Tumor Intrinsic Subtypes and Gene Expression Signatures in Early-Stage ERBB2/HER2-Positive Breast Cancer: A Pooled Analysis of CALGB 40601, NeoALTTO, and NSABP B-41 Trials

Aranzazu Fernandez-Martinez; Mattia Rediti; Gong Tang; Tomás Pascual; Katherine A Hoadley; David Venet; Naim U Rashid; Patricia A Spears; Md N Islam; Sarra El-Abed; Judith Bliss; Matteo Lambertini; Serena Di Cosimo; Jens Huobe; David Goerlitz; Rong Hu; Peter C Lucas; Sandra M Swain; Christos Sotiriou; Charles M Perou; Lisa A Carey

doi:10.1001/jamaoncol.2023.7304

. 2024 Mar 28;10(5):603–611. doi: 10.1001/jamaoncol.2023.7304

Tumor Intrinsic Subtypes and Gene Expression Signatures in Early-Stage ERBB2/HER2-Positive Breast Cancer

A Pooled Analysis of CALGB 40601, NeoALTTO, and NSABP B-41 Trials

Aranzazu Fernandez-Martinez ^1,², Mattia Rediti ³, Gong Tang ^4,⁵, Tomás Pascual ^1,^6,^7,⁸, Katherine A Hoadley ^1,², David Venet ³, Naim U Rashid ⁹, Patricia A Spears ¹, Md N Islam ¹⁰, Sarra El-Abed ¹¹, Judith Bliss ¹², Matteo Lambertini ^13,¹⁴, Serena Di Cosimo ¹⁵, Jens Huobe ¹⁶, David Goerlitz ¹⁷, Rong Hu ¹⁰, Peter C Lucas ^4,¹⁸, Sandra M Swain ^4,¹⁷, Christos Sotiriou ³, Charles M Perou ^1,², Lisa A Carey ^1,^19,^✉

¹Lineberger Comprehensive Center, University of North Carolina, Chapel Hill

²Department of Genetics, University of North Carolina, Chapel Hill

³Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (H.U.B), Université Libre de Bruxelles (ULB), Brussels, Belgium

⁴NSABP Foundation Inc., Pittsburgh, Pennsylvania

⁵Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania

⁶Department of Medical Oncology, Hospital Clínic de Barcelona, Spain

⁷Translational Genomics and Targeted Therapeutics in Solid Tumors, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain

⁸SOLTI Breast Cancer Cooperative Group, Barcelona, Spain

⁹Department of Biostatistics, University of North Carolina, Chapel Hill

¹⁰Genomics and Epigenomics Shared Resource (GESR), Georgetown University Medical Center, Washington, DC

¹¹Breast International Group, Brussels, Belgium

¹²The Institute of Cancer Research, Clinical Trials & Statistics Unit, London, United Kingdom

¹³Department of Internal Medicine and Medical Specialties (DiMI), School of Medicine, University of Genova, Genova, Italy

¹⁴Department of Medical Oncology, UOC Clinica di Oncologia Medica, IRCCS Ospedale Policlinico San Martino, Genova, Italy

¹⁵Integrated Biology Platform, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy

¹⁶Kantonsspital St. Gallen, Brustzentrum, Departement Interdisziplinäre medizinische Dienste, St. Gallen, Switzerland

¹⁷Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC

¹⁸Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania

¹⁹Division of Hematology-Oncology, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

Accepted for Publication: November 8, 2023.

Published Online: March 28, 2024. doi:10.1001/jamaoncol.2023.7304

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Corresponding Author: Lisa A. Carey, MD, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, 450 West Drive, CB7295, Chapel Hill, North Carolina, 27599-7295 (lisa_carey@med.unc.edu).

Author Contributions: Dr Carey had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Venet, Spears, Swain, Perou, Carey.

Acquisition, analysis, or interpretation of data: Fernandez-Martinez, Rediti, Tang, Pascual, Hoadley, Venet, Rashid, Islam, El-abed, Bliss, Lambertini, Di Cosimo, Huober, Goerlitz, Hu, Lucas, Swain, Sotiriou, Perou, Carey.

Drafting of the manuscript: Fernandez-Martinez, Swain, Perou, Carey.

Critical review of the manuscript for important intellectual content: Rediti, Tang, Pascual, Hoadley, Venet, Rashid, Spears, Islam, El-abed, Bliss, Lambertini, Di Cosimo, Huober, Goerlitz, Hu, Lucas, Sotiriou, Perou, Carey.

Statistical analysis: Fernandez-Martinez, Pascual, Venet, Rashid, Perou.

Obtained funding: Swain, Sotiriou, Perou, Carey.

Administrative, technical, or material support: Tang, Hoadley, El-abed, Lambertini, Huober, Goerlitz, Hu, Lucas, Perou, Carey.

Supervision: Venet, El-abed, Goerlitz, Perou, Carey.

