Assessment of Immune Checkpoint Inhibitors and Genomic Alterations by Body Mass Index in Advanced Renal Cell Carcinoma | Nephrology | JAMA Oncology | JAMA Network
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Figure.  Kaplan-Meier Curves for Overall Survival (OS) According to Body Mass Index (BMI)a Status
Kaplan-Meier Curves for Overall Survival (OS) According to Body Mass Index (BMI)a Status

ICI indicates immune checkpoint inhibitor.

aCalculated as weight in kilograms divided by height in meters squared.

Table.  Patient Characteristics
Patient Characteristics
1.
Calle  EE, Kaaks  R.  Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms.   Nat Rev Cancer. 2004;4(8):579-591. doi:10.1038/nrc1408PubMedGoogle ScholarCrossref
2.
Albiges  L, Hakimi  AA, Xie  W,  et al.  Body mass index and metastatic renal cell carcinoma: clinical and biological correlations.   J Clin Oncol. 2016;34(30):3655-3663. doi:10.1200/JCO.2016.66.7311PubMedGoogle ScholarCrossref
3.
Garcia  EP, Minkovsky  A, Jia  Y,  et al.  Validation of OncoPanel: a targeted next-generation sequencing assay for the detection of somatic variants in cancer.   Arch Pathol Lab Med. 2017;141(6):751-758. doi:10.5858/arpa.2016-0527-OAPubMedGoogle ScholarCrossref
4.
Reva  B, Antipin  Y, Sander  C.  Predicting the functional impact of protein mutations: application to cancer genomics.   Nucleic Acids Res. 2011;39(17):e118. doi:10.1093/nar/gkr407PubMedGoogle Scholar
5.
Sanchez  A, Furberg  H, Kuo  F,  et al.  Transcriptomic signatures related to the obesity paradox in patients with clear cell renal cell carcinoma: a cohort study.   Lancet Oncol. 2020;21(2):283-293. doi:10.1016/S1470-2045(19)30797-1PubMedGoogle ScholarCrossref
6.
Gan  CL, Heng  DYC.  New insights into the obesity paradox in renal cell carcinoma.   Nat Rev Nephrol. 2020;16(5):253-254. doi:10.1038/s41581-020-0264-yPubMedGoogle ScholarCrossref
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    Using the full range of body mass index to better understand its prognostic relevance in renal cell cancer
    Helena Furberg Barnes, PhD | Memorial Sloan Kettering Cancer Center
    Lalani et al. (1) analyzed 735 renal cell carcinoma (RCC) patients treated with immune checkpoint inhibitors (ICI) and found that patients with body mass index (BMI) above 25 at treatment start experienced significantly better clinical outcomes than patients with BMI below 25. While this is a striking initial finding, we urge the investigators to conduct additional analyses related to BMI to enhance clinical interpretation and guide future research.

    First, it would be helpful to know the percentage of patients in each of the WHO-based BMI categories (i.e., underweight, normal weight, overweight, obese class I, obese class II and
    obese class III). Next, it would be insightful to present the Hazard Ratios of these BMI levels relative to normal weight patients. This would reveal a more specific pattern of association between BMI and mortality. Is the pattern of association linear or is it a U-shaped curve? Observing a linear, 'dose-response' pattern would support a true protective effect of obesity since mortality risk would decrease as BMI category increases. Alternatively, a U-shaped curve would highlight that patients with the extremes of BMI have higher mortality risks. It is possible that underweight patients have a higher risk of dying due to wasting, while those with the highest BMI values may be more likely to experience fatal heart attacks or strokes. Finally, comparing the extremes of BMI may increase the chances of identifying molecular differences associated with BMI (2). The large sample size of this cohort makes these analyses highly feasible.

    As acknowledged, BMI is a poor marker of adiposity; it also does not distinguish skeletal muscle mass. Caan et al. published a histogram of body composition phenotypes by BMI from a large cohort of colorectal cancer patients (3) which serves as an important example of how dichotomizing BMI at 25 can create heterogeneous body composition groups. They found that many patients in the overweight range have a normal body composition phenotype (i.e., adequate muscle and adiposity in low/middle tertiles). It is not until a BMI of >35 that most patients fall in the upper tertile of adiposity. While most patients with low muscle cluster under a BMI of 22, low muscle is still observed up to a BMI of 45. Each of these body composition phenotypes carry different mortality risks which are obscured when BMI is dichotomized at 25. Ultimately, future research should move beyond BMI to determine the prognostic impact of body composition phenotypes among ICI-treated RCC patients to identify the patients who are most likely to benefit from this treatment and to inform the development of future exercise/dietary interventions.

