Safety, Immunologic, and Clinical Activity of Durvalumab in Combination with Olaparib or Cediranib in Advanced Leiomyosarcoma: Results of the DAPPER Clinical Trial.

Leiomyosarcoma in the advanced/metastatic setting, has a poor prognosis with modest response rates and survival benefit from current systemic treatments including cytotoxic chemotherapy and targeted agents (1–3). There is therefore a need for novel, more effective treatment strategies. In recent clinical trials that evaluated the role of immune checkpoint blockade (ICB) in the management of soft-tissue sarcomas, monotherapy with pembrolizumab or nivolumab showed no responses among patients with leiomyosarcoma (4–6) while the combination of nivolumab with a CTLA-4 inhibitor, ipilimumab yielded objective responses in only 2 of 14 patients with leiomyosarcoma (6).

This lack of clinical benefit from ICB in leiomyosarcoma, as well as evidence of immune infiltrates that are largely composed of suppressor T cells and M2-like macrophages has led to their designation as immunologically “cold” tumor (7–9). Increasing understanding of immune response mechanisms has shown that they are influenced by multiple biological factors that are intrinsic to tumor cells, related to host immunity, and the tumor microenvironment (TME). Novel combinatorial approaches that could overcome the molecular mechanisms for resistance to ICB and potentially convert immune-excluded or “cold” tumors (4, 5, 10, 11) such as leiomyosarcoma into more immunogenic tumors are therefore of great interest (8, 12).

Inhibition of tumor angiogenesis via the VEGF pathway has been shown to normalize the tumor vasculature and permit effector immune cell infiltration (13). It also leads to inhibition of regulatory immune cells and stimulation of antigen-presenting cells that altogether render an immunosuppressive TME more immune-supportive with increased T-cell activation that may be augmented by ICB (8, 14). Similarly, inhibition of the DNA-damage response by PARP inhibition has been shown to enhance antigen presentation by activation of stimulator of interferon genes (STING), dendritic cell activation, increasing tumor-infiltrating lymphocytes numbers, and programmed death ligand-1 (PD-L1) upregulation (15, 16).

We hypothesize that the addition of inhibitors of angiogenesis or DNA damage response (DDR) to ICB will alter the TME to enhance clinical antitumor activity in cold tumors, including leiomyosarcoma. The DAPPER (“Basket Combination Study of Inhibitors of DNA Damage Response, Angiogenesis and Programmed Death Ligand-1 in Patients with Advanced Solid Tumors”) trial was a prospective phase II study to evaluate the safety, immunologic, pharmacodynamic and clinical activity of the PD-L1 inhibitor, durvalumab, given in combination with a PARP inhibitor, olaparib, or a VEGF receptor inhibitor, cediranib, in three disease-specific cohorts (NCT03851614). Here, we present the independent results of the advanced leiomyosarcoma cohort.

DAPPER was an investigator-initiated, phase II, randomized, noncomparative, open-label, clinical trial (NCT03851614). Eligible patients were aged 18 years or older and diagnosed with a histologic diagnosis of leiomyosarcoma, and metastatic or surgically unresectable locally advanced disease. A minimum of one site of measurable disease by CT or MRI (per RECIST v1.1; ref. 17) was required, in addition to at least one site of disease that was safely accessible for pre- and on-treatment core biopsy. Eastern Cooperative Oncology Group (ECOG) performance status ≥ 1 and adequate hematologic, renal, and hepatic function were required. Patients must have received at least one prior line of systemic anticancer therapy and previous treatment with immune checkpoint inhibitors was allowed. For Arm A, prior treatment with a PARP inhibitor was not allowed, but other DDR pathway inhibitors were allowed. Prior angiogenic inhibitor therapy was allowed for patients on Arm B. Key exclusion criteria were active autoimmune disease, active brain metastases, or any serious or uncontrolled medical disorder that would affect study participation. The study protocol was approved by the Research Ethics Board at Princess Margaret Cancer Centre, Toronto, Ontario, Canada. All patients provided written informed consent and the trial was conducted according to Declaration of Helsinki and Good Clinical Practice guidelines.

Enrolled patients were randomized to receive intravenous durvalumab 1,500 mg every 28 days in combination with either oral olaparib 300 mg twice daily (Arm A), or oral cediranib 20 mg daily for 5 days followed by 2 days off each week (Arm B) in a 28-day treatment cycle. Treatment was continued until the development of progressive disease (PD), unacceptable toxicity, completion of 12 months of treatment, or withdrawal of consent.

The co-primary objectives of the DAPPER study were: (i) to evaluate changes in immune biomarkers and (ii) to evaluate the safety and tolerability of treatment with durvalumab in combination with either olaparib or cediranib. The primary biomarker endpoint was fold-change in tumor infiltrative CD4⁺ and CD8⁺ T cells from baseline to first on-treatment biopsy assessed by multiplexed IHC. The primary safety endpoints were the incidence of treatment-related adverse events (TRAE), grade ≥ 3 adverse events (AE), and serious adverse events (SAE). Secondary endpoints were overall response rate (ORR) defined as the proportion of patients who had received one or more doses of study treatment with a best overall response of complete response (CR) or partial response (PR); clinical benefit rate (CBR) defined as the proportion of patients with CR, PR, or stable disease (SD) for ≥ 6 months from the date of the first treatment; progression-free survival (PFS) measured from first study treatment to disease progression or death; overall survival (OS) defined as the time from first study treatment to death by any cause; and tumor growth rate (TGR) defined as the percentage increase in tumor volume per week. ORR, CBR, and PFS were evaluated according to RECIST v1.1 (17) and immune RECIST (iRECIST; ref. 18). Exploratory endpoints were identification of potential predictors of response and resistance by transcriptomic analysis of tumor tissue, immune profiling of the TME, intestinal microbiome, and radiomic profiling.

Safety assessments were performed throughout the study period. AEs were graded according to the NCI Common Terminology Criteria for Adverse Events (CTCAE) v5.0 during treatment and for up to 30 days after treatment discontinuation. No dose modification was allowed for durvalumab, but dose delays were permitted for AEs. A maximum of 2 dose reductions were allowed for olaparib (to 250 mg then 200 mg daily) and for cediranib (to 15 mg then 10 mg, 5 days on and 2 days off).

Radiographic tumor assessments were performed by CT or MRI scans at baseline and then every 3 cycles (12 weeks) in the absence of disease progression. Response evaluation was carried out by investigators using RECIST v1.1 and iRECIST. Patients who required treatment discontinuation for reasons other than disease progression or withdrawal of consent (e.g., for AEs) were followed up for tumor response until disease progression. TGR evaluation was performed on the basis of CT images obtained prior to screening, at baseline (during screening), and at the first on-treatment efficacy assessment using the method developed by Ferte and colleagues, as previously described (19). For clinical relevance, changes in TGR in the period prior to treatment initiation (TGR pretreatment) compared with the period on-treatment (TGR on-treatment) were evaluated.

Biopsies to obtain fresh tumor tissue were performed at baseline (≤28 days before starting study treatment) and on treatment (on cycle 2 day 1 of therapy) unless medically contraindicated. Additional biopsies at the time of radiologic disease progression were optional.

Tissue immunophenotyping was performed by multispectral fluorescent (multiplexed) IHC (mIHC) on paired formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples (see Supplementary Methods) that were collected at baseline, and at cycle 2 day 1 of treatment. Simultaneous staining was carried out with a 7-marker panel (CD3, CD8, CD20, CD68, FOXP3, cytokeratin, and DAPI) and used to assess the cell density (expressed in cells/mm²) of immune cell subsets including double-negative and CD4 T cells (CD3⁺CD8⁻), CD8 T cells (CD3⁺CD8⁺), activated and regulatory CD4 T cells (CD3⁺CD8⁻FOXP3⁺), B cells (CD20⁺), and macrophages (CD68⁺).

Transcriptome analysis was performed by RNA sequencing (RNA-seq) on tumor tissue obtained at baseline (see Supplementary Methods). Relative fractions of 22 cell subsets within the TME lymphocyte population were inferred from the gene-expression profiles using CIBERSORT v.1.06 (20). Gene set enrichment analysis was performed to determine enrichment scores from a validated gene signature of total immune infiltration (ESTIMATE immune score; ref. 21) using the gene set variation analysis R package (v1.42.0; ref. 22). Transcriptomic signatures that have demonstrated potential utility in predicting clinical benefit from immunotherapy (23) were evaluated in this cohort of patients treated on the DAPPER study as well as in an independent cohort of patients with leiomyosarcoma (n = 104) from the publicly-available TCGA dataset, who were not treated with immunotherapy (24). Analysis scripts are available at https://codeocean.com/capsule/2649618/tree/v1.

Stool samples for microbiome assessment were obtained at screening and on treatment (cycle 2 day 1). Metagenomic sequencing of stool samples was used to determine the relative abundance of microbial taxa and alpha diversity (see Supplementary Methods). Image segmentation and radiomic feature extraction was performed on CT images obtained at baseline (see Supplementary Methods). Unsupervised principal component analysis was then used to convert a set of 973 potential correlated radiomic imaging features into a set of 10 linearly uncorrelated features (25) and ranked per contribution from 1 to 10. Statistical correlation of these radiomic features with microbiome features and clinical data was then performed. Analysis scripts are available at https://codeocean.com/capsule/3894632/tree/v1.

Pearson's correlation, point-biserial, or Wilcoxon rank sum test were used for comparison of continuous variables, and Fisher exact tests or Cramer's V were used for categorical variables. Survival probabilities were calculated using Kaplan–Meier methods. Differences in Kaplan–Meier curves were tested by log-rank test. For binary outcomes, a logistic regression model was applied. Univariate Cox proportional hazards model was used to evaluate the degree and direction of statistical association of each microbiome and radiomic parameter with survival. Results were considered significant if P < 0.05. Multiple testing correction of P values was performed using the FDR Benjamini/Hochberg method with a family-wise error rate of 0.05 or 0.1. All statistical analyses are performed using SAS v9.4, Python v3.8.2 and R.

Clinical data presented in this study can be accessed by contacting the corresponding author.

From June 2019 to April 2021, 36 patients with confirmed histologic diagnosis of leiomyosarcoma were screened, among whom 30 fulfilled eligibility criteria and were randomly allocated in a 1:1 ratio to either arm of the study cohort. All randomized patients (15 patients in each arm) received at least 1 dose of study treatment (Fig. 1).

The median age of patients in the overall cohort was 54 years and there was a significant female preponderance (93%) that was at least in part related to the higher proportion of patients (60%) with primary uterine leiomyosarcoma (Table 1). Baseline and demographic characteristics of enrolled patients were in keeping with real-world populations (Supplementary Table S1) and both arms were well balanced, except for a slightly larger proportion of patients in Arm A who had the uterus as their primary tumor location (67% versus 53%). Most of the patients were heavily pretreated with 47% having received 3 or more lines of prior systemic treatment. All patients were immune checkpoint inhibitor treatment naïve. Four patients (2 on each arm) had received prior angiogenesis inhibitor therapy with pazopanib, but none had received prior PARP inhibitor therapy. About half of the patients had received prior radiotherapy (Table 1).

