AIMS 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. MATERIALS & METHODS 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). RESULTS 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. CONCLUSION 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.

Pancreatic steatosis and metabolic-dysfunction-associated steatotic liver disease are characterised by fat accumulation in abdominal organs, but their correlation remains inconclusive. Recently proposed deep learning (DL) for proton density fat fraction (PDFF) estimation, which quantifies organ fat, has primarily been assessed for quantifying liver fat. This study aims to validate DL models for pancreatic PDFF quantification and compare pancreas and liver fat content. We evaluated three DL models—Non-Linear Variables Neural Network (NLV-Net), U-Net, and Multi-Decoder Water-Fat separation Network—against a reference PDFF measured using a graph-cut-based method. NLV-Net showed a strong correlation (Spearman rho) with the reference PDFF in the six-echo pancreatic head (slope: 1.02, rho: 0.95) and body (slope: 1.04, rho: 0.94) and a moderate correlation in the three-echo pancreatic head (slope: 0.44, rho: 0.40) and body (slope: 0.49, rho: 0.34). Weak correlations were found between liver and pancreatic body PDFF using graph cut in six-echo (slope: −0.041, rho: −0.12) and three-echo images (slope: 0.0014, rho: 0.073) and using NLV-Net in six-echo (slope: −0.053, rho: −0.12) and three-echo images (slope: −0.014, rho: −0.033). In conclusion, NLV-Net showed the best agreement with the reference for pancreatic fat quantification, and no correlation was found between liver and pancreas fat.

Abstract Glioma is a rare and debilitating brain cancer with one of the lowest cancer survival rates. Genome-wide association studies have identified 34 genetic susceptibility regions. We sought to discover novel susceptibility regions using approaches that test groups of contiguous genetic markers simultaneously. We analyzed data from three independent glioma studies of European ancestry, GliomaScan (1,316 cases/1,293 controls), Australian Genomics and Clinical Outcomes of Glioma Consortium (560 cases/2,237 controls), and Glioma International Case-Control Study (4,000 cases/2,411 controls), using the machine learning algorithm DEPendency of association on the number of Top Hits and a region-based regression method based on the generalized Berk–Jones (GBJ) statistic, to assess the association of glioma with genomic regions by glioma type and sex. Summary statistics from the UCSF/Mayo Clinic study were used for independent validation. We conducted a meta-analysis using GliomaScan, Australian Genomics and Clinical Outcomes of Glioma Consortium, Glioma International Case-Control Study, and UCSF/Mayo. We identified 11 novel candidate genomic regions for glioma risk common to multiple studies. Two of the 11 regions, 16p13.3 containing RBFOX1 and 1p36.21 containing PRDM2, were significantly associated with female and male glioma risk respectively, based on the results of the meta-analysis. Both regions have been previously linked to glioma tumor progression. Three of the 11 regions contain neurotransmitter receptor genes (7q31.33 GRM8, 5q35.2 DRD1, and 15q13.3 CHRNA7). Our region-based approach identified 11 genomic regions that suggest an association with glioma risk of which two regions, 16p13.3 and 1p36.21, warrant further investigation as genetic susceptibility regions for female and male risk, respectively. Our analyses suggest that genetic susceptibility to glioma may differ by sex and highlight the possibility that synapse-related genes play a role in glioma susceptibility. Significance: Further investigation of the potential susceptibility regions identified in our study may lead to a better understanding of glioma genetic risk and the underlying biological etiology of glioma. Our study suggests sex may play a role in genetic susceptibility and highlights the importance of sex-specific analysis in future glioma research.

Survivors of colorectal cancer (CRC) are at risk of developing another primary colorectal cancer - metachronous CRC. Understanding which pathological features of the first tumour are associated with risk of metachronous CRC might help tailor existing surveillance guidelines. Population-based CRC cases were recruited from the United States, Canada and Australia between 1997 and 2012 and followed prospectively until 2022 by the Colon Cancer Family Registry. Metachronous CRC was defined as a new primary CRC diagnosed at least 1 year after the initial CRC. Those with the genetic cancer predisposition Lynch syndrome or MUTYH mutation carriers were excluded. Cox regression models were fitted to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) for the associations. Of 6085 CRC cases, 138 (2.3%) were diagnosed with a metachronous CRC over a median follow-up time of 12 years (incidence: 2.0 per 1000 person-years). CRC cases with a synchronous CRC were 3.4-fold more likely to develop a metachronous CRC (adjusted HR: 3.36, 95% CI: 1.89-5.98) than those without a synchronous tumour. CRC cases with MMR-deficient tumours had a 72% increased risk of metachronous CRC (adjusted HR: 1.72, 95% CI: 1.11-2.64) compared to those with MMR-proficient tumours. Compared to cases who had an adenocarcinoma histologic type, those with an undifferentiated histologic type were 77% less likely to develop a metachronous CRC (adjusted HR: 0.23, 95% CI: 0.06-0.94). Existing surveillance guidelines for CRC survivors could be updated to include increased surveillance for those whose first CRC was diagnosed with a synchronous CRC or was MMR-deficient.

Abstract Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age‐specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age‐specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome‐wide association study data, and the within‐pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.

Abstract A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that (a) for breast cancer diagnosed at a young age, for example, <50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS‐disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first‐degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.