Cancer heterogeneity is defined by normal cellular trade-offs
Weistuch, C.; Murgas, K. A.; Zhu, J.; Norton, L.; Dill, K. A.; Deasy, J. O.; Tannenbaum, A. R.
Show abstract
Cancer transcriptional patterns exhibit both shared and unique features across diverse cancer types, but whether these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that cancer transcriptional diversity mirrors patterns in normal tissues optimized for distinct functional tasks. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2-breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- KLF4 induces Mesenchymal-Epithelial Transition (MET) by suppressing multiple EMT-inducing transcription factors 93%
- HIF-dependent expression of creatine kinase brain isoform (CKB) promotes breast cancer metastasis, whereas cyclocreatine therapy impairs invasion and improves the efficacy of conventional chemotherapies 93%
- Omics integration analyses reveal the early evolution of malignancy in breast cancer 93%
Similar papers in this journal
Similar papers in this journal
- Molecular Profiles of Matched Primary and Metastatic Tumor Samples Support a Linear Evolutionary Model of Breast Cancer 95%
- The FABRIC Cancer Portal: A Ranked Catalogue of Gene Selection in Tumors over the Human Coding Genome 94%
- Division of labor between YAP and TAZ in non-small cell lung cancer 94%
Similar papers in this journal
- Integrative pan-cancer analysis reveals a common architecture of dysregulated transcriptional networks characterized by loss of enhancer methylation 96%
- miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data 94%
- Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.