Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics

Li, Ding; Bailey, Matthew H.; Porta-Pardo, Eduard; Thórsson, Vésteinn; Colaprico, Antonio; Bertrand, Denis; Gibbs, David L.; Weerasinghe, Amila; Huang, Kuan‐lin; Tokheim, Collin; Cortés-Ciriano, Isidro; Jayasinghe, Reyka G.; Chen, Feng; Yu, Lihua; Sun, Sam Q.; Olsen, Catharina; Kim, Jaegil; Taylor, Alison M.; Cherniack, Andrew D.; Akbani, Rehan; Suphavilai, Chayaporn; Nagarajan, Niranjan; Stuart, Joshua M.; Mills, Gordon B.; Wyczalkowski, Matthew A.; Vincent, Benjamin G.; Hutter, Carolyn M.; Zenklusen, Jean C.; Hoadley, Katherine A.; Wendl, Michael C.; Shmulevich, llya; Lazar, Alexander J.; Wheeler, David A.; Getz, Gad

doi:10.1016/j.cell.2018.03.033

Cited by 313 publications

(258 citation statements)

References 101 publications

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“…The Pan-Cancer Atlas provides a panoramic view of the oncogenic processes that contribute to human cancer. It reveals how genetic variants collaborate in cancer progression and explores the influence of mutations on cell signaling and immune cell composition, providing insight to prioritize the development of new immunotherapies 144 .…”

Section: The Pan-cancer Atlas: Germline Pathogenic Variantsmentioning

confidence: 99%

Pharmacogenomics, biomarker network and allele frequencies in colorectal cancer

López‐Cortés

Paz‐y‐Miño

Guerrero

et al. 2019

Preprint

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Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Over the last decades, several studies have shown that tumor-related genomic alterations predict tumor prognosis, drug response and toxicity. These observations have led to the development of a number of precision therapies based on individual genomic profiles. As part of these approaches, pharmacogenomics analyses genomic alterations that may predict an efficient therapeutic response. Studying these mutations as biomarkers for predicting drug response is of a great interest to improve precision medicine. Here we conduct a comprehensive review of the main pharmacogenomics biomarkers and genomic alterations affecting enzyme activity, transporter capacity, channels and receptors, and therefore the new advances in CRC precision medicine to select the best therapeutic strategy in populations worldwide, with a focus on Latin America. mutations in the transforming growth factor-β (TGFβ), WNT-β-catenin, PI3K, EGFR and downstream MAPK pathways induces CRC 11,[13][14][15] .On the other hand, the development of CRC also occurs when chromosomal instability (CIN) occurs, progress due to defects in telomere stability, chromosomal segregation and mutations in TP53 gene 16 . The 15% of early-stage colorectal tumors present mismatch repair-deficient (MMRd) system, triggering hypermutation and microsatellite instability (MSI) 14 . According to Dienstmann et al., the epigenetic profile of tumors with CIN present mutations in APC, KRAS, TP53, SMAD4 and PIK3CA, promoting the formation of the non-hypermutated consensus molecular subtypes (CMSs): CMS2, CMS3 and CMS4 1 .Whereas tumors with MSI harbor mutations in the MSH6, RNF43, ATM, TGFBR2, BRAF and PTEN genes of the hypermutated molecular subtype CMS1 1 . A consensus of molecular subtypesGene expression-based subtyping is widely accepted as a relevant source of disease stratification 17 . Nevertheless, the translational utility is hampered by divergent results that are probably related to differences in algorithms applied to sample preparation methods, gene expression platforms and racial/ethnic disparities 18,19 . Inspection of the published gene expression-based CRC classification revealed an absence of a clear methodological 'gold standard' 8,9,16,[20][21][22][23] . To facilitate clinical translation, the CRC Subtyping Consortium (CRCSC) was formed to assess the core subtype patterns among existing gene expressionbased CRC subtyping algorithms 18,24 .

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Section: The Pan-cancer Atlas: Germline Pathogenic Variantsmentioning

confidence: 99%

Pharmacogenomics, biomarker network and allele frequencies in colorectal cancer

López‐Cortés

Paz‐y‐Miño

Guerrero

et al. 2019

Preprint

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“…We also found clonal indels in NF2 in two tumors (cdRCC and mixRCC), and MET (mixRCC), SMARCB1 (pRCC1) and ROS1 (pRCC2) indels in one tumor each. We found no mutations in TP53, mutated in a high proportion of cases across cancer types 15 , and no mutations in the 5'UTR region of TERT, which has been reported as mutated in a sizeable fraction of ccRCC 10 (Fig.1c and Fig. S2 and Table S5 and Table S6).…”

Section: Mutation Rates Frequency Of Driver Mutations and Germline mentioning

confidence: 51%

The genomic and epigenomic evolutionary history of papillary renal cell carcinomas

Zhu¹,

Poeta

Costantini

et al. 2018

Preprint

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We investigated intratumor heterogeneity and clonal evolution of papillary renal cell carcinoma (pRCC) and rarer kidney cancer subtypes, integrating whole-genome sequencing and DNA methylation data. In 29 tumors, up to 10 samples from the center to the periphery of each tumor, and metastatic samples in 2 cases, enabled phylogenetic analysis of spatial features of clonal expansion, which showed congruent patterns of genomic and epigenomic evolution. In contrast to previous studies of clear cell renal cell carcinoma, in pRCC driver gene mutations and most arm-level somatic copy number alterations (SCNAs) were clonal. These findings suggest that a single biopsy would be sufficient to identify the important genetic drivers and targeting largescale SCNAs may improve pRCC treatment, which is currently poor. While type 1 pRCC displayed near absence of structural variants (SVs), the more aggressive type 2 pRCC and the rarer subtypes had numerous SVs, which should be pursued for prognostic significance. WebsitesPicard tools

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“…Other studies map the mutation landscape during oncogenesis with respect to the context-depending impact of a mutation on different gene regulatory levels, e.g. epigenome and transcriptome [56], or provided a unique clustering of tumor types and subtypes using multi-omic datasets [57]. …”

Section: Proteogenomics Across Populations Of Cancer Patientsmentioning

confidence: 99%

Integration of large-scale multi-omic datasets: A protein-centric view

Rendleman

Choi

Vogel

2018

Current Opinion in Systems Biology

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Innovative mass spectrometry-based proteomics has enabled routine measurements of protein abundance, localization, interactions, and modifications, covering unique aspects of gene expression regulation and function. It is now time to move from isolated analyses of these datasets toward true integration of proteomics with other data types to gain insights from the interactions and interdependencies of biomolecules. When combined with genomic or transcriptomic data, proteomics expands genome annotation to identify variant or missing genes. Dynamic proteomic measurements can move analysis from predominantly concentration-based framework to that of synthesis and degradation of proteins. Proteomic data from thousands of cancer patients can foster identification of novel pathogenic mutations via detection of protein sequence changes that lead to dysregulated pathways in various tumors. Such comprehensive efforts can exploit the synergy arising from large and complex datasets to advance virtually every field of biology.

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Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics

Cited by 313 publications

References 101 publications

Pharmacogenomics, biomarker network and allele frequencies in colorectal cancer

Pharmacogenomics, biomarker network and allele frequencies in colorectal cancer

The genomic and epigenomic evolutionary history of papillary renal cell carcinomas

Integration of large-scale multi-omic datasets: A protein-centric view

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