Our proof-of-concept study reveals the potential of risk stratification by the combined effects of age, polygenic risk scores (PRS), and non-genetic risk factors in increasing the risk-benefit balance of rapidly emerging non-invasive multicancer early detection (MCED) liquid biopsy tests. We develop and validate sex-specific pan-cancer risk scores (PCRSs), defined by the combination of body mass index, smoking, family history of cancers, and cancer-specific polygenic risk scores (PRSs), to predict the absolute risk of developing at least one of the many common cancer types. We demonstrate the added value of PRSs in improving the predictive performance of the risk factors only model and project the positive and negative predictive values for two promising multicancer screening tests across risk strata defined by age and PCRS. Identifying individuals at the subclinical or asymptomatic stage provides a unique window of opportunity for early intervention that has been shown to improve survival2. In a general population with a relatively low prevalence of cancer, ideally, a screening test needs to be broadly accessible, highly specific, and sensitive. A screening test should be specific to minimize overdiagnosis-related psychological and financial burdens and risks associated with unnecessary follow-up treatments and sensitive to prevent missed or interval cases. For these reasons, to date, the United States Preventive Service Tasks Forces has recommended only a handful of age-based single-cancer screening modalities such as colonoscopy3 (37.1% to 79.4% sensitivity (se) and 86.7% to 97.3% specificity (sp)) for colorectal cancer4 mammogram5 (86.9% se and 88.9% sp) for breast cancer6, low-dose computerized tomography7 (59% to 100% se and 26.4 to 99.7% sp) scan for lung cancer8, and pap test9 (70% to 80% se and 95% sp) for cervical cancer10.Developing effective screening tools for early cancer detection has long been a pressing interest due to the poor prognosis and survival associated with advancing cancer stage13, low positive predictive value (PPV) or high false positives14, and missed or interval cancer cases15. Additionally, there are no presently accepted screening tools for many cancers with poor prognoses or high late-stage diagnosis rates for cancer detection in asymptomatic individuals. In this context, multicancer early detection (MCED) liquid biopsy tests using analytes such as cell-free DNA (cfDNA) are gaining traction26. Several recent studies have started to explore the feasibility of such approaches for early cancer detection in a limited clinical setting28. GRAIL’s Galleri test26 and Thrive’s DETECT-A24 (Detecting cancers Earlier Through Elective Mutation-based blood Collection and Testing) are of note.Existing single-cancer screening tools face several challenges, including lack of adherence to screening recommendations29. The values of PPV for tests are anticipated to be highly influenced by specificity, and for a constant specificity value, by the combination of sensitivity and prevalence. The current MCED tests have high specificity (99% or higher) but typically have low PPV for the general population due to modest sensitivity and low prevalence of cancer in the general population. While sensitivity for some of these tests can be substantially higher for some specific cancers and may be improved further through the incorporation of additional features in cfDNA28, the PPV in most settings will still be expected to remain low as the prevalence of individual cancers is even lower. Thus, in the future, a risk-stratified approach is likely to be needed to enhance PPV and the risk-benefit balance of these tests. In the multicancer setting, however, population risk stratification becomes more challenging as one needs to consider risk factors across many cancers. There are also new opportunities due to the emergence of polygenic risk scores (PRSs) from genome-wide association studies (GWASs) across many cancers30. Recently, a study investigated the potential utility of PRSs and other risk factors to build a model for predicting risk for at least one of several cancers to understand the impact of lifestyle modifications on overall cancer risk30. However, the prospects of risk stratification in multicancer screening and the added values of PRS in addition to classical risk factors are yet to be investigated in the context of emerging MCED tests.MCED has the promise to lower cancer mortality, especially through early detection of cancers for which there is currently no screening available. However, as many recent studies have shown, the issue of low PPV persists as a significant limitation of the newly developed multicancer tests , colorectum, endometrium, kidney, lung, melanoma, non-Hodgkin’s lymphoma, ovary, pancreas, and prostate , respectively, over the course of follow-up. We used sex-specific Cox proportional hazards models where the baseline hazard is specified as a function of age and assumed the multiplicative effects of the risk factors32 with the outcome as the first cancer incidence of the above-mentioned cancers. PRSs were calculated for eleven  cancer types  study and the US population cancer incidence rates to estimate future cancer risk given individuals’ genetic and nongenetic profiles. We identified the top ten incident cancer types for females and males with sufficiently large and publicly available GWAS (see Methods)As anticipated, PCRSs were strongly associated with the risk of developing at least one cancer during the follow-up of the UKBB study in both females  and males   varied substantially by the level of the underlying risk of the population strata and the diagnostic accuracies of the liquid biopsy test in question Figs. , 2d–f. FAs the first step to integrating these new multicancer screening tests in the clinical setting, one can also consider a scenario in which a fixed threshold for PPV is employed as a metric to recommend early multicancer screening in the asymptomatic stage. The eligibility will strongly vary by both age and PCRS percentile. For example, at a threshold of 40% PPV, females could be eligible for the Galleri test as early as age 50 and in the 95th PCRS risk percentile and above Fig. . However36, improvements in cfDNA analyses will be needed to overcome these challenges, potentially through the use of mutation-agnostic methods36. Additional empirical data are needed to explore the potential heterogeneity of the diagnostic accuracy of MCED tests by age and other risk factors. In our multicancer risk prediction models, we included a limited set of risk factors, namely two major lifestyle-related factors that influence the risk of multiple cancers, family history of the most common cancers, and PRSs for each cancer type. We further assumed a proportional hazard model for all cancer risks, assuming multiplicative effects of age and all the other risk factors. Additional efforts are needed to build and validate more refined multicancer risk models in prospective cohort studies by including additional risk factors and interaction effects. Further, models38 that incorporate extensive family history information and carrier status for rare high-penetrant mutations would be important for individuals in high-risk families with strong clustering of related cancers. Excluding variants with minor allele frequency <0.01 is another limitation of our study.Our analysis has several limitations. We assumed that the reported sensitivity and specificity of a test like DETECT-A and Galleri would be applicable across all age, sex, and risk groups. Given the increase in observed sequence alterations in cfDNA resulting from clonal hematopoiesis in older individualsWe did not account for all cancer types in our multicancer model. To build a more robust and complete multicancer risk prediction model, cancer-specific risk models should first be developed and then combined to generate the risk of composite outcomes, like that of any cancer among several. The use of site-specific models will also allow PPV calculations to take into consideration underlying variations in the diagnostic accuracy of the MCED tests across different cancer types. Finally, our model-building effort was restricted to the participants of White British ancestry in the UKBB due to the limited sample size of other ancestry groups in this study, and also the lack of well-validated PRS in non-European ancestry populations. Large studies of diverse populations are urgently needed to study the accuracy of MCED tests and to build and validate robust multicancer risk prediction models across different racial and ethnic groups.In summary, we conducted a first-of-a-kind study highlighting the potential for a risk-based approach in multicancer screening. We observed the added value of PRS in improving the degree of risk stratification for composite cancer outcomes compared to the model defined by age, family history, smoking, and BMI. In the context of population-level early cancer screening, the addition of PRS could allow the detection of high-risk individuals even in the absence of conventional risk factors. In the future, well-powered empirical studies are needed in diverse populations to prospectively evaluate the utility of the multicancer liquid biopsy tests for their use in personalized early cancer detection.31. First, we surveyed the NHGRI-EBI Catalog of Published Genome-Wide Association Studies 39 and the Polygenic Risk Score (PGS) Catalog40 to select the largest European ancestry-based GWAS as of May 2020 for each cancer type. We additionally browsed PubMed41 for large cancer-specific GWASs that were not included in the GWAS Catalog or PGS Catalog. For breast and colorectal cancer, we searched for prior European sample-based large-scale polygenic risk score (PRS) studies as of July 2020 and selected studies reporting the best-performing PRS  were included in our analysis. For the full list of source literature and GWAS summary statistics included in our analysis, see Supplementary Based on the report from the 2013–2017 United States Cancer Statistics (USCS) database, we identified the top ten malignant incident cancer types for females and males, after excluding non-melanoma skin cancer44. Individuals aged 40–69 at baseline were recruited across the United Kingdom (UK) from 2006–201044. A wide range of genotypic and phenotypic information, including personal medical and family history and lifestyle data, were collected at enrollment44. UKBB data is regularly updated by completing follow-up questionnaires, linkage to national cancer and mortality registries, and hospital inpatient electronic medical records systems44. With linkage to the national cancer registry data, cancer diagnosis date and type ) were available for participants diagnosed with cancer44. For our analysis, we used ICD-10 codes for cancer classification (see Supplementary Table UK Biobank (UKBB) is a prospective epidemiological cohort study with over 500,000 participantsWe then filtered to unrelated UKBB participants of White British ancestry with imputed genotype data. We excluded individuals who were lost to follow-up, with genetic sex and self-reported sex mismatch, those with any cancer diagnosis prior to baseline assessment , and participants with missing data in any one of the classical risk factors . In UKBB, family history of all cancers is not available. UK Biobank only reports family history of the top three cancer incident types for females  and males . These quality control procedures resulted in a study population involving 133,830 females and 115,207 males.p-value. We excluded variants with minor allele frequency (MAF) < 0.01 and ambiguous SNPs  with MAF > 0.40. We filtered to variants with a MAF difference of less than 0.10 relative to the UK Biobank data. We removed variants with allele mismatches that could not be resolved by strand or dosage flips and/or SNPs with complete information mismatch, based on RSID, chromosome number, and position, to the European 1000 Genome reference panel45 or the UK Biobank data. We filtered to variants with an information score ≥0.90 based on the UK Biobank imputed genotype data. Finally, we used the fixed threshold approach to calculate PRS for each cancer. Using Plink46, we performed LD clumping at a p-value threshold of 5E-8, r2 of 0.1, and 1000 kb window with the European 1000 Genome reference panel45 as the reference panel to remove SNPs in linkage disequilibrium within each cancer type.After determining the source literature . Data were split into 2/3 training set and 1/3 of test set—independent validation datasets used for model performance evaluation and subsequent analysis.32 was fitted to the training set with the outcome as an incidence