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Summary: Network analysis (NA) has recently emerged as a new paradigm by which to model the symptom patterns of patients with complex illnesses such as cancer. NA uses graph theory-based methods to capture the interplay between symptoms and identify which symptoms may be most impactful to patient quality of life and are therefore most critical to treat/prevent. Despite NA's increasing popularity in research settings, its clinical applicability is hindered by the lack of a unified platform that consolidates all the software tools needed to perform NA, and by the lack of methods for capturing heterogeneity across patient cohorts. Addressing these limitations, we present PRONA, an R-package for Patient Reported Outcomes Network Analysis. PRONA not only consolidates previous NA tools into a unified, easy-to-use analysis pipeline, but also augments the traditional approach with functionality for performing unsupervised discovery of patient subgroups with distinct symptom patterns., Availability and Implementation: PRONA is implemented in R. Source code, installation, and use instructions are available on GitHub at https://github.com/bbergsneider/PRONA., Supplementary Information: Supplementary information is available at Bioinformatics online., (Published by Oxford University Press 2024.)

Regulatory T cells (Treg) are important players in the tumor microenvironment. However, the mechanisms behind their immunosuppressive effects are poorly understood. We found that CCR6-CCL20 activity in tumor-infiltrating Tregs is associated with greater glycolytic activity and ablation of Ccr6 reduced glycolysis and lactic acid production while increasing compensatory glutamine metabolism. Immunosuppressive activity toward CD8+ T cells was abrogated in Ccr6-/- Tregs due to reduction in activation-induced glycolysis. Furthermore, Ccr6-/- mice exhibited improved survival across multiple tumor models compared to wild-type mice and Treg and CD8+ T-cell depletion abrogated the improvement. In addition, Ccr6 ablation further promoted the efficacy of anti-PD-1 therapy in a preclinical glioma model. Follow-up knockdown of Ccl20 with siRNA also demonstrated improvement in antitumor efficacy. Our results unveil CCR6 as a marker and regulator of Treg-induced immunosuppression and identify approaches to target the metabolic determinants of Treg immunosuppressive activity., (©2024 American Association for Cancer Research.)

Background: Interindividual variability in oncology patients' symptom experiences poses significant challenges in prioritizing symptoms for targeted intervention(s). In this study, computational approaches were used to unbiasedly characterize the heterogeneity of the symptom experience of oncology patients to elucidate symptom patterns and drivers of symptom burden., Methods: Severity ratings for 32 symptoms on the Memorial Symptom Assessment Scale from 3088 oncology patients were analyzed. Gaussian Graphical Model symptom networks were constructed for the entire cohort and patient subgroups identified through unsupervised clustering of symptom co-severity patterns. Network characteristics were analyzed and compared using permutation-based statistical tests. Differences in demographic and clinical characteristics between subgroups were assessed using multinomial logistic regression., Results: Network analysis of the entire cohort revealed three symptom clusters: constitutional, gastrointestinal-epithelial, and psychological. Lack of energy was identified as central to the network which suggests that it plays a pivotal role in patients' overall symptom experience. Unsupervised clustering of patients based on shared symptom co-severity patterns identified six patient subgroups with distinct symptom patterns and demographic and clinical characteristics. The centrality of individual symptoms across the subgroup networks differed which suggests that different symptoms need to be prioritized for treatment within each subgroup. Age, treatment status, and performance status were the strongest determinants of subgroup membership., Conclusions: Computational approaches that combine unbiased stratification of patients and in-depth modeling of symptom relationships can capture the heterogeneity in patients' symptom experiences. When validated, the core symptoms for each of the subgroups and the associated clinical determinants may inform precision-based symptom management., (© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Background: Precision health approaches to managing symptom burden in primary brain tumor (PBT) patients are imperative to improving patient outcomes and quality of life, but require tackling the complexity and heterogeneity of the symptom experience. Network Analysis (NA) can identify complex symptom co-severity patterns, and unsupervised clustering can unbiasedly stratify patients into clinically relevant subgroups based on symptom patterns. We combined these approaches in a novel study seeking to understand PBT patients' clinical and demographic determinants of symptom burden., Methods: MDASI-BT symptom severity data from a two-institutional cohort of 1128 PBT patients were analyzed. Gaussian Graphical Model networks were constructed for the all-patient cohort and subgroups identified by unsupervised clustering based on co-severity patterns. Network characteristics were analyzed and compared using permutation-based statistical tests., Results: NA of the all-patient cohort revealed 4 core dimensions that drive the overall symptom burden of PBT patients: Cognitive, physical, focal neurologic, and affective. Fatigue/drowsiness was identified as pivotal to the symptom experience based on the network characteristics. Unsupervised clustering discovered 4 patient subgroups: PC1 ( n = 683), PC2 ( n = 244), PC3 ( n = 92), and PC4 ( n = 109). Moderately accurate networks could be constructed for PC1 and PC2. The PC1 patients had the highest interference scores among the subgroups and their network resembled the all-patient network. The PC2 patients were older and their symptom burden was driven by cognitive symptoms., Conclusions: In the future, the proposed framework might be able to prioritize symptoms for targeting individual patients, informing more personalized symptom management., (Published by Oxford University Press on behalf of the Society for Neuro-Oncology and the European Association of Neuro-Oncology 2022.)