Intrinsic Subtype and Therapeutic Response Among HER2-Positive Breast Tumors from the NCCTG (Alliance) N9831 Trial

Background: Genomic data from human epidermal growth factor receptor 2–positive (HER2+) tumors were analyzed to assess the association between intrinsic subtype and clinical outcome in a large, well-annotated patient cohort

Dissecting the predictive value of MAPK/AKT/estrogen-receptor phosphorylation axis in primary breast cancer to treatment response for tamoxifen over exemestane: a Translational Report of the Intergroup Exemestane Study (IES)-PathIES

Purpose The prognostic and predictive values of the MAPK/AKT/ERα phosphorylation axis (pT202/T204MAPK, pT308AKT, pS473AKT, pS118ERα and pS167ERα) in primary tumours were assessed to determine whether these markers can differentiate between patient responses for switching adjuvant endocrine therapy after 2–3 years from tamoxifen to exemestane and continued tamoxifen monotherapy in the Intergroup Exemestane Study (IES). Methods Of the 4724 patients in IES, 1506 were managed in a subset of centres (N = 89) participating in PathIES. These centres recruited 1282 (85%, 1282/1506) women into PathIES of whom 1036 had phospho-marker data. All phospho-markers were analysed by immunohistochemistry staining. Multivariable Cox proportional hazards models of the phospho-markers for disease-free survival (DFS) and overall survival (OS) were adjusted for clinicopathological factors. Treatment effects on the biomarker expression were determined by interaction tests. Benjamini–Hochberg adjustment for multiple testing with a false discovery rate of 10% was applied (pBH). Results Phospho-T202/T204MAPK, pS118ERα and pS167ERα were all found to be correlated (pBH = 0.0002). These markers were not associated with either DFS or OS when controlling for the established clinicopathological factors. Interaction terms between the phospho-markers and treatment strategies for either DFS or OS were not statistically significant (pBH > 0.05 for all). Conclusions This PathIES study confirmed previously described associations between the phosphorylation site markers of AKT, MAPK and ERα activity in postmenopausal breast cancer patients. No prognostic correlations between the phosphorylation markers and clinical outcome were found, nor were they predictive for clinical outcomes among patients who switched therapy over those treated with tamoxifen alone

Prognostic Significance of Progesterone Receptor–Positive Tumor Cells Within Immunohistochemically Defined Luminal A Breast Cancer

Current immunohistochemical (IHC)-based definitions of luminal A and B breast cancers are imperfect when compared with multigene expression-based assays. In this study, we sought to improve the IHC subtyping by examining the pathologic and gene expression characteristics of genomically defined luminal A and B subtypes

Heterogeneity in global gene expression profiles between biopsy specimens taken peri-surgically from primary ER-positive breast carcinomas

Abstract Background Gene expression is widely used for the characterisation of breast cancers. Variability due to tissue heterogeneity or measurement error or systematic change due to peri-surgical procedures can affect measurements but is poorly documented. We studied the variability of global gene expression between core-cuts of primary ER+ breast cancers and the impact of delays to tissue stabilisation due to sample X-ray and of diagnostic core cutting. Methods Twenty-six paired core-cuts were taken immediately after tumour excision and up to 90 minutes delay due to sample X-ray; 57 paired core-cuts were taken at diagnosis and 2 weeks later at surgical excision. Whole genome expression analysis was conducted on extracted RNA. Correlations and differences were assessed between the expression of individual genes, gene sets/signatures and intrinsic subtypes. Results Twenty-three and 56 sample pairs, respectively, were suitable for analysis. The range of correlations for both sample sets were similar with the majority being >0.97 in both. Correlations between pairs for 18 commonly studied genes were also similar between the studies and mainly with Pearson correlation coefficients >0.6 except for a small number of genes, which had a narrow-dynamic range (e.g. MKI67, SNAI2). There was no systematic difference in intrinsic subtyping between the first and second sample of either set but there was c.15 % discordance between the subtype assignments between the pairs, mainly where the subtyping of individual samples was less certain. Increases in the expression of several stress/early-response genes (e.g. FOS, FOSB, JUN) were found in both studies and confirmed findings in earlier smaller studies. Increased expression of IL6, IGFBP2 and MYC (by 17 %, 14 % and 44 %, respectively) occurred between the samples taken 2 weeks apart and again confirmed findings from an earlier study. Conclusions There is generally good correlation in gene expression between pairs of core-cuts except where genes have a narrow dynamic range. Similar correlation coefficients to the average gene expression profiles of intrinsic subtype, particularly LumA and LumB, can lead to discordances between assigned subtypes. Substantial changes in expression of early-response genes occur within an hour after surgery and in IL6, IGFB2 and MYC as a result of diagnostic core-cut biopsy. Trial registration Trial number CRUK/07/015 . Study start date September 2008

