Immunogenetic Determinants of Susceptibility to Head and Neck Cancer in the Million Veteran Program Cohort.

Increasing rates of human papillomavirus (HPV)-driven oropharyngeal cancer (OPC) have largely offset declines in tobacco-associated head and neck squamous cell carcinoma (HNSCC) at non-OPC sites. Host immunity is an important modulator of HPV infection, persistence, and clearance, and also of immune evasion in both virally- and nonvirally-driven cancers. However, the association between collective known cancer-related immune gene variants and HNSCC susceptibility has not been fully characterized. Here, we conducted a genetic association study in the multiethnic Veterans Affairs Million Veteran Program cohort, evaluating 16,050 variants in 1,576 immune genes in 4,012 HNSCC cases (OPC = 1,823; non-OPC = 2,189) and 16,048 matched controls. Significant polymorphisms were further examined in a non-Hispanic white (NHW) validation cohort (OPC = 1,206; non-OPC = 955; controls = 4,507). For overall HNSCC susceptibility in NHWs, we discovered and validated a novel 9q31.1 SMC2 association and replicated the known 6p21.32 HLA-DQ-DR association. Six loci/genes for overall HNSCC susceptibility were selectively enriched in African-Americans (6p21.32 HLA-G, 9q21.33 GAS1, 11q12.2 CD6, 11q23.2 NCAM1/CD56, 17p13.1 CD68, 18q22.2 SOCS6); all 6 genes function in antigen-presenting regulation and T-cell activation. Two additional loci (10q26 DMBT1, 15q22.2 TPM1) were uncovered for non-OPC susceptibility, and three loci (11q24 CRTAM, 16q21 CDH5, 18q12.1 CDH2) were identified for HPV-positive OPC susceptibility. This study underscores the role of immune gene variants in modulating susceptibility for both HPV-driven and non-HPV-driven HNSCC. Additional large studies, particularly in racially diverse populations, are needed to further validate the associations and to help elucidate other potential immune factors and mechanisms that may underlie HNSCC risk. SIGNIFICANCE: Several inherited variations in immune system genes are significantly associated with susceptibility to head and neck cancer, which could help improve personalized cancer risk estimates.

Targeted sequencing in chromosome 17q linkage region identifies familial glioma candidates in the Gliogene Consortium.

Glioma is a rare, but highly fatal, cancer that accounts for the majority of malignant primary brain tumors. Inherited predisposition to glioma has been consistently observed within non-syndromic families. Our previous studies, which involved non-parametric and parametric linkage analyses, both yielded significant linkage peaks on chromosome 17q. Here, we use data from next generation and Sanger sequencing to identify familial glioma candidate genes and variants on chromosome 17q for further investigation. We applied a filtering schema to narrow the original list of 4830 annotated variants down to 21 very rare (<0.1% frequency), non-synonymous variants. Our findings implicate the MYO19 and KIF18B genes and rare variants in SPAG9 and RUNDC1 as candidates worthy of further investigation. Burden testing and functional studies are planned.

Germline mutations in shelterin complex genes are associated with familial glioma.

Gliomas are the most common brain tumor, with several histological subtypes of various malignancy grade. The genetic contribution to familial glioma is not well understood. Using whole exome sequencing of 90 individuals from 55 families, we identified two families with mutations in POT1 (p.G95C, p.E450X), a member of the telomere shelterin complex, shared by both affected individuals in each family and predicted to impact DNA binding and TPP1 binding, respectively. Validation in a separate cohort of 264 individuals from 246 families identified an additional mutation in POT1 (p.D617Efs), also predicted to disrupt TPP1 binding. All families with POT1 mutations had affected members with oligodendroglioma, a specific subtype of glioma more sensitive to irradiation. These findings are important for understanding the origin of glioma and could have importance for the future diagnostics and treatment of glioma.

Loss of LRIG1 locus increases risk of early and late relapse of stage I/II breast cancer.

Gains and losses at chromosome 3p12-21 are common in breast tumors and associated with patient outcomes. We hypothesized that the LRIG1 gene at 3p14.1, whose product functions in ErbB-family member degradation, is a critical tumor modifier at this locus. We analyzed 971 stage I/II breast tumors using Affymetrix Oncoscan molecular inversion probe arrays that include 12 probes located within LRIG1. Copy number results were validated against gene expression data available in the public database. By partitioning the LRIG1 probes nearest exon 12/13, we confirm a breakpoint in the gene and show that gains and losses in the subregions differ by tumor and patient characteristics including race/ethnicity. In analyses adjusted for known prognostic factors, loss of LRIG1 was independently associated with risk of any relapse (HR, 1.90; 95% CI, 1.32-2.73), relapse≥5 years (HR, 2.39; 95% CI, 1.31-4.36), and death (HR, 1.55; 95% CI, 1.11-2.16). Analyses of copy number across chromosome 3, as well as expression data from pooled, publicly available datasets, corroborated the hypothesis of an elevated and persistent risk among cases with loss of or low LRIG1. We concluded that loss/low expression of LRIG1 is an independent risk factor for breast cancer metastasis and death in stage I/II patients. Increased hazard in patients with loss/low LRIG1 persists years after diagnosis, suggesting that LRIG1 is acting as a critical suppressor of tumor metastasis and is an early clinical indicator of risk for late recurrences in otherwise low-risk patients.

