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 |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

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.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets