Drug-resistant cell-states in advanced metastatic breast cancer - evolution, convergence, and metastatic-site immune modulation

The commonplace efforts to study the molecular underpinnings of drug resistance in cancer focus on mutations. While genomics remains indispensable, they cannot capture epigenetic mechanisms, fail to reveal the cellular strategies that underscore the phenotype, and are underpowered when facing the intractable challenge of many low-frequency mutations.

We address this challenge by uncovering evolutionary convergent drug-resistant transcriptional states in Metastatic Breast Cancer (MBC). (1) Using whole-exome sequencing pre and post-progression, we determine evolutionary acquired mutations in clinical-grade MBC and highlight key resistance modalities, including ER and Growth-Factor Receptors (GFR) pathways activated states. (2) Using RNA-seq in perturbed cells and in tumors, we infer mutation-associated transcriptional states and uncover the main characteristics of the GFR-activated state, including ER-reprogramming, predominant MAPK signaling, and stem-like mesenchymal features. (3) Using in-vitro multi-drug functional characterization, we reveal differential responses to clinically relevant drugs and substantiate the functional implications of the convergent drug-resistant states. (4) Studying transcriptional programs in clinical grade samples, we uncover drug-specific differential responses. (5) Leveraging >100k single-cell RNA-seq (scRNA-seq), we underscore the central transcriptional programs in malignant MBC, recapitulating our bulk-cohort results at the single-cell level. (6) Finally, comparing the tumor microenvironment among metastatic sites with scRNA-seq, we reveal a site-specific microenvironment with substantial immune modulation in metastatic liver sites.

Our study represents a generalizable approach to infer malignant states in clinical samples and predict drug response as a step towards precision oncology at the cell-state level.