Genomic copy number imbalances associated with bone and non-bone metastasis of early-stage breast cancer.

The aim of this study is to identify and validate copy number aberrations in early-stage primary breast tumors associated with bone or non-bone metastasis. Whole-genome molecular inversion probe arrays were used to evaluate copy number imbalances (CNIs) in breast tumors from 960 early-stage patients with information about site of metastasis. The CoxBoost algorithm was used to select metastasis site-related CNIs and to fit a Cox proportional hazards model. Gains at 1q41 and 1q42.12 and losses at 1p13.3, 8p22, and Xp11.3 were significantly associated with bone metastasis. Gains at 2p11.2, 3q21.3-22.2, 3q27.1, 10q23.1, and 14q13.2-3 and loss at 7q21.11 were associated with non-bone metastasis. To examine the joint effect of CNIs and clinical predictors, patients were stratified into three risk groups (low, intermediate, and high) based on the sum of predicted linear hazard ratios. For bone metastasis, the hazard (95 % confidence interval) for the low-risk group was 0.32 (0.11-0.92) compared to the intermediate-risk group and 2.99 (1.74-5.11) for the high-risk group. For non-bone metastasis, the hazard for the low-risk group was 0.34 (0.17-0.66) and 2.33 (1.59-3.43) for the high-risk group. The prognostic value of loss at 8p22 for bone metastasis and gains at 10q23.1 for non-bone metastasis, and gain at 11q13.5 for both bone and non-bone metastases were externally validated in 335 breast tumors pooled from four independent cohorts. Distinct CNIs are independently associated with bone and non-bone metastasis for early-stage breast cancer patients across cohorts. These data warrant consideration for tailoring surveillance and management of metastasis risk.

Selective genomic copy number imbalances and probability of recurrence in early-stage breast cancer.

A number of studies of copy number imbalances (CNIs) in breast tumors support associations between individual CNIs and patient outcomes. However, no pattern or signature of CNIs has emerged for clinical use. We determined copy number (CN) gains and losses using high-density molecular inversion probe (MIP) arrays for 971 stage I/II breast tumors and applied a boosting strategy to fit hazards models for CN and recurrence, treating chromosomal segments in a dose-specific fashion (-1 [loss], 0 [no change] and +1 [gain]). The concordance index (C-Index) was used to compare prognostic accuracy between a training (n = 728) and test (n = 243) set and across models. Twelve novel prognostic CNIs were identified: losses at 1p12, 12q13.13, 13q12.3, 22q11, and Xp21, and gains at 2p11.1, 3q13.12, 10p11.21, 10q23.1, 11p15, 14q13.2-q13.3, and 17q21.33. In addition, seven CNIs previously implicated as prognostic markers were selected: losses at 8p22 and 16p11.2 and gains at 10p13, 11q13.5, 12p13, 20q13, and Xq28. For all breast cancers combined, the final full model including 19 CNIs, clinical covariates, and tumor marker-approximated subtypes (estrogen receptor [ER], progesterone receptor, ERBB2 amplification, and Ki67) significantly outperformed a model containing only clinical covariates and tumor subtypes (C-Index(full model), train[test]  =  0.72[0.71] ± 0.02 vs. C-Index(clinical + subtype model), train[test]  =  0.62[0.62] ± 0.02; p<10(-6)). In addition, the full model containing 19 CNIs significantly improved prognostication separately for ER-, HER2+, luminal B, and triple negative tumors over clinical variables alone. In summary, we show that a set of 19 CNIs discriminates risk of recurrence among early-stage breast tumors, independent of ER status. Further, our data suggest the presence of specific CNIs that promote and, in some cases, limit tumor spread.

Evaluation of changes in serum protein profiles during neoadjuvant chemotherapy in HER2-positive breast cancer using an LC-MALDI-TOF/MS procedure.

