On-treatment biopsies to predict response to neoadjuvant chemotherapy for breast cancer

On-treatment biopsies to predict response to neoadjuvant chemotherapy for breast cancer | 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 On-treatment biopsies to predict response to neoadjuvant chemotherapy for breast cancer Bruno Valentin Sinn, Katharina Sychra, Michael Untch, Thomas Karn, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4483953/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Sep, 2024 Read the published version in Breast Cancer Research → Version 1 posted 9 You are reading this latest preprint version Abstract Background Patients with pathologic complete response (pCR) to neoadjuvant chemotherapy for invasive breast cancer (BC) have better outcomes, potentially warranting less extensive surgical and systemic treatments. Early prediction of treatment response could aid in adapting therapies. Methods On-treatment biopsies from 297 patients with invasive BC in three randomized, prospective neoadjuvant trials were assessed. BC quantity, tumor-infiltrating lymphocytes (TILs), and the proliferation marker Ki-67 were compared to pre-treatment samples. The study investigated the correlation between residual cancer, changes in Ki-67 and TILs, and their impact on pathologic complete response (pCR) and disease-free survival (DFS). Results Among the 297 samples, 138 (46%) were hormone receptor-positive (HR+)/human epidermal growth factor 2-negative (HER2-), 87 (29%) were triple-negative (TNBC), and 72 (24%) were HER2+. Invasive tumor cells were found in 70% of on-treatment biopsies, with varying rates across subtypes (HR+/HER2-: 84%, TNBC: 62%, HER2+: 51%; p < 0.001). Patients with residual tumor on-treatment had an 8% pCR rate post-treatment (HR+/HER2-: 3%, TNBC: 19%, HER2+: 11%), while those without any invasive tumor had a 50% pCR rate (HR+/HER2-: 27%; TNBC: 48%, HER2+: 66%). Sensitivity for predicting residual disease was 0.81, with positive and negative predictive values of 0.92 and 0.50, respectively. Increasing TILs from baseline to on-treatment biopsy (if residual tumor was present) were linked to higher pCR likelihood in the overall cohort (OR 1.034, 95% CI 1.013–1.056 per % increase; p = 0.001) and with a longer DFS in TNBC (HR 0.980, 95% CI 0.963–0.997 per % increase; p = 0.026). Persisting or increased Ki-67 was associated with lower pCR probability in the overall cohort (OR 0.957, 95% CI 0.928–0.986; p = 0.004) and shorter DFS in TNBC (HR 1.023, 95% CI 1.001–1.047; p = 0.04). Conclusion On-treatment biopsies can predict patients unlikely to achieve pCR post-therapy. This could facilitate therapy adjustments for TNBC or HER2 + BC. They also might offer insights into therapy resistance mechanisms. Future research should explore whether standardized or expanded sampling enhances the accuracy of on-treatment biopsy procedures. Trial Registration GeparQuattro (EudraCT 2005-001546-17; Start date: 28.06.2005), GeparQuinto (EudraCT 2006-005834-19; Start date: 27.10.2007) and GeparSixto (EudraCT 2011-000553-23; Start date: 29.09.2011). breast cancer neoadjuvant therapy serial biopsies TILs Ki-67 Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Retrospective analyses of prospective breast cancer (BC) trials have shown comparable efficacy between chemotherapy administered in the adjuvant versus neoadjuvant settings ( 1 ). Excellent response, defined as achieving pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT), varies across breast cancer (BC) subtypes and is strongly influenced by the treatment regimen used. During the analyzed trials, pCR rates were approximately 50% and 30% for patients with triple-negative and HER2-positive disease, respectively. ( 2 ). In recent developments, incorporating immune checkpoint inhibitors into NACT for TNBC resulted in pCR rates of 64.8% ( 3 ), while employing dual anti-HER2 blockade yielded pCR rates of 66.2% in HER2-positive disease, depending on hormone receptor status ( 4 ). Excellent response serves as a prognostic indicator for patient survival, especially in triple-negative and HER2-positive disease ( 5 ), and neoadjuvant therapy can serve as an in vivo assay for chemotherapy response. Prediction of therapy response is of clinical interest offering the potential to customize treatment approaches and enhance response rates. With the emergence of new therapies and refined treatment protocols, there arises the question of whether de-escalating local and/or systemic treatment is viable for patients with a strong likelihood of achieving pCR ( 6 ). For example, PET-based imaging can be used to predict pCR in patients with HER2-positive BC during neoadjuvant treatment with dual anti-HER2 treatment ( 7 ). The conventional method for identifying response markers involves correlating genomic measurements from pre-therapeutic samples with clinical outcomes ( 8 ). On-treatment tissue samples offer the opportunity for microscopic confirmation of response, biomarker examination, and tissue provision for translational research. However, the reliability of biopsy procedures and histopathological assessment for predicting on-treatment pCR remains uncertain. The RESPONDER trial examines if vacuum-assisted biopsies can be used to predict response in the breast with a false negative rate below 10% ( 9 ). In neoadjuvant aromatase inhibition for HR + BC, gene expression analysis of on-treatment samples has been shown to predict treatment response and patient survival. ( 10 ). Ki-67 immunohistochemistry can indicate the need to switch to neoadjuvant chemotherapy if Ki-67 levels remain elevated during endocrine treatment alone ( 11 , 12 ). In the context of neoadjuvant chemotherapy (NACT), we assessed on-treatment response using ultrasound in the GeparTrio (G3)( 13 ) and GeparQuinto (G5)( 14 ) trials. In G3, we could demonstrate that response-guided switch of chemotherapy regimens can improve patient outcome. During neoadjuvant chemotherapy (NACT), on-treatment samples can be utilized to uncover molecular mechanisms linked to therapy response, such as immune and proliferation signatures ( 15 ) and to pinpoint potential markers of resistance/response through comparative gene expression analysis between responders and non-responders ( 16 ). Aim of this retrospective-prospective biomarker study was to evaluate the frequency of residual cancer cells in on-treatment samples from neoadjuvant clinical chemotherapy trials for BC, and to correlate their presence, proliferative activity and accompanying immune cells with response to treatment. METHODS Patients and samples Patients were treated within the randomized, multi-center neoadjuvant clinical trials GeparQuattro (G4) ( 17 – 19 ), GeparQuinto ( 14 , 20 , 21 ) and GeparSixto (G6) ( 22 ). Details on the study designs and outcomes are available in the original publications. In brief, G4 was a phase III study comparing the simultaneous or sequential use of capecitabine with epirubicin, cyclophosphamide and docetaxel (EC-T) with concomitant trastuzumab in HER2 + disease. G5 was a phase III study to evaluate EC-T with or without bevacizumab (B) in HER2-negative BC (setting I), to compare pCR rates of patients treated with paclitaxel with or without everolimus with HER2-negative BC without sonographic response after four cycles EC ± B (setting II) and to compare pCR rates in patients treated with EC-T followed by trastuzumab or lapatinib in HER2-positive disease (setting III). G6 was a phase II trial to evaluate the addition of carboplatin to neoadjuvant treatment for patients with triple-negative or HER2-positive BC. Biopsies were obtained at the time of diagnosis and during chemotherapy: in G4 and G5 after 4 of 8 cycles and in G6 after 2 of 6 cycles. All patients with available material in the central GBG tumor bank were eligible for this retrospective biomarker analysis. Of the 1495, 1948 and 588 patients in the G4, G5, and G6 trials, respectively, 106, 145 and 61 matched pre-therapeutic and on-treatment biopsies were available in the biobank and included in the study, resulting in 312 matched pairs. 15 samples had to be excluded due to insufficient pre-treatment material, resulting in a total of 297 matched samples. Table 1 details the baseline patient characteristics. Table 1 Baseline characteristics of the study cohort All G4 G5 G6 Subtype HR-/HER2- 87 (29,3%) 19 (19%) 43 (30,9%) 25 (43,1%) HR+/HER2- 138 (46,5%) 53 (53%) 85 (61,2%) 0 (0%) HER2+ 72 (24,2%) 28 (28%) 11 (7,9%) 33 (56,9%) Response no pCR 235 (79,1%) 81 (81%) 125 (89,9%) 29 (50%) pCR 62 (20,9%) 19 (19%) 14 (10,1%) 29 (50%) Intermediate biopsy tu+ 207 (69,7%) 69 (69%) 112 (80,6%) 26 (44,8%) tu- 90 (30,3%) 31 (31%) 27 (19,4%) 32 (55,2%) cT stage T1 25 (8,4%) 0 (0%) 11 (7,9%) 14 (24,1%) T2 181 (60,9%) 68 (68%) 81 (58,3%) 32 (55,2%) T3 42 (14,1%) 16 (16%) 17 (12,2%) 9 (15,5%) T4 49 (16,5%) 16 (16%) 30 (21,6%) 3 (5,2%) cN stage N0 125 (42,1%) 43 (43%) 51 (36,7%) 31 (53,4%) N1-3 171 (57,6%) 57 (57%) 88 (63,3%) 26 (44,8%) NA 1 (0,3%) 0 (0%) 0 (0%) 1 (1,7%) Grading G1-2 156 (52,5%) 55 (55%) 76 (54,7%) 25 (43,1%) G3 136 (45,8%) 40 (40%) 63 (45,3%) 33 (56,9%) NA 5 (1,7%) 5 (5%) 0 (0%) 0 (0%) Histology NST 268 (90,2%) 89 (89%) 122 (87,8%) 57 (98,3%) Lobular 23 (7,7%) 9 (9%) 14 (10,1%) 0 (0%) Other 6 (2%) 2 (2%) 3 (2,2%) 1 (1,7%) TILs TILs = 60% 25 (8,4%) 5 (5%) 7 (5%) 13 (22,4%) NA 193 (65%) 75 (75%) 118 (84,9%) 0 (0%) All patients provided written informed consent for participation in the study and the utilization of biomaterials for translational research purposes. The study protocol received approval from the relevant ethics committee and national competent authority. Biomarker analysis An experienced pathologist reassessed the biopsies on an H&E-stained slide to identify the presence of invasive breast cancer (BC). On-treatment biopsies were categorized as positive for invasive tumor (tu+) if residual invasive cancer cells were observed. Ductal carcinoma in situ or other precursor lesions were not included in this classification. The presence and quantity of tumor-infiltrating lymphocytes (TILs) in the stromal compartment were documented following the guidelines of the international TIL working group. This involved comparing the H&E-stained slide under review to standardized reference images. ( 23 ). Immunostaining for Ki-67 was performed on a Ventana Discovery XT instrument (Ventana, Tucson, AZ) using the MIB-1 clone (diluted 1:50). Quantification of stained tumor cells was performed using a digital software solution (VMScope, Berlin, Germany) according to recommendation of the Ki-67 in BC working group ( 24 ). For each case, three areas were chosen and counted, and the mean value of the different areas was used for analysis. Statistical considerations Pathologic complete response (pCR) was defined as the absence of invasive or non-invasive BC in the breast and lymph nodes after completion of neoadjuvant treatment (ypT0 ypN0). Disease-free survival (DFS) was defined as the time from study entry to distant or local relapse or death from any cause. Statistical analyses were computed in R 4.0.3 (R Project for Statistical Computing, RRID:SCR_001905). The change of TILs (Δ TILs ) and Ki-67 (Δ Ki−67 ) was calculated as the difference between on-treatment and pre-treatment as a continuous parameter. To test the association of positive on-treatment biopsies (tu+) with tumor characteristics and pCR, chi-squared test was used. The Kaplan Meier method with log rank test was used to illustrate the association of response parameters with DFS. Uni- and bivariate Cox proportional hazard regression models were fit to examine the association of biomarkers with DFS. Logistic regression models were fit to examine the association of biomarkers with pCR. RESULTS Frequencies of residual cancer cells and their association with patient and tumor characteristics (Fig. 1 ) Residual cancer cells were present in biopsies of 207 patients (tu+; 70%) after 4 of 8 cycles (G4, G5) and 2 of 6 cycles chemotherapy (G6), respectively. 90 biopsies showed no residual disease (tu-; 30%). The highest frequencies of tu- biopsies were observed in patients with HER2+ (49%) and TNBC (38%) BC (Fig. 1 ). The highest frequency of tu- patients was observed among patients of the G6 trial (all patients had triple-negative or HER2-positive disease). The frequency of tu- patients was also higher in patients with small tumors. There was no statistically significant association with lymph node status, histological grading, TILs or histologic subtype (Fig. 1 ). Frequencies of residual cancer cells and their association with response to treatment ( Figs. 2 and 3 ) In tu- patients a pCR was observed in 50% (45/90) (Fig. 2 A). In contrast, only 17 of 207 (8%) patients with positive biopsies (tu+) had a pCR after completion of the full treatment course, and 92% had residual diseases (190/207). A similar association could be observed in the different BC subtypes (Fig. 2 A). The distribution of patients with pCR or non-pCR after completion of chemotherapy according to on-treatment biopsies with (tu+) or without (tu-) residual cancer cells is demonstrated in a Sankey plot (Fig. 3 ) in detail. Sensitivity to predict residual disease was 0.81 (specificity 0.72). The positive and negative predictive values were 0.92 and 0.50, respectively. In univariate Cox regression analyses, the absence of tumor cells in on-treatment biopsies was associated with a lower risk of relapse in patients with triple-negative disease (Table 2 ). However, the effect was not statistically significant when adjusted for pCR in a bivariate model (Table 3 ). The relationship between the presence of residual disease during and/or after chemotherapy and patient survival was demonstrated in a Kaplan-Meier analysis (Fig. 2 B). Patients with residual cancer cells during chemotherapy (tu+) and residual disease (RD) after completion of the full course show the highest risk of relapse. Table 2 Univariate Cox regression models to predict disease-free survival according to residual cancer in on-treatment biopsies during neoadjuvant chemotherapy Subtype Covariate Hazard ratio (95% CI) P HR-/HER2- tu- vs. tu+ 0.402 (0.163–0.987) 0.047 HR+/HER2- tu- vs. tu+ 1.039 (0.428–2.525) 0.933 HER2+ tu- vs. tu+ 1.77 (0.577–5.43) 0.318 Table 3 Bivariate Cox regression models to predict disease-free survival according to residual cancer in biopsies during neoadjuvant chemotherapy and after treatment Subtype Covariate Hazard ratio (95% CI) P HR-/HER2- tu- vs. tu+ 0.544 (0.217–1.369) 0.196 pCR vs. no pCR 0.282 (0.083–0.964) 0.043 HR+/HER2- tu- vs. tu+ 1.14 (0.442–2.939) 0.787 pCR vs. no pCR 0.689 (0.149–3.18) 0.633 HER2+ tu- vs. tu+ 2.114 (0.589–7.584) 0.251 pCR vs. no pCR 0.704 (0.197–2.516) 0.589 Patients with false negative predictions, i.e. those without residual tumor cells on-treatment but residual disease after completion of treatment were analyzed (Figure S1 ). A higher frequency of false negative predictions was observed in HR+/HER2- disease. Tumors with a low quantity of TILs were prone to false negative predictions based on the on-treatment sample. There was no statistically significant association with tumor stage, nodal status or grade. Patients with false positive predictions, i.e. those with residual cancer cells on-treatment, but with pCR after chemotherapy were demonstrated in Figure S2 . This was more frequently observed in HR-/HER2- cases, in the GeparSixto trial, in smaller tumors, patients with negative clinical lymph node status, and in tumors with a higher quantity of TILs. Dynamic change of TILs and Ki-67 and its association with patient outcome (Fig. 4 ) There was no association between the dynamic change in Ki-67 and dynamic change of TILs (Figure S3 ). In patients with residual invasive cancer cells in the on-treatment biopsy an increase of TILs in a subset of patients was observed, while only a few patients showed a decrease (Fig. 4 A). An increase of TILs was associated with a higher probability of pCR in the overall study cohort, but not within the BC subtypes (Fig. 4 C). It was associated with a lower risk of relapse in patients with triple negative disease. The proliferation index as measured by Ki-67 immunohistochemistry decreased in most patients during chemotherapy (Fig. 4 B). The lack of decrease in Ki-67 was associated with a low probability of pCR in all patients and was associated with a higher risk of relapse in patients with triple negative disease (Fig. 4 C). In bivariate Cox regression analyses in patients with triple-negative disease adjusted for pCR, the dynamic change of TILs was statistically significantly associated with DFS. The change of Ki-67 was not significantly associated with relapse-free survival in a bivariate model adjusted for pCR (Table 4 ). Table 4 Bivariate Cox regression models to predict disease-free survival according to pCR and dynamic change in TILs or Ki-67, respectively (hazard ratio for Delta TILs/Ki-67 per % unit). Covariate Hazard ratio (95% CI) P HR-/HER2- Delta TILs 0.979 (0.959–1.000) 0.048 pCR vs. no pCR 0.190 (0.025–1.418) 0.105 HR-/HER2- Delta Ki-67 1.019 (0.996–1.043) 0.098 pCR vs. no pCR 0.211 (0.028–1.600) 0.132 Discussion In this retrospective research study, we analyzed on-treatment biopsies obtained during neoadjuvant chemotherapy (NACT) for breast cancer (BC). 30% of on-treatment biopsies showed no cancer cells. However, if residual cancer cells were detected, achieving a pathologic complete response (pCR) post-treatment was less likely. This suggests the potential for early treatment adjustment, including alternative chemotherapy agents or targeted therapies, to enhance response rates, particularly within the framework of clinical trials. In the GeparTrio and GeparQuinto neoadjuvant trials ( 13 , 14 ), treatment was adjusted according to evaluation of on-treatment response using ultrasound and led to improved patient’s survival in GeparTrio. Biopsy procedures might be an additional tool to identify tumors prone to treatment failure early on treatment in future trials. If no cancer cells were detected during treatment, this information could not reliably predict chemotherapy outcome, as the rate of pCR was 50% in this group in the current study. This suggests that sampling error of the residual disease may have led to a false negative on-treatment sample. False negative prediction was more frequent in HR+/HER2- disease and in tumors with a lower quantity of TILs, reflecting a tumor biology with a lower a priori probability for pCR. False positive prediction (cancer cells on-treatment but pCR after therapy) were more frequent in HR-/HER2- tumors, in the GeparSixto trial, in smaller tumors and those with a higher quantity of TILs, reflecting patients with a higher a priori probability of pCR. As our study collected on-treatment samples for translational research rather than for predicting pCR or guiding treatment decisions, its comparability to studies focused on on-treatment pCR prediction may be limited (reviewed in ( 6 )). The reported negative predictive values of these studies vary, ranging from 71% for core needle biopsy (CNB) to as high as 95% for vacuum-assisted biopsy, which offers a larger specimen for analysis. However, with its false negative rates reaching 49.3%, presents a considerable margin for error ( 25 , 26 ). However, these investigations aimed to evaluate pathologic complete response (pCR) after completion of neoadjuvant chemotherapy (NACT) before surgery, utilizing minimally invasive methods to identify patients potentially eligible for surgery omission. These trials did not assess the capability to predict pCR early during treatment. Regarding survival, patients with triple-negative tumors and residual disease both on- and post-treatment had the highest risk of relapse. Patients without the evidence of cancer cells on-treatment but residual disease after the completion of the full course of treatment had a lower risk. This observation could be explained by the fact that the biopsy procedure is more likely to miss smaller tumors or tumors with only a minimal amount of residual disease. Pre-treatment levels of TILs can be used to predict response to chemotherapy and patient’s survival in BC ( 27 ). Chemotherapy might induce or augment a cytotoxic immune response( 28 ) and an influx of TILs during treatment is associated with better response ( 29 ). Moreover, their presence in surgical specimens is associated with better outcome( 30 ) and a gene signature for prediction of TILs in post-treatment samples is predictive of survival ( 31 ). In this study, we observed an increase of TILs in a subset of patients and only a few cases showed a decrease. An increase of TILs was associated with a higher probability of pCR in all patients and with a lower risk of relapse in patients with TNBC reflecting their known predictive value in triple-negative disease. This observation is particularly interesting, as it can identify patients early on treatment that still harbor invasive tumor residuals, where continuing standard therapy might be the option that is superior to a change in treatment plan. Further investigation in this group of patients could help to refine adapting tumor response into treatment plans and could ultimately allow to move the risk assessment after NACT to an earlier timepoint. The marker of tumor cell proliferation Ki-67 can be used to predict response to NACT and patient’s survival ( 32 , 33 ). High levels are typically associated with better response to cytotoxic treatment but shorter long-term outcome due to more aggressive tumor biology. In the context of neoadjuvant aromatase inhibition, on-treatment evaluation of Ki-67 can predict patient outcome ( 11 , 12 ). In this study, most patients showed a decrease in Ki-67 index and this was associated with a higher probability of response in triple-negative disease. It was also associated with a lower risk of relapse in triple negative disease, but not in other subtypes. Bivariate survival analyses demonstrated that these effects were probably due to the association with pCR and its strong association with survival in this subtype. From a translational research perspective, these observations suggest that on-treatment biopsies could be valuable for studying mechanisms of therapy resistance and predicting failure to achieve pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT). However, they are not suitable for identifying markers of chemotherapy sensitivity, as highly sensitive tumors would not contain residual cancer cells in on-treatment biopsies. To address this, further analyses should focus on revisiting naive biopsies from patients who achieved early pCR. On a molecular level, NACT induces global changes in gene expression ( 34 ). Examples of alterations are genes involved in proliferation, epithelial-mesenchymal transition and metabolic processes ( 35 ). Analysis of serial biopsy samples during chemotherapy allows the characterization of mechanisms of early response and adaption to therapy. In such a study, a decreased expression of genes related to immune response and proliferation could be observed and the downregulation of cell-cycle inhibitors was associated with worse response ( 15 ). The use of on-treatment biopsies for patient stratification should be further explored in the context to clinical trials and should be a part of the study protocol. Several limitations of the study warrant consideration. Firstly, it's important to note that the biopsy procedure was not strictly standardized according to the study protocol. Additionally, variations in the timing of the biopsy procedure across the three trials were inevitable due to differences in study design. In GeparQuattro and GeparQuinto, on-treatment samples were obtained after 4 of 8 cycles, in GeparSixto after 2 of 6 cycles. Moreover, GeparQuattro and GeparQuinto switched from anthracycline to taxane therapy following the biopsy, whereas GeparSixto continued with the same regimen (concurrent taxane/anthracycline). The collection of on-treatment biopsies was not a mandatory part of the study protocol with a potential selection bias and comparably small samples sizes in subgroup analyses. The study was primarily designed to collect material for translational research purposes. It has to be considered that patients within this trial had been treated before 2012 which possibly limits accuracy of on-treatment biopsy due to less experienced examiners and less refined examination instruments. Regimen not matching current standards thereby limiting the chances of achieving a pCR, are also able to influence false negative rates and predictive values of on treatment biopsies. In summary, our findings show that on-treatment biopsies can effectively predict non-pCR across breast cancer (BC) subtypes when residual cancer is present. This discovery presents potential avenues for tailoring therapy concepts in future clinical trials, such as implementing de-escalation strategies for responders and exploring experimental treatments for non-responders. Moreover, analyzing sequential biopsies could be instrumental in identifying molecular markers of therapy resistance. Further research is warranted to determine whether more standardized or extensive sampling procedures, or their combination with additional clinicopathological features, could enhance the sensitivity of on-treatment biopsy procedures. Abbreviations B bevacizumab CI Confidence interval DFS Disease-free survival EC-T epirubicin, cyclophosphamide and docetaxel G4 GeparQuattro G5 GeparQuinto G6 GeparSixto HER2 Human epidermal growth factor receptor HR Hazard ratio NACT Neoadjuvant chemotherapy pCR Pathological complete response RD Residual disease TILs Tumor-infiltrating lymphocytes TNBC Triple negative breast cancer tu+ Residual tumor tu- No residual tumor Declarations Ethics approval and consent to participate The study was performed in accordance with good clinical practice guidelines, national laws and the Declaration of Helsinki. Informed written consent for analysis was ontained from all patients. The GeparQuattro (EudraCT 2005-001546-17) and GeparQuinto (EudraCT 2006-005834-19) studies were approved by the Ethics Committee of the special field ‘Medicine’ at the Johann Wolfgang Goethe University Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt (ethical vote number 110/05 for GeparQuattro and 44/07 for Gepar Quinto). The GeparSixto study (EudraCT 2011-000553-23) was approved by the Ethics Committee of the Medical Association (Aerztekammer) Nordrhein, Tersteegenstr.9, 40474 Düsseldorf (ethical vote number 2011154. Consent for publication Not applicable Availability of data and materials The authors confirm that the data supporting the findings of this study are available within the article. Data sharing statement Will individual participant data be available (including data dictionaries)? Yes What data in particular will be shared? Individual participant data that underlie the results reported in this article, after final analysis and publication of all secondary efficacy endpoints What other documents will be available? Study protocol; Statistical report (if necessary for the project) When will data be available (start and end dates)? Beginning after final analysis and publication of all secondary efficacy endpoints; no end date With whom? Researchers who provide translational research proposals. Proposals should be approved by the GBG scientific board. For what types of analyses? To achieve aims in the approved proposal By what mechanism will data be made available? Proposal forms should be requested from [email protected] ; once the application has been approved and a data transfer agreement has been signed, researchers will be given access to the data. Competing interests B. Sinn is an employee of BioNTech SE, has received consultancy honoraria from Sanofi and holds a pending patent WO2020109570A1. V. Nekljudova declares to be GBG Forschungs GmbH employee. GBG Forschungs GmbH received funding for research grants from Abbvie, Amgen, AstraZeneca, BMS, Daiichi-Sankyo, Gilead, Molecular Health, Novartis, Pfizer and Roche (paid to the institution); other (non-financial/medical writing) from Daiichi-Sankyo, Gilead, Novartis, Pfizer, Roche and Seagen (paid to the institution). GBG Forschungs GmbH has licensing fees from VMscope GmbH. In addition, GBG Forschungs GmbH has a patent EP21152186.9 pending, a patent EP19808852.8 pending, and a patent EP14153692.0 pending. T. Karn reports a patent for EP18209672 pending. V. Mueller reports speaker honoraria from Astra Zeneca, Daiichi-Sankyo, Eisai, Pfizer, MSD, Medac, Novartis, Roche, Seagen, Onkowissen, high5 Oncology, Medscape, Gilead, Pierre Fabre and iMED Institut; receives consultancy honoraria from Roche, Pierre Fabre, PINK, ClinSol, Novartis, MSD, Daiichi-Sankyo, Eisai, Lilly, Seagen, Gilead and Stemline; he also declares to receive institutional research support from Novartis, Roche, Seagen, Genentech and Astra Zeneca; and reports travel grants from Astra Zeneca, Roche, Pfizer, Daiichi Sankyo and Gilead. C. Schem declares honoraria from Roche, Lilly, AstraZeneca, MSD Oncology, Exact Sciences and Novartis; operate in a consulting or advisory role for Novartis, AstraZeneca and Roche; reports honoraria for speakers’ bureau from Roche, AstraZeneca and Novartis; CS also reports to receive research funding from Roche (Inst.), Daiichi Sankyo Europe GmbH (Inst.), AstraZeneca (Inst.), GlaxoSmithKline (Inst.), Novartis (Inst.) and Lilly (Inst.); and to receive travel accommodations and expenses from Pfizer, Roche, AstraZeneca, Novartis and Gilead Sciences. M. Untch reports honoraria from AstraZeneca, Art tempi, Amgen, Daiji Sankyo, Lilly, Roche, Pfizer, MSD Oncology, Pierre Fabre, Sanofi-Aventis, Myriad, Seagen, Gilead, Novartis and Stemline; to act in a consulting or advisory Role for Amgen, Lilly, Roche, Pfizer, Lilly, Pierre Fabre, Novartis, MSD Oncology, Roche, Agendia, Seagen, Gilead, Lily, Stemline, Genzyme and Onkowissen.de. All honoraria and fees to the employer/institution. J. Huober declares to receive honoraria from Lilly, Novartis, Roche, Pfizer, AstraZeneca, Seagen, Gilead