Summary
Background
Previous studies have independently validated the prognostic relevance of residual cancer burden (RCB) after neoadjuvant chemotherapy. We used results from several independent cohorts in a pooled patient-level analysis to evaluate the relationship of RCB with long-term prognosis across different phenotypic subtypes of breast cancer, to assess generalisability in a broad range of practice settings.
Methods
In this pooled analysis, 12 institutes and trials in Europe and the USA were identified by personal communications with site investigators. We obtained participant-level RCB results, and data on clinical and pathological stage, tumour subtype and grade, and treatment and follow-up in November, 2019, from patients (aged ≥18 years) with primary stage I–III breast cancer treated with neoadjuvant chemotherapy followed by surgery. We assessed the association between the continuous RCB score and the primary study outcome, event-free survival, using mixed-effects Cox models with the incorporation of random RCB and cohort effects to account for between-study heterogeneity, and stratification to account for differences in baseline hazard across cancer subtypes defined by hormone receptor status and HER2 status. The association was further evaluated within each breast cancer subtype in multivariable analyses incorporating random RCB and cohort effects and adjustments for age and pretreatment clinical T category, nodal status, and tumour grade. Kaplan-Meier estimates of event-free survival at 3, 5, and 10 years were computed for each RCB class within each subtype.
Findings
We analysed participant-level data from 5161 patients treated with neoadjuvant chemotherapy between Sept 12, 1994, and Feb 11, 2019. Median age was 49 years (IQR 20–80). 1164 event-free survival events occurred during follow-up (median follow-up 56 months [IQR 0–186]). RCB score was prognostic within each breast cancer subtype, with higher RCB score significantly associated with worse event-free survival. The univariable hazard ratio (HR) associated with one unit increase in RCB ranged from 1·55 (95% CI 1·41–1·71) for hormone receptor-positive, HER2-negative patients to 2·16 (1·79–2·61) for the hormone receptor-negative, HER2-positive group (with or without HER2-targeted therapy; p<0·0001 for all subtypes). RCB score remained prognostic for event-free survival in multivariable models adjusted for age, grade, T category, and nodal status at baseline: the adjusted HR ranged from 1·52 (1·36–1·69) in the hormone receptor-positive, HER2-negative group to 2·09 (1·73–2·53) in the hormone receptor-negative, HER2-positive group (p<0·0001 for all subtypes).
Interpretation
RCB score and class were independently prognostic in all subtypes of breast cancer, and generalisable to multiple practice settings. Although variability in hormone receptor subtype definitions and treatment across patients are likely to affect prognostic performance, the association we observed between RCB and a patient’s residual risk suggests that prospective evaluation of RCB could be considered to become part of standard pathology reporting after neoadjuvant therapy.
Funding
National Cancer Institute at the US National Institutes of Health.
Introduction
Neoadjuvant chemotherapy is at least as effective as adjuvant therapy and has several additional advantages.
Compared with adjuvant therapy, neoadjuvant therapy permits less extensive breast and axillary surgery by downstaging the tumour and allows monitoring of the treatment response, which provides important prognostic information. Pathological complete response (pCR) to neoadjuvant chemotherapy, defined as the absence of residual invasive disease in the breast and axilla, is strongly associated with improved long-term survival outcomes.
,
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The influential meta-analysis of the Collaborative Trials in Neoadjuvant Breast Cancer (CTNeoBC) showed that patients with pCR have improved event-free survival and overall survival, with the greatest prognostic value in patients with highly proliferative tumours.
Consequently, the US Food and Drug Administration and European Medicines Agency issued initial guidance in 2012–14 for the use of pCR as a regulatory endpoint for accelerated approval of new drugs for neoadjuvant chemotherapy of breast cancer. Since these guidances were issued, contemporary trials have incorporated standardised pathological assessments of surgical resection specimens and validated pCR as a reliable prognostic marker.
Increasingly, the presence or absence of residual disease is being used to guide adjuvant decisions following neoadjuvant chemotherapy.
