The prognostic ease and difficulty of invasive breast carcinoma
Ontology highlight
ABSTRACT: Breast carcinoma (BC) have been extensively profiled by high-throughput technologies for over a decade, and broadly speaking, these studies can be grouped into those that seek to identify patient subtypes (studies of heterogeneity) or those that seek to identify gene signatures with prognostic or predictive capacity. The shear number of reported signatures has led to speculation that everything is prognostic in BC. Here we show that this ubiquity is an apparition caused by a poor understanding of the inter- relatedness between subtype and the molecular determinants of prognosis. Our approach constructively shows how to avoid confounding due to a patient's subtype, clinicopathological or treatment profile. The approach identifies patients who are predicted to have good outcome at time of diagnosis by all available clinical and molecular markers, but who experience a distant metastasis within five years. These inherently difficult patients (~7% of BC) are prioritized for investigations of intra-tumoral heterogeneity.
Project description:Breast carcinoma (BC) have been extensively profiled by high-throughput technologies for over a decade, and broadly speaking, these studies can be grouped into those that seek to identify patient subtypes (studies of heterogeneity) or those that seek to identify gene signatures with prognostic or predictive capacity. The shear number of reported signatures has led to speculation that everything is prognostic in BC. Here we show that this ubiquity is an apparition caused by a poor understanding of the inter- relatedness between subtype and the molecular determinants of prognosis. Our approach constructively shows how to avoid confounding due to a patient's subtype, clinicopathological or treatment profile. The approach identifies patients who are predicted to have good outcome at time of diagnosis by all available clinical and molecular markers, but who experience a distant metastasis within five years. These inherently difficult patients (~7% of BC) are prioritized for investigations of intra-tumoral heterogeneity. 321 samples from breast cancer patients.
Project description:We seek to investigate the genetic relationships between different Bantu-speaking populations and the migratory routes they followed during various phases of the Bantu expansion. Genomic and whole genome amplified DNA were genotyped on the Illumina InfiniumOmni2.5Exome-8v1.3 array. For quality control filtering, we used the PLINK v1.9 software and custom scripts.
Project description:Here we present a functional multi-omics method, interaction-Correlated Multi-omic Aberration Patterning or iC-MAP, which dissects intra-tumor heterogeneity and identifies in situ in real time the oncogenic consequences of multi-omics aberrations that drive proliferative/invasive tumor in individuals with poor prognosis. First, given epigenetic aberrations resulting from complex interactions between genetic susceptibility and environmental influences are the primary driving force of tumorigenesis, we applied our chromatin activity-based chemoproteomics (ChaC) method to characterize the tumor-phenotypic epiproteomes (epigenetic regulatory proteomes) in breast cancer (BC) patient tissues. A biotinylated ChaC probe UNC0965 that specifically binds to the oncogenically active histone methyltransferase G9a enabled the sorting/enrichment of a G9a-interacting epiproteome representing the predominant BC subtype in a tissue, which is separated from other cell types, especially non-malignant cells where G9a is less enzymatically active. ChaC then identified UNC0965-captured G9a interactors that are mostly involved in the oncogenic pathways associated with tumor cell viability and invasion. Using BC patient transcriptomic/genomic data we retrospectively identified the G9a interactor-encoding genes that show individualized iC-MAP in BC-subtypic patients with incurable metastatic disease, revealing essential drivers of proliferative or invasive BC phenotypes. Our iC-MAP findings can not only act as new diagnostic/prognostic markers to identify patient subsets with metastatic disease but also create precision therapeutic strategies that can match proliferative or invasive potential of individual patients.
Project description:Clinical management of breast cancer (BC) metastasis remains an unmet need as it accounts for 90% of BC-associated mortality. Although the luminal subtype, which represents >70% of BC cases, is generally associated with a favorable outcome, it is susceptible to metastatic relapse as late as 15 years after treatment discontinuation. Seeking therapeutic approaches as well as screening tools to properly identify those patients with a higher risk of recurrence is therefore essential. Here, we report that the lipid-degrading enzyme fatty acid amide hydrolase (FAAH) is a predictor of long-term survival in patients with luminal BC, and that it blocks tumor progression and lung metastasis in cell and mouse models of BC. Together, our findings highlight the potential of FAAH as a biomarker with prognostic value in luminal BC and as a therapeutic target in metastatic disease.
Project description:Breast Cancer (BC) patient stratification is mainly driven by tumour receptor status and histological grading and subtyping, with about twenty percent of patients for which absence of any actionable biomarkers results in no clear therapeutic intervention to apply. Here, we evaluated the potential of single-cell transcriptomics for automated diagnosis and drug treatment of breast cancer. We transcriptionally profiled 35,276 individual cells from 32 BC cell-lines covering all main BC subtypes to yield a Breast Cancer Single Cell Atlas. We show that single cell transcriptomics can successfully detect clinically relevant BC biomarkers and that atlas can be used to automatically predict cancer subtype and composition from a patient’s tumour biopsy. We found that BC cell lines harbour a high degree of heterogeneity in the expression of clinically relevant BC biomarkers and that such heterogeneity enables cells with differential drug sensitivity to co-exist even within a genomically stable isogenic cell line. Finally, we developed a novel bioinformatics approach named DREEP (Drug Response Estimation from Expression Profiles) to automatically predict responses to more than 450 anticancer agents starting from single-cell transcriptional profiles. We validated DREEP both in-silico and in-vitro by selectively inhibiting the growth of the HER2-deficient subpopulation in the MDAMB361 cell line. Our work shows that transcriptional heterogeneity is common, dynamic and that its plasticity plays a relevant role in determining drug sensitivity. Moreover, our Breast Cancer Single Cell Atlas and DREEP approach are a unique resource for the BC research community and to advance the use of single-cell sequencing in the clinic.
Project description:Metabolic plasticity is a hallmark of cancer, and metabolic alterations represent a promising therapeutic target. Since cellular metabolism is controlled by membrane traffic at multiple levels, we investigated the involvement of TBC1 domain-containing proteins (TBC1Ds) in the regulation of cancer metabolism. These proteins are characterized by the presence of a RAB-GAP domain, the TBC1 domain, and typically function as attenuators of RABs, the master switches of membrane traffic. However, a number of TBC1Ds harbor mutations in their catalytic residues, predicting biological functions different from direct regulation of RAB activities. Herein, we report that several genes encoding for TBC1Ds are expressed at higher levels in triple-negative breast cancers (TNBC) vs. other subtypes of breast cancers (BC), and predict prognosis. Orthogonal transcriptomics/metabolomics analysis revealed that the expression of prognostic TBC1Ds correlates with elevated glycolytic metabolism in BC cell lines. In-depth investigations of the three top hits from the previous analyses (TBC1D31, TBC1D22B and TBC1D7) revealed that their elevated expression is causal in determining a glycolytic phenotype in TNBC cell lines. We further showed that the impact of TBC1D7 on glycolytic metabolism of BC cells is independent of its known participation in the TSC1/TSC2 complex and consequent downregulation of mTORC1 activity. Since TBC1D7 behaves as an independent prognostic biomarker in TNBC, it could be used to distinguish good prognosis patients who could be spared aggressive therapy from those with a poor prognosis who might benefit from anti-glycolytic targeted therapies. Together, our results highlight how TBC1Ds connect disease aggressiveness with metabolic alterations in TNBC. Given the high level of heterogeneity among this BC subtype, TBC1Ds could represent important tools in predicting prognosis and guiding therapy decision-making.
Project description:Enriched tumor epithelium, tumor-associated stroma, and whole tissue were collected by laser microdissection from thin sections across spatially separated levels of ten high-grade serous ovarian carcinomas (HGSOC) and analyzed by mass spectrometry. Unsupervised analyses of protein abundance data revealed independent clustering of enriched stroma and enriched tumor epithelium, with whole tumor tissue clustering driven by overall tumor “purity”. Comparing these data to previously defined prognostic HGSOC molecular subtypes revealed protein expression from tumor epithelium correlated with the differentiated subtype, whereas stromal proteins correlated with the mesenchymal subtype. Protein abundance in tumor epithelium and stroma exhibited decreased correlation in samples collected just hundreds of microns apart. These data reveal substantial tumor microenvironment protein heterogeneity that directly bears on prognostic signatures, biomarker discovery, and cancer pathophysiology, and underscore the need to enrich cellular subpopulations for expression profiling.
Project description:Since a detailed inventory of endothelial cell (EC) heterogeneity in breast cancer (BC) is lacking, we perform single cell RNA-sequencing of 26,515 cells (including 8,433 ECs) from 9 BC patients and compare them to published EC taxonomies from lung tumors. Angiogenic ECs are phenotypically similar, while other EC subtypes are different. Predictive interactome analysis reveals known but also previously unreported receptor-ligand interactions between ECs and immune cells, suggesting an involvement of breast EC subtypes in immune responses. We also identify a capillary EC subtype (LIPEC (Lipid Processing EC)), which expresses genes involved in lipid processing that are regulated by PPAR-gamma and is more abundant in peri-tumoral breast tissue. Retrospective analysis of 4,648 BC patients reveals that treatment with metformin (an indirect PPAR-gamma agonist) provides long-lasting clinical benefit and is positively associated with LIPEC abundance. Our findings warrant further exploration of this LIPEC/PPAR-gamma link for BC treatment.
Project description:Estrogens play an important role in breast cancer (BC) development and progression, where the two isoforms of the estrogen receptor (ER? and ER?) are generally co-expressed and mediate the effects of these hormones in cancer cells. ER? has been suggested to exert an antagonist role toward the oncogenic activities of ER?, and for this reason it is considered an oncosuppressor. As clinical evidence regarding a prognostic role for this receptor subtype in hormone-responsive BC is still limited and conflicting, more knowledge is required on the biological functions of ER? in cancer cells. We described previously the ER? and ER? interactomes of BC cells, identifying specific and distinct patterns of protein interactions for the two receptors. In particular, we identified factors involved in mRNA splicing and maturation as important components of both ER? and ER? pathways. Guided by these findings, we investigated here in depth the differences in the early transcriptional events and RNA splicing patterns induced in ER? vs ER?+ER? cells, by expressing ER? in ER?+ human BC MCF-7 cells. High-throughput mRNA sequencing was then performed in both cell lines after stimulation with 17b-estradiol, and the results obtained were compared. We investigated here in depth the differences in the early transcriptional events and RNA splicing patterns induced in ER? vs ER?+ER? cells, by expressing ER? in ER?+ human BC MCF-7 cells. High-throughput mRNA sequencing was then performed in both cell lines after stimulation with 17b-estradiol, and the results obtained were compared.
Project description:Clinical heterogeneity of esrtrogen receptor-negative, progesterone receptor-negative [ER(-)/PR(-)] breast cancer (BC) suggests biological heterogeneity. We performed gene expression analysis of primary BCs and BC cell lines to identify the underlying biology of ER(-)/PR(-) disease, define subsets, and identify potential therapeutic targets. geral-00143 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133A Organism: Homo sapiens (ncbitax) Material Types: cRNA, cell, OrganismPart, whole_organism, total_RNA Disease States: breast carcinomas