Family history and genetic risk score combined to guide cancer risk stratification: A prospective cohort study.

Family history (FH) of cancer and polygenic risk scores (PRS) are pivotal for cancer risk assessment, yet their combined impact remains unclear. Participants in the UK Biobank (UKB) were recruited between 2006 and 2010, with complete follow-up data updated until February 2020 for Scotland and January 2021 for England and Wales. Using UKB data (N = 442,399), we constructed PRS and incidence-weighted overall cancer PRS (CPRS). FH was assessed through self-reported standardized questions. Among 202,801 men (34.6% with FH) and 239,598 women (42.0% with FH), Cox regression was used to examine the associations between FH, PRS, and cancer risk. We found a significant dose-response relationship between FH of cancer and corresponding cancer risk (Ptrend < .05), with over 10 significant pairs of cross-cancer effects of FH. FH and PRS are positively correlated and independent. Joint effects of FH of cancer (multiple cancers) and PRS (CPRS) on corresponding cancer risk were observed: for instance, compared with participants with no FH of cancer and low PRS, men with FH of cancer and high PRS had the highest risk of colorectal cancer (hazard ratio [HR]: 3.69, 95% confidence interval [CI]: 3.01-4.52). Additive interactions were observed in prostate and overall cancer risk for men and breast cancer for women, with the most significant result being a relative excess risk of interaction (RERI) of 2.98, accounting for ~34% of the prostate cancer risk. In conclusion, FH and PRS collectively contribute to cancer risk, supporting their combined application in personalized risk assessment and early intervention strategies.

Abnormal TP53 Predicts Risk of Progression in Patients With Barrett's Esophagus Regardless of a Diagnosis of Dysplasia.

BACKGROUND AND AIMS: Barrett's esophagus (BE) is the precursor to esophageal adenocarcinoma. A major challenge is identifying the small group with BE who will progress to advanced disease from the many who will not. Assessment of p53 status has promise as a predictive biomarker, but analytic limitations and lack of validation have precluded its use. The aim of this study was to develop a robust criteria for grading abnormal immunohistochemical (IHC) expression of p53 and to test its utility as a biomarker for progression in BE. METHODS: Criteria for abnormal IHC of p53 were developed in BE biopsies and validated with sequencing to assess TP53 mutations. The utility of p53 IHC as a biomarker for progression of BE was tested retrospectively in 561 patients with BE with or without known progression. The findings were prospectively validated in a clinical practice setting in 1487 patients with BE. RESULTS: Abnormal p53 IHC highly correlated with TP53 mutation status (90.6% agreement) and was strongly associated with neoplastic progression in the retrospective cohorts, regardless of histologic diagnosis (P < .001). In the retrospective cohort, abnormal p53 was associated with a hazard ratio of 5.03 (95% confidence interval, 3.88-6.5) and a hazard ratio of 5.27 (95% confidence interval, 3.93-7.07) for patients with exclusively nondysplastic disease before progression. In the prospective validation cohort, p53 IHC predicted progression among nondysplastic BE, indefinite for dysplasia, and low-grade dysplasia (P < .001). CONCLUSIONS: p53 IHC identifies patients with BE at higher risk of progression, including in patients without evidence of dysplasia. p53 IHC is inexpensive, easily integrated into routine practice, and should be considered in biopsies from all BE patients without high-grade dysplasia or cancer.

Women's perceptions of PERSPECTIVE: a breast cancer risk stratification e-platform.

BACKGROUND: Breast cancer risk stratification categorizes a woman's potential risk of developing the disease as near-population, intermediate, or high. In accordance, screening and follow up for breast cancer can readily be tailored following risk assessment. Recent efforts have focussed on developing more accessible means to convey this information to women. This study sought to document the relevance of an informational e-platform developed for these purposes. OBJECTIVE: To begin to assess a newly developed breast cancer risk stratification and decision support e-platform called PERSPECTIVE (PErsonalised Risk Stratification for Prevention and Early deteCTIon of breast cancer) among women who do not know their personal breast cancer risk (Phase 1). Changes (pre- and post- e-platform exposure) in knowledge of breast cancer risk and interest in undergoing genetic testing were assessed in addition to perceptions of platform usability and acceptability. METHODS: Using a pre-post design, women (N = 156) of differing literacy and education levels, aged 30 to 60, with no previous breast cancer diagnosis were recruited from the general population and completed self-report e-questionnaires. RESULTS: Mean e-platform viewing time was 18.67 min (SD 0.65) with the most frequently visited pages being breast cancer-related risk factors and risk assessment. Post-exposure, participants reported  significantly higher breast cancer-related knowledge (p < .001). Increases in knowledge relating to obesity, alcohol, breast density, menstruation, and the risk estimation process remained even when sociodemographic variables age and education were controlled. There were no significant changes in genetic testing interest post-exposure. Mean ratings for e-platform acceptability and usability were high: 26.19 out of 30 (SD 0.157) and 42.85 out of 50 (SD 0.267), respectively. CONCLUSIONS: An informative breast cancer risk stratification e-platform targeting healthy women in the general population can significantly increase knowledge as well as support decisions around breast cancer risk and assessment. Currently underway, Phase 2, called PERSPECTIVE, is seeking further content integration and broader implementation .

