Passenger mutations in 2500 cancer genomes: Overall molecular functional impact and consequences

AbstractThe Pan-cancer Analysis of Whole Genomes (PCAWG) project provides an unprecedented opportunity to comprehensively characterize a vast set of uniformly annotated coding and non-coding mutations present in thousands of cancer genomes. Classical models of cancer progression posit that only a small number of these mutations strongly drive tumor progression and that the remaining ones (termed “putative passengers”) are inconsequential for tumorigenesis. In this study, we leveraged the comprehensive variant data from PCAWG to ascertain the molecular functional impact of each variant. The impact distribution of PCAWG mutations shows that, in addition to high- and low-impact mutations, there is a group of medium-impact putative passengers predicted to influence gene activity. Moreover, the predicted impact relates to the underlying mutational signature: different signatures confer divergent impact, differentially affecting distinct regulatory subsystems and gene categories. We also find that impact varies based on subclonal architecture (i.e., early vs. late mutations) and can be related to patient survival. Finally, we note that insufficient power due to limited cohort sizes precludes identification of weak drivers using standard recurrence-based approaches. To address this, we adapted an additive effects model derived from complex trait studies to show that aggregating the impact of putative passenger variants (i.e. including yet undetected weak drivers) provides significant predictability for cancer phenotypes beyond the PCAWG identified driver mutations (12.5% additive variance). Furthermore, this framework allowed us to estimate the frequency of potential weak driver mutations in the subset of PCAWG samples lacking well-characterized driver alterations.

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Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes

Cancers ◽

10.3390/cancers13102366 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2366

Author(s):

Shayantan Banerjee ◽

Karthik Raman ◽

Balaraman Ravindran

Keyword(s):

Cancer Progression ◽

Nucleotide Sequences ◽

Feature Representation ◽

Estimation Methods ◽

Driver Mutations ◽

Cancer Mutation ◽

Evolutionary Features ◽

Cancer Genomes ◽

Passenger Mutations ◽

Pan Cancer

Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5′ and 3′ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments provided comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural, and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with three other commonly used driver prediction tools (FATHMM (cancer), CONDEL, and MutationTaster) significantly outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of using raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

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Sequence neighborhoods enable reliable prediction of pathogenic mutations in cancer genomes

10.1101/2021.02.09.430460 ◽

2021 ◽

Author(s):

Shayantan Banerjee ◽

Karthik Raman ◽

Balaraman Ravindran

Keyword(s):

Cancer Progression ◽

Nucleotide Sequences ◽

Feature Representation ◽

Estimation Methods ◽

Driver Mutations ◽

Cancer Mutation ◽

Evolutionary Features ◽

Cancer Genomes ◽

Passenger Mutations ◽

Pan Cancer

AbstractIdentifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on utilizing the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5’ and 3’ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments gave comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with two other commonly used driver prediction tools (CONDEL and Mutation Taster) outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of utilizing raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

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Mutational likeliness and entropy help to identify driver mutations and their functional role in cancer

10.1101/354324 ◽

2018 ◽

Author(s):

Giorgio Mattiuz ◽

Salvatore Di Giorgio ◽

Lorenzo Tofani ◽

Antonio Frandi ◽

Francesco Donati ◽

...

Keyword(s):

Cancer Progression ◽

Somatic Mutations ◽

Driver Mutations ◽

Cancer Evolution ◽

Loss Of Function ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Genomes ◽

Passenger Mutations ◽

Mutational Processes

AbstractAlterations in cancer genomes originate from mutational processes taking place throughout oncogenesis and cancer progression. We show that likeliness and entropy are two properties of somatic mutations crucial in cancer evolution, as cancer-driver mutations stand out, with respect to both of these properties, as being distinct from the bulk of passenger mutations. Our analysis can identify novel cancer driver genes and differentiate between gain and loss of function mutations.

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DriverPower: Combined burden and functional impact tests for cancer driver discovery

10.1101/215244 ◽

2017 ◽

Cited By ~ 4

Author(s):

Shimin Shuai ◽

Steven Gallinger ◽

Lincoln Stein ◽

Keyword(s):

Driver Mutations ◽

Functional Interpretation ◽

Functional Impact ◽

Genomic Features ◽

Cancer Driver ◽

Mutational Burden ◽

Mutation Model ◽

Whole Genomes ◽

Cancer Genomes ◽

Pan Cancer

AbstractWe describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify cancer driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1,373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across a variety of tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2,583 cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Group, DriverPower has the highest F1-score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.

