Breast cancers exhibit genome-wide aberrant DNA methylation patterns. To investigate how these affect the transcriptome and which changes are linked to transformation or progression, we apply genome-wide expression–methylation quantitative trait loci (emQTL) analysis between DNA methylation and gene expression. On a whole genome scale, in cis and in trans, DNA methylation and gene expression have remarkably and reproducibly conserved patterns of association in three breast cancer cohorts (n = 104, n = 253 and n = 277). The expression–methylation quantitative trait loci associations form two main clusters; one relates to tumor infiltrating immune cell signatures and the other to estrogen receptor signaling. In the estrogen related cluster, using ChromHMM segmentation and transcription factor chromatin immunoprecipitation sequencing data, we identify transcriptional networks regulated in a cell lineage-specific manner by DNA methylation at enhancers. These networks are strongly dominated by ERα, FOXA1 or GATA3 and their targets were functionally validated using knockdown by small interfering RNA or GRO-seq analysis after transcriptional stimulation with estrogen.

How mixtures of immune cells associate with cancer cell phenotype and affect pathogenesis is still unclear. In 15 breast cancer gene expression datasets, we invariably identify three clusters of patients with gradual levels of immune infiltration. The intermediate immune infiltration cluster (Cluster B) is associated with a worse prognosis independently of known clinicopathological features. Furthermore, immune clusters are associated with response to neoadjuvant chemotherapy. In silico dissection of the immune contexture of the clusters identified Cluster A as immune cold, Cluster C as immune hot while Cluster B has a pro-tumorigenic immune infiltration. Through phenotypical analysis, we find epithelial mesenchymal transition and proliferation associated with the immune clusters and mutually exclusive in breast cancers. Here, we describe immune clusters which improve the prognostic accuracy of immune contexture in breast cancer. Our discovery of a novel independent prognostic factor in breast cancer highlights a correlation between tumor phenotype and immune contexture.

We have previously shown that cisplatin triggers an early acid sphingomyelinase (aSMase)-dependent ceramide generation concomitantly with an increase in membrane fluidity and induces apoptosis in HT29 cells. The present study further explores the role and origin of membrane fluidification in cisplatin-induced apoptosis. The rapid increase in membrane fluidity following cisplatin treatment was inhibited by membrane-stabilizing agents such as cholesterol or monosialoganglioside-1. In HT29 cells, these compounds prevented the early aggregation of Fas death receptor and of membrane lipid rafts on cell surface and significantly inhibited cisplatininduced apoptosis without altering drug intracellular uptake or cisplatin DNA adducts formation. Early after cisplatin treatment, Na + /H + membrane exchanger-1 (NHE1) was inhibited leading to intracellular acidification, aSMase was activated, and ceramide was detected at the cell membrane. Treatment of HT29 cells with Staphylococcus aureus sphingomyelinase increased membrane fluidity. Moreover, pretreatment with cariporide, a specific inhibitor of NHE1, inhibited cisplatin-induced intracellular acidification, aSMase activation, ceramide membrane generation, membrane fluidification, and apoptosis. Finally, NHE1-expressing PS120 cells were more sensitive to cisplatin than NHE1-deficient PS120 cells. Altogether, these findings suggest that the apoptotic pathway triggered by cisplatin involves a very early NHE1-dependent intracellular acidification leading to aSMase activation and increase in membrane fluidity. These events are independent of cisplatin-induced DNA adducts formation. The membrane exchanger NHE1 may be another potential target of cisplatin, increasing cell sensitivity to this compound. [Cancer Res 2007;67(16):7865-74]

Digital analysis of pathology whole-slide images is fast becoming a game changer in cancer diagnosis and treatment. Specifically, deep learning methods have shown great potential to support pathology analysis, with recent studies identifying molecular traits that were not previously recognized in pathology H&E whole-slide images. Simultaneous to these developments, it is becoming increasingly evident that tumor heterogeneity is an important determinant of cancer prognosis and susceptibility to treatment, and should therefore play a role in the evolving practices of matching treatment protocols to patients. State of the art diagnostic procedures, however, do not provide automated methods for characterizing and/or quantifying tumor heterogeneity, certainly not in a spatial context. Further, existing methods for analyzing pathology whole-slide images from bulk measurements require many training samples and complex pipelines. Our work addresses these two challenges. First, we train deep learning models to spatially resolve bulk mRNA and miRNA expression levels on pathology whole-slide images (WSIs). Our models reach up to 0.95 AUC on held-out test sets from two cancer cohorts using a simple training pipeline and a small number of training samples. Using the inferred gene expression levels, we further develop a method to spatially characterize tumor heterogeneity. Specifically, we produce tumor molecular cartographies and heterogeneity maps of WSIs and formulate a heterogeneity index (HTI) that quantifies the level of heterogeneity within these maps. Applying our methods to breast and lung cancer slides, we show a significant statistical link between heterogeneity and survival. Our methods potentially open a new and accessible approach to investigating tumor heterogeneity and other spatial molecular properties and their link to clinical characteristics, including treatment susceptibility and survival.

Polycyclic aromatic hydrocarbons (PAH), such as benzo[a]pyrene (B[a]P), are ubiquitous genotoxic environmental pollutants. Their DNA-damaging effects lead to apoptosis induction, through similar pathways to those identified after exposure to other DNA-damaging stimuli with activation of p53-related genes and the involvement of the intrinsic apoptotic pathway. However, at a low concentration of B[a]P (50 nM), our previous results pointed to the involvement of intracellular pH (pHi) variations during B[a]P-induced apoptosis in a rat liver epithelial cell line (F258). In the present work, we identified the mitochondrial F0F1-ATPase activity reversal as possibly responsible for pHi decrease. This acidification not only promoted executive caspase activation, but also activated leucocyte elastase inhibitor/leucocyte-derived DNase II (LEI/L-DNase II) pathway. p53 appeared to regulate mitochondria homeostasis, by initiating F0F1-ATPase reversal and endonuclease G (Endo G) release. In conclusion, a low dose of B[a]P induced apoptosis by recruiting a large panel of executioners apparently depending on p53 phosphorylation and, for some of them, on acidification.

The role of the hepatocyte plasma membrane structure in the development of oxidative stress during alcoholic liver diseases is not yet fully understood. Previously, we have established the pivotal role of membrane fluidity in ethanol-induced oxidative stress, but no study has so far tested the involvement of lipid rafts. In this study, methyl-␤-cyclodextrin or cholesterol oxidase, which were found to disrupt lipid rafts in hepatocytes, inhibited both reactive oxygen species production and lipid peroxidation, and this suggested a role for these microstructures in oxidative stress. By immunostaining of lipid raft components, a raft clustering was detected in ethanol-treated hepatocytes. In addition, we found that rafts were modified by formation of malondialdehyde adducts and disulfide bridges. Interestingly, pretreatment of cells by 4-methyl-pyrazole (to inhibit ethanol metabolism) and various antioxidants prevented the ethanol-induced raft aggregation. In addition, treatment of hepatocytes by a stabilizing agent (ursodeoxycholic acid) or a fluidizing compound [2-(2-methoxyethoxy)-ethyl 8-(cis-2-n-octylcyclopropyl)octanoate] led to inhibition or enhancement of raft clustering, respectively, which pointed to a relationship between membrane fluidity and lipid rafts during ethanol-induced oxidative stress. We finally investigated the involvement of phospholipase C in raft-induced oxidative stress upon ethanol exposure. Phospholipase C was shown to be translocated into rafts and to participate in oxidative stress by controlling hepatocyte iron content. Conclusion: Membrane structure, depicted as membrane fluidity and lipid rafts, plays a key role in ethanol-induced oxidative stress of the liver, and its modulation may be of therapeutic relevance. (HEPATOLOGY 2008;47:59-70.)

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