Cell-free multi-omics analysis reveals tumor status-informative signatures in gastrointestinal cancer patients’ plasma

Abstract

During cancer development, host's tumorigenesis and immune signals are released to and informed by circulating molecules, like cell-free DNA (cfDNA) and RNA (cfRNA) in blood. However, these two kinds of molecules are still not systematically compared in gastrointestinal cancer. Here, we profiled 4 types of cell-free omics data from colorectal and stomach cancer patients, and assayed 15 types of genomic, epi-genomic, and transcriptomic variations. First, we demonstrated that the multi-omics data were more capable of detecting cancer genes than the single-omics data, where cfRNAs were more sensitive and informative than cfDNAs in terms of detection ratio, variation type, altered number, and enriched functional pathway. Moreover, we revealed several peripheral immune signatures that were suppressed in cancer patients and originated from specific circulating and tumor-microenvironment cells. Particularly, we defined a γδ-T-cell score and a cancer-associated-fibroblast (CAF) score using the cfRNA-seq data of 143 cancer patients. They were informative of clinical status like cancer stage, tumor size, and survival. In summary, our work reveals the cell-free multi-molecular landscape of colorectal and stomach cancer, and provides a potential monitoring utility in blood for the personalized cancer treatment.

Pathformer: a biological pathway informed Transformer integrating multi-omics data for disease diagnosis and prognosis

“…For benchmark testing, we collected 20 datasets across different cancer types from TCGA to evaluate classification performance of Pathformer and existing comparison methods, which consists of 10 datasets for cancer early- and late-stage classification, and 10 datasets for cancer low- and high-risk survival classification. Besides, to further verify the effect of Pathformer in cancer diagnosis, we also collected three types of body fluid datasets: the plasma dataset (comprising 373 samples assayed by total cell-free RNA-seq 32,33 ), the extracellular vesicle (EV) dataset (comprising 477 samples from two studies assayed by exosomal RNA-seq 34,35 ), and the platelet dataset (comprising 918 sample from two studies assayed by tumor-educated blood platelet RNA-seq 36,37 ). Through our biological information pipeline, totally 3 and 7 biological modalities are obtained for TCGA dataset and liquid biopsy dataset, respectively.…”