Single-cell sequencing promises a high-resolution view of genetic heterogeneity and clonal evolution in cancer. However, methods to infer tumor evolution from single-cell sequencing data lag behind methods developed for bulk-sequencing data. Here, we present OncoNEM, a probabilistic method for inferring intra-tumor evolutionary lineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellular subpopulations and infers their genotypes as well as a tree describing their evolutionary relationships. In simulation studies, we assess OncoNEM’s robustness and benchmark its performance against competing methods. Finally, we show its applicability in case studies of muscle-invasive bladder cancer and essential thrombocythemia.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0929-9) contains supplementary material, which is available to authorized users.
Genetic risk for breast cancer is conferred by a combination of multiple variants of small effect. To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms. We created a breast cancer gene regulatory network between transcription factors (TFs) and putative target genes (regulons) and asked whether specific regulons are enriched for genes associated with risk loci via eQTLs. We identified 36 overlapping regulons that were enriched and formed a distinct cluster within the network, suggesting shared biology. The risk-TFs driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to ER+ luminal A/B and to ER− basal-like cancers and to different, luminal epithelial cell populations in the adult mammary gland. Our network approach provides a foundation to reveal the regulatory circuits governing breast cancer, to identify targets for intervention, and is transferable to other disease settings.
Summary The detailed molecular characterization of lethal cancers is a prerequisite to understanding resistance to therapy and escape from cancer immunoediting. We performed extensive multi-platform profiling of multi-regional metastases in autopsies from 10 patients with therapy-resistant breast cancer. The integrated genomic and immune landscapes show that metastases propagate and evolve as communities of clones, reveal their predicted neo-antigen landscapes, and show that they can accumulate HLA loss of heterozygosity (LOH). The data further identify variable tumor microenvironments and reveal, through analyses of T cell receptor repertoires, that adaptive immune responses appear to co-evolve with the metastatic genomes. These findings reveal in fine detail the landscapes of lethal metastatic breast cancer.
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