Toxicity and Outcomes of the KEYNOTE-522 Chemo-immunotherapy Regimen in Non-Metastatic Triple-negative Breast Cancer," for consideration for publication in the

Toxicity and Outcomes of the KEYNOTE-522 Chemo-immunotherapy Regimen in Non-Metastatic Triple-negative Breast Cancer," for consideration for publication in the | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Toxicity and Outcomes of the KEYNOTE-522 Chemo-immunotherapy Regimen in Non-Metastatic Triple-negative Breast Cancer," for consideration for publication in the Lina M. Adwer, Mackenzie Jones, Kelsey R. Tieken, Sara B. Cartwright, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6849772/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Real-world implementation of the KEYNOTE-522 regimen presents substantial challenges, including high treatment-modification burden and adherence barriers, particularly in community and non-trial settings. Methods We conducted a retrospective cohort analysis of stage II-III TNBC patients treated at the University of Nebraska Medical Center from 2018–2024. Patients receiving neoadjuvant pembrolizumab with chemotherapy (KEYNOTE-522 regimen) (N = 50) were compared with a historical control group receiving dose-dense anthracycline-cyclophosphamide-taxane (ddACT) chemotherapy alone (N = 28). The primary focus was evaluating treatment-related toxicities, adherence challenges, pathologic complete response (pCR), and implications for surgical decision-making and clinical practice. Results Among 78 patients, 50 received the KEYNOTE-522 regimen and 28 received ddACT. High treatment-modification burden was more frequent in the pembrolizumab group, as reflected by treatment modifications or discontinuations (50.0% vs. 17.9%, p = 0.0051). pCR rates were higher with pembrolizumab (42.0% vs. 28.6%, p = 0.2391), and all pCRs corresponded to RCB 0. Recurrence occurred exclusively in non-pCR patients. At 12 months, recurrence and mortality were lower in the pembrolizumab group (16.0% vs. 44.4%, p = 0.0367; 8.0% vs. 29.6%, p = 0.0777), despite shorter median follow-up (17.7 vs. 40.4 months). EFS did not differ significantly by treatment (p = 0.1240), but was strongly associated with RCB class (p = 0.0118) and prognostic stage (p < 0.0001). In ddACT patients, treatment modification predicted worse EFS (p = 0.0215). No demographic or clinical variables were independently associated with pCR. Conclusions In this real-world cohort, the KEYNOTE-522 regimen was associated with high treatment-modification burden and adherence challenges, particularly among medically and socially complex patients. These findings suggest that implementing pembrolizumab outside clinical trials may require biomarker-guided patient selection and equity-focused implementation strategies before routine adoption in under-resourced settings. triple-negative breast cancer pembrolizumab immune checkpoint inhibitors real-world evidence treatment toxicity neoadjuvant chemotherapy health disparities treatment adherence rural health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Triple-negative breast cancer (TNBC) remains one of the most aggressive and therapeutically challenging breast cancer subtypes. Although it comprises only 15% of cases, TNBC accounts for a disproportionate share of breast cancer mortality 1 . Defined by the absence of estrogen receptor, progesterone receptor, and HER2 expression, TNBC is marked by rapid progression, high recurrence rates, and limited treatment options 2 . Historically, neoadjuvant chemotherapy utilizing dose-dense anthracycline- and taxane-based regimens has formed the cornerstone of TNBC management. Even with aggressive therapy, pCR rates remain modest, and long-term survival outcomes are poor 3 . Recently, the introduction of immune checkpoint inhibitors has significantly transformed the treatment paradigm for early-stage TNBC. The KEYNOTE-522 trial reported improved pCR, event-free survival (EFS), and overall survival with pembrolizumab plus chemotherapy, findings that established a benchmark for exploratory evaluation in real-world settings 4 . This advancement culminated in its Food and Drug Administration (FDA) approval as an integral component of stage II-III TNBC standard care 5 . Despite these promising trial results, translating these findings into real-world effectiveness and safety remains uncertain 6 7 . Those participating in trials like KEYNOTE-522 are highly selected, generally healthier, and have fewer comorbidities and better access to care than typical patients 8 . In contrast, real-world populations tend to be more heterogeneous, comprising individuals with more advanced disease, socioeconomic challenges, and medical complexities that may significantly affect therapeutic response and tolerability. As a result, real-world data on pembrolizumab’s effectiveness, toxicity, and feasibility in TNBC remain limited 9 . Beyond efficacy, real-world tolerability of pembrolizumab-based regimens remains a concern. Checkpoint inhibitors carry substantial risks of immune-related adverse events, including thyroiditis, adrenal insufficiency, pneumonitis, hepatitis, and colitis. These adverse effects may necessitate treatment delays, dose reductions, or premature discontinuation of therapy. This may disrupt systemic therapy and affect surgical planning, highlighting the need for data from routine clinical settings 10 . Moreover, integrating immunotherapy into neoadjuvant regimens may hold significant implications for the surgical management of TNBC. Because pCR is associated with improved outcomes, it has been proposed as a potential marker for de-escalating surgery, including more breast-conserving approaches and fewer axillary dissections 4 . Recent studies indicate that patients attaining pCR may be able to safely forgo extensive axillary surgery, thereby minimizing morbidity while maintaining favorable oncologic outcomes. However, it's unclear whether high pCR rates in trials translate to fewer surgeries in real-world settings, where populations are diverse and toxicity and treatment interruptions are more common 9 . To address this gap between trial efficacy and real-world feasibility, we conducted a retrospective cohort study at an academic center serving a largely rural and socioeconomically diverse population. Rather than replicating trial efficacy endpoints, this study evaluated real-world toxicity, adherence, and implementation feasibility associated with the KEYNOTE-522 regimen. We hypothesized that real-world pembrolizumab use would be associated with substantial toxicity and treatment modifications. Efficacy-related outcomes such as pCR and EFS were included as exploratory endpoints to provide contextual reference, not as primary measures of clinical benefit. Methods We conducted a retrospective cohort study at the University of Nebraska Medical Center, evaluating patients with non-metastatic TNBC treated between January 2018 and August 2024. The Institutional Review Board approved the study before data collection (IRB protocol 0430-23-EP). Eligible patients had histologically confirmed TNBC, defined as < 1% ER/PR expression and HER2 negativity per ASCO/CAP guidelines. Inclusion required clinical stage II–III disease (T2 and/or node-positive), neoadjuvant chemotherapy with curative intent, and definitive breast and axillary surgery. Exclusion criteria included metastatic disease at diagnosis, prior systemic therapy, or incomplete clinical data. Of 85 patients screened, 7 were excluded for incomplete treatment or missing data, leaving a final cohort of 78. Patients were divided into two cohorts based on the timing and type of neoadjuvant therapy received. From July 2021 onward, patients in the pembrolizumab cohort received the KEYNOTE-522 regimen: 12 weeks of paclitaxel and carboplatin, followed by dose-dense doxorubicin and cyclophosphamide (ddAC) every 2 weeks for 4 cycles, with pembrolizumab given throughout and continued postoperatively for 1 year. The historical control group (pre-July 2021) received ddACT alone: ddAC every 2 weeks for 4 cycles, followed by weekly paclitaxel for 12 weeks. ddACT was selected as the comparator as it was the institutional standard-of-care for TNBC before pembrolizumab approval. We acknowledge that this historical comparator introduces potential temporal confounding (evolving supportive care, toxicity recognition, and surgical decision-making practices over time), limiting any causal inference regarding efficacy. All patients proceeded to definitive breast and axillary surgery after neoadjuvant therapy. Surgical management, including the choice of lumpectomy versus mastectomy and sentinel lymph node biopsy versus axillary dissection, was performed at the discretion of treating breast surgeons following institutional practices and multidisciplinary tumor board recommendations, recognizing that some variation in surgical decision-making may still exist. Follow-up duration was measured from the date of definitive surgery to the date of last clinical contact, recurrence, or death, whichever occurred first. Median follow-up for each cohort is reported in the Results. The primary outcome was pCR, defined as the absence of residual invasive cancer in both breast and axillary lymph nodes (ypT0/is, ypN0). pCR determinations were based on final surgical pathology reports and reviewed by board-certified breast pathologists using standardized institutional protocols. For descriptive graphics, we calculated pCR rates among the subset of patients with more than 30 months of follow-up to visually illustrate long-term responders. Still, those were not used in any hypothesis-testing analyses. Residual Cancer Burden (RCB) scores were also extracted when available. Secondary outcomes included recurrence, all-cause mortality, grade ≥ 3 adverse events, and treatment modifications (dose reductions or discontinuation). Adverse events were identified retrospectively through manual chart review. Toxicity attribution was not formally adjudicated, and grade ≥ 3 events were captured regardless of presumed etiology. AE grading followed CTCAE v5 when documentation allowed. Data extracted included patient demographics (age, sex, race, ethnicity, body mass index [BMI]), socioeconomic vulnerability indicators, including Area Deprivation Index (ADI) state and national percentiles 11 , and rurality classification using Rural-Urban Commuting Area (RUCA) codes, to assess structural barriers to care and proxy financial hardship risk 12 . Clinical and pathologic variables included AJCC stage, histologic grade, HER2 status, and Ki-67. Reproductive history and performance status were also recorded. Surgical approach and chemotherapy completion were recorded to assess adherence. Given the retrospective, feasibility-focused design of this study, comparative efficacy outcomes such as pCR and EFS were included as secondary, exploratory measures rather than primary endpoints. We used descriptive statistics to compare baseline characteristics between cohorts. Categorical variables were compared with chi-square or Fisher’s exact tests, and continuous variables with two-sample t-tests. Fisher’s test was used when chi-square assumptions were violated (> 20% of cells with expected counts < 5). Pathologic complete response rates were calculated with 95% confidence intervals (CIs). EFS (from surgery to recurrence or death) was estimated using Kaplan-Meier analysis and compared by log-rank tests. Logistic regression assessed exploratory associations with pCR, including treatment group, prognostic stage, and treatment modifications. Recurrence was also added post-hoc as a descriptive proxy for aggressive tumor biology. Potential confounders (age, BMI, comorbidities, ADI) were included based on clinical relevance or univariate p < 0.10. All statistical tests were two-sided; p-values < 0.05 were considered statistically significant. No power calculation was performed, consistent with the retrospective design and inclusion of all eligible patients over six years. Although modest in size, the cohort reflects an unselected real-world population, including rural patients, high ADI scores, and comorbidities often excluded from trials. These features represent groups most vulnerable to financial toxicity, access barriers, and treatment discontinuation. Lastly, the study was not powered to detect subtle differences in efficacy outcomes such as pCR or EFS, nor was that its primary intent. Rather, the analysis was designed to assess feasibility, toxicity, and adherence outcomes associated with pembrolizumab-based neoadjuvant therapy in routine clinical practice. Results A total of 78 patients with non-metastatic TNBC were included: 50 received the KEYNOTE-522 (KN-522) regimen (pembrolizumab plus chemotherapy), and 28 received dose-dense doxorubicin-cyclophosphamide followed by paclitaxel (ddACT). Unless otherwise noted, percentages are reported within treatment groups (KN-522 n=50, ddACT n=28). Although efficacy outcomes are presented, these were not the study’s primary focus and are interpreted as exploratory context for understanding feasibility and tolerability in real-world settings. Baseline demographic and clinical features were broadly comparable between cohorts (Table 1A). The mean age was 52.7 years (SD 13.7) in KN-522 and 56.1 years (SD 12.4) in ddACT (p=0.2756), and most patients in both groups were White (79.6% vs. 77.8%). BMI was also similar between the two cohorts (mean BMI of 30.7 vs. 31.7 kg/m², p=0.5387). However, patients treated with KN-522 more frequently presented with more advanced prognostic stage disease (IIB–IIIC: 81.6% vs. 57.7%, p=0.0303; Table 1A). The pCR rate was higher in the KN-522 group at 42.0% (21/50, 95% CI 28.3-55.7) compared to 28.6% (8/28, 95% CI 11.8-45.3) in ddACT, although this