Conflict of Interest Disclosures: Dr Pascual reported personal fees from Pfizer, Veracyte, AstraZeneca, Novartis, and Pfizer outside the submitted work. Dr El-abed reported grants from Novartis, Roche/Genentech, Pfizer, AstraZeneca, and BCRF via institution outside the submitted work. Dr Bliss reported grants from AstraZeneca and Merck Sharp & Dohme to ICR-CTSU, grants from Puma Technology, Clovis Oncology, Pfizer, Janssen-Cilag, Novartis, Eli Lilly, and Roche to ICR-CTSU outside the submitted work. Dr Lambertini reported advisory roles and speaker honoraria from Roche, Lilly, Novartis, Pfizer, and AstraZeneca, an advisory role at Seagen, an advisory role, travel expenses, and research funding (to the institution) from Gilead, advisory roles at MSD, Exact Sciences, speaker honoraria from Takeda, speaker honoraria and travel support from Daiichi Sankyo, speaker honoraria from Knight, Ipsen, Sandoz Speaker, and Libbs outside the submitted work. Dr Huober reported personal fees from Roche, Lilly, Pfizer, Novartis, MSD, AstraZeneca, Gilead, Seagen, and Daiichi outside the submitted work. Dr Lucas reported equity interest from Amgen outside the submitted work; in addition, Dr Lucas had a patent for “Small Molecules and their use as MALT1 Inhibitors” issued (WO 2022/204386). Dr Swain reported grants from BCRF, Alexander Savchuk Foundation, and NSABP Foundation during the conduct of the study; grants from Genentech/Roche to institution for other trials, personal fees from Genentech/Roche (nonpromotional speaking and consulting honoraria), nonfinancial support from Genentech/Roche for in-kind travel, fees from Genentech/Roche for in-kind third party writing, and board of directors nonfinancial support from NSABP outside the submitted work. Dr Perou reported grants from NCI Breast SPORE program P50-CA58223 during the conduct of the study; personal fees from Bioclassifier LLC as an equity stockholder and royalties and personal fees from Reveal Genomics equity stockholder and consultant outside the submitted work; in addition, Dr Perou had a patent for US Patent No. 12 995 459 with royalties paid from Bioclassifier. No other disclosures were reported.

Funding/Support: Research from CALGB 40601 was supported by the National Cancer Institute of the National Institutes of Health under AwardNumbers U10CA180821, U10CA180882, and U24CA196171 (to the Alliance for Clinical Trials in Oncology), R01CA229409 (Dr Carey), UG1CA233160, UG1CA233180, UG1CA233290, UG1CA233329, UG1CA233333, UG1CA233373, P50CA058223 (Dr Carey, Dr Perou); U10CA180888, UG1CA233160 (SWOG) and from The Breast Cancer Research Foundation (Alliance, Dr Carey, Dr Perou), Susan G. Komen (Dr Carey, Dr Perou). https://acknowledgments.alliancefound.org. Research from NSABP B-41 was partially supported by the Alexandr Savchuk Foundation, Breast Cancer Research Foundation, GlaxoSmithKline, NSABP Foundation, Inc., and the Genomics & Epigenomics Shared Resource and Histopathology & Tissue Shared Resource at the Georgetown Lombardi Comprehensive Cancer Center (P30CA051008). The conduct of the NeoALTTO study was funded by GlaxoSmithKline and later Novartis. The RNA sequencing in NeoALTTO, on which part of the analyses described in this manuscript are based, was funded by GlaxoSmithKline. Related to the NeoALTTO trial, research reported in this publication was supported by the Fondation contre le cancer, the Breast Cancer Research Foundation, Association Jules Bordet, and the Belgian Fonds National de la Recherche Scientifique (F.R.S-FNRS). Other funding sources include CERCA Programme / Generalitat de Catalunya & Fundación SEOM, Becas FSEOM para Formación en Investigación en Centros de Referencia en el Extranjero 2018 (Dr Pascual); Télévie and the Belgian Fonds National de la Recherche Scientifique (F.R.S.-FNRS), and by Fondation Rose et Jean Hoguet (Dr Rediti).

Role of the Funder/Sponsor: The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the authors’ responsibility and does not necessarily represent the official views of the National Institutes of Health.

Meeting Presentation: This study was presented at the 2022 ASCO Annual Meeting, June 3-7, 2022; Chicago, Illinois.

Data Sharing Statement: See Supplement 2.

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Corresponding author.

PMCID: PMC10979363 PMID: 38546612

Key Points

Question

What is the quantitative association between pathologic complete response (pCR) and event-free survival (EFS) by intrinsic subtype and other gene expression signatures in patients with ERBB2/HER2-positive early breast cancer (EBC) treated in the neoadjuvant setting?

Findings

In this retrospective pooled analysis of 3 randomized clinical trials including 1289 patients with ERBB2/HER2-positive EBC, the association between pCR and EFS differed by tumor intrinsic subtype, and the benefit of dual ERBB2/HER2-blockade was limited to ERBB2-enriched tumors. Immune-activated signatures were associated with higher pCR rates and better EFS, whereas luminal signatures were associated with lower pCR rates.

Meaning

Intrinsic subtype and immune gene expression biomarkers may help guide personalized treatment in patients with ERBB2/HER2-postive EBC.

This retrospective analysis of data from 3 randomized clinical trials examines the association of pathologic complete response with event-free survival by intrinsic subtype and other gene expression signatures in patients with ERBB2/HER2-positive early breast cancer treated in the neoadjuvant setting

Abstract

Importance

Biologic features may affect pathologic complete response (pCR) and event-free survival (EFS) after neoadjuvant chemotherapy plus ERBB2/HER2 blockade in ERBB2/HER2-positive early breast cancer (EBC).

Objective

To define the quantitative association between pCR and EFS by intrinsic subtype and by other gene expression signatures in a pooled analysis of 3 phase 3 trials: CALGB 40601, NeoALTTO, and NSABP B-41.

Design, Setting, and Participants

In this retrospective pooled analysis, 1289 patients with EBC received chemotherapy plus either trastuzumab, lapatinib, or the combination, with a combined median follow-up of 5.5 years. Gene expression profiling by RNA sequencing was obtained from 758 samples, and intrinsic subtypes and 618 gene expression signatures were calculated. Data analyses were performed from June 1, 2020, to January 1, 2023.

Main Outcomes and Measures

The association of clinical variables and gene expression biomarkers with pCR and EFS were studied by logistic regression and Cox analyses.

