Tumor Infiltrating Lymphocytes (TILs) as a Predictive Marker of Pathological Complete Response (pCR) in a Diverse Patient Population with Early Triple Negative Breast Cancer (TNBC) Treated with Neoadjuvant Real-World KEYNOTE-522 Regimen

Tumor Infiltrating Lymphocytes (TILs) as a Predictive Marker of Pathological Complete Response (pCR) in a Diverse Patient Population with Early Triple Negative Breast Cancer (TNBC) Treated with Neoadjuvant Real-World KEYNOTE-522 Regimen | 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 Tumor Infiltrating Lymphocytes (TILs) as a Predictive Marker of Pathological Complete Response (pCR) in a Diverse Patient Population with Early Triple Negative Breast Cancer (TNBC) Treated with Neoadjuvant Real-World KEYNOTE-522 Regimen Riya Albert, Joshua Thomas, Navid Sadeghi, Sangeetha Reddy, Glenda Delgado, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9096569/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 7 You are reading this latest preprint version Abstract Introduction Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by poor prognosis. Based on the KEYNOTE-522 trial, neoadjuvant pembrolizumab plus chemotherapy has become the standard of care due to significantly improved pathological complete response (pCR) rates. The presence of tumor-infiltrating lymphocytes (TILs) is a predictive biomarker of pCR. This retrospective cohort study examines a diverse patient population treated with the K522 regimen to determine if TILs predict pCR relative to other clinical and tumor-specific factors. Methods We retrospectively reviewed 187 patients with early-stage TNBC at two institutions (one tertiary care, one safety-net) who completed neoadjuvant K522 treatment between 2021–2024. Statistical analyses included Chi-squared tests, Z-tests, and univariate logistic regression to evaluate associations between TILs, ethnicity, tumor grade, and pCR. Results The overall pCR rate was 57%; TILs were present in 52.8% of cases. TILs were associated with a significantly higher pCR rate (70% vs. 48% without TILs; p = 0.0027). While pCR rates were similar across ethnicities, Hispanic patients with TILs had significantly higher pCR than those without (80.0% vs. 51.5%; p = 0.0254). Controlling for grade, patients with TILs were 2.442 times more likely to achieve pCR (CI: 1.310–4.553; p = 0.0050). Grade 3 tumors and node-positivity with TILs also showed statistically significant rates of pCR. Conclusion TILs serve as a strong predictive biomarker for immunotherapy response in a real-world TNBC population. Our findings regarding Hispanic and node-positive patients suggest TILs could guide treatment de-escalation to reduce K522-related toxicity. Standardizing TILs reporting is critical to optimizing treatment strategies and improving outcomes in underrepresented populations. tumor-infiltrating lymphocytes triple-negative breast cancer neoadjuvant therapy immunotherapy pathological complete response Figures Figure 1 Introduction Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer defined by its lack of estrogen receptors (ER), progesterone receptors (PR), and HER2/neu. It accounts for 15% of all breast cancer diagnoses, yet due to the lack of typical receptor biomarkers, there are limited effective targeted therapies in early-stage disease. Patients with TNBC tend to present with higher-stage disease and generally have worse prognoses than other breast cancer subtypes. Previously, the standard practice for treatment was neoadjuvant chemotherapy (NAC), using anthracycline- and taxane-based regimens. In July 2021, based on results from the KEYNOTE-522 (K522) trial, the FDA approved pembrolizumab (Keytruda) with chemotherapy as neoadjuvant therapy for high-risk early-stage TNBC, followed by adjuvant monotherapy after surgery, which significantly improved prognosis[ 1 , 2 ]. The K522 trial examined rates of pathologic complete response (pCR) as a preliminary efficacy outcome measure due to its association with good prognosis and long-term disease-free survival[ 3 ]. Compared with neoadjuvant chemotherapy alone, the K522 regimen was associated with a higher pCR rate (64.8% vs. 51.2%) and a clinically significant improvement in event-free survival at 60 months (86.6% vs. 81.7%) Despite these promising improvements in outcomes, response to neoadjuvant therapy with the K522 regimen in early-stage TNBC is still variable, and our understanding of what underlying characteristics may be associated with successful treatment response is limited. Investigating the clinical and pathology-based variables linked to pCR following the K522 regimen may help identify predictive factors for anticipating treatment response. The presence of tumor-infiltrating lymphocytes (TILs) has been identified as a predictive biomarker for TNBC, as high levels of TILs are associated with better overall survival and disease-free survival.[ 4 ] Previous studies correlate high TIL levels with better response to NAC, especially in aggressive subtypes, such as TNBC.[ 5 ] [ 6 ] However, the extent to which TILs predict response to neoadjuvant immunotherapy in early-stage TNBC, particularly in comparison to other tumor and clinical factors, remains unclear. To investigate the predictive value of TILs in early-stage TNBC, we conducted a retrospective cohort study in a clinically diverse patient population who received the K522 regimen to determine whether the presence of TILs confers a predictive value in response to neoadjuvant pembrolizumab treatment compared to various other tumor and clinical factors. Methods Patient Selection We reviewed electronic medical records of 271 early-stage TNBC patients who received neoadjuvant treatment in accordance with the KEYNOTE-522 regimen at an NCI-designated comprehensive cancer center, UT Southwestern Harold C. Simmons Comprehensive Cancer Center (UTSW-SCCC), and its affiliated safety-net hospital, Parkland Health (PH), between August 2021 to December 2024. Using retrospective chart review from both institutions, we identified 184 patients who completed the neoadjuvant portion of the study and underwent surgery at the time of chart review. Patients were excluded if they had metastatic disease at the time of diagnosis, did not proceed with surgery due to disease progression or adverse events, or had not undergone surgery at the time of data collection. This study was approved by the Institutional Review Board (IRB) of UT Southwestern Medical Center and Parkland Health. Data was collected from each institution through medical record review and included the following details: patient ID, site, age at diagnosis, date of birth (DOB), race, ethnicity, tumor type, tumor stage (T and N), chemotherapy regimen, number of cycles, presence of TILs in the resected primary breast tumor, post-surgery tumor characteristics (ypT, ypN, residual cancer burden [RCB]), hormone receptor status (estrogen receptor [ER] and progesterone receptor [PR] expression at diagnosis and on surgical samples, reported as percentage of tumor cell nuclei staining positive via immunohistochemistry), HER2 status (by IHC and FISH at diagnosis and on surgical samples), tumor grade, Ki-67 proliferation index at diagnosis and on surgical samples, history of autoimmune disease, and additional notes. TNBC was defined as the absence of estrogen receptor (ER) and progesterone receptor (PR) expression (< 10%) and a HER2-negative status (score of 0+, 1+, or 2 + with a confirmatory non-amplified result from fluorescent in situ hybridization), in accordance with the most recent American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) guidelines[ 7 ]. RCB ranged from RCB 0, indicating pCR, to RCB III, indicating extensive residual disease. The classifications were determined using MD Anderson’s web calculator for residual cancer burden. pCR was defined as the lack of residual invasive carcinoma in both breast tumor bed tissue and axillary lymph nodes found at the time of surgical resection following neoadjuvant therapy, with final pathologic stages as ypT0/Tis and ypN0. Immunohistochemistry and TILs assessment Tumor-infiltrating lymphocytes (TILs) were evaluated in formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples stained with hematoxylin and eosin (H&E) from baseline biopsies, in accordance with the guidelines established by the International TILs Working Group[ 8 ] ( Fig. 1 ). TILs were assessed in both intratumoral regions (invasive tumor nests, including intraepithelial areas) and stromal regions (intratumoral stroma. The evaluation prioritized intratumoral stromal TILs, followed by peripheral stromal TILs, particularly within 1–2 mm of the tumor-stroma interface. Both regions were included in the final TILs assessment, with greater emphasis placed on intratumoral stromal TILs. Based on the guidelines of the International TILs Working Group, TILs were quantified as a percentage of the total stromal or intratumoral area occupied by mononuclear cells (including lymphocytes and plasma cells)[ 8 ]. While TILs are typically reported in 10% increments, pathologists also estimated percentages in smaller increments when necessary. TILs were considered significant when they constituted approximately 25–30% or more of the assessed area. Statistical analysis Statistical analysis was conducted to evaluate the association between pathological complete response (pCR) rates and various demographic and clinical factors, including tumor-infiltrating lymphocytes (TILs), tumor grade, and residual cancer burden (RCB). Two-proportion Z-tests and Chi-squared tests for independence were employed to compare pCR rates across different demographic groups, including ethnicity and the presence or absence of TILs. Additionally, univariate logistic regression models were used to assess the relationship between the presence of TILs, tumor grade, and RCB with pCR outcomes. Results The median age of our patient population was 51 years, with an age range of 24 to 83 years. Of which, 34.8% self-identified as Caucasian (C), 31.0% Hispanic (H), 25.5% Black (B), and 8.7% other. All patients were female. Patient demographics, tumor type, size (T), and nodal status (N) are shown in Table 1 . Along with immunotherapy, most patients received an anthracycline/taxane-based chemotherapy regimen, starting with four cycles of paclitaxel and carboplatin followed by another four cycles of doxorubicin and cyclophosphamide. 68.6% of patients completed 8 or more cycles of neoadjuvant therapy, and 81.5% of patients completed at least 75% of treatment. Among patients who completed less than 8 cycles, 81.1% discontinued due to adverse reactions. Table 1 Patient Demographics and Clinical Characteristics Patient Demographics n = 184 Parameters Median SD Age (Years) 51 13.6 BMI (kg/m 2 ) 30.1 6.8 n (%) Ethnicity White 64 (34.8) Black 46 (25.0) Hispanic 57 (30.1) Asian/Other 16 (8.7) Tumor Characteristics Type Ductal 171 (92.9) Lobular 5 (2.7) Mixed 8 (4.4) Grade 1 0 (0.0) 2 28 (15.2) 3 156 (84.8) Tumor Classification (T) T1,2 139 (75.5) T3,4 45 (24.5) Nodal Status (N) Negative 83 (45.1) Positive 101 (54.9) Response to Treatment Pathological Response RCB 0 (pCR) 105 (57.1) RCB 1 12 (6.5) RCB 2 42 (22.8) RCB 3 25 (13.6) Pathological Complete Response Rate in Subpopulations Overall, 57% of patients achieved a pathological complete response (pCR). TILs were present and reported in 52.8% of pathology reports. We did not see an increased presence of TILs in any ethnic population (χ² = 2.5806, p = 0.4609). Univariate logistic regression models were used to evaluate the association between age, ethnicity, tumor stage, tumor grade, and presence of TILs, with pCR (RCB 0) versus residual disease (RCB 1, 2, 3). For a secondary analysis, responses were grouped as RCB 0, 1 versus RCB 2, 3 to further assess potential associations, as shown in Table 2 . Tumor grade and the presence of TILs were the only variables significantly associated with pCR in the first grouping; however, only TILs remained significant in the second grouping. Specifically, the presence of TILs was associated with increased pCR rates compared to those without TILs ( 69.6 % vs 49.1%) , which was statistically significant by univariate analysis ( p = 0.0027) and Chi-squared test (χ² = 10.75, p = 0.013). Within ethnic subpopulations, Hispanic patients with TILs demonstrated a higher pCR rate (80.0%) compared to those without TILs (51.5%) (p = 0.0254). Although a trend toward significance was observed, the sample was underpowered to detect a definitive association. No other ethnic group demonstrated an association between the presence of TILs and pCR. Univariate logistic regression models were used to evaluate associations between ethnicity, tumor stage, nodal status, tumor grade, presence of TILs, RCB, and pCR. Only tumor grade and the presence of TILs were significantly associated with pCR. Patients with grade 3 tumors were 2.89 times more likely to achieve pCR than those with lower-grade disease (95% CI: 1.28–11.82; p = 0.017), and higher tumor grade remained associated with increased odds of pCR in a separate model comparing grade 3 to grade 2 (OR = 2.76; 95% CI: 0.998–7.62; p = 0.0505). After adjusting for tumor grade, patients with TILs were 2.44 times more likely to achieve pCR (95% CI: 1.31–4.55; p = 0.005). A model incorporating both TILs and tumor grade demonstrated moderate predictive performance, with an AUC of 0.64 (95% CI: 0.562–0.708). The ROC curve for the stepwise logistic regression model, which included TILs and tumor grade, demonstrated moderate predictive performance, with an AUC of 0.64 (95% CI: 0.562–0.708). Table 2 pCR (RCB = 0) Rates in Subpopulations pCR (RCB = 0) RD (RCB ≥ 1) All p Value n = 108 n = 79 n = 184 Patient Demographics Age at diagnosis—x̄±s 49.6 ± 11.2 53.3 ± 12.6 51 ± 11.9 0.035 Ethnicity—No. (%) 0.465 Asian 7 (43.8) 9 (56.3) 16 (8.6) Black 25 (53.2) 22 (46.8) 47 (25.3) Hispanic 37 (63.8) 21 (36.2) 58 (31.8) White 38 (58.5) 27 (41.5) 65 (34.9) Hispanic—No. (%) 0.245 Hispanic 37 (63.8) 21(36.2) 58 (31.2) Non-Hispanic 70 (54.7) 58 (45.3) 128 (68.8) White—No. (%) 0.850 Non-White 69 (57.0) 52 (43.0) 121 (65.1) White 38 (58.5) 27 (41.5) 65 (34.9) Tumor Characteristics Tumor Classification (T)—No. (%) 0.565 T 1,2 76 (58.9) 53 (41.1) 129 (69.4) T 3,4 31 (54.4) 26 (45.6) 57 (30.7) Nodal Status (N)—No. (%) 0.703 Negative 53 (56.4) 41 (43.6) 94 (50.3) Positive 55 (59.1) 38 (40.9) 93 (49.7) TILs present—No. (%) 0.005 Absent 53 (49.1) 55 (50.9) 108 (57.8) Present 55 (69.6) 24 (30.4) 79 (42.2) Grade—No. (%) 0.013 2 5 (29.4) 12 (70.6) 17 (9.1) 3 103 (60.1) 67 (39.4) 170 (90.9) TILs by Ethnicity Hispanic)—No. (%) n = 57 0.025 TILs present 20 (80.0) 5 (20.0) 25 (39.7 ) TILs absent 17 (51.5 ) 16 (48.5 ) 33 (60.3) Black)—No. (%) n = 46 0.096 TILs present 14 (66.7 ) 7 (33.3) 21 (45.7) TILs absent 11 (16.9) 15 (83.1) 65 (54.3) White)—No. (%) n = 64 0.214 TILs present 20 (66.7) 10 (33.3) 30 (46.9) TILs absent 18 (51.4) 17 (48.6) 35 (53.1) Asian—No. (%) n = 17 0.671 TILs present 2 (50) 2 (50) 4 (23.5) TILs absent 5 (41.7) 7 (58.3) 12 (76.5) Node Positivity and Association with pCR in Patients with TILs Within the TIL population, node positivity (N) was associated with statistically significant improvement in pCR rates (p = 0.0112). This association was further pronounced when these patients were regrouped into RCB 0,1 and RCB 2,3 (p = 0.0051). However, the presence of TILs was not associated with improvement in pCR in node-negative patients regardless of grouping (RCB 0 vs 1,2,3: p = 0.1471 and RCB 0,1 vs 2,3: p = 0.1567). Discussion In this real-world retrospective analysis, the presence of TILs in early-stage TNBC patients demonstrated a strong predictive value for achieving pCR response to the K522 regimen. This association was particularly pronounced in specific subpopulations, with TILs more strongly correlated with pCR in Hispanic patients and patients with positive nodal involvement. Our findings are consistent with the results of the K522 trial and suggest potential subgroups in which the role of TILs may warrant further investigation as predictors of treatment response. Previous studies have demonstrated that pCR rates to neoadjuvant chemo-immunotherapy in TNBC are overall higher than with chemotherapy alone, yet response varies by patient demographics. In the original K522 trial, addition of pembrolizumab significantly improved pCR rates (64.8%) compared to chemotherapy alone (51.2%). 1 However, secondary subgroup analyses demonstrated lower efficacy in different study populations, specifically patients enrolled in Asian countries.[ 9 ] Likewise, analyses of real-world applications of the K522 regimen have also reported lower pCR rates than those seen in the controlled trial setting.[ 9 – 12 ] Prior research on neoadjuvant chemotherapy response has shown that Black patients with TNBC have lower pCR rates,[ 13 ] and that older age is associated with reduced treatment response.[ 12 ] , [ 14 ] Our study mirrors these real-world findings, reporting a higher overall pCR rate with pembrolizumab, though slightly lower (57%) than that observed in the original trial. This difference may be attributable to the demographic composition of our cohort, which included a higher proportion of Hispanic and Black patients and an older median age. While our sample size was not sufficient to detect statistically significant differences across subgroups, these results reinforce the need to consider demographic variability when evaluating immunotherapy responses in real-world settings. Regardless, the relative benefit of the K522 regimen was seen across all ethnicities. While the K522 trial did not evaluate TILs in relation to immunotherapy response, growing evidence highlights their role as a surrogate of the host anti-tumor immune response. Their presence has been associated with favorable outcomes in both treated and untreated TNBC.[ 15 ] The International TILs Working Group established a standardized scoring system with high interobserver reproducibility, supporting TILs as a viable and standardizable biomarker. 9 Several studies evaluated the predictive value of TILs in the context of chemotherapy. For example, a pooled analysis showed that patients with primary TNBC treated with NAC had a stepwise increase in pCR with increasing TILs, as well as improved disease-free and overall survival with incremental increase in TIL density.[ 15 ] Although the predictive role of TILs in response to immunotherapy is less defined, emerging data is encouraging. In metastatic TNBC, biomarker analyses from the KEYNOTE-086 and KEYNOTE-119 trials showed associations between TIL levels and both treatment response and overall outcomes with single-agent pembrolizumab.[ 16 , 17 ] In early-stage disease, although the GeparNuevo trial found that TILs predicted higher pCR rates in both neoadjuvant immunotherapy and placebo arms, a dynamic increase in TILs density was independently associated with increased pCR in the immunotherapy group alone.[ 18 ] Furthermore, the KEYNOTE-173 and I-SPY2 trials demonstrated correlations between higher baseline TILs and pCR after treatment with pembrolizumab and NAC.[ 19 , 20 ] Similar to these prior studies, our findings also demonstrated a significant association between TILs and increased pCR. Collectively, these data support the potential use of TILs as a predictive marker of response in early TNBC. The predictive significance of TILs was particularly pronounced in Hispanic patients within our cohort. We speculate that this enhanced predictive value of TILs may be related to underlying diversity in genetic ancestry and allelic predisposition. One well-characterized example of a population-based polymorphism is the human leukocyte antigen (HLA) gene. Greater heterozygosity of HLA alleles has been associated with more effective T-cell response to malignancies, higher density of TILs, and better outcomes with immunotherapy, likely due to the presentation of a broader range of tumor antigens and higher density of TILs. Notably, Hispanics in the U.S have been found to have increased allelic heterozygosity at the HLA locus, reflecting a complex genetic ancestry.[ 21 , 22 ] Evidence of increased genetic diversity, especially HLA polymorphism, may contribute to the observed strength of association between TILs and treatment response in Hispanic patients, and immunotherapy responses among Hispanic patient populations, highlighting the importance of incorporating genetic ancestry into immunotherapy research and biomarker development. In the original K522 trial, patients with node-positive disease or with high-grade (grade 3) tumors had higher pCR rates with the addition of pembrolizumab compared to those with low-stage or grade disease, suggesting that tumor burden may enhance immunotherapy responsiveness. 1 Our findings also demonstrated that high-grade tumors had increased pCR rates with the K522 regimen. In addition, for patients with node-positive disease, the presence of TILs was predictive for pCR. Lymph node involvement reflects more aggressive tumor biology, characterized by higher mutational burden, and promotes TIL recruitment and immune activation. Our study confirms and extends these findings, as the predictive value of TILs was more pronounced in patients with node-positive disease. Patients with high-risk features such as high tumor stage or grade have a higher baseline risk of recurrence, so the relationship between TILs and pCR may have prognostic value for long-term outcomes in these patients. Conclusion In conclusion, the findings in this study demonstrate a promising predictive value of TILs in a diverse, real-world patient population receiving the K522 regimen. The ability of TILs to predict response to immunotherapy, especially within specific sub-populations, may provide guidance to de-escalate chemotherapy for these patients. Given the high potential for toxicity with the K522 regimen, identifying patients likely to respond to de-escalated treatment based on immune biomarkers could help avoid overtreatment and reduce unnecessary adverse events. Establishing TILs as a predictive marker could not only enhance patient stratification for tailored therapies but also lead to improved outcomes in diverse populations affected by early-stage TNBC. This study is subject to several limitations. Our study was underpowered to detect definitive associations within each subpopulation, although certain groups demonstrated trends toward significance. Larger, targeted studies focusing on Hispanic patients and those with node positive TNBC are warranted to better understand the predictive value of TILs. We excluded patients who experienced disease during neoadjuvant therapy in the statistical analysis, and including these patients could have resulted in a notable difference in efficacy, favoring patients with TIL expression. Furthermore, TILs data were extracted from pathology reports, and biopsy slides were not independently reviewed for purposes of TILs identification or quantification. The presence of TILS is routinely documented in pathology reports at our institution. More nuanced information on the quantity of TILs would be more informative; however, quantitative assessment based on the standardized criteria established by the International TILs Working Group is not part of routine practice. Furthermore, our analyses did not account for specific characteristics of TILs such as variations in location and functional activity, which may also influence treatment response. We recommend further study of the predictive value of TILs based on density and specific histopathologic features. This information may highlight clinically meaningful thresholds that more specifically correlate with treatment response. Current College of American Pathologists guidelines do not mandate standardized reporting of TILs in pathology reports. However, to fully assess the potential of TILs as a predictive marker in early-stage TNBC, future prospective studies that incorporate standardized reporting are critical, especially to explore the role of TILs in guiding de-escalation strategies for chemotherapy. Declarations Funding : The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Riya Albert and Joshua Thomas. The first draft of the manuscript was written by Riya Albert, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data availability: The data presented in the current study are available from the corresponding author on reasonable request. Ethics Approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the University of Texas Southwestern (April 29, 2024/STU 062016-056). Consent to Participate: Informed consent was obtained from all individual participants included in the study. The authors affirm that informed consent was provided for publication of the images in Figure 1. References Schmid P, Cortes J, Pusztai L et al (2020) Pembrolizumab for Early Triple-Negative Breast Cancer. N Engl J Med 382:810–821. https://doi.org/10.1056/NEJMoa1910549 Schmid P, Cortes J, Dent R et al (2024) Overall Survival with Pembrolizumab in Early-Stage Triple-Negative Breast Cancer. 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Hum Immunol 61:334–340. https://doi.org/10.1016/S0198-8859(99)00155-X Arrieta-Bolaños E, Hernández-Zaragoza DI, Barquera R (2023) An HLA map of the world: A comparison of HLA frequencies in 200 worldwide populations reveals diverse patterns for class I and class II. Front Genet 14:866407. https://doi.org/10.3389/fgene.2023.866407 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-9096569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621759890,"identity":"f9a5631a-93cc-47f8-9588-908f434a8dc4","order_by":0,"name":"Riya Albert","email":"data:image/png;base64,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","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Riya","middleName":"","lastName":"Albert","suffix":""},{"id":621759891,"identity":"232e35e7-1210-419c-bc69-1d906d689b20","order_by":1,"name":"Joshua Thomas","email":"","orcid":"","institution":"The University of Texas at Austin","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Thomas","suffix":""},{"id":621759892,"identity":"5d191695-83ed-485e-95b8-361e0a52694b","order_by":2,"name":"Navid Sadeghi","email":"","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Navid","middleName":"","lastName":"Sadeghi","suffix":""},{"id":621759893,"identity":"a3a35a5b-9d5f-426d-ab03-84f3b294fd06","order_by":3,"name":"Sangeetha Reddy","email":"","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Sangeetha","middleName":"","lastName":"Reddy","suffix":""},{"id":621759894,"identity":"d7a5c7ca-e483-4057-a3ec-3c0c7a3a2710","order_by":4,"name":"Glenda Delgado","email":"","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Glenda","middleName":"","lastName":"Delgado","suffix":""},{"id":621759895,"identity":"453fa65c-febd-48df-bbdb-fd22567b78ce","order_by":5,"name":"Heather McArthur","email":"","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Heather","middleName":"","lastName":"McArthur","suffix":""},{"id":621759896,"identity":"72b0bd23-16a0-4495-9680-096a516cd29d","order_by":6,"name":"Samira Syed","email":"","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Samira","middleName":"","lastName":"Syed","suffix":""},{"id":621759897,"identity":"a9b975c0-1e95-4a65-bdd9-1a01bc30818e","order_by":7,"name":"Deborah Farr","email":"","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Deborah","middleName":"","lastName":"Farr","suffix":""},{"id":621759898,"identity":"1dece4e3-067f-4b1d-bd5e-24af83f9c459","order_by":8,"name":"Nisha Unni","email":"","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Nisha","middleName":"","lastName":"Unni","suffix":""}],"badges":[],"createdAt":"2026-03-11 16:23:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9096569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9096569/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107446822,"identity":"765cd196-c944-41e0-9dfb-ae8412982718","added_by":"auto","created_at":"2026-04-21 14:52:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExample pathology slide of TILs in TNBC patient low (a) and high power (b)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig.png","url":"https://assets-eu.researchsquare.com/files/rs-9096569/v1/810e82bd40c63165f26f4f6e.png"},{"id":107490077,"identity":"84d276a3-0dbe-4208-b164-9c7e1ca7b238","added_by":"auto","created_at":"2026-04-22 02:50:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":519969,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9096569/v1/a2e4818d-44c6-4b76-b60c-97c2e8f43168.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eTumor Infiltrating Lymphocytes (TILs) as a Predictive Marker of Pathological Complete Response (pCR) in a Diverse Patient Population with Early Triple Negative Breast Cancer (TNBC) Treated with Neoadjuvant Real-World KEYNOTE-522 Regimen\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTriple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer defined by its lack of estrogen receptors (ER), progesterone receptors (PR), and HER2/neu. It accounts for 15% of all breast cancer diagnoses, yet due to the lack of typical receptor biomarkers, there are limited effective targeted therapies in early-stage disease. Patients with TNBC tend to present with higher-stage disease and generally have worse prognoses than other breast cancer subtypes. Previously, the standard practice for treatment was neoadjuvant chemotherapy (NAC), using anthracycline- and taxane-based regimens.\u003c/p\u003e \u003cp\u003eIn July 2021, based on results from the KEYNOTE-522 (K522) trial, the FDA approved pembrolizumab (Keytruda) with chemotherapy as neoadjuvant therapy for high-risk early-stage TNBC, followed by adjuvant monotherapy after surgery, which significantly improved prognosis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The K522 trial examined rates of pathologic complete response (pCR) as a preliminary efficacy outcome measure due to its association with good prognosis and long-term disease-free survival[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Compared with neoadjuvant chemotherapy alone, the K522 regimen was associated with a higher pCR rate (64.8% vs. 51.2%) and a clinically significant improvement in event-free survival at 60 months (86.6% vs. 81.7%)\u003c/p\u003e \u003cp\u003eDespite these promising improvements in outcomes, response to neoadjuvant therapy with the K522 regimen in early-stage TNBC is still variable, and our understanding of what underlying characteristics may be associated with successful treatment response is limited. Investigating the clinical and pathology-based variables linked to pCR following the K522 regimen may help identify predictive factors for anticipating treatment response.\u003c/p\u003e \u003cp\u003eThe presence of tumor-infiltrating lymphocytes (TILs) has been identified as a predictive biomarker for TNBC, as high levels of TILs are associated with better overall survival and disease-free survival.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Previous studies correlate high TIL levels with better response to NAC, especially in aggressive subtypes, such as TNBC.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] However, the extent to which TILs predict response to neoadjuvant immunotherapy in early-stage TNBC, particularly in comparison to other tumor and clinical factors, remains unclear. To investigate the predictive value of TILs in early-stage TNBC, we conducted a retrospective cohort study in a clinically diverse patient population who received the K522 regimen to determine whether the presence of TILs confers a predictive value in response to neoadjuvant pembrolizumab treatment compared to various other tumor and clinical factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Selection\u003c/h2\u003e \u003cp\u003e We reviewed electronic medical records of 271 early-stage TNBC patients who received neoadjuvant treatment in accordance with the KEYNOTE-522 regimen at an NCI-designated comprehensive cancer center, UT Southwestern Harold C. Simmons Comprehensive Cancer Center (UTSW-SCCC), and its affiliated safety-net hospital, Parkland Health (PH), between August 2021 to December 2024. Using retrospective chart review from both institutions, we identified 184 patients who completed the neoadjuvant portion of the study and underwent surgery at the time of chart review. Patients were excluded if they had metastatic disease at the time of diagnosis, did not proceed with surgery due to disease progression or adverse events, or had not undergone surgery at the time of data collection.