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Evidence before this study
The seminal meta-analysis of the Collaborative Trials in Neoadjuvant Breast Cancer working group published in 2014 showed that on an individual level, achieving a pathological complete response (pCR) following neoadjuvant chemotherapy was associated with improved long-term survival outcomes. On Sept 13, 2021, a search of the PubMed database using the term “pathological complete response and breast cancer prognosis” yielded 1531 published articles between Jan 1, 2014, and Dec 31, 2019. However, pCR does not provide distinction among patients with residual disease. The residual cancer burden (RCB) method was proposed in 2007 as a standardised methodological approach to evaluate and quantitate the extent of residual disease in breast and axillary lymph nodes following neoadjuvant chemotherapy. A further search of the PubMed database (Sept 13, 2021), using the term “residual cancer burden and breast cancer prognosis” identified 166 articles published between Jan 1, 2007, and Dec 31, 2019. Among these articles, single-institution studies and multicentre trials had validated RCB as a prognostic score in breast cancer.
Added value of this study
Individually, the cohorts in previous studies evaluating the prognostic value of RCB are too small to obtain accurate estimates within the various molecular subtypes of breast cancer. By assembling a pooled cohort of 5161 patients across 12 participating groups from Europe and the USA representing a variety of clinical settings, our study was able to validate the prognostic value of RCB overall and within each hormone receptor and HER2 subtype, with regard to event-free survival and distant relapse-free survival. In addition, by evaluating RCB as a continuous measure in a model that allows for non-linear effect within each subtype, we were able to further characterise how risk of recurrence or death changes with increasing RCB and compare this relationship between different breast cancer subtypes.
Implications of all the available evidence
The prognostic importance of pCR (equivalent to RCB score 0) is well established. RCB score adds substantially to the binary assessment of pCR versus residual disease in predicting long-term survival. The observed prognostic consistency of RCB collected across different countries and clinical settings highlights the generalisability of implementing the RCB methodology. RCB score has the potential to be used in a subtype-specific context to predict a patient’s residual risk after neoadjuvant chemotherapy in a prospective setting with standardised evaluation of post-treatment resection specimens, especially given the increasing options for adjuvant therapy in the setting of residual disease.
It yields a continuous score, with pCR being the equivalent of an RCB score of zero. Empirically derived cutpoints are applied to the continuous score to define four RCB classes, from RCB-0 to RCB-3, that represent an increasing residual disease burden. RCB assessments are highly reproducible between pathologists;
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and both RCB and its classes have been validated as prognostic in single-institution studies
,
,
,
and multicentre trials.
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,
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However, individually, these cohorts are too small to obtain accurate estimates of prognosis related to RCB within the various subtypes of breast cancer. Therefore, we performed a pooled participant-level analysis of multiple clinical trials and cohorts to evaluate the overall association between RCB and long-term outcomes, with emphasis on the breast cancer subtypes defined by hormone receptor and HER2 receptor status. Our aim was to understand the prognostic value of RCB relative to pCR in the context of breast cancer subtypes, to optimise the interpretation of RCB and better inform patient management across a broad array of practice settings.
Methods
Study design and patient cohorts
the I-SPY 2 trial,
,
the ARTemis trial,
and a trial led by the Instituto de Investigación Sanitaria Gregorio Marañón (IISGM; Madrid, Spain).
Two of the trials included investigational therapies: the ARTemis study, in which bevacizumab was the investigational drug; and I-SPY 2, in which nine investigational drugs were adaptively randomised (4:1) against a concurrent control.
,
I-SPY 1 and the IISGM trials were both observational, evaluating standard chemotherapies without any experimental arms.
the NEOREP cohort (CNIL declaration number 157270) from the Curie Institute (Paris, France),
the triple-negative breast cancer PROGECT registry of the University of Kansas Medical Center (KUMC; Kansas City, KS, USA),
the TransNEO cohort from the University of Cambridge (Cambridge, UK; European Genome-Phenome number EGAS00001004582), and cohorts from the Edinburgh Breast Unit at Western General Hospital (Edinburgh, UK; Edinburgh Cancer Information Programme Board reference number CIR21166), the Mayo Clinic (Rochester, MN, USA), the Netherlands Cancer Institute (Amsterdam, Netherlands),
and Yale University (New Haven, CT, USA).
