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 (R2 = 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.

BackgroundRecent efforts of genome-wide gene expression profiling analyses have improved our understanding of the biological complexity and diversity of triple-negative breast cancers (TNBCs) reporting, at least six different molecular subtypes of TNBC namely Basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL) and luminal androgen receptor (LAR). However, little is known regarding the potential driving molecular events within each subtype, their difference in survival and response to therapy. Further insight into the underlying genomic alterations is therefore needed.Patients and methodsThis study was carried out using copy-number aberrations, somatic mutations and gene expression data derived from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas. TNBC samples (n = 550) were classified according to Lehmann’s molecular subtypes using the TNBCtype online subtyping tool (http://cbc.mc.vanderbilt.edu/tnbc/).ResultsEach subtype showed significant clinic-pathological characteristic differences. Using a multivariate model, IM subtype showed to be associated with a better prognosis (HR = 0.68; CI = 0.46–0.99; P = 0.043) whereas LAR subtype was associated with a worst prognosis (HR = 1.47; CI = 1.0–2.14; P = 0.046). BL1 subtype was found to be most genomically instable subtype with high TP53 mutation (92%) and copy-number deletion in genes involved in DNA repair mechanism (BRCA2, MDM2, PTEN, RB1 and TP53). LAR tumours were associated with higher mutational burden with significantly enriched mutations in PI3KCA (55%), AKT1 (13%) and CDH1 (13%) genes. M and MSL subtypes were associated with higher signature score for angiogenesis. Finally, IM showed high expression levels of immune signatures and check-point inhibitor genes such as PD1, PDL1 and CTLA4.ConclusionOur findings highlight for the first time the substantial genomic heterogeneity that characterize TNBC molecular subtypes, allowing for a better understanding of the disease biology as well as the identification of several candidate targets paving novel approaches for the development of anticancer therapeutics for TNBC.

Gene expression measurements are a powerful tool in molecular biology, but when applied to heterogeneous samples containing more than one cellular type the results are difficult to interpret. We present here a new approach to this problem allowing to deduce the gene expression profile of the various cellular types contained in a set of samples directly from the measurements taken on the whole sample.

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.

Central statistical monitoring can both optimize on-site monitoring and improve data quality and as such provides a cost-effective way of meeting regulatory requirements for clinical trials.