Results

In the pooled analysis, of 758 women, median age was 49 years, 12% were Asian, 6% Black, and 75% were White. Overall, pCR results were associated with EFS in the ERBB2-enriched (hazard ratio [HR], 0.45; 95% CI, 0.29-0.70; P < .001) and basal-like (HR, 0.19; 95% CI, 0.04-0.86; P = .03) subtypes but not in luminal A or B tumors. Dual trastuzumab plus lapatinib blockade over trastuzumab alone had a trend toward EFS benefit in the intention-to-treat population; however, in the ERBB2-enriched subtype there was a significant and independent EFS benefit of trastuzumab plus lapatinib vs trastuzumab alone (HR, 0.47; 95% CI, 0.27-0.83; P = .009). Overall, 275 of 618 gene expression signatures (44.5%) were significantly associated with pCR and 9 of 618 (1.5%) with EFS. The ERBB2/HER2 amplicon and multiple immune signatures were significantly associated with pCR. Luminal-related signatures were associated with lower pCR rates but better EFS, especially among patients with residual disease and independent of hormone receptor status. There was significant adjusted HR for pCR ranging from 0.45 to 0.81 (higher pCR) and 1.21-1.94 (lower pCR rate); significant adjusted HR for EFS ranged from 0.71 to 0.94.

Conclusions and relevance

In patients with ERBB2/HER2-positive EBC, the association between pCR and EFS differed by tumor intrinsic subtype, and the benefit of dual ERBB2/HER2 blockade was limited to ERBB2-enriched tumors. Immune-activated signatures were concordantly associated with higher pCR rates and better EFS, whereas luminal signatures were associated with lower pCR rates.

Introduction

The highly aggressive ERBB2/HER2-positive breast cancer accounts for 20% of all breast tumors. However, the success of multiple ERBB2/HER2-targeting drugs has markedly improved outcomes. In ERBB2/HER2-positive early breast cancer (EBC), neoadjuvant treatment is now the standard of care given the surgical benefits of tumor downstaging¹ and the benefits of tailoring adjuvant anti-ERBB2/HER2 drugs based on the presence of residual disease at surgery.² Pathologic complete response (pCR) at surgery has been associated with improved survival,³ but a predictable quantitative relationship between pCR benefit and survival outcomes has been elusive.

In randomized neoadjuvant trials, dual ERBB2/HER2-targeting has been associated with higher pCR rates than single ERBB2/HER2 targeting.^4,5,6,7 However, the magnitude of this effect has differed across trial populations and drugs, and neoadjuvant trials are typically underpowered for long-term outcomes. This was true of 3 phase 3 trials investigating dual (trastuzumab and lapatinib) vs single (trastuzumab or lapatinib) anti-ERBB2/HER2 drugs added to chemotherapy: NeoALTTO (NCT00553358), CALGB 40601 (NCT00770809), and NSABP B-41 (NCT00486668). All 3 studies demonstrated higher pCR rates and better survival with dual therapy, which was statistically significant in NeoALTTO for pCR,⁴ and CALGB 40601 for relapse-free survival.⁸ In a trial-level meta-analysis including these 3 phase 3 studies and a similar phase 2 trial, Cher-LOB, dual blockade with trastuzumab plus lapatinib in combination with neoadjuvant chemotherapy significantly prolonged relapse-free and overall survival.⁹ This result contrasts with the findings of the phase 3 adjuvant trial ALTTO,¹⁰ in which patients with ERBB2/HER2-positive EBC treated with trastuzumab plus lapatinib blockade as adjuvant therapy had a 16% reduction in disease-free survival (DFS) with a hazard ratio (HR) that did not meet statistical significance. Dual therapy with trastuzumab plus pertuzumab in the phase 3 APHINITY trial resulted in a significant 24% improvement in invasive DFS HR and is the current standard of care in patients with high-risk disease.¹¹

Differences in the effect of dual ERBB2/HER2 blockade and robustness of the association of pCR with EFS could be partly explained by differences in tumor and microenvironment biology. ERBB2/HER2-positive EBC is not a singular biological entity; instead, it is characterized by heterogeneity of both cancer and immune cell components. At a tumor level, all the intrinsic molecular subtypes (ie, luminal A, luminal B, ERBB2/HER2-enriched, and basal-like) can be found within ERBB2/HER2-positive breast cancer tumors.¹² This intrinsic tumor heterogeneity has clinical implications. ERBB2/HER2-positive/ERBB2-enriched tumors have been systematically associated with higher pCR rates for ERBB2/HER2-targeted therapies,¹³ and ERBB2/HER2-positive/ERBB2-enriched and ERBB2/HER2-positive/basal-like tumors have been associated with worse prognoses compared with ERBB2/HER2-positive/luminal tumors, particularly in those with residual disease.^8,14

Similarly, the immune microenvironment is both predictive and prognostic. Tumor-infiltrating lymphocytes (TILs), which correlate strongly with T-cell expression signatures, have been significantly associated with higher pCR rates and EFS in multiple ERBB2/HER2-positive neoadjuvant studies.^{7,8,15,16,17,18,19,20,21,22,23,24,25} In a recent analysis, immune gene signatures appear more valuable than TILs in pCR prediction.²⁶ Another important finding is that immune signatures and TILs are associated with both higher pCR rates and longer EFS, unlike intrinsic subtypes, which often work in opposite directions for predicting response and survival.⁸

We hypothesized that the biological tumor and immune heterogeneity of ERBB2/HER2-positive EBC contribute to the inconsistent results coming from different neoadjuvant and adjuvant clinical trials. In this analysis, we examined how intrinsic subtype, immune activation status, and other gene expression signatures contribute to pCR and EFS, and the benefit of dual therapy with trastuzumab and lapatinib compared with single-agent trastuzumab, by performing individual patient-level biomarker analysis of 3 phase 3 clinical trials with similar designs: NeoALTTO,^{4,15,21,27,28,29,30} CALGB 40601,^7,8,26 and NSABP B-41.^6,14,31,32

Methods

Neoadjuvant Trials: Study Designs and Patients

All 3 phase 3 trials have had pCR, correlative, and/or survival end points published previously (NeoALTTO,^{4,15,21,27,28,29,30} CALGB 40601,^7,8,26 NSABP B-41^6,14,31,32). All trials involved previously untreated patients with early ERBB2/HER2-positive breast cancer randomized to chemotherapy with single trastuzumab, single lapatinib, or dual trastuzumab and lapatinib anti-ERBB2/HER2 therapy (eFigure 1A in Supplement 1). Trial differences included a 6-week lead-in phase of the randomized anti-ERBB2/HER2 agent(s) in the NeoALTTO trial,^{4,15,21,27,28,29,30} all chemotherapy given as neoadjuvant therapy in B-41, neoadjuvant durations varying from 16 weeks (CALGP 40601) to 28 weeks (B-41), and that the adjuvant anti-ERBB2/HER2 therapy was as randomized in the NeoALTTO trial but was single-agent trastuzumab in the other 2 trials.

