Brandon Bergsneider

All Publications

• Symptom Network Analysis and Unsupervised Clustering of Oncology Patients Identifies Drivers of Symptom Burden and Patient Subgroups With Distinct Symptom Patterns. Cancer medicine Bergsneider, B. H., Armstrong, T. S., Conley, Y. P., Cooper, B., Hammer, M., Levine, J. D., Paul, S., Miaskowski, C., Celiku, O. 2024; 13 (19): e70278

  Abstract

  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.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.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.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.

  View details for DOI 10.1002/cam4.70278

  View details for PubMedID 39377555

  View details for PubMedCentralID PMC11460217

• The CCR6-CCL20 axis promotes regulatory T cell glycolysis and immunosuppression in tumors. Cancer immunology research Pant, A., Jain, A., Chen, Y., Patel, K., Saleh, L., Tzeng, S., Nitta, R. T., Zhao, L., Wu, C. Y., Bederson, M., Wang, W. L., Bergsneider, B. H., Choi, J., Medikonda, R., Verma, R., Cho, K. B., Kim, L. H., Kim, J. E., Yazigi, E., Lee, S. Y., Rajendran, S., Rajappa, P., Mackall, C. L., Li, G., Tyler, B., Brem, H., Pardoll, D. M., Lim, M., Jackson, C. M. 2024

  Abstract

  Regulatory T cells (Tregs) 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 towards 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 wildtype 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.

  View details for DOI 10.1158/2326-6066.CIR-24-0230

  View details for PubMedID 39133127

• The cytokine Meteorin-like inhibits anti-tumor CD8+ T cell responses by disrupting mitochondrial function. Immunity Jackson, C. M., Pant, A., Dinalankara, W., Choi, J., Jain, A., Nitta, R., Yazigi, E., Saleh, L., Zhao, L., Nirschl, T. R., Kochel, C. M., Hwa-Lin Bergsneider, B., Routkevitch, D., Patel, K., Cho, K. B., Tzeng, S., Neshat, S. Y., Kim, Y. H., Smith, B. J., Ramello, M. C., Sotillo, E., Wang, X., Green, J. J., Bettegowda, C., Li, G., Brem, H., Mackall, C. L., Pardoll, D. M., Drake, C. G., Marchionni, L., Lim, M. 2024

  Abstract

  Tumor-infiltrating lymphocyte (TIL) hypofunction contributes to the progression of advanced cancers and is a frequent target of immunotherapy. Emerging evidence indicates that metabolic insufficiency drives T cell hypofunction during tonic stimulation, but the signals that initiate metabolic reprogramming in this context are largely unknown. Here, we found that Meteorin-like (METRNL), a metabolically active cytokine secreted by immune cells in the tumor microenvironment (TME), induced bioenergetic failure of CD8+ T cells. METRNL was secreted by CD8+ T cells during repeated stimulation and acted via both autocrine and paracrine signaling. Mechanistically, METRNL increased E2F-peroxisome proliferator-activated receptor delta (PPARδ) activity, causing mitochondrial depolarization and decreased oxidative phosphorylation, which triggered a compensatory bioenergetic shift to glycolysis. Metrnl ablation or downregulation improved the metabolic fitness of CD8+ T cells and enhanced tumor control in several tumor models, demonstrating the translational potential of targeting the METRNL-E2F-PPARδ pathway to support bioenergetic fitness of CD8+ TILs.

  View details for DOI 10.1016/j.immuni.2024.07.003

  View details for PubMedID 39111315

• CCR2 and CCR5 co-inhibition modulates immunosuppressive myeloid milieu in glioma and synergizes with anti-PD-1 therapy. Oncoimmunology Pant, A., Hwa-Lin Bergsneider, B., Srivastava, S., Kim, T., Jain, A., Bom, S., Shah, P., Kannapadi, N., Patel, K., Choi, J., Cho, K. B., Verma, R., Yu-Ju Wu, C., Brem, H., Tyler, B., Pardoll, D. M., Jackson, C., Lim, M. 2024; 13 (1): 2338965

  Abstract

  Immunotherapy has revolutionized the treatment of cancers. Reinvigorating lymphocytes with checkpoint blockade has become a cornerstone of immunotherapy for multiple tumor types, but the treatment of glioblastoma has not yet shown clinical efficacy. A major hurdle to treat GBM with checkpoint blockade is the high degree of myeloid-mediated immunosuppression in brain tumors that limits CD8 T-cell activity. A potential strategy to improve anti-tumor efficacy against glioma is to use myeloid-modulating agents to target immunosuppressive cells, such as myeloid-derived suppressor cells (MDSCs) in the tumor microenvironment. We found that the co-inhibition of the chemokine receptors CCR2 and CCR5 in murine model of glioma improves the survival and synergizes robustly with anti-PD-1 therapy. Moreover, the treatment specifically reduced the infiltration of monocytic-MDSCs (M-MDSCs) into brain tumors and increased lymphocyte abundance and cytokine secretion by tumor-infiltrating CD8 T cells. The depletion of T-cell subsets and myeloid cells abrogated the effects of CCR2 and CCR5 blockade, indicating that while broad depletion of myeloid cells does not improve survival, specific reduction in the infiltration of immunosuppressive myeloid cells, such as M-MDSCs, can boost the anti-tumor immune response of lymphocytes. Our study highlights the potential of CCR2/CCR5 co-inhibition in reducing myeloid-mediated immunosuppression in GBM patients.