and Daiichi; to have a consulting or advisory relationship with Lilly, Novartis, Roche, Pfizer, AstraZeneca, Gilead and Daiichi; and to receive honoraria for travel expenses from Roche, Novartis, Daiichi and Gilead. J. Holtschmidt reports personal fees and non-financial support from Daiichi Sankyo, non-financial support from Hologic, personal fees from MSD Oncology, Novartis, Palleos Health Care, Pfizer, Roche Pharma and Seagen, outside the submitted work; he also declares to be GBG Forschungs GmbH employee. GBG Forschungs GmbH received funding for research grants from Abbvie, Amgen, AstraZeneca, BMS, Daiichi-Sankyo, Gilead, Molecular Health, Novartis, Pfizer and Roche (paid to the institution); other (non-financial/medical writing) from Daiichi-Sankyo, Gilead, Novartis, Pfizer, Roche and Seagen (paid to the institution). GBG Forschungs GmbH has licensing fees from VMscope GmbH. In addition, GBG Forschungs GmbH has a patent EP21152186.9 pending, a patent EP19808852.8 pending, and a patent EP14153692.0 pending. M. van Mackelenbergh reiceived personal fees, honoraria or travel grants from Amgen, AstraZeneca, Daiichi Sankyo, Gilead, GSK, Lilly, Molecular Health, Mylan, MSD, Novartis, Pfizer, PierreFabre, Roche and Seagen. S. Loibl declares to be GBG Forschungs GmbH employee (CEO);The company receives grants from AbbVie, AstraZeneca, Celgene, Daiichi-Sankyo, Immunomedics/Gilead, Molecular Health, Novartis, Pfizer and Roche; honoraria for Advisory board from Abbvie, Amgen, AstraZeneca, BMS, Celgene, DSI, EirGenix, Gilead, GSK, Lilly, Merck, Novartis, Olema, Pfizer, Pierre Fabre, Relay Therapeutics, Roche, Sanofi and Seagen; honoraria as invented speaker from AstraZeneca, DSI, Gilead, Novartis, Pfizer, Roche, Seage and Medscape. S. Loibl reports non-financial interest as advisory role in AGO Kommission Mamma, PI Aphinity (principal investigator) as member in AGO, ASCO, DKG, ESMO and other non-financial interest from AstraZeneca, Daiichi-Sankyo, Gilead, Novartis, Pfizer, Roche and Seagen. GBG Forschungs GmbH has following /patents pending: EP14153692.0, EP21152186.9, EP19808852.8 and receives licensing fees from VM Scope GmbH. C.D. reports grants from European Commission H2020, grants from German Cancer Aid Translational Oncology, grants from German Breast Group, grants from BMBF, to the institution during the conduct of the study; personal fees from Novartis, personal fees from Roche, personal fees from MSD Oncology, personal fees from Daiichi Sankyo, personal fees from AstraZeneca, from Molecular Health, grants from Myriad, personal fees from Merck, other from Sividon diagnostics, outside the submitted work; In addition, Dr. Denkert has a patent VMScope digital pathology software with royalties paid, a patent WO2020109570A1 - cancer immunotherapy pending, and a patent WO2015114146A1 and WO2010076322A1- therapy response issued. No other potential conflict of interest relevant to this article was reported. Funding This analysis was funded by GBG. The GeparQuattro trial received funding support from Roche and Sanofi-Aventis. The GeparQuinto trial received funding support from GlaxoSmithKline, Novartis, Roche and Sanofi Aventis. The GeparSixto trial received funding support from Cephalon, GlaxoSmithKline and Roche. The funders had no access to the study database and were not involved in the analysis and interpretation of the results. No grant number applicable. Authors’ contributions The analysis was designed by the members of the translational research subcommittee of the German Breast Group (BS, TK, MvM, FM, CS, ES, PAF, VM, SL and CD). BS and VN has analysed the data. BS and VN had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors interpreted the data. The first draft of the manuscript was written by BS. The decision to submit the manuscript for publication was made by all authors. All authors contributed to the review of the manuscript. No persons other than the listed authors contributed to the writing of the manuscript. Acknowledgements We would like to thank all patients, investigators and study personnel who supported the trials. References Asselain B, Barlow W, Bartlett J, Bergh J, Bergsten-Nordström E, Bliss J, et al. Long-term outcomes for neoadjuvant versus adjuvant chemotherapy in early breast cancer: meta-analysis of individual patient data from ten randomised trials. Lancet Oncol. 2018;19:27–39. von Minckwitz G, Schneeweiss A, Loibl S, Salat C, Denkert C, Rezai M, et al. Neoadjuvant carboplatin in patients with triple-negative and HER2-positive early breast cancer (GeparSixto; GBG 66): a randomised phase 2 trial. Lancet Oncol. 2014;15:747–56. Schmid P, Cortes J, Pusztai L, McArthur H, Kümmel S, Bergh J, et al. Pembrolizumab for Early Triple-Negative Breast Cancer. New England Journal of Medicine. 2020;382:810–21. Schneeweiss A, Chia S, Hickish T, Harvey V, Eniu A, Hegg R, et al. 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Neoadjuvant bevacizumab and anthracycline-taxane-based chemotherapy in 678 triple-negative primary breast cancers; results from the geparquinto study (GBG 44). Annals of oncology. 2013;24:2978–84. Von Minckwitz G, Schneeweiss A, Loibl S, Salat C, Denkert C, Rezai M, et al. Neoadjuvant carboplatin in patients with triple-negative and HER2-positive early breast cancer (GeparSixto; GBG 66): A randomised phase 2 trial. The Lancet Oncology. 2014;15:747–56. Salgado R, Denkert C, Demaria S, Sirtaine N, Klauschen F, Pruneri G, et al. The evaluation of tumor-infiltrating lymphocytes (TILS) in breast cancer: Recommendations by an International TILS Working Group 2014. Annals of Oncology. 2015;26:259–71. Dowsett M, Nielsen TO, A’Hern R, Bartlett J, Coombes RC, Cuzick J, et al. Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. Journal of the National Cancer Institute. 2011;103:1656–64. 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Demaria S, Volm MD, Shapiro RL, Yee HT, Oratz R, Formenti SC, et al. Development of tumor-infiltrating lymphocytes in breast cancer after neoadjuvant paclitaxel chemotherapy. Clinical Cancer Research. 2001;7:3025–30. Dieci M V., Criscitiello C, Goubar A, Viale G, Conte P, Guarneri V, et al. Prognostic value of tumor-infiltrating lymphocytes on residual disease after primary chemotherapy for triple-negative breast cancer: A retrospective multicenter study. Annals of Oncology. 2014;25:611–8. Criscitiello C, Bayar MA, Curigliano G, Symmans FW, Desmedt C, Bonnefoi H, et al. A gene signature to predict high tumor-infiltrating lymphocytes after neoadjuvant chemotherapy and outcome in patients with triple-negative breast cancer. Annals of Oncology. 2018;29:162–9. Denkert C, Loibl S, Müller BM, Eidtmann H, Schmitt WD, Eiermann W, et al. Ki67 levels as predictive and prognostic parameters in pretherapeutic breast cancer core biopsies: A translational investigation in the neoadjuvant gepartrio trial. Annals of Oncology. 2013;24:2786–93. Von Minckwitz G, Schmitt WD, Loibl S, Müller BM, Blohmer JU, Sinn B V., et al. Ki67 measured after neoadjuvant chemotherapy for primary breast cancer. Clinical Cancer Research. 2013;19:4521–31. Hannemann J, Oosterkamp HM, Bosch CAJ, Velds A, Wessels LFA, Loo C, et al. Changes in gene expression associated with response to neoadjuvant chemotherapy in breast cancer. Journal of Clinical Oncology. 2005;23:3331–42. Klintman M, Buus R, Cheang MCU, Sheri A, Smith IE, Dowsett M. Changes in expression of genes representing key biologic processes after neoadjuvant chemotherapy in breast cancer, and prognostic implications in residual disease. Clinical Cancer Research. 2016;22:2405–16. Additional Declarations Competing interest reported. B. Sinn is an employee of BioNTech SE, has received consultancy honoraria from Sanofi and holds a pending patent WO2020109570A1. V. Nekljudova declares to be GBG Forschungs GmbH employee. GBG Forschungs GmbH received funding for research grants from Abbvie, Amgen, AstraZeneca, BMS, Daiichi-Sankyo, Gilead, Molecular Health, Novartis, Pfizer and Roche (paid to the institution); other (non-financial/medical writing) from Daiichi-Sankyo, Gilead, Novartis, Pfizer, Roche and Seagen (paid to the institution). GBG Forschungs GmbH has licensing fees from VMscope GmbH. In addition, GBG Forschungs GmbH has a patent EP21152186.9 pending, a patent EP19808852.8 pending, and a patent EP14153692.0 pending. T. Karn reports a patent for EP18209672 pending. V. Mueller reports speaker honoraria from Astra Zeneca, Daiichi-Sankyo, Eisai, Pfizer, MSD, Medac, Novartis, Roche, Seagen, Onkowissen, high5 Oncology, Medscape, Gilead, Pierre Fabre and iMED Institut; receives consultancy honoraria from Roche, Pierre Fabre, PINK, ClinSol, Novartis, MSD, Daiichi-Sankyo, Eisai, Lilly, Seagen, Gilead and Stemline; he also declares to receive institutional research support from Novartis, Roche, Seagen, Genentech and Astra Zeneca; and reports travel grants from Astra Zeneca, Roche, Pfizer, Daiichi Sankyo and Gilead. C. Schem declares honoraria from Roche, Lilly, AstraZeneca, MSD Oncology, Exact Sciences and Novartis; operate in a consulting or advisory role for Novartis, AstraZeneca and Roche; reports honoraria for speakers’ bureau from Roche, AstraZeneca and Novartis; CS also reports to receive research funding from Roche (Inst.), Daiichi Sankyo Europe GmbH (Inst.), AstraZeneca (Inst.), GlaxoSmithKline (Inst.), Novartis (Inst.) and Lilly (Inst.); and to receive travel accommodations and expenses from Pfizer, Roche, AstraZeneca, Novartis and Gilead Sciences. M. Untch reports honoraria from AstraZeneca, Art tempi, Amgen, Daiji Sankyo, Lilly, Roche, Pfizer, MSD Oncology, Pierre Fabre, Sanofi-Aventis, Myriad, Seagen, Gilead, Novartis and Stemline; to act in a consulting or advisory Role for Amgen, Lilly, Roche, Pfizer, Lilly, Pierre Fabre, Novartis, MSD Oncology, Roche, Agendia, Seagen, Gilead, Lily, Stemline, Genzyme and Onkowissen.de. All honoraria and fees to the employer/institution. J. Huober declares to receive honoraria from Lilly, Novartis, Roche, Pfizer, AstraZeneca, Seagen, Gilead and Daiichi; to have a consulting or advisory relationship with Lilly, Novartis, Roche, Pfizer, AstraZeneca, Gilead and Daiichi; and to receive honoraria for travel expenses from Roche, Novartis, Daiichi and Gilead. J. Holtschmidt reports personal fees and non-financial support from Daiichi Sankyo, non-financial support from Hologic, personal fees from MSD Oncology, Novartis, Palleos Health Care, Pfizer, Roche Pharma and Seagen, outside the submitted work; he also declares to be GBG Forschungs GmbH employee. GBG Forschungs GmbH received funding for research grants from Abbvie, Amgen, AstraZeneca, BMS, Daiichi-Sankyo, Gilead, Molecular Health, Novartis, Pfizer and Roche (paid to the institution); other (non-financial/medical writing) from Daiichi-Sankyo, Gilead, Novartis, Pfizer, Roche and Seagen (paid to the institution). GBG Forschungs GmbH has licensing fees from VMscope GmbH. In addition, GBG Forschungs GmbH has a patent EP21152186.9 pending, a patent EP19808852.8 pending, and a patent EP14153692.0 pending. M. van Mackelenbergh reiceived personal fees, honoraria or travel grants from Amgen, AstraZeneca, Daiichi Sankyo, Gilead, GSK, Lilly, Molecular Health, Mylan, MSD, Novartis, Pfizer, PierreFabre, Roche and Seagen. S. Loibl declares to be GBG Forschungs GmbH employee (CEO);The company receives grants from AbbVie, AstraZeneca, Celgene, Daiichi-Sankyo, Immunomedics/Gilead, Molecular Health, Novartis, Pfizer and Roche; honoraria for Advisory board from Abbvie, Amgen, AstraZeneca, BMS, Celgene, DSI, EirGenix, Gilead, GSK, Lilly, Merck, Novartis, Olema, Pfizer, Pierre Fabre, Relay Therapeutics, Roche, Sanofi and Seagen; honoraria as invented speaker from AstraZeneca, DSI, Gilead, Novartis, Pfizer, Roche, Seage and Medscape. S. Loibl reports non-financial interest as advisory role in AGO Kommission Mamma, PI Aphinity (principal investigator) as member in AGO, ASCO, DKG, ESMO and other non-financial interest from AstraZeneca, Daiichi-Sankyo, Gilead, Novartis, Pfizer, Roche and Seagen. GBG Forschungs GmbH has following /patents pending: EP14153692.0, EP21152186.9, EP19808852.8 and receives licensing fees from VM Scope GmbH. C.D. reports grants from European Commission H2020, grants from German Cancer Aid Translational Oncology, grants from German Breast Group, grants from BMBF, to the institution during the conduct of the study; personal fees from Novartis, personal fees from Roche, personal fees from MSD Oncology, personal fees from Daiichi Sankyo, personal fees from AstraZeneca, from Molecular Health, grants from Myriad, personal fees from Merck, other from Sividon diagnostics, outside the submitted work; In addition, Dr. Denkert has a patent VMScope digital pathology software with royalties paid, a patent WO2020109570A1 - cancer immunotherapy pending, and a patent WO2015114146A1 and WO2010076322A1- therapy response issued. No other potential conflict of interest relevant to this article was reported. Supplementary Files S1falseneg.pdf Figure S1 Comparison of tumors with false negative predictions (no residual cancer on-treatment but non-pCR after surgery) with tumors with true negative predictions. False negative predictions were more frequent in HR+/HER2- disease (A). There was no statistically significant association with the clinical trial (B), tumor stage (C), nodal status (D) or histological subtype (E). Tumors with a low quantity of tumor infiltrating lymphocytes were more likely to result in a false negative intermediate sample (F). S2falsepos.pdf Figure S2 Comparison of tumors with false positive predictions (residual cancer on-treatment but pCR after treatment) with tumors with true positive predictions. False positive predictions were more frequent in HR-/HER2- disease (A), in the GeparSixto trial (B), smaller tumors (C). There was no association grade (E). Tumors with a high quantity of tumor infiltrating lymphocytes were more likely to result in a false positive prediction (F). S3.pdf Figure S3 The change in Ki-67 between the two time points is plotted against the change in tumor-infiltrating lymphocytes. There was no association between the two. Cite Share Download PDF Status: Published Journal Publication published 24 Sep, 2024 Read the published version in Breast Cancer Research → Version 1 posted Editorial decision: Revision requested 17 Jul, 2024 Reviews received at journal 17 Jul, 2024 Reviews received at journal 10 Jul, 2024 Reviewers agreed at journal 22 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers invited by journal 17 Jun, 2024 Editor assigned by journal 07 Jun, 2024 Submission checks completed at journal 07 Jun, 2024 First submitted to journal 27 May, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4483953","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":316362889,"identity":"f221de09-84e6-4a48-8947-e4e6eb2ef61e","order_by":0,"name":"Bruno Valentin