Procedures
RCB was evaluated prospectively for five of the 12 cohorts (KUMC, I-SPY 2, IISGM, Mayo Clinic, and Yale cohorts), whereas RCB was determined in a retrospective review by the original investigators for the other seven cohorts (appendix pp 2–3). RCB values used in this analysis were based on reporting at the treating centre and were not centrally reviewed.
Oestrogen receptor (encoded by ESR1) and progesterone receptor (encoded by PGR) status used in this analysis were as defined and provided by the institutions. Two cohorts (ARTemis and TransNEO) only recorded oestrogen receptor status and not progesterone receptor status. Thus, for our analysis, hormone receptor status was determined based on oestrogen and progesterone receptor status if both were available; or oestrogen receptor status alone if progesterone receptor status was not available. In the ARTemis trial, the TransNEO cohort, and Edinburgh cohort, hormone receptor status was defined as positive if the Allred score was 3 or higher. In other cohorts, hormone receptor status was defined by the percentage of cells that stained positive on immunohistochemistry at either a 1% or 10% threshold, depending on the institution. HER2 (ERBB2) status was determined according to international guidelines at all institutions.
Hormone receptor and HER2 status were used to define four phenotypic subtypes (hormone receptor-negative, HER2-negative; hormone receptor-negative, HER2-positive; hormone receptor-positive, HER2-positive; and hormone receptor-positive, HER2-negative) for analysis. Treatment information, such as neoadjuvant HER2-targeted therapy use for HER2-positive patients, and histological-type data were also collected from the cohorts. Data collected in each cohort for the purposes of this study are summarised in the appendix (pp 2–3).
Outcomes
Any locoregional recurrence, distant recurrence, or death from any cause was considered as an event-free survival event, and patients without an event were censored at the date of last follow-up. The secondary endpoint was distant relapse-free survival, defined as time from start of neoadjuvant therapy to distant recurrence or death from any cause. Patients without an event were censored at the date of last follow-up. Follow-up was calculated from the start date of neoadjuvant chemotherapy.
Statistical analysis
Baseline characteristics are presented as number and proportion for categorical variables and median (IQR) for continuous variables. IQR bounds were calculated with the formula: Q1–(1·5×IQR) and Q3+(1·5×IQR). Both the primary and secondary outcomes were assessed in all patients in the pooled analysis cohort. The association between RCB score and event-free survival and distant relapse-free survival in the pooled population was assessed with mixed-effects Cox models, which included random cohort and RCB effects to account for between-cohort heterogeneity, and stratification to account for differences in baseline hazard across biological breast cancer subtypes. The significance of the association was determined by the significance of the mean hazard ratio (HR) associated with a 1-unit increase in RCB score on a log-transformed scale, with a p value lower than 0·05 as the significance threshold. Similar mixed-effects models were used in prespecified subgroup analyses to assess the associations between RCB score and event-free survival within each breast cancer subtype. In addition, multivariable mixed-effects Cox analysis incorporating random cohort and RCB effects and adjusting for age, pretreatment T category (T0–1, T3, or T4 vs T2), pretreatment nodal status (positive vs negative), and pretreatment tumour grade (3 vs 1–2; all as fixed effects) as covariates were done (overall and within each subtype) to evaluate whether or not RCB remains significantly prognostic independent of these clinical covariates. We also evaluated associations within each participating cohort using fixed-effects univariable Cox models stratified by subtype. For the two HER2-positive subtypes, post-hoc analyses of patient subsets who received neoadjuvant HER2-targeted therapies in addition to neoadjuvant chemotherapy, which is now standard of care, was also performed. Results of the HER2-targeted subset are preferentially presented over results for the entire set of HER2-positive subtypes due to clinical relevance. In addition, to evaluate the non-linear effect of RCB on survival, we used B-splines with two degrees of freedom in our mixed-effects models and constructed relative event rate plots (with an RCB score of 0 as the reference) as a function of increasing RCB. Mixed-effects analysis was conducted with the coxme package in R (version 3.4.3). Kaplan-Meier plots of event-free survival and distant relapse-free survival by RCB class, overall and within breast cancer subtypes, were constructed with survival times truncated at 12 years (a time at which around 10% of the smallest patient group [RCB-1] remained at risk for an event); survival estimates at 3, 5, and 10 years were computed.