    1. Lalani AA, Bakouny Z, Farah S, et al. Assessment of Immune Checkpoint Inhibitors and Genomic Alterations by Body Mass Index in Advanced Renal Cell Carcinoma. JAMA Oncology. 2021.
    2. Sanchez A, Furberg H, Kuo F, et al. Transcriptomic signatures related to the obesity paradox in patients with clear cell renal cell carcinoma: a cohort study. The Lancet Oncology. 2020;21(2):283-293.
    3. Caan BJ, Meyerhardt JA, Kroenke CH, et al. Explaining the Obesity Paradox: The Association between Body Composition and Colorectal Cancer Survival (C-SCANS Study). Cancer Epidemiol Biomarkers Prev. 2017;26(7):1008-1015.

    Helena Furberg, PhD
    Alejandro Sanchez, MD
    Bette Caan, PhD
    CONFLICT OF INTEREST: None Reported
    READ MORE
    Research Letter
    March 4, 2021

    Assessment of Immune Checkpoint Inhibitors and Genomic Alterations by Body Mass Index in Advanced Renal Cell Carcinoma

    Author Affiliations
    • 1Department of Oncology, Juravinski Cancer Centre, Hamilton, Ontario, Canada
    • 2Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
    • 3Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
    • 4Department of Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
    • 5Department of Medicine, William Osler Health System, Brampton, Ontario, Canada
    • 6Department of Medical Oncology, Tom Baker Cancer Centre, Calgary, Alberta, Canada
    JAMA Oncol. 2021;7(5):773-775. doi:10.1001/jamaoncol.2021.0019

    An elevated body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) has been associated with an increased risk of renal cell carcinoma (RCC).1 Previously, higher BMI was shown to be a positive prognostic factor for patients with metastatic clear cell RCC (mRCC) who were treated during the vascular endothelial growth factor (VEGF)–targeted therapy era.2 However, the treatment landscape has shifted to include immune checkpoint inhibitors (ICIs) for most patients. We investigated this obesity paradox in patients with mRCC who were treated with programmed cell death 1 protein/programmed cell death 1 ligand 1 (PD-1/PD-L1)–based ICIs and explored potential genomic alterations according to BMI status.

    Methods

    Using the International Metastatic RCC Database Consortium (IMDC) database, we included patients treated with PD-1/PD-L1–based ICI alone or in combinations. Institutional review board approval was obtained from the Dana-Farber Cancer Institute and each participating site within the IMDC consortium, and participants provided written informed consent. Comparisons between patients who were defined as having a high BMI (≥25) vs low (<25) were conducted using χ2 and Fisher tests. We investigated the association of BMI with overall survival (OS; time from ICI initiation to death or censoring at last follow-up), time to treatment failure (TTF; time from ICI initiation to treatment cessation, progression, death, or censoring), and objective response (complete/partial response, by Response Evaluation Criteria in Solid Tumor, version 1.1]). Associations of BMI were assessed in multivariable logistic (objective response rate [ORR]) and Cox (TTF; OS) regression, which were adjusted for IMDC risk classifications (favorable, intermediate, or poor), age, sex, race/ethnicity, histology, sarcomatoid features, line, and type of ICI. In patients with available next-generation sequencing data (OncoPanel, 275-447 genes3), genomic alteration frequencies (nonsense, insertions/deletions, and missense by a Polyphen-2/Mutation Assessor4), and tumor mutational burden were compared by BMI status using Fisher exact and Mann-Whitney U tests. Statistical tests were 2-sided and performed using SAS, version 9.4 (SAS Institute), and R, version 3.6.1 (R Foundation). Results were considered statistically significant if P < .05 or q < 0.05.

