Patruno, L. | Search bioRxiv

bioRxiv2023

Epigenetic heritability of cell plasticity drives cancer drug resistance through one-to-many genotype to phenotype mapping

Cancer drug resistance is multi-factorial, driven by heritable (epi)genetic changes but also phenotypic plasticity. Here we dissect it by perturbing colorectal cancer patient-derived organoids longitudinally with drugs in sequence. Combining longitudinal tracking, single cell omics, evolutionary modelling, and machine leaning, we found that different targeted drugs select for distinct subclones, supporting rationally designed drug sequences. The cellular memory was encoded as a heritable epigenetic configuration, from which multiple transcriptional programmes could run, supporting a one-to-many (epi)genotype-to- phenotype map that explains how clonal expansions and plasticity manifest together. This may ensure drug resistance subclones can exhibit distinct phenotypes in changing environments while still preserving the cellular memory encoding for their selective advantage. Chemotherapies resistance was instead entirely driven by plasticity. Inducing further chromosomal instability before drug application changed clonal evolution but not convergent transcriptional programmes. Collectively, our data show how genetic and epigenetic alterations are selected to "permissive epigenome" enabling phenotypic plasticity.

cancer biology↗

bioRxiv2023

A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing

Single-cell RNA and ATAC sequencing technologies allow one to probe expression and chromatin accessibility states as a proxy for cellular phenotypes at the resolution of individual cells. A key challenge of cancer research is to consistently map such states on genetic clones, within an evolutionary framework. To this end we introduce CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles generated from independent or multimodal assays on the latent space of copy numbers clones. CONGAS+ can detect tumour subclones associated with aneuploidy by clustering cells with the same ploidy profile. The framework is implemented in a probabilistic language that can scale to analyse thousands of cells thanks to GPU deployment. Our tool exhibits robust performance on simulations and real data, highlighting the advantage of detecting aneuploidy from two distinct molecules as opposed to other single-molecule models, and also leveraging real multi-omic data. In the application to prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ did retrieve complex subclonal architectures while providing a coherent mapping among ATAC and RNA, facilitating the study of genotype-phenotype mapping, and their relation to tumour aneuploidy. Author summaryAneuploidy is a condition caused by copy number alterations (CNAs), which brings cells to acquire or lose chromosomes. In the context of cancer progression and treatment response, aneuploidy is a key factor driving cancer clonal dynamics, and measuring CNAs from modern sequencing assays is therefore important. In this framing, we approach this problem from new single-cell assays that measure both chromatin accessibility and RNA transcripts. We model the relation between single-cell data and CNAs and, thanks to a sophisticated Bayesian model, we are capable of determining tumour clones from clusters of cells with the same copy numbers. Our model works when input cells are sequenced independently for both assays, or even when modern multi-omics protocols are used. By linking aneuploidy to gene expression and chromatin conformation, our new approach provides a novel way to map complex genotypes with phenotype-level information, one of the missing factors to understand the molecular basis of cancer heterogeneity.

bioinformatics↗