\u003c/p\u003e \u003cp\u003e This study was approved by the Institutional Review Board (IRB) of UT Southwestern Medical Center and Parkland Health.\u003c/p\u003e \u003cp\u003eData was collected from each institution through medical record review and included the following details: patient ID, site, age at diagnosis, date of birth (DOB), race, ethnicity, tumor type, tumor stage (T and N), chemotherapy regimen, number of cycles, presence of TILs in the resected primary breast tumor, post-surgery tumor characteristics (ypT, ypN, residual cancer burden [RCB]), hormone receptor status (estrogen receptor [ER] and progesterone receptor [PR] expression at diagnosis and on surgical samples, reported as percentage of tumor cell nuclei staining positive via immunohistochemistry), HER2 status (by IHC and FISH at diagnosis and on surgical samples), tumor grade, Ki-67 proliferation index at diagnosis and on surgical samples, history of autoimmune disease, and additional notes.\u003c/p\u003e \u003cp\u003eTNBC was defined as the absence of estrogen receptor (ER) and progesterone receptor (PR) expression (\u0026lt;\u0026thinsp;10%) and a HER2-negative status (score of 0+, 1+, or 2\u0026thinsp;+\u0026thinsp;with a confirmatory non-amplified result from fluorescent in situ hybridization), in accordance with the most recent American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) guidelines[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRCB ranged from RCB 0, indicating pCR, to RCB III, indicating extensive residual disease. The classifications were determined using MD Anderson\u0026rsquo;s web calculator for residual cancer burden. pCR was defined as the lack of residual invasive carcinoma in both breast tumor bed tissue and axillary lymph nodes found at the time of surgical resection following neoadjuvant therapy, with final pathologic stages as ypT0/Tis and ypN0.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImmunohistochemistry and TILs assessment\u003c/h3\u003e\n\u003cp\u003eTumor-infiltrating lymphocytes (TILs) were evaluated in formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples stained with hematoxylin and eosin (H\u0026amp;E) from baseline biopsies, in accordance with the guidelines established by the International TILs Working Group[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e TILs were assessed in both intratumoral regions (invasive tumor nests, including intraepithelial areas) and stromal regions (intratumoral stroma. The evaluation prioritized intratumoral stromal TILs, followed by peripheral stromal TILs, particularly within 1\u0026ndash;2 mm of the tumor-stroma interface. Both regions were included in the final TILs assessment, with greater emphasis placed on intratumoral stromal TILs.\u003c/p\u003e \u003cp\u003eBased on the guidelines of the International TILs Working Group, TILs were quantified as a percentage of the total stromal or intratumoral area occupied by mononuclear cells (including lymphocytes and plasma cells)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While TILs are typically reported in 10% increments, pathologists also estimated percentages in smaller increments when necessary. TILs were considered significant when they constituted approximately 25\u0026ndash;30% or more of the assessed area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted to evaluate the association between pathological complete response (pCR) rates and various demographic and clinical factors, including tumor-infiltrating lymphocytes (TILs), tumor grade, and residual cancer burden (RCB). Two-proportion Z-tests and Chi-squared tests for independence were employed to compare pCR rates across different demographic groups, including ethnicity and the presence or absence of TILs. Additionally, univariate logistic regression models were used to assess the relationship between the presence of TILs, tumor grade, and RCB with pCR outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe median age of our patient population was 51 years, with an age range of 24 to 83 years. Of which, 34.8% self-identified as Caucasian (C), 31.0% Hispanic (H), 25.5% Black (B), and 8.7% other. All patients were female. Patient demographics, tumor type, size (T), and nodal status (N) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Along with immunotherapy, most patients received an anthracycline/taxane-based chemotherapy regimen, starting with four cycles of paclitaxel and carboplatin followed by another four cycles of doxorubicin and cyclophosphamide. 68.6% of patients completed 8 or more cycles of neoadjuvant therapy, and 81.5% of patients completed at least 75% of treatment. Among patients who completed less than 8 cycles, 81.1% discontinued due to adverse reactions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient Demographics and Clinical Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003ePatient Demographics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;184\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eParameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e51\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e13.6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e30.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e6.8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e64 (34.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e46 (25.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e57 (30.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAsian/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e16 (8.7)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTumor Characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDuctal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e171 (92.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLobular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e5 (2.7)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e8 (4.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0 (0.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e28 (15.2)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e156 (84.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTumor Classification (T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e139 (75.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT3,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e45 (24.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNodal Status (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e83 (45.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e101 (54.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eResponse to Treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePathological Response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRCB 0 (pCR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e105 (57.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRCB 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e12 (6.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRCB 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e42 (22.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRCB 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e25 (13.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePathological Complete Response Rate in Subpopulations\u003c/h3\u003e\n\u003cp\u003eOverall, 57% of patients achieved a pathological complete response (pCR). TILs were present and reported in 52.8% of pathology reports. We did not see an increased presence of TILs in any ethnic population (χ\u0026sup2; = 2.5806, p\u0026thinsp;=\u0026thinsp;0.4609).