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.

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 −1.2% in the all-cancer mortality ELR and mean difference of −6.4% in the mortality BLR. Whereas for incidence, the effect of adjusting by cancer-specific SMRs resulted in a mean change of −14.4% 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.

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.

One of the hallmarks of cancer is the existence of a high mutational load in driver genes, which is balanced by upregulation (downregulation) of DNA repair pathways, since almost complete DNA repair is required for mitosis. The prediction of cancer survival with gene expression has been investigated by many groups, however, results of a comprehensive re-evaluation of the original data adjusted by the PCNA metagene indicate that only a small proportion of genes are truly predictive of survival. However, little is known regarding the effect of the PCNA metagene on survival prediction specifically by DNA repair genes. We investigated prediction of overall survival (OS) in 18 cancers by using normalized RNA-Seq data for 126 DNA repair genes with expression available in TCGA. Transformations for normality and adjustments for age at diagnosis, stage, and PCNA metagene were performed for all DNA repair genes. We also analyzed genomic event rates (GER) for somatic mutations, deletions, and amplification in driver genes and DNA repair genes. After performing empirical p-value testing with use of randomly selected gene sets, it was observed that OS could be predicted significantly by sets of DNA repair genes for 61% (11/18) of the cancers. Pathway activation analysis indicates that in the presence of dysfunctional driver genes, the initial damage signaling and minor single-gene repair mechanisms may be abrogated, but with later pathway genes fully activated and intact. Neither PARP1 or PARP2 were significant predictors of survival for any of the 11 cancers. Results from cluster analysis of GERs indicates that the most opportunistic set of cancers warranting further study are AML, colorectal, and renal papillary, because of their lower GERs for mutations, deletions, and amplifications in DNA repair genes. However, the most opportunistic cancer to study is likely to be AML, since it showed the lowest GERs for mutations, deletions, and amplifications, suggesting that DNA repair pathway activation in AML is intact and unaltered genomically. In conclusion, our hypothesis-driven focus to target DNA repair gene expression adjusted for the PCNA metagene as a means of predicting OS in various cancers resulted in statistically significant sets of genes. Author summaryThe proliferating cell nuclear antigen (PCNA) protein is a homotrimer and activator of polymerase δ, which encircles DNA during transcription to recruit other proteins October 2, 2018 1/18 1 INTRODUCTION 1 Recent developments in cancer research based on next-generation sequencing 2 technology indicates that a hallmark of sporadic cancers is that they exhibit a lifelong 3 accumulation of pathogenic somatic mutations in "driver genes," otherwise known as 4 tumor suppressor genes and oncogenes [1]. There are many different biological 5 activities and molecular functions of driver genes, including, for example, DNA 6 binding, protein binding, transcription factor activity, receptor activity, magnesium 7 and calcium ion binding, actin binding, protein kinase activity, etc. A...

Removal of the proliferation component of gene expression by PCNA adjustment has been addressed in numerous survival prediction studies for breast cancer and all cancers in the TCGA. These studies indicate that widespread co-regulation of proliferation upwardly biases survival prediction when gene selection is performed on a genome-wide basis. In addition, removal of the correlative effects of proliferation does not reduce the random bias associated with survival prediction using random gene selection. Since most cancers become addicted to DNA repair as a result of forced cellular replication, increased oxidation, and repair deficiencies from oncogenic loss or genetic polymorphisms, we pursued an investigation to remove the proliferation component of expression in DNA repair genes to determine survival prediction. This translational hypothesis-driven focus on DNA repair genes is directly amenable to finding new sets of DNA repair genes that could potentially be studied for inhibition therapy. Overall survival (OS) prediction was evaluated in 18 cancers by using normalized RNA-Seq data for 126 DNA repair genes with expression available in TCGA. Transformations for normality and adjustments for age at diagnosis, stage, and PCNA metagene expression were performed for all DNA repair genes. We also analyzed genomic event rates (GER) for somatic mutations, deletions, and amplification in driver genes and DNA repair genes. After performing empirical p-value testing with use of randomly selected gene sets, it was observed that OS could be predicted significantly by sets of DNA repair genes for 61% (11/18) of the cancers. Interestingly, PARP1 was not a significant predictor of survival for any of the 11 cancers. Results from cluster analysis of GERs indicates that the most opportunistic cancers for inhibition therapy may be AML, colorectal, and renal papillary, because of potentially less confounding due to lower GERs for mutations, deletions, and amplifications in DNA repair genes. However, the most opportunistic cancer for inhibition therapy is likely to be AML, since it showed the lowest GERs for mutations, deletions, and amplifications in DNA repair genes. In conclusion, our hypothesis-driven focus to target DNA repair gene expression adjusted for the PCNA metagene as a means of predicting OS in various cancers resulted in statistically significant sets of genes.