difference was not statistically significant (p=0.2391). Figure 1A illustrates a strictly descriptive 30-month landmark subset of patients who had already reached the 30-month landmark (KN-522 n = 25; ddACT n = 27). RCB class distributions were also similar between treatment groups (p=0.5494; Table 1A). All patients who achieved pCR were classified as RCB 0 in both cohorts, confirming complete internal concordance between these measures (p < 0.0001; Figure 1B). Prognostic stage was reported in full categories (IIA, IIB, IIIA, IIIB, IIIC) in descriptive tables, but combined into Early (IIA-IIB) versus Advanced (IIIA-IIIC) groups for multivariable analyses (Table 4) to enhance statistical power. To account for differences in follow-up duration, recurrence and mortality were assessed at a 12-month timepoint in both groups, restricted to patients with available follow-up through that time (KN-522 n=25, ddACT n=27). Recurrence occurred in 4 of 25 KN-522 patients (16.0%, 95% CI 1.6–30.4) and 12 of 27 ddACT patients (44.4%, 95% CI 25.7-63.2) (p=0.0367), and mortality was also lower in the KN-522 group (8.0% vs. 29.6%, p= 0.0777; Table 1B). All recurrence events in both treatment arms occurred exclusively in patients who did not achieve pCR (Figure 2B). Kaplan-Meier analysis showed no statistically significant difference in EFS between groups (log-rank p=0.1240; Figure 2A). At 12 months, EFS was 92.0% (95% CI 85.3-100%) in KN-522 vs. 75.0% (95% CI 61.0-89.0%) in ddACT. Median EFS was not reached in either cohort, reflecting shorter follow-up in the KN-522 group (median 17.7 months) relative to ddACT (40.4 months). The differential follow-up duration also introduces potential immortal time bias, which may contribute to the observed differences in recurrence and survival. Visual inspection of survival curves showed a steeper early decline in KN-522. Median follow-up was shorter in this group as well. Stratified analyses showed significant differences in EFS by RCB class across all patients (p=0.0118; Figure 2C), but no statistically significant differences by rurality (p=0.1841; Figure 3), or ERBB2 status (p=0.6449; Figure 4). The prognostic stage was also significantly associated with EFS across the full cohort (p<0.0001; Figure 5). Among ddACT patients, those who did not require dose reductions or early discontinuation had significantly longer EFS than those with modifications (log-rank p=0.0215; Figure 6). Dose reductions or pembrolizumab discontinuation occurred more frequently in KN-522 (25/50 patients, 50.0%) than in ddACT (5/28, 17.9%) (p=0.0051; Table 1A). While formal grading of adverse events was not available, chart documentation included high treatment-modification burden due to those adverse events, most commonly in the KN-522 cohort. However, specific attribution to immunotherapy versus chemotherapy and precise alignment of toxicity grade with treatment modifications could not be determined from available records. Multivariable logistic regression (Table 4) found no significant association between treatment group and pCR (OR 1.139, 95 % CI 0.337–3.854; p = 0.8343). Recurrence, entered post-hoc as a non-causal descriptor of aggressive tumor biology, showed an association with lower odds of pCR (OR 0.040, 95 % CI 0.002–0.756; p = 0.0319). Prognostic stage, recategorized as Early (IIA/IIB) versus Advanced (IIIA/IIIB/IIIC), and treatment modifications were not independently associated with pCR. Lastly, Tables 2 and 3 confirmed that no other demographic or clinical variables, including age, BMI, performance status, Area Deprivation Index, rurality, Ki-67, or ERBB2 status, were significantly associated with pCR in either treatment group. Discussion This study was designed to assess real-world feasibility, toxicity, and adherence to the KEYNOTE-522 regimen in a socioeconomically diverse population, not to test efficacy. As such, pCR and recurrence data were included as exploratory signals to contextualize feasibility and toxicity outcomes. In that context, this retrospective cohort study compared pembrolizumab-based chemoimmunotherapy with ddACT chemotherapy alone in non-metastatic TNBC patients treated in routine practice. Patients in the KN-522 group had more advanced disease, potentially biasing against favorable outcomes. While the observed pCR rate was numerically higher in the pembrolizumab group (42.0% vs. 28.6%), the difference lacked statistical significance despite complete concordance between pCR and RCB 0 status. The higher percentages shown in Fig. 1 A reflect only patients who had ≥ 30 months of follow-up and are meant to illustrate durability rather than serve as a basis for statistical comparison. These findings highlight the limitations of trial-derived efficacy metrics in heterogeneous populations and reinforce the study’s aim in evaluating real-world implementation feasibility rather than treatment effectiveness. Compared to recent findings emphasizing improved pCR rates and surgical outcomes, our study highlights substantial practical challenges in implementing KEYNOTE-522, primarily related to toxicity, adherence, and clinical feasibility in real-world clinical settings outside of highly controlled trials. Our findings reinforce that trial-level pCR gains may not be sufficient to support widespread use of chemoimmunotherapy in real-world practice without addressing the accompanying implementation hurdles, as efficacy observed in trials may not directly translate to all real-world scenarios 9 13 . Although recurrence and mortality were lower in the KN-522 group, the shorter follow-up (median 17.7 months) limits conclusions about long-term outcomes. The apparent survival trend may reflect early signals or temporal bias, and future follow-up is needed to determine whether these differences persist. Indeed, longer-term follow-up in clinical trials like KEYNOTE-522 showed more evident survival advantages emerging only after extended observation 13 14 . Additionally, the use of a historical comparator group introduces potential temporal confounding. Changes in imaging, multidisciplinary management, and toxicity recognition over time could contribute to the observed differences in recurrence and mortality. This temporal bias and unequal follow-up duration reinforce that our recurrence and mortality findings should be considered hypothesis-generating rather than definitive evidence of treatment effect. A particularly concerning finding was the high rate of treatment modifications (50%) in patients receiving pembrolizumab-based regimens, significantly more frequent than in the ddACT group (17.9%, p = 0.0051). While our dataset does not capture toxicity grading or specific attribution to immunotherapy versus chemotherapy, the need for dose reductions or discontinuation underscores real-world tolerability challenges. These findings raise important feasibility and implementation concerns when translating this regimen to general oncology practice and are consistent with prior reports highlighting barriers to widespread adoption of chemoimmunotherapy in non-trial settings 9 10 13 . Among ddACT patients, dose reductions or early discontinuation were significantly associated with worse EFS, emphasizing that treatment adherence, regardless of regimen, is a key determinant of outcomes. These results highlight the importance of careful patient selection, particularly avoiding indiscriminate use of chemoimmunotherapy in patients with lower-risk disease or significant comorbidities. A key limitation in our study was the lack of granular data on the type, timing, and attribution of adverse events. Grade ≥ 3 events were captured inclusively and may reflect combined effects of immunotherapy, chemotherapy, and baseline patient comorbidities. Without detailed attribution, it is not possible to distinguish immune-related toxicities from those due to cytotoxic agents or clinical status. This reinforces the need for improved toxicity monitoring infrastructure in real-world practice, where causal assignment is often ambiguous and underreported. While biomarker-driven selection strategies for those undergoing treatment are promising, they remain largely investigational. Our study did not evaluate biomarker status, and thus, we cannot determine whether specific molecular profiles correlated with benefit or toxicity 15 16 . However, it is worth mentioning that emerging data suggest that biomarkers such as PD-L1 expression, tumor-infiltrating lymphocytes (TILs), and immune gene signatures may identify TNBC patients who benefit most significantly from checkpoint inhibitors compared to those who don’t. Our findings emphasize the need to integrate such biomarkers into clinical practice to guide pembrolizumab use and potentially spare low-likelihood responders from substantial toxicity. Future trials and real-world studies should prioritize biomarker stratification to enable hypothesis-driven personalization of therapy and explore de-escalated regimens in patients with favorable immune profiles 15 16 17 . Furthermore, our data reaffirm that pCR remains a powerful prognostic marker, with no recurrence events observed among pCR achievers in either group. Yet, the non-pCR population remains at high risk, accounting for all recurrences and deaths. This finding emphasizes the importance of post-neoadjuvant therapy strategies for patients with residual disease 18 . Notably, studies show that capecitabine and Olaparib have demonstrated survival benefits in this setting, and their role may be particularly important in patients unable to tolerate prolonged pembrolizumab exposure or those who fail to achieve pCR despite immunotherapy 19 20 . Our cohort included a socioeconomically and geographically diverse population, reflected by elevated ADI scores and substantial rural representation, factors associated with delayed diagnosis, limited treatment access, and higher rates of treatment noncompletion. These vulnerabilities amplify the risk of financial toxicity, particularly with prolonged, resource-intensive regimens like pembrolizumab, which require specialized toxicity management, extended monitoring, and multidisciplinary care, resources often lacking in rural or under-resourced settings. Coupled with uncertain clinical benefit in all subgroups, these practical limitations raise significant equity concerns. Patients from underserved backgrounds may experience delayed diagnosis and limited access to specialized toxicity management, exacerbating existing disparities in TNBC outcomes 21 22 . Given the substantial resource demands of pembrolizumab, implementation in real-world settings should incorporate equity frameworks and cost-effectiveness considerations. While our dataset did not capture direct indicators of financial burden, such as out-of-pocket costs, employment disruption, or insurance status, high ADI scores and rurality classifications serve as reasonable proxies for socioeconomic vulnerability. These findings highlight the importance of aligning future clinical guidelines and implementation efforts with real-world feasibility, especially in settings where healthcare access is already constrained. This study was intentionally exploratory, not powered to detect statistically significant differences in pathologic response or long-term survival outcomes, nor was it designed for that purpose. Given the modest sample size, the analysis was intended as exploratory, with a focus on real-world feasibility, toxicity, and adherence outcomes, which are domains that are underrepresented in randomized trial literature but nevertheless important for clinical implementation. Therefore, the lack of statistical significance in pCR or EFS does not diminish the study’s contribution but rather highlights the limitations of trial-based efficacy measures when applied to complex, real-world populations. This lack of statistical significance is unsurprising given the sample size and reflects the exploratory nature of the efficacy comparisons. These findings are meant to provide early insight into the practical challenges and risks of implementing the KEYNOTE-522 regimen across socioeconomically and geographically diverse patient populations outside of the clinical trial settings. They should inform future biomarker-driven, equity-focused implementation efforts. Conclusions In conclusion, this real-world study was designed not to re-evaluate efficacy, but to investigate the feasibility, toxicity, and adherence challenges of the KEYNOTE-522 regimen in a socioeconomically and geographically diverse population. Within that scope, our findings reveal that while pembrolizumab-based neoadjuvant therapy may provide clinical benefit in some patients, its implementation outside of trial settings is met with substantial toxicity, frequent treatment modifications, and adherence barriers. These challenges raise concerns about equity, safety, and sustainability. Future clinical guidelines may benefit from incorporating validated biomarkers to support individualized patient selection, pending prospective evidence. Until such infrastructure is established, broad adoption of the KEYNOTE-522 regimen across all eligible TNBC patients should be approached with caution. Declarations Prior presentations: This study was presented during the 2025 American Society of Breast Surgeons meeting in Las Vegas, NV, May 2025 Conflicts of Interest The authors declare no conflicts of interest related to this study. Funding Statement: This research received no specific grant from public, commercial, or not-for-profit funding agencies. Author Contribution LMA – collected and curated clinical data and drafted the manuscript.MJ, KRT and SBC – collected clinical data and assisted with data curation.JZ and LMS – performed statistical analysis, verified the underlying data, prepared Figures 1-6, prepared Tables 1-4, and contributed to data interpretation.JAS – conceived the study, obtained IRB approval, supervised all phases of the project, and provided substantial critical revisions to the manuscript.HJ, SM, SA, MK, AY, AW, JCM, JM and JK – contributed to patient management, manuscript review, and feedback. Acknowledgement The authors thank the University of Nebraska Medical Center Department of Surgery, Division of Surgical Oncology, and the Fred & Pamela Buffett Cancer Center for supporting this work. No external funding was received for this study. We also thank the patients whose experiences and outcomes made this research possible. References Dent R, Trudeau M, Pritchard KI, et al. 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N Engl J Med . 2017;376(22):2147-2159. doi:10.1056/NEJMoa1612645 Tutt ANJ, Garber JE, Kaufman B, et al. Adjuvant Olaparib for Patients with BRCA1- or BRCA2-Mutated Breast Cancer. N Engl J Med . 2021;384(25):2394-2405. doi:10.1056/NEJMoa2105215 Arora A, Seenu V, Parshad R, et al. A phase II, randomized, open-labeled study to evaluate low-dose pembrolizumab in addition to neoadjuvant chemotherapy for triple-negative breast cancer (TNBC). Trials . 2025;26(1):73. doi:10.1186/s13063-025-08726-9 Rizzo A, Schipilliti FM, Di Costanzo F, et al. Discontinuation rate and serious adverse events of chemoimmunotherapy as neoadjuvant treatment for triple-negative breast cancer: a systematic review and meta-analysis. ESMO Open . 2023;8(6):102198. doi:10.1016/j.esmoop.2023.102198 Tables Table 1A. Baseline Characteristics and Key Clinical Outcomes by Treatment Group (Full Cohort, N=78) All patients with non-metastatic TNBC were treated with either ddACT (N=28) or KN-522 (N=50), which forms the parent population for all subsequent subgroup tables. Treatment Group ddACT (N=28) KEYNOTE-522 (N=50) Total (N=78) P-value Age 0.2756 1 Mean (SD) 56.1 (12.35) 52.7 (13.65) 53.9 (13.21) Race, n (%) 0.9105 2 African American 4 (14.8%) 6 (12.2%) 10 (13.2%) Hispanic 2 (7.4%) 4 (8.2%) 6 (7.9%) White 21 (77.8%) 39 (79.6%) 60 (78.9%) BMI (kg/m2) 0.5387 1 Mean (SD) 31.7 (7.15) 30.7 (7.75) 31.0 (7.51) ADI State Decile 0.6671 1 Mean (SD) 5.1 (3.08) 4.8 (3.00) 4.9 (3.01) ADI National Percentile 0.5586 1 Mean (SD) 61.4 (22.35) 58.2 (23.00) 59.3 (22.67) Rurality (RUCA), n (%) 0.2521 3 1 22 (78.6%) 31 (63.3%) 53 (68.8%) 2 1 (3.6%) 7 (14.3%) 8 (10.4%) 4.0+ 5 (17.9%) 11 (22.4%) 16 (20.8%) ERBB2, n (%) 0.0519 3 0 12 (42.9%) 34 (68.0%) 46 (59.0%) 1+ 9 (32.1%) 6 (12.0%) 15 (19.2%) 2+ 7 (25.0%) 10 (20.0%) 17 (21.8%) Path Response, n (%) 0.2391 3 NON-PCR 20 (71.4%) 29 (58.0%) 49 (62.8%) pCR 8 (28.6%) 21 (42.0%) 29 (37.2%) RCB Score Class, n (%) 0.5494 2 0 8 (28.6%) 21 (42.0%) 29 (37.2%) 1 5 (17.9%) 7 (14.0%) 12 (15.4%) 2 9 (32.1%) 16 (32.0%) 25 (32.1%) 3 6 (21.4%) 6 (12.0%) 12 (15.4%) Prognostic Staging, n (%) 0.0303 2 iia 11 (42.3%) 9 (18.4%) 20 (26.7%) iib 4 (15.4%) 18 (36.7%) 22 (29.3%) iiia 2 (7.7%) 0 (0.0%) 2 (2.7%) iiib 4 (15.4%) 12 (24.5%) 16 (21.3%) iiic 5 (19.2%) 10 (20.4%) 15 (20.0%) Dose Reductions or Discontinuation, n (%) 0.0051 3 No 23 (82.1%) 25 (50.0%) 48 (61.5%) Yes 5 (17.9%) 25 (50.0%) 30 (38.5%) 1 Unequal variance two sample t-test; 2 Fisher Exact p-value; 3 Chi-Square p-value; Footnotes: Data are reported for all patients unless otherwise indicated. For some variables, totals may not add up to the full cohort size due to missing values (e.g., incomplete documentation, unavailable data in EHR). Column totals may vary slightly between variables as each row reflects only patients with complete data for that characteristic. Percentages are calculated using the available number of patients per variable as the denominator. p-values were calculated using unequal variance t-test for continuous variables, and Chi-square or Fisher’s exact test for categorical variables, as appropriate. Table 1B. Key Clinical Outcomes at 12-Month Follow-Up (Subset of Full Cohort) Subset of Table 1A patients with ≥12 months of follow-up. Treatment Group ddACT (N=27) KEYNOTE-522 (N=25) Total (N=52) P-value Age 0.4391 1 Mean (SD) 56.4 (12.45) 53.5 (14.10) 55.0 (13.22) Race, n (%) 0.4208 2 African American 4 (15.4%) 1 (4.0%) 5 (9.8%) Hispanic 2 (7.7%) 2 (8.0%) 4 (7.8%) White 20 (76.9%) 22 (88.0%) 42 (82.4%) BMI (kg/m2) 0.1611 1 Mean (SD) 32.0 (7.15) 29.3 (6.67) 30.7 (7.00) ADI State Decile 0.4902 1 Mean (SD) 5.1 (3.13) 4.5 (3.00) 4.8 (3.05) ADI National Percentile 0.3674 1 Mean (SD) 61.6 (22.75) 55.6 (24.04) 58.7 (23.34) Rurality (RUCA), n (%) 0.0915 2 1 22 (81.5%) 16 (66.7%) 38 (74.5%) 2 0 (0.0%) 4 (16.7%) 4 (7.8%) 4.0+ 5 (18.5%) 4 (16.7%) 9 (17.6%) ERBB2, n (%) 0.0650 2 0 12 (44.4%) 18 (72.0%) 30 (57.7%) 1+ 9 (33.3%) 2 (8.0%) 11 (21.2%) 2+ 6 (22.2%) 5 (20.0%) 11 (21.2%) Dose Reductions or Discontinuation, n (%) 0.0089 2 No 22 (81.5%) 11 (44.0%) 33 (63.5%) Yes 5 (18.5%) 14 (56.0%) 19 (36.5%) Performance Status, n (%) 1.0000 2 0 26 (96.3%) 24 (96.0%) 50 (96.2%) 1 1 (3.7%) 1 (4.0%) 2 (3.8%) Death, n (%) 0.0777 2 No 19 (70.4%) 23 (92.0%) 42 (80.8%) Yes 8 (29.6%) 2 (8.0%) 10 (19.2%) Recurrence, n (%) 0.0367 2 No 15 (55.6%) 21 (84.0%) 36 (69.2%) Yes 12 (44.4%) 4 (16.0%) 16 (30.8%) 1 Unequal variance two sample t-test; 2 Fisher Exact p-value; 3 Chi-Square p-value; Footnotes: Table includes only patients with ≥12 months of clinical follow-up. Differences in sample size from Table 1A reflect this restriction. For certain variables, totals may not add to the full 12-month cohort due to missing data. Each row reflects only patients with non-missing values for that characteristic. Percentages are based on available denominators per variable. p-values calculated as in Table 1A. Table 2. KN-522 Subgroup: Baseline Characteristics by pCR Status (N=50) Subset of Table 1A. Compares patients who did vs. did not achieve pCR after KN-522 neoadjuvant therapy. NON-PCR (N=29) pCR (N=21) Total (N=50) P-value Age 0.9749 1 Mean (SD) 52.8 (13.43) 52.7 (14.27) 52.7 (13.65) Race, n (%) 0.8547 2 African American 4 (14.3%) 2 (9.5%) 6 (12.2%) Hispanic 2 (7.1%) 2 (9.5%) 4 (8.2%) White 22 (78.6%) 17 (81.0%) 39 (79.6%) BMI (kg/m2) 0.1379 1 Mean (SD) 31.9 (9.01) 28.9 (5.26) 30.7 (7.75) ADI State Decile 0.5762 1 Mean (SD) 5.0 (2.99) 4.5 (3.06) 4.8 (3.00) ADI National Percentile 0.6429 1 Mean (SD) 59.5 (23.43) 56.4 (22.83) 58.2 (23.00) Rurality (RUCA), n (%) 0.6599 3 1 19 (67.9%) 12 (57.1%) 31 (63.3%) 2 3 (10.7%) 4 (19.0%) 7 (14.3%) 4.0+ 6 (21.4%) 5 (23.8%) 11 (22.4%) ERBB2, n (%) 0.3856 3 0 19 (65.5%) 15 (71.4%) 34 (68.0%) 1+ 5 (17.2%) 1 (4.8%) 6 (12.0%) 2+ 5 (17.2%) 5 (23.8%) 10 (20.0%) RCB Score Class, n (%) <.0001 2 0 0 (0.0%) 21 (100.0%) 21 (42.0%) 1 7 (24.1%) 0 (0.0%) 7 (14.0%) 2 16 (55.2%) 0 (0.0%) 16 (32.0%) 3 6 (20.7%) 0 (0.0%) 6 (12.0%) Prognostic Staging, n (%) 0.9173 2 iia 6 (21.4%) 3 (14.3%) 9 (18.4%) iib 10 (35.7%) 8 (38.1%) 18 (36.7%) iiib 6 (21.4%) 6 (28.6%) 12 (24.5%) iiic 6 (21.4%) 4 (19.0%) 10 (20.4%) Dose Reductions or Discontinuation, n (%) 0.3900 3 No 13 (44.8%) 12 (57.1%) 25 (50.0%) Yes 16 (55.2%) 9 (42.9%) 25 (50.0%) Performance Status, n (%) 0.8193 2 0 24 (82.8%) 19 (90.5%) 43 (86.0%) 1 4 (13.8%) 2 (9.5%) 6 (12.0%) 2 1 (3.4%) 0 (0.0%) 1 (2.0%) 1 Unequal variance two sample t-test; 2 Fisher Exact p-value; 3 Chi-Square p-value; Footnotes: Analysis limited to patients in the KN-522 group (N=50). Sample sizes vary by variable due to missing data, as indicated in the “N” row. Denominators for each characteristic reflect the number of patients with available data. Percentages reflect within-group distributions. p-values calculated as in Table 1A. pCR defined as ypT0/is, ypN0. Table 3. ddACT Subgroup: Baseline Characteristics by pCR Status (N=28) Subset of Table 1A. Compares patients who did vs. did not achieve pCR after ddACT neoadjuvant therapy. non pCR (N=20) pCR (N=8) Total (N=28) P-value Age 0.1051 1 Mean (SD) 58.5 (12.08) 50.0 (11.54) 56.1 (12.35) Race, n (%) 0.7905 2 African American 3 (15.8%) 1 (12.5%) 4 (14.8%) Hispanic 1 (5.3%) 1 (12.5%) 2 (7.4%) White 15 (78.9%) 6 (75.0%) 21 (77.8%) BMI (kg/m2) 0.4960 1 Mean (SD) 31.1 (7.16) 33.3 (7.37) 31.7 (7.15) ADI State Decile 0.1281 1 Mean (SD) 4.5 (2.84) 6.6 (3.29) 5.1 (3.08) ADI National Percentile 0.1484 1 Mean (SD) 57.3 (21.62) 71.5 (22.22) 61.4 (22.35) Rurality (RUCA), n (%) 0.6914 3 1 16 (80.0%) 6 (75.0%) 22 (78.6%) 2 1 (5.0%) 0 (0.0%) 1 (3.6%) 4.0+ 3 (15.0%) 2 (25.0%) 5 (17.9%) ERBB2, n (%) 0.6271 3 0 8 (40.0%) 4 (50.0%) 12 (42.9%) 1+ 6 (30.0%) 3 (37.5%) 9 (32.1%) 2+ 6 (30.0%) 1 (12.5%) 7 (25.0%) RCB Score Class, n (%) <.0001 2 0 0 (0.0%) 8 (100.0%) 8 (28.6%) 1 5 (25.0%) 0 (0.0%) 5 (17.9%) 2 9 (45.0%) 0 (0.0%) 9 (32.1%) 3 6 (30.0%) 0 (0.0%) 6 (21.4%) Prognostic Staging, n (%) 0.4291 2 iia 7 (36.8%) 4 (57.1%) 11 (42.3%) iib 2 (10.5%) 2 (28.6%) 4 (15.4%) iiia 2 (10.5%) 0 (0.0%) 2 (7.7%) iiib 3 (15.8%) 1 (14.3%) 4 (15.4%) iiic 5 (26.3%) 0 (0.0%) 5 (19.2%) Dose Reductions or Discontinuation, n (%) 0.6397 3 No 16 (80.0%) 7 (87.5%) 23 (82.1%) Yes 4 (20.0%) 1 (12.5%) 5 (17.9%) Performance Status, n (%) 0.5195 3 0 19 (95.0%) 8 (100.0%) 27 (96.4%) 1 1 (5.0%) 0 (0.0%) 1 (3.6%) 1 Unequal variance two sample t-test; 2 Fisher Exact p-value; 3 Chi-Square p-value; Footnotes: Analysis limited to patients in the ddACT group (N=28). Sample sizes vary by variable due to missing data, as indicated in the “N” row. Denominators for each characteristic reflect the number of patients with available data. Percentages reflect within-group distributions. p-values calculated as in Table 1A. pCR defined as ypT0/is, ypN0. Table 4. Multivariable Exploratory Correlates of pCR in Full Cohort (N=78) Logistic regression model for pathologic complete response (pCR), including treatment group, prognostic staging, and treatment-modification status. Recurrence was included post-hoc as a descriptive, non-causal proxy for tumor aggressiveness. Prognostic stage was grouped as Early (stage IIA–IIB) vs. Advanced (stage III). Treatment modification is defined as chemotherapy dose reduction or early discontinuation. OR (95% CI) p-value Treatment Group 1.139 (0.337-3.854) 0.8343 Recurrence 0.040 (0.002-0.756) 0.0319 Prognostic Stage 1.003 (0.349-2.886) 0.9950 Treatment Modification 0.631 (0.215-1.852) 0.4023 Footnotes: Multivariable logistic regression for pCR across the full cohort (N = 78); all coefficients are descriptive and hypothesis-generating given the small event count and the post-treatment timing of recurrence. Model includes treatment group, recurrence status, prognostic stage (early [IIA–IIB] vs. advanced [IIIA–IIIC]), and treatment modification (dose reduction or early discontinuation). Variables with missing data were excluded from the model. OR = odds ratio; CI = confidence interval. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6849772","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485159898,"identity":"7b4b9f0c-bdc6-4b06-909c-28136bf5733a","order_by":0,"name":"Lina M. 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Santamaria-Barria","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACxgYUrgGDHJTFTFgLD1SLMUEtcMADpRMbCGlhnpH87AHDH7s8e/bTiZ8rCuzSN9zuTvzAUGEN04vpsBlp5gaMbcnFPDy5myXPGCTnbrhzdrMEw5l0PFoSzCQYG5gTexhyN0g2GDDnbriRu42Bse0wHi3p3yQY/tQn9vC/3fyzwaA+3QCs5R8+LTlmEgxshxN7JHK3AW05nADR0oBHS8+bMonEtuOJPTfebrNsMDhuOPNG7maJhGPpxri0GLanb5P48Kc6sb0/d/PNhj/V8nw3cjd++FBjLYtTC0giAUMYUwQB5PHIjYJRMApGwSiAAAAzxluW9rR6fgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Nebraska Medical Center, Nebraska Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Juan","middleName":"A.","lastName":"Santamaria-Barria","suffix":""}],"badges":[],"createdAt":"2025-06-09 01:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6849772/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6849772/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86953013,"identity":"aee5579c-4c57-4a6e-8c00-ba5781ce54df","added_by":"auto","created_at":"2025-07-17 14:39:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57138,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Pathologic Complete Response (pCR) Rate by Treatment Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBar graph comparing pCR rates between pembrolizumab plus chemotherapy (KN-522) and ddACT. pCR was defined as no residual invasive cancer in breast or lymph nodes (ypT0/is, ypN0). Bars depict a descriptive 30-month landmark subset (patients with ≥ 30 months follow-up) to illustrate durability; because follow-up differs between cohorts no statistical comparison was performed on this subset. Full-cohort pCR rates (42.0 % vs 28.6 %) appear in Table 1A.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Concordance Between pCR and RCB Score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStacked bar graph demonstrating perfect concordance between pCR and RCB 0 status in both treatment groups (p\u0026lt;0.0001). All patients who achieved pCR were classified as RCB 0 on final pathology.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6849772/v1/47b9d26ff1b7a63fcb421940.png"},{"id":86953450,"identity":"41784035-ef6f-42f7-bba1-3e7e34356423","added_by":"auto","created_at":"2025-07-17 14:47:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Kaplan-Meier Curve for Event-Free Survival (EFS) by Treatment Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier curve comparing EFS between KN-522 and ddACT groups. No statistically significant difference observed (log-rank p=0.1240). Median EFS was not reached in either group at the time of analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Kaplan-Meier Curve for EFS Stratified by pCR Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier curve showing significantly improved EFS among patients achieving pCR versus non-pCR across both treatment groups. All recurrence events occurred exclusively in patients without pCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. Kaplan-Meier Curve for EFS by RCB Class\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier curves demonstrating EFS stratified by RCB class (0, 1, 2, 3) across all patients (p=0.0118). Increasing RCB class was associated with worse EFS.