Knowledge of the exact distribution of meiotic crossovers (COs) and gene conversions (GCs) is essential for understanding many aspects of population genetics and evolution, from haplotype structure and long-distance genetic linkage to the generation of new allelic variants of genes. To this end, we resequenced the four products of 13 meiotic tetrads along with 10 doubled haploids derived from Arabidopsis thaliana hybrids. GC detection through short reads has previously been confounded by genomic rearrangements. Rigid filtering for misaligned reads allowed GC identification at high accuracy and revealed an ∼80-kb transposition, which undergoes copy-number changes mediated by meiotic recombination. Non-crossover associated GCs were extremely rare most likely due to their short average length of ∼25–50 bp, which is significantly shorter than the length of CO-associated GCs. Overall, recombination preferentially targeted non-methylated nucleosome-free regions at gene promoters, which showed significant enrichment of two sequence motifs.DOI: http://dx.doi.org/10.7554/eLife.01426.001

Resequencing or reference-based assemblies reveal large parts of the small-scale sequence variation. However, they typically fail to separate such local variation into colinear and rearranged variation, because they usually do not recover the complement of large-scale rearrangements, including transpositions and inversions. Besides the availability of hundreds of genomes of diverse Arabidopsis thaliana accessions, there is so far only one full-length assembled genome: the reference sequence. We have assembled 117 Mb of the A. thaliana Landsberg erecta (Ler) genome into five chromosome-equivalent sequences using a combination of short Illumina reads, long PacBio reads, and linkage information. Whole-genome comparison against the reference sequence revealed 564 transpositions and 47 inversions comprising ∼3.6 Mb, in addition to 4.1 Mb of nonreference sequence, mostly originating from duplications. Although rearranged regions are not different in local divergence from colinear regions, they are drastically depleted for meiotic recombination in heterozygotes. Using a 1.2-Mb inversion as an example, we show that such rearrangement-mediated reduction of meiotic recombination can lead to genetically isolated haplotypes in the worldwide population of A. thaliana. Moreover, we found 105 single-copy genes, which were only present in the reference sequence or the Ler assembly, and 334 single-copy orthologs, which showed an additional copy in only one of the genomes. To our knowledge, this work gives first insights into the degree and type of variation, which will be revealed once complete assemblies will replace resequencing or other reference-dependent methods.

The vast majority of cancer next-generation sequencing data consist of bulk samples composed of mixtures of cancer and normal cells. To study tumor evolution, subclonal reconstruction approaches based on machine learning are used to separate subpopulation of cancer cells and reconstruct their ancestral relationships. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in subclonal reconstruction if tumor evolution is not accounted for, and that those errors increase when multiple samples are taken from the same tumor. To address this issue, we present a novel approach for model-based subclonal reconstruction that combines data-driven machine learning with evolutionary theory. Using public, synthetic and newly generated data, we show the method is more robust and accurate than current techniques in both single-sample and multi-region sequencing data. With careful data curation and interpretation, we show how the method allows minimizing the confounding factors that affect non-evolutionary methods, leading to a more accurate recovery of the evolutionary history of human tumors..

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BackgroundNatural selection shapes cancer genomes. Previous studies used signatures of positive selection to identify genes driving malignant transformation. However, the contribution of negative selection against somatic mutations that affect essential tumor functions or specific domains remains a controversial topic.ResultsHere, we analyze 7546 individual exomes from 26 tumor types from TCGA data to explore the portion of the cancer exome under negative selection. Although we find most of the genes neutrally evolving in a pan-cancer framework, we identify essential cancer genes and immune-exposed protein regions under significant negative selection. Moreover, our simulations suggest that the amount of negative selection is underestimated. We therefore choose an empirical approach to identify genes, functions, and protein regions under negative selection. We find that expression and mutation status of negatively selected genes is indicative of patient survival. Processes that are most strongly conserved are those that play fundamental cellular roles such as protein synthesis, glucose metabolism, and molecular transport. Intriguingly, we observe strong signals of selection in the immunopeptidome and proteins controlling peptide exposition, highlighting the importance of immune surveillance evasion. Additionally, tumor type-specific immune activity correlates with the strength of negative selection on human epitopes.ConclusionsIn summary, our results show that negative selection is a hallmark of cell essentiality and immune response in cancer. The functional domains identified could be exploited therapeutically, ultimately allowing for the development of novel cancer treatments.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1434-0) contains supplementary material, which is available to authorized users.

Sézary syndrome is a leukemic form of cutaneous T-cell lymphoma with an aggressive clinical course. The genetic etiology of the disease is poorly understood, with chromosomal abnormalities and mutations in some genes being involved in the disease. The goal of our study was to understand the genetic basis of the disease by looking for driver gene mutations and fusion genes in 15 erythrodermic patients with circulating Sézary cells, 14 of them fulfilling the diagnostic criteria of Sézary syndrome. We have discovered genes that could be involved in the pathogenesis of Sézary syndrome. Some of the genes that are affected by somatic point mutations include ITPR1, ITPR2, DSC1, RIPK2, IL6, and RAG2, with some of them mutated in more than one patient. We observed several somatic copy number variations shared between patients, including deletions and duplications of large segments of chromosome 17. Genes with potential function in the T-cell receptor signaling pathway and tumorigenesis were disrupted in Sézary syndrome patients, for example, CBLB, RASA2, BCL7C, RAMP3, TBRG4, and DAD1. Furthermore, we discovered several fusion events of interest involving RASA2, NFKB2, BCR, FASN, ZEB1, TYK2, and SGMS1. Our work has implications for the development of potential therapeutic approaches for this aggressive disease.