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Review of applications of CRISPR-Cas9 gene-editing technology in cancer research

Biological Procedures Online ◽

10.1186/s12575-021-00151-x ◽

2021 ◽

Vol 23 (1) ◽

Author(s):

Ziyi Zhao ◽

Chenxi Li ◽

Fei Tong ◽

Jingkuang Deng ◽

Guofu Huang ◽

...

Keyword(s):

Cancer Research ◽

Cancer Progression ◽

Tumor Suppressors ◽

Genetic Screening ◽

Gene Editing ◽

Causes Of Death ◽

Cancer Models ◽

Cancer Genomes ◽

Passenger Mutations ◽

Cancer Studies

AbstractCharacterized by multiple complex mutations, including activation by oncogenes and inhibition by tumor suppressors, cancer is one of the leading causes of death. Application of CRISPR-Cas9 gene-editing technology in cancer research has aroused great interest, promoting the exploration of the molecular mechanism of cancer progression and development of precise therapy. CRISPR-Cas9 gene-editing technology provides a solid basis for identifying driver and passenger mutations in cancer genomes, which is of great value in genetic screening and for developing cancer models and treatments. This article reviews the current applications of CRISPR-Cas9 gene-editing technology in various cancer studies, the challenges faced, and the existing solutions, highlighting the potential of this technology for cancer treatment.

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A tug-of-war between driver and passenger mutations in cancer and other adaptive processes

10.1101/003053 ◽

2014 ◽

Cited By ~ 1

Author(s):

Christopher Dennis McFarland ◽

Leonid A Mirny ◽

Kirill S Korolev

Keyword(s):

Mutation Rate ◽

Cancer Progression ◽

Evolutionary Dynamics ◽

Cancer Genomics ◽

Treatment Strategies ◽

Driver Mutations ◽

Fitness Advantage ◽

Adaptive Processes ◽

Passenger Mutations ◽

Stochastic Population Model

Cancer progression is an example of a rapid adaptive process where evolving new traits is essential for survival and requires a high mutation rate. Precancerous cells acquire a few key mutations that drive rapid population growth and carcinogenesis. Cancer genomics demonstrates that these few driver mutations occur alongside thousands of random passenger mutationsa natural consequence of cancer's elevated mutation rate. Some passengers can be deleterious to cancer cells, yet have been largely ignored in cancer research. In population genetics, however, the accumulation of mildly deleterious mutations has been shown to cause population meltdown. Here we develop a stochastic population model where beneficial drivers engage in a tug-of-war with frequent mildly deleterious passengers. These passengers present a barrier to cancer progression that is described by a critical population size, below which most lesions fail to progress, and a critical mutation rate, above which cancers meltdown. We find support for the model in cancer age-incidence and cancer genomics data that also allow us to estimate the fitness advantage of drivers and fitness costs of passengers. We identify two regimes of adaptive evolutionary dynamics and use these regimes to rationalize successes and failures of different treatment strategies. We find that a tumors load of deleterious passengers can explain previously paradoxical treatment outcomes and suggest that it could potentially serve as a biomarker of response to mutagenic therapies. The collective deleterious effect of passengers is currently an unexploited therapeutic target. We discuss how their effects might be exacerbated by both current and future therapies.

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

Cell ◽

10.1016/j.cell.2020.01.032 ◽

2020 ◽

Vol 180 (5) ◽

pp. 915-927.e16 ◽

Cited By ~ 13

Author(s):

Sushant Kumar ◽

Jonathan Warrell ◽

Shantao Li ◽

Patrick D. McGillivray ◽

William Meyerson ◽

...

Keyword(s):

Functional Impact ◽

Cancer Genomes ◽

Passenger Mutations

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Clonal approaches to understanding the impact of mutations on hematologic disease development

Blood ◽

10.1182/blood-2018-11-835405 ◽

2019 ◽

Vol 133 (13) ◽

pp. 1436-1445 ◽

Cited By ~ 6

Author(s):

Jyoti Nangalia ◽

Emily Mitchell ◽

Anthony R. Green

Keyword(s):

Somatic Mutations ◽

Clonal Selection ◽

Single Cells ◽

Driver Mutations ◽

Great Promise ◽

Tumor Evolution ◽

Disease Development ◽

Hematopoietic Tissue ◽

The Impact

Abstract Interrogation of hematopoietic tissue at the clonal level has a rich history spanning over 50 years, and has provided critical insights into both normal and malignant hematopoiesis. Characterization of chromosomes identified some of the first genetic links to cancer with the discovery of chromosomal translocations in association with many hematological neoplasms. The unique accessibility of hematopoietic tissue and the ability to clonally expand hematopoietic progenitors in vitro has provided fundamental insights into the cellular hierarchy of normal hematopoiesis, as well as the functional impact of driver mutations in disease. Transplantation assays in murine models have enabled cellular assessment of the functional consequences of somatic mutations in vivo. Most recently, next-generation sequencing–based assays have shown great promise in allowing multi-“omic” characterization of single cells. Here, we review how clonal approaches have advanced our understanding of disease development, focusing on the acquisition of somatic mutations, clonal selection, driver mutation cooperation, and tumor evolution.

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Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning

Scientific Reports ◽

10.1038/s41598-021-93917-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Marleen M. Nieboer ◽

Luan Nguyen ◽

Jeroen de Ridder

Keyword(s):

Multiple Instance Learning ◽

Cancer Diagnostics ◽

Common Mechanism ◽

Open Chromatin ◽

Driver Genes ◽

3D Genome ◽

Whole Genomes ◽

Cancer Genomes ◽

Cancer Types ◽

The Impact

AbstractOver the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. Here, we introduce svMIL2, an improved version of our Multiple Instance Learning-based method to study the effect of somatic non-coding SVs disrupting boundaries of TADs and CTCF loops in 1646 cancer genomes. We demonstrate that svMIL2 predicts pathogenic non-coding SVs with an average AUC of 0.86 across 12 cancer types, and identifies non-coding SVs affecting well-known driver genes. The disruption of active (super) enhancers in open chromatin regions appears to be a common mechanism by which non-coding SVs exert their pathogenicity. Finally, our results reveal that the contribution of pathogenic non-coding SVs as opposed to driver SNVs may highly vary between cancers, with notably high numbers of genes being disrupted by pathogenic non-coding SVs in ovarian and pancreatic cancer. Taken together, our machine learning method offers a potent way to prioritize putatively pathogenic non-coding SVs and leverage non-coding SVs to identify driver genes. Moreover, our analysis of 1646 cancer genomes demonstrates the importance of including non-coding SVs in cancer diagnostics.

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FRI0612-HPR EFFECT OF THERMOFORMABLE ORTHOSES ON FOOT FUNCTION IN RHEUMATOID ARTHRITIS PATIENTS: PRELIMINARY RESULTS FROM AN OPEN CLINICAL TRIAL.

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.6308 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 911.2-912

Author(s):

I. Aachari ◽

H. Rkain ◽

F. Safaa ◽

L. Benzakour ◽

T. Latifa ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Clinical Trial ◽

Small Sample ◽

Activity Limitation ◽

Foot Orthoses ◽

Functional Impact ◽

Pain Disability ◽

Before And After ◽

Foot Function ◽

The Impact

Background:Orthoses and footwear can play an important role in managing foot pathology in patients whose systemic disease is controlled. Foot orthoses are frequently prescribed in clinical practice as an intervention for people with rheumatoid arthritis (RA).Objectives:The aim of our study is to evaluate the impact of thermoformable orthoses on the functional index of the foot (FFI) in patients with rheumatoid arthritis.Methods:We conducted an open clinical trial, having consecutively included 14 patients (85.7% female, average age 54.8 ± 10 years) suffering from rheumatoid arthritis (median progression time of 9 years [5 - 12]). The average DAS28 was 2.7 ± 1.2 and the functional impact objectified by the Health Assessment Questionnaire (HAQ) was on average 0.9 ± 0.7.The median deadline from the start of RA and the onset of the foot problem was 3 years [0 – 7,75]. The foot problem was bilateral in 100% of the cases and inaugural in 85.7% of the cases.We evaluated the functional impact of foot injury for all our patients at baseline and 8 weeks after the use of thermoformable orthoses, based on the FFI (Foot function Index) measuring the impact of foot pathology on function in terms of pain, disability and activity limitation.The comparison of the FFI domains before and after the use of orthoses was carried out using parametric or nonparametric paired tests using The SPSS statistical software.Results:With the use of foot orthoses, FFI values decreased in all subscales (p=0,024) (pain, disability and activity limitation). This reduction was significant for disability (0,011) but not for pain and activity limitation.There were no significant correlations between the global FFI and the progression of RA, the duration of foot damage and the functional impact measured by the HAQ.Table 1. The comparison of the FFI domains before and after the use of orthoses.psignificatif if< 0,05; Test used: Non-parametric test for two linked samples.Conclusion:Foot orthoses were effective as an adjuvant in the management of rheumatoid foot. They significantly reduced disability as measured by the FFI. The absence of factors associated with pain and limitation of activity could possibly be related to the small sample size.Disclosure of Interests:None declared

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