Dataset Information

A Precision Medicine Drug Discovery Pipeline Identifies Combined CDK2 and 9 Inhibition as a Novel Therapeutic Strategy in Colorectal Cancer.

ABSTRACT: Colorectal cancer is the third most common cancer in the United States and responsible for over 50,000 deaths each year. Therapeutic options for advanced colorectal cancer are limited, and there remains an unmet clinical need to identify new treatments for this deadly disease. To address this need, we developed a precision medicine pipeline that integrates high-throughput chemical screens with matched patient-derived cell lines and patient-derived xenografts (PDX) to identify new treatments for colorectal cancer. High-throughput screens of 2,100 compounds were performed across six low-passage, patient-derived colorectal cancer cell lines. These screens identified the CDK inhibitor drug class among the most effective cytotoxic compounds across six colorectal cancer lines. Among this class, combined targeting of CDK1, 2, and 9 was the most effective, with IC₅₀s ranging from 110 nmol/L to 1.2 μmol/L. Knockdown of CDK9 in the presence of a CDK2 inhibitor (CVT-313) showed that CDK9 knockdown acted synergistically with CDK2 inhibition. Mechanistically, dual CDK2/9 inhibition induced significant G₂-M arrest and anaphase catastrophe. Combined CDK2/9 inhibition in vivo synergistically reduced PDX tumor growth. Our precision medicine pipeline provides a robust screening and validation platform to identify promising new cancer therapies. Application of this platform to colorectal cancer pinpointed CDK2/9 dual inhibition as a novel combinatorial therapy to treat colorectal cancer.

SUBMITTER: Somarelli JA

PROVIDER: S-EPMC7718319 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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A Precision Medicine Drug Discovery Pipeline Identifies Combined CDK2 and 9 Inhibition as a Novel Therapeutic Strategy in Colorectal Cancer.

Somarelli Jason A JA Roghani Roham Salman RS Moghaddam Ali Sanjari AS Thomas Beatrice C BC Rupprecht Gabrielle G Ware Kathryn E KE Altunel Erdem E Mantyh John B JB Kim So Young SY McCall Shannon J SJ Shen Xiling X Mantyh Christopher R CR Hsu David S DS

Molecular cancer therapeutics 20201106 12

Colorectal cancer is the third most common cancer in the United States and responsible for over 50,000 deaths each year. Therapeutic options for advanced colorectal cancer are limited, and there remains an unmet clinical need to identify new treatments for this deadly disease. To address this need, we developed a precision medicine pipeline that integrates high-throughput chemical screens with matched patient-derived cell lines and patient-derived xenografts (PDX) to identify new treatments for ...[more]

PMID: 33158998

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Project description:Purpose: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, with high mortality and a lack of targeted therapies. To identify and prioritize druggable targets, we performed genome analysis together with genome-scale siRNA and oncology drug profiling using low-passage tumor cells derived from a patient with treatment-resistant HPV-negative HNSCC.Experimental Design: A tumor cell culture was established and subjected to whole-exome sequencing, RNA sequencing, comparative genome hybridization, and high-throughput phenotyping with a siRNA library covering the druggable genome and an oncology drug library. Secondary screens of candidate target genes were performed on the primary tumor cells and two nontumorigenic keratinocyte cell cultures for validation and to assess cancer specificity. siRNA screens of the kinome on two isogenic pairs of p53-mutated HNSCC cell lines were used to determine generalizability. Clinical utility was addressed by performing drug screens on two additional HNSCC cell cultures derived from patients enrolled in a clinical trial.Results: Many of the identified copy number aberrations and somatic mutations in the primary tumor were typical of HPV(-) HNSCC, but none pointed to obvious therapeutic choices. In contrast, siRNA profiling identified 391 candidate target genes, 35 of which were preferentially lethal to cancer cells, most of which were not genomically altered. Chemotherapies and targeted agents with strong tumor-specific activities corroborated the siRNA profiling results and included drugs that targeted the mitotic spindle, the proteasome, and G2-M kinases WEE1 and CHK1 We also show the feasibility of ex vivo drug profiling for patients enrolled in a clinical trial.Conclusions: High-throughput phenotyping with siRNA and drug libraries using patient-derived tumor cells prioritizes mutated driver genes and identifies novel drug targets not revealed by genomic profiling. Functional profiling is a promising adjunct to DNA sequencing for precision oncology. Clin Cancer Res; 24(12); 2828-43. ©2018 AACR.

Project description:PurposeDeveloping innovative precision and personalized cancer therapeutics is essential to enhance cancer survivability, particularly for prevalent cancer types such as colorectal cancer. This study aims to demonstrate various approaches for discovering new targets for precision therapies using artificial intelligence (AI) on a Polish cohort of colorectal cancer patients.MethodsWe analyzed 71 patients with histopathologically confirmed advanced resectional colorectal adenocarcinoma. Whole exome sequencing was performed on tumor and peripheral blood samples, while RNA sequencing (RNAseq) was conducted on tumor samples. We employed three approaches to identify potential targets for personalized and precision therapies. First, using our in-house neoantigen calling pipeline, ARDentify, combined with an AI-based model trained on immunopeptidomics mass spectrometry data (ARDisplay), we identified neoepitopes in the cohort. Second, based on recurrent mutations found in our patient cohort, we selected corresponding cancer cell lines and utilized knock-out gene dependency scores to identify synthetic lethality genes. Third, an AI-based model trained on cancer cell line data was employed to identify cell lines with genomic profiles similar to selected patients. Copy number variants and recurrent single nucleotide variants in these cell lines, along with gene dependency data, were used to find personalized synthetic lethality pairs.ResultsWe identified approximately 8,700 unique neoepitopes, but none were shared by more than two patients, indicating limited potential for shared neoantigenic targets across our cohort. Additionally, we identified three synthetic lethality pairs: the well-known APC-CTNNB1 and BRAF-DUSP4 pairs, along with the recently described APC-TCF7L2 pair, which could be significant for patients with APC and BRAF variants. Furthermore, by leveraging the identification of similar cancer cell lines, we uncovered a potential gene pair, VPS4A and VPS4B, with therapeutic implications.ConclusionOur study highlights three distinct approaches for identifying potential therapeutic targets in cancer patients. Each approach yielded valuable insights into our cohort, underscoring the relevance and utility of these methodologies in the development of precision and personalized cancer therapies. Importantly, we developed a novel AI model that aligns tumors with representative cell lines using RNAseq and methylation data. This model enables us to identify cell lines closely resembling patient tumors, facilitating accurate selection of models needed for in vitro validation.

Dataset Information

A Precision Medicine Drug Discovery Pipeline Identifies Combined CDK2 and 9 Inhibition as a Novel Therapeutic Strategy in Colorectal Cancer.

Publications

A Precision Medicine Drug Discovery Pipeline Identifies Combined CDK2 and 9 Inhibition as a Novel Therapeutic Strategy in Colorectal Cancer.

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