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. 2025 Jan 19;13(1):e009734.
doi: 10.1136/jitc-2024-009734.

Pan-tumor analysis to investigate the obesity paradox in immune checkpoint blockade

Stephanie L Alden  1 , Soren Charmsaz  2 , Howard L Li  1 , Hua-Ling Tsai  1 , Ludmila Danilova  1   3 , Kabeer Munjal  2 , Madelena Brancati  2 , Aanika Warner  1 , Kathryn Howe  2 , Ervin Griffin  2 , Mari Nakazawa  2 , Chris Thoburn  1 , Jennifer Gizzi  1 , Alexei Hernandez  2 , Nicole E Gross  2 , Erin M Coyne  2 , Elsa Hallab  2 , Sarah S Shin  2 , Jennifer Durham  2 , Evan J Lipson  2 , Yasser Ged  2 , Marina Baretti  2 , Jean Hoffman-Censits  2 , Tanguy Y Seiwert  2 , Aditi Guha  4 , Sanjay Bansal  4 , Laura Tang  4 , G Scott Chandler  5 , Rajat Mohindra  5 , Rachel Garonce-Hediger  5 , Elizabeth M Jaffee  2   3 , Won Jin Ho  2   3 , Chester Kao  2 , Mark Yarchoan  6   3
Affiliations

Pan-tumor analysis to investigate the obesity paradox in immune checkpoint blockade

Stephanie L Alden et al. J Immunother Cancer. .

Abstract

Background: Obesity is a risk factor for developing cancer but is also associated with improved outcomes after treatment with immune checkpoint inhibitors (ICIs), a phenomenon called the obesity paradox. To interrogate mechanisms of divergent immune responses in obese and non-obese patients, we examined the relationship among obesity status, clinical responses, and immune profiles from a diverse, pan-tumor cohort of patients treated with ICI-based therapy.

Methods: From June 2021 to March 2023, we prospectively collected serial peripheral blood samples from patients with advanced or metastatic solid tumors who received ICI as standard of care at Johns Hopkins. Patients were stratified by obesity status at treatment initiation, with obesity defined as body mass index (BMI)≥30 at treatment initiation and BMI≥18.5 and <30 considered non-obese; underweight patients (BMI<18.5) were excluded. We evaluated the concentration of 37 cytokines and used cytometry by time of flight to characterize immune cell clusters and cell-surface expression markers at baseline and on-treatment.

Results: We enrolled 94 patients, of whom 30 (32%) were obese and 64 (68%) were non-obese. Compared with non-obese patients, obese patients had superior progression-free survival (HR: 0.44 (95% CI: 0.24 to 0.81), p=0.01) and overall survival (OS) (HR: 0.24 (95% CI: 0.07 to 0.80), p=0.02). Obese patients had lower serum IL-15 levels at treatment baseline and lower on-treatment levels of IL-6, IL-8, and IL-15. Low on-treatment IL-6 was associated with improved OS (HR: 0.27 (95% CI: 0.08 to 0.88), p=0.03), as was low on-treatment IL-8 (HR: 0.19 (95% CI: 0.05 to 0.70), p=0.01). Obese patients demonstrated lower levels of T effector cells with reduced expression of cytotoxicity markers and higher expression of exhaustion markers at baseline and on-treatment.

Conclusions: Obese and non-obese patients with cancer have divergent immunological responses to ICIs. Obesity is associated with reduced levels of certain inhibitory cytokines and higher expression of T-cell exhaustion markers. ICI-based therapy may more effectively reverse T-cell dysfunction in obese patients, potentially contributing to the paradoxically improved responses in this population.

Keywords: Cytokine; Immune Checkpoint Inhibitor; Immunotherapy; Solid tumor; T cell.

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Conflict of interest statement

Competing interests: SSS reports travel honoraria from Standard BioTools. EJL reports funding from the Marilyn and Michael Glosserman Fund for Basal Cell Carcinoma and Melanoma Research, Bloomberg∼Kimmel Institute for Cancer Immunotherapy, Moving for Melanoma of Delaware, the Barney Family Foundation, the Laverna Hahn Charitable Trust, and the Julie Ann Robertson Cashour Memorial Fund outside the submitted work. RGH, GSC, and RM are employees of F. Hoffman-La Roche, and AG, SB, and LT are employees of Genentech. EMJ reports other support from AbMeta, other support from Adventris, personal fees from Achilles, personal fees from DragonFly, non-financial support from Parker Institute, personal fees from Surge, grants from Lustgarten, grants from Genentech, personal fees from Mestag, personal fees from Medical Home Group, non-financial support from BMS, grants from Break Through Cancer, personal fees from CPRIT, personal fees from Neuvogen, non-financial support from HDT Bio, and personal fees from NeoTX outside the submitted work. WJH reports support from NIH/NCI P30CA006973 and NIH/NCI R21CA264004, as well as patent royalties from Rodeo/Amgen, received grants from Sanofi and NeoTX (to Johns Hopkins), and speaking/travel honoraria from Exelixis and Standard BioTools. MY reports research funding (to Johns Hopkins) from Genentech, Bristol-Myers Squibb, and Incyte; consulting fees from Genentech, Exelixis, Eisai, AstraZeneca, Replimune, Hepion, and Lantheus; and equity in Adventris. The other authors declare no competing interests.

Figures

Figure 1
Figure 1. When comparing obese with non-obese patients, there was a significant difference in median (A) progression free survival (PFS) (log-rank p<0.01), with 12-month PFS 51.7% (95% CI: 35.5% to 75.1%) and 26.5% (95% CI: 17.1% to 41.0%), respectively, and (B) overall survival (OS) (log-rank p=0.01), with 12-month OS 88.5% (95% CI: 77.0% to 100.0%) and 58.0% (95% CI: 44.5% to 75.5%), respectively. (C) While there was a difference in PFS and OS, there was no difference in the best objective response (BOR) (p=0.21) comparing obese with non-obese patients. *Select percentages may not add up to 100% due to rounding. CR, complete response; PD, progressive disease; PR, partial response; SD, stable disease
Figure 2
Figure 2. When comparing obese with non-obese patients (A) at baseline, there was a significant difference in IL-15 concentration, while (B) on-treatment, there were significant differences in concentration of IL-6, IL-8, and IL-15. (C) When comparing the ratio of on-treatment to baseline concentration, IL-6, IL-17E, and VEGF-α differed significantly comparing obese with non-obese patients. BMI, body mass index.
Figure 3
Figure 3. (A) Using the entire cohort, samples were assayed with a 37-marker panel for CyTOF, with a FlowSOM algorithm used to make 35 metaclusters, which were finalized into 26 clusters, with the scaled marker profile shown. (B) UMAP plots with the annotated cell clusters. Boxplots comparing obese with non-obese patients (C) at baseline, where there were significant differences in the number of DNT_V, TcEFF_II, TcEFF_III, and Th2CM_II cell populations, and (D) on-treatment, where there were significant differences in the number of DNT_V, TcEFF_II, TcEFF_III, Th2CM_II, and Treg cell populations. CyTOF, cytometry by time of flight; DNT, double-negative T cells; MMI, mean metal intensity; TcEFF, T-effector cells; Th2, T-helper 2; Treg, T-regulatory;UMAP, uniform manifold approximation and projection.
Figure 4
Figure 4. (A) At baseline, obese patients had higher expression of activation and exhaustion markers, with non-obese patients demonstrating higher expression of markers of cytotoxicity, (B) with increased expression of CD27 and TIM-3 in obese as compared with non-obese patients. (C) On-treatment, obese patients continued to have higher expression of activation and exhaustion markers, with non-obese patients again showing higher expression of markers of cytotoxicity, (D) with increased expression of CD27, TIM-3, PD-1, and CTLA-4 observed in obese patients on-treatment. BMI, body mass index.
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