Role of the funding source
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Results
Table 1Patient characteristics overall and by breast cancer subtype
Data are n, n (%), or median (IQR). Negative values for the lower IQR bound are truncated at 0. RCB=residual cancer burden.
Figure 2Prognostic value of RCB score and RCB class in the overall pooled analysis cohort
Plots of log relative hazard rate for event-free survival events (A) and distant relapse-free survival events (B) as a function of RCB score. Splines approximation of RCB with two degrees of freedom was used to allow for non-linear effect. A log linear increase in relative hazard rate implies that the hazard ratio associated with change in RCB remains constant over the range of RCB. Thresholds for corresponding RCB classes (RCB-0 to RCB-3) are shown for reference (vertical dashed lines). Vertical bars represent all RCB scores recorded on a continuous scale. Kaplan-Meier plots of event-free survival (C) and distant relapse-free survival (D) stratified by RCB class. Crosses denote patients censored. RCB=residual cancer burden. pCR=pathological complete response.
Table 2Multivariable mixed-effects Cox models of event-free survival as a function of RCB
Figure 3Prognostic value of RCB score within hormone receptor and HER2 subtypes
Figure 4Prognostic value of RCB class for hormone receptor and HER2 subtypes
Discussion
More than 10 years ago, the degree of residual invasive disease in breast cancer was not believed to be of crucial importance for patient management, in part because mastectomy was the gold standard for patients with locally advanced breast cancer. Use of neoadjuvant chemotherapy increased as improved systemic therapies emerged and research evidence showed that breast conservation following neoadjuvant chemotherapy led to similar outcomes to mastectomy.
Several studies have since shown the strong prognostic relationship between the presence or extent of residual disease and the risk of locoregional and distant recurrences.
,
,
In this analysis, the number of event-free survival and distant relapse-free survival events was similar (1164 vs 1072), indicating that distant recurrences are the predominant risk for patients selected for neoadjuvant chemotherapy. Our definitions of event-free survival and distant relapse-free survival endpoints are consistent with the CTNeoBC meta-analysis
and the standardised definitions for efficacy endpoints system (commonly known as STEEP), which recommends the date of first therapy as the starting point for time-to-event calculations.
,
and the prognostic value of RCB has been validated in several single-centre studies and multicentre trials.
,
,
,
,
,
,
,
Indeed, in this pooled analysis, we observed significant associations between RCB and event-free survival or distant metastasis-free survival in the overall population, within all breast cancer subtypes, and across all cohorts (except in the smallest cohort for event-free survival). Since our pooled cohorts represent a variety of clinical settings, this result implies a broad generalisability of the association between RCB and prognosis in the overall patient population and within each molecular subtype of breast cancer.
This result highlights the importance of subtypes in prognostication and suggests that use of RCB for recurrence risk prediction after neoadjuvant therapy should be performed within a subtype-specific context.
Variation in how hormone receptor positivity was defined across sites might also have an important role in the higher than expected early recurrence rates in the hormone receptor-positive, HER2-negative RCB-0 group. Three groups used Allred score, three groups defined positivity as more than 1% of cells with oestrogen receptor-positive staining, and others defined it as more than 10%, reflecting uncertainty on how to classify hormone receptor-low tumours. Five of the early recurrences in the hormone receptor-positive, HER2-negative RCB-0 group were observed in oestrogen receptor-negative (progesterone receptor-low) or oestrogen receptor-low (progesterone receptor-negative) cases. Whether these hormone receptor-low cases were more similar to hormone receptor-negative tumours or to their strongly hormone receptor-positive counterparts remains an unanswered question. Characterisation with various molecular subtypes, previously shown to associate with responsiveness to therapy and prognosis, might be informative.
,
Additionally, not all participating groups performed extensive metastatic workup as part of standard clinical care before neoadjuvant therapy, and the length of follow-up differed among the included cohorts. Furthermore, the proportion of lobular cancers in our study was less than the proportion of lobular cases in the overall breast cancer population,
,
possibly reflecting the common belief among clinicians that lobular cancers do not respond well to neoadjuvant chemotherapy, and therefore clinicians do not select patients with lobular cancers for neoadjuvant therapy.
This is a shortcoming of retrospective pathology review because inaccurate sampling of the surgical specimen is the greatest potential source of residual disease misdiagnosed as pCR, and sampling accuracy cannot be determined by reviewing the slides. This limitation is particularly relevant in the hormone receptor-positive, HER2-negative subtype in which the proportion of diffuse disease is greater than in other subtypes,
increasing the likelihood that sampling could affect the classification of RCB-0 and RCB-1. Additionally, only the most recent cohorts in our analysis used clips as standard practice to mark the sites, assuring that the original tumour bed was sampled. Prospective assessment of RCB, along with careful identification of the initial site of disease, might improve the overall prognostic performance of RCB. This approach should particularly apply in the setting of mastectomy, because it allows pathologists to identify the original site of disease using specimen radiographs and the clip placed during the biopsy at diagnosis for careful characterisation of the tumour bed.
Despite these limitations, the consistency of the prognostic importance of RCB across participating groups in our study highlights the generalisability of implementing and standardising the entire RCB methodology, from the stage of tissue acquisition to the final pathology assessment, across different countries, neoadjuvant chemotherapy treatments, and clinical settings. Altogether, our findings suggest that the RCB score has potential to be calibrated in a subtype-specific context to predict a patient’s residual risk after neoadjuvant chemotherapy in a prospective setting with standardised evaluation of post-treatment resection specimens. Given the increasing options for escalation and de-escalation of adjuvant therapy in the setting of residual disease, prospective evaluation of RCB as part of standard pathology reporting following neoadjuvant therapy might be warranted.
Contributors
All authors have reviewed the data analyses, reviewed or revised the manuscript for intellectual content, approved the final version to be published, and agree to be accountable for all aspects of the work. CY, M-OK, MO, MvdN, and SS had access to the raw data. LJE is guarantor of the manuscript. CY, LJE, and WFS conceptualised and supervised the study. MO, MvdN, SS, DdC, A-SH, TGS, MdM-M, TH, RG, EP, JST, AG, PH, LM, FF, KS, and AMDeM curated data. MO and CY accessed and verified the data. CY and M-OK formally analysed and validated data. DdC, A-SH, EP, JST, AG, PH, LM, FF, KC, and CY contributed to investigations. M-OK and WFS developed methodology and software. MO, MvdN, JaW, and SS provided administrative support. DdC, A-SH, ML, FR, GSS, TGS, MvS, JeW, MM, MdM-M, SL-T, JCB, MPG, TH, RG, VV, SBE, JEA, JMSB, CC, JD, HE, LaH, LoH, S-JS, DC, AKG, KS, PS, AMDeM, LP, LJvV, LJE, and WFS provided resources. CY was responsible for data visualisation. CY and MO wrote the initial draft. All authors had full access to all the summarised or analysed data and had final responsibility for the decision to submit for publication.
