Building a Statistical Model for Predicting Cancer Genes

More than 400 cancer genes have been identified in the human genome. The list is not yet complete. Statistical models predicting cancer genes may help with identification of novel cancer gene candidates. We used known prostate cancer (PCa) genes (identified through KnowledgeNet) as a training set to build a binary logistic regression model identifying PCa genes. Internal and external validation of the model was conducted using a validation set (also from KnowledgeNet), permutations, and external data on genes with recurrent prostate tumor mutations. We evaluated a set of 33 gene characteristics as predictors. Sixteen of the original 33 predictors were significant in the model. We found that a typical PCa gene is a prostate-specific transcription factor, kinase, or phosphatase with high interindividual variance of the expression level in adjacent normal prostate tissue and differential expression between normal prostate tissue and primary tumor. PCa genes are likely to have an antiapoptotic effect and to play a role in cell proliferation, angiogenesis, and cell adhesion. Their proteins are likely to be ubiquitinated or sumoylated but not acetylated. A number of novel PCa candidates have been proposed. Functional annotations of novel candidates identified antiapoptosis, regulation of cell proliferation, positive regulation of kinase activity, positive regulation of transferase activity, angiogenesis, positive regulation of cell division, and cell adhesion as top functions. We provide the list of the top 200 predicted PCa genes, which can be used as candidates for experimental validation. The model may be modified to predict genes for other cancer sites

Chemotherapy and Survival for Patients With Multiple Myeloma: Findings From a Large Nationwide and Population-Based Cohort

Objective: To assess the patterns of chemotherapy use for patients with multiple myeloma and to determine if chemotherapy is effective in prolonging survival outside the clinical trial settings. Methods: We studied a nationwide and population-based retrospective cohort of 4902 patients ≥65 years of age with stage II or III multiple myeloma from 1992 to 1999, identified from the Surveillance, Epidemiology, and End-Results-Medicare data. Multivariate logistic regression was used to estimate the odds ratio of receiving chemotherapy and Cox proportional hazard model was used to estimate the hazard ratio of mortality associated with chemotherapy. Results: Of 4902 patients with stage II or III multiple myeloma, 52.0% received chemotherapy during the course of the disease. The receipt of chemotherapy decreased significantly with age from 65.7% in the 65- to 69-year age group to 34.3% in those ≥80 years. Blacks (47.6%) were less likely to receive chemotherapy than whites (52.8%). Use of chemotherapy decreased significantly with comorbidity scores and increased over time. Risk of all-cause mortality was significantly reduced in patients who received chemotherapy compared with those who did not (adjusted hazard ratio = 0.65; 95% confidence interval = 0.61-0.69). A similar pattern as observed for myeloma-specific mortality (0.61; 0.56-0.67). Survival benefit increased with increasing cycles of chemotherapy (P \u3c 0.001 for trend) and was significant across different age groups, gender, ethnic groups, and comorbidity scores. Conclusion: Chemotherapy was significantly associated with increased survival in patients with multiple myeloma outside the clinical trial settings. This survival benefit was significant across different groups by age, gender, race, and comorbidity. A substantial number of patients with multiple myeloma did not receive chemotherapy

Long-Term Survival After Radical Prostatectomy Compared to Other Treatments in Older Men With Local or Regional Prostate Cancer

