Dataset Information

Lentiviral-Driven Discovery of Cancer Drug Resistance Mutations.

ABSTRACT: Identifying resistance mutations in a drug target provides crucial information. Lentiviral transduction creates multiple types of mutations due to the error-prone nature of the HIV-1 reverse transcriptase (RT). Here we optimized and leveraged this property to identify drug resistance mutations, developing a technique we term LentiMutate. This technique was validated by identifying clinically relevant EGFR resistance mutations, then applied to two additional clinical anticancer drugs: imatinib, a BCR-ABL inhibitor, and AMG 510, a KRAS G12C inhibitor. Novel deletions in BCR-ABL1 conferred resistance to imatinib. In KRAS-G12C or wild-type KRAS, point mutations in the AMG 510 binding pocket or oncogenic non-G12C mutations conferred resistance to AMG 510. LentiMutate should prove highly valuable for clinical and preclinical cancer-drug development. SIGNIFICANCE: LentiMutate can evaluate a drug's on-target activity and can nominate resistance mutations before they occur in patients, which could accelerate and refine drug development to increase the survival of patients with cancer.

SUBMITTER: Yenerall P

PROVIDER: S-EPMC8448967 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Antibody-drug conjugate therapy has attracted considerable attention in recent years. Since the selection of appropriate targets is a critical aspect of antibody-drug conjugate research and development, a big data research for discovery of candidate targets per tumor type is outstanding and of high interest. Thus, the purpose of this study was to identify and prioritize candidate antibody-drug conjugate targets with translational potential across common types of cancer by mining the Human Protein Atlas, as a unique big data resource. To perform a multifaceted screening process, XML and TSV files including immunohistochemistry expression data for 45 normal tissues and 20 tumor types were downloaded from the Human Protein Atlas website. For genes without high protein expression across critical normal tissues, a quasi H-score (range, 0-300) was computed per tumor type. All genes with a quasi H - score ≥ 150 were extracted. Of these, genes with cell surface localization were selected and included in a multilevel validation process. Among 19670 genes that encode proteins, 5520 membrane protein-coding genes were included in this study. During a multistep data mining procedure, 332 potential targets were identified based on the level of the protein expression across critical normal tissues and 20 tumor types. After validation, 23 cell surface proteins were identified and prioritized as candidate antibody-drug conjugate targets of which two have interestingly been approved by the FDA for use in solid tumors, one has been approved for lymphoma, and four have currently been entered in clinical trials. In conclusion, we identified and prioritized several candidate targets with translational potential, which may yield new clinically effective and safe antibody-drug conjugates. This large-scale antibody-based proteomic study allows us to go beyond the RNA-seq studies, facilitates bench-to-clinic research of targeted anticancer therapeutics, and offers valuable insights into the development of new antibody-drug conjugates.

Dataset Information

Lentiviral-Driven Discovery of Cancer Drug Resistance Mutations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets