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Tumour heterogeneity and personalized treatment screening based on single-cell transcriptomics
Xinying Zhang1, Jiajie Xie1, Zixin Yang1
1School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
Abstract:
According to global cancer statistics for the year 2022, based on updated estimates from the International Agency for Research on Cancer, there were approximately 20 million new cases of cancer in 2022 alongside 9.7 million related deaths. Lung, breast, colorectal, gastric, and liver cancers are the most common types of cancer. Despite advancements in anticancer drugs and optimised chemotherapy regimens that have improved cure rates for malignant tumours, the presence of tumour heterogeneity has resulted in substantial variations among patients in terms of disease progression, clinical response, sensitivity to therapy, and prognosis, posing significant challenges in attaining optimal therapeutic outcomes for each patient. Here, we collected five single-cell transcriptome datasets from patients with lung, breast, colorectal, gastric, and liver cancers and constructed multiple cancer blueprints of tumour cell heterogeneity. By integrating multiple bioinformatics analyses, we explored the biological differences underlying tumour cell heterogeneity at the single-cell level and identified tumour cell subcluster-specific biomarkers and potential therapeutic drugs for each subcluster. Interestingly, although tumour cell subpopulations exhibit dramatic differences within the same cancer type and between different cancers at both the genomic and transcriptomic levels, some demonstrate similar oncogenic pathway activities and phenotypes. Tumour cell subpopulations from the five cancers listed above were classified into three major groups corresponding to different treatment strategies. The findings of this study not only focus on the differences but also on the similarities among tumour cell subpopulations across different cancers, providing new insights for individualised therapy.
Insights
Cancer cells vary greatly, even within the same tumor. This study identifies specific biomarkers and treatments for lung, breast, colorectal, gastric, and liver cancer cell subpopulations, paving the way for personalized cancer therapy.
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.
