ConsensusDriver improves upon individual algorithms for predicting driver alterations in different cancer types and individual patients – a toolbox for precision oncology

Bertrand, Denis; Drissler, Sibyl; Chia, Burton K.; Koh, Jia Yu; Li, Chenhao; Suphavilai, Chayaporn; Tan, Iain Beehuat; Nagarajan, Niranjan

doi:10.1101/127985

Cited by 3 publications

(5 citation statements)

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“…[58] MAF files were parsed into individual sample files and converted to vcf files using MAF2VCF perl script (https://github.com/mskcc/vcf2maf) and passed through the ConsensusDriver Algorithm using colon adenocarcinoma and rectum adenocarcinoma cancer subtype options as described by Bertrand et al[14] The resulting genes were combined into a list. Known false positive driver mutations, as previously identified by Bertrand et al[14] were removed from our analysis. Frequencies of the identified genes were compared among each tumor location in each data set.…”

Section: Methodsmentioning

confidence: 99%

“…Consensus Driver is a relatively new driver prediction algorithm that integrates previous driver prediction algorithms (fathmm, CHASM, OncoIMPACT, DriverNet, MutSigCV, OncodriveFM) and significantly improves the quality of predictions and discovery of novel significantly mutated genes. [58] MAF files were parsed into individual sample files and converted to vcf files using MAF2VCF perl script (https://github.com/mskcc/vcf2maf) and passed through the ConsensusDriver Algorithm using colon adenocarcinoma and rectum adenocarcinoma cancer subtype options as described by Bertrand et al[14] The resulting genes were combined into a list. Known false positive driver mutations, as previously identified by Bertrand et al[14] were removed from our analysis.…”

Section: Methodsmentioning

confidence: 99%

“…[13] Despite the many recent molecular colorectal cancer studies, to our knowledge none have specifically explored ensemble gene level clonal evolution differences and proteomic differences among RCC, LCC and rectal cancers. We employed the ConsensusDriver algorithm[14] that takes advantage of combining multiple previously described algorithms to increase driver mutation discovery. We also examined amino acid hotspots, DNA copy number, RNA, miRNA and protein expressions in these various tumors.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Proteogenomic Analysis of Right-sided Colon Cancer, Left-sided Colon Cancer and Rectal Cancers Reveal Distinct Mutational Profiles

Imperial

Ahmed

Toor

et al. 2018

Preprint

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To understand the molecular differences between right-sided colon cancer (RCC), left-sided colon cancer (LCC) and rectal cancer, we analyzed colorectal tumors at the DNA, RNA, miRNA and protein levels using previously sequenced data from The Cancer Genome Atlas and Memorial Sloan Kettering Cancer Center. Clonal evolution analysis identified the same tumor-initiating events involving APC, KRAS and TP53 genes in RCC, LCC and rectal cancers. However, the individual role-played by each event, their order in tumor dynamics and selection of downstream mutations were distinct in all three anatomical locations, with some similarities noted between LCC and rectal cancer. We found a potentially targetable alteration APC R1450* specific to RCC that has not been previously described. Differential gene expression analysis revealed multiple genes within the homeobox, G-protein coupled receptor binding and transcription regulation families were dysregulated in RCC, LCC, and rectal cancers and may have a pathological role in these cancers. Further, using a novel in silico proteomic analytic tool developed by our research group, we found distinct central or hub proteins with unique interactomes in each location. Protein expression signatures were not necessarily concordant with the tumor profiles obtained at the DNA and RNA levels, underscoring the relevance of post-transcriptional events in defining the biology of these cancers beyond molecular changes at the DNA and/or RNA level. Ultimately, not only tumor location and the respective genomic profile but also protein-protein interactions will need to be taken into account to improve treatment outcomes of colorectal cancers. Further studies that take into account the alterations found in this study may help in developing more tailored, and perhaps more effective, treatment strategies.Author summaryPatients with right-sided colon cancer (RCC) has a worse prognosis compared to left-sided colon cancer (LCC). Recent data has also shown that wild-type RAS metastatic RCC’s have poor outcomes when treated with the combination of chemotherapy and anti-EGFR therapy compared to LCC and rectal cancers. Therefore, There is an urgent unmet need to understand the molecular differences between RCC, LCC, and rectal cancers. In this study, we demonstrate clonal evolutionary trajectory and the order of mutations in RCC, LCC, and rectal cancers are distinct with some similarities between LCC and rectal cancers. The order of the mutations that lead to the acquisition of crucial driver alterations may have prognostic and therapeutic implications. We also discovered a novel targetable alteration, APC R1450* to be significantly enriched in early, late and metastatic RCC but not in LCC and rectal cancers. Amazingly, proteomic signatures were discordant with DNA and RNA levels. These distinct differences in DNA, RNA and post-transcriptional events may contribute to their unique clinicopathological features.Conflict of Interest StatementAshiq Masood Advisory board and speaker Bureau Bristol-Myers Squibb and Boehringer IngelheimJanakiraman Subramanian Advisory board - Astra Zeneca, Pfizer, Boehringer Ingelheim, Alexion, Paradigm, Bristol-Myers Squibb Speakers Bureau - Astra Zeneca, Boehringer Ingelheim, Lilly Research Support - Biocept and ParadigmArif Hussain Advisory board – Novartis, Bayer, Astra Zeneca Consultant – Bristol-Myers-Squibb All other authors have no conflict of interest.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Proteogenomic Analysis of Right-sided Colon Cancer, Left-sided Colon Cancer and Rectal Cancers Reveal Distinct Mutational Profiles

Imperial

Ahmed

Toor

et al. 2018

Preprint

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“…As the number of patients/tumors with molecular data increases across cancer types, enabled particularly by large-scale studies such as TCGA and ICGC (Weinstein et al, 2013;Zhang et al, 2011), the identification of cancer driver genes has benefited greatly (Cerami et al, 2012;Zhang et al, 2011;Weinstein et al, 2013;Bertrand et al, 2017). However, these data sources typically lack drug response information and are therefore not suitable for identifying drug response biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Predicting Cancer Drug Response using a Recommender System

2018

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Section: Introductionmentioning

confidence: 99%

Predicting Cancer Drug Response Using a Recommender System

Suphavilai

Bertrand

Nagarajan

2017

Preprint

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Motivation: As we move towards an era of precision medicine, the ability to predict patient-specific drug responses in cancer based on molecular information such as gene expression data represents both an opportunity and a challenge. In particular, methods are needed that can accommodate the high-dimensionality of data to learn interpretable models capturing drug response mechanisms, as well as providing robust predictions across datasets. Results:We propose a method based on ideas from "recommender systems" (CaDRReS) that predicts cancer drug responses for unseen cell-lines/patients based on learning projections for drugs and celllines into a latent "pharmacogenomic" space. Comparisons with other proposed approaches for this problem based on large public datasets (CCLE, GDSC) shows that CaDRReS provides consistently good models and robust predictions even across unseen patient-derived cell-line datasets. Analysis of the pharmacogenomic spaces inferred by CaDRReS also suggests that they can be used to understand drug mechanisms, identify cellular subtypes, and further characterize drug-pathway associations.Availability: Source code and datasets are available at https://github.

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ConsensusDriver improves upon individual algorithms for predicting driver alterations in different cancer types and individual patients – a toolbox for precision oncology

Abstract: Background: In recent years, several large-scale cancer genomics studies have helped generate

Cited by 3 publications

References 67 publications

Comparative Proteogenomic Analysis of Right-sided Colon Cancer, Left-sided Colon Cancer and Rectal Cancers Reveal Distinct Mutational Profiles

Comparative Proteogenomic Analysis of Right-sided Colon Cancer, Left-sided Colon Cancer and Rectal Cancers Reveal Distinct Mutational Profiles

Predicting Cancer Drug Response using a Recommender System

Predicting Cancer Drug Response Using a Recommender System

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