Recent research has highlighted a strong correlation between tissue-specific cancer risk and the lifetime number of tissue-specific stem-cell divisions. Whether such correlation implies a high unavoidable intrinsic cancer risk has become a key public health debate with the dissemination of the ‘bad luck’ hypothesis. Here we provide evidence that intrinsic risk factors contribute only modestly (less than ~10–30% of lifetime risk) to cancer development. First, we demonstrate that the correlation between stem-cell division and cancer risk does not distinguish between the effects of intrinsic and extrinsic factors. We then show that intrinsic risk is better estimated by the lower bound risk controlling for total stem-cell divisions. Finally, we show that the rates of endogenous mutation accumulation by intrinsic processes are not sufficient to account for the observed cancer risks. Collectively, we conclude that cancer risk is heavily influenced by extrinsic factors. These results are important for strategizing cancer prevention, research and public health.

Barcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of cell lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing—from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs or sgRNAs to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq clustering are not well developed. Available methods often yield a high frequency of under-clustering artifacts that result in spurious barcodes, or over-clustering artifacts that group distinct barcodes together. Here, we developed Bartender, an accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data. In contrast with existing methods that cluster based on sequence similarity alone, Bartender uses a modified two-sample proportion test that also considers cluster size. This modification results in higher accuracy and lower rates of under- and over-clustering artifacts. Additionally, Bartender includes unique molecular identifier handling and a ‘multiple time point’ mode that matches barcode clusters between different clustering runs for seamless handling of time course data. Bartender is a set of simple-to-use command line tools that can be performed on a laptop at comparable run times to existing methods. Bartender is available at no charge for non-commercial use at https://github.com/LaoZZZZZ/bartender-1.1. Supplementary data are available at Bioinformatics online.

Tumor suppressor gene p53 plays an important role in the maintenance of the genomic integrity, and mutation in the gene may alter an individual’s susceptibility to various carcinomas. P53 Arg72Pro or codon 72 polymorphism has been indicated to increase the risk of developing certain cancers such as bladder cancer and cervical cancer. Human papillomavirus (HPV) infection has been shown as a risk factor for certain cancers such as cervical cancer and oral cancer as well, and the HPV oncoprotein E6 may induce the degradation of p53 function. However, the association between p53 Arg72Pro polymorphism and the risk of oral cancer with HPV infection remains inconclusive. Therefore, this meta-analysis involving 5,614 participants was performed to investigate the relations among the p53 Arg72Pro polymorphism, HPV infection, and the risk of developing oral cancer. A search of the literature by PubMed, Embase, Web of Science, and China National Knowledge Infrastructure databases was conducted to identify studies based on the inclusion and exclusion criteria. Odds ratios with 95 % confidence intervals were combined using a random-effect model or a fixed-effect model. The current study was conducted with 13 studies consisting of 2,413 cases and 3,201 controls. Neither overall analysis nor stratified analyses detected any obvious evidence of association between p53 Arg72Pro polymorphism and oral cancer susceptibility in all genetic models. However, a significant association between p53 Arg72Pro polymorphism and the risk of oral cancer with HPV infection was detected in the Arg/Arg vs. Arg/Pro + Pro/Pro model. In the current meta-analysis which used the quantitative data synthesis for the first time, our study demonstrated that p53 Arg72Pro polymorphism together with HPV infection might jointly alter an individual’s susceptibility to the risk of oral cancer. Our results suggested that p53 Arg72Pro polymorphism may partly contribute to the pathogenesis of oral cancer development.

Next generation sequencing (NGS) technology has been widely used in biomedical research, particularly on those genomics-related studies. One of NGS applications is the high-throughput mRNA sequencing (RNA-seq), which is usually applied to evaluate gene expression level (i.e. copies of isoforms), to identify differentially expressed genes, and to discover potential alternative splicing events. Popular tools for differential expression (DE) analysis using RNA-seq data include edgeR and DESeq. These methods tend to identify DE genes at the gene-level, which only allows them to compare the total size of isoforms, that is, sum of an isoform’s copy number times its length over all isoforms. Naturally, these methods may fail to detect DE genes when the total size of isoforms remains similar but isoform-wise expression levels change dramatically. Other tools can perform isoform-level DE analysis only if isoform structures are known but would still fail for many non-model species whose isoform information are missing. To overcome these disadvantages, we developed an isoform-free (without need to pre-specify isoform structures) splicing-graph based negative binomial (SGNB) model for differential expression analysis at isoform level. Our model detects not only the change in the total size of isoforms but also the change in the isoform-wise expression level and hence is more powerful. We performed extensive simulations to compare our method with edgeR and DESeq. Under various scenarios, our method consistently achieved a higher detection power, while controlling pre-specified type I error. We also applied our method to a real data set to illustrate its applicability in practice.

Single nucleotide polymorphisms (SNPs), the most common genetic markers in genome-wide association studies, are usually in linkage disequilibrium (LD) with each other within a small genomic region. Both single- and two-marker-based LD mapping methods have been developed by taking advantage of the LD structures. In this study, a more general LD mapping framework with an arbitrary number of markers has been developed to further improve LD mapping and its detection power. This method is referred as multi-marker linkage disequilibrium mapping (mmLD). For the parameter estimation, we implemented a two-phase estimation procedure: first, haplotype frequencies were estimated for known markers; then, haplotype frequencies were updated to include the unknown quantitative trait loci based on estimates from the first step. For the hypothesis testing, we proposed a novel sequential likelihood ratio test procedure, which iteratively removed haplotypes with zero frequency and subsequently determined the proper degree of freedom. To compare the proposed mmLD method with other existing mapping methods, e.g. the adjusted single-marker LD mapping and the SKAT_C, we performed extensive simulations under various scenarios. The simulation results demonstrated that the mmLD has the same or higher power than the existing methods, while maintaining the correct type I errors. We further applied the mmLD to a public data set, ‘GAW17’, to investigate its applicability. The result showed the good performance of mmLD. We concluded that this improved mmLD method will be useful for future genome-wide association studies and genetic association analyses.

Adaptive designs for integrated phase I/II trials of drug combinations are increasingly utilized to speed up the drug development process and enhance drug efficacy, particularly in the realm of cancer therapy. The model-based Continual Reassessment Method (CRM) for dose-finding is widely used to leverage accumulated data in guiding patient allocation, drawing on principles from the Bayesian framework. In this paper, we present crm12Comb , an R package we developed to streamline the Phase I/II adaptive design process for drug combinations using the CRM approach. This package supports patients’ assignment guidance in a single trial based on existing data, as well as simulation studies for conducting extensive simulations with multiple trial parameters to evaluate operating characteristics and create visual representations. It accounts for toxicity and efficacy as binary outcomes, applying partial orderings to the dose-toxicity and dose-efficacy relationships for drug combinations. crm12Comb allows for a wide range of user-specified parameters, including maximum number of patients, cohort size, drug combinations, and a variety of link functions with prior distributions, offering flexibility to accommodate diverse clinical scenarios.