Studying Cancer Evolution in Barrett's Esophagus and Esophageal Adenocarcinoma.

Technological advances in genome sequencing and copy number analysis have allowed researchers to catalog the wide variety of genomic alterations that occur across diverse cancer types. For most cancer types, the lack of high-frequency alterations and the heterogeneity observed both within and between tumors suggest neoplastic progression proceeds through a branched evolutionary pathway as proposed by Nowell in 1976, as opposed to the linear pathway that has dominated medical science for the last century. To understand how cancer evolves over time and space in the body, new study designs are needed that can distinguish between alterations that develop in patients who progress to cancer from to those who don't. Here we present approaches developed in the study of Barrett's esophagus, a premalignant precursor of esophageal adenocarcinoma, and discuss strategies for applying the results from these analyses to address the critical clinical problems of overdiagnosis of benign disease, early detection of life-threatening cancer, and effective risk stratification.

A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules.

Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.

Establishing a Urine-Based Biomarker Assay for Prostate Cancer Risk Stratification.

One of the major features of prostate cancer (PCa) is its heterogeneity, which often leads to uncertainty in cancer diagnostics and unnecessary biopsies as well as overtreatment of the disease. Novel non-invasive tests using multiple biomarkers that can identify clinically high-risk cancer patients for immediate treatment and monitor patients with low-risk cancer for active surveillance are urgently needed to improve treatment decision and cancer management. In this study, we identified 14 promising biomarkers associated with PCa and tested the performance of these biomarkers on tissue specimens and pre-biopsy urinary sediments. These biomarkers showed differential gene expression in higher- and lower-risk PCa. The 14-Gene Panel urine test (PMP22, GOLM1, LMTK2, EZH2, GSTP1, PCA3, VEGFA, CST3, PTEN, PIP5K1A, CDK1, TMPRSS2, ANXA3, and CCND1) was assessed in two independent prospective and retrospective urine study cohorts and showed high diagnostic accuracy to identify higher-risk PCa patients with the need for treatment and lower-risk patients for surveillance. The AUC was 0.897 (95% CI 0.939-0.855) in the prospective cohort (n = 202), and AUC was 0.899 (95% CI 0.964-0.834) in the retrospective cohort (n = 97). In contrast, serum PSA and Gleason score had much lower accuracy in the same 202 patient cohorts [AUC was 0.821 (95% CI 0.879-0.763) for PSA and 0.860 (95% CI 0.910-0.810) for Gleason score]. In addition, the 14-Gene Panel was more accurate at risk stratification in a subgroup of patients with Gleason scores 6 and 7 in the prospective cohort (n = 132) with AUC of 0.923 (95% CI 0.968-0.878) than PSA [AUC of 0.773 (95% CI 0.852-0.794)] and Gleason score [AUC of 0.776 (95% CI 0.854-0.698)]. Furthermore, the 14-Gene Panel was found to be able to accurately distinguish PCa from benign prostate with AUC of 0.854 (95% CI 0.892-0.816) in a prospective urine study cohort (n = 393), while PSA had lower accuracy with AUC of 0.652 (95% CI 0.706-0.598). Taken together, the 14-Gene Panel urine test represents a promising non-invasive tool for detection of higher-risk PCa to aid treatment decision and lower-risk PCa for active surveillance.

Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk.

The purpose of this study was to develop and assess a new quantitative four-view mammographic image feature based fusion model to predict the near-term breast cancer risk of the individual women after a negative screening mammography examination of interest. The dataset included fully-anonymized mammograms acquired on 870 women with two sequential full-field digital mammography examinations. For each woman, the first "prior" examination in the series was interpreted as negative (not recalled) during the original image reading. In the second "current" examination, 430 women were diagnosed with pathology verified cancers and 440 remained negative ("cancer-free"). For each of four bilateral craniocaudal and mediolateral oblique view images of left and right breasts, we computed and analyzed eight groups of global mammographic texture and tissue density image features. A risk prediction model based on three artificial neural networks was developed to fuse image features computed from two bilateral views of four images. The risk model performance was tested using a ten-fold cross-validation method and a number of performance evaluation indices including the area under the receiver operating characteristic curve (AUC) and odds ratio (OR). The highest AUC = 0.725 ± 0.026 was obtained when the model was trained by gray-level run length statistics texture features computed on dense breast regions, which was significantly higher than the AUC values achieved using the model trained by only two bilateral one-view images (p < 0.02). The adjustable OR values monotonically increased from 1.0 to 11.8 as model-generated risk score increased. The regression analysis of OR values also showed a significant increase trend in slope (p < 0.01). As a result, this preliminary study demonstrated that a new four-view mammographic image feature based risk model could provide useful and supplementary image information to help predict the near-term breast cancer risk.