    Results

    Within the IMDC database, 735 patients with mRCC with a recorded BMI were treated with PD-1/PD-L1–based ICI. Overall, 229 (31%) received first-line ICI, and 230 (31%) received combination ICI (142 [19%] with VEGF; 88 [12%] with CTLA-4 (cytotoxic T-lymphocyte–associated protein 4)/other therapies). At ICI initiation, 274 patients (37%) had what was considered low BMI and 461 (63%) a high BMI (Table). Median follow-up was 13.5 months (range, <1 to 78.6 months). Patients with a high BMI displayed significantly improved OS compared with those with a low BMI (1-year OS: 79% vs 66%; adjusted hazard ratio, 0.75; 95% CI, 0.57-0.97; P = .03) (Figure). This association was consistent in subgroup analysis by sex, IMDC group, histology, and type/line of therapy. Patients with higher BMIs also had a numerically higher ORR (30% vs 21%) and TTF (median [95% CI], 7.4 [6.7-9.0] vs 4.9 [3.8-6.9] months), although these were not statistically significant in multivariable models (ORR: adjusted odds ratio, 1.51; 95% CI, 0.98-2.32; P = .06; TTF: adjusted hazard ratio, 0.98; 95% CI, 0.80-1.20; P = .83). In 319 patients with available next-generation sequencing data, genomic alteration frequencies (all q > 0.50), and tumor mutational burden (6.8 vs 6.8 mutations per megabase; P = .90) were found to be similar between BMI groups.

    Discussion

    In this multinational analysis from the IMDC, an elevated BMI was independently associated with improved OS in patients with mRCC who were treated with PD-1/PD-L1–based ICIs. These findings are consistent with the obesity paradox that was previously seen during the VEGF-targeted therapy era.2 Several hypotheses have attempted to explain this clinical observation in RCC. Low fatty acid synthase gene expression, which is inversely correlated with BMI, was associated with longer OS in VEGF-treated patients.2 Transcriptomic analysis suggests that patients with obesity have tumors with increased angiogenesis gene signatures and peritumoral adipose tissues with increased hypoxia, inflammation, and immune cell infiltration signatures.5 The limitations of this study include biases that were associated with the retrospective analysis and lack of robust gene-expression profiling. While baseline characteristics differed between groups, we adjusted for key prognostic variables in multivariable models. Further, BMI may have limitations as a surrogate marker of adiposity; more sophisticated, although cumbersome, radiological measurements could better identify sarcopenic obesity.6 Ultimately, further correlative work is required to explore the biological underpinnings for similar findings across other solid tumors that are treated with ICIs.

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    Article Information

    Accepted for Publication: December 17, 2020.

    Published Online: March 4, 2021. doi:10.1001/jamaoncol.2021.0019

    Corresponding Author: Toni K. Choueiri, MD, Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, 450 Brookline Ave, Dana 1230, Boston MA 02215 (toni_choueiri@dfci.harvard.edu).

    Author Contributions: Drs Heng and Choueiri had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Lalani, Choueiri.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Lalani, Farah, Heng.

    Critical revision of the manuscript for important intellectual content: Lalani, Bakouny, Donskov, Dudani, Heng, Choueiri.

    Statistical analysis: Lalani, Bakouny, Farah.

    Administrative, technical, or material support: Lalani, Heng, Choueiri.

    Supervision: Choueiri.