To homogenize the clinical outcomes from the 3 clinical trials, for this patient-level pooled analysis, pCR was defined as the absence of invasive tumor cells in the breast (ypT0/is). We used EFS for long-term outcome, defined as the time from randomization to the event (ie, local recurrence, regional recurrence, distant recurrence, nonbreast second primary tumors, contralateral invasive breast cancer, and death of any cause). A slight difference in the event definitions between the 3 clinical trials included progressions during the neoadjuvant phase, which were regarded as events in the CALGB 40601 and NeoALTTO trials but not in the B-41 trial. However, only 3 patients from the NeoALTTO and 5 from the B-41 trials progressed during the neoadjuvant phase. In the intention-to-treat (ITT) cohort, 10 patients from NSABP B-41 did not have EFS events and/or time information collected.

Ethics committee and relevant health authorities at each participating site approved the NeoALTTO, CALGB 40601, and NSABP B-41 studies, and all patients provided written informed consent, including future biomarker research.

Tumor Gene Expression Analyses

Gene expression profiles from pretreatment core biopsies were obtained from 249 of 455 participants (54.7%) in the NeoALTTO, 264 of 305 participants (86.6%) in the CALGB 40601, and 245 of 529 participants (46.3%) in the NSABP B-41 trials, respectively (CONSORT diagram, eFigure 1B in Supplement 1). The tumor preservation methods, RNA extraction, RNAseq library preparation, sequencing parameters, bioinformatic algorithms, and data preprocessing are summarized in the eMethods in Supplement 1. A principal component analysis (PCA) plot before and after the batch effect correction can be found in eFigure 2 in Supplement 1.

For the 3 studies, intrinsic subtypes and a collection of 618 gene expression signatures (GES) representing diverse cell types and biologic pathways were obtained from RNAseq gene expression data as described previously^8,26 (eMethods, eTable 1 in Supplement 1).

Statistical Analysis

Comparisons of differences in baseline clinicopathologic variables among the trials were made using a Wilcoxon rank-sum for continuous variables and χ² or Fisher exact tests for categorical variables. Proportions and P values are provided. For the survival analyses, the 5-year EFS proportions for each group were estimated using the Kaplan-Meier method. The association between clinical and genomic biomarkers with pCR and EFS was assessed using univariable and multivariable logistic and Cox regression models, respectively. Clinical variables considered for multivariable models included clinical trial (ie, CALGB 40601, NeoALTTO, and NSABP B-41, where CALGB 40601 was the reference group), treatment arm (ie, trastuzumab, trastuzumab plus lapatinib, or lapatinib, where trastuzumab was the reference arm), hormone-receptor status (hormone receptor positive vs hormone receptor negative), clinical tumor size (T1-T2 vs T3-T4a-c), and clinical node involvement (node positive vs node negative). Inflammatory breast cancer was excluded in all trials. All Cox models were stratified by clinical trial. Odds ratios (ORs), hazard ratios (HRs), and 95% CIs were calculated for each variable. The significance level was set to a 2-sided α of .05, and P values were adjusted for multiple testing using the Benjamini and Hochberg method to control the false discovery rate. To avoid a potential guarantee time bias in the multivariable EFS models including pCR status, we performed a 30-week landmark analysis. The landmark subpopulation included only patients without events and being followed up at 30 weeks after randomization.^27,33

All the analyses were based on the study clinical database frozen on May 26, 2016, in the NeoALTTO trial, on June 10, 2021, in the CALGB 40601 trial, and on December 31, 2016, in the NSABP B-41 trial, and were performed using R (version 3.5.2; R Foundation for Statistical Computing) and Python statistical software (version 3.6; Python Software Foundation). Data analyses were performed from June 1, 2020, to January 1, 2023.

Results

Clinicopathologic Characteristics and Efficacy Analysis in the ITT Cohort

There were 1289 patients with ERBB2/HER2-positive EBC included. Although generally similar, several baseline clinicopathologic features differed among the 3 trials, including larger tumors, more node-positive, and a higher proportion of Asian participants in the NeoALTTO trial (eTable 2 in Supplement 1).

In the ITT population, a multivariable analysis for pCR prediction found that patients treated with trastuzumab plus lapatinib had significantly higher pCR rates than those treated with trastuzumab (adjusted OR [aOR], 1.80; 95% CI, 1.36-2.39; P < .001), with no significant differences between the lapatinib and trastuzumab arms (eTable 3 in Supplement 1). The Kaplan-Meier estimates of 5-year EFS by treatment were 83%, 79%, and 73% for the lapatinib and trastuzumab, trastuzumab, and lapatinib arms, respectively (eFigure 3 in Supplement 1), a difference that was not significant in a multivariable Cox analysis (lapatinib and trastuzumab vs trastuzumab: adjusted HR [aHR], 0.74; 95% CI, 0.54-1.01; P = .056) (eTable 4 in Supplement 1).

When comparing the EFS among the 3 studies, the NeoALTTO trial had significantly worse EFS outcomes than the CALGB 40601 trial (HR, 1.92; 95% CI, 1.39-2.66; P < .001) (eFigure 4 in Supplement 1). In the overall cohort of patients that were treated with trastuzumab in the neoadjuvant setting (n = 887), patients with pCR had a significantly better EFS outcome than patients with residual disease in a multivariable model stratified by clinical trial and adjusted by treatment arm, hormone receptor status, tumor size, and node status (aHR for pCR vs residual disease: 0.47; 95% CI, 0.34-0.66; P < .001). The Kaplan-Meier estimates of 5-year EFS were 88% for pCR and 74% for patients with residual disease (eFigure 5A in Supplement 1). Similar results were observed when the landmark analysis was performed (n = 856) (eFigure 5B in Supplement 1).