of any first cancer included in the analysis. The models specified a baseline hazard as a function of age and assumed multiplicative effects of the risk factors32:Cox proportional hazard regression (Cox) modelz = : set of covariates (risk factors) included in the Cox modelβ = : set of coefficients (log hazard ratios) for the predictors48. In the underlying analysis of the UK Biobank data using the Cox proportional hazard model, the “event” is defined as the occurrence of any of these cancers, and the “time-to-event” is the time to first onset of any of these cancers. Thus, if an individual has multiple cancers, e.g., lung cancer first and then prostate, the individual is censored at the onset of the lung cancer. Further, if an individual first develops cancer of a type other than the ones included in our list, then they are censored at the first onset of those cancer types. Further, deaths from non-cancer causes were also treated as censoring events. Thus, the underlying hazard ratio parameters of the model can be interpreted as the instantaneous risk of developing at least one among the set of selected cancers, given a person was free of all cancers up to that time point.Polygenic risk scores for each cancer  missing, but all other individual variables had a small missing rate (<5%). For demonstrating the risk-stratification ability of models, a complete-case analysis is more desirable as imputation and model averaging will cause a diminishing of risk-stratification compared to the full potential of the model. In other words, our goal is to demonstrate the risk-stratification ability of the models for a population in which the underlying risk factors could be fully observed. From that point of view, a complete-case analysis is more desirable.We computed pan-cancer risk scores (PCRS) or cancer-specific risk scores for all UKBB participants as the weighted sum of the predictors, with weights for each predictor as the estimated log hazard ratio (HRs) from the fitted Cox model. Then, in the test set, we assessed the discriminatory accuracy of the pan-cancer risk score (PCRS) or the cancer-specific risk score  using Harrel’s concordance index (C-statistic) and area under the curve (AUC) at five years of follow-up.49 to estimate absolute risk. Detailed methodology for absolute risk model building is described in Choudhury et al. 202049. Briefly, risk estimates for each individual in the test set were obtained by feeding age-specific cancer incidence rates by 1-year strata, log HR parameters from the Cox model, and the reference dataset into the model. We used 2016 cancer incidence rates in white individuals of the SEER*Stat database50. Site-specific cancer incidence rates were obtained and then added to get the overall incidence rates for any cancer included in our study. Cancer incidence rates for a given age and sex were determined by the following year’s cancer incidence rates. For instance, in our study, cancer incidence rates for females aged 50–51 will correspond to SEER*Stat’s cancer incidence rates for females aged 51–52. This is to account for the fact that the DETECT-A test was performed at study enrollment, and the female participants were followed up over the course of 12 months. DETECT-A and Galleri will both be used to detect cancers early, prior to conventional diagnosis. The reference dataset was obtained by simulating 10,000 samples representative of the underlying UKBB population using the normal distribution with PCRS or cancer-specific risk score mean and standard deviation.We used iCARE 24. We wanted to select a time window for absolute risk estimation so that the PPV for females aged 65–75 is equal to the point estimate of 19.4% reported in the DETECT-A study24. We varied the time window by one month around one year and calculated the weighted average PPV for females aged 65–75 based on the UKBB PCRS distribution and age distribution as reported by the US Census Bureau51. We found that a time window of 11 months provided the best match for the overall PPV for the 65–75 group to the empirically determined PPV value of 19.4%. Thus, subsequently, we calculated PPV and NPV for different age and PCRS risk groups based on underlying 11-month absolute risk.DETECT-A study reported an overall sensitivity of 27.1% at 98.9% specificity and an empirical PPV value of 19.4% (95% CI: 13.1–27.1%)26. For DETECT-A, we omit the calculation of projected PPVs and NPVs for males as it does not include prostate cancer  as one of the detectable cancer types50.Galleri reported an overall sensitivity of 51.5% at 99.5% specificity. For Galleri, we used a time window of 1-yearx, the positive predictive value and negative predictive value of the multicancer liquid biopsy test can be calculated using the formula below:Given the absolute risk estimate, 26.The absolute risk estimate can be written as a function of age and risk factors. We assumed that the sensitivity and specificity of the multicancer liquid biopsy test do not depend on the underlying risk factors, and we used the value of these as reported from the DETECT-A and Galleri study (Supplementary Table https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics).This study was conducted under UK Biobank Application Number 17712 (PI: Dr. Nilanjan Chatterjee). The study analyzes existing UK Biobank data and does not involve new human research participants. UK Biobank was approved by the North West Multi-center Research Ethics Committee (Further information on research design is available in the Supplementary DataSupplementary InformationREPORTING SUMMARY


An important prerequisite for actively engaging in cancer prevention and early detection measures, which is particularly recommended in cases of familial cancer risk, is the acquisition of information. Although a lot of cancer information is available, not all social groups are equally well reached because information needs and communicative accessibility differ. Previous research has shown that a live chat service provided by health professionals could be an appropriate, low-threshold format to meet individual information needs on sensitive health topics such as familial cancer risk. An established German Cancer Information Service is currently developing such a live chat service. As it is only worthwhile if accepted by the target groups, formative evaluation is essential in the course of the chat service’s development and implementation.This study aimed to explore the acceptance of a live chat on familial cancer risk by patients with cancer and their relatives (research question [RQ] 1) and examine the explanatory power of factors associated with their intentions to use such a service (RQ2). Guided by the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2), we examined the explanatory power of the following UTAUT2 factors: performance expectancy, effort expectancy, social influence, facilitating conditions, and habit, supplemented by perceived information insufficiency, perceived susceptibility, perceived severity, and cancer diagnosis as additional factors related to information seeking about familial cancer.We conducted a cross-sectional survey via a German web-based access panel in March 2022 that was stratified by age, gender, and education (N=1084). The participants are or have been diagnosed with cancer themselves (n=144) or have relatives who are or have been affected (n=990). All constructs were measured with established scales. To answer RQ1, descriptive data  were used. For RQ2, a blockwise multiple linear regression analysis was conducted.Overall, 32.7% of participants were (rather) willing, 28.9% were undecided, and 38.4% were (rather) not willing to use a live chat on familial cancer risk in the future. A multiple linear regression analysis explained 47% of the variance. It revealed that performance expectancy, social influence, habit, perceived susceptibility, and perceived severity were positively associated with the intention to use a live chat on familial cancer risk. Effort expectancy, facilitating conditions, information insufficiency, and cancer diagnosis were not related to usage intentions.A live chat seems promising for providing information on familial cancer risk. When promoting the service, the personal benefits should be addressed in particular. UTAUT2 is an effective theoretical framework for explaining live chat usage intentions and does not need to be extended in the context of familial cancer risk. Cancer is one of the leading causes of death worldwide , accountAlthough there is already a lot of cancer information available, not all social groups are equally well reached because information needs and access differ, for example, due to a lack of barrier-free information or the users’ insufficient health literacy ,9. In paAgainst this background, the German Cancer Information Service (Krebsinformationsdienst) of the German Cancer Research Center (Deutsches Krebsforschungszentrum) is currently expanding its information services to implement a web-based real time written chat for providing tailored information on familial cancer risk. This live chat enables synchronous, 1-to-1 written conversations between physicians of the Cancer Information Service and individuals interested in cancer information  in a web-based environment . It focuses on individual information about genetic and lifestyle-related cancer risks, as well as, the promotion of appropriate coping strategies, especially in the form of prevention and early detection measures. The chat is therefore aimed at interested individuals in Germany who would like general information on familial cancer risk, prevention, or early detection, but especially at individuals who suspect an increased risk of cancer due to an accumulation of individuals with cancer in the family or who already know about a genetic predisposition.However, such a live chat requires many resources on the part of the provider, needs to be adjusted to the users’ preferences, and is only worthwhile if accepted by the target groups. Formative evaluation is thus essential in the course of the chat’s development and implementation. Guided by the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) , this stA widely accepted theory for explaining consumers’ intentions to use digital technologies such as live chats is the UTAUT2 , which hIn the health context, performance expectancy is related to the expected personal benefit of using a specific technology such as a live chat on familial cancer . It is one of the strongest predictors of behavioral intentions having a moderate to large effect ,22,23,25As the use of health-related technology is often free of charge and medical contexts require a sensitive approach that is not necessarily enjoyable, the UTAUT2 factors hedonic motivation and price value have often not been considered in contexts of health ,20,23,33In line with the current state of research, we postulated the following hypothesis:H1: Performance expectancy (H1a), effort expectancy (H1b), social influence (H1c), facilitating conditions (H1d), and habit (H1e) are positively associated with the intention to use a live chat on familial cancer risk.Since the UTAUT2 contains mostly application-centered factors and is often criticized for not being specific enough to the health context , we aimeFirst, information insufficiency, which is the perceived information need regarding familial cancer risk, was integrated as an additional factor. It describes the difference between the perceived current level of knowledge and the perceived required level to adequately deal with familial cancer risk; it is a frequently considered predictor of health- and risk-related information seeking ,35,36. TSecond, risk perceptions about familial cancer risk were integrated. Risk perceptions describe an individual’s perceived susceptibility and severity, that is, the likelihood of being affected by a familial cancer risk and its threat assessment that increases the salience of the disease . They arFinally, a cancer diagnosis could be an additional factor associated with the intention to use the live chat. Lower health status is related to more frequent health-related information seeking in general ,46,47 anH2: Beyond the UTAUT2 factors, information insufficiency (H2a), perceived susceptibility (H2b), perceived severity (H2c), and a cancer diagnosis (H2d) provide additional explanatory power for the intention to use a live chat on familial cancer risk (positive effects assumed).An overview of the proposed research model is shown in To test our hypotheses and answer our RQ, we conducted a cross-sectional web-based survey with a sample of 1100 German individuals from March 16, 2022, to March 22, 2022. All participants were internet