Comparison of protein profiles of sera acquired before and after preoperative chemotherapy for breast cancer may reveal tumor markers that could be used to monitor tumor response. In this study, we analyzed pre- and post-chemotherapy protein profiles of sera from 39 HER2-postive breast cancer patients (n=78 samples) who received 6 months of preoperative chemotherapy using LC-MALDI-TOF/MS technology. We detected qualitative and quantitative differences in pair-wise comparison of pre- and post chemotherapy samples that were different in patients who achieved pathological complete response (pCR, n=21) compared with those with residual disease (n=18). We identified 2329 and 3152 peaks as differentially expressed in the pre-chemotherapy samples of the responders and non-responders. Comparison of matching pre- and post-chemotherapy samples identified 34 (32 decreased, two increased) and 304 peaks (157 decreased, 147 increased) that significantly changed (p<0.01, false discovery rate ≤ 20%) after treatment in responders and non-responders, respectively. The top 11 most significantly altered peptide peaks with the greatest change in intensity were positively identified. These corresponded to eight proteins including α-2-macroglobulin, complement 3, hemopexin, and serum amyloid P in the responder group and chains C and A of apolipoprotein A-I, hemopexin precursor, complement C, and amyloid P component in the non-responding groups. All proteins decreased after therapy, except chain C apolipoprotein A and hemopexin precursor that increased. These results suggest that changes in serum protein levels occur in response to chemotherapy and these changes partly appear different in patients who are highly sensitive to chemotherapy compared with those with lesser response.

Inherited variation in immune genes and pathways and glioblastoma risk.

To determine whether inherited variations in immune function single-nucleotide polymorphisms (SNPs), genes or pathways affect glioblastoma risk, we analyzed data from recent genome-wide association studies in conjunction with predefined immune function genes and pathways. Gene and pathway analyses were conducted on two independent data sets using 6629 SNPs in 911 genes on 17 immune pathways from 525 glioblastoma cases and 602 controls from the University of California, San Francisco (UCSF) and a subset of 6029 SNPs in 893 genes from 531 cases and 1782 controls from MD Anderson (MDA). To further assess consistency of SNP-level associations, we also compared data from the UK (266 cases and 2482 controls) and the Mayo Clinic (114 cases and 111 controls). Although three correlated epidermal growth factor receptor (EGFR) SNPs were consistently associated with glioblastoma in all four data sets (Mantel-Haenzel P values = 1 × 10⁻5 to 4 × 10⁻³), independent replication is required as genome-wide significance was not attained. In gene-level analyses, eight immune function genes were significantly (minP < 0.05) associated with glioblastoma; the IL-2RA (CD25) cytokine gene had the smallest minP values in both UCSF (minP = 0.01) and MDA (minP = 0.001) data sets. The IL-2RA receptor is found on the surface of regulatory T cells potentially contributing to immunosuppression characteristic of the glioblastoma microenvironment. In pathway correlation analyses, cytokine signaling and adhesion-extravasation-migration pathways showed similar associations with glioblastoma risk in both MDA and UCSF data sets. Our findings represent the first systematic description of immune genes and pathways that characterize glioblastoma risk.

Gamma-radiation sensitivity and polymorphisms in RAD51L1 modulate glioma risk.

BACKGROUND: DNA strand breaks pose the greatest threat to genomic stability. Genetically determined mutagen sensitivity predisposes individuals to a variety of cancers, including glioma. However, polymorphisms in DNA strand break repair genes that may determine mutagen sensitivity are not well studied in cancer risk, especially in gliomas. METHODS: We correlated genotype data for tag single-nucleotide polymorphisms (tSNPs) of DNA strand break repair genes with a gamma-radiation-induced mutagen sensitivity phenotype [expressed as mean breaks per cell (B/C)] in samples from 426 glioma patients. We also conducted analysis to assess joint and haplotype effects of single-nucleotide polymorphisms (SNPs) on mutagen sensitivity. We further validate our results in an independent external control group totaling 662 subjects. RESULTS: Of the 392 tSNPs examined, we found that mutagen sensitivity was modified by one tSNP in the EME2 gene and six tSNPs in the RAD51L1 gene (P < 0.01). Among the six RAD51L1 SNPs tested in the validation set, one (RAD51L1 rs2180611) was significantly associated with mutagen sensitivity (P = 0.025). Moreover, we found a significant dose-response relationship between the mutagen sensitivity and the number of adverse tSNP genotypes. Furthermore, haplotype analysis revealed that RAD51L1 haplotypes F-A (zero adverse allele) and F-E (six adverse alleles) exhibited the lowest (0.42) and highest (0.93) mean B/C values, respectively. A similar dose-response relationship also existed between the mutagen sensitivity and the number of adverse haplotypes. CONCLUSION: These results suggest that polymorphisms in and haplotypes of the RAD51L1 gene, which is involved in the double-strand break repair pathway, modulate gamma-radiation-induced mutagen sensitivity.

A comprehensive study of the association between the EGFR and ERBB2 genes and glioma risk.