\u003c/p\u003e \u003cp\u003eUnivariate logistic regression models were used to evaluate the association between age, ethnicity, tumor stage, tumor grade, and presence of TILs, with pCR (RCB 0) versus residual disease (RCB 1, 2, 3). For a secondary analysis, responses were grouped as RCB 0, 1 versus RCB 2, 3 to further assess potential associations, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Tumor grade and the presence of TILs were the only variables significantly associated with pCR in the first grouping; however, only TILs remained significant in the second grouping. Specifically, the presence of TILs was associated with increased pCR rates compared to those without TILs (\u003cem\u003e69.6\u003c/em\u003e% vs \u003cem\u003e49.1%)\u003c/em\u003e, which was statistically significant by univariate analysis \u003cem\u003e(\u003c/em\u003ep\u0026thinsp;=\u0026thinsp;0.0027) and Chi-squared test (χ\u0026sup2; = 10.75, p\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e \u003cp\u003eWithin ethnic subpopulations, Hispanic patients with TILs demonstrated a higher pCR rate (80.0%) compared to those without TILs (51.5%) (p\u0026thinsp;=\u0026thinsp;0.0254). Although a trend toward significance was observed, the sample was underpowered to detect a definitive association. No other ethnic group demonstrated an association between the presence of TILs and pCR.\u003c/p\u003e \u003cp\u003eUnivariate logistic regression models were used to evaluate associations between ethnicity, tumor stage, nodal status, tumor grade, presence of TILs, RCB, and pCR. Only tumor grade and the presence of TILs were significantly associated with pCR. Patients with grade 3 tumors were 2.89 times more likely to achieve pCR than those with lower-grade disease (95% CI: 1.28\u0026ndash;11.82; p\u0026thinsp;=\u0026thinsp;0.017), and higher tumor grade remained associated with increased odds of pCR in a separate model comparing grade 3 to grade 2 (OR\u0026thinsp;=\u0026thinsp;2.76; 95% CI: 0.998\u0026ndash;7.62; p\u0026thinsp;=\u0026thinsp;0.0505). After adjusting for tumor grade, patients with TILs were 2.44 times more likely to achieve pCR (95% CI: 1.31\u0026ndash;4.55; p\u0026thinsp;=\u0026thinsp;0.005). A model incorporating both TILs and tumor grade demonstrated moderate predictive performance, with an AUC of 0.64 (95% CI: 0.562\u0026ndash;0.708).\u003c/p\u003e \u003cp\u003eThe ROC curve for the stepwise logistic regression model, which included TILs and tumor grade, demonstrated moderate predictive performance, with an AUC of 0.64 (95% CI: 0.562\u0026ndash;0.708).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003epCR (RCB\u0026thinsp;=\u0026thinsp;0) Rates in Subpopulations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epCR (RCB\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRD (RCB\u0026thinsp;\u0026ge;\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;108\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;79\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;184\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatient Demographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis\u0026mdash;x̄\u0026plusmn;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e49.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e53.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e51\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u0026mdash;No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.465\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e7 (43.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e9 (56.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e16 (8.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e25 (53.2)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e22 (46.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e47 (25.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e37 (63.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e21 (36.2)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e58 (31.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e38 (58.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e27 (41.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e65 (34.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u0026mdash;No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.245\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e37 (63.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e21(36.2)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e58 (31.2)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e70 (54.7)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e58 (45.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e128 (68.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u0026mdash;No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.850\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e69 (57.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e52 (43.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e121 (65.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e38 (58.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e27 (41.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e65 (34.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor Characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Classification (T)\u0026mdash;No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.565\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT 1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e76 (58.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e53 (41.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e129 (69.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT 3,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e31 (54.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e26 (45.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e57 (30.7)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodal Status (N)\u0026mdash;No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.703\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e53 (56.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e41 (43.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e94 (50.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e55 (59.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e38 (40.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e93 (49.7)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs present\u0026mdash;No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e53 (49.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e55 (50.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e108 (57.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e55 (69.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e24 (30.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e79 (42.2)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u0026mdash;No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e5 (29.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e12 (70.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e17 (9.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e103 (60.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e67 (39.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e170 (90.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTILs by Ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic)\u0026mdash;No. (%)\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e20 (80.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5 (20.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e25 (39.7 )\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs absent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e17 (51.5 )\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e16 (48.5 )\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e33 (60.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack)\u0026mdash;No. (%)\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.096\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e14 (66.7 )\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e7 (33.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e21 (45.7)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs absent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e11 (16.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e15 (83.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e65 (54.