users aged 18 years and older, and are currently or have been in the past diagnosed with cancer themselves or have relatives  who are or have been affected by this disease and thus could be potentially interested in using the live chat. The fieldwork was conducted by the German market research institute Cint/GapFish using quota sampling. The sample was stratified by gender , age (population-representative distribution), and education . Participants were invited until the desired sample size and quotas were reached. To determine whether they fit the target group and quotas, they were first asked about their personal and familial cancer history and provided information about their gender, age, and level of education. Individuals who did not fit the target group and quotas were excluded from further questioning. The study is described according to the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) guidelines [f²=0.085, α=.05). During data cleaning, 16 individuals who gave implausible answers or indicated that they did not understand some of the questions were excluded. Thus, the final sample size was 1084.The intended sample size was 1100. A statistical a priori power analysis with G*Power (Kiel University) showed that this sample size is sufficient for a multiple linear regression analysis with 12 factors to reveal small to moderate effects with a statistical power of 0.95  and severity  of familial cancer risk were measured with single items adapted from Kahlor  see Mul. Both itTo answer the first RQ, descriptive data  were used. For the second RQ, a blockwise multiple linear regression analysis was conducted to examine the explanatory power of the UTAUT2 factors (H1) and factors related to information seeking about familial cancer (H2) for the intention to use the live chat. In the first block, sociodemographic factors were included as control variables. In the second block, the UTAUT2 factors were considered, whereas, in the third block, additional factors related to information seeking about familial cancer were integrated. All variables were included in the model based on theoretical assumptions.The study received ethical approval from the Central Ethics Committee at Leibniz University Hannover, Germany (EVLUH20/2021). All participants were informed of the investigator, purpose, content, data storage, and duration of the survey on the first page of the questionnaire and provided informed consent before participating. All study data are anonymous. The participants were financially compensated by the market research institute that conducted the survey.The 1084 participants who were included for data analysis are currently or have been in the past diagnosed with cancer themselves (n=144) or have relatives who are or have been affected by this disease (n=990). They were aged between 18 and 87  years. Overall, 547 (50.5%) were female and 537 (49.5%) were male. In addition, 543 (50.1%) had a low level of education and 541 (49.9%) were highly educated.The first RQ addressed the acceptance of a live chat on familial cancer risk by patients with cancer and their relatives. Overall, the participants showed a moderate intention to use the live chat . A total of 355 (32.7%) participants were (rather) willing, 313 (28.9%) were undecided, and 416 (38.4%) were (rather) not willing to use such a service in the future.F2,1081=22.21; P<.001; partial η²=0.04). Moreover, the proportion of people with high education level was higher in the group of interested people (44.2% low-educated vs 55.8% high-educated) and lower in the groups of uninterested (53.6% low-educated vs 46.4% high-educated) and undecided (52.1% low-educated vs 47.9% high-educated) people . In contrast, the subgroups did not differ with respect to gender .Additional exploratory analyses for sociodemographic characterization of the 3 subgroups revealed that the average age in the uninterested subgroup was slightly higher  than in the undecided  and interested  groups  and explained 47% of the variance in the intention to use the live chat. The results are presented in The second RQ focused on factors associated with the intention to use a live chat on familial cancer risk and their explanatory power. The overall model of the blockwise multiple linear regression analysis was significant . Thus, men tend to be more willing to use the chat than women. Age  and education level  were not related to usage intentions.R2=0.392). Performance expectancy , social influence , and habit  were positively associated with the intention to use the live chat. Thus, individuals who expect a higher personal benefit from using the live chat, individuals who perceive that important others expect them to use the service, and individuals who use live chats more frequently in their daily lives, tended to have higher intentions to use the live chat on familial cancer risk. In contrast, effort expectancy  and facilitating conditions  were not significantly associated with the intention to use the live chat. Thus, hypotheses H1a, H1c, and H1e were accepted, whereas hypotheses H1b and H1d had to be rejected.The second block included the UTAUT2 factors. In H1, we assumed that performance expectancy (H1a), effort expectancy (H1b), social influence (H1c), facilitating conditions (H1d), and habit (H1e) are positively associated with the intention to use the live chat. Indeed, these factors accounted for the greatest share of explained variance , perceived susceptibility (H2b), perceived severity (H2c), and a cancer diagnosis (H2d) are additionally positively associated with the intention to use the live chat on familial cancer risk. The inclusion of these variables increased the amount of explained variance by only 3.4%. Perceived susceptibility  and perceived severity  were positively associated with the intention to use the live chat. Accordingly, individuals who perceive a higher likelihood of having a familial cancer risk and who perceive such a risk to be more threatening tend to have higher intentions to use the live chat on familial cancer risk. Information insufficiency  and a cancer diagnosis , on the other hand, did not contribute to the explanation of the intention to use the live chat. Thus, hypotheses H2b and H2c were accepted, whereas hypotheses H2a and H2d had to be rejected.This study explored the intentions of patients with cancer and their relatives to use a live chat on familial cancer risk and examined the explanatory power of potential associated factors. With regard to the first RQ on live chat acceptance, the survey results suggest that about one-third of the participants are interested in using such a service in the future. Since the live chat is limited in terms of subject matter and individuals have different channel-specific preferences for obtaining health- and cancer-related information, this finding can certainly be considered promising. It is also in line with the findings of a chat evaluation in the context of cancer genetic testing, which revealed an actual usage rate of 32% .The high number of undecided participants indicates that it may have been difficult for some to decide whether or not they would use the live chat based only on a description and without trying it out. In addition, the variance in usage intentions demonstrates that individual cancer information and communication preferences may vary, underlining the need for different communication strategies in order to reach a broad target group. Another explanation could be that the variance hides different subgroups that differ in their intentions to use the chat. Our results provide initial evidence of sociodemographic differences with respect to age and education level. However, these differences lost relevance when the other factors were integrated into the model.Regarding the second RQ, our study identified performance expectancy, that is, the perceived usage and expected benefits, social influence of important others whose opinions the participants value, and habit of using live chats in everyday life as the strongest associated and thus determining factors of the intention to use the live chat service. This is in line with previous research, that has often identified performance expectancy ,23,25,51However, the perceived ease of use, as manifested by effort expectancy and facilitating conditions, did not seem to be relevant in the context of using a cancer-related live chat. In line with our findings, recent studies on usage intentions of health-related technology revealed that fulfillment of technical requirements has a rather subordinate significance ,22,24,26Beyond the UTAUT2 factors, several additional theoretically based and empirically supported associated factors contributed to the explanation of live chat usage intentions. In line with our hypothesis, perceived susceptibility and severity of a  familial cancer risk were positively associated with the intention to use the live chat. In particular, participants who perceived a higher threat showed higher usage intentions, whereas the perceived likelihood of being affected by a familial cancer risk seemed to have a subordinate importance. Perceived susceptibility, which may be associated with fear , is a stOther factors proved not to be relevant. Information insufficiency, that is, the perceived lack of knowledge regarding familial cancer risk, was not associated with the intention to use the live chat. Considering our theoretical derivation of information insufficiency as a frequently considered predictor in health information seeking models ,36, thisAs opposed to our hypothesis, a cancer diagnosis was not associated with higher intentions to use the live chat. Individuals with a cancer diagnosis and those who are not themselves affected by cancer but have at least 1 case of cancer in their family did not differ in their intentions to use the live chat, indicating similar personal importance of familial cancer risk in both groups. This demonstrates that not only affected individuals themselves but also relatives of patients with cancer require information on familial cancer risk and their needs should be addressed with communication and information measures.In summary, the UTAUT2 is an effective theoretical framework for explaining the intention to use a live chat service on familial cancer risk. Other contextual factors related to information seeking about familial cancer were of minor importance and had only little explanatory power. From our results, there is no need to expand the UTAUT2 in the context of a live chat on familial cancer risk.This study is not without limitations and provides a starting point for further research. First, the survey was a cross-sectional survey and does not allow for causal inferences. Future studies should use longitudinal and experimental designs to examine the reciprocal effects of perceptions, expectations, and usage intentions. Second, our sample consisted of overall more digitally savvy people limiting the generalizability of our results. This should be considered when interpreting the results, as less digitally savvy people are likely to have more difficulties using health-related technologies, which could decrease their intentions to use such a service . Thus, tIn conclusion, a live chat appears promising for providing information on familial cancer risk. However, as individual information and communication preferences vary, it is reasonable to combine different information channels and communication strategies to reach a broader target group and achieve the greatest effects in cancer prevention and early detection. When promoting live chat, the personal benefits of using such a service should be addressed. In addition, the social environment seems to have an important influence suggesting that not only the advice seekers themselves but their family members and friends should also be addressed regarding the benefits of such a live chat service. Since the habit of using live chats is also of central importance for usage intentions, comparisons with chat formats familiar in everyday life such as instant messaging services could be made to lower the inhibition threshold of use. Moreover, people who perceive a high threat of a  familial cancer risk and contact other services  should be made aware of the availability of live chat services on familial cancer risk by the respective health care providers. Finally, health care providers should increase education about the risk of a cluster of cancers in a family to emphasize the importance of appropriate prevention and early detection measures and to highlight the relevance of the live chat on familial cancer risk—even for individuals who have not previously perceived a familial risk of cancer.