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6849772/v1/f0051b1b854ad6aae4ceb1bc.png"},{"id":86953451,"identity":"488fbdd8-4948-4fe3-8549-b473ed43dca0","added_by":"auto","created_at":"2025-07-17 14:47:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30309,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Curve for EFS by Rurality (RUCA Classification)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier curves comparing EFS among patients classified as urban (RUCA 1) versus rural (RUCA ≥2). No statistically significant difference observed (log-rank p=0.1841), though rural patients exhibited numerically worse early outcomes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6849772/v1/8e29f87491efa7a7e00c954d.png"},{"id":86953018,"identity":"fa22c542-90b2-4980-94dc-60d6dc8f5f73","added_by":"auto","created_at":"2025-07-17 14:39:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30333,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Curve for EFS by ERBB2 Status (0, 1+, 2+)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier curves stratifying EFS by ERBB2 IHC score (0, 1+, 2+). No statistically significant association observed (log-rank p=0.6449).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6849772/v1/8707cca7fcfc5cac1efc9ebb.png"},{"id":86953452,"identity":"5bd152cd-e3c7-432f-8964-eebdf15acdb0","added_by":"auto","created_at":"2025-07-17 14:47:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":57552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Curve for EFS by Prognostic Stage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier curves showing EFS stratified by prognostic stage. The top curve displays outcomes across full prognostic subgroups (IA, IIB, IIIA–IIIC), demonstrating significantly worse EFS with more advanced disease (log-rank p \u0026lt; 0.0001). Given small sample sizes within individual subgroups, an additional curve is included comparing grouped stages (Stage I vs Stage II), which also demonstrated a significant difference in EFS (log-rank p = 0.0174). Stages were merged post hoc to facilitate interpretation and improve statistical power.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6849772/v1/aa66a50f30a4a3a0886562b4.png"},{"id":86953019,"identity":"7162bcfc-3d32-4a22-b7e7-878f3f7cbcf5","added_by":"auto","created_at":"2025-07-17 14:39:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":30642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Curve for EFS by Treatment Modification Status in ddACT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier curve comparing EFS among ddACT patients with versus without chemotherapy dose reductions or early discontinuation. Patients completing full-dose ddACT had significantly longer EFS (log-rank p=0.0215).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6849772/v1/6260a8a7164237f69390d944.png"},{"id":88253680,"identity":"d9b6c2f1-3f47-427a-81f2-1f0bf8312a79","added_by":"auto","created_at":"2025-08-04 14:02:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2179364,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6849772/v1/9c91fbb5-79de-4bc0-b10b-491609d71d3f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Toxicity and Outcomes of the KEYNOTE-522 Chemo-immunotherapy Regimen in Non-Metastatic Triple-negative Breast Cancer,\" for consideration for publication in the","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTriple-negative breast cancer (TNBC) remains one of the most aggressive and therapeutically challenging breast cancer subtypes. Although it comprises only 15% of cases, TNBC accounts for a disproportionate share of breast cancer mortality\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Defined by the absence of estrogen receptor, progesterone receptor, and HER2 expression, TNBC is marked by rapid progression, high recurrence rates, and limited treatment options\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Historically, neoadjuvant chemotherapy utilizing dose-dense anthracycline- and taxane-based regimens has formed the cornerstone of TNBC management. Even with aggressive therapy, pCR rates remain modest, and long-term survival outcomes are poor\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRecently, the introduction of immune checkpoint inhibitors has significantly transformed the treatment paradigm for early-stage TNBC. The KEYNOTE-522 trial reported improved pCR, event-free survival (EFS), and overall survival with pembrolizumab plus chemotherapy, findings that established a benchmark for exploratory evaluation in real-world settings\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. This advancement culminated in its Food and Drug Administration (FDA) approval as an integral component of stage II-III TNBC standard care\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Despite these promising trial results, translating these findings into real-world effectiveness and safety remains uncertain\u003csup\u003e\u003cb\u003e6\u003c/b\u003e \u003cb\u003e7\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThose participating in trials like KEYNOTE-522 are highly selected, generally healthier, and have fewer comorbidities and better access to care than typical patients\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. In contrast, real-world populations tend to be more heterogeneous, comprising individuals with more advanced disease, socioeconomic challenges, and medical complexities that may significantly affect therapeutic response and tolerability. As a result, real-world data on pembrolizumab\u0026rsquo;s effectiveness, toxicity, and feasibility in TNBC remain limited\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBeyond efficacy, real-world tolerability of pembrolizumab-based regimens remains a concern. Checkpoint inhibitors carry substantial risks of immune-related adverse events, including thyroiditis, adrenal insufficiency, pneumonitis, hepatitis, and colitis. These adverse effects may necessitate treatment delays, dose reductions, or premature discontinuation of therapy. This may disrupt systemic therapy and affect surgical planning, highlighting the need for data from routine clinical settings\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMoreover, integrating immunotherapy into neoadjuvant regimens may hold significant implications for the surgical management of TNBC. Because pCR is associated with improved outcomes, it has been proposed as a potential marker for de-escalating surgery, including more breast-conserving approaches and fewer axillary dissections\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Recent studies indicate that patients attaining pCR may be able to safely forgo extensive axillary surgery, thereby minimizing morbidity while maintaining favorable oncologic outcomes. However, it's unclear whether high pCR rates in trials translate to fewer surgeries in real-world settings, where populations are diverse and toxicity and treatment interruptions are more common\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo address this gap between trial efficacy and real-world feasibility, we conducted a retrospective cohort study at an academic center serving a largely rural and socioeconomically diverse population. Rather than replicating trial efficacy endpoints, this study evaluated real-world toxicity, adherence, and implementation feasibility associated with the KEYNOTE-522 regimen. We hypothesized that real-world pembrolizumab use would be associated with substantial toxicity and treatment modifications. Efficacy-related outcomes such as pCR and EFS were included as exploratory endpoints to provide contextual reference, not as primary measures of clinical benefit.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe conducted a retrospective cohort study at the University of Nebraska Medical Center, evaluating patients with non-metastatic TNBC treated between January 2018 and August 2024. The Institutional Review Board approved the study before data collection (IRB protocol 0430-23-EP). Eligible patients had histologically confirmed TNBC, defined as \u0026lt;\u0026thinsp;1% ER/PR expression and HER2 negativity per ASCO/CAP guidelines. Inclusion required clinical stage II\u0026ndash;III disease (T2 and/or node-positive), neoadjuvant chemotherapy with curative intent, and definitive breast and axillary surgery. Exclusion criteria included metastatic disease at diagnosis, prior systemic therapy, or incomplete clinical data. Of 85 patients screened, 7 were excluded for incomplete treatment or missing data, leaving a final cohort of 78.\u003c/p\u003e\u003cp\u003ePatients were divided into two cohorts based on the timing and type of neoadjuvant therapy received. From July 2021 onward, patients in the pembrolizumab cohort received the KEYNOTE-522 regimen: 12 weeks of paclitaxel and carboplatin, followed by dose-dense doxorubicin and cyclophosphamide (ddAC) every 2 weeks for 4 cycles, with pembrolizumab given throughout and continued postoperatively for 1 year. The historical control group (pre-July 2021) received ddACT alone: ddAC every 2 weeks for 4 cycles, followed by weekly paclitaxel for 12 weeks. ddACT was selected as the comparator as it was the institutional standard-of-care for TNBC before pembrolizumab approval. We acknowledge that this historical comparator introduces potential temporal confounding (evolving supportive care, toxicity recognition, and surgical decision-making practices over time), limiting any causal inference regarding efficacy.\u003c/p\u003e\u003cp\u003eAll patients proceeded to definitive breast and axillary surgery after neoadjuvant therapy. Surgical management, including the choice of lumpectomy versus mastectomy and sentinel lymph node biopsy versus axillary dissection, was performed at the discretion of treating breast surgeons following institutional practices and multidisciplinary tumor board recommendations, recognizing that some variation in surgical decision-making may still exist. Follow-up duration was measured from the date of definitive surgery to the date of last clinical contact, recurrence, or death, whichever occurred first. Median follow-up for each cohort is reported in the Results.\u003c/p\u003e\u003cp\u003eThe primary outcome was pCR, defined as the absence of residual invasive cancer in both breast and axillary lymph nodes (ypT0/is, ypN0). pCR determinations were based on final surgical pathology reports and reviewed by board-certified breast pathologists using standardized institutional protocols. For descriptive graphics, we calculated pCR rates among the subset of patients with more than 30 months of follow-up to visually illustrate long-term responders. Still, those were not used in any hypothesis-testing analyses. Residual Cancer Burden (RCB) scores were also extracted when available. Secondary outcomes included recurrence, all-cause mortality, grade\u0026thinsp;\u0026ge;\u0026thinsp;3 adverse events, and treatment modifications (dose reductions or discontinuation). Adverse events were identified retrospectively through manual chart review. Toxicity attribution was not formally adjudicated, and grade\u0026thinsp;\u0026ge;\u0026thinsp;3 events were captured regardless of presumed etiology. AE grading followed CTCAE v5 when documentation allowed.\u003c/p\u003e\u003cp\u003eData extracted included patient demographics (age, sex, race, ethnicity, body mass index [BMI]), socioeconomic vulnerability indicators, including Area Deprivation Index (ADI) state and national percentiles\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e, and rurality classification using Rural-Urban Commuting Area (RUCA) codes, to assess structural barriers to care and proxy financial hardship risk\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Clinical and pathologic variables included AJCC stage, histologic grade, HER2 status, and Ki-67. Reproductive history and performance status were also recorded. Surgical approach and chemotherapy completion were recorded to assess adherence. Given the retrospective, feasibility-focused design of this study, comparative efficacy outcomes such as pCR and EFS were included as secondary, exploratory measures rather than primary endpoints.\u003c/p\u003e\u003cp\u003eWe used descriptive statistics to compare baseline characteristics between cohorts. Categorical variables were compared with chi-square or Fisher\u0026rsquo;s exact tests, and continuous variables with two-sample t-tests. Fisher\u0026rsquo;s test was used when chi-square assumptions were violated (\u0026gt;\u0026thinsp;20% of cells with expected counts\u0026thinsp;\u0026lt;\u0026thinsp;5). Pathologic complete response rates were calculated with 95% confidence intervals (CIs). EFS (from surgery to recurrence or death) was estimated using Kaplan-Meier analysis and compared by log-rank tests. Logistic regression assessed exploratory associations with pCR, including treatment group, prognostic stage, and treatment modifications. Recurrence was also added post-hoc as a descriptive proxy for aggressive tumor biology. Potential confounders (age, BMI, comorbidities, ADI) were included based on clinical relevance or univariate p\u0026thinsp;\u0026lt;\u0026thinsp;0.10. All statistical tests were two-sided; p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003cp\u003eNo power calculation was performed, consistent with the retrospective design and inclusion of all eligible patients over six years. Although modest in size, the cohort reflects an unselected real-world population, including rural patients, high ADI scores, and comorbidities often excluded from trials. These features represent groups most vulnerable to financial toxicity, access barriers, and treatment discontinuation. Lastly, the study was not powered to detect subtle differences in efficacy outcomes such as pCR or EFS, nor was that its primary intent. Rather, the analysis was designed to assess feasibility, toxicity, and adherence outcomes associated with pembrolizumab-based neoadjuvant therapy in routine clinical practice.