Throughout their development, tumors are challenged by the immune system, and they acquire features to evade its surveillance. A systematic view of these traits, which shed light on how tumors ...respond to immunotherapies, is still lacking.
Here, we computed the relative abundance of an array of immune cell populations to measure the immune infiltration pattern of 9,174 tumors of 29 solid cancers. We then clustered tumors with similar infiltration pattern to define immunophenotypes. Finally, we identified genomic and transcriptomic traits associated to these immunophenotypes across cancer types.
In highly cytotoxic immunophenotypes, we found tumors with low clonal heterogeneity enriched for alterations of genes involved in epigenetic regulation, ubiquitin-mediated proteolysis, antigen presentation, and cell-cell communication, which may drive resistance in combination with the ectopic expression of negative immune checkpoints. Tumors with immunophenotypes of intermediate cytotoxicity are characterized by an upregulation of processes involved in neighboring tissue invasion and remodeling that may foster the recruitment of immunosuppressive cells. Tumors with poorly cytotoxic immunophenotype tend to be of more advanced stages and bear a greater burden of copy number alterations and frequent alterations of cell cycle, hedgehog, β-catenin, and TGFβ pathways, which may cause immune depletion.
We provide a comprehensive landscape of the characteristics of solid tumors that may influence (or be influenced by) the characteristics of their immune infiltrate. These results may help interpret the response of solid tumors to immunotherapies and guide the development of novel drug combination strategies.
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A fundamental goal in cancer research is to understand the mechanisms of cell transformation. This is key to developing more efficient cancer detection methods and therapeutic approaches. One ...milestone towards this objective is the identification of all the genes with mutations capable of driving tumours. Since the 1970s, the list of cancer genes has been growing steadily. Because cancer driver genes are under positive selection in tumorigenesis, their observed patterns of somatic mutations across tumours in a cohort deviate from those expected from neutral mutagenesis. These deviations, which constitute signals of positive selection, may be detected by carefully designed bioinformatics methods, which have become the state of the art in the identification of driver genes. A systematic approach combining several of these signals could lead to a compendium of mutational cancer genes. In this Review, we present the Integrative OncoGenomics (IntOGen) pipeline, an implementation of such an approach to obtain the compendium of mutational cancer drivers. Its application to somatic mutations of more than 28,000 tumours of 66 cancer types reveals 568 cancer genes and points towards their mechanisms of tumorigenesis. The application of this approach to the ever-growing datasets of somatic tumour mutations will support the continuous refinement of our knowledge of the genetic basis of cancer.
Abstract
Chemotherapies may increase mutagenesis of healthy cells and change the selective pressures in tissues, thus influencing their evolution. However, their contributions to the mutation burden ...and clonal expansions of healthy somatic tissues are not clear. Here, exploiting the mutational footprint of some chemotherapies, we explore their influence on the evolution of hematopoietic cells. Cells of Acute Myeloid Leukemia (AML) secondary to treatment with platinum-based drugs show the mutational footprint of these drugs, indicating that non-malignant blood cells receive chemotherapy mutations. No trace of the 5-fluorouracil (5FU) mutational signature is found in AMLs secondary to exposure to 5FU, suggesting that cells establishing the leukemia could be quiescent during treatment. Using the platinum-based mutational signature as a barcode, we determine that the clonal expansion originating the secondary AMLs begins after the start of the cytotoxic treatment. Its absence in clonal hematopoiesis cases is consistent with the start of the clonal expansion predating the exposure to platinum-based drugs.
Some cancer therapies damage DNA and cause mutations in both cancerous and healthy cells. Therapy-induced mutations may underlie some of the long-term and late side effects of treatments, such as ...mental disabilities, organ toxicity and secondary neoplasms. Nevertheless, the burden of mutation contributed by different chemotherapies has not been explored. Here we identify the mutational signatures or footprints of six widely used anticancer therapies across more than 3,500 metastatic tumors originating from different organs. These include previously known and new mutational signatures generated by platinum-based drugs as well as a previously unknown signature of nucleoside metabolic inhibitors. Exploiting these mutational footprints, we estimate the contribution of different treatments to the mutation burden of tumors and their risk of contributing coding and potential driver mutations in the genome. The mutational footprints identified here allow for precise assessment of the mutational risk of different cancer therapies to understand their long-term side effects.
While recent studies have identified higher than anticipated heterogeneity of mutation rate across genomic regions, mutations in exons and introns are assumed to be generated at the same rate. Here ...we find fewer somatic mutations in exons than expected from their sequence content and demonstrate that this is not due to purifying selection. Instead, we show that it is caused by higher mismatch-repair activity in exonic than in intronic regions. Our findings have important implications for understanding of mutational and DNA repair processes and knowledge of the evolution of eukaryotic genes, and they have practical ramifications for the study of evolution of both tumors and species.
An algorithm that uses evolutionary data to predict disease variants makes a case for embracing computational evidence for clinical interpretation of genetic variation.
Mutation rates along the genome are highly variable and influenced by several chromatin features. Here, we addressed how nucleosomes, the most pervasive chromatin structure in eukaryotes, affect the ...generation of mutations. We discovered that within nucleosomes, the somatic mutation rate across several tumor cohorts exhibits a strong 10 base pair (bp) periodicity. This periodic pattern tracks the alternation of the DNA minor groove facing toward and away from the histones. The strength and phase of the mutation rate periodicity are determined by the mutational processes active in tumors. We uncovered similar periodic patterns in the genetic variation among human and Arabidopsis populations, also detectable in their divergence from close species, indicating that the same principles underlie germline and somatic mutation rates. We propose that differential DNA damage and repair processes dependent on the minor groove orientation in nucleosome-bound DNA contribute to the 10-bp periodicity in AT/CG content in eukaryotic genomes.
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•Somatic and germline mutation rates show a 10-bp periodicity in nucleosome-occupied DNA•This periodicity tracks DNA minor groove facing toward and away from the histones•The orientation of the periodicity depends on the mutational processes active in the tissue•This has contributed to the AT/CG 10-bp periodicity in eukaryotic genomes
The pattern of exposure of the minor groove of the DNA wrapped around nucleosomes determines a periodicity of the somatic and germline mutation rate, providing broad insights into the human cancer mutational landscape and eukaryotic genome evolution.
Despite the existence of good catalogues of cancer genes.sup.1,2, identifying the specific mutations of those genes that drive tumorigenesis across tumour types is still a largely unsolved problem. ...As a result, most mutations identified in cancer genes across tumours are of unknown significance to tumorigenesis.sup.3. We propose that the mutations observed in thousands of tumours--natural experiments testing their oncogenic potential replicated across individuals and tissues--can be exploited to solve this problem. From these mutations, features that describe the mechanism of tumorigenesis of each cancer gene and tissue may be computed and used to build machine learning models that encapsulate these mechanisms. Here we demonstrate the feasibility of this solution by building and validating 185 gene-tissue-specific machine learning models that outperform experimental saturation mutagenesis in the identification of driver and passenger mutations. The models and their assessment of each mutation are designed to be interpretable, thus avoiding a black-box prediction device. Using these models, we outline the blueprints of potential driver mutations in cancer genes, and demonstrate the role of mutation probability in shaping the landscape of observed driver mutations. These blueprints will support the interpretation of newly sequenced tumours in patients and the study of the mechanisms of tumorigenesis of cancer genes across tissues.
Abstract
Motivation
Identification of the genomic alterations driving tumorigenesis is one of the main goals in oncogenomics research. Given the evolutionary principles of cancer development, ...computational methods that detect signals of positive selection in the pattern of tumor mutations have been effectively applied in the search for cancer genes. One of these signals is the abnormal clustering of mutations, which has been shown to be complementary to other signals in the detection of driver genes.
Results
We have developed OncodriveCLUSTL, a new sequence-based clustering algorithm to detect significant clustering signals across genomic regions. OncodriveCLUSTL is based on a local background model derived from the simulation of mutations accounting for the composition of tri- or penta-nucleotide context substitutions observed in the cohort under study. Our method can identify known clusters and bona-fide cancer drivers across cohorts of tumor whole-exomes, outperforming the existing OncodriveCLUST algorithm and complementing other methods based on different signals of positive selection. Our results indicate that OncodriveCLUSTL can be applied to the analysis of non-coding genomic elements and non-human mutations data.
Availability and implementation
OncodriveCLUSTL is available as an installable Python 3.5 package. The source code and running examples are freely available at https://bitbucket.org/bbglab/oncodriveclustl under GNU Affero General Public License.
Supplementary information
Supplementary data are available at Bioinformatics online.
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