Sinn","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3PsQrCMBCA4SsHcTn3Sgq+QkSwZOqrVAQnBUEQBykFQZfi3MG3EJwrBae6dxJdnCuCOIjYqotL6uiQf0kC+cgFQKf7w8RnJQCM8L0BMH8kzH0R+pUUlwV+sJrYPGkdMvAsmwfny2Cyt5yKb6SZgshFz26EEJNc7lY83A6JKEIZqgZLqMUJIhJpf43EXCLTZZzKiZeT3gnpkZP6gfF7OcGCMKzOileAcdX3ZcBGtVDEOek2eXWRk6Q9lYGC2IRrMxt7jkg7xwtdXacyjzfpTfXMe7yvk+GXAp1Op9OpewLo7D0NfqYD8AAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Pathology, Charité – Universitätsmedizin Berlin, Berlin","correspondingAuthor":true,"prefix":"","firstName":"Bruno","middleName":"Valentin","lastName":"Sinn","suffix":""},{"id":316362890,"identity":"e5e24393-ae43-44ec-bb86-5e21d4a44c79","order_by":1,"name":"Katharina Sychra","email":"","orcid":"","institution":"Department of Pathology, Charité – Universitätsmedizin Berlin, Berlin","correspondingAuthor":false,"prefix":"","firstName":"Katharina","middleName":"","lastName":"Sychra","suffix":""},{"id":316362891,"identity":"55aea57b-d554-48b2-a983-d08158b10163","order_by":2,"name":"Michael Untch","email":"","orcid":"","institution":"Department of Gynecology, Helios Kliniken Berlin-Buch, Berlin","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Untch","suffix":""},{"id":316362892,"identity":"1230bf46-4606-4e47-9f48-95d0a0b18bfc","order_by":3,"name":"Thomas Karn","email":"","orcid":"","institution":"Department of Gynecology and Obstetrics, Goethe-University, Frankfurt","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Karn","suffix":""},{"id":316362893,"identity":"d7858131-46a3-4586-a623-6529fde1b311","order_by":4,"name":"Marion van Mackelenbergh","email":"","orcid":"","institution":"Department of Gynecology and Obstetrics, Universitätsklinikum Schleswig-Holstein, Kiel","correspondingAuthor":false,"prefix":"","firstName":"Marion","middleName":"van","lastName":"Mackelenbergh","suffix":""},{"id":316362894,"identity":"29c057a8-abb4-499e-a3ad-8de7d68e45be","order_by":5,"name":"Jens Huober","email":"","orcid":"","institution":"Breast Center St. Gallen, Kantonsspital St. Gallen, St. Gallen","correspondingAuthor":false,"prefix":"","firstName":"Jens","middleName":"","lastName":"Huober","suffix":""},{"id":316362896,"identity":"ceaa066d-1501-4fe4-ba82-8925eee9a226","order_by":6,"name":"Wolfgang Schmitt","email":"","orcid":"","institution":"Department of Pathology, Charité – Universitätsmedizin Berlin, Berlin","correspondingAuthor":false,"prefix":"","firstName":"Wolfgang","middleName":"","lastName":"Schmitt","suffix":""},{"id":316362897,"identity":"b4e14ca8-9083-40f4-9ec7-566a08edd870","order_by":7,"name":"Frederik Marmé","email":"","orcid":"","institution":"Department of Gynecology, Universitätsklinikum Mannheim, Mannheim","correspondingAuthor":false,"prefix":"","firstName":"Frederik","middleName":"","lastName":"Marmé","suffix":""},{"id":316362898,"identity":"e377553e-757b-4fb6-8840-dce35c298233","order_by":8,"name":"Christian Schem","email":"","orcid":"","institution":"Mammazentrum Hamburg","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Schem","suffix":""},{"id":316362900,"identity":"4ee18907-2010-4560-bf07-13a216a600ef","order_by":9,"name":"Christine Solbach","email":"","orcid":"","institution":"Breast Center, Universitätsklinikum Frankfurt, Frankfurt","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Solbach","suffix":""},{"id":316362901,"identity":"072855c8-dd0e-4d30-aeb3-6408ab0857d8","order_by":10,"name":"Elmar Stickeler","email":"","orcid":"","institution":"Department of Gynecology, Uniklinik RWTH Aachen, Aachen","correspondingAuthor":false,"prefix":"","firstName":"Elmar","middleName":"","lastName":"Stickeler","suffix":""},{"id":316362902,"identity":"d8d4882b-46fc-4b09-bfc0-4aee8e839d22","order_by":11,"name":"Hans Tesch","email":"","orcid":"","institution":"Centrum für Hämatologie und Onkologie Bethanien, Hamburg","correspondingAuthor":false,"prefix":"","firstName":"Hans","middleName":"","lastName":"Tesch","suffix":""},{"id":316362903,"identity":"29824518-3edb-40ce-a5bf-51651eefe1cb","order_by":12,"name":"Peter A. Fasching","email":"","orcid":"","institution":"Department of Gynecology and Obstetrics, University Hospital Erlangen","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"A.","lastName":"Fasching","suffix":""},{"id":316362904,"identity":"7a87d202-ee76-488e-88c1-dc4b299b1488","order_by":13,"name":"Andreas Schneeweiss","email":"","orcid":"","institution":"Universitätsfrauenklinik Heidelberg, Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Schneeweiss","suffix":""},{"id":316362905,"identity":"dfdf08f5-1df9-45d0-a5cf-da861bbde907","order_by":14,"name":"Volkmar Müller","email":"","orcid":"","institution":"Department of Gynecology, Universitätsklinikum Hamburg-Eppendorf, Hamburg","correspondingAuthor":false,"prefix":"","firstName":"Volkmar","middleName":"","lastName":"Müller","suffix":""},{"id":316362906,"identity":"c3144900-e24d-4095-9433-f4a048d5edbc","order_by":15,"name":"Johannes Holtschmidt","email":"","orcid":"","institution":"German Breast Group, Neu-Isenburg","correspondingAuthor":false,"prefix":"","firstName":"Johannes","middleName":"","lastName":"Holtschmidt","suffix":""},{"id":316362907,"identity":"46da09e3-cec6-48b9-88ee-4e0935018587","order_by":16,"name":"Valentina Nekljudova","email":"","orcid":"","institution":"German Breast Group, Neu-Isenburg","correspondingAuthor":false,"prefix":"","firstName":"Valentina","middleName":"","lastName":"Nekljudova","suffix":""},{"id":316362908,"identity":"ed32045a-5638-4661-b1c3-72855274f9bf","order_by":17,"name":"Sibylle Loibl","email":"","orcid":"","institution":"German Breast Group, Neu-Isenburg","correspondingAuthor":false,"prefix":"","firstName":"Sibylle","middleName":"","lastName":"Loibl","suffix":""},{"id":316362909,"identity":"08febb27-aa4d-4b0a-a782-b28889ba812a","order_by":18,"name":"Carsten Denkert","email":"","orcid":"","institution":"Department of Pathology, Philipps-University Marburg and University Hospital Marburg (UKGM), Marburg","correspondingAuthor":false,"prefix":"","firstName":"Carsten","middleName":"","lastName":"Denkert","suffix":""}],"badges":[],"createdAt":"2024-05-27 09:47:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4483953/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4483953/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13058-024-01883-w","type":"published","date":"2024-09-24T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59139519,"identity":"720239bd-c563-4248-a721-0c0ec667125b","added_by":"auto","created_at":"2024-06-26 19:20:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":233525,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of on-treatment biopsies without tumor cells according to tumor subtype (A) clinical trial (B), tumor stage (C), lymph node status (D), histological grading (E), tumor-infiltrating lymphocytes (F), and histology (G).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4483953/v1/8e0f9298b9c719c3045a2ea3.png"},{"id":59138917,"identity":"a475f856-828e-4785-a03d-718e89c3cc12","added_by":"auto","created_at":"2024-06-26 19:12:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161489,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Percent of patients with pCR after completion of chemotherapy according to on-treatment biopsies with (tu+) or without (tu-) residual cancer cells. (B) Disease-free survival in HR-/HER2- BC patients according to the two the presence of residual disease on- and after treatment.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4483953/v1/4832c09482c823556ea4456a.png"},{"id":59138911,"identity":"8b544653-cfef-4e15-a8af-50c4c68c3de7","added_by":"auto","created_at":"2024-06-26 19:12:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101178,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of patients with pCR or non-pCR after completion of chemotherapy according to on-treatment biopsies with (tu+) or without (tu-) residual cancer cells.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4483953/v1/3791e12141bbd60ec072adaa.png"},{"id":59138913,"identity":"0d695bdd-8aac-4d42-be1a-ad1418cae069","added_by":"auto","created_at":"2024-06-26 19:12:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":325109,"visible":true,"origin":"","legend":"\u003cp\u003eThe quantity of tumor-infiltrating lymphocytes (A) and Ki67 levels (B) in on-treatment biopsies are plotted against the pre-treatment sample. Only samples with residual cancer in the intermediate biopsies are shown. An increase in TILs was associated with the probability of a pCR across tumor subtypes (C) and with a lower risk of relapse in patients with triple-negative disease (D). A lack in decrease of Ki-67 was associated with a lower probability of pCR across subtypes and with a higher risk of relapse in patients with triple-negative disease. The dots indicate the hazard ratio (HR) and odds ratio (OR), respectively (per % increase), the bars indicate the 95 % confidence interval.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4483953/v1/050a8e3a4e6d83c8bf3c9b13.png"},{"id":65628083,"identity":"5385f8a8-b5fe-4604-b00a-1fbb5a8aafde","added_by":"auto","created_at":"2024-09-30 16:17:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1508108,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4483953/v1/2db2362a-10c3-4e2b-a2e7-fa967d3d3de9.pdf"},{"id":59138914,"identity":"f57cfaaf-80a5-4d11-b10f-b3e72f687bf1","added_by":"auto","created_at":"2024-06-26 19:12:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1\u003c/strong\u003e Comparison of tumors with false negative predictions (no residual cancer on-treatment but non-pCR after surgery) with tumors with true negative predictions. False negative predictions were more frequent in HR+/HER2- disease (A). There was no statistically significant association with the clinical trial (B), tumor stage (C), nodal status (D) or histological subtype (E). Tumors with a low quantity of tumor infiltrating lymphocytes were more likely to result in a false negative intermediate sample (F).\u003c/p\u003e","description":"","filename":"S1falseneg.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4483953/v1/5f553263963b368b02c952cc.pdf"},{"id":59139518,"identity":"c195acca-2135-4601-aae2-53ad739c8308","added_by":"auto","created_at":"2024-06-26 19:20:59","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2\u003c/strong\u003e Comparison of tumors with false positive predictions (residual cancer on-treatment but pCR after treatment) with tumors with true positive predictions. False positive predictions were more frequent in HR-/HER2- disease (A), in the GeparSixto trial (B), smaller tumors (C). There was no association grade (E). Tumors with a high quantity of tumor infiltrating lymphocytes were more likely to result in a false positive prediction (F).\u003c/p\u003e","description":"","filename":"S2falsepos.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4483953/v1/6dbc6f8c995fd6616ac626c9.pdf"},{"id":59138918,"identity":"913139c4-9c64-4219-9510-fc91830a63ff","added_by":"auto","created_at":"2024-06-26 19:13:00","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":26532,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3 \u003c/strong\u003eThe change in Ki-67 between the two time points is plotted against the change in tumor-infiltrating lymphocytes. There was no association between the two.\u003c/p\u003e","description":"","filename":"S3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4483953/v1/c88dbffa07540648e34197d5.pdf"}],"financialInterests":"Competing interest reported. B. Sinn is an employee of BioNTech SE, has received consultancy honoraria from Sanofi and holds a pending patent WO2020109570A1.\nV. Nekljudova declares to be GBG Forschungs GmbH employee. GBG Forschungs GmbH received funding for research grants from Abbvie, Amgen, AstraZeneca, BMS, Daiichi-Sankyo, Gilead, Molecular Health, Novartis, Pfizer and Roche (paid to the institution); other (non-financial/medical writing) from Daiichi-Sankyo, Gilead, Novartis, Pfizer, Roche and Seagen (paid to the institution). GBG Forschungs GmbH has licensing fees from VMscope GmbH. In addition, GBG Forschungs GmbH has a patent EP21152186.9 pending, a patent EP19808852.8 pending, and a patent EP14153692.0 pending.\nT. Karn reports a patent for EP18209672 pending.\nV. Mueller reports speaker honoraria from Astra Zeneca, Daiichi-Sankyo, Eisai, Pfizer, MSD, Medac, Novartis, Roche, Seagen, Onkowissen, high5 Oncology, Medscape, Gilead, Pierre Fabre and iMED Institut; receives consultancy honoraria from Roche, Pierre Fabre, PINK, ClinSol, Novartis, MSD, Daiichi-Sankyo, Eisai, Lilly, Seagen, Gilead and Stemline; he also declares to receive institutional research support from Novartis, Roche, Seagen, Genentech and Astra Zeneca; and reports travel grants from Astra Zeneca, Roche, Pfizer, Daiichi Sankyo and Gilead.\n\nC. Schem declares honoraria from Roche, Lilly, AstraZeneca, MSD Oncology, Exact Sciences and Novartis; operate in a consulting or advisory role for Novartis, AstraZeneca and Roche; reports honoraria for speakers’ bureau from Roche, AstraZeneca and Novartis; CS also reports to receive research funding from Roche (Inst.), Daiichi Sankyo Europe GmbH (Inst.), AstraZeneca (Inst.), GlaxoSmithKline (Inst.), Novartis (Inst.) and Lilly (Inst.); and to receive travel accommodations and expenses from Pfizer, Roche, AstraZeneca, Novartis and Gilead Sciences. \nM. Untch reports honoraria from AstraZeneca, Art tempi, Amgen, Daiji Sankyo, Lilly, Roche, Pfizer, MSD Oncology, Pierre Fabre, Sanofi-Aventis, Myriad, Seagen, Gilead, Novartis and Stemline; to act in a consulting or advisory Role for Amgen, Lilly, Roche, Pfizer, Lilly, Pierre Fabre, Novartis, MSD Oncology, Roche, Agendia, Seagen, Gilead, Lily, Stemline, Genzyme and Onkowissen.de.\nAll honoraria and fees to the employer/institution.\n\nJ. Huober declares to receive honoraria from Lilly, Novartis, Roche, Pfizer, AstraZeneca, Seagen, Gilead and Daiichi; to have a consulting or advisory relationship with Lilly, Novartis, Roche, Pfizer, AstraZeneca, Gilead and Daiichi; and to receive honoraria for travel expenses from Roche, Novartis, Daiichi and Gilead.\nJ. Holtschmidt reports personal fees and non-financial support from Daiichi Sankyo, non-financial support from Hologic, personal fees from MSD Oncology, Novartis, Palleos Health Care, Pfizer, Roche Pharma and Seagen, outside the submitted work; he also declares to be GBG Forschungs GmbH employee. GBG Forschungs GmbH received funding for research grants from Abbvie, Amgen, AstraZeneca, BMS, Daiichi-Sankyo, Gilead, Molecular Health, Novartis, Pfizer and Roche (paid to the institution); other (non-financial/medical writing) from Daiichi-Sankyo, Gilead, Novartis, Pfizer, Roche and Seagen (paid to the institution). GBG Forschungs GmbH has licensing fees from VMscope GmbH. In addition, GBG Forschungs GmbH has a patent EP21152186.9 pending, a patent EP19808852.8 pending, and a patent EP14153692.0 pending.\n\nM. van Mackelenbergh reiceived personal fees, honoraria or travel grants from Amgen, AstraZeneca, Daiichi Sankyo, Gilead, GSK, Lilly, Molecular Health, Mylan, MSD, Novartis, Pfizer, PierreFabre, Roche and Seagen.