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 78 patients with non-metastatic TNBC were included: 50 received the KEYNOTE-522 (KN-522) regimen (pembrolizumab plus chemotherapy), and 28 received dose-dense doxorubicin-cyclophosphamide followed by paclitaxel (ddACT). Unless otherwise noted, percentages are reported within treatment groups (KN-522 n=50, ddACT n=28). Although efficacy outcomes are presented, these were not the study’s primary focus and are interpreted as exploratory context for understanding feasibility and tolerability in real-world settings.\u003c/p\u003e\n\u003cp\u003eBaseline demographic and clinical features were broadly comparable between cohorts (Table 1A). The mean age was 52.7 years (SD 13.7) in KN-522 and 56.1 years (SD 12.4) in ddACT (p=0.2756), and most patients in both groups were White (79.6% vs. 77.8%). BMI was also similar between the two cohorts (mean BMI of 30.7 vs. 31.7 kg/m², p=0.5387). However, patients treated with KN-522 more frequently presented with more advanced prognostic stage disease (IIB–IIIC: 81.6% vs. 57.7%, p=0.0303; Table 1A).\u003c/p\u003e\n\u003cp\u003eThe pCR rate was higher in the KN-522 group at 42.0% (21/50, 95% CI 28.3-55.7) compared to 28.6% (8/28, 95% CI 11.8-45.3) in ddACT, although this difference was not statistically significant (p=0.2391). Figure 1A illustrates a strictly descriptive 30-month landmark subset of patients who had already reached the 30-month landmark (KN-522 n = 25; ddACT n = 27). RCB class distributions were also similar between treatment groups (p=0.5494; Table 1A). All patients who achieved pCR were classified as RCB 0 in both cohorts, confirming complete internal concordance between these measures (p \u0026lt; 0.0001; Figure 1B). Prognostic stage was reported in full categories (IIA, IIB, IIIA, IIIB, IIIC) in descriptive tables, but combined into Early (IIA-IIB) versus Advanced (IIIA-IIIC) groups for multivariable analyses (Table 4) to enhance statistical power.\u003c/p\u003e\n\u003cp\u003eTo account for differences in follow-up duration, recurrence and mortality were assessed at a 12-month timepoint in both groups, restricted to patients with available follow-up through that time (KN-522 n=25, ddACT n=27). Recurrence occurred in 4 of 25 KN-522 patients (16.0%, 95% CI 1.6–30.4) and 12 of 27 ddACT patients (44.4%, 95% CI 25.7-63.2) (p=0.0367), and mortality was also lower in the KN-522 group (8.0% vs. 29.6%, p= 0.0777; Table 1B). All recurrence events in both treatment arms occurred exclusively in patients who did not achieve pCR (Figure 2B).\u003c/p\u003e\n\u003cp\u003eKaplan-Meier analysis showed no statistically significant difference in EFS between groups (log-rank p=0.1240; Figure 2A). At 12 months, EFS was 92.0% (95% CI 85.3-100%) in KN-522 vs. 75.0% (95% CI 61.0-89.0%) in ddACT. Median EFS was not reached in either cohort, reflecting shorter follow-up in the KN-522 group (median 17.7 months) relative to ddACT (40.4 months). The differential follow-up duration also introduces potential immortal time bias, which may contribute to the observed differences in recurrence and survival. Visual inspection of survival curves showed a steeper early decline in KN-522. Median follow-up was shorter in this group as well.\u003c/p\u003e\n\u003cp\u003eStratified analyses showed significant differences in EFS by RCB class across all patients (p=0.0118; Figure 2C), but no statistically significant differences by rurality (p=0.1841; Figure 3), or ERBB2 status (p=0.6449; Figure 4). The prognostic stage was also significantly associated with EFS across the full cohort (p\u0026lt;0.0001; Figure 5). Among ddACT patients, those who did not require dose reductions or early discontinuation had significantly longer EFS than those with modifications (log-rank p=0.0215; Figure 6).\u003c/p\u003e\n\u003cp\u003eDose reductions or pembrolizumab discontinuation occurred more frequently in KN-522 (25/50 patients, 50.0%) than in ddACT (5/28, 17.9%) (p=0.0051; Table 1A). While formal grading of adverse events was not available, chart documentation included high treatment-modification burden due to those adverse events, most commonly in the KN-522 cohort. However, specific attribution to immunotherapy versus chemotherapy and precise alignment of toxicity grade with treatment modifications could not be determined from available records.\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression (Table 4) found no significant association between treatment group and pCR (OR 1.139, 95 % CI 0.337–3.854; p = 0.8343). Recurrence, entered post-hoc as a non-causal descriptor of aggressive tumor biology, showed an association with lower odds of pCR (OR 0.040, 95 % CI 0.002–0.756; p = 0.0319). Prognostic stage, recategorized as Early (IIA/IIB) versus Advanced (IIIA/IIIB/IIIC), and treatment modifications were not independently associated with pCR.\u003c/p\u003e\n\u003cp\u003eLastly, Tables 2 and 3 confirmed that no other demographic or clinical variables, including age, BMI, performance status, Area Deprivation Index, rurality, Ki-67, or ERBB2 status, were significantly associated with pCR in either treatment group.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study was designed to assess real-world feasibility, toxicity, and adherence to the KEYNOTE-522 regimen in a socioeconomically diverse population, not to test efficacy. As such, pCR and recurrence data were included as exploratory signals to contextualize feasibility and toxicity outcomes. In that context, this retrospective cohort study compared pembrolizumab-based chemoimmunotherapy with ddACT chemotherapy alone in non-metastatic TNBC patients treated in routine practice. Patients in the KN-522 group had more advanced disease, potentially biasing against favorable outcomes. While the observed pCR rate was numerically higher in the pembrolizumab group (42.0% vs. 28.6%), the difference lacked statistical significance despite complete concordance between pCR and RCB 0 status. The higher percentages shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA reflect only patients who had\u0026thinsp;\u0026ge;\u0026thinsp;30 months of follow-up and are meant to illustrate durability rather than serve as a basis for statistical comparison. These findings highlight the limitations of trial-derived efficacy metrics in heterogeneous populations and reinforce the study\u0026rsquo;s aim in evaluating real-world implementation feasibility rather than treatment effectiveness.\u003c/p\u003e\u003cp\u003eCompared to recent findings emphasizing improved pCR rates and surgical outcomes, our study highlights substantial practical challenges in implementing KEYNOTE-522, primarily related to toxicity, adherence, and clinical feasibility in real-world clinical settings outside of highly controlled trials. Our findings reinforce that trial-level pCR gains may not be sufficient to support widespread use of chemoimmunotherapy in real-world practice without addressing the accompanying implementation hurdles, as efficacy observed in trials may not directly translate to all real-world scenarios\u003csup\u003e\u003cb\u003e9\u003c/b\u003e \u003cb\u003e13\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough recurrence and mortality were lower in the KN-522 group, the shorter follow-up (median 17.7 months) limits conclusions about long-term outcomes. The apparent survival trend may reflect early signals or temporal bias, and future follow-up is needed to determine whether these differences persist. Indeed, longer-term follow-up in clinical trials like KEYNOTE-522 showed more evident survival advantages emerging only after extended observation\u003csup\u003e\u003cb\u003e13\u003c/b\u003e \u003cb\u003e14\u003c/b\u003e\u003c/sup\u003e. Additionally, the use of a historical comparator group introduces potential temporal confounding. Changes in imaging, multidisciplinary management, and toxicity recognition over time could contribute to the observed differences in recurrence and mortality. This temporal bias and unequal follow-up duration reinforce that our recurrence and mortality findings should be considered hypothesis-generating rather than definitive evidence of treatment effect.\u003c/p\u003e\u003cp\u003eA particularly concerning finding was the high rate of treatment modifications (50%) in patients receiving pembrolizumab-based regimens, significantly more frequent than in the ddACT group (17.9%, p\u0026thinsp;=\u0026thinsp;0.0051). While our dataset does not capture toxicity grading or specific attribution to immunotherapy versus chemotherapy, the need for dose reductions or discontinuation underscores real-world tolerability challenges. These findings raise important feasibility and implementation concerns when translating this regimen to general oncology practice and are consistent with prior reports highlighting barriers to widespread adoption of chemoimmunotherapy in non-trial settings\u003csup\u003e\u003cb\u003e9\u003c/b\u003e \u003cb\u003e10\u003c/b\u003e \u003cb\u003e13\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAmong ddACT patients, dose reductions or early discontinuation were significantly associated with worse EFS, emphasizing that treatment adherence, regardless of regimen, is a key determinant of outcomes. These results highlight the importance of careful patient selection, particularly avoiding indiscriminate use of chemoimmunotherapy in patients with lower-risk disease or significant comorbidities. A key limitation in our study was the lack of granular data on the type, timing, and attribution of adverse events. Grade\u0026thinsp;\u0026ge;\u0026thinsp;3 events were captured inclusively and may reflect combined effects of immunotherapy, chemotherapy, and baseline patient comorbidities. Without detailed attribution, it is not possible to distinguish immune-related toxicities from those due to cytotoxic agents or clinical status. This reinforces the need for improved toxicity monitoring infrastructure in real-world practice, where causal assignment is often ambiguous and underreported.\u003c/p\u003e\u003cp\u003eWhile biomarker-driven selection strategies for those undergoing treatment are promising, they remain largely investigational. Our study did not evaluate biomarker status, and thus, we cannot determine whether specific molecular profiles correlated with benefit or toxicity\u003csup\u003e\u003cb\u003e15\u003c/b\u003e \u003cb\u003e16\u003c/b\u003e\u003c/sup\u003e. However, it is worth mentioning that emerging data suggest that biomarkers such as PD-L1 expression, tumor-infiltrating lymphocytes (TILs), and immune gene signatures may identify TNBC patients who benefit most significantly from checkpoint inhibitors compared to those who don\u0026rsquo;t. Our findings emphasize the need to integrate such biomarkers into clinical practice to guide pembrolizumab use and potentially spare low-likelihood responders from substantial toxicity. Future trials and real-world studies should prioritize biomarker stratification to enable hypothesis-driven personalization of therapy and explore de-escalated regimens in patients with favorable immune profiles\u003csup\u003e\u003cb\u003e15\u003c/b\u003e \u003cb\u003e16\u003c/b\u003e \u003cb\u003e17\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFurthermore, our data reaffirm that pCR remains a powerful prognostic marker, with no recurrence events observed among pCR achievers in either group. Yet, the non-pCR population remains at high risk, accounting for all recurrences and deaths. This finding emphasizes the importance of post-neoadjuvant therapy strategies for patients with residual disease\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Notably, studies show that capecitabine and Olaparib have demonstrated survival benefits in this setting, and their role may be particularly important in patients unable to tolerate prolonged pembrolizumab exposure or those who fail to achieve pCR despite immunotherapy\u003csup\u003e\u003cb\u003e19\u003c/b\u003e \u003cb\u003e20\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur cohort included a socioeconomically and geographically diverse population, reflected by elevated ADI scores and substantial rural representation, factors associated with delayed diagnosis, limited treatment access, and higher rates of treatment noncompletion. These vulnerabilities amplify the risk of financial toxicity, particularly with prolonged, resource-intensive regimens like pembrolizumab, which require specialized toxicity management, extended monitoring, and multidisciplinary care, resources often lacking in rural or under-resourced settings. Coupled with uncertain clinical benefit in all subgroups, these practical limitations raise significant equity concerns. Patients from underserved backgrounds may experience delayed diagnosis and limited access to specialized toxicity management, exacerbating existing disparities in TNBC outcomes\u003csup\u003e\u003cb\u003e21\u003c/b\u003e\u003cb\u003e22\u003c/b\u003e\u003c/sup\u003e. Given the substantial resource demands of pembrolizumab, implementation in real-world settings should incorporate equity frameworks and cost-effectiveness considerations. While our dataset did not capture direct indicators of financial burden, such as out-of-pocket costs, employment disruption, or insurance status, high ADI scores and rurality classifications serve as reasonable proxies for socioeconomic vulnerability. These findings highlight the importance of aligning future clinical guidelines and implementation efforts with real-world feasibility, especially in settings where healthcare access is already constrained.