Breast, colorectal, ovarian, and endometrial cancers constitute approximately 30% of newly diagnosed cancer cases in Switzerland, affecting more than 12,000 individuals annually. Hundreds of these patients are likely to carry germline pathogenic variants associated with hereditary breast ovarian cancer (HBOC) or Lynch syndrome (LS). Genetic services (counseling and testing) for hereditary susceptibility to cancer can prevent many cancer diagnoses and deaths through early identification and risk management. Cascade screening is the systematic identification and testing of relatives of a known mutation carrier. It determines whether asymptomatic relatives also carry the known variant, needing management options to reduce future harmful outcomes. Specific aims of the CASCADE study are to (1) survey index cases with HBOC or LS from clinic-based genetic testing records and determine their current cancer status and surveillance practices, needs for coordination of medical care, psychosocial needs, patient-provider and patient-family communication, quality of life, and willingness to serve as advocates for cancer genetic services to blood relatives, (2) survey first- and second-degree relatives and first-cousins identified from pedigrees or family history records of HBOC and LS index cases and determine their current cancer and mutation status, cancer surveillance practices, needs for coordination of medical care, barriers and facilitators to using cancer genetic services, psychosocial needs, patient-provider and patient-family communication, quality of life, and willingness to participate in a study designed to increase use of cancer genetic services, and (3) explore the influence of patient-provider communication about genetic cancer risk on patient-family communication and the acceptability of a family-based communication, coping, and decision support intervention with focus group(s) of mutation carriers and relatives. CASCADE is a longitudinal study using surveys (online or paper/pencil) and focus groups, designed to elicit factors that enhance cascade genetic testing for HBOC and LS in Switzerland. Repeated observations are the optimal way for assessing these outcomes. Focus groups will examine barriers in patient-provider and patient-family communication, and the acceptability of a family-based communication, coping, and decision-support intervention. The survey will be developed in English, translated into three languages (German, French, and Italian), and back-translated into English, except for scales with validated versions in these languages. Descriptive analyses will include calculating means, standard deviations, frequencies, and percentages of variables and participant descriptors. Bivariate analyses (Pearson correlations, chi-square test for differences in proportions, and t test for differences in means) will assess associations between demographics and clinical characteristics. Regression analyses will incorporate generalized estimating equations for pairing index cases with their relatives and explore whether predictors are in direct, mediating, or moderating relationship to an outcome. Focus group data will be transcribed verbatim and analyzed for common themes. Robust evidence from basic science and descriptive population-based studies in Switzerland support the necessity of cascade screening for genetic predisposition to HBOC and LS. CASCADE is designed to address translation of this knowledge into public health interventions. ClinicalTrials.gov NCT03124212; https://clinicaltrials.gov/ct2/show/NCT03124212 (Archived by WebCite at http://www.webcitation.org/6tKZnNDBt).

Introduction: Approximately 20%-45% of familial melanoma (FM) cases are associated with genetic predisposition. Objectives: This single-center retrospective study aimed to assess the frequency of pathogenic variants (PV) in the main melanoma-predisposing genes in patients with cutaneous melanoma and investigate the clinical predictors of genetic predisposition. Methods: Patients included were those diagnosed with cutaneous melanoma at the Dermatology Unit of the University Hospital, Verona, Italy, from 2000 to 2022, presenting at least one of the following: multiple melanomas (≥3); personal/family history of pancreatic cancer (PC) (up to second-degree relatives); ≥2 first-degree relatives with melanoma; ≥1 first-degree relatives with early onset (<45 years) melanoma and tested for CDKN2A, CDK4, POT1, BAP1, MITF, ATM, and TERT. Results: During the study period, 35 out of 1,320 patients (2.7%) underwent genetic testing. Four patients (11.4%) harbored a PV in a melanoma-predisposing gene, 3 in CDKN2A (8.6%), and 1 in MITF (2.9%). Variants currently classified as being of unknown clinical significance (VUS) were detected in CDKN2A (n=1), MITF (n=1), and ATM (n=2). Family history of PC and ≥5 melanomas, personal history of ≥50 nevi, and ≥4 melanomas were significantly associated with PV in tested genes (P<0.05). Conclusions: The prevalence of PV in predisposing genes in FM was lower than previously reported in Italian registries. Possible reasons include deleterious variants in untested intermediate-/low-penetrance genes or yet-to-be-discovered high-penetrance genes and environmental risk factors. A family history of PC, a high number of nevi and melanomas predict a monogenic predisposition to melanoma.

(1) Background: The identification of tumor subtypes is fundamental in precision medicine for accurate diagnoses and personalized therapies. Cancer development is often driven by the accumulation of somatic mutations that can cause alterations in tissue functions and morphologies. In this work, a method based on a deep neural network integrated into a network-based stratification framework (D3NS) is proposed to stratify tumors according to somatic mutations. (2) Methods: This approach leverages the power of deep neural networks to detect hidden information in the data by combining the knowledge contained in a network of gene interactions, as typical of network-based stratification methods. D3NS was applied using real-world data from The Cancer Genome Atlas for bladder, ovarian, and kidney cancers. (3) Results: This technique allows for the identification of tumor subtypes characterized by different survival rates and significant associations with several clinical outcomes (tumor stage, grade or response to therapy). (4) Conclusion: D3NS can provide a base model in cancer research and could be considered as a useful tool for tumor stratification, offering potential support in clinical settings.