\n\nS. Loibl declares to be GBG Forschungs GmbH employee (CEO);The company receives grants from AbbVie, AstraZeneca, Celgene, Daiichi-Sankyo, Immunomedics/Gilead, Molecular Health, Novartis, Pfizer and Roche; honoraria for Advisory board from Abbvie, Amgen, AstraZeneca, BMS, Celgene, DSI, EirGenix, Gilead, GSK, Lilly, Merck, Novartis, Olema, Pfizer, Pierre Fabre, Relay Therapeutics, Roche, Sanofi and Seagen; honoraria as invented speaker from AstraZeneca, DSI, Gilead, Novartis, Pfizer, Roche, Seage and Medscape. S. Loibl reports non-financial interest as advisory role in AGO Kommission Mamma, PI Aphinity (principal investigator) as member in AGO, ASCO, DKG, ESMO and other non-financial interest from AstraZeneca, Daiichi-Sankyo, Gilead, Novartis, Pfizer, Roche and Seagen. GBG Forschungs GmbH has following /patents pending: EP14153692.0, EP21152186.9, EP19808852.8 and receives licensing fees from VM Scope GmbH.\nC.D. reports grants from European Commission H2020, grants from German Cancer Aid Translational Oncology, grants from German Breast Group, grants from BMBF, to the institution during the conduct of the study; personal fees from Novartis, personal fees from Roche, personal fees from MSD Oncology, personal fees from Daiichi Sankyo, personal fees from AstraZeneca, from Molecular Health, grants from Myriad, personal fees from Merck, other from Sividon diagnostics, outside the submitted work; In addition, Dr. Denkert has a patent VMScope digital pathology software with royalties paid, a patent WO2020109570A1 - cancer immunotherapy pending, and a patent WO2015114146A1 and WO2010076322A1- therapy response issued. \nNo other potential conflict of interest relevant to this article was reported.","formattedTitle":"On-treatment biopsies to predict response to neoadjuvant chemotherapy for breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRetrospective analyses of prospective breast cancer (BC) trials have shown comparable efficacy between chemotherapy administered in the adjuvant versus neoadjuvant settings (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Excellent response, defined as achieving pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT), varies across breast cancer (BC) subtypes and is strongly influenced by the treatment regimen used. During the analyzed trials, pCR rates were approximately 50% and 30% for patients with triple-negative and HER2-positive disease, respectively. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In recent developments, incorporating immune checkpoint inhibitors into NACT for TNBC resulted in pCR rates of 64.8% (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), while employing dual anti-HER2 blockade yielded pCR rates of 66.2% in HER2-positive disease, depending on hormone receptor status (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Excellent response serves as a prognostic indicator for patient survival, especially in triple-negative and HER2-positive disease (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), and neoadjuvant therapy can serve as an \u003cem\u003ein vivo\u003c/em\u003e assay for chemotherapy response.\u003c/p\u003e \u003cp\u003ePrediction of therapy response is of clinical interest offering the potential to customize treatment approaches and enhance response rates. With the emergence of new therapies and refined treatment protocols, there arises the question of whether de-escalating local and/or systemic treatment is viable for patients with a strong likelihood of achieving pCR (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). For example, PET-based imaging can be used to predict pCR in patients with HER2-positive BC during neoadjuvant treatment with dual anti-HER2 treatment (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe conventional method for identifying response markers involves correlating genomic measurements from pre-therapeutic samples with clinical outcomes (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). On-treatment tissue samples offer the opportunity for microscopic confirmation of response, biomarker examination, and tissue provision for translational research. However, the reliability of biopsy procedures and histopathological assessment for predicting on-treatment pCR remains uncertain. The RESPONDER trial examines if vacuum-assisted biopsies can be used to predict response in the breast with a false negative rate below 10% (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn neoadjuvant aromatase inhibition for HR\u0026thinsp;+\u0026thinsp;BC, gene expression analysis of on-treatment samples has been shown to predict treatment response and patient survival. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Ki-67 immunohistochemistry can indicate the need to switch to neoadjuvant chemotherapy if Ki-67 levels remain elevated during endocrine treatment alone (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In the context of neoadjuvant chemotherapy (NACT), we assessed on-treatment response using ultrasound in the GeparTrio (G3)(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and GeparQuinto (G5)(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) trials. In G3, we could demonstrate that response-guided switch of chemotherapy regimens can improve patient outcome.\u003c/p\u003e \u003cp\u003eDuring neoadjuvant chemotherapy (NACT), on-treatment samples can be utilized to uncover molecular mechanisms linked to therapy response, such as immune and proliferation signatures (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and to pinpoint potential markers of resistance/response through comparative gene expression analysis between responders and non-responders (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAim of this retrospective-prospective biomarker study was to evaluate the frequency of residual cancer cells in on-treatment samples from neoadjuvant clinical chemotherapy trials for BC, and to correlate their presence, proliferative activity and accompanying immune cells with response to treatment.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and samples\u003c/h2\u003e \u003cp\u003ePatients were treated within the randomized, multi-center neoadjuvant clinical trials GeparQuattro (G4) (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), GeparQuinto (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and GeparSixto (G6) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Details on the study designs and outcomes are available in the original publications. In brief, G4 was a phase III study comparing the simultaneous or sequential use of capecitabine with epirubicin, cyclophosphamide and docetaxel (EC-T) with concomitant trastuzumab in HER2\u0026thinsp;+\u0026thinsp;disease. G5 was a phase III study to evaluate EC-T with or without bevacizumab (B) in HER2-negative BC (setting I), to compare pCR rates of patients treated with paclitaxel with or without everolimus with HER2-negative BC without sonographic response after four cycles EC\u0026thinsp;\u0026plusmn;\u0026thinsp;B (setting II) and to compare pCR rates in patients treated with EC-T followed by trastuzumab or lapatinib in HER2-positive disease (setting III). G6 was a phase II trial to evaluate the addition of carboplatin to neoadjuvant treatment for patients with triple-negative or HER2-positive BC. Biopsies were obtained at the time of diagnosis and during chemotherapy: in G4 and G5 after 4 of 8 cycles and in G6 after 2 of 6 cycles. All patients with available material in the central GBG tumor bank were eligible for this retrospective biomarker analysis. Of the 1495, 1948 and 588 patients in the G4, G5, and G6 trials, respectively, 106, 145 and 61 matched pre-therapeutic and on-treatment biopsies were available in the biobank and included in the study, resulting in 312 matched pairs. 15 samples had to be excluded due to insufficient pre-treatment material, resulting in a total of 297 matched samples. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e details the baseline patient characteristics.\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\u003eBaseline characteristics of the study cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR-/HER2-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (29,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (30,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (43,1%)\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 \u003cp\u003eHR+/HER2-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (46,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85 (61,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\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 \u003cp\u003eHER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (24,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (7,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33 (56,9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno pCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235 (79,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125 (89,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (50%)\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 \u003cp\u003epCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (20,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (10,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etu+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207 (69,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112 (80,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26 (44,8%)\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 \u003cp\u003etu-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (30,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (19,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (55,2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (8,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (7,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (24,1%)\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 \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181 (60,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (58,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (55,2%)\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 \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (14,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (12,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (15,5%)\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 \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (16,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (21,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (5,2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125 (42,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51 (36,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (53,4%)\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 \u003cp\u003eN1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171 (57,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (63,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26 (44,8%)\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 \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (1,7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (52,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76 (54,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (43,1%)\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 \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (45,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (45,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33 (56,9%)\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 \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268 (90,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 (87,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57 (98,3%)\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 \u003cp\u003eLobular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (7,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (10,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\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 \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (1,7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTILs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTILs\u0026thinsp;\u0026lt;\u0026thinsp;60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (26,6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (10,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (77,6%)\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 \u003cp\u003eTILs\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (8,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (22,4%)\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 \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118 (84,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll patients provided written informed consent for participation in the study and the utilization of biomaterials for translational research purposes. The study protocol received approval from the relevant ethics committee and national competent authority.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBiomarker analysis\u003c/h2\u003e \u003cp\u003eAn experienced pathologist reassessed the biopsies on an H\u0026amp;E-stained slide to identify the presence of invasive breast cancer (BC). On-treatment biopsies were categorized as positive for invasive tumor (tu+) if residual invasive cancer cells were observed. Ductal carcinoma in situ or other precursor lesions were not included in this classification.\u003c/p\u003e \u003cp\u003eThe presence and quantity of tumor-infiltrating lymphocytes (TILs) in the stromal compartment were documented following the guidelines of the international TIL working group. This involved comparing the H\u0026amp;E-stained slide under review to standardized reference images. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImmunostaining for Ki-67 was performed on a Ventana Discovery XT instrument (Ventana, Tucson, AZ) using the MIB-1 clone (diluted 1:50). Quantification of stained tumor cells was performed using a digital software solution (VMScope, Berlin, Germany) according to recommendation of the Ki-67 in BC working group (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). For each case, three areas were chosen and counted, and the mean value of the different areas was used for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical considerations\u003c/h2\u003e \u003cp\u003ePathologic complete response (pCR) was defined as the absence of invasive or non-invasive BC in the breast and lymph nodes after completion of neoadjuvant treatment (ypT0 ypN0). Disease-free survival (DFS) was defined as the time from study entry to distant or local relapse or death from any cause.\u003c/p\u003e \u003cp\u003eStatistical analyses were computed in R 4.0.3 (R Project for Statistical Computing, RRID:SCR_001905). The change of TILs (Δ\u003csub\u003eTILs\u003c/sub\u003e) and Ki-67 (Δ\u003csub\u003eKi\u0026minus;67\u003c/sub\u003e) was calculated as the difference between on-treatment and pre-treatment as a continuous parameter. To test the association of positive on-treatment biopsies (tu+) with tumor characteristics and pCR, chi-squared test was used. The Kaplan Meier method with log rank test was used to illustrate the association of response parameters with DFS. Uni- and bivariate Cox proportional hazard regression models were fit to examine the association of biomarkers with DFS. Logistic regression models were fit to examine the association of biomarkers with pCR.