Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomics

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Global cancer statistics show millions of new cases and deaths annually, with lung, breast, colorectal, gastric, and liver cancers being most common.
• Tumor heterogeneity presents a major challenge in cancer treatment, leading to varied patient responses and prognoses despite advancements in therapies.
• Understanding cellular diversity within tumors is crucial for improving therapeutic outcomes.

Purpose of the Study:

• To construct comprehensive cancer blueprints of tumor cell heterogeneity using single-cell transcriptome data.
• To explore biological differences and identify subcluster-specific biomarkers and potential therapeutic targets at the single-cell level.
• To investigate similarities and differences in oncogenic pathways and phenotypes across tumor subpopulations in common cancers.

Main Methods:

• Collected and analyzed five single-cell transcriptome datasets from patients with lung, breast, colorectal, gastric, and liver cancers.
• Integrated multiple bioinformatics analyses to explore tumor cell heterogeneity.
• Classified tumor cell subpopulations into three major groups based on distinct treatment strategies.

Main Results:

• Identified significant biological differences underlying tumor cell heterogeneity at the single-cell level across five major cancer types.
• Discovered tumor cell subcluster-specific biomarkers and potential therapeutic drugs for individual subpopulations.
• Found that despite genomic and transcriptomic differences, some tumor cell subpopulations share similar oncogenic pathway activities and phenotypes.
• Classified tumor cell subpopulations into three groups, each associated with distinct treatment approaches.

Conclusions:

• Tumor heterogeneity is a complex characteristic of common cancers, with significant variations and similarities among cell subpopulations.
• The identification of subcluster-specific biomarkers and therapeutic targets offers new avenues for personalized cancer therapy.
• Classifying tumor cell subpopulations based on treatment strategies provides a framework for developing more effective, individualized treatment plans.