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Intratumoral heterogeneity (ITH) is a fundamental property of cancer; however, the origins of ITH remain poorly understood. We performed single-cell transcriptome profiling of peritoneal carcinomatosis (PC) from 15 patients with gastric adenocarcinoma (GAC), constructed a map of 45,048 PC cells, profiled the transcriptome states of tumor cell populations, incisively explored ITH of malignant PC cells and identified significant correlates with patient survival. The links between tumor cell lineage/state compositions and ITH were illustrated at transcriptomic, genotypic, molecular and phenotypic levels. We uncovered the diversity in tumor cell lineage/state compositions in PC specimens and defined it as a key contributor to ITH. Single-cell analysis of ITH classified PC specimens into two subtypes that were prognostically independent of clinical variables, and a 12-gene prognostic signature was derived and validated in multiple large-scale GAC cohorts. The prognostic signature appears fundamental to GAC carcinogenesis and progression and could be practical for patient stratification.

When using next generation sequencing (NGS) data of tumor and its paired normal to detect somatic copy number alteration (SCNA) regions, the imbalance read’s PCR amplification process and system noise introduced by the twice sequencing procedures cause the result of existing algorithms to contain many false positive breakpoints and SCNA regions. These false positive breakpoints and SCNA regions further affect SCNA based subclonal population inferring tool that uses these SCNA regions as input. We present p-SCNAClonal, a tool that improves tumor subclonal population inferring by merging the SCNA regions according to the read count and B-allele frequency (BAF) information to reduce the number of false positive SCNA breakpoints. p-SCNAClonal then locates the baseline SCNA (not containing any SCNAs) and infers the absolute copy number of SCNA and its subclonal populations through a probabilistic model. We show p-SCNAClonal’s superiority to existing SCNA based subclonal population inferring method. p-SCNAClonal is publicly available as a Python package at https://github.com/Billy-Nie/pSCNAClonal

Tumours contain multiple, genetically diverse subclonal populations of cells that have evolved from a single progenitor population. Currently, next-generation sequencing (NGS) and the third generation sequencing (TGS) have recently allowed us to develop algorithms to quantitatively dissect the extent of heterogeneity within a tumour, resolve cancer evolution history and identify the somatic variations and aneuploidy events with subclonal frequency. However, existing tumour NGS data has no ground truth annotation to validate all these NGS based tumour analysis algorithms. To benchmark these algorithms, a powerful tumour genome simulation tool which could simulate all the distinct subclonal genomes with diverse aneuploidy events and somatic variations according to the given tumour evolution history is in need. We provide a simulation package, Pysubsim-tree, which could simulate the tumour genomes according to their evolution history defined by the somatic variations and aneuploidy events. Pysubsim-tree is free, open source, available at: https://github.com/dustincys/pysubsimtree.

Protein-protein interactions PPIs are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. This paper aims at exploring more and removing PPIs falsely predicted PPIs involved in glucosinolate biosynthesis in Arabidopsis. A symmetric kernel function is proposed according to the approach of feature representation which combines the domain and domain-domain interaction DDI information in this paper. The performance of this kernel indicates SVM based PPIs predictor trained with this kernel is highly effective. According to the prediction result, proteins with Arabidopsis Genome Initiative AGI numbers AT4G14800 and AT5G54810, AT5G05730 and AT4G18040, AT1G04510 and AT5G05260 are affirmed as interactive among the 237 low level of confidence PPIs pairs. Furthermore, the SVM-based PPIs predictor is used to explore PPIs of AT1G74090 and AT5G07690 both of which are members of the four glucosinolate biosynthesis pathway proteins absent from AtPIN.