Single-cell expression profiles of melanoma Tumors harbor multiple cell types that are thought to play a role in the development of resistance to drug treatments. Tirosh et al. used single-cell sequencing to investigate the distribution of these differing genetic profiles within melanomas. Many cells harbored heterogeneous genetic programs that reflected two different states of genetic expression, one of which was linked to resistance development. Following drug treatment, the resistance-linked expression state was found at a much higher level. Furthermore, the environment of the melanoma cells affected their gene expression programs. Science, this issue p. 189 Melanoma cells show transcriptional heterogeneity. To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

Intratumor Heterogeneity: The Rosetta Stone of Therapy Resistance

Overcoming Endocrine Resistance in Breast Cancer.

Therapeutic resistance continues to be an indominable foe in our ambition for curative cancer treatment. Recent insights into the molecular determinants of acquired treatment resistance in the clinical and experimental setting have challenged the widely held view of sequential genetic evolution as the primary cause of resistance and brought into sharp focus a range of non-genetic adaptive mechanisms. Notably, the genetic landscape of the tumour and the non-genetic mechanisms used to escape therapy are frequently linked. Remarkably, whereas some oncogenic mutations allow the cancer cells to rapidly adapt their transcriptional and/or metabolic programme to meet and survive the therapeutic pressure, other oncogenic drivers convey an inherent cellular plasticity to the cancer cell enabling lineage switching and/or the evasion of anticancer immunosurveillance. The prevalence and diverse array of non-genetic resistance mechanisms pose a new challenge to the field that requires innovative strategies to monitor and counteract these adaptive processes. In this Perspective we discuss the key principles of non-genetic therapy resistance in cancer. We provide a perspective on the emerging data from clinical studies and sophisticated cancer models that have studied various non-genetic resistance pathways and highlight promising therapeutic avenues that may be used to negate and/or counteract the non-genetic adaptive pathways.

Non-genetic mechanisms of therapeutic resistance in cancer.

Cancer represents an evolutionary process through which growing malignant populations genetically diversify, leading to tumour progression, relapse and resistance to therapy. In addition to genetic diversity, the cell-to-cell variation that fuels evolutionary selection also manifests in cellular states, epigenetic profiles, spatial distributions and interactions with the microenvironment. Therefore, the study of cancer requires the integration of multiple heritable dimensions at the resolution of the single cell - the atomic unit of somatic evolution. In this Review, we discuss emerging analytic and experimental technologies for single-cell multi-omics that enable the capture and integration of multiple data modalities to inform the study of cancer evolution. These data show that cancer results from a complex interplay between genetic and non-genetic determinants of somatic evolution.

Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics

KDM5 histone demethylase activity links cellular transcriptomic heterogeneity to therapeutic resistance

Individuals in a population often have different fitnesses even when they have identical genotypes, but the effect of this variation on the evolution of a population through complicated fitness landscapes is unknown. Here, we investigate how populations with non-genetic fitness variation cross fitness valleys, common barriers to adaptation in rugged fitness landscapes in which a population must pass through a deleterious intermediate to arrive at a final advantageous stage. We develop a stochastic computational model describing the dynamics of an asexually reproducing population crossing a fitness valley, in which individuals of the same evolutionary stage can have variable fitnesses. We find that fitness variation that persists over multiple generations increases the rate of valley crossing through a novel evolutionary mechanism different from previously characterized mechanisms such as stochastic tunneling. By reducing the strength of selection against deleterious intermediates, persistent fitness variation allows for faster adaptation through rugged fitness landscapes.

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Fitness variation in isogenic populations leads to a novel evolutionary mechanism for crossing fitness valleys

The Elston-Stewart peeling algorithm enables estimation of an individual's probability of harboring germline risk alleles based on pedigree data, and serves as the computational backbone of important genetic counseling tools. However, it remains limited to the analysis of risk alleles at a small number of genetic loci because its computing time grows exponentially with the number of loci considered. We propose a novel, approximate version of this algorithm, dubbed the peeling and paring algorithm, which scales polynomially in the number of loci. This allows extending peeling-based models to include many genetic loci. The algorithm creates a trade-off between accuracy and speed, and allows the user to control this trade-off. We provide exact bounds on the approximation error and evaluate it in realistic simulations. Results show that the loss of accuracy due to the approximation is negligible in important applications. This algorithm will improve genetic counseling tools by increasing the number of pathogenic risk alleles that can be addressed. To illustrate we create an extended five genes version of BRCAPRO, a widely used model for estimating the carrier probabilities of BRCA1 and BRCA2 risk alleles and assess its computational properties.

Thomas Madsen

Papers

KDM5 histone demethylase activity links cellular transcriptomic heterogeneity to therapeutic resistance

Fitness variation in isogenic populations leads to a novel evolutionary mechanism for crossing fitness valleys

Efficient computation of the joint probability of multiple inherited risk alleles from pedigree data.