\u003c/p\u003e\u003cp\u003eThis study was intentionally exploratory, not powered to detect statistically significant differences in pathologic response or long-term survival outcomes, nor was it designed for that purpose. Given the modest sample size, the analysis was intended as exploratory, with a focus on real-world feasibility, toxicity, and adherence outcomes, which are domains that are underrepresented in randomized trial literature but nevertheless important for clinical implementation. Therefore, the lack of statistical significance in pCR or EFS does not diminish the study\u0026rsquo;s contribution but rather highlights the limitations of trial-based efficacy measures when applied to complex, real-world populations. This lack of statistical significance is unsurprising given the sample size and reflects the exploratory nature of the efficacy comparisons. These findings are meant to provide early insight into the practical challenges and risks of implementing the KEYNOTE-522 regimen across socioeconomically and geographically diverse patient populations outside of the clinical trial settings. They should inform future biomarker-driven, equity-focused implementation efforts.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this real-world study was designed not to re-evaluate efficacy, but to investigate the feasibility, toxicity, and adherence challenges of the KEYNOTE-522 regimen in a socioeconomically and geographically diverse population. Within that scope, our findings reveal that while pembrolizumab-based neoadjuvant therapy may provide clinical benefit in some patients, its implementation outside of trial settings is met with substantial toxicity, frequent treatment modifications, and adherence barriers. These challenges raise concerns about equity, safety, and sustainability. Future clinical guidelines may benefit from incorporating validated biomarkers to support individualized patient selection, pending prospective evidence. Until such infrastructure is established, broad adoption of the KEYNOTE-522 regimen across all eligible TNBC patients should be approached with caution.\u003c/p\u003e"},{"header":"Declarations","content":"Prior presentations: This study was presented during the 2025 American Society of Breast Surgeons meeting in Las Vegas, NV, May 2025\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\u003cp\u003eThe authors declare no conflicts of interest related to this study.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Statement:\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from public, commercial, or not-for-profit funding agencies.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLMA \u0026ndash; collected and curated clinical data and drafted the manuscript.MJ, KRT and SBC \u0026ndash; collected clinical data and assisted with data curation.JZ and LMS \u0026ndash; performed statistical analysis, verified the underlying data, prepared Figures 1-6, prepared Tables 1-4, and contributed to data interpretation.JAS \u0026ndash; conceived the study, obtained IRB approval, supervised all phases of the project, and provided substantial critical revisions to the manuscript.HJ, SM, SA, MK, AY, AW, JCM, JM and JK \u0026ndash; contributed to patient management, manuscript review, and feedback.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the University of Nebraska Medical Center Department of Surgery, Division of Surgical Oncology, and the Fred \u0026amp; Pamela Buffett Cancer Center for supporting this work. No external funding was received for this study. We also thank the patients whose experiences and outcomes made this research possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDent R, Trudeau M, Pritchard KI, et al. Triple-negative breast cancer: clinical features and patterns of recurrence. \u003cem\u003eClin Cancer Res\u003c/em\u003e. 2007;13(15 Pt 1):4429-4434. doi:10.1158/1078-0432.CCR-06-3045\u003c/li\u003e\n\u003cli\u003eFoulkes WD, Smith IE, Reis-Filho JS. Triple-Negative Breast Cancer. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2010;363(20):1938-1948. doi:10.1056/NEJMra1001389\u003c/li\u003e\n\u003cli\u003eVan Den Ende NS, Nguyen AH, Jager A, Kok M, Debets R, Van Deurzen CHM. Triple-Negative Breast Cancer and Predictive Markers of Response to Neoadjuvant Chemotherapy: A Systematic Review. \u003cem\u003eIJMS\u003c/em\u003e. 2023;24(3):2969. doi:10.3390/ijms24032969\u003c/li\u003e\n\u003cli\u003eSchmid P, Cortes J, Pusztai L, et al. Pembrolizumab for Early Triple-Negative Breast Cancer. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2020;382(9):810-821. doi:10.1056/NEJMoa1910549\u003c/li\u003e\n\u003cli\u003eShah M, Osgood CL, Amatya AK, et al. FDA Approval Summary: Pembrolizumab for Neoadjuvant and Adjuvant Treatment of Patients with High-Risk Early-Stage Triple-Negative Breast Cancer. \u003cem\u003eClinical Cancer Research\u003c/em\u003e. 2022;28(24):5249-5253. doi:10.1158/1078-0432.CCR-22-1110\u003c/li\u003e\n\u003cli\u003eDeng H, Wang L, Wang N, et al. Neoadjuvant checkpoint blockade in combination with Chemotherapy in patients with tripe-negative breast cancer: exploratory analysis of real-world, multicenter data. \u003cem\u003eBMC Cancer\u003c/em\u003e. 2023;23(1):29. doi:10.1186/s12885-023-10515-z\u003c/li\u003e\n\u003cli\u003eFreeman JQ, Huo D, Shubeck SP, et al. Trends and Disparities in the Use of Immunotherapy for Triple-Negative Breast Cancer in the US. \u003cem\u003eJAMA Netw Open\u003c/em\u003e. 2025;8(2):e2460243. doi:10.1001/jamanetworkopen.2024.60243\u003c/li\u003e\n\u003cli\u003eCorrigan KL, Rooney MK, Kouzy R, Manzar G, Thomas CR, Ludmir EB. Selection and Prejudice: Addressing Clinical Trial Disparities With a Review of Current Shortcomings and Future Directions. \u003cem\u003eSeminars in Radiation Oncology\u003c/em\u003e. 2023;33(4):367-373. doi:10.1016/j.semradonc.2023.06.002\u003c/li\u003e\n\u003cli\u003eConnors C, Valente SA, ElSherif A, et al. Real-World Outcomes with the KEYNOTE-522 Regimen in Early-Stage Triple-Negative Breast Cancer. \u003cem\u003eAnn Surg Oncol\u003c/em\u003e. 2025;32(2):912-921. doi:10.1245/s10434-024-16390-7\u003c/li\u003e\n\u003cli\u003eJayan A, Sukumar JS, Fangman B, et al. Real-World Immune-Related Adverse Events in Patients With Early Triple-Negative Breast Cancer Who Received Pembrolizumab. \u003cem\u003eJCO Oncol Pract\u003c/em\u003e. Published online October 10, 2024:OP.24.00371. doi:10.1200/OP.24.00371\u003c/li\u003e\n\u003cli\u003eKind AJH, Jencks S, Brock J, et al. Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study. \u003cem\u003eAnn Intern Med\u003c/em\u003e. 2014;161(11):765-774. doi:10.7326/M13-2946\u003c/li\u003e\n\u003cli\u003eHart LG, Larson EH, Lishner DM. Rural definitions for health policy and research. \u003cem\u003eAm J Public Health\u003c/em\u003e. 2005;95(7):1149-1155. doi:10.2105/AJPH.2004.042432\u003c/li\u003e\n\u003cli\u003eSchmid P, Cortes J, Dent R, et al. Event-free Survival with Pembrolizumab in Early Triple-Negative Breast Cancer. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2022;386(6):556-567. doi:10.1056/NEJMoa2112651\u003c/li\u003e\n\u003cli\u003eSchmid P, Cortes J, Dent R, et al. Overall Survival with Pembrolizumab in Early-Stage Triple-Negative Breast Cancer. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2024;391(21):1981-1991. doi:10.1056/NEJMoa2409932\u003c/li\u003e\n\u003cli\u003eDixon-Douglas J, Loi S. Immunotherapy in Early-Stage Triple-Negative Breast Cancer: Where Are We Now and Where Are We Headed? \u003cem\u003eCurr Treat Options Oncol\u003c/em\u003e. 2023;24(8):1004-1020. doi:10.1007/s11864-023-01087-y\u003c/li\u003e\n\u003cli\u003eHu H, Kaklamani V. Updates on the preoperative immunotherapy for triple-negative breast cancer. \u003cem\u003eTransl Breast Cancer Res\u003c/em\u003e. 2023;4:17. doi:10.21037/tbcr-23-16\u003c/li\u003e\n\u003cli\u003eNanda R, Liu MC, Yau C, et al. Effect of Pembrolizumab Plus Neoadjuvant Chemotherapy on Pathologic Complete Response in Women With Early-Stage Breast Cancer: An Analysis of the Ongoing Phase 2 Adaptively Randomized I-SPY2 Trial. \u003cem\u003eJAMA Oncol\u003c/em\u003e. 2020;6(5):676-684. doi:10.1001/jamaoncol.2019.6650\u003c/li\u003e\n\u003cli\u003eSpring LM, Fell G, Arfe A, et al. Pathologic Complete Response after Neoadjuvant Chemotherapy and Impact on Breast Cancer Recurrence and Survival: A Comprehensive Meta-analysis. \u003cem\u003eClin Cancer Res\u003c/em\u003e. 2020;26(12):2838-2848. doi:10.1158/1078-0432.CCR-19-3492\u003c/li\u003e\n\u003cli\u003eMasuda N, Lee SJ, Ohtani S, et al. Adjuvant Capecitabine for Breast Cancer after Preoperative Chemotherapy. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2017;376(22):2147-2159. doi:10.1056/NEJMoa1612645\u003c/li\u003e\n\u003cli\u003eTutt ANJ, Garber JE, Kaufman B, et al. Adjuvant Olaparib for Patients with BRCA1- or BRCA2-Mutated Breast Cancer. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2021;384(25):2394-2405. doi:10.1056/NEJMoa2105215\u003c/li\u003e\n\u003cli\u003eArora A, Seenu V, Parshad R, et al. A phase II, randomized, open-labeled study to evaluate low-dose pembrolizumab in addition to neoadjuvant chemotherapy for triple-negative breast cancer (TNBC). \u003cem\u003eTrials\u003c/em\u003e. 2025;26(1):73. doi:10.1186/s13063-025-08726-9\u003c/li\u003e\n\u003cli\u003eRizzo A, Schipilliti FM, Di Costanzo F, et al. Discontinuation rate and serious adverse events of chemoimmunotherapy as neoadjuvant treatment for triple-negative breast cancer: a systematic review and meta-analysis. \u003cem\u003eESMO Open\u003c/em\u003e. 2023;8(6):102198. doi:10.1016/j.esmoop.2023.102198\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1A. Baseline Characteristics and Key Clinical Outcomes by Treatment Group (Full Cohort, N=78)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients with non-metastatic TNBC were treated with either ddACT (N=28) or KN-522 (N=50), which forms the parent population for all subsequent subgroup tables.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 235px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 185px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eddACT\u003cbr\u003e\u0026nbsp;(N=28)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKEYNOTE-522\u003cbr\u003e\u0026nbsp;(N=50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003cbr\u003e\u0026nbsp;(N=78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.2756\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e56.1 (12.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e52.7 (13.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e53.9 (13.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.9105\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e6 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e4 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e6 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e21 (77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e39 (79.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e60 (78.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.5387\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e31.7 (7.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e30.7 (7.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e31.0 (7.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADI State Decile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.6671\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5.1 (3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e4.8 (3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4.9 (3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADI National Percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.5586\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e61.4 (22.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e58.2 (23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e59.3 (22.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRurality (RUCA), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.2521\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e22 (78.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e31 (63.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e53 (68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e7 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e8 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 4.0+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e11 (22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e16 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eERBB2, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.0519\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e12 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e34 (68.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e46 (59.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 1+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e6 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e15 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e10 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e17 (21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath Response, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.2391\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; NON-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e20 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e29 (58.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e49 (62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; pCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e8 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e21 (42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e29 (37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRCB Score Class, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.5494\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e8 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e21 (42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e29 (37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e7 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e12 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e16 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e25 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e6 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e6 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e12 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrognostic Staging, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.0303\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; iia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e11 (42.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e9 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e20 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; iib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e18 (36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e22 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; iiia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; iiib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e12 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e16 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; iiic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e10 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e15 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDose Reductions or Discontinuation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.0051\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e23 (82.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e25 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e48 (61.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e25 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e30 (38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 642px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eUnequal variance two sample t-test; \u003csup\u003e2\u003c/sup\u003eFisher Exact p-value; \u003csup\u003e3\u003c/sup\u003eChi-Square\u0026nbsp;p-value;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFootnotes: Data are reported for all patients unless otherwise indicated. For some variables, totals may not add up to the full cohort size due to missing values (e.g., incomplete documentation, unavailable data in EHR). Column totals may vary slightly between variables as each row reflects only patients with complete data for that characteristic. Percentages are calculated using the available number of patients per variable as the denominator. p-values were calculated using unequal variance t-test for continuous variables, and Chi-square or Fisher\u0026rsquo;s exact test for categorical variables, as appropriate.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1B. Key Clinical Outcomes at 12-Month Follow-Up (Subset of Full Cohort)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubset of Table 1A patients with \u0026ge;12 months of follow-up.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 235px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eddACT\u003cbr\u003e\u0026nbsp;(N=27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKEYNOTE-522\u003cbr\u003e\u0026nbsp;(N=25)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003cbr\u003e\u0026nbsp;(N=52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.4391\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e56.4 (12.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e53.5 (14.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e55.0 (13.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.4208\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eAfrican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e5 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e20 (76.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e22 (88.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e42 (82.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.1611\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e32.0 (7.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e29.3 (6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e30.7 (7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADI State Decile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.4902\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e5.1 (3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4.5 (3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4.8 (3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADI National Percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.3674\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e61.6 (22.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e55.6 (24.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e58.7 (23.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRurality (RUCA), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.0915\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e22 (81.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e16 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e38 (74.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e4.0+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e5 (18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e9 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eERBB2, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.0650\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e12 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e18 (72.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e30 (57.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e9 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e11 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e6 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e11 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDose Reductions or Discontinuation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.0089\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e22 (81.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e11 (44.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e33 (63.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e5 (18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e14 (56.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e19 (36.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerformance Status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e1.0000\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e26 (96.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e24 (96.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e50 (96.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.0777\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e19 (70.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e23 (92.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e42 (80.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e8 (29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e10 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecurrence, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.0367\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e15 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e21 (84.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e36 (69.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e12 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e16 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 642px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eUnequal variance two sample t-test; \u003csup\u003e2\u003c/sup\u003eFisher Exact p-value; \u003csup\u003e3\u003c/sup\u003eChi-Square\u0026nbsp;p-value;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFootnotes: Table includes only patients with \u0026ge;12 months of clinical follow-up. Differences in sample size from Table 1A reflect this restriction. For certain variables, totals may not add to the full 12-month cohort due to missing data. Each row reflects only patients with non-missing values for that characteristic. Percentages are based on available denominators per variable. p-values calculated as in Table 1A.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. KN-522 Subgroup: Baseline Characteristics by pCR Status (N=50)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubset of Table 1A. Compares patients who did vs. did not achieve pCR after KN-522 neoadjuvant therapy.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNON-PCR\u003cbr\u003e\u0026nbsp;(N=29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epCR\u003cbr\u003e\u0026nbsp;(N=21)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003cbr\u003e\u0026nbsp;(N=50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.9749\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e52.8 (13.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e52.7 (14.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e52.7 (13.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.8547\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eAfrican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e4 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e2 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e2 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e2 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e22 (78.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e17 (81.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e39 (79.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.1379\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e31.9 (9.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e28.9 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e30.7 (7.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADI State Decile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.5762\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e5.0 (2.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e4.5 (3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4.8 (3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADI National Percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.6429\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e59.5 (23.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e56.4 (22.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e58.2 (23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRurality (RUCA), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.6599\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e19 (67.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e12 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e31 (63.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e3 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e4 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e4.0+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e6 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e5 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e11 (22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eERBB2, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.3856\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e19 (65.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e15 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e34 (68.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e1+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e5 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e5 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e5 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e10 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRCB Score Class, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e21 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e21 (42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e7 (24.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e16 (55.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e16 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e6 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrognostic Staging, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.9173\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eiia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e6 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e3 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e9 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eiib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e10 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e8 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e18 (36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eiiib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e6 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e6 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e12 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eiiic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e6 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e4 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e10 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDose Reductions or Discontinuation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.3900\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e13 (44.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e12 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e25 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e16 (55.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e9 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e25 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerformance Status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.8193\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e24 (82.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e19 (90.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e43 (86.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e4 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e2 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 221px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e1 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 633px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eUnequal variance two sample t-test; \u003csup\u003e2\u003c/sup\u003eFisher Exact p-value; \u003csup\u003e3\u003c/sup\u003eChi-Square\u0026nbsp;p-value;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFootnotes: Analysis limited to patients in the KN-522 group (N=50). Sample sizes vary by variable due to missing data, as indicated in the \u0026ldquo;N\u0026rdquo; row. Denominators for each characteristic reflect the number of patients with available data. Percentages reflect within-group distributions. p-values calculated as in Table 1A. pCR defined as ypT0/is, ypN0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. ddACT Subgroup: Baseline Characteristics by pCR Status (N=28)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubset of Table 1A. Compares patients who did vs. did not achieve pCR after ddACT neoadjuvant therapy.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"644\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e\u003cstrong\u003enon pCR\u003cbr\u003e\u0026nbsp;(N=20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u003cstrong\u003epCR\u003cbr\u003e\u0026nbsp;(N=8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003cbr\u003e\u0026nbsp;(N=28)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e0.1051\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e58.5 (12.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e50.0 (11.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e56.1 (12.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e0.7905\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eAfrican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e3 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e1 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e4 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e1 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e1 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e2 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e15 (78.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e6 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e21 (77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e0.4960\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e31.1 (7.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e33.3 (7.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e31.7 (7.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADI State Decile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e0.1281\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e4.5 (2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e6.6 (3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e5.1 (3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADI National Percentile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e0.1484\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e57.3 (21.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e71.5 (22.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e61.4 (22.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRurality (RUCA), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e0.6914\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e16 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e6 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e22 (78.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e1 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e1 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e4.0+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e3 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e2 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e5 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eERBB2, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e0.6271\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e8 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e4 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e12 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e1+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e6 (30.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e3 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e9 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e6 (30.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e1 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e7 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRCB Score Class, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e8 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e8 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e5 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e5 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e9 (45.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e9 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e6 (30.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e6 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrognostic Staging, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e0.4291\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eiia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e7 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e4 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e11 (42.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eiib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e2 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e2 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e4 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eiiia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e2 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e2 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eiiib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e3 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e1 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e4 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eiiic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e5 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e5 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDose Reductions or Discontinuation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e0.6397\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e16 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e7 (87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e23 (82.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e4 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e1 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e5 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerformance Status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e0.5195\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e19 (95.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e8 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e27 (96.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1477%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7783%;\"\u003e\n \u003cp\u003e1 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0716%;\"\u003e\n \u003cp\u003e1 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.5292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 91.4518%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003eUnequal variance two sample t-test; \u003csup\u003e2\u003c/sup\u003eFisher Exact p-value; \u003csup\u003e3\u003c/sup\u003eChi-Square\u0026nbsp;p-value;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFootnotes: Analysis limited to patients in the ddACT group (N=28). Sample sizes vary by variable due to missing data, as indicated in the \u0026ldquo;N\u0026rdquo; row. Denominators for each characteristic reflect the number of patients with available data. Percentages reflect within-group distributions. p-values calculated as in Table 1A. pCR defined as ypT0/is, ypN0.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Multivariable Exploratory Correlates of pCR in Full Cohort (N=78)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLogistic regression model for pathologic complete response (pCR), including treatment group, prognostic staging, and treatment-modification status. Recurrence was included post-hoc as a descriptive, non-causal proxy for tumor aggressiveness. Prognostic stage was grouped as Early (stage IIA\u0026ndash;IIB) vs. Advanced (stage III). Treatment modification is defined as chemotherapy dose reduction or early discontinuation.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1.139 (0.337-3.854)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.8343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e0.040 (0.002-0.756)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.0319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrognostic Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e1.003 (0.349-2.886)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.9950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment Modification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e0.631 (0.215-1.852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.4023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFootnotes: Multivariable logistic regression for pCR across the full cohort (N = 78); all coefficients are descriptive and hypothesis-generating given the small event count and the post-treatment timing of recurrence. Model includes treatment group, recurrence status, prognostic stage (early [IIA\u0026ndash;IIB] vs. advanced [IIIA\u0026ndash;IIIC]), and treatment modification (dose reduction or early discontinuation). Variables with missing data were excluded from the model. OR = odds ratio; CI = confidence interval.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"triple-negative breast cancer, pembrolizumab, immune checkpoint inhibitors, real-world evidence, treatment toxicity, neoadjuvant chemotherapy, health disparities, treatment adherence, rural health","lastPublishedDoi":"10.21203/rs.3.rs-6849772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6849772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e\u003cp\u003eReal-world implementation of the KEYNOTE-522 regimen presents substantial challenges, including high treatment-modification burden and adherence barriers, particularly in community and non-trial settings.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort analysis of stage II-III TNBC patients treated at the University of Nebraska Medical Center from 2018\u0026ndash;2024. Patients receiving neoadjuvant pembrolizumab with chemotherapy (KEYNOTE-522 regimen) (N\u0026thinsp;=\u0026thinsp;50) were compared with a historical control group receiving dose-dense anthracycline-cyclophosphamide-taxane (ddACT) chemotherapy alone (N\u0026thinsp;=\u0026thinsp;28). The primary focus was evaluating treatment-related toxicities, adherence challenges, pathologic complete response (pCR), and implications for surgical decision-making and clinical practice.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 78 patients, 50 received the KEYNOTE-522 regimen and 28 received ddACT. High treatment-modification burden was more frequent in the pembrolizumab group, as reflected by treatment modifications or discontinuations (50.0% vs. 17.9%, p\u0026thinsp;=\u0026thinsp;0.0051). pCR rates were higher with pembrolizumab (42.0% vs. 28.6%, p\u0026thinsp;=\u0026thinsp;0.2391), and all pCRs corresponded to RCB 0. Recurrence occurred exclusively in non-pCR patients. At 12 months, recurrence and mortality were lower in the pembrolizumab group (16.0% vs. 44.4%, p\u0026thinsp;=\u0026thinsp;0.0367; 8.0% vs. 29.6%, p\u0026thinsp;=\u0026thinsp;0.0777), despite shorter median follow-up (17.7 vs. 40.4 months). EFS did not differ significantly by treatment (p\u0026thinsp;=\u0026thinsp;0.1240), but was strongly associated with RCB class (p\u0026thinsp;=\u0026thinsp;0.0118) and prognostic stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In ddACT patients, treatment modification predicted worse EFS (p\u0026thinsp;=\u0026thinsp;0.0215). No demographic or clinical variables were independently associated with pCR.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eIn this real-world cohort, the KEYNOTE-522 regimen was associated with high treatment-modification burden and adherence challenges, particularly among medically and socially complex patients. These findings suggest that implementing pembrolizumab outside clinical trials may require biomarker-guided patient selection and equity-focused implementation strategies before routine adoption in under-resourced settings.\u003c/p\u003e","manuscriptTitle":"Toxicity and Outcomes of the KEYNOTE-522 Chemo-immunotherapy Regimen in Non-Metastatic Triple-negative Breast Cancer,\" for consideration for publication in the","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-17 14:39:33","doi":"10.21203/rs.3.rs-6849772/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c34739ff-e186-4bc7-88a5-756da559e91b","owner":[],"postedDate":"July 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T13:54:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-17 14:39:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6849772","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6849772","identity":"rs-6849772","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}