Breast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on associations with long-term breast cancer risk are limited. We examined LIBRA assessments and breast cancer risk and compared results to prior assessments using Cumulus, an established computer-assisted method requiring manual thresholding.We conducted a cohort study among 21,150 non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were 40–74 years at enrollment, followed for up to 10 years, and had archived processed screening mammograms acquired on Hologic or General Electric full-field digital mammography (FFDM) machines and prior Cumulus density assessments available for analysis. Dense area (DA), non-dense area (NDA), and percent density (PD) were assessed using LIBRA software. Cox regression was used to estimate hazard ratios (HRs) for breast cancer associated with DA, NDA and PD modeled continuously in standard deviation (SD) increments, adjusting for age, mammogram year, body mass index, parity, first-degree family history of breast cancer, and menopausal hormone use. We also examined differences by machine type and breast view.The adjusted HRs for breast cancer associated with each SD increment of DA, NDA and PD were 1.36 , 0.85 (0.77–0.93) and 1.44 (1.26–1.66) for LIBRA and 1.44 (1.33–1.55), 0.81 (0.74–0.89) and 1.54 (1.34–1.77) for Cumulus, respectively. LIBRA results were generally similar by machine type and breast view, although associations were strongest for Hologic machines and mediolateral oblique views. Results were also similar during the first 2 years, 2–5 years and 5–10 years after the baseline mammogram.Associations with breast cancer risk were generally similar for LIBRA and Cumulus density measures and were sustained for up to 10 years. These findings support the suitability of fully automated LIBRA assessments on processed FFDM images for large-scale research on breast density and cancer risk.The online version contains supplementary material available at 10.1186/s13058-023-01685-6. Mammographic density, or the extent of the breast that appears radiopaque on a mammogram, is an established breast cancer risk factor , 2. ClinThere are multiple methods for quantitating breast density from FFDMs . One of The aim of this study was to examine the association between density measures by LIBRA and long-term breast cancer risk in a large cohort of over 21,000 women undergoing screening mammography by FFDM and followed for up to 10 years. We also compare associations with breast cancer risk obtained using LIBRA density measures with those using Cumulus in the same breast cancer screening cohort.This study is ancillary to a genome-wide association study of mammographic density . The parThe EHR was used to identify potentially eligible screening FFDMs on the cohort. Processed FFDMs in the KPNC imaging archive came from 37 different KPNC mammography facilities, with 1–5 machines per facility. Over 90% of FFDM machines were manufactured by Hologic or General Electric (GE).2, non-dense area (NDA) in cm2, and percent density (PD), defined as the dense area divided by the total breast area and expressed as a percentage. Cumulus assessments were done in batches by a single trained reader using the left craniocaudal (CC) view for ~ 90% of women; the right CC view was randomly selected for ~ 10% of women to blind the reader to breast cancer history, because the prior study included the right CC view for women with prior breast cancer in the left breast. We excluded women who had bilateral breast cancer, bilateral breast implants, breasts too large to be completely imaged on a single exposure, unreadable images or unavailable images [The study was restricted to the 24,800 non-Hispanic white women with Cumulus density measures who met eligibility criteria for a prior study . Brieflye images .n = 1110) , or they did not have at least one mediolateral oblique (MLO) and one CC view (n = 13)  program of cancer registries. The KPNC registry records information on all new primary cancers (except non-melanoma skin cancer) diagnosed among KPNC members. Data elements and quality assurance measures are similar to SEER. Age at mammogram was determined based on date of birth and date of the mammogram, both from the EHR. We used the body mass index (BMI) from the EHR measured at the patient visit closest to mammogram date. The RPGEH survey provided self-reported information on parity and family history of breast cancer. The KPNC pharmacy database, which records all dispensed outpatient and inpatient prescriptions, was used to determine use of menopausal hormones within the 5 years prior to FFDM.2), these variables were modeled using splines. Parity was categorized as nulliparous, parous, or missing. History of breast cancer in a first-degree family member was categorized as yes or no. The use of menopausal hormones was categorized as none, estrogen alone, or estrogen plus progestin within the 5 years prior to the index mammogram. Analyses of Cumulus density measurements were also adjusted for image batch [We generated scatter plots and Pearson correlation coefficients to compare density measures from LIBRA vs. Cumulus. Cox regression was used to estimate hazard ratios (HRs) for breast cancer associated with DA, NDA and PD, with time since baseline mammogram as the time scale. Women entered the cohort at the time of their baseline mammogram, and were followed until diagnosis of breast cancer (event) or censored at death, end of KPNC membership or end of study period 12/31/2021), whichever came first. We modeled DA, NDA and PD as continuous variables in units of the standard deviation (SD) in the full cohort. Prior studies have applied different transformations to density measures, including no transformation, log and square-root transformation, , 11 so w/31/2021,ge batch . SeparatThe eligible study population included a total of 21,150 women and 988 incident breast cancer diagnoses within 10 years of follow up (Table r = 0.97), moderate correlation for DA (r = 0.69), and moderate to good correlation for PD (r = 0.80)  versus Cumulus  on CC views acquired on Hologic FFDM machines show very high correlation for NDA  and long-term (5–10 years). These findings provide further evidence that associations of mammographic density with near-term breast cancer risk are not largely explained by masking, and reflect breast tissue characteristics that predispose to future malignant transformation.Our findings are largely consistent with a few smaller case–control studies that have also reported results on the association between LIBRA density assessments and breast cancer risk with comparison to Cumulus measurements , 9, 11. Our study has several strengths and limitations. It is the first large cohort study of automated area-based measures of mammographic density and breast cancer risk and information was available on important risk factors. Given the cohort design and duration of follow-up, we were able to examine associations between density and breast cancer risk in both the near-term (< 2 years) and long-term (5–10 years) periods after the baseline screening mammogram. The associations of LIBRA measures and breast cancer risk were examined by breast view (MLO and CC) and by machine type (Hologic and GE). Cumulus measures were performed by a single radiological technologist and were shown to be strongly associated with breast cancer risk . HoweverOur findings, together with the results from other studies, provide substantial support for the use of the fully automated and publicly available density measurement tool, LIBRA, for large-scale studies of mammographic density using processed FFDM images from either Hologic or GE machines. Moreover, LIBRA and Cumulus density measurements were significantly associated with both near- and long-term breast cancer risk and the magnitude of the associations did not attenuate over the 10-year follow-up period. LIBRA allowed us to generate density measures in a fraction of the time and cost needed for a trained reader to measure density using Cumulus. By providing reliable and robust density measures, LIBRA will enable future large longitudinal studies to address multiple questions related to the determinants of mammographic density and its relationship to breast cancer risk, and how these relationships may vary over time.Additional file 1. Quality control steps. LIBRA quality control steps (from D. Kontos and team).Additional file 2. Figure S1. Splines of HRs for DA when untransformed, log and square root transformed; splines of HRs for NDA when untransformed, log and square root transformed; Splines of HRs for PD when untransformed, log and square root transformed.Additional file 3. Table S1. Hazard ratios* (adjusted for age and BMI only) for breast density assessments and breast cancer risk, by view and machine type.Additional file 4. Table S2. Hazard ratios per SD of LIBRA breast density assessments and breast cancer risk, by time since mammogram.


