Computational Discovery of Cancer Immunotherapy Targets by Intercellular CRISPR Screens

Tumor immune evasion: insights from CRISPR screens and future directions

Despite the clinical success of cancer immunotherapies including immune checkpoint blockade and adoptive cellular therapies across a variety of cancer types, many patients do not respond or ultimately relapse; however, the molecular underpinnings of this are not fully understood. Thus, a system-level understating of the routes to tumor immune evasion is required to inform the design of the next generation of immunotherapy approaches. CRISPR screening approaches have proved extremely powerful in identifying genes that promote tumor immune evasion or sensitize tumor cells to destruction by the immune system. These large-scale efforts have brought to light decades worth of fundamental immunology and have uncovered the key immune-evasion pathways subverted in cancers in an acquired manner in patients receiving immune-modulatory therapies. The comprehensive discovery of the main pathways involved in immune evasion has spurred the development and application of novel immune therapies to target this process. Although successful, conventional CRISPR screening approaches are hampered by a number of limitations, which obfuscate a complete understanding of the precise molecular regulation of immune evasion in cancer. Here, we provide a perspective on screening approaches to interrogate tumor-lymphocyte interactions and their limitations, and discuss further development of technologies to improve such approaches and discovery capability.

Comparative Analysis of Gene Correlation Networks of Breast Cancer Patients Based on Mutations in TP53

Breast cancer is one of the most prevalent cancers in females, with more than 450,000 deaths each year worldwide. Among the subtypes of breast cancer, basal-like breast cancer, also known as triple-negative breast cancer, shows the lowest survival rate and does not have effective treatments yet. Somatic mutations in the TP53 gene frequently occur across all breast cancer subtypes, but comparative analysis of gene correlations with respect to mutations in TP53 has not been done so far. The primary goal of this study is to identify gene correlations in two groups of breast cancer patients and to derive potential prognostic gene pairs for breast cancer. We partitioned breast cancer patients into two groups: one group with a mutated TP53 gene (mTP53) and the other with a wild-type TP53 gene (wtTP53). For every gene pair, we computed the hazard ratio using the Cox proportional hazard model and constructed gene correlation networks (GCNs) enriched with prognostic information. Our GCN is more informative than typical GCNs in the sense that it indicates the type of correlation between genes, the concordance index, and the prognostic type of a gene. Comparative analysis of correlation patterns and survival time of the two groups revealed several interesting findings. First, we found several new gene pairs with opposite correlations in the two GCNs and the difference in their correlation patterns was the most prominent in the basal-like subtype of breast cancer. Second, we obtained potential prognostic genes for breast cancer patients with a wild-type TP53 gene. From a comparative analysis of GCNs of mTP53 and wtTP53, we found several gene pairs that show significantly different correlation patterns in the basal-like breast cancer subtype and obtained prognostic genes for patients with a wild-type TP53 gene. The GCNs and prognostic genes identified in this study will be informative for the prognosis of survival and for selecting a drug target for breast cancer, in particular for basal-like breast cancer. To the best of our knowledge, this is the first attempt to construct GCNs for breast cancer patients with or without mutations in the TP53 gene and to find prognostic genes accordingly.