Evaluation of agreement between common clustering strategies for DNA methylation-based subtyping of breast tumours.Epigenomics • December 23, 2024
Elaheh Zarean, Shuai Li, Ee Wong, Enes Makalic, Roger Milne, Graham Giles, Catriona Mclean, Melissa Southey, Pierre-antoine Dugué
Clustering algorithms have been widely applied to tumor DNA methylation datasets to define methylation-based cancer subtypes. This study aimed to evaluate the agreement between subtypes obtained from common clustering strategies. We used tumor DNA methylation data from 409 women with breast cancer from the Melbourne Collaborative Cohort Study (MCCS) and 781 breast tumors from The Cancer Genome Atlas (TCGA). Agreement was assessed using the adjusted Rand index for various combinations of number of CpGs, number of clusters and clustering algorithms (hierarchical, K-means, partitioning around medoids, and recursively partitioned mixture models). Inconsistent agreement patterns were observed for between-algorithm and within-algorithm comparisons, with generally poor to moderate agreement (ARI <0.7). Results were qualitatively similar in the MCCS and TCGA, showing better agreement for moderate number of CpGs and fewer clusters (K = 2). Restricting the analysis to CpGs that were differentially-methylated between tumor and normal tissue did not result in higher agreement. Our study highlights that common clustering strategies involving an arbitrary choice of algorithm, number of clusters and number of methylation sites are likely to identify different DNA methylation-based breast tumor subtypes.
GWAS meta-analysis identifies five susceptibility loci for endometrial cancer.EBioMedicine • January 23, 2025
Dhanya Ramachandran, Xuemin Wang, Triin Laisk, Ying Zheng, Nathan Ingold, Daffodil Canson, Pik Kho, Bianca Naumann, Carly Chapman, Kristine Bousset, Anna Krause, Peter Schürmann, Britta Wieland, Patricia Hanel, Fabienne Hülse, Norman Häfner, Ingo Runnebaum, Natalia Dubrowinskaja, Nurzhan Turmanov, Tatyana Yugay, Zura Yessimsiitova, Frédéric Amant, Daniela Annibali, Matthias Beckmann, Clara Bodelon, Daniel Buchanan, Chu Chen, Megan Clarke, Linda Cook, Immaculata De Vivo, Wout De Wispelaere, Mengmeng Du, Douglas Easton, Julius Emons, Peter Fasching, Christine Friedenreich, Grace Gallagher, Graham Giles, Ellen Goode, Holly Harris, David Hunter, David Kolin, Peter Kraft, James Lacey, Diether Lambrechts, Lingeng Lu, George Mutter, Jeffin Naduparambil, Kelli O'connell, Alpa Patel, Paul D Pharoah, Timothy Rebbeck, Fulvio Ricceri, Harvey Risch, Matthias Ruebner, Carlotta Sacerdote, Rodney Scott, V Setiawan, Xiao-ou Shu, Melissa Southey, Emma Tham, Ian Tomlinson, Constance Turman, Nicolas Wentzensen, Wanghong Xu, Herbert Yu, Wei Zheng, Amanda Spurdle, Yosef Yarden, Peter Hillemanns, Dylan Glubb, Thilo Dörk, Tracy O'mara
Background: Endometrial cancer is the most common gynaecological cancer in high-income countries. In addition to environmental risk factors, genetic predisposition contributes towards endometrial cancer development but is still incompletely defined.
Methods: Building on genome-wide association studies (GWASs) by the Endometrial Cancer Association Consortium, we conducted a GWAS meta-analysis of 17,278 endometrial cancer cases and 289,180 controls, incorporating biobank samples from the UK, Finland, Estonia and Japan.
Results: GWAS analysis identified five additional risk loci (3p25.2, 3q25.2, 6q22.31, 12q21.2, and 17q24.2). Corresponding gene-based analyses supported findings for three of the five loci, at NAV3 (12q21.2), PPARG (3p25.2), and BPTF (17q24.2), as well as two additional candidate risk regions at ATF7IP2 (16p13.2-p13.13) and RPP21 (6p22.1). Validation genotyping in further independent case-control series replicated the most significant locus at 12q21.2 and corroborated risk variants located intronic to NAV3, the gene for Neuron Navigator 3. Downregulation of NAV3 in endometrial cell lines accelerated cell division and wound healing capacity whereas NAV3 overexpression reduced cell survival and increased cell death, indicating that NAV3 acts as a tumour suppressor in endometrial cells.
Conclusions: Our large study extends the number of genome-wide significant risk loci identified for endometrial carcinoma by about one-third and proposes a role of NAV3 as a tumour suppressor in this common cancer. Background: This study was mainly supported by funding from the Wilhelm Sander Foundation, Germany, and the National Health and Medical Research Council (NHMRC) of Australia. A complete list of funding organisations is provided in the acknowledgements.
Allergic disease and risk of multiple myeloma: A case-control study.Cancer Epidemiology • December 22, 2024
Simon Cheah, Adrian Lowe, Nina Afshar, Julie Bassett, Fiona Bruinsma, Wendy Cozen, Simon Harrison, John Hopper, Harindra Jayasekara, H Prince, Claire Vajdic, Nicole Doo, Graham Giles, Shyamali Dharmage, Roger Milne
Objective: Multiple myeloma (MM) is responsible for significant morbidity and mortality, yet our knowledge regarding MM aetiology remains limited. We investigated whether a history of allergic conditions is associated with MM risk.
Methods: Incident cases (n = 782) of MM were recruited via cancer registries in Victoria and NSW. Controls (n = 733) were siblings (n = 436) or spouses (n = 297) of cases. Unconditional logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for associations between self-reported allergic conditions (asthma, eczema, food allergy, hay fever) and MM risk.
Results: Eczema was inversely associated with MM risk (OR = 0.54, 95 %CI = 0.42-0.70), as was a combined history of food allergy and eczema (OR = 0.52, 95 %CI = 0.29-0.93). There was an inverse association between a history of any allergic condition (compared with none) and risk of MM (OR = 0.68, 95 %CI = 0.55-0.84). In the mean-centred dose-risk analysis the OR was 0.87 (95 %CI = 0.73-1.04) per additional allergic condition of interest. No notable associations were identified for food allergy, asthma, or hay fever alone.
Conclusions: We found that a history of allergic disease, particularly eczema, was associated with reduced MM risk. Further research is recommended to confirm findings and investigate potential mechanisms.
Hormone therapy use and young-onset breast cancer: a pooled analysis of prospective cohorts included in the Premenopausal Breast Cancer Collaborative Group.The Lancet. Oncology • December 04, 2024
Katie O'brien, Melissa House, Mandy Goldberg, Michael Jones, Clarice Weinberg, Amy De Gonzalez, Kimberly Bertrand, William Blot, Jessica Dehart, Fergus Couch, Montserrat Garcia Closas, Graham Giles, Victoria Kirsh, Cari Kitahara, Woon-puay Koh, Hannah Park, Roger Milne, Julie Palmer, Alpa Patel, Thomas Rohan, Minouk Schoemaker, Anthony Swerdlow, Lauren Teras, Celine Vachon, Kala Visvanathan, Jian-min Yuan, Wei Zheng, Hazel Nichols, Dale Sandler
Background: Oestrogen plus progestin hormone therapy is an established risk factor for breast cancer in postmenopausal women. We examined the less well-studied association between exogenous hormones and breast cancer in young women, who might use hormone therapy after gynaecological surgery or to relieve perimenopausal symptoms.
Methods: In this pooled cohort analysis, we investigated the relationship between exogenous hormones and breast cancer in young women using data from 10-13 prospective cohorts from North America, Europe, Asia, and Australia. The participating cohorts followed up women for incident breast cancer until age 55 years. We used cohort-stratified, multivariable-adjusted Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% CI for associations of hormone therapy with incident young-onset breast cancer. We also estimated risk differences based on cumulative risk until age 55 years.
Results: We included 459 476 women aged 16-54 years (mean 42·0 years [IQR 35·5-49·2]), of whom 8455 (2%) developed young-onset breast cancer (diagnosed before age 55 years; median follow-up 7·8 years [5·2-11·2]). Overall, 15% of participants reported using hormone therapy, with oestrogen plus progestin hormone therapy (6%) and unopposed oestrogen (5%) being the most common types. Cumulative risk of young-onset breast cancer was 4·1% in non-users. Hormone therapy of any type was not associated with incident young-onset breast cancer (HR 0·96 [95% CI 0·88 to 1·04]), but ever oestrogen hormone therapy use was inversely associated (0·86 [0·75 to 0·98]; risk difference -0·5% [-1·0 to -0·0]). The HR for ever oestrogen plus progestin hormone therapy and young-onset breast cancer was 1·10 (0·98 to 1·24), with positive associations observed for long-term use (1·18 [1·01 to 1·38] for >2 years) and use among women without hysterectomy or bilateral oophorectomy (1·15 [1·02 to 1·31]). Oestrogen hormone therapy and young-onset breast cancer association was similar for all breast cancer subtypes, but oestrogen plus progestin hormone therapy was more strongly associated with oestrogen receptor negative (1·44 [1·11 to 1·88]) and triple-negative disease (1·50 [1·02 to 2·20]) than with other subtypes.
Conclusions: Oestrogen hormone therapy use was inversely associated with young-onset breast cancer, and oestrogen plus progestin hormone therapy was associated with higher young-onset breast cancer incidence among women with intact uterus and ovaries. These findings largely parallel results from studies of hormone use and later-onset breast cancer and provide novel evidence for establishing clinical recommendations among younger women. Background: NIH Intramural Research Program.
Smart Nonuniformity for Calibrating Sequencing Depth of a Targeted Gene Panel to Simultaneously Detect Somatic and Germline Variants.The Journal Of Molecular Diagnostics : JMD • October 24, 2024
Robert O'reilly, Philip Harraka, Jared Burke, Daniele Belluoccio, Paul Yeh, Kerryn Howlett, Kiarash Behrouzfar, Amanda Rewse, Helen Tsimiklis, Graham Giles, John Hopper, Kristen Bubb, Stephen Nicholls, Roger Milne, Melissa Southey
Targeted gene panel sequencing that measures genomic variation at different depths has potential diagnostic application. A targeted gene panel, smart nonuniformity sequencing, was developed to detect somatic variants associated with clonal hematopoiesis of indeterminate potential (CHIP), which requires an optimal sequencing depth of >500×; and germline variants requiring a lower ≥50× depth (panel 1). This was achieved by adjusting probe ratios for genomic regions relevant to identifying CHIP in comparison to those relevant to germline variation analysis. An additional custom panel containing only the genomic regions relevant to the identification of CHIP (panel 2) was also manufactured to confirm that panel 1 did not miss variants because of the complex design. Both panels were used to sequence 150 blood-derived DNAs; 94 DNAs from research participants aged 64 to 75 years; 16 DNAs with known germline variants; 16 DNAs with known germline variants (titrated from 0% to 100%); 24 DNAs from individuals aged <40 years; and 3 commercial CHIP controls and 3 high-molecular-weight DNA controls. The sequencing median depth ratio between the CHIP and germline relevant genomic regions was 4.7:1. Fourteen CHIP-associated variants were called in both panel 1 (1382× median variant depth) and panel 2 (1665× median variant depth). All known germline variants were identified (251× median variant depth). Smart nonuniformity sequencing reliably detects variants with allele frequency in the range >0.01 to 1 in one workflow.