The serum metabolome is a potential source of molecular biomarkers associated with the risk of breast cancer. Here we aimed to analyze metabolites present in pre-diagnostic serum samples collected from healthy women participating in the Norwegian Trøndelag Health Study (HUNT2 study) for whom long-term information about developing breast cancer was available.n=453 case-control pairs). Using a high-resolution mass spectrometry approach 284 compounds were quantitatively analyzed, including 30 amino acids and biogenic amines, hexoses, and 253 lipids .Women participating in the HUNT2 study who developed breast cancer within a 15-year follow-up period (BC cases) and age-matched women who stayed breast cancer-free were selected  were observed in the subgroup of younger women (<45 years old). Noteworthy, increased levels of glycerides, phosphatidylcholines, and sphingolipids were associated with reduced risk of cancer in younger and middle-aged women (≤64 years old). On the other hand, increased levels of serum lipids were associated with an enhanced risk of breast cancer in older women (>64 years old). Moreover, several metabolites could be detected whose serum levels were different between BC cases diagnosed earlier (<5 years) and later (>10 years) after sample collecting, yet these compounds were also correlated with the age of participants. Current results were coherent with the results of the NMR-based metabolomics study performed in the cohort of HUNT2 participants, where increased serum levels of VLDL subfractions were associated with reduced risk of breast cancer in premenopausal women.Changes in metabolite levels detected in pre-diagnostic serum samples, which reflected an impaired lipid and amino acid metabolism, were associated with long-term risk of breast cancer in an age-dependent manner. In 2020, there were over 2 million new cases and approximately 685,000 deaths due to BC worldwide; by 2040, both numbers are expected to increase by 40-50 percent . PrimaryNew opportunities for cancer diagnostics are created by metabolomics - a science that involves studying the most dynamically changing system in the human body - the metabolome. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two analytical techniques most often used in metabolomics. The analysis of metabolites in blood and other biological samples allows the detection of even subtle changes related to the development of a disease, including BC –9. SeverRecently, our group published the results of a study on risk assessment of breast cancer in the Norwegian population of women using an NMR-based metabolomics approach . The ser22.1The material used in our study came from the second part of the Norwegian Trøndelag Health Study where about 65,000 healthy participants were included [the HUNT2 study ]; serum 2.2The Absolute IDQ p400 HR kit  was used to perform targeted quantitative analysis of the metabolites, according to the manufacturer’s protocol. The kit allows the quantitative measurement of up to 408 compounds (or their isomer groups) covering 11 classes of metabolites phosphatidylcholines, sphingolipids, and cholesteryl esters) in 10 µl of sample thanks to the combination of direct flow injection and liquid chromatography (LC) with mass spectrometry (MS). The MS analyses were performed using Orbitrap Q Exactive Plus spectrometer , which was equipped with a 1290 Infinity UHPLC  system; Xcalibur 4.1 software  controlled the whole system. The concentrations of metabolites in µM were established by processing the registered spectra and chromatograms using Xcalibur 4.1. and MetIDQ DB110-2976 software (Biocrates Life Sciences AG) .2.3The metabolomics dataset consisted of 906 samples, each described by measured levels of 389 metabolites. Two types of missing values were detected in the dataset: (i) values below the limit of detection , and (ii) measurements lacking due to the internal standard error . Fifty and ten percent of missing values in all samples were allowed for either type of error, respectively, as recommended by ; otherwi2.4https://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml, accessed on 26 August 2022).The difference in baseline characteristics between future breast cancer patients and controls was tested with Student’s t-test for continuous variables and Pearson’s chi-square test for categorical variables. The Mann-Whitney U test was applied to test the statistical significance of differences between the analyzed groups of individuals in measured levels of 284 compounds qualified for quantitative analysis. The Benjamini-Hochberg procedure was applied to minimize the number of false positive results. All statistical hypotheses were tested at the 5% significance level. Moreover, following the Mann-Whitney U test, the “r” effect size was calculated according to the formula: ct size) . The chi3Metabolite profiles were analyzed by a mass spectrometry-based approach in a set of 906 serum samples collected from healthy women participating in the HUNT2 population-based study. The set included 453 women who developed breast cancer within a follow-up period and 453 age-matched women who remained free of breast cancer during the follow-up . Baseline characteristics for the study cohort are presented in 3.1The mean concentrations of quantified compounds and the strength of differences between the groups of participants are presented in 3.2Considering the strong correlations between metabolite concentrations and participants’ age, we analyzed differences between the BC and Ctr groups in smaller subgroups to limit age-related variance. We assumed that three age-defined subgroups could be hypothetically distinguished: “younger than average”, “average/middle-aged”, and “older than average”. Based on the actual age distribution in the study cohort these three subgroups were identified as components of the Gaussian mixture model: below 45-year-old (112 BC/Ctr pairs), 45-64-year-old (206 BC/Ctr pairs), and above 64-year-old (135 BC/Ctr pairs), which is depicted in 3.3To further assess the association between the risk of breast cancer and the concentration of serum metabolites, odds ratios  were estimated for all 284 compounds in each age-defined subgroup separately. Though a few specific metabolites showed some association with cancer risk, it was generally weak in all age-defined subgroups (3.4We observed that differences in serum lipid concentrations between BC cases and controls were more frequent in the subgroup of younger women (below 45 years old), which were putatively pre-menopausal . Hence, this observation was coherent with our previous results of the NMR-based metabolomics study performed in the cohort of HUNT2 study participants . In that3.5The method of selection of participants for the current study  was aimed to analyze whether time to cancer diagnosis affected molecular differences observed between BC and Ctr groups. Hence, in the next step, we compared differences between BC cases and controls in subgroups where breast cancer was diagnosed up to 5-year follow-up (“early” diagnosis), during 5 to 10 years follow-up, and during 10 to 15 years follow-up (“late” diagnosis). However, this analysis revealed similar numbers of discriminatory compounds in all three time-of-diagnosis-related subgroups. There were 16, 18, and 18 compounds with higher than negligible effect size (r>0.1) between BC and Ctr in subgroups <5, 5-10, and 10-15 years-to-diagnosis, respectively (details in 3.6We noted that the number of metabolites differentiating BC cases with early/late diagnosis was similar in the subgroups of younger and older participants: 104 and 93 metabolites with higher than negligible effect size (r>0.1), respectively; median values of effect sizes equal to 0.078 and 0.065, respectively , phosphatidylcholines, and sphingolipids (ceramides and sphingolipids) were associated with reduced risk of cancer. This general observation was coherent with the results of previously published reports. Brantley and coworkers reported that poly-unsaturated triglycerides and cholesteryl esters were inversely associated with breast cancer in the American Nurses’ Health Study . His andSimilar to the MS-based studies mentioned above, NMR-based profiling of plasma in the French SU.VI.MAX cohort revealed that women with lower levels of glycerol-derived compounds, unsaturated lipids, and lipoproteins, had a higher risk of developing breast cancer . AnotherExcept for lipids, serum concentration of several “small molecule” metabolites  was addressed in the current study, and such compounds were also associated with breast cancer risk (though the magnitude of differences established by effect size was relatively low). Nevertheless, a few amino acids showed a similar trend of association with breast cancer risk as described in previous studies. This included the association between a reduced risk of cancer and higher levels of Arg, Gln, Lys, and Thr, previously reported by others , 15. MorData presented in our study, in coherence with previously published reports, revealed that reduced serum levels of several lipids and amino acids were associated with an increased risk of developing breast cancer. It has been observed that reduced levels of certain metabolites in the blood of actual cancer patients could reflect the increased transfer to tumor tissue due to higher consumption of these metabolites by cancer cells . HoweverIn general, breast cancer represents a hormone-dependent malignancy, hence conditions affecting hormonal homeostasis are among known risk factors, including reproduction-related factors   and the In conclusion, differences in concentration of several metabolites were noted in pre-diagnostic serum samples between women who eventually developed breast cancer and women who stayed breast cancer-free. Metabolomics features detected at the baseline  were associated with the long-term risk of breast cancer in an age-dependent manner and could be observed primarily in younger  women. Changes in metabolite profiles putatively reflected the reprogramming of metabolism connected to cancer-promoting conditions, including changes in hormone homeostasis. The study indicated that impaired metabolism of lipids and amino acids is an etiological factor involved in breast cancer, that could be observed many years before cancer diagnosis. However, despite a relatively large number of participants and the direct quantitative value of the applied analytical platform, which were strengths of the study, the associations between specific metabolites and breast cancer risk were generally modest. Hence, the predictive value of a hypothetical metabolomics signature appeared too low for application in cancer-screening strategies. On the other hand, thanks to the inclusion of a relatively large and heterogenous cohort, the correlation between metabolite concentration and age has been convincingly documented and demonstrated as an important obstacle in the cancer risk biomarker-oriented study. Moreover, the extended knowledge of metabolomics profiles associated with breast cancer risk could help to develop rational prevention strategies based on modified dietary and lifestyle patterns.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Ethics Committee of Central Norway. The patients/participants provided their written informed consent to participate in this study.Conceptualization: PW, GG, TB. Methodology: KM, KJ. Investigation: KM, KJ. Data curation: AK. Formal analysis: AK, JD. Writing- original draft: PW, KM, Writing- Review & Editing: KJ, JD, GG, TB. Visualization: KM, JD. Funding: PW. All authors contributed to the article and approved the submitted version.


RAD52, a 13.4 kb deletion in HSD17B14 and a 64 kb partial duplication of RAD51C. Notably, all these genes encode proteins involved in pathways previously identified as essential for breast cancer development. Variants were genotyped in geographically matched cases and controls . The RAD52 delins and HSD17B14 deletion both showed significant enrichment among cases with indications of hereditary disease susceptibility. RAD52 delins was identified in 7/278 cases  and HSD17B14 deletion in 8/278 cases , the frequency of both variants in the controls being 11/1229 (0.9%). This suggests a role for RAD52 and HSD17B14 in hereditary breast cancer susceptibility. The RAD51C duplication was very rare, identified only in 2/278 of hereditary cases and 2/1229 controls . The identification of recurrent CNVs in these genes, and especially the relatively high frequency of RAD52 and HSD17B14 alterations in the Finnish population, highlights the importance of studying CNVs alongside single nucleotide variants when searching for genetic factors underlying hereditary disease predisposition.Copy number variants (CNVs) are a major source of genetic variation and can disrupt genes or affect gene dosage. They are known to be causal or underlie predisposition to various diseases. However, the role of CNVs in inherited breast cancer susceptibility has not been thoroughly investigated. To address this, we performed whole-exome sequencing based analysis of rare CNVs in 98 high-risk Northern Finnish breast cancer cases. After filtering, selected candidate alleles were validated and characterized with a combination of orthogonal methods, including PCR-based approaches, optical genome mapping and long-read sequencing. This revealed three recurrent alterations: a 31 kb deletion co-occurring with a retrotransposon insertion (delins) in  RAD52, RAD51C and HSD17B14 genes. Of these, RAD52 and RAD51C are involved in DNA damage response pathway, whereas HSD17B14 encodes a protein involved in steroid hormone metabolism. These CNVs were characterized at nucleotide level and genotyped in additional Northern Finnish case-control cohorts. The CNVs that disrupted RAD52 and HSD17B14 genes showed enrichment among cases with a family history of the disease and/or early disease onset, suggesting a role for these alterations in breast cancer susceptibility. Overall, the identification of these gene-disrupting recurrent CNVs emphasizes the need to consider CNV detection in the analysis pipelines when searching for novel predisposing alleles for breast cancer.For breast cancer susceptibility, CNVs represent an inadequately studied class of genetic variation that could explain a part of the inherited risk. Here, we performed whole-exome sequencing based analysis of rare CNVs in 98 hereditary breast cancer cases and identified recurrent alterations in  BRCA1 and BRCA2, along with PALB2, TP53, ATM, RAD51C and CHEK2. All these genes encode proteins involved in the DNA double-strand break (DSB) signaling pathway and their mutations confer moderate-to-high risk for breast cancer. However, less than half of the familial component of breast cancer is explained by the known susceptibility factors  (hereafter referred as RAD52 delins). Based on the sequencing results, the deletion had masked the transposon-insertion in OGM analysis, and the extreme GC-content together with the repetitive sequences within the SVA-insertion had rendered the PCR-based approaches unsuccessful. The obtained nucleotide-level information allowed to design PCR-based genotyping assay  from the cohort showing an indication of inherited disease susceptibility . Eleven carriers were identified in the controls  and this indicated a 2.9-fold increased risk, which is in the 2 to 4-fold range defined for moderate-risk alleles for breast cancer , the mean for this cohort otherwise being 45 years (range 26–89 years). In the unselected breast cancer cohort (patients unselected for the family history and age at disease onset), additional twenty RAD52 delins carriers were identified , but this frequency remained similar to that in controls (0.9%). Even though the cases from the hereditary cohort were diagnosed at relatively young age, in the unselected cohort there was no difference in the average age at disease onset between the carriers  and non-carriers . No associations with the clinical parameters or the 5-year breast cancer-specific survival (BCSS) were detected  identified in the whole-exome sequenced discovery cohort were compound heterozygotes, also carrying either RAD52 p.Tyr415Ter or p.Ser346Ter, which are polymorphic protein-truncating variants (PTVs) located in the last two exons of RAD52 (gnomAD). The p.Tyr415Ter variant resides in the last exon and p.Ser346Ter in the second last exon of RAD52 with minor allele frequencies (MAFs) of 0.0135 and 0.0153 in Finns, respectively (http://www.sisuproject.fi). Due to this observation of compound heterozygosity, all the cases from the hereditary cohort (n = 278) and unselected cohort (n = 1983) were genotyped for these relatively common PTVs (Table D in RAD52 delins/p.Ser346Ter compound heterozygote was identified (Uns1). In total, 3 out of 27 (11.1%) RAD52 delins carriers harbored a common PTV in the remaining RAD52 allele, while none of the 11 delins carriers in the healthy controls were compound heterozygotes. Albeit the observation of compound heterozygotes among affected individuals was interesting, this did not reach statistical significance  were diagnosed with breast cancer at the age of 47 and 50 years, respectively. Her7 had five other breast cancer cases in the family, of which two were confirmed as RAD52 delins carriers along with one male pancreatic cancer case, but no additional compound heterozygotes were identified. Uns1 had three breast cancer cases in first-degree relatives, but additional samples were not available for testing . Her6, Fig D in . AltogetFig D in . No homoHSD17B14 deletion was confirmed by qPCR and OGM, and the precise breakpoints were defined by breakpoint-spanning PCR .The HSD17B14 deletion was detected in altogether eight cases in the hereditary cohort  and in eleven cases in the control cohort , all confirmed as heterozygotes . In the unselected cohort, additional 24 carriers were identified , but the frequency did not significantly differ from the 11/1229 (0.9%) in controls . The mean age at disease onset for the unselected carriers was 61 years (range 42–80 years), which was similar to the mean of the cohort (58 years). Interestingly, all but one of the HSD17B14 deletion carrier breast tumors with pathology data available were of Luminal A subtype , and all were negative for HER2 amplification . 21 out of 24 carriers (87.5%) were postmenopausal (age at diagnosis 52 years or more), which did not significantly differ from the rest of the unselected cohort  were also diagnosed with polycythemia vera. This myeloproliferative neoplasm is very rare  in the studied breast cancer cohort and their family members. Her14 was diagnosed with breast cancer at the age of 56 and polycythemia vera 6 years later, while the other case (sister of Uns32) was diagnosed with cervical cancer at the age of 68 and polycythemia vera 2 years later.The evidence for  100,000 ) and repRAD51C duplication was confirmed as the same chr17 c.-31512_965+1210dup{insCTTTTGTGAG} allele that has previously been reported in breast and ovarian cancer cases from a Southern Finnish cohort  [RAD51C gene. The duplication is absent from the gnomAD database indicating that it is a rare Finnish founder allele. In the current study, two RAD51C duplication carriers were observed in the hereditary cohort . The mean age at disease onset for the currently identified unselected carriers was 60 years (range 39–80 years), which was similar to the mean of the cohort (58 years). All six carriers from the unselected cohort with tumor pathology data available had estrogen (ER) and progesterone receptor (PR) positive tumors  had both the RAD51C duplication and RAD52 delins/Tyr415Ter variants, while Uns38 (breast cancer at 65 years) had both the RAD51C duplication and HSD17B14 deletion. No double carriers for any of the currently studied CNVs were identified in the controls.Interestingly, two cases with RAD51C, RAD52 and HSD17B14 genes. Of these, RAD51C duplication has previously been described [RAD52 delins and HSD17B14 deletion CNVs were characterized and investigated here for the first time for their association with breast cancer susceptibility.Genome-wide investigation of rare coding region CNVs in 98 high-risk Northern Finnish breast cancer cases revealed recurrent alterations in RAD51C mutations have previously been reported to increase the risk for breast and ovarian cancer and to also be causative for a recessively inherited Fanconi anemia-like disorder [BRCA1, BRCA2 and PALB2 mutations. These genes are important players in the DNA DSB repair pathway through homologous recombination (HR) [RAD51C mutations has been estimated higher for ovarian cancer (risk ratio 7.55) than it is for breast cancer (risk ratio 1.99), which places RAD51C in the category of moderate-risk genes [RAD51C duplication CNV allele had two-fold frequency in the breast cancer cases compared to controls. However, potentially due to its rarity this remained under statistical significance, even when combined with the results from the Southern Finnish cohort. We found no supporting evidence for increased ovarian cancer risk, as there was only one reported ovarian cancer case in the eight currently identified carrier families. Pathogenic RAD51C variants have previously also been linked particularly to ER-negative and triple-negative breast cancer subtypes [disorder , similarion (HR) . HR-defiion (HR) . The relsk genes . In the subtypes ,30, but RAD52 also encodes a protein involved in the DSB repair pathway with best-defined role as a key mediator of the repair by single-strand annealing (SSA) [RAD52 and any of the genes from the BRCA1/BRCA2/PALB2 axis is lethal and shows significant reduction in RAD51 foci [ng (SSA) . SSA repng (SSA) . RAD52 ang (SSA) . In humaD51 foci .RAD52 harbors two polymorphic PTV alleles, p.Ser346Ter and p.Tyr415Ter, both located at the 3’ end of the gene (gnomAD). Of these, p.Ser346Ter has previously been investigated in the context of BRCA1/BRCA2 mutations. In pathogenic BRCA2 and to lesser extent BRCA1 germline mutation carriers, the presence of RAD52 p.Ser346Ter was associated with reduced risk of breast cancer [RAD52 delins abolishes a large part of the gene and is most likely a null allele. Large loss-of-function deletions in RAD52 are very rare according to public databases (gnomAD), and the currently described is the first one reported with nearly 1% carrier frequency in general population in Northern Finland and 2.5% occurrence in cases with an indication of inherited breast cancer predisposition. As the two of the identified RAD52 delins carriers were compound heterozygotes for p.Ser346Ter, and one for p.Tyr415Ter , this suggests potential additive effects for the observed RAD52 alterations in breast cancer predisposition. To support this, no compound heterozygotes were observed in the controls.t cancer . Since pt cancer . In contRAD52 deletion co-occurred with an insertion, which exhibited 99% homology to the retrotransposon SVA_F element H6_1084. Similar insertions have also been reported to be involved in pathogenic deletions of NF1 in neurofibromatosis [SMARCB1 [Long-read whole-genome sequencing revealed that the omatosis . Another[SMARCB1 . As the [SMARCB1 . For thiHSD17B14 (17β-hydroxysteroid dehydrogenase), which encodes an enzyme involved in steroid metabolism and converting intracellular 17β-estradiol into estrone in human tissues, including mammary fat [HSD17B14 expression has been associated with poor survival in women with ER-positive breast cancer, and a high estrone (E1):17β-estradiol (E2) ratio in postmenopausal women has been shown to drive inflammation and stimulate ER-positive breast cancer tumor growth [HSD17B14 deletion carriers with available tumor data from the unselected cohort, all but one had ER-positive tumors, but the menopausal status did not significantly differ compared to the rest of the unselected cohort. The HSD17B14 deletion was most prevalent in the hereditary cohort, and the risk is estimated as moderate with 3-fold enrichment compared to controls. This CNV is one of the two predicted loss-of-function HSD17B14 CNV alleles reported in the gnomAD database. Although rare, they could represent alleles related to breast cancer predisposition in other populations as well, warranting further investigations.The third recurrent CNV was detected in mary fat . As a mer growth . Consequr growth . Of the RAD52 and HSD17B14 genes, along with previously described duplication in RAD51C, suggests that rare CNVs contribute to increased breast cancer risk, potentially in combination with other moderate-risk alleles as indicated by the identification of several double-mutants. As the current discovery cohort was limited in sample size, it is possible that additional causal CNVs exist, warranting studies in larger cohorts. The combined prevalence for the RAD52 and HSD17B14 alleles in the hereditary breast cancer cohort was as high as 5%. Whereas RAD51C is a previously established cancer susceptibility gene, both RAD52 and HSD17B14 also encode proteins involved in pathways previously identified as essential for breast cancer development: the DNA damage response pathway is frequently aberrated in breast cancer and strongly linked to inherited predisposition, whereas steroid hormone metabolism can drive breast cancer progression. The current results highlight the need for careful examination of CNVs alongside SNVs and small indels when searching for new breast cancer risk factors, and when evaluating the potential combined effects of moderate-risk alleles.Overall, the identification of recurrent deletions in The study was approved by the Ethics Committee of the Northern Ostrobothnia Hospital District  with informed consent of the participants . The study was conducted in compliance with the Declaration of Helsinki.BRCA1/BRCA2/PALB2 founder mutation-negative Northern Finnish breast cancer cases with indications of hereditary disease susceptibility was used for CNV calling. This discovery cohort consisted of: 1) index cases from families with ≥3 breast and/or ovarian cancer cases in first- or second-degree relatives (n = 83), 2) index cases from families with two cases of breast, or breast and ovarian cancer in first- or second-degree relatives, of which at least one with early disease onset (<35 years), bilateral breast cancer or multiple primary tumors (n = 7) and 3) breast cancer cases diagnosed at or below the age of 40 (n = 8). The early-onset breast cancer cases were included based on the assumption that when a woman below the age of 40 years develops breast cancer, a hereditary predisposition can be suspected regardless of the family history [Whole-exome sequencing data from 98  history . Whole-e history .BRCA1, BRCA2, PALB2, TP53, ATM, RAD51C and CHEK2, all the observed putative CNVs were evaluated further  in three carriers. Briefly, ultra-high molecular weight (UHMW) gDNA was isolated from frozen EDTA blood, following the manufacturer’s guidelines . For each sample, 750 ng of purified UHMW gDNA was labeled with DL-green fluorophores with the Direct Labeling Enzyme (DLE-1) chemistry and cleaned up with membrane adsorption . Labeled gDNA samples were loaded on the Saphyr chips for linearization and imaging on the Saphyr instrument. 800 Gbp of data was collected per sample using GRCh38/hg38 as a reference. The de novo assembly and SV annotation pipeline were executed with Bionano Solve software Solve3.7 using Access v1.7 (Bionano genomics).OGM was performed as described in Mantere et al. 2021  to confiRAD52 and RAD51C CNV alleles were characterized at nucleotide level using HiFi long-read genome sequencing as described in Sabatella et al. 2021 using the Sequel II system (Pacific Biosciences) [ciences) . Brieflyciences) .BRCA1, BRCA2, PALB2 and MCPH1 founder mutation negative [The association of the three recurrent CNVs with breast cancer susceptibility was investigated using geographically matched case-control cohorts. Additional breast cancer cases with an indication of inherited predisposition were used as a replication cohort and consisted of 180 negative –48 indexn = 759 females and n = 470 males).Unselected breast cancer cohort included 1983 consecutive Northern Finnish cases diagnosed at the Oulu University Hospital during the years 2000–2019 and were unselected for the family history of cancer and age at disease onset. The mutational status for known predisposing gene mutations was either unknown or not used as an exclusion criterion for this cohort. Clinical parameters of the breast tumors were obtained from pathology reports. Geographically matched anonymous, cancer-free Northern Finnish Red Cross blood donors (n = 1229) were used as population controls (RAD52 (rs4987207 and rs4987208) was performed with HRM analysis and iPLEX . All primers used for genotyping and sequencing are presented in Table A in CNV genotyping was performed using DNA extracted from peripheral blood by multiplex high-resolution melt (HRM) analysis . For each CNV, breakpoint-spanning allele-specific PCR primers together with control PCR reaction was used. Samples with verified CNVs were used as positive controls. All the positive samples from PCR-based genotyping were confirmed with Sanger sequencing , and the zygosity of the deletion alleles was defined by separate wild-type specific PCR. In the segregation analysis, formalin-fixed paraffin-embedded (FFPE) tissue samples from relatives were genotyped using shorter amplicons and direct sequencing. The genotyping of the polymorphic 3´ end PTVs in RAD52 PTVs (stop-gain variants rs4987207 and rs4987208) in the RAD52 delins carriers was evaluated with Hardy-Weinberg Multilocus analysis [Statistical analyses were performed with IBM SPSS Statistics 26.0 (IBM Corporation). Mutation carrier frequencies between cases and controls, and differences in the tumor characteristics between carrier and non-carrier cases were both compared with Fisher’s exact test. The mean age at diagnosis between carriers and non-carriers in the unselected cohort was compared with Mann-Whitney U test. The potential enrichment of analysis . The 5-yS1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.


Prospective population-level analyses provide evidence supporting the importance of butyrate production in reduction of colorectal cancer risk by whole grain consumption. N = 343,621) with less detailed dietary assessment, only individuals with a high genetically predicted butyrate production had a lower risk of colorectal cancer per 5 g/day intake of bread and cereal fiber. This study suggests that colorectal cancer risk varies by intake of carbohydrate types and sources, and the impact of whole grain intake may be modified by SCFA production.Whole grain and fiber intakes may decrease the risk of colorectal cancer. The interplay between host genetic factors, colonization of specific bacteria, production of short-chain fatty acids (SCFA), and intake of whole grains and fiber could alter the protective role of carbohydrates against colorectal cancer. Here, we assessed intakes of types and sources of carbohydrates in 114,217 UK Biobank participants with detailed dietary data (2–5 24-hour dietary assessments), and a host polygenic score (PGS) was applied to categorize participants as high or low for intraluminal microbial SCFA production, namely, butyrate and propionate. Multivariable Cox proportional hazards models were used to determine the associations of carbohydrates and SCFA with colorectal cancer incidence. During a median follow-up of 9.4 years, 1,193 participants were diagnosed with colorectal cancer. Risk was inversely associated with intakes of non-free sugar and whole grain fiber. Evidence of heterogeneity was observed by the butyrate PGS; consuming higher amounts of whole grain starch was only associated with a lower risk of colorectal cancer in those with predicted high SCFA production. Similarly, in additional analyses utilizing the larger UK Biobank cohort (Prospective population-level analyses provide evidence supporting the importance of butyrate production in reduction of colorectal cancer risk by whole grain consumption. Colorectal cancer is the third most common cause of cancer death for both men and women worldwide . Smokingin vivo studies has suggested that butyrate, which is used as an energy source by colonocytes, may be protective against carcinogenesis  butyrate, propionate, and acetate . Evidencogenesis . Large gogenesis . As suchWe, therefore, aimed to examine the associations of carbohydrate types and sources with colorectal cancer risk in 114,217 participants from the UK Biobank with detailed dietary data. We also examined if host genetic variants related to colonic/microbial SCFA production modulated the associations of carbohydrate and fiber intakes with the risk of colorectal cancer.http://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/Anthropometry.pdf), and blood samples were also collected. Ethical approval was obtained from the North West Multi-Centre Research Ethics Committee (reference number 21/NW/0157) and the UK Biobank is conducted with oversight from the UK Biobank Ethics Advisory Committee. A full description of the study assessment, protocol, and ethical approval can be found on the UK Biobank website (https://www.ukbiobank.ac.uk/).In total, 503,317 individuals (5.5% response rate) ages 37 to 73 years provided written informed consent to enroll in the UK Biobank between 2006 and 2010 . EligiblA subsample of participants completed the Oxford WebQ, a validated 24-hour dietary assessment , on at lCarbohydrate intakes were estimated in each 24-hour dietary assessment based on the foods and beverages consumed by participants using the UK Nutrient Databank food composition tables . Percentn = 922), had a diagnosis of cancer before recruitment , their genetic sex did not match their reported sex (n = 321), or they did not complete a 24-hour dietary assessment . We also excluded 24-hour dietary assessments if participants did not report a reliable energy intake , date of death, or last day of follow-up from HES and SMR data or Welsh cancer registry . Cancer registry data were available until February 29, 2020, for English participants and January 31, 2021, for Scottish participants. After this time, participants from England and Scotland were followed using HES and SMR databases, respectively; for Wales, hospital episode data were not used because the cancer registry had longer follow-up data. Participants were coded as having an event if they had an incident diagnosis of colorectal cancer , or if no prior incident diagnosis was reported and their primary underlying cause of death was colorectal cancer.Incident cancer diagnoses were determined using a combination of the NHS Digital records and Public Health England for participants from England and Wales, the NHS Central Register for participants from Scotland as well as Hospital Episode Statistics (HES) data for English participants, and Scottish Morbidity Records (SMR) for Scottish participants  including 807,411 single-nucleotide polymorphisms (SNP), and the UK Biobank Axiom , which included 825,927 SNPs and shared 95% of marker content with the UK BiLEVE. Over 90 million variants were imputed from this using the Haplotype Reference Consortium and UK10K + 1000 Genomes reference panels .The genotyping of participants in the UK Biobank has been described in detail elsewhere . Brieflyn = 1,998) or reported they were not of white British ancestry . We also excluded participants with low call rates , sex chromosome aneuploidy (n = 66), and related individuals , leaving a maximum of 87,417 participants and 909 cases of colorectal cancer in these analyses.To estimate the potential influence of host genetic factors that may affect microbial SCFA production on the associations of intakes of carbohydrate types and sources with colorectal cancer, we used a 9 SNP polygenic score (PGS) for butyrate and a 3 SNP PGS for propionate . These SParticipants were categorized into quartiles of percentage of energy intake from each carbohydrate type and source as well as quartiles by g/day of fiber sources. Intakes of carbohydrates were also modeled as a per 5% energy increment for each source, and intakes of fiber sources were modeled as a 5 g/day increment for each fiber source. Baseline characteristics were summarized across quartiles of carbohydrate and fiber intakes.a priori based on probable and known risk factors for colorectal cancer. Minimally adjusted models were stratified by sex and age at recruitment and adjusted for region at recruitment.Cox proportional hazards regressions, with age as the underlying time variable, were used to calculate hazard ratios (HR) and 95% confidence intervals (CI) for colorectal cancer incidence. All covariates added to the model were selected The main multivariable Cox regression models were further adjusted for height, physical activity, Townsend deprivation index, education, employment status, smoking status, alcohol consumption estimated from the recruitment questionnaire, ethnicity, diabetes status, nonsteroidal anti-inflammatory drug (NSAID) use, body mass index (BMI), consumption of red and processed meat from the touchscreen questionnaire at recruitment, intake of fruit and vegetables estimated from the 24-hour dietary assessments , energy intake, and women-specific covariates: menopausal hormone therapy use and menopausal status. For covariates in which responses were unknown or missing, participants were categorized into an unknown/missing category. Further explanation of the categorization of covariates can be found in the Supplementary Methods.2 statistic for including the carbohydrate types or sources or fiber source was estimated using LRTs comparing models without the carbohydrate/fiber source to models with the carbohydrate/fiber source (modeled as 5% energy increment for carbohydrates and 5 g/day for fiber). The χ2 value provides a quantitative measure of the extent intake of carbohydrate types or sources improves model fit for colorectal cancer risk. To assess for residual confounding and the extent to which adjustment for confounders influenced the associations of intakes of carbohydrate types and sources with risk of colorectal cancer, the change of χ2 values was estimated using minimally adjusted models χ2 as the reference value (The likelihood ratio test (LRT) χce value . Further2) by using LRTs comparing the main model to a model including an interaction term between the carbohydrate source (5% energy increment) or fiber (5 g/day increment) and sex or BMI. We further explored if associations varied by tumor site . In 68 instances, the diagnosis of colon and rectal cancer coincided, and these participants were removed from the subgroup analyses by tumor site. For heterogeneity by tumor site, we stratified Cox models using a competing risks approach  or fiber intake (per 5 g/day increment) with the butyrate PGS or propionate PGS, separating participants by the median PGS for both butyrate and propionate (low or high PGS). We also adjusted models for the first 10 principal components of ancestry to reduce the potential impact of population stratification.n = 343,621; 4,191 cases of colorectal cancer). The fiber from breads and cereals variable was calculated using the consumption frequency and type of bread and cereals reported by individuals at their recruitment visit. Specifically, participants were asked how many slices of bread or bowls of cereal they consumed per week as well as the type of bread and cereal they mainly consumed. From these responses, the NSP contents (in grams) of slices of bread and breakfast cereals were determined based on the frequency of consumption and types of bread or cereal reported in the touchscreen questionnaire. The estimated intakes of fiber from cereal and fiber from bread were then summed to obtain the total intake of fiber from breads and cereals. Further details of the calculation of the fiber from breads and cereals from the touchscreen questionnaire have been described in greater detail elsewhere . For every 5% higher energy intakes of total sugars and non-free sugars, an inverse association with colorectal cancer was observed ; however, when whole grain intake was modeled as absolute grams of whole grain foods instead of % energy from starch from whole grain, an inverse association with colorectal cancer risk was observed . No other associations between carbohydrate intake and colorectal cancer risk were observed.Multivariable-adjusted HRs for intakes of total carbohydrates and carbohydrates from different types and sources and colorectal cancer risk are shown in P = 0.047; Q4 vs. Q1: 0.81, 0.69–0.96).Multivariable-adjusted HRs for fiber intake and colorectal cancer are presented in Pheterogeneity = 0.023; Pheterogeneity = 0.012; In participants with a high PGS for butyrate production, intake of whole grain starch was inversely associated with colorectal cancer risk , whereas no association was observed between intake of starch from whole grain and colorectal cancer risk for individuals with a low host butyrate PGS , we observed evidence of heterogeneity by the butyrate production PGS: a 5 g/day higher intake of fiber from breads and cereals was inversely associated with colorectal cancer risk for individuals with a high butyrate PGS , whereas no association was observed for individuals in the low butyrate PGS . Similarly, evidence of heterogeneity was observed for intake of fiber from fruit, which was inversely associated with rectal cancer risk  but was not associated with colon cancer risk .No evidence of heterogeneity across sex and BMI categories was observed for the main analyses using 24-hour dietary assessments . Some evidence of heterogeneity was observed when looking at tumor sites ; non-free sugar intake was inversely associated with rectal cancer risk  but was not associated with colon cancer risk  and removing the first 2 years of follow-up , associations remained largely unchanged with CIs widening, probably due to a smaller number of cases and participants, and thus reduced statistical power .In sensitivity analyses restricting to participants who completed a minimum of three 24-hour dietary assessments  with less detailed dietary questions.In this analysis of 114,217 participants from the UK Biobank, intakes of carbohydrates, total sugar, non-free sugars, total whole grains, and fiber from whole grains were inversely associated with colorectal cancer risk. Moreover, we found suggestive evidence of effect modification by host genetically predicted SCFA production, with higher intakes of starch from whole grains being inversely associated with colorectal cancer risk only in those with a high host genetically predicted butyrate. Similar evidence of heterogeneity was observed for intake of fiber from cereals and breads in the larger UK Biobank sample  was associated with a 17% reduction in colorectal cancer risk . We obseSCFAs from fiber and whole grain microbiota fermentation have been thought to modulate colorectal cancer risk, with butyrate potentially having an anticarcinogenic effect . BesidesSCFAs, such as butyrate, have been shown to modulate cell histone deacetylases in the colorectum and function to reinforce cellular junctions, which may reduce low-grade inflammation, and lead to a lower risk of colorectal cancer . SeveralWe also observed heterogeneity by the butyrate PGS for the association of total starch intake and colorectal cancer risk; participants in the low butyrate PGS category and consuming higher amounts of total starch had a higher risk of colorectal cancer, whereas no association was observed for those in the high butyrate PGS. Higher intake of certain sources of starch, such as potatoes, may promote a greater insulin response  and the Strengths of this analysis include using repeated 24-hour dietary assessments to reduce random measurement error and the prospective nature of this study using linkage to health records, thus minimizing loss to follow-up. Due to the design of the 24-hour dietary assessments, we were able to look at sources and types of carbohydrates in detail to assess potential associations with colorectal cancer risk. Moreover, the UK Biobank collected detailed information from participants regarding socioeconomic and lifestyle factors and took standardized physical measurements, allowing us to adjust for these potential confounders in the analysis. In addition, genotyping was conducted in nearly the entire cohort allowing us to separate participants by host SCFA genetic factors.Several limitations should be considered. With just over 1,000 cases of colorectal cancer in the main analyses, results may also be underpowered to detect modest associations. As well, numerous tests were performed, and if we correct for multiple testing, no significant findings would be observed, thus these results need to be interpreted cautiously. All self-reported dietary intake is subject to measurement error and some participants (∼39%) only completed two 24-hour dietary assessments and estimates will therefore be affected by random error, which biases the results toward the null. Although we adjusted for multiple confounders, unmeasured and residual confounding may still influence the associations observed. Reverse causality is also plausible due to colorectal cancer symptom onset potentially altering the consumption of specific foods. We did explore the potential for reverse causality in sensitivity analyses by removing the first 2 years of follow-up and estimates remained the same although with wider CIs; however, longer follow-up may still be required to account for the long latency period of this cancer. The UK Biobank is generally a healthy sample, with the subsample of participants completing the 24-hour dietary assessments being healthier, thus the results may not be generalizable to a wider population.Eubacterium rectale and Roseburia intestinalis) in fecal samples (For analyses assessing effect modification by host genetically predicted SCFA production, the genetic scores utilized were derived from one sample of 952 individuals , limitin samples . NeverthIn summary, our results of this analysis of participants from the UK Biobank suggested that intakes of total carbohydrates, total sugars, non-free sugars, whole grain foods, and fiber from whole grains might be inversely associated with colorectal cancer risk. Some evidence of heterogeneity was observed by genetically predicted butyrate production, where the intake of starch from whole grains was inversely associated with colorectal cancer risk only in participants with high host genetically predicted butyrate synthesis. Although these results may support the possible importance of SCFA production in these associations, further research in other large prospective cohorts is needed to replicate these findings.Supplementary MaterialSupplementary MaterialClick here for additional data file.