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.

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.

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.

Background : Mammographic (or breast) density is an established risk factor for breast cancer. There are a variety of approaches to measurement including quantitative, semi-automated and automated approaches. We present a new automated measure, AutoCumulus, learnt from applying deep learning to semi-automated measures. Methods: We used mammograms of 9,057 population-screened women in the BRAIx study for which semi-automated measurements of mammographic density had been made by experienced readers using the CUMULUS software. The dataset was split into training, testing, and validation sets (80%, 10%, 10%, respectively). We applied a deep learning regression model (fine-tuned ConvNeXtSmall) to estimate percentage density and assessed performance by the correlation between estimated and measured percent density and a Bland-Altman plot. The automated measure was tested on an independent CSAW-CC dataset in which density had been measured using the LIBRA software, comparing measures for left and right breasts, sensitivity for high sensitivity, and areas under the receiver operating characteristic curve (AUCs). Results: Based on the testing dataset, the correlation in percent density between the automated and human measures was 0.95, and the differences were only slightly larger for women with higher density. Based on the CSAW-CC dataset, AltoCumulus outperformed LIBRA in correlation between left and right breast (0.95 versus 0.79; P<0.001), specificity for 95% sensitivity (13% versus 10% (P<0.001)), and AUC (0.638 cf. 0.597; P<0.001). Conclusion: We have created an automated measure of mammographic density that is accurate and gives superior performance on repeatability within a woman, and for prediction of interval cancers, than another well-established automated measure.

In Australia, there is a high burden of acute rheumatic fever (ARF) among Aboriginal and Torres Strait Islander peoples. Clinical diagnostic criteria can result in a diagnosis of ‘definite’, ‘probable’ or ‘possible’ ARF and outcomes range from recovery to severe rheumatic heart disease (RHD). We compared outcomes by ARF diagnosis, where the main outcome was defined as disease progression from: possible to probable ARF, definite ARF or RHD; probable to definite ARF or RHD; or definite ARF to definite ARF recurrence or RHD. Data were extracted from the Northern Territory RHD register for Indigenous Australians with an initial diagnosis of ARF during the 5.5-year study period (01/01/2013–30/06/2019). Descriptive statistics were used to describe cohort characteristics, probability of survival, and cumulative incidence risk of disease progression. Cox proportional hazards regression was used to determine whether time to disease progression differed according to ARF diagnosis. Sub-analyses on RHD outcome, clinical manifestations, and antibiotic adherence were also performed. In total there were 913 cases with an initial ARF diagnosis. Of these, 92 (13%) experienced disease progression. The probability of disease progression significantly differed between ARF diagnoses (p = 0.0043; log rank test). Cumulative incidence risk of disease progression at 5.5 years was 33.6% (95% CI 23.6–46.2) for definite, 13.5% (95% CI 8.8–20.6) for probable and 11.4% (95% CI 6.0–21.3) for possible ARF. Disease progression was 2.19 times more likely in those with definite ARF than those with possible ARF (p = 0.026). Progression to RHD was reported in 52/732 (7%) of ARF cases with normal baseline echocardiography. There was a significantly higher risk of progression from no RHD to RHD if the initial diagnosis was definite compared to possible ARF (p<0.001). These data provide a useful way to stratify risk and guide prognosis for people diagnosed with ARF and can help inform practice.

BACKGROUND Cirrus is an automated risk predictor for breast cancer that comprises texture-based mammographic features and is mostly independent of mammographic density. We investigated genetic and environmental variance of variation in Cirrus. METHODS We measured Cirrus for 3195 breast-cancer-free participants, including 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1599 siblings of twins. Multivariate normal models were used to estimate the variance and familial correlations of age-adjusted Cirrus as a function of age. The classic twin model was expanded to allow the shared environment effects to differ by zygosity. The single-nucleotide polymorphism (SNP)-based heritability was estimated for a subset of 2356 participants. RESULTS There was no evidence that the variance or familial correlations depended on age. The familial correlations were 0.52(standard error[SE]=0.03) for MZ pairs and 0.16(SE=0.03) for DZ and non-twin sister pairs combined. Shared environmental factors specific to MZ pairs accounted for 20% of the variance. Additive genetic factors accounted for 32%(SE=5%) of the variance, consistent with the SNP-based heritability of 36%(SE=16%). CONCLUSIONS Cirrus is substantially familial due to genetic factors and an influence of shared environmental factors that was evident for MZ twin pairs only. The latter could be due to non-genetic factors operating in utero or in early life that are shared by MZ twins. IMPACT Early-life factors shared more by MZ pairs than DZ/non-twin sister pairs, could play a role in the variation in Cirrus, consistent with early life being recognised as a critical window of vulnerability to breast carcinogens.

we assessed 12 lifestyle-related epigenetic scores for their association with

BACKGROUND DEPendency of association on the number of Top Hits (DEPTH) is an approach to identify candidate susceptibility regions by considering the risk signals from overlapping groups of sequential variants across the genome. METHODS We conducted a DEPTH analysis using a sliding window of 200 SNPs to colorectal cancer (CRC) data from the Colon Cancer Family Registry (CCFR) (5,735 cases and 3,688 controls), and GECCO (8,865 cases and 10,285 controls) studies. A DEPTH score >1 was used to identify candidate susceptibility regions common to both studies. We compared DEPTH results against those from conventional GWAS analyses of these two studies as well as against 132 published susceptibility regions. RESULTS Initial DEPTH analysis revealed 2,622 (CCFR) and 3,686 (GECCO) candidate susceptibility regions, of which 569 were common to both studies. Bootstrapping revealed 40 and 49 candidate susceptibility regions in the CCFR and GECCO data sets, respectively. Notably, DEPTH identified at least 82 regions that would not be detected using conventional GWAS methods, nor had they been identified by previous CRC GWASs. We found four reproducible candidate susceptibility regions (2q22.2, 2q33.1, 6p21.32, 13q14.3). The highest DEPTH scores were in the HLA locus at 6p21 where the strongest associated SNPs were rs762216297, rs149490268, rs114741460, and rs199707618 for the CCFR data, and rs9270761 for the GECCO data. CONCLUSIONS DEPTH can identify candidate susceptibility regions for CRC not identified using conventional analyses of larger datasets. IMPACT DEPTH has potential as a powerful complementary tool to conventional GWAS analyses for discovering susceptibility regions within the genome.

Abstract Background The extent to which known and unknown factors explain how much people of the same age differ in disease risk is fundamental to epidemiology. Risk factors can be correlated in relatives, so familial aspects of risk (genetic and non-genetic) must be considered. Development We present a unifying model (VALID) for variance in risk, with risk defined as log(incidence) or logit(cumulative incidence). Consider a normally distributed risk score with incidence increasing exponentially as the risk increases. VALID’s building block is variance in risk, Δ2, where Δ = log(OPERA) is the difference in mean between cases and controls and OPERA is the odds ratio per standard deviation. A risk score correlated r between a pair of relatives generates a familial odds ratio of exp(rΔ2). Familial risk ratios, therefore, can be converted into variance components of risk, extending Fisher’s classic decomposition of familial variation to binary traits. Under VALID, there is a natural upper limit to variance in risk caused by genetic factors, determined by the familial odds ratio for genetically identical twin pairs, but not to variation caused by non-genetic factors. Application For female breast cancer, VALID quantified how much variance in risk is explained—at different ages—by known and unknown major genes and polygenes, non-genomic risk factors correlated in relatives, and known individual-specific factors. Conclusion VALID has shown that, while substantial genetic risk factors have been discovered, much is unknown about genetic and familial aspects of breast cancer risk especially for young women, and little is known about individual-specific variance in risk.

Background: Outcomes after acute rheumatic fever (ARF) diagnosis are variable, ranging from recovery to development of severe rheumatic heart disease (RHD). There is no diagnostic test. Evaluation using the Australian clinical diagnostic criteria can result in a diagnosis of definite, probable or possible ARF. The possible category was introduced in 2013 in Australias Northern Territory (NT). Our aim was to compare longitudinal outcomes after a diagnosis of definite, probable or possible ARF. Methods: We extracted data from the NT RHD register for Indigenous Australians with an initial diagnosis of ARF during the 5.5-year study period (01/01/2013 - 30/06/2019). Descriptive statistics were used to describe the demographic and clinical characteristics at initial ARF diagnosis. Kaplan-Meier curves were used to assess the probability of survival free of disease progression and the cumulative incidence risk at each year since initial diagnosis was calculated. Cox proportional hazards regression was used to determine whether time to disease progression differed according to ARF diagnosis and whether progression was associated with specific predictors at diagnosis. A multinomial logistic regression model was performed to assess whether ARF diagnosis was associated with RHD outcome and to assess associations between ARF diagnosis and clinical manifestations. A generalised linear mixed model (GLMM) was developed to assess any differences in the long-term antibiotic adherence between ARF diagnosis categories and to examine longitudinal trends in adherence. Results: There were 913 initial ARF cases, 732 with normal baseline echocardiography. Of these, 92 (13%) experienced disease progression: definite ARF 61/348 (18%); probable ARF 20/181 (11%); possible ARF 11/203 (5%). The proportion of ARF diagnoses that were uncertain (i.e. possible or probable) increased over time, from 22/78 (28%) in 2013 to 98/193 (51%) in 2018. Cumulative incidence risk of any disease progression at 5.5 years was 33.6 (23.6-46.2) for definite ARF, 13.5 (8.8-20.6) for probable and 11.4% (95% CI 6.0-21.3) for possible ARF. The probability of disease-free survival was lowest for definite ARF and highest for possible ARF (p=0.004). Cox proportional hazards regression indicated that disease progression was 2.19 times more likely in those with definite ARF than those with possible ARF (p=0.026). Progression to RHD was reported in 37/348 (11%) definite ARF, 10/181 (6%) probable ARF, and 5/203 (2%) possible ARF. The multinomial logistic regression model demonstrated a significantly higher risk of progression from no RHD to RHD if the initial diagnosis was definite compared to possible ARF (p<0.001 for both mild and moderate-severe RHD outcomes). The GLMM estimated that patients with definite ARF had a significantly higher adherence to antibiotic prophylaxis compared with probable ARF (p=0.024). Conclusion: These data indicate that the ARF diagnostic categories are being applied appropriately, are capturing more uncertain cases over time, provide a useful way to stratify risk and guide prognosis, and can help inform practice. Possible ARF is not entirely benign; some cases progress to RHD.

Supplemental material is available for this article. Keywords: Mammography, Screening, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license. See also the commentary by Cadrin-Chênevert in this issue.