L1 retrotransposons are an abundant class of transposable elements which pose a threat to genome stability and may play a role in age-related pathologies such as cancer. Recent evidence indicates that L1s become more active in somatic tissues during the course of aging; the mechanisms underlying this phenomenon remain unknown, however. Here we report that the longevity regulating protein, SIRT6, is a powerful repressor of L1 activity. Specifically, SIRT6 binds to the 5′UTR of L1 loci, where it mono-ADP ribosylates the nuclear corepressor protein, KAP1, and facilitates KAP1 interaction with the heterochromatin factor, HP1α, thereby contributing to the packaging of L1 elements into transcriptionally repressive heterochromatin. During the course of aging, and also in response to DNA damage, however, we find that SIRT6 is depleted from L1 loci, allowing for the activation of these previously silenced retroelements.

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Objective Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed 10 phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Data Sciences and Informatics (OHDSI) network. Materials and Methods We constructed classifiers using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE) R-package, an open-source framework for learning phenotype classifiers using datasets in the Observational Medical Outcomes Partnership Common Data Model. We labeled training data based on the presence of multiple mentions of disease-specific codes. Performance was evaluated on cohorts derived using rule-based definitions and real-world disease prevalence. Classifiers were developed and evaluated across 3 medical centers, including 1 international site. Results Compared to the multiple mentions labeling heuristic, classifiers showed a mean recall boost of 0.43 with a mean precision loss of 0.17. Performance decreased slightly when classifiers were shared across medical centers, with mean recall and precision decreasing by 0.08 and 0.01, respectively, at a site within the USA, and by 0.18 and 0.10, respectively, at an international site. Discussion and Conclusion We demonstrate a high-throughput pipeline for constructing and sharing phenotype classifiers across sites within the OHDSI network using APHRODITE. Classifiers exhibit good portability between sites within the USA, however limited portability internationally, indicating that classifier generalizability may have geographic limitations, and, consequently, sharing the classifier-building recipe, rather than the pretrained classifiers, may be more useful for facilitating collaborative observational research.

Background Elderly patients with gastrointestinal cancer and mental illness have significant comorbidities that can impact the quality of their care. We investigated the relationship between mental illness and frequent emergency department (ED) use in the last month of life, an indicator for poor end‐of‐life care quality, among elderly patients with gastrointestinal cancers. Methods We used SEER‐Medicare data to identify decedents with gastrointestinal cancers who were diagnosed between 2004 and 2013 and were at least 66 years old at time of diagnosis (median age: 80 years, range: 66–117 years). We evaluated the association between having a diagnosis of depression, bipolar disorders, psychotic disorders, anxiety, dementia, and/or substance use disorders and ED use in the last 30 days of life using logistic regression models. Results Of 160,367 patients included, 54,661 (34.1%) had a mental illness diagnosis between one year prior to cancer diagnosis and death. Patients with mental illness were more likely to have > 1 ED visit in the last 30 days of life (15.6% vs. 13.3%, p < 0.01). ED use was highest among patients with substance use (17.7%), bipolar (16.5%), and anxiety disorders (16.4%). Patients with mental illness who were male, younger, non‐white, residing in lower income areas, and with higher comorbidity were more likely to have multiple end‐of‐life ED visits. Patients who received outpatient treatment from a mental health professional were less likely to have multiple end‐of‐life ED visits (adjusted odds ratio 0.82, 95% confidence interval 0.78–0.87). Conclusions In elderly patients with gastrointestinal cancers, mental illness is associated with having multiple end‐of‐life ED visits. Increasing access to mental health services may improve quality of end‐of‐life care in this vulnerable population.

Background We evaluated whether pre- and mid-treatment metabolic tumor volume (MTV) predicts per lesion local recurrence (LR) in patients treated with definitive radiation therapy (RT, dose≥60 Gy) for locally advanced non-small cell lung cancer (NSCLC). Methods We retrospectively reviewed records of patients with stage III NSCLC treated from 2006 to 2018 with pre- and mid-RT PET-CT. We measured the MTV of treated lesions on the pre-RT (MTVpre) and mid-RT (MTVmid) PET-CT. LR was defined per lesion as recurrence within the planning target volume. Receiver operating characteristic (ROC) curves, cumulative incidence rates, and uni- and multivariable (MVA) competing risk regressions were used to evaluate the association between MTV and LR. Results We identified 111 patients with 387 lesions (112 lung tumors and 275 lymph nodes). Median age was 68 years, 69.4% were male, 46.8% had adenocarcinoma, 39.6% had squamous cell carcinoma, and 95.5% received concurrent chemotherapy. Median follow-up was 38.7 months. 3-year overall survival was 42.3%. 3-year cumulative incidence of LR was 26.8% per patient and 11.9% per lesion. Both MTVpre and MTVmid were predictive of LR by ROC (AUC = 0.71 and 0.76, respectively) and were significantly associated with LR on MVA (P = 0.004 and P = 7.1e-5, respectively). Among lesions at lower risk of LR based on MTVpre, higher MTVmid was associated with LR (P = 0.001). Conclusion Per-lesion, larger MTVpre and MTVmid predicted for increased risk of LR. MTVmid was more highly predictive of LR than MTVpre and if validated may allow for further discrimination of high-risk lesions at mid-RT informing dose painting strategies.