SummaryThe genetics of renal cancer is dominated by inactivation of the VHL tumour suppressor gene in clear cell carcinoma (ccRCC), the commonest histological subtype. A recent large-scale screen of ~3500 genes by PCR-based exon re-sequencing identified several new cancer genes in ccRCC including UTX (KDM6A)1, JARID1C (KDM5C) and SETD22. These genes encode enzymes that demethylate (UTX, JARID1C) or methylate (SETD2) key lysine residues of histone H3. Modification of the methylation state of these lysine residues of histone H3 regulates chromatin structure and is implicated in transcriptional control3. However, together these mutations are present in fewer than 15% of ccRCC, suggesting the existence of additional, currently unidentified cancer genes. Here, we have sequenced the protein coding exome in a series of primary ccRCC and report the identification of the SWI/SNF chromatin remodeling complex gene PBRM14 as a second major ccRCC cancer gene, with truncating mutations in 41% (92/227) of cases. These data further elucidate the somatic genetic architecture of ccRCC and emphasize the marked contribution of aberrant chromatin biology.

Background Myelodysplastic syndromes are a diverse and common group of chronic hematologic cancers. The identification of new genetic lesions could facilitate new diagnostic and therapeutic strategies. Methods We used massively parallel sequencing technology to identify somatically acquired point mutations across all protein-coding exons in the genome in 9 patients with low-grade myelodysplasia. Targeted resequencing of the gene encoding RNA splicing factor 3B, subunit 1 (SF3B1), was also performed in a cohort of 2087 patients with myeloid or other cancers. Results We identified 64 point mutations in the 9 patients. Recurrent somatically acquired mutations were identified in SF3B1. Follow-up revealed SF3B1 mutations in 72 of 354 patients (20%) with myelodysplastic syndromes, with particularly high frequency among patients whose disease was characterized by ring sideroblasts (53 of 82 [65%]). The gene was also mutated in 1 to 5% of patients with a variety of other tumor types. The observed mutations were less deleterious than was expected on the basis of chance, suggesting that the mutated protein retains structural integrity with altered function. SF3B1 mutations were associated with down-regulation of key gene networks, including core mitochondrial pathways. Clinically, patients with SF3B1 mutations had fewer cytopenias and longer event-free survival than patients without SF3B1 mutations. Conclusions Mutations in SF3B1 implicate abnormalities of messenger RNA splicing in the pathogenesis of myelodysplastic syndromes. (Funded by the Wellcome Trust and others.)

SummaryThe extensive genetic heterogeneity of cancers can greatly affect therapy success due to the existence of subclonal mutations conferring resistance. However, the characterization of subclones in mixed-cell populations is computationally challenging due to the short length of sequence reads that are generated by current sequencing technologies. Here, we report cloneHD, a probabilistic algorithm for the performance of subclone reconstruction from data generated by high-throughput DNA sequencing: read depth, B-allele counts at germline heterozygous loci, and somatic mutation counts. The algorithm can exploit the added information present in correlated longitudinal or multiregion samples and takes into account correlations along genomes caused by events such as copy-number changes. We apply cloneHD to two case studies: a breast cancer sample and time-resolved samples of chronic lymphocytic leukemia, where we demonstrate that monitoring the response of a patient to therapy regimens is feasible. Our work provides new opportunities for tracking cancer development.

The spectrum of mutations discovered in cancer genomes can be explained by the activity of a few elementary mutational processes. We present a novel probabilistic method, EMu, to infer the mutational signatures of these processes from a collection of sequenced tumors. EMu naturally incorporates the tumor-specific opportunity for different mutation types according to sequence composition. Applying EMu to breast cancer data, we derive detailed maps of the activity of each process, both genome-wide and within specific local regions of the genome. Our work provides new opportunities to study the mutational processes underlying cancer development. EMu is available at http://www.sanger.ac.uk/resources/software/emu/.

Populations can evolve to adapt to external changes. The capacity to evolve and adapt makes successful treatment of infectious diseases and cancer difficult. Indeed, therapy resistance has become a key challenge for global health. Therefore, ideas of how to control evolving populations to overcome this threat are valuable. Here we use the mathematical concepts of stochastic optimal control to study what is needed to control evolving populations. Following established routes to calculate control strategies, we first study how a polymorphism can be maintained in a finite population by adaptively tuning selection. We then introduce a minimal model of drug resistance in a stochastically evolving cancer cell population and compute adaptive therapies. When decisions are in this manner based on monitoring the response of the tumor, this can outperform established therapy paradigms. For both case studies, we demonstrate the importance of high-resolution monitoring of the target population to achieve a given control objective, thus quantifying the intuition that to control, one must monitor.stochastic optimal control | adaptive cancer therapy | drug resistance