Integrative analysis of mutated genes and mutational processes reveals seven colorectal cancer subtypes

AbstractColorectal cancer (CRC) is one of the leading causes of cancer-related deaths in the world. It has been reported that ∼10%-15% of individuals with colorectal cancer experience a causative mutation in the known susceptibility genes, highlighting the importance of identifying mutations for early detection in high risk individuals. Through extensive sequencing projects such as the International Cancer Genome Consortium (ICGC), a large number of somatic point mutations have been identified that can be used to identify cancer-associated genes, as well as the signature of mutational processes defined by the tri-nucleotide sequence context (motif) of mutated sites. Mutation is the hallmark of cancer genome, and many studies have reported cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes, however, none of these cancer subtyping methods consider these features simultaneously. This highlights the need for a better and more inclusive subtype classification approach to enable biomarker discovery and thus inform drug development for CRC. In this study, we developed a statistical pipeline based on a novel concept ‘gene-motif’, which merges mutated gene information with tri-nucleotide motif of mutated sites, to identify cancer subtypes, in this case CRCs. Our analysis identified for the first time, 3,131 gene-motif combinations that were significantly mutated in 536 ICGC colorectal cancer samples compared to other cancer types, identifying seven CRC subtypes with distinguishable phenotypes and biomarkers. Interestingly, we identified several genes that were mutated in multiple subtypes but with unique sequence contexts. Taken together, our results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers.

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Evaluation of colorectal cancer subtypes and cell lines using deep learning

Life Science Alliance ◽

10.26508/lsa.201900517 ◽

2019 ◽

Vol 2 (6) ◽

pp. e201900517 ◽

Cited By ~ 7

Author(s):

Jonathan Ronen ◽

Sikander Hayat ◽

Altuna Akalin

Keyword(s):

Colorectal Cancer ◽

Deep Learning ◽

Cell Lines ◽

Biomarker Discovery ◽

Early Stage ◽

Point Mutations ◽

Disease Models ◽

Latent Factors ◽

Selection Of

Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cell lines. To maximize the translatability and clinical relevance of in vitro studies, the selection of optimal cancer models is imperative. We have developed a deep learning–based method to measure the similarity between CRC tumors and disease models such as cancer cell lines. Our method efficiently leverages multiomics data sets containing copy number alterations, gene expression, and point mutations and learns latent factors that describe data in lower dimensions. These latent factors represent the patterns that are clinically relevant and explain the variability of molecular profiles across tumors and cell lines. Using these, we propose refined CRC subtypes and provide best-matching cell lines to different subtypes. These findings are relevant to patient stratification and selection of cell lines for early-stage drug discovery pipelines, biomarker discovery, and target identification.

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Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer

Cancers ◽

10.3390/cancers13174376 ◽

2021 ◽

Vol 13 (17) ◽

pp. 4376

Author(s):

Amin Ghareyazi ◽

Amir Mohseni ◽

Hamed Dashti ◽

Amin Beheshti ◽

Abdollah Dehzangi ◽

...

Keyword(s):

Pancreatic Cancer ◽

Signaling Pathways ◽

Molecular Mechanisms ◽

De Novo ◽

Treatment Options ◽

Point Mutations ◽

Growth Factor Receptor ◽

Causative Mutation ◽

Whole Genome Analysis ◽

Cancer Subtypes

It is now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.

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An Investigation of Point Mutations Discovers Novel Genes and Their Corresponding Motifs in Pancreatic Cancer

10.20944/preprints202108.0368.v1 ◽

2021 ◽

Author(s):

Amin Ghareyazi ◽

Amir Mohseni ◽

Hamed Dashti ◽

Abdollah Dehzangi ◽

Hamid R. Rabiee ◽

...

Keyword(s):

Pancreatic Cancer ◽

Signaling Pathways ◽

Molecular Mechanisms ◽

Treatment Options ◽

Point Mutations ◽

Growth Factor Receptor ◽

Causative Mutation ◽

Cancer Subtypes ◽

P53 Signaling ◽

Pancreatic Cancers

It has now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4,211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.

