Project description:Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes, while the fourth is a previously undescribed neuroendocrine variant (NEv2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.
Project description:Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC.
Project description:We investigated the gene expression changes in a library of small cell lung carcinoma (SCLC) patient-derived xenograft (PDX) models.
Project description:Triple-negative breast cancer (TNBC) is considered the most aggressive type of breast cancer with limited options for therapy. TNBC is a heterogeneous disease and tumours has been classified into TNBC subtypes using gene expression profiling to distinguish basal-like1 (BL1), basal-like2 (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal/stem-like (MSL), luminal androgen receptor (LAR) and one non-classifiable group (called unstable, UNS). The aim of this study was to verify the clinical relevance of molecular subtyping of TNBCs by expression analysis to improve the individual indication of systemic therapy.
Project description:We thoroughly analyzed the molecular subtyping of Triple-Negative Breast Cancer and its different manifestations, and located T cells and Tumor cells related to the survival of breast cancer through single cell sequencing and space transcriptome according to our score, and finally investigated their relationship with the therapeutic efficacy, providing a scientific basis for the treatment of triple negative breast cancer.
Project description:We thoroughly analyzed the molecular subtyping of Triple-Negative Breast Cancer and its different manifestations, and located T cells and Tumor cells related to the survival of breast cancer through single cell sequencing and space transcriptome according to our score, and finally investigated their relationship with the therapeutic efficacy, providing a scientific basis for the treatment of triple negative breast cancer.