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

Dual human epidermal growth factor receptor 2 (HER2) targeting can increase pathologic complete response rates (pCRs) to neoadjuvant therapy and improve progression-free survival in metastatic disease. CALGB 40601 examined the impact of dual HER2 blockade consisting of trastuzumab and lapatinib added to paclitaxel, considering tumor and microenvironment molecular features

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.Peer reviewe

Carboplatin in BRCA1/2-mutated and triple-negative breast cancer BRCAness subgroups: the TNT Trial

Germline mutations in BRCA1/2 predispose individuals to breast cancer (termed germline-mutated BRCA1/2 breast cancer, gBRCA-BC) by impairing homologous recombination (HR) and causing genomic instability. HR also repairs DNA lesions caused by platinum agents and PARP inhibitors. Triple-negative breast cancers (TNBCs) harbor subpopulations with BRCA1/2 mutations, hypothesized to be especially platinum-sensitive. Cancers in putative ‘BRCAness’ subgroups—tumors with BRCA1 methylation; low levels of BRCA1 mRNA (BRCA1 mRNA-low); or mutational signatures for HR deficiency and those with basal phenotypes—may also be sensitive to platinum. We assessed the efficacy of carboplatin and another mechanistically distinct therapy, docetaxel, in a phase 3 trial in subjects with unselected advanced TNBC. A prespecified protocol enabled biomarker–treatment interaction analyses in gBRCA-BC and BRCAness subgroups. The primary endpoint was objective response rate (ORR). In the unselected population (376 subjects; 188 carboplatin, 188 docetaxel), carboplatin was not more active than docetaxel (ORR, 31.4% versus 34.0%, respectively; P = 0.66). In contrast, in subjects with gBRCA-BC, carboplatin had double the ORR of docetaxel (68% versus 33%, respectively; biomarker, treatment interaction P = 0.01). Such benefit was not observed for subjects with BRCA1 methylation, BRCA1 mRNA-low tumors or a high score in a Myriad HRD assay. Significant interaction between treatment and the basal-like subtype was driven by high docetaxel response in the nonbasal subgroup. We conclude that patients with advanced TNBC benefit from characterization of BRCA1/2 mutations, but not BRCA1 methylation or Myriad HRD analyses, to inform choices on platinum-based chemotherapy. Additionally, gene expression analysis of basal-like cancers may also influence treatment selection

Novel 18-gene signature for predicting relapse in ER-positive, HER2-negative breast cancer.

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were madeBACKGROUND: Several prognostic signatures for early oestrogen receptor-positive (ER+) breast cancer have been established with a 10-year follow-up. We tested the hypothesis that signatures optimised for 0-5-year and 5-10-year follow-up separately are more prognostic than a single signature optimised for 10 years. METHODS: Genes previously identified as prognostic or associated with endocrine resistance were tested in publicly available microarray data set using Cox regression of 747 ER+/HER2- samples from post-menopausal patients treated with 5 years of endocrine therapy. RNA expression of the selected genes was assayed in primary ER+/HER2- tumours from 948 post-menopausal patients treated with 5 years of anastrozole or tamoxifen in the TransATAC cohort. Prognostic signatures for 0-10, 0-5 and 5-10 years were derived using a penalised Cox regression (elastic net). Signature comparison was performed with likelihood ratio statistics. Validation was done by a case-control (POLAR) study in 422 samples derived from a cohort of 1449. RESULTS: Ninety-three genes were selected by the modelling of microarray data; 63 of these were significantly prognostic in TransATAC, most similarly across each time period. Contrary to our hypothesis, the derived early and late signatures were not significantly more prognostic than the 18-gene 10-year signature. The 18-gene 10-year signature was internally validated in the TransATAC validation set, showing prognostic information similar to that of Oncotype DX Recurrence Score, PAM50 risk of recurrence score, Breast Cancer Index and IHC4 (score based on four IHC markers), as well as in the external POLAR case-control set. CONCLUSIONS: The derived 10-year signature predicts risk of metastasis in patients with ER+/HER2- breast cancer similar to commercial signatures. The hypothesis that early and late prognostic signatures are significantly more informative than a single signature was rejected.This work was supported by Breast Cancer Now working in partnership with Walk the Walk, as well as by the National Institute for Health Research Royal Marsden/ICR Biomedical Research Centre. ARB was funded by Cancer Research UK (grant number C569/A16891). The study was supported by funds from Skåne County Council’s Research and Development Foundation, Governmental Funding of Clinical Research within the National Health Service (grant number ALFSKANE-350191 [to MK]), the Swedish Breast Cancer Association (BRO), the Mrs Berta Kamprad Foundation and The Inger Persson Research Foundation. IS and JC were supported by Cancer Research UK (programme grant C569/A10404)

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC