Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

Cancer-mutation network and the number and specificity of driver mutations

Cancer genomics has produced extensive information on cancer-associated genes, but the number and specificity of cancer-driver mutations remains a matter of debate. We constructed a bipartite network in which 7,665 tumors from 30 cancer types are connected via shared mutations in 198 previously identified cancer genes. We show that about 27% of the tumors can be assigned to statistically supported modules, most of which encompass one or two cancer types. The rest of the tumors belong to a diffuse network component suggesting lower gene specificity of driver mutations. Linear regression of the mutational loads in cancer genes was used to estimate the number of drivers required for the onset of different cancers. The mean number of drivers in known cancer genes is approximately two, with a range of one to five. Cancers that are associated with modules had more drivers than those from the diffuse network component, suggesting that unidentified and/or interchangeable drivers exist in the latter.

Clinical and Biologic Significance of MYC Genetic Mutations in De Novo Diffuse Large B-cell Lymphoma

PURPOSE

MYC is a critical driver oncogene in many cancers, and its deregulation in the forms of translocation and overexpression has been implicated in lymphomagenesis and progression of diffuse large B-cell lymphoma (DLBCL). The MYC mutational profile and its roles in DLBCL are unknown. This study aims to determine the spectrum of MYC mutations in a large group of patients with DLBCL, and to evaluate the clinical significance of MYC mutations in patients with DLBCL treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) immunochemotherapy.

EXPERIMENTAL DESIGN

We identified MYC mutations in 750 patients with DLBCL using Sanger sequencing and evaluated the prognostic significance in 602 R-CHOP-treated patients.

RESULTS

The frequency of MYC mutations was 33.3% at the DNA level (mutations in either the coding sequence or the untranslated regions) and 16.1% at the protein level (nonsynonymous mutations). Most of the nonsynonymous mutations correlated with better survival outcomes; in contrast, T58 and F138 mutations (which were associated with MYC rearrangements), as well as several mutations occurred at the 3' untranslated region, correlated with significantly worse survival outcomes. However, these mutations occurred infrequently (only in approximately 2% of DLBCL). A germline SNP encoding the Myc-N11S variant (observed in 6.5% of the study cohort) was associated with significantly better patient survival, and resulted in reduced tumorigenecity in mouse xenografts.

CONCLUSIONS

Various types of MYC gene mutations are present in DLBCL and show different impact on Myc function and clinical outcomes. Unlike MYC gene translocations and overexpression, most MYC gene mutations may not have a role in driving lymphomagenesis. Clin Cancer Res; 22(14); 3593-605. ©2016 AACR.

Why Are Some Driver Mutations Rare?

Understanding why driver mutations that promote cancer are sometimes rare is important for precision medicine since it would help in their identification. Driver mutations are largely discovered through their frequencies. Thus, rare mutations often escape detection. Unlike high-frequency drivers, low-frequency drivers can be tissue specific; rare drivers have extremely low frequencies. Here, we discuss rare drivers and strategies to discover them. We suggest that allosteric driver mutations shift the protein ensemble from the inactive to the active state. Rare allosteric drivers are statistically rare since, to switch the protein functional state, they cooperate with additional mutations, and these are not considered in the patient cancer-specific protein sequence analysis. A complete landscape of mutations that drive cancer will reveal tumor-specific therapeutic vulnerabilities.

Next-Generation Sequencing and Result Interpretation in Clinical Oncology: Challenges of Personalized Cancer Therapy

The tools of next-generation sequencing (NGS) technology, such as targeted sequencing of candidate cancer genes and whole-exome and -genome sequencing, coupled with encouraging clinical results based on the use of targeted therapeutics and biomarker-guided clinical trials, are fueling further technological advancements of NGS technology. However, NGS data interpretation is associated with challenges that must be overcome to promote the techniques' effective integration into clinical oncology practice. Specifically, sequencing of a patient's tumor often yields 30-65 somatic variants, but most of these variants are "passenger" mutations that are phenotypically neutral and thus not targetable. Therefore, NGS data must be interpreted by multidisciplinary decision-support teams to determine mutation actionability and identify potential "drivers," so that the treating physician can prioritize what clinical decisions can be pursued in order to provide cancer therapy that is personalized to the patient and his or her unique genome.

In silico learning of tumor evolution through mutational time series

Cancer is caused by the effects of somatic mutations known as drivers. Although a number of major cancer drivers have been identified, it is suspected that many more comparatively rare and conditional drivers exist, and the interactions between different cancer-associated mutations that might be relevant for tumor progression are not well understood. We applied an advanced neural network approach to learn the sequence of mutations and the mutational burden in colon and lung cancers and to identify mutations that are associated with individual drivers. A significant ordering of driver mutations is demonstrated, and numerous, previously undetected conditional drivers are identified. These findings broaden the existing understanding of the mechanisms of tumor progression and have implications for therapeutic strategies.

Cancer arises through the accumulation of somatic mutations over time. Understanding the sequence of mutation occurrence during cancer progression can assist early and accurate diagnosis and improve clinical decision-making. Here we employ long short-term memory (LSTM) networks, a class of recurrent neural network, to learn the evolution of a tumor through an ordered sequence of mutations. We demonstrate the capacity of LSTMs to learn complex dynamics of the mutational time series governing tumor progression, allowing accurate prediction of the mutational burden and the occurrence of mutations in the sequence. Using the probabilities learned by the LSTM, we simulate mutational data and show that the simulation results are statistically indistinguishable from the empirical data. We identify passenger mutations that are significantly associated with established cancer drivers in the sequence and demonstrate that the genes carrying these mutations are substantially enriched in interactions with the corresponding driver genes. Breaking the network into modules consisting of driver genes and their interactors, we show that these interactions are associated with poor patient prognosis, thus likely conferring growth advantage for tumor progression. Thus, application of LSTM provides for prediction of numerous additional conditional drivers and reveals hitherto unknown aspects of cancer evolution.

Distinct developmental trajectories of endometriotic epithelium and stroma: implications for the origins of endometriosis

Endometriosis is a common gynecological disease characterized by the ectopic growth of endometrium-like tissue. Despite the widespread prevalence of endometriosis, its pathogenesis remains poorly understood. A recent study by Noë et al provides evidence that the epithelium and stroma within the same endometriotic lesions follow distinct and independent developmental trajectories. They used droplet digital polymerase chain reaction analysis of laser-captured epithelium-enriched and stroma-enriched endometriosis tissue, and found that all 19 somatic passenger mutations analyzed were enriched exclusively in the epithelial compartment. These findings are consistent with the clonal expansion of epithelial cells, whereas stromal cells may be continuously regenerated or recruited over the course of disease. Further findings of differing allelic frequencies among passenger mutations within the epithelium of the same endometriotic lesions are suggestive of subclonality or the existence of multiple clones in some cases. Overall, the authors' observations of clonally dominant somatic passenger mutations in the epithelium and not the stroma of endometriosis add to the recent description of cancer-associated mutations in such lesions, and provide clues to the pathogenesis of endometriosis. Further studies to determine where and when these mutations occur and whether they can be used to develop the first biologically informed classification system for endometriosis are warranted. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

An unbiased in vitro screen for activating epidermal growth factor receptor mutations

Cancer tissues harbor thousands of mutations, and a given oncogene may be mutated at hundreds of sites, yet only a few of these mutations have been functionally tested. Here, we describe an unbiased platform for the functional characterization of thousands of variants of a single receptor tyrosine kinase (RTK) gene in a single assay. Our in vitro screen for activating mutations (iSCREAM) platform enabled rapid analysis of mutations conferring gain-of-function RTK activity promoting clonal growth. The screening strategy included a somatic model of cancer evolution and utilized a library of 7,216 randomly mutated epidermal growth factor receptor (EGFR) single-nucleotide variants that were tested in murine lymphoid Ba/F3 cells. These cells depend on exogenous interleukin-3 (IL-3) for growth, but this dependence can be compensated by ectopic EGFR overexpression, enabling selection for gain-of-function EGFR mutants. Analysis of the enriched mutants revealed EGFR A702V, a novel activating variant that structurally stabilized the EGFR kinase dimer interface and conferred sensitivity to kinase inhibition by afatinib. As proof of concept for our approach, we recapitulated clinical observations and identified the EGFR L858R as the major enriched EGFR variant. Altogether, iSCREAM enabled robust enrichment of 21 variants from a total of 7,216 EGFR mutations. These findings indicate the power of this screening platform for unbiased identification of activating RTK variants that are enriched under selection pressure in a model of cancer heterogeneity and evolution.

Heritable one-hit events defining cancer prevention?

Over 100 years ago (1902-1914) Theodor Boveri suggested a role for mutations in cancer. Boveri's ideas were derived from the then "just-emerging" chromosome theory of inheritance. While demonstrating chromosomal aberrations as a cause of genetic imbalance, Boveri suggested that possible causes of malignancy may include events such as aneuploidy that are now defined as gene mutations, asserting all the while that malignancy occurs at the cellular level. Indeed, studies to date essentially uniformly show that cancer is a genetic disease.

Validated Bayesian Differentiation of Causative and Passenger Mutations

In many contexts, the problem arises of determining which of many candidate mutations is the most likely to be causative for some phenotype. It is desirable to have a way to evaluate this probability that relies as little as possible on previous knowledge, to avoid bias against discovering new genes or functions. We have isolated mutants with blocked cell cycle progression in Chlamydomonas and determined mutant genome sequences. Due to the intensity of UV mutagenesis required for efficient mutant collection, the mutants contain multiple mutations altering coding sequence. To provide a quantitative estimate of probability that each individual mutation in a given mutant is the causative one, we developed a Bayesian approach. The approach employs four independent indicators: sequence conservation of the mutated coding sequence with Arabidopsis; severity of the mutation relative to Chlamydomonas wild-type based on Blosum62 scores; meiotic mapping information for location of the causative mutation relative to known molecular markers; and, for a subset of mutants, the transcriptional profile of the candidate wild-type genes through the mitotic cell cycle. These indicators are statistically independent, and so can be combined quantitatively into a single probability calculation. We validate this calculation: recently isolated mutations that were not in the training set for developing the indicators, with high calculated probability of causality, are confirmed in every case by additional genetic data to indeed be causative. Analysis of “best reciprocal BLAST” (BRB) relationships among Chlamydomonas and other eukaryotes indicate that the temperature sensitive-lethal (Ts-lethal) mutants that our procedure recovers are highly enriched for fundamental cell-essential functions conserved broadly across plants and other eukaryotes, accounting for the high information content of sequence alignment to Arabidopsis.

The 129 strain-derived passenger mutations in ACKR1-deficient mice alter the expression of PYHIN and Fc-gamma receptor genes

Most genetically modified mice have been produced using 129 strain-derived embryonic stem cells. Despite ample backcrosses with other strains, these may retain characteristics for 129 passenger mutations, leading to confounding phenotypes unrelated to targeted genes. Here we show that widely used Ackr1-/-129ES mice have approximately 6 Mb of the 129-derived genome retained adjacently to the Ackr1 locus on chromosome 1, including several characteristic polymorphisms. These most notably affect the expression of PYHIN and Fc-gamma receptor genes in myeloid cells, resulting in the overproduction of IL-1β by activated macrophages and the loss of Fc-gamma receptors on myeloid progenitor cells. Therefore, caution is warranted when interpreting Ackr1-/-129ES mouse phenotypes as being solely due to the ACKR1 deficiency. Our findings call for a careful reevaluation of data from previous studies using Ackr1-/-129ES mice and underscore the limitations and pitfalls inherent to mouse models produced using traditional genetic engineering techniques involving 129 embryonic stem cells.