Data sharing
Declaration of interests
AKG reports personal fees from Sinochips Diagnostics. CC reports institutional funding from Genentech, Roche, Servier, and AstraZeneca; and participation in a data and safety monitoring advisory board for iMED External Science Panel. CY reports institutional funding from Quantum Leap Healthcare Collaborative. DC reports institutional research funding from Novartis, AstraZeneca, Pfizer, Roche, Eli-Lilly, Puma Biotechnology, Daiichi Sankyo, Synthon, Seagen, Zymeworks, Elsevier, European Cancer Organisation, Celgene, Succinct Medical Communications, Prima BioMed (now Immutep), Oncolytics Biotech (US), Celldex Therapeutics, San Antonio Breast Cancer Consortium, Highfield Communication, Samsung Bioepis, prIME Oncology, Merck Sharp & Dohme, Prima BioMed (now Immutep), RTI Health Solutions, and Eisai. WFS owns stocks in Delphi Diagnostics; and reports the patent “method of measuring residual cancer and predicting patient survival” (US Patent and Trademark Office [USPTO] number 7711494B2). GSS reports institutional research funding from AstraZeneca, Merck, Novartis, and Roche. HE reports institutional research funding from Roche Sanofi-Aventis; is a consultant for Daiichi-Sankyo, AstraZeneca, Intas Pharmaceuticals, and prIME Oncology; and reports travel support from Daiichi-Sankyo, AstraZeneca, Intas Pharmaceuticals, Pfizer, and Amgen. JEA reports institutional research funding from AstraZeneca; and honoraria from Pfizer and Eisai. JMSB reports grants from Thermo Fisher Scientific, Geoptix, Agendia, NanoString Technologies, Stratifyer, and Biotheranostics; is a consultant for Insight Genetics, BioNTech, Biotheranostics, Pfizer, RNA Diagnostics, and OncoXchange; reports honoraria from NanoString Technology, Oncology Education, and Biotheranostics; reports travel support from Biotheranostics and Nanostring Technologies; reports patents “histone gene module predicts anthracycline benefit” (Patent Cooperation Treaty [PCT] number CA2016/000247); “95-gene signature of residual risk following endocrine treatment” (PCT number CA2016/000304); “immune gene signature predicts anthracycline benefit” (PCT number CA2016/000305); and applied for patents “methods and devices for predicting anthracycline treatment efficacy” (USPTO application number 15/325,472; European Patent Office number 15822898.1; Canada, not yet assigned) and “systems, devices and methods for constructing and using a biomarker” (USPTO application number 15/328,108; European Patent Office number 15824751.0; Canada, not yet assigned). JCB reports institutional research funding from Eli Lilly. LP is a consultant for and receives honoraria from AstraZeneca, Merck, Novartis, Genentech, Eisai, Pieris, Immunomedics, Seattle Genetics, Almac, H3 Biomedicine, Clovis, and Syndax; and reports the patent “method of measuring residual cancer and predicting patient survival” (US Patent Number 7711494B2). LaH reports individual research grants from Roche and Sanofi-Aventis; and travel support from Roche, AstraZeneca, Pfizer, and Sanofi-Aventis. LJE reports institutional research funding from Merck; participation in an advisory board for Blue Cross Blue Shield; and personal fees from UpToDate. LJvV is an employee of and owns stock in Agendia. MPG reports individual research grants from Pfizer, Sermonix, and Eli Lilly; and is a consultant for Pfizer, Eli Lilly, Novartis, Biotheranostics, Sermonix, Context Therapeutics, and Eagle Therapeutics. MM reports grants from Roche, Puma, and Novartis; is a consultant for AstraZeneca, Amgen, Glaxo, Taiho Oncology, Roche, Novartis, PharmaMar, Eli Lilly, Puma Biotechnology, Daiichi Sankyo, and Pfizer; reports honoraria from AstraZeneca, Amgen, Roche, Novartis, and Pfizer; and reports personal fees from Pfizer and Eli Lilly. PS reports institutional research funding from Novartis, Merck, and Bristol Myers Squibb; and is a consultant for Merck, Novartis, Seattle Genetics, Gilead Immunomedics, AstraZeneca, and ExactSciences. SL-T has received consulting fees from AstraZeneca, Novartis, Roche, Pfizer, Celgene, Pierre-Fabre, Eisai, and Eli Lilly; reports honoraria from Eli Lilly; and reports travel support from Novartis, Celgene, Merck Sharp & Dohme, Roche, and Pfizer. SBE reports institutional research funding from Pfizer. All other authors declare no competing interests.
Acknowledgments
The project described was supported by grant number P01CA210961 from the National Cancer Institute at the US National Institutes of Health. Additional support was provided from grants RP#180712 from the Cancer Prevention and Research Institute of Texas and BCRF-158 from the Breast Cancer Research Foundation. The trial led by IISGM was supported by research grants from the Carlos III Health Institute (PI15/00117, PI18/01775), co-funded by El Fondo Europeo de Desarrollo Regional. Contracts between the different institutes and groups were centralised and organised by the legal team at the University of California, San Francisco. Jeffrey B Matthews, a scientific writer funded under National Cancer Institute grant P01CA210961, provided editing assistance for this manuscript.
Supplementary Material
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Published: December 10, 2021
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