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. 2022 Oct 10;40(10):1161-1172.e5.
doi: 10.1016/j.ccell.2022.08.022. Epub 2022 Sep 29.

Ancestry-driven recalibration of tumor mutational burden and disparate clinical outcomes in response to immune checkpoint inhibitors

Amin H Nassar  1 , Elio Adib  2 , Sarah Abou Alaiwi  2 , Talal El Zarif  3 , Stefan Groha  4 , Elie W Akl  5 , Pier Vitale Nuzzo  3 , Tarek H Mouhieddine  6 , Tomin Perea-Chamblee  7 , Kodi Taraszka  8 , Habib El-Khoury  9 , Muhieddine Labban  10 , Christopher Fong  7 , Kanika S Arora  11 , Chris Labaki  3 , Wenxin Xu  3 , Guru Sonpavde  3 , Robert I Haddad  12 , Kent W Mouw  13 , Marios Giannakis  12 , F Stephen Hodi  14 , Noah Zaitlen  8 , Adam J Schoenfeld  15 , Nikolaus Schultz  7 , Michael F Berger  16 , Laura E MacConaill  17 , Guruprasad Ananda  18 , David J Kwiatkowski  5 , Toni K Choueiri  3 , Deborah Schrag  19 , Jian Carrot-Zhang  20 , Alexander Gusev  21
Affiliations

Ancestry-driven recalibration of tumor mutational burden and disparate clinical outcomes in response to immune checkpoint inhibitors

Amin H Nassar et al. Cancer Cell. .

Abstract

The immune checkpoint inhibitor (ICI) pembrolizumab is US FDA approved for treatment of solid tumors with high tumor mutational burden (TMB-high; ≥10 variants/Mb). However, the extent to which TMB-high generalizes as an accurate biomarker in diverse patient populations is largely unknown. Using two clinical cohorts, we investigated the interplay between genetic ancestry, TMB, and tumor-only versus tumor-normal paired sequencing in solid tumors. TMB estimates from tumor-only panels substantially overclassified individuals into the clinically important TMB-high group due to germline contamination, and this bias was particularly pronounced in patients with Asian/African ancestry. Among patients with non-small cell lung cancer treated with ICIs, those misclassified as TMB-high from tumor-only panels did not associate with improved outcomes. TMB-high was significantly associated with improved outcomes only in European ancestries and merits validation in non-European ancestry populations. Ancestry-aware tumor-only TMB calibration and ancestry-diverse biomarker studies are critical to ensure that existing disparities are not exacerbated in precision medicine.

Keywords: biomarker; cancer disparities; genetic ancestry; genomics; immunotherapy; tumor mutational burden.

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Conflict of interest statement

