Evaluating a New Digital App-Based Program for Heart Health: Feasibility and Acceptability Pilot Study.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in the United States, affecting a significant proportion of adults. Digital health lifestyle change programs have emerged as a promising method of CVD prevention, offering benefits such as on-demand support, lower cost, and increased scalability. Prior research has shown the effectiveness of digital health interventions in reducing negative CVD outcomes. This pilot study focuses on the Lark Heart Health program, a fully digital artificial intelligence (AI)-powered smartphone app, providing synchronous CVD risk counseling, educational content, and personalized coaching. OBJECTIVE: This pilot study evaluated the feasibility and acceptability of a fully digital AI-powered lifestyle change program called Lark Heart Health. Primary analyses assessed (1) participant satisfaction, (2) engagement with the program, and (3) the submission of health screeners. Secondary analyses were conducted to evaluate weight loss outcomes, given that a major focus of the Heart Health program is weight management. METHODS: This study enrolled 509 participants in the 90-day real-world single-arm pilot study of the Heart Health app. Participants engaged with the app by participating in coaching conversations, logging meals, tracking weight, and completing educational lessons. The study outcomes included participant satisfaction, app engagement, the completion of screeners, and weight loss. RESULTS: On average, Heart Health study participants were aged 60.9 (SD 10.3; range 40-75) years, with average BMI indicating class I obesity. Of the 509 participants, 489 (96.1%) stayed enrolled until the end of the study (dropout rate: 3.9%). Study retention, based on providing a weight measurement during month 3, was 80% (407/509; 95% CI 76.2%-83.4%). Participant satisfaction scores indicated high satisfaction with the overall app experience, with an average score of ≥4 out of 5 for all satisfaction indicators. Participants also showed high engagement with the app, with 83.4% (408/489; 95% CI 80.1%-86.7%) of the sample engaging in ≥5 coaching conversations in month 3. The results indicated that participants were successfully able to submit health screeners within the app, with 90% (440/489; 95% CI 87%-92.5%) submitting all 3 screeners measured in the study. Finally, secondary analyses showed that participants lost weight during the program, with analyses showing an average weight nadir of 3.8% (SD 2.9%; 95% CI 3.5%-4.1%). CONCLUSIONS: The study results indicate that participants in this study were satisfied with their experience using the Heart Health app, highly engaged with the app features, and willing and able to complete health screening surveys in the app. These acceptability and feasibility results provide a key first step in the process of evidence generation for a new AI-powered digital program for heart health. Future work can expand these results to test outcomes with a commercial version of the Heart Health app in a diverse real-world sample.

Comparing the value of mono- vs coculture for high-throughput compound screening in hematological malignancies.

Large-scale compound screens are a powerful model system for understanding variability of treatment response and discovering druggable tumor vulnerabilities of hematological malignancies. However, as mostly performed in a monoculture of tumor cells, these assays disregard modulatory effects of the in vivo microenvironment. It is an open question whether and to what extent coculture with bone marrow stromal cells could improve the biological relevance of drug testing assays over monoculture. Here, we established a high-throughput platform to measure ex vivo sensitivity of 108 primary blood cancer samples to 50 drugs in monoculture and coculture with bone marrow stromal cells. Stromal coculture conferred resistance to 52% of compounds in chronic lymphocytic leukemia (CLL) and 36% of compounds in acute myeloid leukemia (AML), including chemotherapeutics, B-cell receptor inhibitors, proteasome inhibitors, and Bromodomain and extraterminal domain inhibitors. Only the JAK inhibitors ruxolitinib and tofacitinib exhibited increased efficacy in AML and CLL stromal coculture. We further confirmed the importance of JAK-STAT signaling for stroma-mediated resistance by showing that stromal cells induce phosphorylation of STAT3 in CLL cells. We genetically characterized the 108 cancer samples and found that drug-gene associations strongly correlated between monoculture and coculture. However, effect sizes were lower in coculture, with more drug-gene associations detected in monoculture than in coculture. Our results justify a 2-step strategy for drug perturbation testing, with large-scale screening performed in monoculture, followed by focused evaluation of potential stroma-mediated resistances in coculture.

Proteogenomics refines the molecular classification of chronic lymphocytic leukemia.

Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic-based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterize the proteome and transcriptome alongside genetic and ex-vivo drug response profiling in a clinically annotated CLL discovery cohort (n = 68). Unsupervised clustering of the proteome data reveals six subgroups. Five of these proteomic groups are associated with genetic features, while one group is only detectable at the proteome level. This new group is characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). Classifiers developed to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n = 165, n = 169) confirm that ASB-CLL comprises about 20% of CLL patients. The inferior overall survival in ASB-CLL is also independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling.

Performance Evaluation of MHC Class-I Binding Prediction Tools Based on an Experimentally Validated MHC-Peptide Binding Data Set.

Knowing whether a protein can be processed and the resulting peptides presented by major histocompatibility complex (MHC) is highly important for immunotherapy design. MHC ligands can be predicted by in silico peptide-MHC class-I binding prediction algorithms. However, prediction performance differs considerably, depending on the selected algorithm, MHC class-I type, and peptide length. We evaluated the prediction performance of 13 algorithms based on binding affinity data of 8- to 11-mer peptides derived from the HPV16 E6 and E7 proteins to the most prevalent human leukocyte antigen (HLA) types. Peptides from high to low predicted binding likelihood were synthesized, and their HLA binding was experimentally verified by in vitro competitive binding assays. Based on the actual binding capacity of the peptides, the performance of prediction algorithms was analyzed by calculating receiver operating characteristics (ROC) and the area under the curve (AROC). No algorithm outperformed others, but different algorithms predicted best for particular HLA types and peptide lengths. The sensitivity, specificity, and accuracy of decision thresholds were calculated. Commonly used decision thresholds yielded only 40% sensitivity. To increase sensitivity, optimal thresholds were calculated, validated, and compared. In order to make maximal use of prediction algorithms available online, we developed MHCcombine, a web application that allows simultaneous querying and output combination of up to 13 prediction algorithms. Taken together, we provide here an evaluation of peptide-MHC class-I binding prediction tools and recommendations to increase prediction sensitivity to extend the number of potential epitopes applicable as targets for immunotherapy.