HKU Data Repository
Browse

Supporting data for Stratifying gene vulnerabilities among patient-derived gastric cancer organoids by CRISPR-Cas9 screens

dataset
posted on 2024-12-06, 01:22 authored by Hiu Yan LeeHiu Yan Lee, Siu Lun WongSiu Lun Wong

Raw data files and analysed data for thesis titled "Stratifying gene vulnerabilities among patient-derived gastric cancer organoids by CRISPR-Cas9 screens".Related computational scripts for graph plotting and sequencing alignments.

Gastric cancer is the fifth most common malignant tumour worldwide, with an estimated 5-year survival rate of less than 20%. Gastric cancer is classified into multiple subtypes based on the Lauren Classification. The main subtypes based on histomorphology are the intestinal and diffuse subtypes, while there is also a mixed subtype. Gastric cancer carrying ARHGAP fusion is shown to have a worse prognosis when compared to ARHGAP fusion-negative ones, and ARHGAP fusion mutation is highly associated with the diffuse subtype. There are different fusion mutations present in gastric cancer, and among the fusion mutations, CLDN18-ARHGAP26 and CTNND1-ARHGAP26 are the fusion mutations with high prevalence in the population. This research aims to identify the genetic factors contributing to gastric cancer development, with a particular focus on identifying potential therapeutic targets for ones with ARHGAP fusion mutations. Building upon our previous genome-wide CRISPR-Cas9 screening data in an ARHGAP fusion-containing organoid line, we selected top-performing genes to construct sub-pool libraries for screening in a panel of organoid lines derived from patients with different gastric cancer subtypes. Through high-throughput CRISPR screening, computational analyses (MAGeCK, BAGEL, JACKs and Trinity CTAT), and downstream validation (Growth competition assays), this work identifies common and distinct vulnerabilities among gastric cancer subtypes.

History

Usage metrics

    Research Postgraduates

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC