Polygenic risk prediction models for colorectal cancer: a systematic review

Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

Disparities in Fruit and Vegetable Intake at the Intersection of Gender and Education in Northern Sweden: A Cross-Sectional Study

Background: Even though the existence of inequalities in fruit and vegetable consumption has been well established, it is not clear how it is patterned across intersections of multiple social positions and identities. This study aims to investigate disparities in fruit and vegetable intake between groups at the intersection of education and gender in northern Sweden, and to estimate the discriminatory accuracy of the intersectional groups.Methods: Cross-sectional data from the 2018 Health on Equal Terms survey conducted in four regions in northern Sweden was used (N=21,853). Four intersectional groups were created: high and low educated men, and high and low educated women. Prevalence differences corresponding to joint, referent, and excess intersectional inequalities, were estimated using linear binomial regression for three binary outcome variables: inadequate fruit and vegetable intake combined, inadequate fruit intake, and inadequate vegetable intake. The analysis was adjusted for potential confounders. The discriminatory accuracy of the intersectional groups was estimated by the area under the receiver operating characteristic curve.Results: Low educated men had the highest prevalence of inadequate intake of fruits and vegetables combined (81.4%), fruits (53.5%), and vegetables (54.2%), while high educated women had the lowest (47.7%, 28.6%, and 19.2% respectively). The joint disparities between high educated women and low educated men were both significant and substantial for all outcome variables (34.6 pp, 28.7pp, and 34.9pp respectively, adjusted). They were mostly explained by the two referent disparities for gender and education – the part of the joint disparity explained by differences in gender and education, respectively. An inconsistent direction, magnitude, and significance between outcomes was observed for the excess intersectional disparity - the part of the joint disparity not explained by either referent disparity (-5.5pp, 0.1pp, and 3.9pp respectively, adjusted). The discriminatory accuracy of the intersectional groups was moderate (0.67, 0.64, and 0.66 respectively).Conclusion: An intersectional approach can provide a more detailed view of inequalities in fruit and vegetable consumption between groups combining several social positions. The moderate discriminatory accuracy observed here suggests that interventions and policies aiming to reduce diet inequalities should not solely be targeted at certain groups, but also be universal.

Prospective Evaluation over 15 Years of Six Breast Cancer Risk Models

Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models (IBIS, BOADICEA, BRCAPRO, BRCAPRO-BCRAT, BCRAT, and iCARE-lit). 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50–65 years and unaffected at commencement of follow-up two (conducted in 2003–2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group (50–54, 55–59, 60–65 years) and family history of breast cancer. C-statistics were higher for BOADICEA (0.59, 95% confidence interval (CI) 0.56–0.62) and IBIS (0.57, 95% CI 0.54–0.61) than the other models (p-difference ≤ 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk (p-value < 0.03). The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum.

A Validated Risk Prediction Model for Breast Cancer in US Black Women

PURPOSE Breast cancer risk prediction models are used to identify high-risk women for early detection, targeted interventions, and enrollment into prevention trials. We sought to develop and evaluate a risk prediction model for breast cancer in US Black women, suitable for use in primary care settings. METHODS Breast cancer relative risks and attributable risks were estimated using data from Black women in three US population-based case-control studies (3,468 breast cancer cases; 3,578 controls age 30-69 years) and combined with SEER age- and race-specific incidence rates, with incorporation of competing mortality, to develop an absolute risk model. The model was validated in prospective data among 51,798 participants of the Black Women's Health Study, including 1,515 who developed invasive breast cancer. A second risk prediction model was developed on the basis of estrogen receptor (ER)–specific relative risks and attributable risks. Model performance was assessed by calibration (expected/observed cases) and discriminatory accuracy (C-statistic). RESULTS The expected/observed ratio was 1.01 (95% CI, 0.95 to 1.07). Age-adjusted C-statistics were 0.58 (95% CI, 0.56 to 0.59) overall and 0.63 (95% CI, 0.58 to 0.68) among women younger than 40 years. These measures were almost identical in the model based on estrogen receptor–specific relative risks and attributable risks. CONCLUSION Discriminatory accuracy of the new model was similar to that of the most frequently used questionnaire-based breast cancer risk prediction models in White women, suggesting that effective risk stratification for Black women is now possible. This model may be especially valuable for risk stratification of young Black women, who are below the ages at which breast cancer screening is typically begun.

Discriminatory accuracy of the SOFA score for determining clinical decompensation in patients presenting with COVID-19

Introduction While the global dissemination of vaccines targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a decline in the incidence of infections, the case fatality rates have remained relative stable. A major objective of managing hospitalized patients with documented or suspected COVID-19 infection is the rapid identification of features associated with severe illness using readily available laboratory tests and clinical tools. The sequential organ failure assessment (SOFA) score is a validated tool to facilitate the identification of patients at risk of dying from sepsis. Purpose The aim of this study was to assess the discriminatory accuracy of the SOFA score in predicting clinical decompensation in patients hospitalized with COVID-19 infection. Methods We conducted a retrospective analysis at a three-hospital health system, comprised of one tertiary and two community hospitals, located in the Chicago metropolitan area. All patients had positive SARS-CoV-2 testing and were hospitalized for COVID-19 infection. The primary outcome was clinical decompensation, defined as the composite endpoint of death, ICU admission, or need for intubation. We utilized the most abnormal laboratory values observed during the admission to calculate the SOFA score. Receiver Operating Curves (ROC) were then constructed to determine the sensitivity and specificity of SOFA scores. Results Between March 1st and May 31st 2020, 1029 patients were included in our analysis with 367 patients meeting the study endpoint. The median SOFA score was 2.0 IQR (Q1, Q3 1,4) for the entire cohort. Patients who had in-hospital mortality had a median SOFA score of 4.0 (Q1,Q3 3,7). In patients that met the primary composite endpoint, the median SOFA score was 3.0, IQR (Q1, Q3 2,6). The ROC was 0.776 (95% CI 0.746–0.806, p&lt;0.01). Conclusion The SOFA score demonstrates strong discriminatory accuracy for prediction of clinical decompensation in patients presenting with COVID-19 at our urban hospital system. FUNDunding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Loyola University Medical Center

Population heterogeneity in associations between hormonal contraception and antidepressant use in Sweden: a prospective cohort study applying intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

ObjectivesFrom a reproductive justice framework, we aimed to investigate how a possible association between hormonal contraceptive (HC) and antidepressants use (as a proxy for depression) is distributed across intersectional strata in the population. We aimed to visualise how intersecting power dynamics may operate in combination with HC use to increase or decrease subsequent use of antidepressants. Our main hypothesis was that the previously observed association between HC and antidepressants use would vary between strata, being more pronounced in more oppressed intersectional contexts. For this purpose, we applied an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy approach.DesignObservational prospective cohort study using record linkage of national Swedish registers.SettingThe population of Sweden.ParticipantsAll 915 954 women aged 12–30 residing in Sweden 2010, without a recent pregnancy and alive during the individual 1-year follow-up.Primary outcome measureUse of any antidepressant, meaning being dispensed at least one antidepressant (ATC: N06A) during follow-up.ResultsPreviously mentally healthy HC users had an OR of 1.79 for use of antidepressants compared with non-users, whereas this number was 1.28 for women with previous mental health issues. The highest antidepressant use were uniformly found in strata with previous mental health issues, with highest usage in women aged 24–30 with no immigrant background, low income and HC use (51.4%). The largest difference in antidepressant use between HC users and non-users was found in teenagers, and in adult women of immigrant background with low income. Of the total individual variance in the latent propensity of using antidepressant 9.01% (healthy) and 8.16% (with previous mental health issues) was found at the intersectional stratum level.ConclusionsOur study suggests teenagers and women with immigrant background and low income could be more sensitive to mood effects of HC, a heterogeneity important to consider moving forward.

A Unique Urinary Metabolic Feature for the Determination of Bladder Cancer, Prostate Cancer, and Renal Cell Carcinoma

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most prevalent cancer among urological cancers. However, there are no cancer-specific symptoms that can differentiate them as well as early clinical signs of urological malignancy. Furthermore, many metabolic studies have been conducted to discover their biomarkers, but the metabolic profiling study to discriminate between these cancers have not yet been described. Therefore, in this study, we aimed to investigate the urinary metabolic differences in male patients with PCa (n = 24), BCa (n = 29), and RCC (n = 12) to find the prominent combination of metabolites between cancers. Based on 1H NMR analysis, orthogonal partial least-squares discriminant analysis was applied to find distinct metabolites among cancers. Moreover, the ranked analysis of covariance by adjusting a potential confounding as age revealed that 4-hydroxybenzoate, N-methylhydantoin, creatinine, glutamine, and acetate had significantly different metabolite levels among groups. The receiver operating characteristic analysis created by prominent five metabolites showed the great discriminatory accuracy with AUC > 0.7 for BCa vs. RCC, PCa vs. BCa, and RCC vs. PCa. This preliminary study compares the metabolic profiles of BCa, PCa, and RCC, and reinforces the exploratory role of metabolomics in the investigation of human urine.

Proteomics of Primary Uveal Melanoma: Insights into Metastasis and Protein Biomarkers

Uveal melanoma metastases are lethal and remain incurable. A quantitative proteomic analysis of 53 metastasizing and 47 non-metastasizing primary uveal melanoma (pUM) was pursued for insights into UM metastasis and protein biomarkers. The metastatic status of the pUM specimens was defined based on clinical data, survival histories, prognostic analyses, and liver histopathology. LC MS/MS iTRAQ technology, the Mascot search engine, and the UniProt human database were used to identify and quantify pUM proteins relative to the normal choroid excised from UM donor eyes. The determined proteomes of all 100 tumors were very similar, encompassing a total of 3935 pUM proteins. Proteins differentially expressed (DE) between metastasizing and non-metastasizing pUM (n = 402) were employed in bioinformatic analyses that predicted significant differences in the immune system between metastasizing and non-metastasizing pUM. The immune proteins (n = 778) identified in this study support the immune-suppressive nature and low abundance of immune checkpoint regulators in pUM, and suggest CDH1, HLA-DPA1, and several DE immune kinases and phosphatases as possible candidates for immune therapy checkpoint blockade. Prediction modeling identified 32 proteins capable of predicting metastasizing versus non-metastasizing pUM with 93% discriminatory accuracy, supporting the potential for protein-based prognostic methods for detecting UM metastasis.

The prognostic performance of PREDICT+ in patients (pts) with HER2-positive (HER2+) early-stage breast cancer (EBC).

524 Background: PREDICT+ is a widely used, free, online tool based on traditional clinico-pathological features, including HER2, developed to predict individual mortality of EBC pts and to aid clinical decision making for adjuvant therapy. However, its prognostic role in HER2+ EBC pts treated with chemotherapy (CT) and anti-HER2 therapies remains unclear. We aimed to investigate the prognostic performance of PREDICT+ in HER2+ EBC pts enrolled in the ALTTO trial. Methods: ALTTO is a phase III study evaluating adjuvant lapatinib (L) +/- trastuzumab (T) vs. T alone in pts with HER2+ EBC. Pts enrolled in the ALTTO trial and receiving T-based therapy started concurrently with CT were eligible for this analysis. We calculated PREDICT+ estimates using variables extracted from ALTTO database, blinded to pts outcomes. The prognostic performance of PREDICT+ was evaluated by assessing its calibration and discriminatory accuracy. For calibration, median predicted 5-year (5-yr) overall survival (OS) was compared to observed 5-yr OS. For discriminatory accuracy, the area under the receiver-operator characteristic (AUC under the ROC) curve and corresponding 95% confidence intervals (CI) for predicted 5-yr OS were calculated. Subgroup analyses were performed according to type of anti-HER2 therapy, type of CT, age, hormone receptor (HR) status, nodal status and tumor size. Results: This analysis included 2,794 pts. After a median follow-up of 6.0 years (IQR, 5.8-6.7), 182 deaths were observed. Overall, PREDICT+ underestimated 5-yr OS by 6.7% (95% CI, 5.8-7.6): observed 5-year OS was 94.7% vs. predicted 88.0%. The underestimation was consistent across all subgroups (Table). For discriminatory accuracy, AUC under the ROC curve was 73.7% (95%CI 69.7-77.8) in the overall population, ranging between 61.7% and 77.7% across the analysed subgroups. Conclusions: In HER2+ EBC pts enrolled in the ALTTO trial, the PREDICT+ score highly underestimated OS. The low performance of this prognostic tool was consistent across all pts subgroups. PREDICT+ should be used with caution to give prognostic estimation in HER2+ EBC pts treated in the modern era with effective chemotherapy and anti-HER2 targeted therapies.[Table: see text]