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFrequencies of residual cancer cells and their association with patient and tumor characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResidual cancer cells were present in biopsies of 207 patients (tu+; 70%) after 4 of 8 cycles (G4, G5) and 2 of 6 cycles chemotherapy (G6), respectively. 90 biopsies showed no residual disease (tu-; 30%). The highest frequencies of tu- biopsies were observed in patients with HER2+ (49%) and TNBC (38%) BC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The highest frequency of tu- patients was observed among patients of the G6 trial (all patients had triple-negative or HER2-positive disease). The frequency of tu- patients was also higher in patients with small tumors. There was no statistically significant association with lymph node status, histological grading, TILs or histologic subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFrequencies of residual cancer cells and their association with response to treatment (\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn tu- patients a pCR was observed in 50% (45/90) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In contrast, only 17 of 207 (8%) patients with positive biopsies (tu+) had a pCR after completion of the full treatment course, and 92% had residual diseases (190/207). A similar association could be observed in the different BC subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The distribution of patients with pCR or non-pCR after completion of chemotherapy according to on-treatment biopsies with (tu+) or without (tu-) residual cancer cells is demonstrated in a Sankey plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) in detail. Sensitivity to predict residual disease was 0.81 (specificity 0.72). The positive and negative predictive values were 0.92 and 0.50, respectively.\u003c/p\u003e \u003cp\u003eIn univariate Cox regression analyses, the absence of tumor cells in on-treatment biopsies was associated with a lower risk of relapse in patients with triple-negative disease (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the effect was not statistically significant when adjusted for pCR in a bivariate model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The relationship between the presence of residual disease during and/or after chemotherapy and patient survival was demonstrated in a Kaplan-Meier analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Patients with residual cancer cells during chemotherapy (tu+) and residual disease (RD) after completion of the full course show the highest risk of relapse.\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\u003eUnivariate Cox regression models to predict disease-free survival according to residual cancer in on-treatment biopsies during neoadjuvant chemotherapy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR-/HER2-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etu- vs. tu+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.402 (0.163\u0026ndash;0.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR+/HER2-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etu- vs. tu+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.039 (0.428\u0026ndash;2.525)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etu- vs. tu+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.77 (0.577\u0026ndash;5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate Cox regression models to predict disease-free survival according to residual cancer in biopsies during neoadjuvant chemotherapy and after treatment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR-/HER2-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etu- vs. tu+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.544 (0.217\u0026ndash;1.369)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.196\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 \u003cp\u003epCR vs. no pCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.282 (0.083\u0026ndash;0.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR+/HER2-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etu- vs. tu+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14 (0.442\u0026ndash;2.939)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.787\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 \u003cp\u003epCR vs. no pCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.689 (0.149\u0026ndash;3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etu- vs. tu+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.114 (0.589\u0026ndash;7.584)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.251\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 \u003cp\u003epCR vs. no pCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.704 (0.197\u0026ndash;2.516)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePatients with false negative predictions, i.e. those without residual tumor cells on-treatment but residual disease after completion of treatment were analyzed (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A higher frequency of false negative predictions was observed in HR+/HER2- disease. Tumors with a low quantity of TILs were prone to false negative predictions based on the on-treatment sample. There was no statistically significant association with tumor stage, nodal status or grade. Patients with false positive predictions, i.e. those with residual cancer cells on-treatment, but with pCR after chemotherapy were demonstrated in Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. This was more frequently observed in HR-/HER2- cases, in the GeparSixto trial, in smaller tumors, patients with negative clinical lymph node status, and in tumors with a higher quantity of TILs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDynamic change of TILs and Ki-67 and its association with patient outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere was no association between the dynamic change in Ki-67 and dynamic change of TILs (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn patients with residual invasive cancer cells in the on-treatment biopsy an increase of TILs in a subset of patients was observed, while only a few patients showed a decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). An increase of TILs was associated with a higher probability of pCR in the overall study cohort, but not within the BC subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). It was associated with a lower risk of relapse in patients with triple negative disease.\u003c/p\u003e \u003cp\u003eThe proliferation index as measured by Ki-67 immunohistochemistry decreased in most patients during chemotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The lack of decrease in Ki-67 was associated with a low probability of pCR in all patients and was associated with a higher risk of relapse in patients with triple negative disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eIn bivariate Cox regression analyses in patients with triple-negative disease adjusted for pCR, the dynamic change of TILs was statistically significantly associated with DFS. The change of Ki-67 was not significantly associated with relapse-free survival in a bivariate model adjusted for pCR (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate Cox regression models to predict disease-free survival according to pCR and dynamic change in TILs or Ki-67, respectively (hazard ratio for Delta TILs/Ki-67 per % unit).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR-/HER2-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelta TILs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.979 (0.959\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048\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 \u003cp\u003epCR vs. no pCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.190 (0.025\u0026ndash;1.418)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR-/HER2-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelta Ki-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.019 (0.996\u0026ndash;1.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098\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 \u003cp\u003epCR vs. no pCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.211 (0.028\u0026ndash;1.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective research study, we analyzed on-treatment biopsies obtained during neoadjuvant chemotherapy (NACT) for breast cancer (BC).\u003c/p\u003e \u003cp\u003e30% of on-treatment biopsies showed no cancer cells. However, if residual cancer cells were detected, achieving a pathologic complete response (pCR) post-treatment was less likely. This suggests the potential for early treatment adjustment, including alternative chemotherapy agents or targeted therapies, to enhance response rates, particularly within the framework of clinical trials. In the GeparTrio and GeparQuinto neoadjuvant trials (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), treatment was adjusted according to evaluation of on-treatment response using ultrasound and led to improved patient\u0026rsquo;s survival in GeparTrio. Biopsy procedures might be an additional tool to identify tumors prone to treatment failure early on treatment in future trials.\u003c/p\u003e \u003cp\u003eIf no cancer cells were detected during treatment, this information could not reliably predict chemotherapy outcome, as the rate of pCR was 50% in this group in the current study. This suggests that sampling error of the residual disease may have led to a false negative on-treatment sample. False negative prediction was more frequent in HR+/HER2- disease and in tumors with a lower quantity of TILs, reflecting a tumor biology with a lower a priori probability for pCR. False positive prediction (cancer cells on-treatment but pCR after therapy) were more frequent in HR-/HER2- tumors, in the GeparSixto trial, in smaller tumors and those with a higher quantity of TILs, reflecting patients with a higher a priori probability of pCR.\u003c/p\u003e \u003cp\u003eAs our study collected on-treatment samples for translational research rather than for predicting pCR or guiding treatment decisions, its comparability to studies focused on on-treatment pCR prediction may be limited (reviewed in (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)). The reported negative predictive values of these studies vary, ranging from 71% for core needle biopsy (CNB) to as high as 95% for vacuum-assisted biopsy, which offers a larger specimen for analysis. However, with its false negative rates reaching 49.3%, presents a considerable margin for error (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, these investigations aimed to evaluate pathologic complete response (pCR) after completion of neoadjuvant chemotherapy (NACT) before surgery, utilizing minimally invasive methods to identify patients potentially eligible for surgery omission. These trials did not assess the capability to predict pCR early during treatment.\u003c/p\u003e \u003cp\u003eRegarding survival, patients with triple-negative tumors and residual disease both on- and post-treatment had the highest risk of relapse. Patients without the evidence of cancer cells on-treatment but residual disease after the completion of the full course of treatment had a lower risk. This observation could be explained by the fact that the biopsy procedure is more likely to miss smaller tumors or tumors with only a minimal amount of residual disease.\u003c/p\u003e \u003cp\u003ePre-treatment levels of TILs can be used to predict response to chemotherapy and patient\u0026rsquo;s survival in BC (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Chemotherapy might induce or augment a cytotoxic immune response(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) and an influx of TILs during treatment is associated with better response (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Moreover, their presence in surgical specimens is associated with better outcome(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and a gene signature for prediction of TILs in post-treatment samples is predictive of survival (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we observed an increase of TILs in a subset of patients and only a few cases showed a decrease. An increase of TILs was associated with a higher probability of pCR in all patients and with a lower risk of relapse in patients with TNBC reflecting their known predictive value in triple-negative disease. This observation is particularly interesting, as it can identify patients early on treatment that still harbor invasive tumor residuals, where continuing standard therapy might be the option that is superior to a change in treatment plan. Further investigation in this group of patients could help to refine adapting tumor response into treatment plans and could ultimately allow to move the risk assessment after NACT to an earlier timepoint.\u003c/p\u003e \u003cp\u003eThe marker of tumor cell proliferation Ki-67 can be used to predict response to NACT and patient\u0026rsquo;s survival (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). High levels are typically associated with better response to cytotoxic treatment but shorter long-term outcome due to more aggressive tumor biology. In the context of neoadjuvant aromatase inhibition, on-treatment evaluation of Ki-67 can predict patient outcome (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, most patients showed a decrease in Ki-67 index and this was associated with a higher probability of response in triple-negative disease. It was also associated with a lower risk of relapse in triple negative disease, but not in other subtypes. Bivariate survival analyses demonstrated that these effects were probably due to the association with pCR and its strong association with survival in this subtype.\u003c/p\u003e \u003cp\u003eFrom a translational research perspective, these observations suggest that on-treatment biopsies could be valuable for studying mechanisms of therapy resistance and predicting failure to achieve pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT). However, they are not suitable for identifying markers of chemotherapy sensitivity, as highly sensitive tumors would not contain residual cancer cells in on-treatment biopsies. To address this, further analyses should focus on revisiting naive biopsies from patients who achieved early pCR. On a molecular level, NACT induces global changes in gene expression (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Examples of alterations are genes involved in proliferation, epithelial-mesenchymal transition and metabolic processes (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Analysis of serial biopsy samples during chemotherapy allows the characterization of mechanisms of early response and adaption to therapy. In such a study, a decreased expression of genes related to immune response and proliferation could be observed and the downregulation of cell-cycle inhibitors was associated with worse response (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The use of on-treatment biopsies for patient stratification should be further explored in the context to clinical trials and should be a part of the study protocol.\u003c/p\u003e \u003cp\u003eSeveral limitations of the study warrant consideration. Firstly, it's important to note that the biopsy procedure was not strictly standardized according to the study protocol. Additionally, variations in the timing of the biopsy procedure across the three trials were inevitable due to differences in study design. In GeparQuattro and GeparQuinto, on-treatment samples were obtained after 4 of 8 cycles, in GeparSixto after 2 of 6 cycles. Moreover, GeparQuattro and GeparQuinto switched from anthracycline to taxane therapy following the biopsy, whereas GeparSixto continued with the same regimen (concurrent taxane/anthracycline). The collection of on-treatment biopsies was not a mandatory part of the study protocol with a potential selection bias and comparably small samples sizes in subgroup analyses. The study was primarily designed to collect material for translational research purposes. It has to be considered that patients within this trial had been treated before 2012 which possibly limits accuracy of on-treatment biopsy due to less experienced examiners and less refined examination instruments. Regimen not matching current standards thereby limiting the chances of achieving a pCR, are also able to influence false negative rates and predictive values of on treatment biopsies.\u003c/p\u003e \u003cp\u003eIn summary, our findings show that on-treatment biopsies can effectively predict non-pCR across breast cancer (BC) subtypes when residual cancer is present. This discovery presents potential avenues for tailoring therapy concepts in future clinical trials, such as implementing de-escalation strategies for responders and exploring experimental treatments for non-responders. Moreover, analyzing sequential biopsies could be instrumental in identifying molecular markers of therapy resistance. Further research is warranted to determine whether more standardized or extensive sampling procedures, or their combination with additional clinicopathological features, could enhance the sensitivity of on-treatment biopsy procedures.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebevacizumab\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDisease-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEC-T\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eepirubicin, cyclophosphamide and docetaxel\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eG4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeparQuattro\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eG5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeparQuinto\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eG6\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeparSixto\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHER2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman epidermal growth factor receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNACT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeoadjuvant chemotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePathological complete response\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResidual disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTILs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor-infiltrating lymphocytes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriple negative breast cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003etu+\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResidual tumor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003etu-\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNo residual tumor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was performed in accordance with good clinical practice guidelines, national laws and the Declaration of Helsinki. Informed written consent for analysis was ontained from all patients. The GeparQuattro (EudraCT 2005-001546-17) and GeparQuinto (EudraCT 2006-005834-19) studies were approved by the Ethics Committee of the special field \u0026lsquo;Medicine\u0026rsquo; at the Johann Wolfgang Goethe University Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt (ethical vote number 110/05 for GeparQuattro and 44/07 for Gepar Quinto). The GeparSixto study (EudraCT 2011-000553-23) was approved by the Ethics Committee of the Medical Association (Aerztekammer) Nordrhein, Tersteegenstr.9, 40474 D\u0026uuml;sseldorf (ethical vote number 2011154.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data supporting the findings of this study are available within the article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.299674267100976%\" valign=\"top\"\u003eWill individual participant data be available (including data dictionaries)?\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"55.700325732899024%\" valign=\"top\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.299674267100976%\" valign=\"top\"\u003eWhat data in particular will be shared?\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"55.700325732899024%\" valign=\"top\"\u003eIndividual participant data that underlie the results reported in this article, after final analysis and publication of all secondary efficacy endpoints\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.299674267100976%\" valign=\"top\"\u003eWhat other documents will be available?\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"55.700325732899024%\" valign=\"top\"\u003eStudy protocol; Statistical report (if necessary for the project)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.299674267100976%\" valign=\"top\"\u003eWhen will data be available (start and end dates)?\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"55.700325732899024%\" valign=\"top\"\u003eBeginning after final analysis and publication of all secondary efficacy endpoints; no end date\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.299674267100976%\" valign=\"top\"\u003eWith whom?\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"55.700325732899024%\" valign=\"top\"\u003eResearchers who provide translational research proposals. Proposals should be approved by the GBG scientific board.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.299674267100976%\" valign=\"top\"\u003eFor what types of analyses?\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"55.700325732899024%\" valign=\"top\"\u003eTo achieve aims in the approved proposal\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.299674267100976%\" valign=\"top\"\u003eBy what mechanism will data be made available?\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"55.700325732899024%\" valign=\"top\"\u003eProposal forms should be requested from [email protected]; once the application has been approved and a data transfer agreement has been signed, researchers will be given access to the data.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB. Sinn is an employee of BioNTech SE, has received consultancy honoraria from Sanofi and holds a pending patent WO2020109570A1.\u003c/p\u003e\n\u003cp\u003eV. Nekljudova\u0026nbsp;declares to be GBG Forschungs GmbH employee. GBG Forschungs GmbH received funding for research grants from Abbvie, Amgen, AstraZeneca, BMS, Daiichi-Sankyo, Gilead, Molecular Health, Novartis, Pfizer and Roche (paid to the institution); other (non-financial/medical writing) from Daiichi-Sankyo, Gilead, Novartis, Pfizer, Roche and Seagen (paid to the institution). GBG Forschungs GmbH has licensing fees\u0026nbsp;from VMscope GmbH. In addition, GBG Forschungs GmbH has a patent EP21152186.9 pending, a patent EP19808852.8 pending, and a patent EP14153692.0 pending.\u003c/p\u003e\n\u003cp\u003eT. Karn reports a patent for EP18209672 pending.\u003c/p\u003e\n\u003cp\u003eV. Mueller reports speaker honoraria from Astra Zeneca, Daiichi-Sankyo, Eisai, Pfizer, MSD, Medac, Novartis, Roche, \u0026nbsp;Seagen, Onkowissen, high5 Oncology, Medscape, Gilead, Pierre Fabre and iMED Institut; receives consultancy honoraria from Roche, Pierre Fabre, PINK, ClinSol, Novartis, MSD, Daiichi-Sankyo, Eisai, Lilly, Seagen, Gilead and Stemline; he also declares to receive institutional research support from Novartis, Roche, Seagen, Genentech and Astra Zeneca; and reports travel grants from Astra Zeneca, Roche, Pfizer, Daiichi Sankyo and Gilead.\u003c/p\u003e\n\u003cp\u003eC. Schem declares honoraria from Roche, Lilly, AstraZeneca, MSD Oncology, Exact Sciences and Novartis; operate in a consulting or advisory role for Novartis, AstraZeneca and Roche; reports honoraria for speakers\u0026rsquo; bureau from Roche, AstraZeneca and Novartis; CS also reports to receive research funding from Roche (Inst.), Daiichi Sankyo Europe GmbH (Inst.), AstraZeneca (Inst.), GlaxoSmithKline (Inst.), Novartis (Inst.) and Lilly (Inst.); and to receive travel accommodations and expenses from Pfizer, Roche, AstraZeneca, Novartis and Gilead Sciences. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eM. Untch reports honoraria from AstraZeneca, Art tempi, Amgen, Daiji Sankyo, Lilly, Roche, Pfizer, MSD Oncology, Pierre Fabre, Sanofi-Aventis, Myriad, Seagen, Gilead, Novartis and Stemline; to act in a consulting or advisory Role for Amgen, Lilly, Roche, Pfizer, Lilly, Pierre Fabre, Novartis, MSD Oncology, Roche, Agendia, \u0026nbsp;Seagen, Gilead, Lily, Stemline, Genzyme and Onkowissen.de.\u003c/p\u003e\n\u003cp\u003eAll honoraria and fees to the employer/institution.\u003c/p\u003e\n\u003cp\u003eJ. Huober declares to receive honoraria from Lilly, Novartis, Roche, Pfizer, AstraZeneca, Seagen, Gilead and Daiichi; to have a consulting or advisory relationship with\u0026nbsp;Lilly, Novartis, Roche, Pfizer, AstraZeneca, Gilead and Daiichi; and to receive honoraria for travel expenses from\u0026nbsp;Roche, Novartis, Daiichi and Gilead.\u003c/p\u003e\n\u003cp\u003eJ. Holtschmidt reports personal fees and non-financial support from Daiichi Sankyo, non-financial support from Hologic, personal fees from MSD Oncology, Novartis, Palleos Health Care, Pfizer, Roche Pharma and Seagen, outside the submitted work; he also declares to be GBG Forschungs GmbH employee. GBG Forschungs GmbH received funding for research grants from Abbvie, Amgen, AstraZeneca, BMS, Daiichi-Sankyo, Gilead, Molecular Health, Novartis, Pfizer and Roche (paid to the institution); other (non-financial/medical writing) from Daiichi-Sankyo, Gilead, Novartis, Pfizer, Roche and Seagen (paid to the institution). GBG Forschungs GmbH has licensing fees from VMscope GmbH. In addition, GBG Forschungs GmbH has a patent EP21152186.9 pending, a patent EP19808852.8 pending, and a patent EP14153692.0 pending.\u003c/p\u003e\n\u003cp\u003eM. van Mackelenbergh reiceived personal fees, honoraria or travel grants from Amgen, AstraZeneca, Daiichi Sankyo, Gilead, GSK, Lilly, Molecular Health, Mylan, MSD, Novartis, Pfizer, PierreFabre, Roche and Seagen.\u003c/p\u003e\n\u003cp\u003eS. Loibl declares to be GBG Forschungs GmbH employee (CEO);The company receives grants from AbbVie, AstraZeneca, Celgene, Daiichi-Sankyo, Immunomedics/Gilead, Molecular Health, Novartis, Pfizer and Roche; honoraria for Advisory board from Abbvie, Amgen, AstraZeneca, BMS, Celgene, DSI, EirGenix, Gilead, GSK, Lilly, Merck, Novartis, Olema, Pfizer, Pierre Fabre, Relay Therapeutics, Roche, Sanofi and Seagen; honoraria as invented speaker from AstraZeneca, DSI, Gilead, Novartis, Pfizer, Roche, Seage and Medscape. S. Loibl reports non-financial interest as advisory role in AGO Kommission Mamma, PI Aphinity (principal investigator) as member in AGO, ASCO, DKG, ESMO and other non-financial interest from AstraZeneca, Daiichi-Sankyo, Gilead, Novartis, Pfizer, Roche and Seagen. GBG Forschungs GmbH has following /patents pending: EP14153692.0, EP21152186.9, EP19808852.8 and receives licensing fees from VM Scope GmbH.\u003c/p\u003e\n\u003cp\u003eC.D. reports grants from European Commission H2020, grants from German Cancer Aid Translational Oncology, grants from German Breast Group, grants from BMBF, to the institution during the conduct of the study; personal fees from Novartis, personal fees from Roche, personal fees from MSD Oncology, personal fees from Daiichi Sankyo, personal fees from AstraZeneca, \u0026nbsp;from Molecular Health, grants from Myriad, personal fees from Merck, other from Sividon diagnostics, \u0026nbsp;outside the submitted work; \u0026nbsp;In addition, Dr. Denkert has a patent VMScope digital pathology software with royalties paid, a patent WO2020109570A1 - cancer immunotherapy pending, and a patent WO2015114146A1 and \u0026nbsp;WO2010076322A1- therapy response issued. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo other potential conflict of interest relevant to this article was reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis analysis was funded by GBG.\u003c/p\u003e\n\u003cp\u003eThe GeparQuattro trial received funding support from Roche and Sanofi-Aventis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe GeparQuinto trial received funding support from GlaxoSmithKline, Novartis, Roche and Sanofi Aventis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe GeparSixto trial received funding support from Cephalon, GlaxoSmithKline and Roche.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe funders had no access to the study database and were not involved in the analysis and interpretation of the results. No grant number applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis was designed by the members of the translational research subcommittee of the German Breast Group (BS, TK, MvM, FM, CS, ES, PAF, VM, SL and CD). BS and VN has analysed the data. BS and VN had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors interpreted the data. The first draft of the manuscript was written by BS. The decision to submit the manuscript for publication was made by all authors. All authors contributed to the review of the manuscript. No persons other than the listed authors contributed to the writing of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all patients, investigators and study personnel who supported the trials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsselain B, Barlow W, Bartlett J, Bergh J, Bergsten-Nordstr\u0026ouml;m E, Bliss J, et al. Long-term outcomes for neoadjuvant versus adjuvant chemotherapy in early breast cancer: meta-analysis of individual patient data from ten randomised trials. Lancet Oncol. 2018;19:27\u0026ndash;39. \u003c/li\u003e\n\u003cli\u003evon Minckwitz G, Schneeweiss A, Loibl S, Salat C, Denkert C, Rezai M, et al. Neoadjuvant carboplatin in patients with triple-negative and HER2-positive early breast cancer (GeparSixto; GBG 66): a randomised phase 2 trial. Lancet Oncol. 