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Identification of Novel Mutations in Colorectal Cancer Patients Using AmpliSeq Comprehensive Cancer Panel

Journal of Personalized Medicine ◽

10.3390/jpm11060535 ◽

2021 ◽

Vol 11 (6) ◽

pp. 535

Author(s):

Bader Almuzzaini ◽

Jahad Alghamdi ◽

Alhanouf Alomani ◽

Saleh AlGhamdi ◽

Abdullah A. Alsharm ◽

...

Keyword(s):

Colorectal Cancer ◽

Personalized Medicine ◽

Cancer Patients ◽

Biomarker Discovery ◽

Local Population ◽

Network Analyses ◽

Functional Perspective ◽

Novel Variants ◽

Colorectal Cancer Patients ◽

Cancer Panel

Biomarker discovery would be an important tool in advancing and utilizing the concept of precision and personalized medicine in the clinic. Discovery of novel variants in local population provides confident targets for developing biomarkers for personalized medicine. We identified the need to generate high-quality sequencing data from local colorectal cancer patients and understand the pattern of occurrence of variants. In this report, we used archived samples from Saudi Arabia and used the AmpliSeq comprehensive cancer panel to identify novel somatic variants. We report a comprehensive analysis of next-generation sequencing results with a coverage of >300X. We identified 466 novel variants which were previously unreported in COSMIC and ICGC databases. We analyzed the genes associated with these variants in terms of their frequency of occurrence, probable pathogenicity, and clinicopathological features. Among pathogenic somatic variants, 174 were identified for the first time in the large intestine. APC, RET, and EGFR genes were most frequently mutated. A higher number of variants were identified in the left colon. Occurrence of variants in ERBB2 was significantly correlated with those of EGFR and ATR genes. Network analyses of the identified genes provide functional perspective of the identified genes and suggest affected pathways and probable biomarker candidates. This report lays the ground work for biomarker discovery and identification of driver gene mutations in local population.

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Exceptional Chemotherapy Response in Metastatic Colorectal Cancer Associated With Hyper-Indel–Hypermutated Cancer Genome and Comutation of POLD1 and MLH1

JCO Precision Oncology ◽

10.1200/po.16.00015 ◽

2017 ◽

pp. 1-12

Author(s):

Manish R. Sharma ◽

James T. Auman ◽

Nirali M. Patel ◽

Juneko E. Grilley-Olson ◽

Xiaobei Zhao ◽

...

Keyword(s):

Colorectal Cancer ◽

Colon Cancer ◽

Somatic Mutation ◽

Mutation Rate ◽

Germ Line ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Response To Chemotherapy ◽

Cancer Genome Atlas ◽

Genome Atlas

Purpose A 73-year-old woman with metastatic colon cancer experienced a complete response to chemotherapy with dose-intensified irinotecan that has been durable for 5 years. We sequenced her tumor and germ line DNA and looked for similar patterns in publicly available genomic data from patients with colorectal cancer. Patients and Methods Tumor DNA was obtained from a biopsy before therapy, and germ line DNA was obtained from blood. Tumor and germline DNA were sequenced using a commercial panel with approximately 250 genes. Whole-genome amplification and exome sequencing were performed for POLE and POLD1. A POLD1 mutation was confirmed by Sanger sequencing. The somatic mutation and clinical annotation data files from the colon (n = 461) and rectal (n = 171) adenocarcinoma data sets were downloaded from The Cancer Genome Atlas data portal and analyzed for patterns of mutations and clinical outcomes in patients with POLE- and/or POLD1-mutated tumors. Results The pattern of alterations included APC biallelic inactivation and microsatellite instability high (MSI-H) phenotype, with somatic inactivation of MLH1 and hypermutation (estimated mutation rate > 200 per megabase). The extremely high mutation rate led us to investigate additional mechanisms for hypermutation, including loss of function of POLE. POLE was unaltered, but a related gene not typically associated with somatic mutation in colon cancer, POLD1, had a somatic mutation c.2171G>A [p.Gly724Glu]. Additionally, we noted that the high mutation rate was largely composed of dinucleotide deletions. A similar pattern of hypermutation (dinucleotide deletions, POLD1 mutations, MSI-H) was found in tumors from The Cancer Genome Atlas. Conclusion POLD1 mutation with associated MSI-H and hyper-indel–hypermutated cancer genome characterizes a previously unrecognized variant of colon cancer that was found in this patient with an exceptional response to chemotherapy.

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