Glioma is the most common type of adult brain tumor and glioblastoma, its most aggressive form, has a dismal prognosis. Receptor tyrosine kinases such as the epidermal growth factor receptor (EGFR, ERBB2, ERBB3, ERBB4) family, and the vascular endothelial growth factor receptor (VEGFR), play a central role in tumor progression. We investigated the genetic variants of EGFR, ERBB2, VEGFR and their ligands, EGF and VEGF on glioma and glioblastoma risk. In addition, we evaluated the association of genetic variants of a newly discovered family of genes known to interact with EGFR: LRIG2 and LRIG3 with glioma and glioblastoma risk. Methods. We analyzed 191 tag single nucleotide polymorphisms (SNPs) capturing all common genetic variation of EGF, EGFR, ERBB2, LRIG2, LRIG3, VEGF and VEGFR2 genes. Material from four case-control studies with 725 glioma patients (329 of who were glioblastoma patients) and their 1 610 controls was used. Haplotype analyses were conducted using SAS/Genetics software. Results. Fourteen of the SNPs were significantly associated with glioma risk at p< 0.05, and 17 of the SNPs were significantly associated with glioblastoma risk at p< 0.05. In addition, we found that one EGFR haplotype was related to increased glioblastoma risk at p=0.009, Odds Ratio [OR] = 1.67 (95% confidence interval (CI): 1.14, 2.45). The Bonferroni correction made all p-values non-significant. One SNP, rs4947986 next to the intron/exon boundary of exon 7 in EGFR, was validated in an independent data set of 713 glioblastoma and 2 236 controls, [OR] = 1.42 (95% CI: 1.06,1.91). Discussion. Previous studies show that regulation of the EGFR pathway plays a role in glioma progression but the present study is the first to find that certain genotypes of the EGFR gene may be related to glioblastoma risk. Further studies are required to reinvestigate these findings and evaluate the functional significance.

Polymorphisms of LIG4, BTBD2, HMGA2, and RTEL1 genes involved in the double-strand break repair pathway predict glioblastoma survival.

PURPOSE: Glioblastoma (GBM) is the most common and aggressive type of glioma and has the poorest survival. However, a small percentage of patients with GBM survive well beyond the established median. Therefore, identifying the genetic variants that influence this small number of unusually long-term survivors may provide important insight into tumor biology and treatment. PATIENTS AND METHODS: Among 590 patients with primary GBM, we evaluated associations of survival with the 100 top-ranking glioma susceptibility single nucleotide polymorphisms from our previous genome-wide association study using Cox regression models. We also compared differences in genetic variation between short-term survivors (STS; or= 36 months), and explored classification and regression tree analysis for survival data. We tested results using two independent series totaling 543 GBMs. RESULTS: We identified LIG4 rs7325927 and BTBD2 rs11670188 as predictors of STS in GBM and CCDC26 rs10464870 and rs891835, HMGA2 rs1563834, and RTEL1 rs2297440 as predictors of LTS. Further survival tree analysis revealed that patients >or= 50 years old with LIG4 rs7325927 (V) had the worst survival (median survival time, 1.2 years) and exhibited the highest risk of death (hazard ratio, 17.53; 95% CI, 4.27 to 71.97) compared with younger patients with combined RTEL1 rs2297440 (V) and HMGA2 rs1563834 (V) genotypes (median survival time, 7.8 years). CONCLUSION: Polymorphisms in the LIG4, BTBD2, HMGA2, and RTEL1 genes, which are involved in the double-strand break repair pathway, are associated with GBM survival.

Merging microarray data, robust feature selection, and predicting prognosis in prostate cancer.

MOTIVATION: Individual microarray studies searching for prognostic biomarkers often have few samples and low statistical power; however, publicly accessible data sets make it possible to combine data across studies. METHOD: We present a novel approach for combining microarray data across institutions and platforms. We introduce a new algorithm, robust greedy feature selection (RGFS), to select predictive genes. RESULTS: We combined two prostate cancer microarray data sets, confirmed the appropriateness of the approach with the Kolmogorov-Smirnov goodness-of-fit test, and built several predictive models. The best logistic regression model with stepwise forward selection used 7 genes and had a misclassification rate of 31%. Models that combined LDA with different feature selection algorithms had misclassification rates between 19% and 33%, and the sets of genes in the models varied substantially during cross-validation. When we combined RGFS with LDA, the best model used two genes and had a misclassification rate of 15%. AVAILABILITY: Affymetrix U95Av2 array data are available at http://www.broad.mit.edu/cgi-bin/cancer/datasets.cgi. The cDNA microarray data are available through the Stanford Microarray Database (http://cmgm.stanford.edu/pbrown/). GeneLink software is freely available at http://bioinformatics.mdanderson.org/GeneLink/. DNA-Chip Analyzer software is publicly available at http://biosun1.harvard.edu/complab/dchip/.