2014;15:747\u0026ndash;56. \u003c/li\u003e\n\u003cli\u003eSchmid P, Cortes J, Pusztai L, McArthur H, K\u0026uuml;mmel S, Bergh J, et al. Pembrolizumab for Early Triple-Negative Breast Cancer. New England Journal of Medicine. 2020;382:810\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eSchneeweiss A, Chia S, Hickish T, Harvey V, Eniu A, Hegg R, et al. Pertuzumab plus trastuzumab in combination with standard neoadjuvant anthracycline-containing and anthracycline-free chemotherapy regimens in patients with HER2-positive early breast cancer: A randomized phase II cardiac safety study (TRYPHAENA). Annals of Oncology. 2013;24:2278\u0026ndash;84. \u003c/li\u003e\n\u003cli\u003eCortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, et al. Pathological complete response and long-term clinical benefit in breast cancer: The CTNeoBC pooled analysis. The Lancet. 2014;384:164\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eHeil J, Kuerer HM, Pfob A, Rauch G, Sinn HP, Golatta M, et al. Eliminating the breast cancer surgery paradigm after neoadjuvant systemic therapy: current evidence and future challenges. Annals of Oncology. Elsevier Ltd.; 2020;31:61\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rez-Garc\u0026iacute;a JM, Gebhart G, Ruiz Borrego M, Stradella A, Bermejo B, Schmid P, et al. Chemotherapy de-escalation using an 18F-FDG-PET-based pathological response-adapted strategy in patients with HER2-positive early breast cancer (PHERGain): a multicentre, randomised, open-label, non-comparative, phase 2 trial. The Lancet Oncology. Elsevier; 2021;22:858\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003eHatzis C, Pusztai L, Valero V, Booser DJ, Esserman L, Lluch A, et al. A Genomic Predictor of Response and Survival Following Taxane-Anthracycline Chemotherapy for Invasive Breast Cancer. JAMA. 2011;305:1873\u0026ndash;81. \u003c/li\u003e\n\u003cli\u003eHeil J, Sinn P, Richter H, Pfob A, Schaefgen B, Hennigs A, et al. RESPONDER - diagnosis of pathological complete response by vacuum-assisted biopsy after neoadjuvant chemotherapy in breast Cancer - a multicenter, confirmative, one-armed, intra-individually-controlled, open, diagnostic trial. BMC Cancer. 2018;18:1\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eTurnbull AK, Arthur LM, Renshaw L, Larionov AA, Kay C, Dunbier AK, et al. Accurate Prediction and Validation of Response to Endocrine Therapy in Breast Cancer. Journal of Clinical Oncology. 2015;33:2270\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eEllis MJ, Suman VJ, Hoog J, Goncalves R, Sanati S, Creighton CJ, et al. Ki67 Proliferation Index as a Tool for Chemotherapy Decisions During and After Neoadjuvant Aromatase Inhibitor Treatment of Breast Cancer: Results From the American College of Surgeons Oncology Group Z1031 Trial (Alliance). Journal of Clinical Oncology. 2017;35:1061\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eSmith I, Robertson J, Kilburn L, Wilcox M, Evans A, Holcombe C, et al. Long-term outcome and prognostic value of Ki67 after perioperative endocrine therapy in postmenopausal women with hormone-sensitive early breast cancer (POETIC): an open-label, multicentre, parallel-group, randomised, phase 3 trial. The Lancet Oncology. The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license; 2020;21:1443\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eVon Minckwitz G, Blohmer JU, Costa SD, Denkert C, Eidtmann H, Eiermann W, et al. Response-guided neoadjuvant chemotherapy for breast cancer. Journal of Clinical Oncology. 2013;31:3623\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003evon Minckwitz G, Loibl S, Untch M, Eidtmann H, Rezai M, Fasching PA, et al. Survival after neoadjuvant chemotherapy with or without bevacizumab or everolimus for HER2-negative primary breast cancer (GBG 44-GeparQuinto). Annals of oncology. 2014;25:2363\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eMagbanua MJM, Wolf DM, Yau C, Davis SE, Crothers J, Au A, et al. Serial expression analysis of breast tumors during neoadjuvant chemotherapy reveals changes in cell cycle and immune pathways associated with recurrence and response. Breast Cancer Research. 2015;17:73. \u003c/li\u003e\n\u003cli\u003eBownes RJ, Turnbull AK, Martinez-Perez C, Cameron DA, Sims AH, Oikonomidou O. On-treatment biomarkers can improve prediction of response to neoadjuvant chemotherapy in breast cancer. Breast Cancer Research. 2019;21:73. \u003c/li\u003e\n\u003cli\u003eUntch M, Rezai M, Loibl S, Fasching PA, Huober J, Tesch H, et al. Neoadjuvant treatment with trastuzumab in HER2-positive breast cancer: results from the GeparQuattro study. Journal of clinical oncology. 2010;28:2024\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003evon Minckwitz G, Rezai M, Fasching PA, Huober J, Tesch H, Bauerfeind I, et al. Survival after adding capecitabine and trastuzumab to neoadjuvant anthracycline-taxane-based chemotherapy for primary breast cancer (GBG 40--GeparQuattro). Annals of Oncology. 2014;25:81\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003evon Minckwitz G, Rezai M, Loibl S, Fasching PA, Huober J, Tesch H, et al. Capecitabine in addition to anthracycline- and taxane-based neoadjuvant treatment in patients with primary breast cancer: phase III GeparQuattro study. Journal of clinical oncology. 2010;28:2015\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eUntch M, Loibl S, Bischoff J, Eidtmann H, Kaufmann M, Blohmer J-U, et al. Lapatinib versus trastuzumab in combination with neoadjuvant anthracycline-taxane-based chemotherapy (GeparQuinto, GBG 44): a randomised phase 3 trial. The Lancet Oncology. 2012;13:135\u0026ndash;44. \u003c/li\u003e\n\u003cli\u003eGerber B, Loibl S, Eidtmann H, Rezai M, Fasching PA, Tesch H, et al. Neoadjuvant bevacizumab and anthracycline-taxane-based chemotherapy in 678 triple-negative primary breast cancers; results from the geparquinto study (GBG 44). Annals of oncology. 2013;24:2978\u0026ndash;84. \u003c/li\u003e\n\u003cli\u003eVon Minckwitz G, Schneeweiss A, Loibl S, Salat C, Denkert C, Rezai M, et al. Neoadjuvant carboplatin in patients with triple-negative and HER2-positive early breast cancer (GeparSixto; GBG 66): A randomised phase 2 trial. The Lancet Oncology. 2014;15:747\u0026ndash;56. \u003c/li\u003e\n\u003cli\u003eSalgado R, Denkert C, Demaria S, Sirtaine N, Klauschen F, Pruneri G, et al. The evaluation of tumor-infiltrating lymphocytes (TILS) in breast cancer: Recommendations by an International TILS Working Group 2014. Annals of Oncology. 2015;26:259\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003eDowsett M, Nielsen TO, A\u0026rsquo;Hern R, Bartlett J, Coombes RC, Cuzick J, et al. Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. Journal of the National Cancer Institute. 2011;103:1656\u0026ndash;64. \u003c/li\u003e\n\u003cli\u003eKuerer HM, Rauch GM, Krishnamurthy S, Adrada BE, Caudle AS, Desnyder SM, et al. A Clinical Feasibility Trial for Identification of Exceptional Responders in Whom Breast Cancer Surgery Can Be Eliminated Following Neoadjuvant Systemic Therapy. Annals of Surgery. 2018;267:946\u0026ndash;51. \u003c/li\u003e\n\u003cli\u003eHeil J, K\u0026uuml;mmel S, Schaefgen B, Paepke S, Thomssen C, Rauch G, et al. Diagnosis of pathological complete response to neoadjuvant chemotherapy in breast cancer by minimal invasive biopsy techniques. British Journal of Cancer. 2015;113:1565\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eDenkert C, von Minckwitz G, Darb-Esfahani S, Lederer B, Heppner BI, Weber KE, et al. Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy. The Lancet Oncology. 2018;19:40\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eZitvogel L, Apetoh L, Ghiringhelli F, Kroemer G. Immunological aspects of cancer chemotherapy. Nature Reviews Immunology. 2008;8:59\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eDemaria S, Volm MD, Shapiro RL, Yee HT, Oratz R, Formenti SC, et al. Development of tumor-infiltrating lymphocytes in breast cancer after neoadjuvant paclitaxel chemotherapy. Clinical Cancer Research. 2001;7:3025\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eDieci M V., Criscitiello C, Goubar A, Viale G, Conte P, Guarneri V, et al. Prognostic value of tumor-infiltrating lymphocytes on residual disease after primary chemotherapy for triple-negative breast cancer: A retrospective multicenter study. Annals of Oncology. 2014;25:611\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eCriscitiello C, Bayar MA, Curigliano G, Symmans FW, Desmedt C, Bonnefoi H, et al. A gene signature to predict high tumor-infiltrating lymphocytes after neoadjuvant chemotherapy and outcome in patients with triple-negative breast cancer. Annals of Oncology. 2018;29:162\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eDenkert C, Loibl S, M\u0026uuml;ller BM, Eidtmann H, Schmitt WD, Eiermann W, et al. Ki67 levels as predictive and prognostic parameters in pretherapeutic breast cancer core biopsies: A translational investigation in the neoadjuvant gepartrio trial. Annals of Oncology. 2013;24:2786\u0026ndash;93. \u003c/li\u003e\n\u003cli\u003eVon Minckwitz G, Schmitt WD, Loibl S, M\u0026uuml;ller BM, Blohmer JU, Sinn B V., et al. Ki67 measured after neoadjuvant chemotherapy for primary breast cancer. Clinical Cancer Research. 2013;19:4521\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eHannemann J, Oosterkamp HM, Bosch CAJ, Velds A, Wessels LFA, Loo C, et al. Changes in gene expression associated with response to neoadjuvant chemotherapy in breast cancer. Journal of Clinical Oncology. 2005;23:3331\u0026ndash;42. \u003c/li\u003e\n\u003cli\u003eKlintman M, Buus R, Cheang MCU, Sheri A, Smith IE, Dowsett M. Changes in expression of genes representing key biologic processes after neoadjuvant chemotherapy in breast cancer, and prognostic implications in residual disease. Clinical Cancer Research. 2016;22:2405\u0026ndash;16. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, neoadjuvant therapy, serial biopsies, TILs, Ki-67","lastPublishedDoi":"10.21203/rs.3.rs-4483953/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4483953/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePatients with pathologic complete response (pCR) to neoadjuvant chemotherapy for invasive breast cancer (BC) have better outcomes, potentially warranting less extensive surgical and systemic treatments. Early prediction of treatment response could aid in adapting therapies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOn-treatment biopsies from 297 patients with invasive BC in three randomized, prospective neoadjuvant trials were assessed. BC quantity, tumor-infiltrating lymphocytes (TILs), and the proliferation marker Ki-67 were compared to pre-treatment samples. The study investigated the correlation between residual cancer, changes in Ki-67 and TILs, and their impact on pathologic complete response (pCR) and disease-free survival (DFS).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 297 samples, 138 (46%) were hormone receptor-positive (HR+)/human epidermal growth factor 2-negative (HER2-), 87 (29%) were triple-negative (TNBC), and 72 (24%) were HER2+. Invasive tumor cells were found in 70% of on-treatment biopsies, with varying rates across subtypes (HR+/HER2-: 84%, TNBC: 62%, HER2+: 51%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with residual tumor on-treatment had an 8% pCR rate post-treatment (HR+/HER2-: 3%, TNBC: 19%, HER2+: 11%), while those without any invasive tumor had a 50% pCR rate (HR+/HER2-: 27%; TNBC: 48%, HER2+: 66%). Sensitivity for predicting residual disease was 0.81, with positive and negative predictive values of 0.92 and 0.50, respectively. Increasing TILs from baseline to on-treatment biopsy (if residual tumor was present) were linked to higher pCR likelihood in the overall cohort (OR 1.034, 95% CI 1.013\u0026ndash;1.056 per % increase; p\u0026thinsp;=\u0026thinsp;0.001) and with a longer DFS in TNBC (HR 0.980, 95% CI 0.963\u0026ndash;0.997 per % increase; p\u0026thinsp;=\u0026thinsp;0.026). Persisting or increased Ki-67 was associated with lower pCR probability in the overall cohort (OR 0.957, 95% CI 0.928\u0026ndash;0.986; p\u0026thinsp;=\u0026thinsp;0.004) and shorter DFS in TNBC (HR 1.023, 95% CI 1.001\u0026ndash;1.047; p\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOn-treatment biopsies can predict patients unlikely to achieve pCR post-therapy. This could facilitate therapy adjustments for TNBC or HER2\u0026thinsp;+\u0026thinsp;BC. They also might offer insights into therapy resistance mechanisms. Future research should explore whether standardized or expanded sampling enhances the accuracy of on-treatment biopsy procedures.\u003c/p\u003e\u003ch2\u003eTrial Registration\u003c/h2\u003e \u003cp\u003eGeparQuattro (EudraCT 2005-001546-17; Start date: 28.06.2005), GeparQuinto (EudraCT 2006-005834-19; Start date: 27.10.2007) and GeparSixto (EudraCT 2011-000553-23; Start date: 29.09.2011).\u003c/p\u003e","manuscriptTitle":"On-treatment biopsies to predict response to neoadjuvant chemotherapy for breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-26 19:12:54","doi":"10.21203/rs.3.rs-4483953/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-18T03:07:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-17T10:49:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-10T21:55:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313305978804449964444427832412187328989","date":"2024-06-22T05:56:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95895317269427970779578553352168565379","date":"2024-06-19T11:04:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-17T04:31:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-07T14:28:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-07T11:25:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research","date":"2024-05-27T09:46:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brcr","sideBox":"Learn more about [Breast Cancer Research](http://breast-cancer-research.biomedcentral.com)","snPcode":"13058","submissionUrl":"https://submission.nature.com/new-submission/13058/3","title":"Breast Cancer Research","twitterHandle":"@BCRJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dda53314-0421-432e-bf25-c9541425db42","owner":[],"postedDate":"June 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-30T16:11:31+00:00","versionOfRecord":{"articleIdentity":"rs-4483953","link":"https://doi.org/10.1186/s13058-024-01883-w","journal":{"identity":"breast-cancer-research","isVorOnly":false,"title":"Breast Cancer Research"},"publishedOn":"2024-09-24 15:57:58","publishedOnDateReadable":"September 24th, 2024"},"versionCreatedAt":"2024-06-26 19:12:54","video":"","vorDoi":"10.1186/s13058-024-01883-w","vorDoiUrl":"https://doi.org/10.1186/s13058-024-01883-w","workflowStages":[]},"version":"v1","identity":"rs-4483953","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4483953","identity":"rs-4483953","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}