  View details for DOI 10.1080/2162402X.2024.2338965

  View details for PubMedID 38590799

  View details for PubMedCentralID PMC11000615

• Discovery of clinical and demographic determinants of symptom burden in primary brain tumor patients using network analysis and unsupervised clustering. Neuro-oncology advances Bergsneider, B. H., Vera, E., Gal, O., Christ, A., King, A. L., Acquaye, A., Choi, A., Leeper, H. E., Mendoza, T., Boris, L., Burton, E., Lollo, N., Panzer, M., Penas-Prado, M., Pillai, T., Polskin, L., Wu, J., Gilbert, M. R., Armstrong, T. S., Celiku, O. 2023; 5 (1): vdac188

  Abstract

  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.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.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.In the future, the proposed framework might be able to prioritize symptoms for targeting individual patients, informing more personalized symptom management.

  View details for DOI 10.1093/noajnl/vdac188

  View details for PubMedID 36820236

  View details for PubMedCentralID PMC9938652

• Analysis of SARS-CoV-2 infection associated cell entry proteins ACE2, CD147, PPIA, and PPIB in datasets from non SARS-CoV-2 infected neuroblastoma patients, as potential prognostic and infection biomarkers in neuroblastoma. Biochemistry and biophysics reports Bergsneider, B., Bailey, E., Ahmed, Y., Gogineni, N., Huntley, D., Montano, X. 2021; 27: 101081

  Abstract

  SARS-CoV-2 viral contagion has given rise to a worldwide pandemic. Although most children experience minor symptoms from SARS-CoV-2 infection, some have severe complications including Multisystem Inflammatory Syndrome in Children. Neuroblastoma patients may be at higher risk of severe infection as treatment requires immunocompromising chemotherapy and SARS-CoV-2 has demonstrated tropism for nervous cells. To date, there is no sufficient epidemiological data on neuroblastoma patients with SARS-CoV-2. Therefore, we evaluated datasets of non-SARS-CoV-2 infected neuroblastoma patients to assess for key genes involved with SARS-CoV-2 infection as possible neuroblastoma prognostic and infection biomarkers. We hypothesized that ACE2, CD147, PPIA and PPIB, which are associated with viral-cell entry, are potential biomarkers for poor prognosis neuroblastoma and SARS-CoV-2 infection. We have analysed three publicly available neuroblastoma gene expression datasets to understand the specific molecular susceptibilities that high-risk neuroblastoma patients have to the virus. Gene Expression Omnibus (GEO) GSE49711 and GEO GSE62564 are the microarray and RNA-Seq data, respectively, from 498 neuroblastoma samples published as part of the Sequencing Quality Control initiative. TARGET, contains microarray data from 249 samples and is part of the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative. ACE2, CD147, PPIA and PPIB were identified through their involvement in both SARS-CoV-2 infection and cancer pathogenesis. In-depth statistical analysis using Kaplan-Meier, differential gene expression, and Cox multivariate regression analysis, demonstrated that overexpression of ACE2, CD147, PPIA and PPIB is significantly associated with poor-prognosis neuroblastoma samples. These results were seen in the presence of amplified MYCN, unfavourable tumour histology and in patients older than 18 months of age. Previously, we have shown that high levels of the nerve growth factor receptor NTRK1 together with low levels of the phosphatase PTPN6 and TP53 are associated with increased relapse-free survival of neuroblastoma patients. Interestingly, low levels of expression of ACE2, CD147, PPIA and PPIB are associated with this NTRK1-PTPN6-TP53 module, suggesting that low expression levels of these genes are associated with good prognosis. These findings have implications for clinical care and therapeutic treatment. The upregulation of ACE2, CD147, PPIA and PPIB in poor-prognosis neuroblastoma samples suggests that these patients may be at higher risk of severe SARS-CoV-2 infection. Importantly, our findings reveal ACE2, CD147, PPIA and PPIB as potential biomarkers and therapeutic targets for neuroblastoma.

  View details for DOI 10.1016/j.bbrep.2021.101081

  View details for PubMedID 34307909

  View details for PubMedCentralID PMC8286873

• Next-Generation Sequencing and Personalized Medicine for Brain Cancer CURRENT SURGERY REPORTS Bergsneider, B. H., Liau, L. M. 2018; 6 (12)

  View details for DOI 10.1007/s40137-018-0221-x

  View details for Web of Science ID 000447654500002