Context Immune checkpoint inhibitors (ICIs) have gained a revolutionary role in management of many advanced malignancies. However, immune-related endocrine events (irEEs), have been associated with their use. irEEs have non-specific clinical presentations and variable timelines, making their early diagnosis challenging. Objective To identify risk factors, timelines, and prognosis associated with irEEs development. Design and setting Retrospective observational study within the Cleveland Clinic center. Patients Metastatic cancer adult patients who received ICIs were included. Methods 570 charts were reviewed to obtain information on demographics, ICIs used, endocrine toxicities, cancer response to treatment with ICI, and overall survival. Main Outcome Measures Incidence of irEEs, time to irEEs development, and overall survival of patients who develop irEEs. Results The final cohort included 551 patients. The median time for the diagnosis of irEEs was 11 weeks. Melanoma was associated with the highest risk for irEEs (31.3%). Ipilimumab appeared to have the highest percentage of irEEs (29.4%), including the highest risk of pituitary insufficiency (11.7%), the most severe (Grade 4 in 60%) and irreversible (100%) forms of irEEs. 45 % of patients with irEEs had adequate cancer response to ICI compared to 28.3 % of patients without irEEs (p= 0.002). Patients with irEEs had significantly better survival compared to patients without irEEs (P <0.001). Conclusions In the adult population with metastatic cancer receiving treatment with ICI, irEEs development may predict tumor response to immunotherapy and a favorable prognosis. Ipilimumab use, combination ICI therapy, and melanoma are associated with a higher incidence of irEEs.

Summary The IMPEDE VTE score has recently emerged as a novel risk prediction tool for venous thromboembolism (VTE) in multiple myeloma (MM). We retrospectively reviewed 839 patients with newly diagnosed MM between 2010 and 2015 at Cleveland Clinic and included 575 patients in final analysis to validate this score. The c‐statistic of the IMPEDE VTE score to predict VTE within 6 months of treatment start was 0·68 (95% CI: 0·61–0·75). The 6‐month cumulative incidence of VTE was 5·0% (95% CI: 2·1–7·9) in the low risk group, compared to 12·6% (95% CI: 8·9–16·4%) and 24·1% (95% CI: 12·2–36·1) in the intermediate and high risk groups (P < 0·001 for both). In addition, a higher proportion of patients in the VTE cohort had ECOG performance status of ≥2 as compared to the no VTE cohort (33% vs. 16%, P = 0·001). Other MM characteristics such as stage, immunoglobulin subtype, and cytogenetics were not predictors of VTE. In summary, we have validated the IMPEDE VTE score in our patient cohort and our findings suggest that it can be utilized as a VTE risk stratification tool in prospective studies looking into investigating VTE prophylaxis strategies in MM patients.

Venous thromboembolism (VTE) is highly prevalent in Multiple Myeloma (MM) patients, however a reliable VTE prediction tool in MM remains under study. The IMPEDE VTE score has recently emerged as a novel risk prediction tool for VTE in MM but needs external validation in different cohorts. We conducted a retrospective cohort study to validate this score. We reviewed 839 patients who were newly diagnosed with MM between 2010 and 2015 at Cleveland Clinic and included 575 patients in final analysis. The 6-month cumulative incidence of VTE among all patients was 10.7% (95% CI: 8.2 – 13.2) and the c-statistic of the IMPEDE VTE score to predict VTE within 6 months of treatment start was 0.68 (95% CI: 0.61 – 0.75). The 6-month cumulative incidence of VTE was 5.0% (95% CI: 2.1 – 7.9) in the low risk group, compared to 12.6% (95% CI: 8.9% – 16.4%) and 24.1% (95% CI: 12.2 – 36.1) in the intermediate and high risk groups (p<0.001 for both). In addition, a higher proportion of patients in the VTE cohort had ECOG performance status of ≥2 as compared to the no VTE cohort (33% vs. 16%, p=0.001). Other MM characteristics such as stage, immunoglobulin subtype, and cytogenetics were not found to be predictors of VTE. In summary, we have validated the IMPEDE VTE score in our patient cohort and our findings suggest that it can be utilized as a VTE risk stratification tool in prospective studies looking into investigating VTE prophylaxis strategies in MM patients.

Genomic mutations drive the pathogenesis of myelodysplastic syndromes and acute myeloid leukemia. While morphological and clinical features have dominated the classical criteria for diagnosis and classification, incorporation of molecular data can illuminate functional pathobiology. Here we show that unsupervised machine learning can identify functional objective molecular clusters, irrespective of anamnestic clinico-morphological features, despite the complexity of the molecular alterations in myeloid neoplasia. Our approach reflects disease evolution, informed classification, prognostication, and molecular interactions. We apply machine learning methods on 3588 patients with myelodysplastic syndromes and secondary acute myeloid leukemia to identify 14 molecularly distinct clusters. Remarkably, our model shows clinical implications in terms of overall survival and response to treatment even after adjusting to the molecular international prognostic scoring system (IPSS-M). In addition, the model is validated on an external cohort of 412 patients. Our subclassification model is available via a web-based open-access resource (https://drmz.shinyapps.io/mds_latent).

BackgroundGenomic mutations drive the pathogenesis of myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). While morphological and clinical features, complemented by cytogenetics, have dominated the classical criteria for diagnosis and classi cation, incorporation of molecular mutational data can illuminate functional pathobiology. MethodsWe combined cytogenetic and molecular features from a multicenter cohort of 3588 MDS, MDS/ myeloproliferative neoplasm (including chronic myelomonocytic leukemia [CMML]), and secondary AML patients to generate a molecular-based scheme using machine learning methods and then externally validated the model on 412 patients. Molecular signatures driving each cluster were identi ed and used for genomic subclassi cation. FindingsUnsupervised analyses identi ed 14 distinctive and clinically heterogenous molecular clusters (MCs) with unique pathobiological associations, treatment responses, and prognosis. Normal karyotype (NK) was enriched in MC2, MC4, MC6, MC9, MC10, and MC12 with different distributions of TET2, SF3B1, ASXL1, DNMT3A, and RAS mutations. Complex karyotype and trisomy 8 were enriched in MC13 and MC1, respectively. We then identi ed ve risk groups to re ect the biological differences between clusters. Our clustering model was able to highlight the signi cant survival differences among patients assigned to the similar IPSS-R risk group but with heterogenous molecular con gurations. Different response rates to hypomethylating agents (e.g., MC9 and MC13 [OR: 2.2 and 0.6, respectively]) re ected the biological differences between the clusters. Interestingly, our clusters continued to show survival differences regardless of the bone marrow blast percentage. InterpretationDespite the complexity of the molecular alterations in myeloid neoplasia, our model recognized functional objective clusters, irrespective of anamnestic clinico-morphological features, that re ected disease evolution and informed classi cation, prognostication, and molecular interactions. Our subclassi cation model is available via a web-based open-access resource as well (https://drmz.shinyapps.io/mds_latent).