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Homologous recombination deficiency (HRD) is a defining characteristic in BRCA-deficient breast tumors caused by genetic or epigenetic alterations in key pathway genes. We investigated the frequency of BRCA1 promoter hypermethylation in 237 triple-negative breast cancers (TNBCs) from a population-based study using reported whole genome and RNA sequencing data, complemented with analyses of genetic, epigenetic, transcriptomic and immune infiltration phenotypes. We demonstrate that BRCA1 promoter hypermethylation is twice as frequent as BRCA1 pathogenic variants in early-stage TNBC and that hypermethylated and mutated cases have similarly improved prognosis after adjuvant chemotherapy. BRCA1 hypermethylation confers an HRD, immune cell type, genome-wide DNA methylation, and transcriptional phenotype similar to TNBC tumors with BRCA1-inactivating variants, and it can be observed in matched peripheral blood of patients with tumor hypermethylation. Hypermethylation may be an early event in tumor development that progress along a common pathway with BRCA1-mutated disease, representing a promising DNA-based biomarker for early-stage TNBC. BRCA-deficient breast cancer is characterised by homologous recombination deficiency. Here, the authors show that hypermethylated BRCA1 phenotypically copies mutated BRCA1 in triple negative breast cancers. [ABSTRACT FROM AUTHOR]

Purpose: Oestrogen receptor-positive (ER+) and human epidermal receptor 2-negative (HER2–) breast cancers are classified as Luminal A or B based on gene expression, but immunohistochemical markers are used for surrogate subtyping. The aims of this study were to examine the agreement between molecular subtyping (MS) and surrogate subtyping and to identify subgroups consisting mainly of Luminal A or B tumours. Methods: The cohort consisted of 2063 patients diagnosed between 2013–2017, with primary ER+/HER2– breast cancer, analysed by RNA sequencing. Surrogate subtyping was performed according to three algorithms (St. Gallen 2013, Maisonneuve and our proposed Grade-based classification). Agreement (%) and kappa statistics (κ) were used as concordance measures and ROC analysis for luminal distinction. Ki67, progesterone receptor (PR) and histological grade (HG) were further investigated as surrogate markers. Results: The agreement rates between the MS and St. Gallen 2013, Maisonneuve and Grade-based classifications were 62% (κ = 0.30), 66% (κ = 0.35) and 70% (κ = 0.41), respectively. PR did not contribute to distinguishing Luminal A from B tumours (auROC = 0.56). By classifying HG1-2 tumours as Luminal A-like and HG3 as Luminal B-like, agreement with MS was 80% (κ = 0.46). Moreover, by combining HG and Ki67 status, a large subgroup of patients (51% of the cohort) having > 90% Luminal A tumours could be identified. Conclusions: Agreement between MS and surrogate classifications was generally poor. However, a post hoc analysis showed that a combination of HG and Ki67 could identify patients very likely to have Luminal A tumours according to MS. [ABSTRACT FROM AUTHOR]

Background: Transcriptomic profiles have shown promise as predictors of response to neoadjuvant chemotherapy in breast cancer (BC). This study aimed to explore their predictive value in the advanced BC (ABC) setting.Methods: In a Phase 3 trial of first-line chemotherapy in ABC, a fine needle aspiration biopsy (FNAB) was obtained at baseline. Intrinsic molecular subtypes and gene modules related to immune response, proliferation, oestrogen receptor (ER) signalling and recurring genetic alterations were analysed for association with objective response to chemotherapy. Gene-set enrichment analysis (GSEA) of responders vs non-responders was performed independently. Lymphocytes were enumerated in FNAB smears and the absolute abundance of immune cell types was calculated using the Microenvironment Cell Populations counter method.Results: Gene expression data were available for 109 patients. Objective response to chemotherapy was statistically significantly associated with an immune module score (odds ratio (OR)=1.62; 95% confidence interval (CI), 1.03-2.64; P=0.04). Subgroup analysis showed that this association was restricted to patients with ER-positive or luminal tumours (OR=3.54; 95%, 1.43-10.86; P=0.012 and P for interaction=0.04). Gene-set enrichment analysis confirmed that in these subgroups, immune-related gene sets were enriched in responders.Conclusions: Immune-related transcriptional signatures may predict response to chemotherapy in ER-positive and luminal ABC. [ABSTRACT FROM AUTHOR]

