Dataset Information

OncoLoop: A Network-Based Precision Cancer Medicine Framework.

ABSTRACT: Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide.

Significance

OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. This article is highlighted in the In This Issue feature, p. 247.

SUBMITTER: Vasciaveo A

PROVIDER: S-EPMC9905319 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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OncoLoop: A Network-Based Precision Cancer Medicine Framework.

Vasciaveo Alessandro A Arriaga Juan Martín JM de Almeida Francisca Nunes FN Zou Min M Douglass Eugene F EF Picech Florencia F Shibata Maho M Rodriguez-Calero Antonio A de Brot Simone S Mitrofanova Antonina A Chua Chee Wai CW Karan Charles C Realubit Ronald R Pampou Sergey S Kim Jaime Y JY Afari Stephanie N SN Mukhammadov Timur T Zanella Luca L Corey Eva E Alvarez Mariano J MJ Rubin Mark A MA Shen Michael M MM Califano Andrea A Abate-Shen Cory C

Cancer discovery 20230201 2

Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered m ...[more]

PMID: 36374194

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Project description:PurposeData standards and interoperability are critical for improving care for patients with cancer. Recent efforts by ASCO include the Data Standards and Interoperability Summit in 2016, which led to the Omics and Precision Oncology and Advancing Interoperability workshops. To facilitate improved patient care, several recommendations for data sharing and standardization were made to the community.MethodsTo address these recommendations, we developed SMART Cancer Navigator, a Web application that uses application programming interfaces to gather clinical and genomic data from 11 public knowledge bases ranging from basic to clinical content coverage; three (CIViC, ClinVar, and OncoKB) explicitly linked genomic variants to clinical factors such as prognosis and treatment selection. We illustrated the utility of this application by selecting one of the monthly case studies presented by the ASCO University Molecular Oncology Tumor Board: Ovarian Cancer (BRCA Mutation). We also performed analyses on information from the three clinico-genomic knowledge bases to corroborate previous work and illustrate the state of data sharing among publicly available resources.ResultsSMART Cancer Navigator aggregates and contextualizes data from 11 different knowledge bases and stores user queries in a lightweight Web application that can link into Fast Healthcare Interoperability Resources-enabled electronic health records. Potentially relevant clinical trials and/or approved treatments were identified for three mutations found in a hypothetical patient with advanced ovarian cancer. A comparison of the three clinico-genomic knowledge bases indicated substantial differences in coverage at the gene and variant levels.ConclusionSMART Cancer Navigator has immediate relevance to practicing oncologists and others. Additional knowledge bases can be added without undue effort. As a first step toward utility, we generalized and disseminated the resulting implementation (https://smart-cancer-navigator.github.io) and data sets.

Project description:BackgroundIntertumoral heterogeneity represents a significant hurdle to identifying optimized targeted therapies in gastric cancer (GC). To realize precision medicine for GC patients, an actionable gene alteration-based molecular classification that directly associates GCs with targeted therapies is needed.MethodsA total of 207 Japanese patients with GC were included in this study. Formalin-fixed, paraffin-embedded (FFPE) tumor tissues were obtained from surgical or biopsy specimens and were subjected to DNA extraction. We generated comprehensive genomic profiling data using a 435-gene panel including 69 actionable genes paired with US Food and Drug Administration-approved targeted therapies, and the evaluation of Epstein-Barr virus (EBV) infection and microsatellite instability (MSI) status.ResultsComprehensive genomic sequencing detected at least one alteration of 435 cancer-related genes in 194 GCs (93.7%) and of 69 actionable genes in 141 GCs (68.1%). We classified the 207 GCs into four The Cancer Genome Atlas (TCGA) subtypes using the genomic profiling data; EBV (N = 9), MSI (N = 17), chromosomal instability (N = 119), and genomically stable subtype (N = 62). Actionable gene alterations were not specific and were widely observed throughout all TCGA subtypes. To discover a novel classification which more precisely selects candidates for targeted therapies, 207 GCs were classified using hypermutated phenotype and the mutation profile of 69 actionable genes. We identified a hypermutated group (N = 32), while the others (N = 175) were sub-divided into six clusters including five with actionable gene alterations: ERBB2 (N = 25), CDKN2A, and CDKN2B (N = 10), KRAS (N = 10), BRCA2 (N = 9), and ATM cluster (N = 12). The clinical utility of this classification was demonstrated by a case of unresectable GC with a remarkable response to anti-HER2 therapy in the ERBB2 cluster.ConclusionsThis actionable gene-based classification creates a framework for further studies for realizing precision medicine in GC.

Project description:Gastric cancer (GC) is a complex disease linked to a series of environmental factors and unhealthy lifestyle habits, and especially to genetic alterations. GC represents the second leading cause of cancer-related deaths worldwide. Its onset is subtle, and the majority of patients are diagnosed once the cancer is already advanced. In recent years, there have been innovations in the management of advanced GC including the introduction of new classifications based on its molecular characteristics. Thanks to new technologies such as next-generation sequencing and microarray, the Cancer Genome Atlas and Asian Cancer Research Group classifications have also paved the way for precision medicine in GC, making it possible to integrate diagnostic and therapeutic methods. Among the objectives of the subdivision of GC into subtypes is to select patients in whom molecular targeted drugs can achieve the best results; many lines of research have been initiated to this end. After phase III clinical trials, trastuzumab, anti-Erb-B2 receptor tyrosine kinase 2 (commonly known as ERBB2) and ramucirumab, anti-vascular endothelial growth factor receptor 2 (commonly known as VEGFR2) monoclonal antibodies, were approved and introduced into first- and second-line therapies for patients with advanced/metastatic GC. However, the heterogeneity of this neoplasia makes the practical application of such approaches difficult. Unfortunately, scientific progress has not been matched by progress in clinical practice in terms of significant improvements in prognosis. Survival continues to be low in contrast to the reduction in deaths from many common cancers such as colorectal, lung, breast, and prostate cancers. Although several target molecules have been identified on which targeted drugs can act and novel products have been introduced into experimental therapeutic protocols, the overall approach to treating advanced stage GC has not substantially changed. Currently, surgical resection with adjuvant or neoadjuvant radiotherapy and chemotherapy are the most effective treatments for this disease. Future research should not underestimate the heterogeneity of GC when developing diagnostic and therapeutic strategies aimed toward improving patient survival.

Dataset Information

OncoLoop: A Network-Based Precision Cancer Medicine Framework.

Significance

Publications

OncoLoop: A Network-Based Precision Cancer Medicine Framework.

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