Background This study aimed to address long-term survival in a large population-based cohort of men with prostate cancer receiving radical prostatectomy compared to other treatments. Methods We studied 5,845 patients diagnosed with local/regional stage prostate cancer at age 65–74 in 1992 with comorbidity score Results Of 5,845 patients, 10-year all-cause survival rates were the highest for patients receiving radical prostatectomy (81.0%; 95% CI: 79.4–82.4%), followed by radical prostatectomy in combination with radiotherapy (67.6%; 62.0–72.5%), radiotherapy (60.5%; 58.3–62.6%), and were the lowest for watchful-waiting (50.7%; 47.5–53.8%). A similar pattern was found for 10-year prostate cancer-specific survivals by treatments. After adjusting for age, ethnicity, region, Gleason Score, comorbidity, median annual household income, hormone therapy and chemotherapy, the hazard ratio of all-cause mortality was 0.31 (95% CI: 0.25–0.37) for radical prostatectomy and 0.38 (95% CI: 0.28–0.52) for radical prostatectomy plus radiation therapy compared to those with watchful-waiting. Conclusions There was a significant long-term survival benefit in men receiving radical prostatectomy compared to those receiving watchful-waiting or radiotherapy. J. Surg. Oncol. 2008;97:583–591. © 2008 Wiley-Liss, Inc

Genetic variants in ELOVL2 and HSD17B12 predict melanoma‐specific survival

Fatty acids play a key role in cellular bioenergetics, membrane biosynthesis and intracellular signaling processes and thus may be involved in cancer development and progression. In the present study, we comprehensively assessed associations of 14,522 common single‐nucleotide polymorphisms (SNPs) in 149 genes of the fatty‐acid synthesis pathway with cutaneous melanoma disease‐specific survival (CMSS). The dataset of 858 cutaneous melanoma (CM) patients from a published genome‐wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used as the discovery dataset, and the identified significant SNPs were validated by a dataset of 409 CM patients from another GWAS from the Nurses’ Health and Health Professionals Follow‐up Studies. We found 40 noteworthy SNPs to be associated with CMSS in both discovery and validation datasets after multiple comparison correction by the false positive report probability method, because more than 85% of the SNPs were imputed. By performing functional prediction, linkage disequilibrium analysis, and stepwise Cox regression selection, we identified two independent SNPs of ELOVL2 rs3734398 T>C and HSD17B12 rs11037684 A>G that predicted CMSS, with an allelic hazards ratio of 0.66 (95% confidence interval = 0.51–0.84 and p = 8.34 × 10−4) and 2.29 (1.55–3.39 and p = 3.61 × 10−5), respectively. Finally, the ELOVL2 rs3734398 variant CC genotype was found to be associated with a significantly increased mRNA expression level. These SNPs may be potential markers for CM prognosis, if validated by additional larger and mechanistic studies

Joint Effect of Multiple Common SNPs Predicts Melanoma Susceptibility

Single genetic variants discovered so far have been only weakly associated with melanoma. This study aims to use multiple single nucleotide polymorphisms (SNPs) jointly to obtain a larger genetic effect and to improve the predictive value of a conventional phenotypic model. We analyzed 11 SNPs that were associated with melanoma risk in previous studies and were genotyped in MD Anderson Cancer Center (MDACC) and Harvard Medical School investigations. Participants with ≥15 risk alleles were 5-fold more likely to have melanoma compared to those carrying ≤6. Compared to a model using the most significant single variant rs12913832, the increase in predictive value for the model using a polygenic risk score (PRS) comprised of 11 SNPs was 0.07(95% CI, 0.05-0.07). The overall predictive value of the PRS together with conventional phenotypic factors in the MDACC population was 0.69 (95% CI, 0.64-0.69). PRS significantly improved the risk prediction and reclassification in melanoma as compared with the conventional model. Our study suggests that a polygenic profile can improve the predictive value of an individual gene polymorphism and may be able to significantly improve the predictive value beyond conventional phenotypic melanoma risk factors

Genetic variants in the PIWI-piRNA pathway gene DCP1A predict melanoma disease-specific survival