The aim of this was to determine whether the change of size observed at the first response evaluation after initiation of first-line combination chemotherapy correlates with overall survival (OS) in patients with metastatic breast cancer (MBC). The change in size of tumors derived from measurements according to Response Evaluation Criteria In Solid Tumors (RECIST) at the first evaluation on computed tomography (CT) was obtained from a multicenter, randomized phase III trial ('TEX trial,' n = 287) comparing treatment with a combination of epirubicin and paclitaxel alone or with capecitabine (TEX). Cox regression and Kaplan-Meier analyses were performed to evaluate the correlations between the first change in tumor size, response according to RECIST and OS. Data from CT evaluations of 233 patients were available. Appearance of new lesions or progression of non-target lesions (new/non-target) indicated short OS by univariable regression analysis (HR 3.76, 95 % CI 1.90-7.42, p < 0.001). A decrease by >30 % at this early time point was prognostic favorable (HR 0.69, 95 % CI 0.49-0.98, p = 0.04) and not significantly less than the best overall response according to RECIST. After adjustment for previous adjuvant treatment and the treatment given within the frame of the randomized trial, OS was still significantly shorter in patients with new/non-target lesions after a median 8 weeks of treatment (HR 4.41, 95 % CI 2.74-7.11, p < 0.001). Disease progression at the first evaluation correlates with OS in patients with MBC treated with first-line combination chemotherapy. The main reason for early disease progression was the appearance of new lesions or progression of non-target lesions. These patients had poor OS even though more lines of treatment were available. Thus, these factors should be focused on in the response evaluations besides tumor size changes. [ABSTRACT FROM AUTHOR]

In a previous study from our laboratory, high tumor levels of tissue inhibitor of metalloproteinases-1 (TIMP-1) have been associated with an adverse response to chemotherapy in metastatic breast cancer suggesting that TIMP-1, which is known to inhibit apoptosis, may be a new predictive marker in this disease. The purpose of this study was to investigate the association between TIMP-1 and objective response to chemotherapy in an independent patient population consisting of patients with metastatic breast cancer from Sweden and Denmark. TIMP-1 was measured using ELISA in 162 primary tumor extracts from patients who later developed metastatic breast cancer and these levels were related to the objective response to first-line chemotherapy. Increasing levels of TIMP-1 were associated with a decreasing probability of response to treatment, reaching borderline significance (OR = 1.59, 95% CI: 0.97–2.62, P = 0.07). This OR is very similar to the result from our previous study. Increasing levels of TIMP-1 were also associated with a shorter disease-free survival and overall survival, however, not statistically significant. The results from the present study support previous data that TIMP-1 is associated with objective response to chemotherapy for metastatic breast cancer. [ABSTRACT FROM AUTHOR]

Multigene expression signatures provide a molecular subdivision of early breast cancer associated with patient outcome. A gap remains in the validation of such signatures in clinical treatment groups of patients within population-based cohorts of unselected primary breast cancer representing contemporary disease stages and current treatments. A cohort of 3520 resectable breast cancers with RNA sequencing data included in the population-based SCAN-B initiative (ClinicalTrials.gov ID NCT02306096) were selected from a healthcare background population of 8587 patients diagnosed within the years 2010–2015. RNA profiles were classified according to 19 reported gene signatures including both gene expression subtypes (e.g. PAM50, IC10, CIT) and risk predictors (e.g. Oncotype DX, 70-gene, ROR). Classifications were analyzed in nine adjuvant clinical assessment groups: TNBC-ACT (adjuvant chemotherapy, n = 239), TNBC-untreated (n = 82), HER2+/ER− with anti-HER2+ ACT treatment (n = 110), HER2+/ER+ with anti-HER2 + ACT + endocrine treatment (n = 239), ER+/HER2−/LN− with endocrine treatment (n = 1113), ER+/HER2−/LN− with endocrine + ACT treatment (n = 243), ER+/HER2−/LN+ with endocrine treatment (n = 423), ER+/HER2−/LN+ with endocrine + ACT treatment (n = 433), and ER+/HER2−/LN− untreated (n = 200). Gene signature classification (e.g., proportion low-, high-risk) was generally well aligned with stratification based on current immunohistochemistry-based clinical practice. Most signatures did not provide any further risk stratification in TNBC and HER2+/ER– disease. Risk classifier agreement (low-, medium/intermediate-, high-risk groups) in ER+ assessment groups was on average 50–60% with occasional pair-wise comparisons having <30% agreement. Disregarding the intermediate-risk groups, the exact agreement between low- and high-risk groups was on average ~80–95%, for risk prediction signatures across all assessment groups. Outcome analyses were restricted to assessment groups of TNBC-ACT and endocrine treated ER+/HER2−/LN− and ER+/HER2−/LN+ cases. For ER+/HER2− disease, gene signatures appear to contribute additional prognostic value even at a relatively short follow-up time. Less apparent prognostic value was observed in the other groups for the tested signatures. The current study supports the usage of gene expression signatures in specific clinical treatment groups within population-based breast cancer. It also stresses the need of further development to reach higher consensus in individual patient classifications, especially for intermediate-risk patients, and the targeting of patients where current gene signatures and prognostic variables provide little support in clinical decision-making. [ABSTRACT FROM AUTHOR]