Declaration of interests G.S. reports the following disclosures: advisory boards of BMS, Genentech, EMD Serono, Merck, Sanofi, Seattle Genetics/Astellas, Astrazeneca, Exelixis, Janssen, Bicycle Therapeutics, Pfizer, Immunomedics/Gilead, Scholar Rock, and G1 Therapeutics; research support to Sanofi, Astrazeneca, Immunomedics/Gilead, QED, Predicine, and BMS; steering committee of studies of BMS, Bavarian Nordic, Seattle Genetics, QED, and G1 Therapeutics (all unpaid) and Astrazeneca, EMD Serono, Debiopharm (paid); data safety monitoring committee of Mereo; travel costs from BMS (2019) and Astrazeneca (2018); writing/editor fees from UpToDate and as editor of the Elsevier PracticeUpdate Bladder Cancer Center of Excellence; speaking fees from Physicians Education Resource (PER), Onclive, Research to Practice, and Medscape (all educational). M.G. receives research funding from Bristol-Myers Squibb, Merck, Servier, and Janssen. F.S.H. reports grants and other from Bristol-Myers Squibb; personal fees from Merck; personal fees from EMD Serono; grants, personal fees, and other from Novartis; personal fees from Surface; personal fees from Compass Therapeutics; personal fees from Apricity; personal fees from Aduro; personal fees from Sanofi; personal fees from Pionyr; personal fees from Torque; personal fees from Bicara; other from Pieris Pharmaceuticals; personal fees from Eisai; personal fees from Checkpoint Therapeutics; personal fees from Idera; personal fees from Genentech/Roche; personal fees from BioEntre; personal fees from Gossamer; personal fees from Phio; personal fees from Iovance; personal fees from Trillium; personal fees from Abcuro; personal fees from Catalym; personal fees from Immunocore; outside the submitted work. In addition, F.S.H. has the following patents: Methods for Treating MICA-Related Disorders (20100111973) with royalties paid; Tumor Antigens and Uses Thereof (7250291) issued; Angiopoiten-2 Biomarkers Predictive of Anti-immune Checkpoint Response (20170248603) pending; Compositions and Methods for Identification, Assessment, Prevention, and Treatment of Melanoma using PD-L1 Isoforms (20160340407) pending; Therapeutic Peptides (20160046716) pending; Therapeutic Peptides (20140004112) pending; Therapeutic Peptides (20170022275) pending; Therapeutic Peptides (20170008962) pending; Therapeutic Peptides (9402905) issued; Methods of Using Pembrolizumab and Trebananib pending; Vaccine Compositions and Methods for Restoring NKG2D Pathway Function against Cancers (10279021) issued; Antibodies That Bind to MHC Class I Polypeptide-Related Sequence A (10106611) issued; and Anti-galectin Antibody Biomarkers Predictive of Anti-immune Checkpoint and Anti-angiogenesis Responses. T.K.C. reports the following disclosures: research/advisory boards/consultancy/honoraria (institutional and personal, paid and unpaid) for or from AstraZeneca, Aveo, Bayer, Bristol Myers-Squibb, Eisai, EMD Serono, Exelixis, GlaxoSmithKline, IQVA, Ipsen, Kanaph, Lilly, Merck, Nikang, Novartis, Pfizer, Roche, Sanofi/Aventis, Takeda, and Tempest; travel, accommodations, expenses, medical writing in relation to consulting, advisory roles, or honoraria; stock options in Pionyr and Tempest; and other: UpToDate royalties, CME-related events (e.g., OncLIve, PVI, MJH Life Sciences) honoraria, NCI GU steering committee. T.K.C. also has patents filed, royalties, or other intellectual properties (no income as of this writing) related to biomarkers of immune checkpoint blockers and ctDNA. No speaker’s bureau. T.K.C. is supported in part by the Dana-Farber/Harvard Cancer Center Kidney SPORE and Program, the Kohlberg Chair at Harvard Medical School and the Trust Family, Michael Brigham, and Loker Pinard Funds for Kidney Cancer Research at DFCI.

Figures

Figure 1:
Figure 1:. See also Figures S1-S4 and Tables S1-S2 TMB overestimation in tumor-only sequencing panels driven by false positive germline variants in non-Europeans.
A. Annotating somatic and germline variants across self-reported race and effect on TMB calculations. The orange color in the DNA molecule refers to the European component of the genome and the navy-blue color refers to the African component. B. Germline reference panels where non-Europeans are underrepresented. This is driven by genetic ancestry and not by race as an individual with high non-European ancestry will have the same bias in their tumor-only TMB estimate whether they self-report as white or non-white. See also Figures S1-S4 and Tables S1 and S2.
Figure 2:
Figure 2:. See also Figure S5 and Tables S3-S4. Differential TMB estimates across continental ancestral populations in the DFCI cohorts prior and after calibration and effects of TMB calibration on clinical outcomes.
A. Distribution of uncalibrated TMB estimates for each of 7 cancer types shown by genetic ancestry. A two-sided binomial test was used to establish P values (* <0.05, ** <0.01, *** <0.001, **** <0.0001). Data are represented as boxplots. The horizontal lines reflect the median, the lower and upper whiskers indicate 1.5 x the interquartile ranges. Circles are outliers. B. Distribution of calibrated TMB estimates for each of 7 cancer types shown by genetic ancestry. A two-sided binomial test was used to establish P values (* <0.05, ** <0.01, *** <0.001, **** <0.0001). Data are represented as boxplots. The horizontal lines reflect the median, the lower and upper whiskers indicate 1.5 x the interquartile ranges. Circles are outliers. C. 10x10 dot plot showing TMB misclassification rates for TMB-high tumors in each ancestral population in the entire DFCI cohort (n=2800 TMB-high patients). D. Impact of TMB calibration on overall survival in ICI-treated patients at DFCI (n=1840 patients). Patients were stratified into: (a) true TMB-low (raw TMB<10; calibrated TMB<10), true TMB-high (raw TMB≥10; calibrated TMB ≥10), and false TMB-high (raw TMB ≥10, calibrated TMB<10). CRC: colorectal cancer, EGC: esophagogastric cancer (EGC), HNSCC: head and neck squamous cell carcinoma, melanoma, NSCLC: non-small cell lung cancer, UC: urothelial carcinoma, RCC: renal cell carcinoma. See also Figure S5 and Tables S3-S4.
Figure 3.
Figure 3.. Effects of TMB calibration on clinical outcomes in the MSKCC cohort of patients with NSCLC treated with ICI (n=234 patients).
A. Impact of tumor-only versus paired tumor/normal TMB on overall survival in ICI-treated patients with NSCLC at MSKCC. Patients were stratified into: (a) true TMB-low (raw TMB<10; tumor/normal TMB<10), true TMB-high (raw TMB≥10; tumor/normal TMB ≥10), and false TMB-high (raw TMB ≥10, tumor/normal TMB<10). B. Impact of TMB calibration on overall survival in ICI-treated patients with NSCLC at MSKCC. Patients were stratified into: (a) true TMB-low (raw TMB<10; calibrated TMB<10), true TMB-high (raw TMB≥10; calibrated TMB ≥10), and false TMB-high (raw TMB ≥10, calibrated TMB<10). cTMB= calibrated TMB; TMBTO= raw tumor-only TMB; TMBTN= paired tumor/normal TMB.
Figure 4:
Figure 4:. See also Table S5. Adjusted hazard ratios (HR) of clinical outcomes of different ancestral populations treated with ICI therapy in different cancer types.
A. Adjusted HR ratios for time to ICI failure in the DFCI ICI cohort (n=1840). Data are presented as adjusted HR with 95% CI (reference group EUR). B. Adjusted HR ratios for overall survival. All p-values and hazard ratios in A and B are of the Wald x2 test from the Cox regression analysis, adjusted as detailed in the STAR Methods section. Data are presented as adjusted HR with 95% CI (reference group EUR). * A horizontal line is not shown for HNSCC given only 1 patient had an event and confidence interval ranged from 0 to infinity. C. Time to ICI failure and genetic ancestry in DFCI patients with NSCLC treated with ICI. D. Overall survival and genetic ancestry in DFCI patients with NSCLC treated with ICI. See also Table S5.
Figure 5:
Figure 5:. See also Table S6. Ancestry-specific associations between TMB status and overall survival (OS) among ICI-treated patients with NSCLC.
A. Association between TMB and OS in European ancestry in the DFCI cohort. B. Association between TMB and OS in Asian ancestry in the DFCI cohort. C. Association between TMB and OS in African ancestry in the DFCI cohort. D. Association between TMB and OS in European ancestry in the MSKCC cohort. E. Association between TMB and OS in Asian ancestry in the MSKCC cohort. F. Association between TMB and OS in African ancestry in the MSKCC cohort. For the DFCI and MSKCC cohorts, calibrated TMB and tumor/normal TMB were analyzed, respectively. P-values were adjusted for prior lines of therapy, ICI type, TMB-c, treatment prior to sequencing and histologic subtype. See also Table S6.
Figure 6:
Figure 6:. See also Tables S7-S8. Impact of MGA genomic alterations on overall survival (OS) in ICI-treated patients with NSCLC.
A. OS in the DFCI NSCLC cohort of European ancestry. B. OS in the DFCI NSCLC cohort of African ancestry. C. OS in the DFCI NSCLC cohort of Asian ancestry. D. OS in the MSKCC NSCLC cohort of European ancestry. E. OS in the MSKCC NSCLC cohort of African ancestry. F. OS in the MSKCC NSCLC cohort of Asian ancestry. WT: Wild type. P-values were adjusted for prior lines of therapy, ICI type, TMB-c, treatment prior to sequencing and histologic subtype. See also Tables S7-S8.
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