scholarly journals Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Lidia Mateo ◽  
Miquel Duran-Frigola ◽  
Albert Gris-Oliver ◽  
Marta Palafox ◽  
Maurizio Scaltriti ◽  
...  
Keyword(s):  
Survival Data ◽  
Large Scale ◽  
Drug Response ◽  
Progression Free Survival ◽  
Driver Gene ◽  
Precision Oncology ◽  
Response Predictors ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer ◽  
Occurrence Patterns

Abstract Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients’ progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.

scholarly journals Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

2019 ◽  
Author(s):  
Lidia Mateo ◽  
Miquel Duran-Frigola ◽  
Albert Gris-Oliver ◽  
Marta Palafox ◽  
Maurizio Scaltriti ◽  
...  
Keyword(s):  
Survival Data ◽  
Large Scale ◽  
Drug Response ◽  
De Novo ◽  
Progression Free Survival ◽  
Driver Gene ◽  
Precision Oncology ◽  
Response Predictors ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer

AbstractIdentification of actionable genomic vulnerabilities is the cornerstone of precision oncology. Based on a large-scale drug screening in patient derived-xenografts, we uncover connections between driver gene alterations, derive Driver Co-Occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug response predictors attained an average balanced accuracy of 58% in a cross-validation setting, which rose to a 66% for the subset of high-confidence predictions. Morevover, we experimentally validated 12 out of 14 de novo predictions in mice. Finally, we adapted our strategy to obtain drug-response models from patients’ progression free survival data. By revealing unexpected links between oncogenic alterations, our strategy can increase the clinical impact of genomic profiling.

scholarly journals Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Shumei Chia ◽  
Ankur Sharma ◽  
Lorna Tu ◽  
Rafael Peres Da Silva ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Drug Response ◽  
Drug Repurposing ◽  
High Accuracy ◽  
Molecular Heterogeneity ◽  
Precision Oncology ◽  
Scale Analysis ◽  
Rna Seq ◽  
Large Scale Analysis

AbstractWhile understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc’s monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc.

scholarly journals Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

2019 ◽  
Vol 116 (44) ◽  
pp. 22020-22029 ◽  
Author(s):  
Aritro Nath ◽  
Eunice Y. T. Lau ◽  
Adam M. Lee ◽  
Paul Geeleher ◽  
William C. S. Cho ◽  
...  
Keyword(s):  
Anticancer Drug ◽  
Noncoding Rna ◽  
Large Scale ◽  
Drug Response ◽  
Cancer Cell Line ◽  
Systematic Evaluation ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Clinical Biomarkers ◽  
Response Predictors

Large-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long noncoding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue lineage, or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that 2 lncRNAs, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-epidermal growth factor receptor (EGFR) drug response.

Drug response prediction system for personalized cancer therapy

2016 ◽  
Vol 69 ◽  
pp. S81-S82
Author(s):  
R. Kurilov ◽  
D. Juraeva ◽  
D. Weese ◽  
T. Klein ◽  
M. Kapushesky ◽  
...  
Keyword(s):  
Cancer Therapy ◽  
Drug Response ◽  
Response Prediction ◽  
Prediction System ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer

scholarly journals “Personalized Cancer Therapy”: A Publicly Available Precision Oncology Resource

2017 ◽  
Vol 77 (21) ◽  
pp. e123-e126 ◽  
Author(s):  
Katherine C. Kurnit ◽  
Ann M. Bailey ◽  
Jia Zeng ◽  
Amber M. Johnson ◽  
Md. Abu Shufean ◽  
...  
Keyword(s):  
Cancer Therapy ◽  
Precision Oncology ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer

Comprehensive molecular profiling of advanced gynecologic malignancies.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e17082-e17082
Author(s):  
Luis Rojas ◽  
Casey B. Williams ◽  
David Starks ◽  
Kirstin Anne Williams ◽  
Brian Leyland-Jones
Keyword(s):  
Large Scale ◽  
Performance Status ◽  
Progression Free Survival ◽  
Median Number ◽  
Tumor Board ◽  
Molecular Profile ◽  
Precision Oncology ◽  
Gynecologic Malignancies ◽  
Development Of Resistance ◽  
Rational Combination