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite)\u0026mdash;No. (%)\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.214\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e20 (66.7)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e10 (33.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e30 (46.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs absent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e18 (51.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e17 (48.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e35 (53.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u0026mdash;No. (%)\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.671\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e2 (50)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2 (50)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e4 (23.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs absent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e5 (41.7)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e7 (58.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e12 (76.5)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNode Positivity and Association with pCR in Patients with TILs\u003c/h2\u003e \u003cp\u003eWithin the TIL population, node positivity (N) was associated with statistically significant improvement in pCR rates (p\u0026thinsp;=\u0026thinsp;0.0112). This association was further pronounced when these patients were regrouped into RCB 0,1 and RCB 2,3 (p\u0026thinsp;=\u0026thinsp;0.0051). However, the presence of TILs was not associated with improvement in pCR in node-negative patients regardless of grouping (RCB 0 vs 1,2,3: p\u0026thinsp;=\u0026thinsp;0.1471 and RCB 0,1 vs 2,3: p\u0026thinsp;=\u0026thinsp;0.1567).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this real-world retrospective analysis, the presence of TILs in early-stage TNBC patients demonstrated a strong predictive value for achieving pCR response to the K522 regimen. This association was particularly pronounced in specific subpopulations, with TILs more strongly correlated with pCR in Hispanic patients and patients with positive nodal involvement. Our findings are consistent with the results of the K522 trial and suggest potential subgroups in which the role of TILs may warrant further investigation as predictors of treatment response.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that pCR rates to neoadjuvant chemo-immunotherapy in TNBC are overall higher than with chemotherapy alone, yet response varies by patient demographics. In the original K522 trial, addition of pembrolizumab significantly improved pCR rates (64.8%) compared to chemotherapy alone (51.2%).\u003csup\u003e1\u003c/sup\u003e However, secondary subgroup analyses demonstrated lower efficacy in different study populations, specifically patients enrolled in Asian countries.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Likewise, analyses of real-world applications of the K522 regimen have also reported lower pCR rates than those seen in the controlled trial setting.[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Prior research on neoadjuvant chemotherapy response has shown that Black patients with TNBC have lower pCR rates,[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and that older age is associated with reduced treatment response.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Our study mirrors these real-world findings, reporting a higher overall pCR rate with pembrolizumab, though slightly lower (57%) than that observed in the original trial. This difference may be attributable to the demographic composition of our cohort, which included a higher proportion of Hispanic and Black patients and an older median age. While our sample size was not sufficient to detect statistically significant differences across subgroups, these results reinforce the need to consider demographic variability when evaluating immunotherapy responses in real-world settings. Regardless, the relative benefit of the K522 regimen was seen across all ethnicities.\u003c/p\u003e \u003cp\u003eWhile the K522 trial did not evaluate TILs in relation to immunotherapy response, growing evidence highlights their role as a surrogate of the host anti-tumor immune response. Their presence has been associated with favorable outcomes in both treated and untreated TNBC.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] The International TILs Working Group established a standardized scoring system with high interobserver reproducibility, supporting TILs as a viable and standardizable biomarker.\u003csup\u003e9\u003c/sup\u003e Several studies evaluated the predictive value of TILs in the context of chemotherapy. For example, a pooled analysis showed that patients with primary TNBC treated with NAC had a stepwise increase in pCR with increasing TILs, as well as improved disease-free and overall survival with incremental increase in TIL density.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Although the predictive role of TILs in response to immunotherapy is less defined, emerging data is encouraging. In metastatic TNBC, biomarker analyses from the KEYNOTE-086 and KEYNOTE-119 trials showed associations between TIL levels and both treatment response and overall outcomes with single-agent pembrolizumab.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] In early-stage disease, although the GeparNuevo trial found that TILs predicted higher pCR rates in both neoadjuvant immunotherapy and placebo arms, a dynamic increase in TILs density was independently associated with increased pCR in the immunotherapy group alone.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Furthermore, the KEYNOTE-173 and I-SPY2 trials demonstrated correlations between higher baseline TILs and pCR after treatment with pembrolizumab and NAC.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Similar to these prior studies, our findings also demonstrated a significant association between TILs and increased pCR. Collectively, these data support the potential use of TILs as a predictive marker of response in early TNBC.\u003c/p\u003e \u003cp\u003eThe predictive significance of TILs was particularly pronounced in Hispanic patients within our cohort. We speculate that this enhanced predictive value of TILs may be related to underlying diversity in genetic ancestry and allelic predisposition. One well-characterized example of a population-based polymorphism is the human leukocyte antigen (HLA) gene. Greater heterozygosity of HLA alleles has been associated with more effective T-cell response to malignancies, higher density of TILs, and better outcomes with immunotherapy, likely due to the presentation of a broader range of tumor antigens and higher density of TILs. Notably, Hispanics in the U.S have been found to have increased allelic heterozygosity at the HLA locus, reflecting a complex genetic ancestry.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Evidence of increased genetic diversity, especially HLA polymorphism, may contribute to the observed strength of association between TILs and treatment response in Hispanic patients, and immunotherapy responses among Hispanic patient populations, highlighting the importance of incorporating genetic ancestry into immunotherapy research and biomarker development.\u003c/p\u003e \u003cp\u003eIn the original K522 trial, patients with node-positive disease or with high-grade (grade 3) tumors had higher pCR rates with the addition of pembrolizumab compared to those with low-stage or grade disease, suggesting that tumor burden may enhance immunotherapy responsiveness.