Background: The protumor activities of cancer-associated fibroblasts (CAFs) suggest that they are potential therapeutic targets for the treatment of cancer. The mechanism of CAF heterogeneity in gastric cancer (GC) remains unclear and has slowed translational advances in targeting CAFs. Therefore, a comprehensive understanding of the classification, function, activation stage, and spatial distribution of the CAF subsets in GC is urgently needed.

Currently most methods take manual strategies to annotate cell types after clustering the single-cell RNA sequencing (scRNA-seq) data. Such methods are labor-intensive and heavily rely on user expertise, which may lead to inconsistent results. We present SCSA, an automatic tool to annotate cell types from scRNA-seq data, based on a score annotation model combining differentially expressed genes (DEGs) and confidence levels of cell markers from both known and user-defined information. Evaluation on real scRNA-seq datasets from different sources with other methods shows that SCSA is able to assign the cells into the correct types at a fully automated mode with a desirable precision.

Most cancer risk-associated single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) are noncoding and it is challenging to assess their functional impacts. To systematically identify the SNPs that affect gene expression by modulating activities of distal regulatory elements, we adapt the self-transcribing active regulatory region sequencing (STARR-seq) strategy, a high-throughput technique to functionally quantify enhancer activities. From 10,673 SNPs linked with 996 cancer risk-associated SNPs identified in previous GWAS studies, we identify 575 SNPs in the fragments that positively regulate gene expression, and 758 SNPs in the fragments with negative regulatory activities. Among them, 70 variants are regulatory variants for which the two alleles confer different regulatory activities. We analyze in depth two regulatory variants—breast cancer risk SNP rs11055880 and leukemia risk-associated SNP rs12142375—and demonstrate their endogenous regulatory activities on expression of ATF7IP and PDE4B genes, respectively, using a CRISPR-Cas9 approach. By identifying regulatory variants associated with cancer susceptibility and studying their molecular functions, we hope to help the interpretation of GWAS results and provide improved information for cancer risk assessment.

Background: The genomic spectrum of biliary tract carcinoma (BTC) has been characterized and is associated with distinct anatomic and etiologic subtypes, yet limited studies have linked genomic alterations with personalized therapies in BTC patients.

Cellular senescence (CS), a state of permanent growth arrest, is intertwined with tumorigenesis. Due to the absence of specific markers, characterizing senescence levels and senescence-related phenotypes across cancer types remain unexplored. Here, we defined computational metrics of senescence levels as CS scores to delineate CS landscape across 33 cancer types and 29 normal tissues and explored CS-associated phenotypes by integrating multiplatform data from ~20 000 patients and ~212 000 single-cell profiles. CS scores showed cancer type-specific associations with genomic and immune characteristics and significantly predicted immunotherapy responses and patient prognosis in multiple cancers. Single-cell CS quantification revealed intra-tumor heterogeneity and activated immune microenvironment in senescent prostate cancer. Using machine learning algorithms, we identified three CS genes as potential prognostic predictors in prostate cancer and verified them by immunohistochemical assays in 72 patients. Our study provides a comprehensive framework for evaluating senescence levels and clinical relevance, gaining insights into CS roles in cancer- and senescence-related biomarker discovery.

Quantitative genetic epistasis has been hypothesized to be an important factor in the development and progression of complex diseases. Cancers in particular are driven by the accumulation of mutations that may act epistatically during the course of the disease. However, as cancer mutations are uncovered at an unprecedented rate, determining which combinations of genetic alterations interact to produce cancer phenotypes remains a challenge. Here we show that by using combinatorial RNAi screening in cell culture, dense and often previously undetermined interactions among cancer genes were revealed by assessing gene pairs that are frequently co-altered in primary breast cancers. These interacting gene pairs are significantly associated with survival time when co-altered in patients, indicating that genetic interaction mapping may be leveraged to improve risk assessment. As many of these interacting gene pairs involve known drug targets, personalized treatment regimens may be improved by overlaying genetic interactions with mutational profiling.