The Piwi-piRNA pathway is important for germ cell maintenance, genome integrity, DNA methylation and retrotransposon control and thus may be involved in cancer development. In this study, we comprehensively analyzed prognostic roles of 3,116 common SNPs in PIWI-piRNA pathway genes in melanoma disease-specific survival. A published genome-wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used to identify associated SNPs, which were later validated by another GWAS from the Harvard Nurses' Health Study and Health Professionals Follow-up Study. After multiple testing correction, we found that there were 27 common SNPs in two genes (PIWIL4 and DCP1A) with false discovery rate < 0.2 in the discovery dataset. Three tagSNPs (i.e., rs7933369 and rs508485 in PIWIL4; rs11551405 in DCP1A) were replicated. The rs11551405 A allele, located at the 3' UTR microRNA binding site of DCP1A, was associated with an increased risk of melanoma disease-specific death in both discovery dataset [adjusted Hazards ratio (HR) = 1.66, 95% confidence interval (CI) = 1.21-2.27, p =1.50 × 10-3 ] and validation dataset (HR = 1.55, 95% CI = 1.03-2.34, p = 0.038), compared with the C allele, and their meta-analysis showed an HR of 1.62 (95% CI, 1.26-2.08, p =1.55 × 10-4 ). Using RNA-seq data from the 1000 Genomes Project, we found that DCP1A mRNA expression levels increased significantly with the A allele number of rs11551405. Additional large, prospective studies are needed to validate these findings

Genetic variants in the metzincin metallopeptidase family genes predict melanoma survival

Metzincins are key molecules in the degradation of the extracellular matrix and play an important role in cellular processes such as cell migration, adhesion, and cell fusion of malignant tumors, including cutaneous melanoma (CM). We hypothesized that genetic variants of the metzincin metallopeptidase family genes would be associated with CM-specific survival (CMSS). To test this hypothesis, we first performed Cox proportional hazards regression analysis to evaluate the associations between genetic variants of 75 metzincin metallopeptidase family genes and CMSS using the dataset from the genome-wide association study (GWAS) from The University of Texas MD Anderson Cancer Center (MDACC) which included 858 non-Hispanic white patients with CM, and then validated using the dataset from the Harvard GWAS study which had 409 non-Hispanic white patients with invasive CM. Four independent SNPs (MMP16 rs10090371 C>A, ADAMTS3 rs788935 T>C, TLL2 rs10882807 T>C and MMP9 rs3918251 A>G) were identified as predictors of CMSS, with a variant-allele attributed hazards ratio (HR) of 1.73 (1.32-2.29, 9.68E-05), 1.46 (1.15-1.85, 0.002), 1.68 (1.31-2.14, 3.32E-05) and 0.67 (0.51-0.87, 0.003), respectively, in the meta-analysis of these two GWAS studies. Combined analysis of risk genotypes of these four SNPs revealed a decreased CMSS in a dose-response manner as the number of risk genotypes increased (Ptrend < 0.001). An improvement was observed in the prediction model (area under the curve [AUC] = 81.4% vs. 78.6%), when these risk genotypes were added to the model containing non-genotyping variables. Our findings suggest that these genetic variants may be promising prognostic biomarkers for CMSS

Genetic Variants in WNT2B and BTRC Predict Melanoma Survival

Cutaneous melanoma (CM) is the most lethal skin cancer. The Wnt pathway has an impact on development, invasion and metastasis of CM, thus likely affecting CM prognosis. Using data from a published genome-wide association study (GWAS) from The University of Texas M.D. Anderson Cancer Center, we assessed the associations of 19,830 common single-nucleotide polymorphisms (SNPs) in 151 Wnt pathway autosomal genes with CM-specific survival (CMSS) and then validated significant SNPs in another GWAS from Harvard University. In the single-locus analysis, 1,855 SNPs were significantly associated with CMSS at P T and BTRC rs61873997 G>A) that showed a predictive role in CMSS, with an effect-allele-attributed hazards ratio [adjHR of 1.99 (95% confidence interval (CI) = 1.41-2.81, P = 8.10E-05) and 0.61 (0.46-0.80, 3.12E-04), respectively]. Collectively, these variants in the Wnt pathway genes may be biomarkers for outcomes of CM patients, if validated by larger studies