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PDF file 65K, Primers used in assessment of RINT1 transcripts

PDF file 199K, RINT1 c.1334-5delA, c.1334-1_1335delGTT minigene assay

PDF file 69K, Distribution of cases and controls included in the analysis, by study center and ethnic group

Background: We have developed a genome-wide association study analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by considering overlapping groups of contiguous markers (e.g., SNPs) across the genome. DEPTH is a machine learning algorithm for feature ranking of ultra-high dimensional datasets, built from well-established statistical tools such as bootstrapping, penalized regression, and decision trees. Unlike marginal regression, which considers each SNP individually, the key idea behind DEPTH is to rank groups of SNPs in terms of their joint strength of association with the outcome. Our aim was to compare the performance of DEPTH with that of standard logistic regression analysis.Methods: We selected 1,854 prostate cancer cases and 1,894 controls from the UK for whom 541,129 SNPs were measured using the Illumina Infinium HumanHap550 array. Confirmation was sought using 4,152 cases and 2,874 controls, ascertained from the UK and Australia, for whom 211,155 SNPs were measured using the iCOGS Illumina Infinium array.Results: From the DEPTH analysis, we identified 14 regions associated with prostate cancer risk that had been reported previously, five of which would not have been identified by conventional logistic regression. We also identified 112 novel putative susceptibility regions.Conclusions: DEPTH can reveal new risk-associated regions that would not have been identified using a conventional logistic regression analysis of individual SNPs.Impact: This study demonstrates that the DEPTH algorithm could identify additional genetic susceptibility regions that merit further investigation. Cancer Epidemiol Biomarkers Prev; 25(12); 1619–24. ©2016 AACR.

List of the previously identified 100 independent prostate cancer susceptibility SNPs.

Early-onset breast cancer (EOBC) causes substantial loss of life and productivity, creating a major burden among women worldwide. We analyzed 1,265,548 Hapmap3 single-nucleotide polymorphisms (SNP) among a discovery set of 3,523 EOBC incident cases and 2,702 population control women ages ≤ 51 years. The SNPs with smallest P values were examined in a replication set of 3,470 EOBC cases and 5,475 control women. We also tested EOBC association with 19,684 genes by annotating each gene with putative functional SNPs, and then combining their P values to obtain a gene-based P value. We examined the gene with smallest P value for replication in 1,145 breast cancer cases and 1,142 control women. The combined discovery and replication sets identified 72 new SNPs associated with EOBC (P < 4 × 10−8) located in six genomic regions previously reported to contain SNPs associated largely with later-onset breast cancer (LOBC). SNP rs2229882 and 10 other SNPs on chromosome 5q11.2 remained associated (P < 6 × 10−4) after adjustment for the strongest published SNPs in the region. Thirty-two of the 82 currently known LOBC SNPs were associated with EOBC (P < 0.05). Low power is likely responsible for the remaining 50 unassociated known LOBC SNPs. The gene-based analysis identified an association between breast cancer and the phosphofructokinase-muscle (PFKM) gene on chromosome 12q13.11 that met the genome-wide gene-based threshold of 2.5 × 10−6. In conclusion, EOBC and LOBC seem to have similar genetic etiologies; the 5q11.2 region may contain multiple distinct breast cancer loci; and the PFKM gene region is worthy of further investigation. These findings should enhance our understanding of the etiology of breast cancer. Cancer Epidemiol Biomarkers Prev; 23(4); 658–69. ©2014 AACR.

Plot of the first two principal components for the 7,920 participants in the iCOGS dataset.

Technical details of the DEPTH procedure.

PDF file - 197K, Table S1. Discovery set study populations. Table S2. Distribution of discovery set subjects by site, case-control status and chip. Table S3. Discovery set quality control summary. Table S4. SNPs with score-based P-values < 4 X 10-8 among 58016 SNPs for which Plink aborted the logistic regressions. Table S5. Replication set study populations. Table S6. Distribution of replication set subjects by site, case-control status and

PDF file - 272K

BRA-STRAP is an Australia-wide study of breast cancer predisposition that brings together gene-panel data from 30,000 adult Australian women of all ages, across the breast cancer risk spectrum, with and without a diagnosis of breast cancer. The “BRA-STRAP panel” includes 24 genes* that are involved in, or putatively associated with, predisposition to breast and/or ovarian cancer. Despite insufficient evidence for clinical translation for some of these genes, all 24 are commonly included on panel tests for breast cancer predisposition. We present findings from the population-based case-control sub-study of BRA-STRAP, which involved 1451 women diagnosed with breast cancer and 857 age-matched controls participating in the Australian Breast Cancer Family Registry (ABCFR), and 6101 healthy, elderly Australian women enrolled in the ASPREE study. These analyses focus on rare genetic variants predicted to lead to loss of function and/or classified as pathogenic/likely pathogenic (P/LP) in ClinVar. Odds ratios (ORs) for their associations with breast cancer were estimated by aggregating genetic variants for each gene. For the women diagnosed with breast cancer, the median age at diagnosis (inter-quartile range, IQR) was 40.0 (14.0) years and the overall frequency of P/LP variant carriers across all genes was 156/1451 (10.8%). The median age (IQR) of the ABCFR and ASPREE controls were 39.4 (14.9) and 73.9 (5.8) years, respectively. The frequencies of P/LP variant carriers were 33/857 (3.9%) and 268/6101 (4.4%) in the ABCFR and ASPREE controls, respectively. We combined both control datasets and, after adjusting for age and other potential confounders, the ORs associated with P/LP variants in BRCA1 and BRCA2 were 4.1 [95% confidence interval (CI): 1.8-10.2] and 2.9 [95% CI: 1.5-6], respectively. We also found that the OR for P/LP variants in ATM was 4.0 [95% CI: 1.5-10.4] and the OR for P/LP variants in PALB2 was 2.2 [95% CI: 0.75-5.7] although this did not reach statistical significance. These results contribute to international efforts to refine the breast cancer risk estimates for genetic variants identified from population-based screening of unselected women using genes that are included on panel tests and thought to be potentially breast cancer predisposition genes.The case-control-family design of the ABCFR will also allow us to estimate the age specific cumulative risk (penetrance) of these genetic variants, which is important for genetic counselling and the clinical management of carrier families. *ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, FANCM, MLH1, MRE11A, MSH2, MSH6, MUTYH, NBN, NF1, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, RECQL, STK11 and TP53 Citation Format: Tu Nguyen-Dumont, James Dowty, Katherine Tucker, Judy Kirk, Paul James, Alison Trainer, Ingrid Winship, Nicholas Pachter, Nicola Poplawski, Scott Grist, Daniel J Park, Anne-Laure Renault, Fleur Hammet, Maryam Mahmoodi, Helen Tsimiklis, Jason A Steen, Derrick Theys, Amanda Rewse, Amanda Willis, April Morrow, Catherine Speechly, Rebecca Harris, Moeen Riaz, Robert Sebra, Eric Schadt, Paul Lacaze, John McNeil, John L Hopper, Melissa C Southey. Population-based estimates of breast cancer risk for germline pathogenic variants identified by gene-panel testing: An Australian perspective [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS7-04.

Background: Hypomethylating agents (HMA) used in higher-risk myelodysplastic syndromes (MDS) improve survival but HMA-failure has a poor prognosis. Abnormal bone marrow (BM) colony-forming units (CFUs) persist in treated MDS patients despite achievement of complete remission, suggesting persistent abnormal stem cell function. We aim to identify genetic biomarkers following treatment with Azacitidine ± Thalidomide or Lenalidomide that predict clinical outcomes in MDS. Methods: BM cells from patients enrolled in ALLG MDS3 and MDS4 clinical trials at baseline and after 4 cycles of treatment (C4) were grown in Methocult for 14 days. CFUs were pooled at baseline; C4 macroscopically normal and abnormal colonies were harvested separately. mRNA expression was quantified using the Nanostring nCounter PanCancer Pathways panel. Clinical outcomes analysed were: (1) clinical benefit at 12 months (haematological improvement or better as per IWG criteria) (2) best response achieved. Genes expressed above background level in ≥25% of samples were included for statistical analyses, resulting in 516 genes across 56 samples from 23 different patients. R limma package was used for differential expression analysis. Patients were weighted using limma's voomWithQualityWeights function. Moderated t-tests with empirical Bayes were done to identify differentially expressed genes. For testing between colonies, a log-fold-change cut-off of 0.5 was used with limma's treat function. P-values were adjusted for multiple hypothesis testing. Results: 98 genes exhibited significantly different expression (p Conclusion: We identified changes in gene expression following treatment in MDS that predict outcomes in response and clinical benefit. These genetic biomarkers require further validation and could define early markers of resistance for investigation of novel therapies. Citation Format: Lynette Chee, David Ritchie, Jessica Chung, Daniel Park, Mandy Ludford-Menting, Jane Ripley, Melita Kenealy, Rachel Koldej. Genetic biomarkers predict clinical response and survival in myelodysplasia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1548.