Cancers accumulate mutations that lead to neoantigens, novel peptides that elicit an immune response, and consequently undergo evolutionary selection. Here we establish how the clonal structure of neoantigens in a growing cancer is shaped by negative selection, by constructing a mathematical model of neoantigen evolution. The model predicts that, without immune escape, tumour neoantigens are either clonal or absent from large subclones, and hyper-mutated tumours can only establish following the evolution of immune evasion. Strong negative selection on neoantigens leads to an increased number of neutrally-evolving tumours, as a consequence of selective pressure for immune escape. The clone size distribution under negative selection is effectivelyneutral, and becomes more neutral-like under stronger negative selection. These results are consistent with the analysis of neoantigen clone sizes and immune escape in exome and RNA sequencing data from colon, stomach and endometrial cancers. 4 RESULTS Mathematical model of tumour growth predicts distinct antigen-hot and -cold tumoursWe created a mathematical model of neoantigen evolution during tumour growth, based on a stochastic branching process (Figure 1a and Methods). At each step, tumour cells of lineage i produced two surviving offspring at birth rate b=1 per unit time or died with death rate determined by the strength of negative selection against the cumulative antigenicity of neoantigens in the lineage. Neoantigens accumulated at rate µ per cell per division, and had antigenicity s drawn from a pre-specified distribution. s can be interpreted as the effectiveness of immune predation against an antigen: s=0 indicates no selection pressure (neutral evolution), and s<0 strong negative selection (following ref 34 ). Tumour growth was simulated until the tumour reached a predefined population size (approximating a clinically detectable size) or until a sufficiently long time elapsed without tumour establishment (corresponding to no cancer formation within a person's lifetime).We first examined the temporal neoantigen burden in simulated tumours. We defined the 'antigen score' of a tumour as the proportion of tumour cells carrying cumulative antigenicity ≥ ! . Tumours simulated with identical parameters separated into two distinct groups: 'antigen-hot' and 'antigen-cold'. Antigen-hot tumours had an antigen score close to 1, corresponding to every tumour cell in the population being highly antigenic, whereas in antigen-cold tumours the majority of cells lacked immunogenic mutations (Figure 1b&c). The proportion of antigen-hot tumours depended on the selection strength (Figure S1a): increased negative selection for neoantigens decreased the probability of observing antigen-hot tumours. In antigen-cold tumours, the proportion of neoantigen-carrying cells also decreased inversely with negative selection.

Background Immunotherapy with immune checkpoint inhibitors (ICIs) is highly effective in microsatellite instability–high (MSI‐H) metastatic colorectal cancer (mCRC); however, specific predictive biomarkers are lacking. Patients and Methods Data and samples from 85 patients with MSI‐H mCRC treated with ICIs were gathered. Tumor infiltrating lymphocytes (TILs) and tumor mutational burden (TMB) were analyzed in an exploratory cohort of “super” responders and “clearly” refractory patients; TILs were then evaluated in the whole cohort of patients. Primary objectives were the correlation between the number of TILs and TMB and their role as biomarkers of ICI efficacy. Main endpoints included response rate (RR), progression‐free survival (PFS), and overall survival (OS). Results In the exploratory cohort, an increasing number of TILs correlated to higher TMB (Pearson's test, p = .0429). In the whole cohort, median number of TILs was 3.6 in responders compared with 1.8 in nonresponders (Mann‐Whitney test, p = .0448). RR was 70.6% in patients with high number of TILs (TILs‐H) compared with 42.9% in patients with low number of TILs (odds ratio = 3.20, p = .0291). Survival outcomes differed significantly in favor of TILs‐H (PFS: hazard ratio [HR] = 0.42, p = .0278; OS: HR = 0.41, p = .0463). Conclusion A significant correlation between higher TMB and increased number of TILs was shown. A significantly higher activity and better PFS and OS with ICI in MSI‐H mCRC were reported in cases with high number of TILs, thus supporting further studies of TIL count as predictive biomarker of ICI efficacy. Implications for Practice Microsatellite instability is the result of mismatch repair protein deficiency, caused by germline mutations or somatic modifications in mismatch repair genes. In metastatic colorectal cancer (mCRC), immunotherapy (with immune checkpoint inhibitors [ICIs]) demonstrated remarkable clinical benefit in microsatellite instability–high (MSI‐H) patients. ICI primary resistance has been observed in approximately 25% of patients with MSI‐H mCRC, underlining the need for predictive biomarkers. In this study, tumor mutational burden (TMB) and tumor infiltrating lymphocyte (TIL) analyses were performed in an exploratory cohort of patients with MSI‐H mCRC treated with ICIs, demonstrating a significant correlation between higher TMB and increased number of TILs. Results also demonstrated a significant correlation between high number of TILs and clinical responses and survival benefit in a large data set of patients with MSI‐H mCRC treated with ICI. TMB and TILs could represent predictive biomarkers of ICI efficacy in MSI‐H mCRC and should be incorporated in future trials testing checkpoint inhibitors in colorectal cancer.