    Conflict of Interest Disclosures: Dr Lalani reported personal fees from AbbVie, Astellas, AstraZeneca, Bristol Myers Squibb, Eisai, Ipsen, Janssen, Merck, Novartis, Pfizer, Roche, and TerSera and grants from BioCanRx, Bristol Myers Squibb, Novartis, Roche, Ipsen, and EMD Serrono outside the submitted work. Dr Bakouny reported grants from Genentech/imCORE and nonfinancial support from Bristol Myers Squibb outside the submitted work. Dr Donskov reported grants from Pfizer, Ipsen, and MSD outside the submitted work. Dr Heng reported grants from Pfizer, Bristol Myers Squibb, Merck, and Roche outside the submitted work. Dr Choueiri reported personal fees from Alexion Pharmaceuticals, Alligent, Analysis Group, ASCO, AstraZeneca, Bayer, Bristol Myers Squibb, Cerulean Pharma, Clinical Care Options, Corvus Pharmaceuticals, Eisai, EMD Serono, Exelixis, Foundation Medicine, Genentech/Roche, GlaxoSmithKline, Harborside Press, HERON, Ipsen, Kidney Cancer Journal, Lancet Oncology, Lilly, Lpath, Merck, Michael J. Hennessy Associates, Navinata Healthcare, NCCN, New England Journal of Medicine, Novartis, Peloton Therapeutics, Pfizer, PlatformQ Health, Prometheus, Sanofi/Aventis, and UpToDate; research support from Agensys, Analysis Group, AstraZeneca, Bayer, Bristol Myers Squibb, Calithera Biosciences, Celldex, Cerulean Pharma, US Department of Defense, Corvus Pharmaceuticals, Eisai, Exelixis, Foundation Medicine, GATEWAY for Cancer Research, GlaxoSmithKline, Ipsen, Merck, National Cancer Institute, Novartis, Peloton Therapeutics, Pfizer, Prometheus, Roche, Genentech, Seattle Genetics/Astellas, Takeda, and TRACON Pharma; patent applications PCT/US2018/058430 and PCT/US2018/12209; and medical writing and editorial assistance support funded by communications companies funded by pharmaceutical companies such as ClinicalThinking, Health Interactions, Envision Pharma Group, Fishawack Group of Companies, and Parexel. No other disclosures were reported.

    Funding/Support: This research was supported in part by the Dana-Farber/Harvard Cancer Center Kidney SPORE, and the Trust Family, Michael Brigham, and Loker Pinard Funds for Kidney Cancer Research at Dana-Farber Cancer Institute (Dr Choueiri).

    Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: We thank Ronan Flippot, MD (Gustave Roussy), John A. Steinharter, MS (Dana-Farber Cancer Institute), Pier V. Nuzzo, MD (Dana-Farber Cancer Institute), Justin H. Fleischer, BS (Dana-Farber Cancer Institute), Sumanta K. Pal, MD (City of Hope), Nityam Rathi, BSc (Cleveland Clinic Lerner College of Medicine), Aaron R. Hansen, MBBS (Princess Margaret Cancer Center), Takeshi Yuasa, MD (Japanese Foundation for Cancer Research), Ulka Vaishampayan, MD (Rogel Cancer Center), Mathushan Subasri, BSc (University of Western Ontario), J. Connor Wells, MD (Tom Baker Cancer Centre), and Naveen S. Basappa, MD (Cross Cancer Institute) for assistance with data collection and helpful discussion on interpretation. We also thank Wanling Xie, MS (Dana-Farber Cancer Institute) for assistance with data analysis. They were not compensated.

    References
    1.
    Calle  EE, Kaaks  R.  Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms.   Nat Rev Cancer. 2004;4(8):579-591. doi:10.1038/nrc1408PubMedGoogle ScholarCrossref
    2.
    Albiges  L, Hakimi  AA, Xie  W,  et al.  Body mass index and metastatic renal cell carcinoma: clinical and biological correlations.   J Clin Oncol. 2016;34(30):3655-3663. doi:10.1200/JCO.2016.66.7311PubMedGoogle ScholarCrossref
    3.
    Garcia  EP, Minkovsky  A, Jia  Y,  et al.  Validation of OncoPanel: a targeted next-generation sequencing assay for the detection of somatic variants in cancer.   Arch Pathol Lab Med. 2017;141(6):751-758. doi:10.5858/arpa.2016-0527-OAPubMedGoogle ScholarCrossref
    4.
    Reva  B, Antipin  Y, Sander  C.  Predicting the functional impact of protein mutations: application to cancer genomics.   Nucleic Acids Res. 2011;39(17):e118. doi:10.1093/nar/gkr407PubMedGoogle Scholar
    5.
    Sanchez  A, Furberg  H, Kuo  F,  et al.  Transcriptomic signatures related to the obesity paradox in patients with clear cell renal cell carcinoma: a cohort study.   Lancet Oncol. 2020;21(2):283-293. doi:10.1016/S1470-2045(19)30797-1PubMedGoogle ScholarCrossref
    6.
    Gan  CL, Heng  DYC.  New insights into the obesity paradox in renal cell carcinoma.   Nat Rev Nephrol. 2020;16(5):253-254. doi:10.1038/s41581-020-0264-yPubMedGoogle ScholarCrossref
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