Developmental-Status-Aware Transcriptional Decomposition Establishes a Cell State Panorama of Human Cancers

Authors

Yikai Luo
Han Liang

Publication Date

10-28-2024

Journal

Genome Medicine

Abstract

Background: Cancer cells evolve under unique functional adaptations that unlock transcriptional programs embedded in adult stem and progenitor-like cells for progression, metastasis, and therapeutic resistance. However, it remains challenging to quantify the stemness-aware cell state of a tumor based on its gene expression profile.

Methods: We develop a developmental-status-aware transcriptional decomposition strategy using single-cell RNA-sequencing-derived tissue-specific fetal and adult cell signatures as anchors. We apply our method to various biological contexts, including developing human organs, adult human tissues, experimentally induced differentiation cultures, and bulk human tumors, to benchmark its performance and to reveal novel biology of entangled developmental signaling in oncogenic processes.

Results: Our strategy successfully captures complex dynamics in developmental tissue bulks, reveals remarkable cellular heterogeneity in adult tissues, and resolves the ambiguity of cell identities in in vitro transformations. Applying it to large patient cohorts of bulk RNA-seq, we identify clinically relevant cell-of-origin patterns and observe that decomposed fetal cell signals significantly increase in tumors versus normal tissues and metastases versus primary tumors. Across cancer types, the inferred fetal-state strength outperforms published stemness indices in predicting patient survival and confers substantially improved predictive power for therapeutic responses.

Conclusions: Our study not only provides a general approach to quantifying developmental-status-aware cell states of bulk samples but also constructs an information-rich, biologically interpretable, cell-state panorama of human cancers, enabling diverse translational applications.

Keywords

Humans, Neoplasms, Gene Expression Regulation, Neoplastic, Single-Cell Analysis, Transcriptome, Neoplastic Stem Cells, Gene Expression Profiling, Transcription, Genetic

DOI

10.1186/s13073-024-01393-6

PMID

39468667

PMCID

PMC11514945

PubMedCentral® Posted Date

10-28-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes