Tumor mutational burden (TMB) has emerged as a potential predictive biomarker for clinical response to ICI therapy, but whether TMB also predicts toxicity remains unknown. We investigated the relationship between TMB, objective response rate (ORR), overall survival (OS), and toxicity for ICI therapy across multiple cancer types.
We searched MEDLINE, PubMed, and ASCO/ESMO/AACR meetings for clinical trials of anti-PD(L)1, CTLA-4, or combination in 29 cancer types. We assessed ICI administered, responses (complete or partial response), median OS, OS HR, and grade 3/4 toxicity. We conducted a systematic review, meta-analysis and meta-regression using tumor level TMB data from Foundation Medicine.
One hundred seventeen clinical trials, which included 12,450 patients treated with ICI therapy were analyzed. Meta-regression analysis revealed that TMB was significantly associated with ORR for anti-PD(L)1, anti–CTLA-4, and combination (P < 0.0001 for all), but not associated with toxicity in all treatment groups. OS data were unavailable for most studies included in our meta-analysis, and the relationship between TMB and OS in this subset was not significant (P = 0.26). In high TMB tumor types (≥10 mut/megabase) the improvement of ORR and increase in grade 3/4 toxicity with combination ICI therapy as compared with PD(L)1 monotherapy were 21.13% and 25.41%, respectively, as compared with 3.73% and 18.78% in low TMB tumor types (<10 mut/megabase).
There is a positive association between TMB and clinical response with anti-PD(L)1, anti–CTLA-4, and combination ICIs, but no association between TMB and toxicity. These results imply a favorable risk/benefit ratio for ICIs in tumors with a higher TMB.
TMB has emerged as a potential biomarker for clinical response to anti-PD(L)1 therapies, but whether TMB also predicts toxicity from therapy is unclear. Furthermore, the relationship between TMB, toxicity, and therapeutic response in patients treated with single or combination immunotherapies across major tumor types is unknown. Our meta-regression and meta-analysis addresses these questions and is to our knowledge, the largest comprehensive study that simultaneously assesses the relationship between TMB, ORR, OS, and toxicity, of single and combination ICI therapy across multiple tumor types. We find that a higher TMB is associated with a higher response to single and dual checkpoint inhibitors across multiple tumor types, but is not associated with toxicity. Our findings also have implications for patient selection in clinical practice and for mechanisms of immune toxicity resulting from these agents.
Targeting immune checkpoints via programmed cell death protein 1 (PD-1), its ligand (PD-L1) or CTL-associated protein 4 (CTLA-4) has transformed treatment paradigms for numerous cancers (1–4). However, response rates have not been consistent across tumor types. Even within individual cancer types, clinical responses to immune checkpoint inhibitors (ICIs) are variable, and predictive and prognostic biomarkers for ICI therapy are needed. PD-L1 expression by IHC measurement is the most commonly utilized predictive biomarker for immunotherapy, but it has major limitations (5).
Tumor mutational burden (TMB), defined as the total number of nonsynonymous mutations per coding area of a tumor genome, has emerged as a novel potential predictive biomarker for response to anti-PD(L)1 immunotherapy (6–9). Rizvi and colleagues first demonstrated an association between increased TMB and clinical benefit of anti–PD-1 therapy using whole-exome sequencing (WES) data from patients with advanced NSCLC (9). Since that time, a relationship between TMB and clinical benefit from ICIs has been demonstrated within multiple other tumor types (10–15). In addition to predicting responses to ICIs, TMB may also predict improved survival with single and combination immunotherapy within some tumor types (16, 17). TMB is a surrogate for the number of expressed tumor neoantigens; these abnormal proteins are presented on the human leukocyte antigen (HLA) complex and recognized by T cells, thereby stimulating antitumor immunity (18). Because of the high costs of WES, TMB is often estimated for clinical practice using selected targeted gene panels. TMB is independent of PD-L1 expression and may therefore provide unique information about ICI responsiveness (19).
Understanding the relationship between TMB and clinical outcomes, including therapeutic response and adverse events, may have the potential to improve the clinical use and therapeutic development of ICI immunotherapy. If TMB is found to be associated with both response and toxicity, then TMB could eventually be used to identify patients who should be treated more intensively (e.g., with higher doses of anti–CTLA-4 therapy) to meet the most appropriate threshold of therapeutic effect (20). In contrast, if TMB is associated with response but not toxicity, then TMB could emerge as a key biomarker for establishing which tumors benefit outweighs risks of ICI therapy. We conducted a systematic review, meta-analysis, and meta-regression, to evaluate the relationship between objective response rate (ORR), overall survival (OS), toxicity, and TMB for anti–PD-1, anti–PD-L1, anti–CTLA-4 monotherapy, and combination, anti-PD(L)1 plus anti–CTLA-4 therapy across multiple cancer types.
Materials and Methods
This meta-regression and meta-analysis was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines (21). Three independent reviewers (A. Osipov, A. Popovic, and M. Yarchoan) performed the literature search, assessed eligibility criteria, and performed data extraction.
Search strategy and study selection
We initially identified 29 major solid tumor types or subtypes for which TMB has been well characterized using at least 50 tumor specimens (Supplementary Table 1). We conducted the literary search by screening electronic searches of MEDLINE, PubMed (from January 1, 2010 to February 20, 2019), as well as abstracts presented at ASCO, ESMO, AACR meetings 2010–2019 to identify ORR and grade 3/4 toxicity rate for all anti–PD-1, anti–PD-L1, CTLA-4 monotherapies, and combination ICI therapies, anti–PD-1 or anti–PD-L1 plus anti–CTLA-4 therapies, in each of these cancer types. ORR was obtained from studies which reported either the overall ORR, or from the reported complete and partial response rate. Similarly, OS data, including median OS reported and HR, was captured when available. We searched for clinical trials using the specific search terms: nivolumab, BMS-936558, pembrolizumab, MK-3475, atezolizumab, MPDL3280A, durvalumab, MEDI4736, tremelimumab, CP-675,206, Ipilimumab, BMS-734016, MDX-010, MDX-101, MEDI4736, avelumab, MSB0010718C, BMS-936559, cemiplimab, REGN2810, anti–PD-1, anti–PD-L1, and anti–CTLA-4. Only English publications were considered. We also contacted experts in the field to locate additional published trials of these agents that may not have been included in our initial electronic search. We excluded trials with a total sample size or a sample size in the subgroup of interest less than 10. We also excluded studies that investigated anti–PD-1, anti–PD-L1, anti–CTLA-4 therapies in combination with other agents (not including ICI combination alone), and studies that selected patients based on PD-L1 expression or other immune-related biomarkers. Of the remaining studies, only the largest published study for each anti–PD-1, anti-PD-L1, anti–CTLA-4 monotherapy therapy or combination was included in the final assessment of ORR, OS, and grade 3/4 toxicity rate for each cancer type or subtype (Supplementary Table 2). For survival HR analysis, we only included studies whose control arm received standard-of-care treatment. For descriptive analysis of median OS data, we included all studies whose median OS for single- and dual-agent immunotherapy was available.
For each included study or dataset, we extracted the checkpoint inhibitor assessed, number of patients treated, number of responders (complete and partial response) from each treatment group, median overall survival with associated HR and total number of patients experiencing grade 3 and 4 toxicity. The number of treated, as well as number of responders and those who experienced a grade 3/4 toxicity, was used to calculate ORR and rate of grade 3/4 toxicity in each individual study and pooled estimates for each tumor type or subtype. The median TMB for each of the 29 solid tumor types was acquired from a validated targeted TMB assay performed and provided by Foundation Medicine (FoundationOne assay; refs. 22, 23). Details of the assay have been previously reported, which estimates the total number of somatic, coding mutations (including synonymous and nonsynonymous mutations and short indels) per megabase of tumor genome (23).
For each monotherapy and the combination immunotherapy, to evaluate the association between TMB and tumor response, as well as association of TMB and toxicity, meta-regression was performed using a logistic-normal mixed-effects model where the median TMB of the tumor type (log-transformed) was included as a study-level fixed effect. The ability of TMB to explain the heterogeneity across tumor types was summarized as percent reduction of between-study heterogeneity in the model with and without TMB on the logit scale. To evaluate whether the association of response and TMB, as well as toxicity and TMB, differs between anti-PD(L)1 monotherapy and the combination ICI therapy, we tested the interaction term of TMB and treatment group in the logistic-normal mixed-effects model.
Meta-analysis was conducted to summarize response rate and toxicity rate for each tumor type and treatment group. The pooled estimates were obtained by a random-effects model using DerSimonian–Laird method. For tumor types with paired estimates of response rate and toxicity rate for both anti-PD(L)1 monotherapy and the combination ICI therapy, difference between treatment groups was visualized via a heatmap, and Spearman correlation coefficient was computed to correlate ORR and toxicity rate using the pooled estimates. The overall difference in response and toxicity rates between PD(L)1 monotherapy and the combination ICI therapy was obtained by a random-effects model with DerSimonian–Laird method using the pooled estimates from two treatment groups of each tumor type.
To evaluate the association between TMB and HR (log-transformed), meta-regression was performed using a mixed-effects model with DerSimonian–Laird method among studies with HR reported. Similarly, meta-analysis was conducted to summarize HRs of studies with median overall survival reported using a random-effects model with inverse variance weighting method. Because of limited survival data of studies with the combination ICI therapy, these analyses were performed for anti-PD(L)1 monotherapy only.
Of the 260 studies we identified, 117 ICI studies including a total of 12,450 patients met the inclusion criteria and were included in our overall analysis. We identified a total of 75 studies with anti-PD(L)1 monotherapy, 14 studies with anti–CTLA-4 monotherapy, 28 studies with anti-PD(L)1 plus anti–CTLA-4 combination (Fig. 1).
Tumor objective response and tumor mutational burden
A total of 75 trials/studies were included for anti-PD(L)1 monotherapy tumor ORR analysis. In these trials, 29 tumor types were examined, where a total of 8,692 patients were treated with PD(L)1 monotherapy and 1,568 patients (18.04%) responded to the treatment. For anti–CTLA-4 monotherapy, a total of 14 trials were included in the final analysis of tumor response rate. In these trials, 11 tumor types were examined where a total of 1,377 patients were treated with anti–CTLA-4 monotherapy and 130 patients (9.44%) responded to the treatment. With regard to combination ICI therapy with anti-PD(L)1 plus anti–CTLA-4, a total of 28 trials among 19 tumor types were included, where a total of 2,381 patients were treated and 791 (33.22%) patients responded to the treatment.
Meta-regression analysis revealed that TMB was positively associated with ORR, for all ICI treatment groups including anti-PD(L)1 monotherapy, anti–CTLA-4 monotherapy, and anti-PD(L)1 plus anti-CTLA-4 combination (P < 0.0001, respectively; Fig. 2A–C). TMB explained 44.81%, 85.00%, and 45.93% of the heterogeneity in response across tumor types in three treatment groups, respectively. A meta-analysis of pooled response rates for each specific tumor type with each specific ICI therapy within each treatment category (anti-PD(L)1, anti–CTLA-4, and anti-PD(L)1 plus anti–CTLA-4 combination) can be found in Supplementary Figures 1 through 3.
Checkpoint inhibitor toxicity and tumor mutational burden
A total of 60 trials/studies were included in analysis of ICI toxicity in patients treated with anti-PD(L)1 monotherapy. In these studies, 28 tumor types were examined where 8,411 patients were treated with PD(L)1 monotherapy, of which 1,262 (15.00%) patients experienced grade 3/4 toxicity. For anti–CTLA-4 monotherapy, 12 trials were analyzed among nine tumor types that were included in the final analysis for grade 3/4 toxicity. In these studies, a total of 1,317 patients were treated with anti–CTLA-4 monotherapy and among them 450 (34.17%) patients experienced grade 3 and 4 toxicity. In combination ICI therapy with anti-PD(L)1 plus anti–CTLA-4, a total of 28 trials were included in the final analysis of grade 3 and 4 toxicity. Among these studies, 19 tumor types were examined, where among 2,562 patients treated with ICI combination therapy, 1,068 (41.69%) patients experienced a grade 3 and 4 toxicity.
Utilizing meta-regression, TMB was not significantly associated with grade 3 and 4 toxicity among all 3 ICI treatments groups (anti-PD(L)1: P = 0.7819, anti–CTLA-4: P = 0.6269, and anti-PD(L)1 plus anti–CTLA-4, P = 0.7089; Fig. 3A–C). TMB explained only -0.12%, 8.10%, and 0.36% of the heterogeneity in toxicity across tumor types in the treatment groups, respectively. A meta-analysis of pooled grade 3 and 4 toxicity rates for each specific tumor type with each specific ICI therapy within each treatment category (anti-PD(L)1, anti–CTLA-4, and anti-PD(L)1 plus anti–CTLA-4 combination) can be found in Supplementary Figures 4 through 6.
PD(L)1 monotherapy versus combination ICI therapy
Meta-regression examining the association between ORR and TMB between anti-PD(L)1 monotherapy and anti-PD(L)1 plus anti–CTLA-4 combination across tumor types, revealed that the ICI combination group had a significantly higher response rate after adjusting for TMB in comparison with anti-PD(L)1 monotherapy (P = 0.0018). However, in evaluating whether the ORR difference between the combination and anti-PD(L)1 monotherapy varies with TMB, no significant differential effect was observed (test for treatment group by TMB interaction, P = 0.5788; Fig. 4).
