DNA Repair Gene Expression Adjusted by the PCNA Metagene Predicts Survival in Multiple Cancers.

Removal of the proliferation component of gene expression by proliferating cell nuclearantigen (PCNA) adjustment via statistical methods has been addressed in numerous survivalprediction studies for breast cancer and all cancers in the Cancer Genome Atlas (TCGA). Thesestudies indicate that the removal of proliferation in gene expression by PCNA adjustment removesthe statistical significance for predicting overall survival (OS) when gene selection is performed ona genome-wide basis. Since cancers become addicted to DNA repair as a result of forced cellularreplication, increased oxidation, and repair deficiencies from oncogenic loss or geneticpolymorphisms, we hypothesized that PCNA adjustment of DNA repair gene expression does notremove statistical significance for OS prediction. The rationale and importance of this translationalhypothesis is that new lists of repair genes which are predictive of OS can be identified to establishnew targets for inhibition therapy. A candidate gene approach was employed using TCGARNA-Seq data for 121 DNA repair genes in 8 molecular pathways to predict OS for 18 cancers.Statistical randomization test results indicate that after PCNA adjustment, OS could be predictedsignificantly by sets of DNA repair genes for 61% (11/18) of the cancers. These findings suggest thatremoval of the proliferation signal in expression by PCNA adjustment does not remove statisticalsignificance for predicting OS. In conclusion, it is likely that previous studies on PCNA adjustmentand survival were biased because genes identified through a genome-wide approach are stronglyco-regulated by proliferation.

Progression inference for somatic mutations in cancer.

Computational methods were employed to determine progression inference of genomic alterations in commonly occurring cancers. Using cross-sectional TCGA data, we computed evolutionary trajectories involving selectivity relationships among pairs of gene-specific genomic alterations such as somatic mutations, deletions, amplifications, downregulation, and upregulation among the top 20 driver genes associated with each cancer. Results indicate that the majority of hierarchies involved TP53, PIK3CA, ERBB2, APC, KRAS, EGFR, IDH1, VHL, etc. Research into the order and accumulation of genomic alterations among cancer driver genes will ever-increase as the costs of nextgen sequencing subside, and personalized/precision medicine incorporates whole-genome scans into the diagnosis and treatment of cancer.

Adjustment of lifetime risks of space radiation-induced cancer by the healthy worker effect and cancer misclassification.

Background. The healthy worker effect (HWE) is a source of bias in occupational studies of mortality among workers caused by use of comparative disease rates based on public data, which include mortality of unhealthy members of the public who are screened out of the workplace. For the US astronaut corp, the HWE is assumed to be strong due to the rigorous medical selection and surveillance. This investigation focused on the effect of correcting for HWE on projected lifetime risk estimates for radiation-induced cancer mortality and incidence. Methods. We performed radiation-induced cancer risk assessment using Poisson regression of cancer mortality and incidence rates among Hiroshima and Nagasaki atomic bomb survivors. Regression coefficients were used for generating risk coefficients for the excess absolute, transfer, and excess relative models. Excess lifetime risks (ELR) for radiation exposure and baseline lifetime risks (BLR) were adjusted for the HWE using standardized mortality ratios (SMR) for aviators and nuclear workers who were occupationally exposed to ionizing radiation. We also adjusted lifetime risks by cancer mortality misclassification among atomic bomb survivors. Results. For all cancers combined ("Nonleukemia"), the effect of adjusting the all-cause hazard rate by the simulated quantiles of the all-cause SMR resulted in a mean difference (not percent difference) in ELR of 0.65% and mean difference of 4% for mortality BLR, and mean change of 6.2% in BLR for incidence. The effect of adjusting the excess (radiation-induced) cancer rate or baseline cancer hazard rate by simulated quantiles of cancer-specific SMRs resulted in a mean difference of [Formula: see text] in the all-cancer mortality ELR and mean difference of [Formula: see text] in the mortality BLR. Whereas for incidence, the effect of adjusting by cancer-specific SMRs resulted in a mean change of [Formula: see text] for the all-cancer BLR. Only cancer mortality risks were adjusted by simulated quantiles for misclassification, and results indicate a mean change of 1.1% for all-cancer mortality ELR, while the mean change in the all-cancer PC was approximately 4% for males and 6% for females. Conclusions. The typical life table approach for projecting lifetime risk of radiation-induced cancer mortality and incidence for astronauts and radiation workers can be improved by adjusting for HWE while simulating the uncertainty of input rates, input excess risk coefficients, and bias correction factors during multiple Monte Carlo realizations of the life table.