\u003csup\u003e1\u003c/sup\u003e Our findings also demonstrated that high-grade tumors had increased pCR rates with the K522 regimen. In addition, for patients with node-positive disease, the presence of TILs was predictive for pCR. Lymph node involvement reflects more aggressive tumor biology, characterized by higher mutational burden, and promotes TIL recruitment and immune activation. Our study confirms and extends these findings, as the predictive value of TILs was more pronounced in patients with node-positive disease. Patients with high-risk features such as high tumor stage or grade have a higher baseline risk of recurrence, so the relationship between TILs and pCR may have prognostic value for long-term outcomes in these patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the findings in this study demonstrate a promising predictive value of TILs in a diverse, real-world patient population receiving the K522 regimen. The ability of TILs to predict response to immunotherapy, especially within specific sub-populations, may provide guidance to de-escalate chemotherapy for these patients. Given the high potential for toxicity with the K522 regimen, identifying patients likely to respond to de-escalated treatment based on immune biomarkers could help avoid overtreatment and reduce unnecessary adverse events. Establishing TILs as a predictive marker could not only enhance patient stratification for tailored therapies but also lead to improved outcomes in diverse populations affected by early-stage TNBC.\u003c/p\u003e \u003cp\u003eThis study is subject to several limitations. Our study was underpowered to detect definitive associations within each subpopulation, although certain groups demonstrated trends toward significance. Larger, targeted studies focusing on Hispanic patients and those with node positive TNBC are warranted to better understand the predictive value of TILs. We excluded patients who experienced disease during neoadjuvant therapy in the statistical analysis, and including these patients could have resulted in a notable difference in efficacy, favoring patients with TIL expression. Furthermore, TILs data were extracted from pathology reports, and biopsy slides were not independently reviewed for purposes of TILs identification or quantification. The presence of TILS is routinely documented in pathology reports at our institution. More nuanced information on the quantity of TILs would be more informative; however, quantitative assessment based on the standardized criteria established by the International TILs Working Group is not part of routine practice. Furthermore, our analyses did not account for specific characteristics of TILs such as variations in location and functional activity, which may also influence treatment response. We recommend further study of the predictive value of TILs based on density and specific histopathologic features. This information may highlight clinically meaningful thresholds that more specifically correlate with treatment response. Current College of American Pathologists guidelines do not mandate standardized reporting of TILs in pathology reports. However, to fully assess the potential of TILs as a predictive marker in early-stage TNBC, future prospective studies that incorporate standardized reporting are critical, especially to explore the role of TILs in guiding de-escalation strategies for chemotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Riya Albert and Joshua Thomas. The first draft of the manuscript was written by Riya Albert, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the University of Texas Southwestern (April 29, 2024/STU 062016-056).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003eThe authors affirm that informed consent was provided for publication of the images in Figure 1.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchmid P, Cortes J, Pusztai L et al (2020) Pembrolizumab for Early Triple-Negative Breast Cancer. 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Ann Oncol 31:569\u0026ndash;581. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.annonc.2020.01.072\u003c/span\u003e\u003cspan address=\"10.1016/j.annonc.2020.01.072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeon-Ferre RA, Jonas SF, Salgado R et al (2024) Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer. 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Front Genet 14:866407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2023.866407\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2023.866407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"tumor-infiltrating lymphocytes, triple-negative breast cancer, neoadjuvant therapy, immunotherapy, pathological complete response","lastPublishedDoi":"10.21203/rs.3.rs-9096569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9096569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e \u003cp\u003eTriple-negative breast cancer (TNBC) is an aggressive subtype characterized by poor prognosis. Based on the KEYNOTE-522 trial, neoadjuvant pembrolizumab plus chemotherapy has become the standard of care due to significantly improved pathological complete response (pCR) rates. The presence of tumor-infiltrating lymphocytes (TILs) is a predictive biomarker of pCR. This retrospective cohort study examines a diverse patient population treated with the K522 regimen to determine if TILs predict pCR relative to other clinical and tumor-specific factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively reviewed 187 patients with early-stage TNBC at two institutions (one tertiary care, one safety-net) who completed neoadjuvant K522 treatment between 2021\u0026ndash;2024. Statistical analyses included Chi-squared tests, Z-tests, and univariate logistic regression to evaluate associations between TILs, ethnicity, tumor grade, and pCR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall pCR rate was 57%; TILs were present in 52.8% of cases. TILs were associated with a significantly higher pCR rate (70% vs. 48% without TILs; p\u0026thinsp;=\u0026thinsp;0.0027). While pCR rates were similar across ethnicities, Hispanic patients with TILs had significantly higher pCR than those without (80.0% vs. 51.5%; p\u0026thinsp;=\u0026thinsp;0.0254). Controlling for grade, patients with TILs were 2.442 times more likely to achieve pCR (CI: 1.310\u0026ndash;4.553; p\u0026thinsp;=\u0026thinsp;0.0050). Grade 3 tumors and node-positivity with TILs also showed statistically significant rates of pCR.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eTILs serve as a strong predictive biomarker for immunotherapy response in a real-world TNBC population. Our findings regarding Hispanic and node-positive patients suggest TILs could guide treatment de-escalation to reduce K522-related toxicity. Standardizing TILs reporting is critical to optimizing treatment strategies and improving outcomes in underrepresented populations.\u003c/p\u003e","manuscriptTitle":"Tumor Infiltrating Lymphocytes (TILs) as a Predictive Marker of Pathological Complete Response (pCR) in a Diverse Patient Population with Early Triple Negative Breast Cancer (TNBC) Treated with Neoadjuvant Real-World KEYNOTE-522 Regimen","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 14:52:40","doi":"10.21203/rs.3.rs-9096569/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T22:22:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T19:13:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115102348324973360356422579785993122749","date":"2026-05-01T19:46:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T22:58:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-12T06:33:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T06:32:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research and Treatment","date":"2026-03-11T16:13:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e9e6b819-61c6-4592-b8c2-b73f00a285e3","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T22:22:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T19:13:15+00:00","index":21,"fulltext":""},{"type":"reviewerAgreed","content":"115102348324973360356422579785993122749","date":"2026-05-01T19:46:31+00:00","index":20,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T22:38:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 14:52:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9096569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9096569","identity":"rs-9096569","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}