In evaluating the relationship between toxicity and TMB between anti-PD(L)1 monotherapy and anti-PD(L)1 plus anti–CTLA-4 combination, mixed-effects logistic meta-regression showed that that the ICI combination group had a significantly higher grade 3 and 4 toxicity rate compared with anti-PD(L)1 monotherapy after adjusting for TMB (P < 0.0001). However, change in TMB did not lead to a significant variation in toxicity difference between anti-PD(L)1 and anti-PD(L)1 plus anti–CTLA-4 combination (P = 0.8847), indicating that the difference of grade 3 and 4 toxicity rates between two treatment groups does not depend on TMB (Fig. 4).
ORR and grade 3/4 toxicity were simultaneously illustrated through a heatmap with both anti-PD(L)1 monotherapy and anti-PD(L)1 plus anti–CTLA-4 combination among tumor types that were studied in both treatment groups (Fig. 5A). Pooled data for such individual tumor types, comparing response and toxicity rates of anti-PD(L)1 and anti-PD(L)1 plus anti–CTLA-4 for each tumor type, are presented in Supplementary Table 3. ORR and toxicity rates were not significantly correlated in neither anti-PD(L)1 [Spearman correlation coefficient (r = 0.10, P = 0.6863) nor anti-PD(L)1 plus anti–CTLA-4 combination (r = 0.06, P = 0.8147; Fig. 5B)]. The estimated overall difference between anti-PD(L)1 monotherapy and combination ICI therapy among 19 major tumor types across all TMBs is 6.28% for ORR [95% confidence interval (CI), 0.0273–0.0983] and 19.55% for grade 3/4 toxicity (95% CI, 0.1357–0.2553). The estimated overall difference of ORR between anti-PD(L)1 monotherapy and combination ICI therapy in tumors with TMB <10 median mut/megabase, was 3.73% (95% CI, 0.0096–0.0650) where tumors with TMB ≥ 10 mut/megabase was 21.13% (95% CI, 0.1279–0.2947). The estimated overall difference of grade 3/4 toxicity between anti-PD(L)1 monotherapy and combination ICI therapy in tumors with TMB <10 median mut/megabase, was 18.78% (95% CI, 0.1227–0.2529) where tumors with TMB ≥ 10 mut/megabase was 25.41% (95% CI, 0.0664–0.4418).
TMB and overall survival
Of all studies included in our meta-regression and meta-analysis, 10 studies across eight tumor types reported overall survival HRs for anti-PD(L)1 monotherapy versus a standard-of-care comparator. A meta-analysis of anti-PD(L)1 monotherapy using HRs can be found in Supplementary Figure 7. Meta-regression examining the association between TMB and HR revealed that that there was a positive relationship between TMB and OS as defined by HR, but it did not meet statistical significance (P = 0.2621; Fig. 6). Descriptive statistics of median OS survival of anti-PD(L)1 monotherapy, anti–CTLA-4 and anti-PD(L)1 plus anti–CTLA-4 combination can be found in Supplementary Table 4.
We newly demonstrate using clinical trial level data that TMB is positively associated with response to single or dual checkpoint inhibitors across multiple tumor types, but is not associated with a higher likelihood of toxicity. Our results build upon the work of many other groups showing that TMB is associated with response and prolonged survival after treatment with ICI therapy (9, 10, 12, 16, 17). We were unable to confirm that an increased response rate with ICI therapy in high TMB histologies translated into an OS benefit, as OS data were immature or unavailable for the majority of studies included in our meta-analysis.
Our findings broadly suggest that the benefits of adding CTLA-4 inhibition to PD(L)1 therapy will preferentially benefit high TMB tumor types. However, the relationship between TMB and ORR is imperfect, and some tumors have a higher or lower response rate than what would otherwise be anticipated from the TMB alone. For example, both RCC and Merkel cell cancer (MCC) have moderate TMBs but a high response rate to ICI therapy. In RCC, ICI responses may be augmented by the presence of highly immunogenic indel mutations, whereas in the case of MCC, ICI responses may be driven by responses to Merkel cell viral antigens (24, 25). Additional work is needed to determine whether the relationship between TMB and ORR can be further refined by incorporating information about mutational features in addition to mutation number. Additional work is also needed to understand whether the relationship between TMB and ORR is applicable to emerging checkpoint molecules, such as lymphocyte-activation gene 3 (LAG3), for which clinical data are currently limited.
Our findings have implications regarding the mechanism of immune toxicity resulting from ICI therapy. The pathogenesis and underlying mechanisms of ICI toxicity is poorly understood and it has been unclear to what extent autoimmunity in the setting of ICI therapy is driven by tumor features (26–29). In some contexts, cancer-associated autoimmunity may result from an immune response against cancer antigens (30). For example, patients receiving ICI for melanoma often develop vitiligo, an immune mediated attack of nonmalignant melanocytes, providing some initial evidence that tumors may direct the immune response against self in the setting of ICI therapy. Conversely, the observation that patients with genetic CTLA-4 deficiency often develop autoimmunity provides strong evidence that modulation of immune checkpoint pathways can result in immune-related toxicities independently of tumor antigens (31). While our results are not granular enough to report an association between any specific mutations and ICI toxicity, our results broadly support the conclusion that TMB is not a significant biomarker of ICI toxicity. Our results contrast the recent findings by Bomze and colleagues that reported a significant positive association between TMB and immune-related toxicities using postmarketing data from the FDA Adverse Event Reporting System (FAERS; ref. 32). We hypothesize that while the clinical trials included in our analysis restricted enrollment to patients with a good performance status who remained on therapy for at least 12 weeks when most immune-related toxicities would emerge, immune-related adverse events may have been more commonly reported in the FAERS system in higher TMB tumors because such patients would have been more likely to respond and to live long enough to experience toxicities (33). FAERS reporting includes data from not only from physicians, but also other sources such as consumers, potentially resulting in heterogeneity in toxicity reporting. In contrast, our study included only clinical trial data with standardized approaches to toxicity diagnosis and grading.
Strengths of our investigation included the comprehensive nature of our analysis including trials, the use of only published toxicity and response data reported by providers on closely monitored clinical trials, and the use of TMB data from a single validated assay representing a large volume of clinical data points (22, 34). Our analysis is broad and simultaneously evaluates the toxicity, survival, and objective response rate of anti–PD-1, anti–PD-L1, anti–CTLA-4 monotherapy and their combination for 29 tumor types, from 117 clinical trials and 12,450 patients. A limitation is TMB assessments were performed using a limited sequencing panel, and on different patients from which clinical trial responses were assessed. Inferences which arise from our findings applied to individual patients have the potential to lead to incongruent findings as a result of ecologic fallacy. In addition, we cannot exclude the possibility of bias within the individual studies utilized in our clinical trial meta-analysis. Only a minority of studies included in our meta-analysis reported overall survival HRs, limiting our power to determine the relationship between TMB and overall survival. The use of prospective patient-level data is needed to further validate our findings; the hypothesis that TMB can identify patients for combination immunotherapy is under investigation in the CheckMate 848 study (NCT03668119).
In conclusion, a positive relationship exists between TMB and response with single or combination ICIs; however, there is no association between TMB and ICI toxicity. These findings imply that TMB may broadly define the therapeutic index for ICI therapy, with increased benefit of single and dual ICI therapy in higher TMB tumors without significant additional toxicity. Our results identify new opportunities for therapeutic development, by supporting the investigation of combination ICI therapy in higher TMB tumor types and novel combinatorial strategies that go beyond dual ICI therapy in low TMB tumors.
Disclosure of Potential Conflicts of Interest
M. Jaffee reports personal fees from Genocea (advisory), DragonFly (advisory), Stone (advisory), Adaptive Biotech (advisory), Achilles (advisory), and Parker Institute (advisory) outside the submitted work grants and non-financial support from BMS (drug supply). M. Yarchoan reports grants from Incyte, Bristol-Myers Squibb, Exelixis, other from Eisai (advisory board), other from Exelixis (advisory board), and other from Geneos (advisory board) outside the submitted work. No potential conflicts of interest were disclosed by the other authors.
A. Osipov: Conceptualization, data curation, formal analysis, validation, investigation, visualization, methodology, writing original draft, project administration, writing review and editing. S.J. Lim: Software, formal analysis, writing original draft, writing review and editing. A. Popovic: Data curation, writing review and editing. N.S. Azad: Supervision, writing review and editing. D.A. Laheru: Supervision, funding acquisition, writing review and editing. L. Zheng: Supervision, funding acquisition, writing review and editing. E.M. Jaffee: Supervision, funding acquisition, writing review and editing. H. Wang: Data curation, software, formal analysis, supervision, investigation, visualization, methodology, writing original draft, project administration, writing review and editing. M. Yarchoan: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing original draft, project administration, writing review and editing.
This study was funded by the Linda Rubin Endowment Fellowship in Gastrointestinal & Pancreatic Cancers, Johns Hopkins Bloomberg-Kimmel Institute for Cancer Immunotherapy, the Viragh Foundation, NCI Specialized Program of Research Excellence (SPORE) in Gastrointestinal Cancers (P50 CA062924), and the NIH Center Core grant (P30 CA006973).
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