Inferring the role of the microbiome on survival in patients treated with immune checkpoint inhibitors: causal modeling, timing, and classes of concomitant medications
Daniel Spakowicz,
Rebecca Hoyd,
Mitchell Muniak,
Marium Husain,
James S. Bassett,
Lei Wang,
Gabriel Tinoco,
Sandip H. Patel,
Jarred Burkart,
Abdul Miah,
Mingjia Li,
Andrew Johns,
Madison Grogan,
David P. Carbone,
Claire F. Verschraegen,
Kari L. Kendra,
Gregory A. Otterson,
Lang Li,
Carolyn J. Presley,
Dwight H. Owen
Affiliations
Daniel Spakowicz
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Rebecca Hoyd
Mitchell Muniak
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Marium Husain
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
James S. Bassett
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Lei Wang
Department of Biomedical Informatics, The Ohio State University College of Medicine
Gabriel Tinoco
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Sandip H. Patel
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Jarred Burkart
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Abdul Miah
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Mingjia Li
Division of Hospital Medicine, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Andrew Johns
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Madison Grogan
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
David P. Carbone
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Claire F. Verschraegen
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Kari L. Kendra
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Gregory A. Otterson
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Lang Li
Department of Biomedical Informatics, The Ohio State University College of Medicine
Carolyn J. Presley
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Dwight H. Owen
Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center
Abstract Background The microbiome has been shown to affect the response to Immune Checkpoint Inhibitors (ICIs) in a small number of cancers and in preclinical models. Here, we sought to broadly survey cancers to identify those in which the microbiome may play a prognostic role using retrospective analyses of patients with advanced cancer treated with ICIs. Methods We conducted a retrospective analysis of 690 patients who received ICI therapy for advanced cancer. We used a literature review to define a causal model for the relationship between medications, the microbiome, and ICI response to guide the abstraction of electronic health records. Medications with precedent for changes to the microbiome included antibiotics, corticosteroids, proton pump inhibitors, histamine receptor blockers, non-steroid anti-inflammatories and statins. We tested the effect of medication timing on overall survival (OS) and evaluated the robustness of medication effects in each cancer. Finally, we compared the size of the effect observed for different classes of antibiotics to taxa that have been correlated to ICI response using a literature review of culture-based antibiotic susceptibilities. Results Of the medications assessed, only antibiotics and corticosteroids significantly associated with shorter OS. The hazard ratios (HRs) for antibiotics and corticosteroids were highest near the start of ICI treatment but remained significant when given prior to ICI. Antibiotics and corticosteroids remained significantly associated with OS even when controlling for multiple factors such as Eastern Cooperative Oncology Group performance status, Charlson Comorbidity Index score, and stage. When grouping antibiotics by class, β-lactams showed the strongest association with OS across all tested cancers. Conclusions The timing and strength of the correlations with antibiotics and corticosteroids after controlling for confounding factors are consistent with the microbiome involvement with the response to ICIs across several cancers.