Profound synchrony of age-specific incidence rates and tumor suppression for different cancer types as revealed by the multistage-senescence model of carcinogenesis.

The age-specific trend of cancer incidence rates, but not its magnitude, is well described employing the multistage theory of carcinogenesis by Armitage and Doll in combination with the senescence model of Pompei and Wilson. We derived empirical parameters of the multistage-senescence model from U.S. Surveillance, Epidemiology, and End Results (SEER) incidence data from 2000-2003 and 2010-2013 for The Cancer Genome Atlas (TCGA) cancer types. Under the assumption of a constant tumor-specific transition rate between stages, there is an extremely strong linear relationship (P < 0.0001) between the number of stages and the stage transition rate. The senescence tumor suppression factor for 20 non-reproductive cancers is remarkably consistent (0.0099±0.0005); however, five female reproductive cancers have significantly higher tumor suppression. The peak incidence rate for non-reproductive cancers occurs at a younger age for cancers with fewer stages and their carcinogenic stages are of longer duration. Driver gene mutations are shown to contribute on average only about a third of the carcinogenic stages of different tumor types. A tumor's accumulated incidence, calculated using a two-variable (age, stage) model, is strongly associated with intrinsic cancer risk. During both early adulthood and senescence, the pace of tumor suppression appears to be synchronized across most cancer types, suggesting the presence of overlapping evolutionary processes.

A community effort to create standards for evaluating tumor subclonal reconstruction.

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.

ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles.

BACKGROUND: Tumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer. RESULTS: To simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets. CONCLUSIONS: The ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.