Similar distributions of local, distant, and organ sites of recurrence or death comprising the EFS events were seen across the 3 trials (eFigure 6 in Supplement 1). Patients treated only with lapatinib generally experienced more distant recurrences than patients treated with trastuzumab or trastuzumab plus lapatinib, except for brain metastasis, which was more frequent in the trastuzumab arm, suggesting activity of lapatinib in preventing brain relapses as has been noted with other anti-ERBB2/HER2 small molecules (eFigure 7 in Supplement 1).³⁴

Clinical Implications of the Intrinsic Subtypes in the RNAseq Cohort

The CALGB 40601 trial had a higher proportion of patients represented in the RNAseq cohort, whereas the NSABP B-41 trial was more represented in the ITT cohort. Otherwise, there were no significant differences among the clinicopathologic characteristics, response, and EFS survival outcomes of the parent ITT and the RNAseq cohorts (eTable 5 in Supplement 1). In the RNAseq cohort, several baseline clinicopathologic features differed among the 3 trials, including larger tumors, more node positive, and a higher proportion of Asian participants in the NeoALTTO trial (Table).

Table. Comparison of Baseline Clinicopathologic Characteristics of the Patients From the NSABP B-41, CALGB 40601, and NeoALTTO Trials in the 758 Patients in the RNAseq Cohort.

Variable	No. (%)			P value^a
Variable	NSABP B-41 (n = 245)	C40601 (n = 264)	NeoALTTO (n = 249)	P value^a
Age, median (IQR), y	48 (42-56)	49 (41-56)	50 (40-55)	.85
Menopause status
Postmenopausal	110 (44.9)	102 (38.6)	117 (47.0)	.14
Premenopausal	135 (55.1)	162 (61.4)	132 (53.0)	.14
Race
Asian	10 (4.1)	16 (6.1)	68 (27.3)	<.001
Black	24 (9.8)	21 (8.0)	4 (1.6)
Other	7 (2.9)	14 (5.3)	22 (8.8)
White	204 (83.3)	213 (80.7)	155 (62.2)
HR status
HR negative	104 (42.4)	110 (41.7)	115 (46.2)	.55
HR positive	141 (57.6)	154 (58.3)	134 (53.8)	.55
Clinical tumor size
T1-T2	168 (68.6)	181 (68.6)	148 (59.4)	<.001
T3-T4	77 (31.4)	61 (23.1)	101 (40.6)	<.001
Unknown	0	22 (8.3)	0
Tumor size by physical examination, median (IQR), cm	4 (3-6)	4 (3-5)	4 (3-8)	.07
Clinical status of lymph nodes
N positive	129 (52.7)	136 (51.5)	181 (72.7)	<.001
N negative	116 (47.3)	113 (42.8)	67 (26.9)
Unknown	0	15 (5.7)	1 (0.4)
Treatment arm
Trastuzumab	86 (35.1)	104 (39.4)	77 (30.9)	.004
Trastuzumab and lapatinib	75 (30.6)	103 (39.0)	84 (33.7)
Lapatinib	84 (34.3)	57 (21.6)	88 (35.3)

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Abbreviations: HR, hormone receptor; T1-T2, tumor size ≤50 mm; T3-T4, tumor size >50 mm; N, lymph nodes infiltration.

^{^a}

Kruskal-Wallis rank sum test; Pearson χ² test.

When analyzed for RNAseq-based tumor intrinsic subtype on the combined cohort, most tumors were ERBB2 enriched (57.9%), followed by luminal B (15.0%), luminal A (9.9%), basal-like (8.8%), and normal-like (8.3%). There were no significant differences in the intrinsic subtype distribution by study. Subtype distribution significantly differed by hormone-receptor status (eTable 6 in Supplement 1). There were no significant differences in the hormone-receptor status distribution by locoregional and distant relapse location (eFigure 8A in Supplement 1). However, there were significant differences in the intrinsic subtype distribution by the site of relapse: only patients with ERBB2/HER2-positive/ERBB2-enriched and ERBB2/HER2-positive/basal-like tumors had brain metastases. Importantly, 5 of the 55 brain metastases were from hormone-receptor–positive tumors; all had a nonluminal subtype (1 basal-like and 4 ERBB2 enriched). ERBB2/HER2-positive/luminal tumors developed more bone and visceral metastasis (eFigure 8B in Supplement 1).

The association between pCR and EFS was different by tumor intrinsic subtype. In a multivariable Cox model stratified by study and adjusted by treatment arm, pCR status was significantly associated with EFS in patients with ERBB2/HER2-positive/ERBB2-enriched (aHR, 0.45; 95% CI, 0.29-0.70; P < .001) and ERBB2/HER2-positive/basal-like tumors (aHR, 0.19; 95% CI, 0.04-0.86; P = .03), but not in patients with ERBB2/HER2-positive/luminal A or B disease (Figure 1). Similar results were obtained when performing a landmark analysis (eTable 7 in Supplement 1). In a stratified univariable Cox model, a significant EFS benefit of dual trastuzumab and lapatinib vs single trastuzumab ERBB2/HER2-blockade was found only in patients with ERBB2/HER2-positive/ERBB2-enriched disease (aHR for trastuzumab and lapatinib vs trastuzumab alone, 0.48; 95% CI, 0.27-0.83; P = .009) but not in patients with basal-like or luminal ERBB2/HER2-positive EBC (Figure 2).

Figure 1. — Kaplan-Meier event-free survival (EFS) proportions at 5 years are provided. Cox regression models were stratified by clinical trial. Patients with normal-like tumors were removed from the analysis. RD indicates residual disease.

Figure 2. — Kaplan-Meier event-free survival (EFS) proportions at 5 years are provided. Cox regression models were stratified by clinical trial. Patients with normal-like tumors and treated with lapatinib only were removed from the analysis.

pCR and EFS Biomarkers Across Individual Trials and in the Combined Cohort

Using the combined cohort, 275 of 618 gene expression signatures (44.5%) were significantly associated with pCR, and 8 biomarkers were significantly associated with pCR individually in each trial (eTable 8 in Supplement 1). A selection of the signatures more consistently and strongly associated with pCR is shown in Figure 3A. In general, ERBB2/HER2, proliferation, and immune-related signatures were associated with higher pCR rates. Some differences were observed between the studies at an immune signature level; for example, B-cell–related signatures were more associated with pCR in the CALGB 40601 trial, whereas T-cell–related signatures were more associated with pCR in the NeoALTTO trial. As expected, luminal-related signatures were associated with lower pCR rates across the 3 studies and the combined cohort.

Figure 3. — A selection of the most interesting and consistent biomarkers across the 3 studies and the combined cohort is shown. All the models have been stratified by clinical trial and adjusted by tumor size, hormone receptor status, and clinical node involvement. The whiskers indicate the 95% CIs. HR indicates hazard ratio; IgG, immunoglobulin G; OR, odds ratio; PD-L1, programmed death-ligand 1.

In multivariable Cox regression analysis, only 9 of 618 signatures (1.5%), all immune, were significantly associated with EFS in the combined cohort when P values were adjusted by multiplicity (eTable 9 in Supplement 1). Some of the signatures strongly associated with EFS are represented in Figure 3B. Concordant with their association with pCR, B-cell–related immune gene expression signatures were significantly associated with longer EFS, whereas vascular, proliferation, and metastasis signatures were associated with worse prognosis, although these associations were no longer significant when adjusted for multiple comparisons (eTable 9 in Supplement 1).

Significantly better prognosis was seen in the 409 patients with residual disease if these tumors were HR-positive at baseline (aHR, 0.50; 95% CI, 0.34-0.74; P<.001) or luminal expression subtypes (luminal vs HER2-enriched aHR, 0.55; 95% CI, 0.35-0.86; P = .01) (eFigure 9A and 9B in Supplement 1). Among patients with residual disease, those with higher immune infiltration at baseline showed a significantly better prognosis. In contrast, high MAPK activation pathway signature levels and the gene expression of ERBB3 were significantly associated with worse outcomes, although these associations were not significant when adjusted for multiple comparisons (eFigure 9C and eTable 10 in Supplement 1).

Discussion

This pooled analysis of 3 phase 3 clinical trials with similar designs (ie, NSABP B-41, CALGB 40601, and NeoALTTO) illustrates how the association between pCR and survival in ERBB2/HER2-positive EBC varies by genomic intrinsic subtype and is only significant in patients with ERBB2/HER2-positive/ERBB2-enriched and ERBB2/HER2-positive/basal-like tumors. We also found a significant pCR and EFS benefit of dual anti-ERBB2/HER2 therapy limited to patients with ERBB2-enriched tumors, which comprise approximately 60% of ERBB2/HER2-positive EBC.

Prognostic and predictive biomarkers are needed in ERBB2/HER2-positive EBC to guide the future tailoring of treatment strategies. However, one of the biggest obstacles in biomarker research is the lack of validation. In this individual-level pooled analysis of 758 patients, we were able to test hundreds of gene expression signatures for pCR prediction and outcome prognostication, even though these studies were not powered individually for survival outcomes. ERBB2 amplicon genes, proliferation, and immune signature levels at baseline were significantly associated with pCR rates in each trial and the combined cohort. In contrast, luminal genes and signatures were significantly associated with lower pCR rates but did not appear to have EFS implications in this setting. Immune signatures were also significantly associated with better EFS outcomes in the combined cohort. These results support our previous findings,⁸ suggesting that the combination of tumor (ie, ERBB2 amplicon genes, proliferation, and luminal signatures) and immune-related biomarkers provide essential prognostic information to stratify patients with ERBB2/HER2-positive EBC in different groups that could benefit from different treatment strategies; some newer commercially available predictors already integrate these elements into a single assay.²⁴

Limitations

This study has several limitations. First, lapatinib is only approved in the metastatic setting but not for ERBB2/HER2-positive EBC treatment. Second, even though all 3 trials aimed to test a common hypothesis (ie, if dual ERBB2/HER2-blockade with trastuzumab plus lapatinib was better thantrastuzumab alone in terms of pCR), the designs were slightly different: in the NSABP B-41 trial, all chemotherapy was administered before surgery, whereas in the NeoALTTO and CALGB 40601 trials only the taxane component was preoperative, whereas the anthracycline-based regimen was administered after surgery. However, several associations with pCR were consistent across the 3 cohorts. Third, in the NeoALTTO trial, there was a brief induction phase of 6 weeks with only ERBB2/HER2-targeted drugs, and T was administered for 12 weeks, compared with 16 weeks in NSABP B-41 and CALGB 40601. Moreover, in the NeoALTTO adjuvant phase, the ERBB2/HER2-blockade treatment was the same as in the induction phase and not the standard-of-care trastuzumab for 1 year through most of the trial. The variations in treatment as well as the higher proportion of high clinical risk patients may have contributed to the worse EFS seen in NeoALTTO compared with the other trials. All the models performed in our study have been adjusted and/or stratified by the clinical trial to mitigate these differences and adjusted for key clinical variables. Fourth, the proportions of T4a-c tumors within each study were not studied in this pooled analysis. However, the tumor size assessed by physical examination was not significantly different among the 3 studies. Fifth, even when pooling together patients from 3 trials, the number of EFS events is limited when dividing the study cohort by subgroups, which may result in inadequate statistical power for certain statistical comparisons. Finally, there was a slight difference in the EFS event definition in NSABP B-41, in which progression during the neoadjuvant phase was not counted as an event; however, this was an uncommon occurrence, and we did not find variation in local and distant event proportions across the 3 studies.

