David Venet

Bridging the gap between animal or in vitro models and human disease is essential in medical research. Researchers often suggest that a biological mechanism is relevant to human cancer from the statistical association of a gene expression marker (a signature) of this mechanism, that was discovered in an experimental system, with disease outcome in humans. We examined this argument for breast cancer. Surprisingly, we found that gene expression signatures-unrelated to cancer-of the effect of postprandial laughter, of mice social defeat and of skin fibroblast localization were all significantly associated with breast cancer outcome. We next compared 47 published breast cancer outcome signatures to signatures made of random genes. Twenty-eight of them (60%) were not significantly better outcome predictors than random signatures of identical size and 11 (23%) were worst predictors than the median random signature. More than 90% of random signatures >100 genes were significant outcome predictors. We next derived a metagene, called meta-PCNA, by selecting the 1% genes most positively correlated with proliferation marker PCNA in a compendium of normal tissues expression. Adjusting breast cancer expression data for meta-PCNA abrogated almost entirely the outcome association of published and random signatures. We also found that, in the absence of adjustment, the hazard ratio of outcome association of a signature strongly correlated with meta-PCNA (R(2) = 0.9). This relation also applied to single-gene expression markers. Moreover, >50% of the breast cancer transcriptome was correlated with meta-PCNA. A corollary was that purging cell cycle genes out of a signature failed to rule out the confounding effect of proliferation. Hence, it is questionable to suggest that a mechanism is relevant to human breast cancer from the finding that a gene expression marker for this mechanism predicts human breast cancer outcome, because most markers do. The methods we present help to overcome this problem.

BACKGROUND: Recent efforts of gene expression profiling analyses recognized at least four different triple-negative breast cancer (TNBC) molecular subtypes. However, little is known regarding their tumor microenvironment (TME) heterogeneity. METHODS: Here, we investigated TME heterogeneity within each TNBC molecular subtype, including immune infiltrate localization and composition together with expression of targetable immune pathways, using publicly available transcriptomic and genomic datasets from a large TNBC series totaling 1512 samples. Associations between molecular subtypes and specific features were assessed using logistic regression models. All statistical tests were two-sided. RESULTS: We demonstrated that each TNBC molecular subtype exhibits distinct TME profiles associated with specific immune, vascularization, stroma, and metabolism biological processes together with specific immune composition and localization. The immunomodulatory subtype was associated with the highest expression of adaptive immune-related gene signatures and a fully inflamed spatial pattern appearing to be the optimal candidate for immune check point inhibitors. In contrast, most mesenchymal stem-like and luminal androgen receptor tumors showed an immunosuppressive phenotype as witnessed by high expression levels of stromal signatures. Basal-like, luminal androgen receptor, and mesenchymal subtypes exhibited an immune cold phenotype associated with stromal and metabolism TME signatures and enriched in margin-restricted spatial pattern. Tumors with high chromosomal instability and copy number loss in the chromosome 5q and 15q regions, including genomic loss of major histocompatibility complex related genes, showed reduced cytotoxic activity as a plausible immune escape mechanism. CONCLUSIONS: Our results demonstrate that each TNBC subtype is associated with specific TME profiles, setting the ground for a rationale tailoring of immunotherapy in TNBC patients.

Abstract AURORA aims to study the processes of relapse in metastatic breast cancer (MBC) by performing multi-omics profiling on paired primary tumors and early-course metastases. Among 381 patients (primary tumor and metastasis pairs: 252 targeted gene sequencing, 152 RNA sequencing, 67 single nucleotide polymorphism arrays), we found a driver role for GATA1 and MEN1 somatic mutations. Metastases were enriched in ESR1, PTEN, CDH1, PIK3CA, and RB1 mutations; MDM4 and MYC amplifications; and ARID1A deletions. An increase in clonality was observed in driver genes such as ERBB2 and RB1. Intrinsic subtype switching occurred in 36% of cases. Luminal A/B to HER2-enriched switching was associated with TP53 and/or PIK3CA mutations. Metastases had lower immune score and increased immune-permissive cells. High tumor mutational burden correlated to shorter time to relapse in HR+/HER2− cancers. ESCAT tier I/II alterations were detected in 51% of patients and matched therapy was used in 7%. Integration of multi-omics analyses in clinical practice could affect treatment strategies in MBC. Significance: The AURORA program, through the genomic and transcriptomic analyses of matched primary and metastatic samples from 381 patients with breast cancer, coupled with prospectively collected clinical data, identified genomic alterations enriched in metastases and prognostic biomarkers. ESCAT tier I/II alterations were detected in more than half of the patients. This article is highlighted in the In This Issue feature, p. 2659

IMPORTANCE: In neoadjuvant trials, treatment of human epidermal growth factor receptor 2 (HER2)-positive breast cancers with dual HER2 blockade resulted in increased pathologic complete response (pCR) rates compared with each targeted agent alone. Amplification and/or overexpression of HER2 currently remains the only biomarker for therapeutic decisions, but it is insufficient to explain the heterogeneous response to anti-HER2 agents. OBJECTIVE: To investigate the ability of clinically and biologically relevant genes and gene signatures (GSs) measured by RNA sequencing to predict the efficacy of anti-HER2 agents. DESIGN, SETTING, AND PARTICIPANTS: The neoadjuvant NeoALTTO trial randomized 455 women with HER2-positive early-stage breast cancer to trastuzumab, lapatinib, or the combination for 6 weeks followed by the addition of weekly paclitaxel for 12 weeks, followed by 3 cycles of fluorouracil, epirubicin, and cyclophosphamide after surgery. The present substudy, which was planned in the NeoALTTO main protocol, evaluated the association of pretreatment gene expression levels defined using RNA sequencing with pCR and event-free survival (EFS). MAIN OUTCOMES AND MEASURES: Gene expression-based biomarkers using RNA sequencing were examined for their association with response to anti-HER2 therapy and long-term outcome. RESULTS: Sequencing data were available for 254 (56%) of the NeoALTTO participants (mean [SD] age of substudy participants, 48.8 [11.2] years). The expression of ERBB2/HER2 was the most significant predictor of pCR, followed by HER2-enriched subtype, ESR1, treatment arm, ER immunohistochemical analysis scores, Genomic Grade Index, immune, proliferation, and AKT/mTOR GSs. Adjusting for clinicopathological variables and treatment arms, ERBB2/HER2, HER2-enriched subtype, ESR1, and Genomic Grade Index remained significant. Immune GSs were associated with higher pCR only in the combination arm (odds ratio, 2.1; 95% CI, 1.2-4.0; interaction test P = .01), while the stroma GSs were significantly associated with higher pCR in the single arms and with lower pCR in the combination arm (odds ratio, 0.46; 95% CI, 0.25-0.84; P = .009). None of the evaluated variables was associated with EFS after correction for multiple testing, but this analysis was underpowered. CONCLUSIONS AND RELEVANCE: High levels of ERBB2/HER2 and low levels of ESR1 were associated with pCR in all treatment arms. In the combination arm, high expression of immune and stroma GSs were significantly associated with higher and lower pCR rates, respectively, and should be further explored as candidate predictive markers. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00553358.