e17082 Background: Precision medicine has been studied in patients with advanced, heavily-treated cancers by administering molecularly targeted monotherapies. Rational combination approaches that are selected based upon molecular analysis, tailored for each patient based upon their past medical history, performance status and unique molecular profile, and curated to prevent the development of resistance represent a possible solution that warrants further exploration. Methods: The primary aim of this proposal, Identifying Molecular Drivers of Cancer (NCT02470715), is to recognize genetic drivers of cancer by performing comprehensive genomic profiling (CGP) and/or proteomic characterization, and further translational research of patient samples. A molecular tumor board discussed results upon receipt and recommended customized combinations. Final decisions of implementation were the choice of the treating physician. Results: CGP was evaluable in 46/64 (72%) advanced gynecologic malignancies (GYN). The remaining 18 are either awaiting therapy or too early for evaluation. Thirty three/45 (73%) patients received combination therapy matched to the CGP. Fourteen/34 (41%) matched patients achieved CR, 12/34 (35%) achieved a PR, 6/34 (18%) achieved SD, and 2/34 (6%) had progression after 3 cycles. Clinical benefit rate was 94%. For ovarian cancer (25 pts), the response rate was 75% and the median progression-free survival (PFS) is 8.5 months and ongoing in 4 patients. Twelve/46 (26%) patients received CGP, but were treated with standard treatments because of insurance denial or inability to get drugs. Two/12 (17%) achieved a CR, 3/12 (25%) achieved SD, and 7/12 (58%) had progression. The PFS for patients that received standard therapies was 1 month. Median number of prior therapies in all patients was 4. Conclusions: Management of advanced gynecologic malignancies with customized combinations of approved agents based on the patient’s tumor CGP was feasible, resulted in superior therapeutic outcomes and improved PFS compared to standard therapies in heavily pretreated controls within this cohort. This precision oncology therapeutic model for gynecologic malignancies deserves prospective large scale evaluation. Clinical trial information: NCT02470715.

scholarly journals Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis

2018 ◽  
Author(s):  
Mukesh Bansal ◽  
Jing He ◽  
Michael Peyton ◽  
Manjunath Kaustagi ◽  
Archana Iyer ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Large Scale ◽  
Drug Response ◽  
De Novo ◽  
Response Prediction ◽  
Precision Oncology ◽  
Protein Protein Interaction ◽  
Pathway Models ◽  
Protein Protein Interaction Networks ◽  
Signaling Interactions

SummarySignaling pathway models are largely based on the compilation of literature data from heterogeneous cellular contexts. Indeed, de novo reconstruction of signaling interactions from large-scale molecular profiling is still lagging, compared to similar efforts in transcriptional and protein-protein interaction networks. To address this challenge, we introduce a novel algorithm for the systematic inference of protein kinase pathways, and applied it to published mass spectrometry-based phosphotyrosine profile data from 250 lung adenocarcinoma (LUAD) samples. The resulting network includes 43 TKs and 415 inferred, LUAD-specific substrates, which were validated at >60% accuracy by SILAC assays, including “novel’ substrates of the EGFR and c-MET TKs, which play a critical oncogenic role in lung cancer. This systematic, data-driven model supported drug response prediction on an individual sample basis, including accurate prediction and validation of synergistic EGFR and c-MET inhibitor activity in cells lacking mutations in either gene, thus contributing to current precision oncology efforts.

scholarly journals Discovering novel long non-coding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

2019 ◽  
Author(s):  
Aritro Nath ◽  
Eunice Y.T. Lau ◽  
Adam M. Lee ◽  
Paul Geeleher ◽  
William C.S. Cho ◽  
...  
Keyword(s):  
Anticancer Drug ◽  
Large Scale ◽  
Drug Response ◽  
Cancer Cell Line ◽  
Systematic Evaluation ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Clinical Biomarkers ◽  
Response Predictors ◽  
Drugs Analysis

AbstractLarge-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long non-coding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue-lineage or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that two novel lncRNA, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-EGFR drug response.

scholarly journals Iterative sure independent ranking and screening for drug response prediction

2020 ◽  
Vol 20 (S8) ◽  
Author(s):  
Biao An ◽  
Qianwen Zhang ◽  
Yun Fang ◽  
Ming Chen ◽  
Yufang Qin
Keyword(s):  
Drug Response ◽  
Cross Validation ◽  
Marginal Utility ◽  
Cancer Cell Line ◽  
Response Prediction ◽  
Omics Data ◽  
Copy Number Alterations ◽  
Utility Measure ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer

Abstract Background Prediction of drug response based on multi-omics data is a crucial task in the research of personalized cancer therapy. Results We proposed an iterative sure independent ranking and screening (ISIRS) scheme to select drug response-associated features and applied it to the Cancer Cell Line Encyclopedia (CCLE) dataset. For each drug in CCLE, we incorporated multi-omics data including copy number alterations, mutation and gene expression and selected up to 50 features using ISIRS. Then a linear regression model based on the selected features was exploited to predict the drug response. Cross validation test shows that our prediction accuracies are higher than existing methods for most drugs. Conclusions Our study indicates that the features selected by the marginal utility measure, which measures the conditional probability of drug responses given the feature, are helpful for drug response prediction.

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