Conclusions

This analysis shows for the first time 2 main clinical implications of tumor intrinsic subtype differences within ERBB2/HER2-positive EBC, demonstrating that the association of EFS with pCR after chemotherapy plus ERBB2/HER2 targeting is seen only in patients with ERBB2-enriched and basal-like tumors and only ERBB2-enriched patients benefit from dual neoadjuvant ERBB2/HER2-blockade with trastuzumab and lapatinib. Common biomarkers of pCR and EFS included ERBB2 amplicon genes and immune gene signatures, whereas in luminal tumors, pCR was less common but had little prognostic implication. Our results highlight the need to incorporate intrinsic subtype and immune gene expression biomarkers to guide personalized treatment in ERBB2/HER2-positive EBC.

Supplement 1.

eFigure 1. Clinical trial designs and CONSORT diagrams

eFigure 2. Principal Component Analysis (PCA) plot

eFigure 3. Kaplan-Meier (KM) curves

eFigure 4. Kaplan-Meier (KM) curves

eFigure 5. Kaplan-Meier (KM) curves

eFigure 6. Distribution of the types of event-free survival (EFS) events

eFigure 7. Distribution of the locoregional and distant relapse events

eFigure 8. Distribution of the locoregional and distant relapse events

eFigure 9. Association of hormone receptor (HR), intrinsic subtype and gene expression signatures with event-free survival

eMethods

eReferences

eTables

jamaoncol-e237304-s001.pdf^{(1.9MB, pdf)}

Supplement 2.

Data Sharing Statement

jamaoncol-e237304-s002.pdf^{(19.8KB, pdf)}

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eFigure 1. Clinical trial designs and CONSORT diagrams

eFigure 2. Principal Component Analysis (PCA) plot

eFigure 3. Kaplan-Meier (KM) curves

eFigure 4. Kaplan-Meier (KM) curves

eFigure 5. Kaplan-Meier (KM) curves

eFigure 6. Distribution of the types of event-free survival (EFS) events

eFigure 7. Distribution of the locoregional and distant relapse events

eFigure 8. Distribution of the locoregional and distant relapse events

eFigure 9. Association of hormone receptor (HR), intrinsic subtype and gene expression signatures with event-free survival

eMethods

eReferences

eTables

jamaoncol-e237304-s001.pdf^{(1.9MB, pdf)}

Supplement 2.

Data Sharing Statement

jamaoncol-e237304-s002.pdf^{(19.8KB, pdf)}

[coi230096r1] 1.Pilewskie M, Morrow M. Axillary nodal management following neoadjuvant chemotherapy: a review. JAMA Oncol. 2017;3(4):549-555. doi: 10.1001/jamaoncol.2016.4163 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r2] 2.von Minckwitz G, Huang CS, Mano MS, et al. ; KATHERINE Investigators . Trastuzumab emtansine for residual invasive her2-positive breast cancer. N Engl J Med. 2019;380(7):617-628. doi: 10.1056/NEJMoa1814017 [DOI] [PubMed] [Google Scholar]

[coi230096r3] 3.Cortazar P, Zhang L, Untch M, et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet. 2014;384(9938):164-172. doi: 10.1016/S0140-6736(13)62422-8 [DOI] [PubMed] [Google Scholar]

[coi230096r4] 4.Baselga J, Bradbury I, Eidtmann H, et al. ; NeoALTTO Study Team . Lapatinib with trastuzumab for HER2-positive early breast cancer (NeoALTTO): a randomised, open-label, multicentre, phase 3 trial. Lancet. 2012;379(9816):633-640. doi: 10.1016/S0140-6736(11)61847-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r5] 5.Gianni L, Pienkowski T, Im YH, et al. Efficacy and safety of neoadjuvant pertuzumab and trastuzumab in women with locally advanced, inflammatory, or early HER2-positive breast cancer (NeoSphere): a randomised multicentre, open-label, phase 2 trial. Lancet Oncol. 2012;13(1):25-32. doi: 10.1016/S1470-2045(11)70336-9 [DOI] [PubMed] [Google Scholar]

[coi230096r6] 6.Robidoux A, Tang G, Rastogi P, et al. Lapatinib as a component of neoadjuvant therapy for HER2-positive operable breast cancer (NSABP protocol B-41): an open-label, randomised phase 3 trial. Lancet Oncol. 2013;14(12):1183-1192. doi: 10.1016/S1470-2045(13)70411-X [DOI] [PubMed] [Google Scholar]

[coi230096r7] 7.Carey LA, Berry DA, Cirrincione CT, et al. Molecular heterogeneity and response to neoadjuvant human epidermal growth factor receptor 2 targeting in CALGB 40601, a randomized phase III Trial of paclitaxel plus trastuzumab with or without lapatinib. J Clin Oncol. 2016;34(6):542-549. doi: 10.1200/JCO.2015.62.1268 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r8] 8.Fernandez-Martinez A, Krop IE, Hillman DW, et al. Survival, pathologic response, and genomics in CALGB 40601 (Alliance), a neoadjuvant phase III trial of paclitaxel-trastuzumab with or without lapatinib in HER2-positive breast cancer. J Clin Oncol. 2020;38(35):4184-4193. doi: 10.1200/JCO.20.01276 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r9] 9.Guarneri V, Griguolo G, Miglietta F, Conte PF, Dieci MV, Girardi F. Survival after neoadjuvant therapy with trastuzumab-lapatinib and chemotherapy in patients with HER2-positive early breast cancer: a meta-analysis of randomized trials. ESMO Open. 2022;7(2):100433. doi: 10.1016/j.esmoop.2022.100433 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r10] 10.Piccart-Gebhart M, Holmes E, Baselga J, et al. Adjuvant lapatinib and trastuzumab for early human epidermal growth factor receptor 2-positive breast cancer: results from the randomized phase III adjuvant lapatinib and/or trastuzumab treatment optimization trial. J Clin Oncol. 2016;34(10):1034-1042. doi: 10.1200/JCO.2015.62.1797 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r11] 11.Piccart M, Procter M, Fumagalli D, et al. ; APHINITY Steering Committee and Investigators . Adjuvant pertuzumab and trastuzumab in early HER2-positive breast cancer in the APHINITY Trial: 6 years’ follow-up. J Clin Oncol. 2021;39(13):1448-1457. doi: 10.1200/JCO.20.01204 [DOI] [PubMed] [Google Scholar]

[coi230096r12] 12.Prat A, Carey LA, Adamo B, et al. Molecular features and survival outcomes of the intrinsic subtypes within HER2-positive breast cancer. J Natl Cancer Inst. 2014;106(8):dju152. doi: 10.1093/jnci/dju152 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r13] 13.Schettini F, Pascual T, Conte B, et al. HER2-enriched subtype and pathological complete response in HER2-positive breast cancer: a systematic review and meta-analysis. Cancer Treat Rev. 2020;84:101965. doi: 10.1016/j.ctrv.2020.101965 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r14] 14.Swain SM, Tang G, Lucas PC, et al. Pathologic complete response and outcomes by intrinsic subtypes in NSABP B-41, a randomized neoadjuvant trial of chemotherapy with trastuzumab, lapatinib, or the combination. Breast Cancer Res Treat. 2019;178(2):389-399. doi: 10.1007/s10549-019-05398-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r15] 15.Salgado R, Denkert C, Campbell C, et al. Tumor-infiltrating lymphocytes and associations with pathological complete response and event-free survival in HER2-positive early-stage breast cancer treated with lapatinib and trastuzumab: a secondary analysis of the NeoALTTO Trial. JAMA Oncol. 2015;1(4):448-454. doi: 10.1001/jamaoncol.2015.0830 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r16] 16.Nuciforo P, Pascual T, Cortés J, et al. A predictive model of pathologic response based on tumor cellularity and tumor-infiltrating lymphocytes (CelTIL) in HER2-positive breast cancer treated with chemo-free dual HER2 blockade. Ann Oncol. 2018;29(1):170-177. doi: 10.1093/annonc/mdx647 [DOI] [PubMed] [Google Scholar]

[coi230096r17] 17.Dieci MV, Prat A, Tagliafico E, et al. Integrated evaluation of PAM50 subtypes and immune modulation of pCR in HER2-positive breast cancer patients treated with chemotherapy and HER2-targeted agents in the CherLOB trial. Ann Oncol. 2016;27(10):1867-1873. doi: 10.1093/annonc/mdw262 [DOI] [PubMed] [Google Scholar]

[coi230096r18] 18.Denkert C, von Minckwitz G, Darb-Esfahani S, et al. Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol. 2018;19(1):40-50. doi: 10.1016/S1470-2045(17)30904-X [DOI] [PubMed] [Google Scholar]

[coi230096r19] 19.Chic N, Luen SJ, Nuciforo P, et al. Tumor cellularity and infiltrating lymphocytes as a survival surrogate in HER2-positive breast cancer. J Natl Cancer Inst. 2022;114(3):467-470. doi: 10.1093/jnci/djab057 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r20] 20.Barroso-Sousa R, Barry WT, Guo H, et al. The immune profile of small HER2-positive breast cancers: a secondary analysis from the APT trial. Ann Oncol. 2019;30(4):575-581. doi: 10.1093/annonc/mdz047 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r21] 21.Fumagalli D, Venet D, Ignatiadis M, et al. RNA sequencing to predict response to neoadjuvant anti-HER2 therapy: a secondary analysis of the NeoALTTO randomized clinical trial. JAMA Oncol. 2017;3(2):227-234. doi: 10.1001/jamaoncol.2016.3824 [DOI] [PMC free article] [PubMed] [Google Scholar]

[coi230096r22] 22.Ignatiadis M, Singhal SK, Desmedt C, et al. Gene modules and response to neoadjuvant chemotherapy in breast cancer subtypes: a pooled analysis. J Clin Oncol. 2012;30(16):1996-2004. doi: 10.1200/JCO.2011.39.5624 [DOI] [PubMed] [Google Scholar]

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PERMALINK

Tumor Intrinsic Subtypes and Gene Expression Signatures in Early-Stage ERBB2/HER2-Positive Breast Cancer

Aranzazu Fernandez-Martinez, MD

Mattia Rediti, MD

Gong Tang, PhD

Tomás Pascual, MD

Katherine A Hoadley, PhD

David Venet, PhD

Naim U Rashid, PhD

Patricia A Spears, BS

Md N Islam, MS

Sarra El-Abed, MD

Judith Bliss, PhD

Matteo Lambertini, MD

Serena Di Cosimo, MD

Jens Huobe, MD

David Goerlitz, MS

Rong Hu, PhD

Peter C Lucas, MD

Sandra M Swain, MD

Christos Sotiriou, MD

Charles M Perou, PhD

Lisa A Carey, MD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusions and relevance

Introduction

Methods

Neoadjuvant Trials: Study Designs and Patients

Tumor Gene Expression Analyses

Statistical Analysis

Results

Clinicopathologic Characteristics and Efficacy Analysis in the ITT Cohort

Clinical Implications of the Intrinsic Subtypes in the RNAseq Cohort

Table. Comparison of Baseline Clinicopathologic Characteristics of the Patients From the NSABP B-41, CALGB 40601, and NeoALTTO Trials in the 758 Patients in the RNAseq Cohort.

Figure 1. Kaplan-Meier Curves of the Association of Pathologic Complete Response (pCR) With Event-Free Survival by Tumor Intrinsic Subtype.

Figure 2. Kaplan-Meier Curves of the Association of the Treatment Arm With Event-Free Survival by Tumor Intrinsic Subtype.

pCR and EFS Biomarkers Across Individual Trials and in the Combined Cohort

Figure 3. Forest Plots Showing the Association of Gene Expression Biomarker Levels at Baseline With Pathologic Complete Response (PCR) and Event-Free Survival (EFS).

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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