All 30 patients in the cohort were evaluable for toxicity. Twenty-eight patients (14 in each treatment arm) experienced TRAEs of any grade that were at least possibly attributed to study treatment (Table 2). Most TRAEs on both treatment arms were grade 1 or 2, with the most frequent being nausea and fatigue. The most frequent immune-related AEs were hypothyroidism and elevated transaminases, involving 7 (23%) and 3 (10%) patients in the cohort, and none of which were grade ≥ 3. Of patients on Arm A, 3 (20%) experienced grade ≥ 3 TRAEs, including anemia in 2 patients and elevated alkaline phosphatase in a third. On Arm B, one patient (7%) developed grade 3 hypertension that was deemed related to cediranib. There were no grade 4 TRAEs. One grade 5 AE was recorded in a patient on Arm B who died within a few hours of presentation to hospital with sudden onset breathlessness while on a trip abroad, deemed to be possibly related to cediranib and unlikely related to durvalumab. There was no other SAE attributable to study treatment.

At data cutoff on June 30, 2022, the median number of cycles of treatment received was 3 in both arms (range: Arm A, 1–13; Arm B, 1–6). One patient in Arm A completed the full 12 months of study treatment. Two patients (one in each arm) discontinued treatment due to intolerable AEs while 25 patients comprising 12/15 (80%) on Arm A and 13/15 (91%) on Arm B discontinued due to disease progression. Two patients died on study, one related to disease progression, while the other was possibly related to cediranib.

In the overall cohort, 28 patients (14 on each treatment arm) were evaluable for response by RECIST v1. There were no CRs. ORR and CBR were 4% and 18%, respectively (Supplementary Table S2). On Arm A, PR was observed in 1 patient (7%) who completed all planned study treatment and remained on study follow-up without progression for more than 18 months. Five other patients (36%) had SD as their best response, while 8 patients (57%) had PD. None of the patients on Arm B achieved PR, while 4 (29%) and 9 (64%) patients had SD (with one unconfirmed PR) and PD, respectively, as their best response (Supplementary Table S2). Response assessment by iRECIST showed the same proportion of patients with PR, SD, and PD as observed with RECISTv1.1 (Supplementary Table S3).

The median follow-up duration for the entire cohort was 12.6 months (range, 3.2–24.4 months) and a total of 13 patients, comprising 7 (47%) on Arm A and 6 (40%) on Arm B, were alive at data cutoff. In the overall cohort, the median PFS was 2.8 months [95% confidence interval (CI), 2.8–5.4 months]. Patients allocated to Arm A had median PFS of 2.8 months (95% CI, 2.8–10.9 months) with median PFS of 2.8 months (95% CI, 2.7–6 months) for patients allocated to Arm B (Fig. 2A). Median OS was 14.6 months (95% CI, 10.7–not reached) for the overall cohort. Patients on Arm A had median OS of 16.5 months (95% CI, 10.3–not reached) while those on Arm B had median OS of 14.6 months (95% CI, 9.8–not reached). There was no statistically significant difference in the median PFS (P = 0.58), or median OS (P = 0.96) observed in the treatment arms (Fig. 2B). SD that lasted ≥ 6 months was observed in 4 patients (29%) on Arm A only (Fig. 2C).

Twenty patients in the overall cohort (9 on Arm A and 11 on Arm B) had pretreatment scans that were suitable for TGR analysis. A reduction in TGR on-treatment compared with TGR pretreatment was observed for 6 patients (67%) on Arm A and 7 patients (64%) on Arm B. When median TGR pretreatment was compared with the median TGR on-treatment using Fisher exact test (Fig. 2D), there was a significant difference among patients on Arm A (5.1 vs. 0.2; 95% CI, 0.2–4.3; P = 0.031), but not for those on Arm B (2.9 vs. 1.3; 95% CI, 0.2–2.7; P = 0.068). Among 4 patients on Arm A who had the largest reduction in TGR, 3 had SD that lasted ≥ 6 months.

Baseline tumor biopsies were obtained from all 30 enrolled patients (100%) and subsequent on-treatment biopsies were possible for 24 patients (80%). mIHC was successfully performed on paired tissue samples for 19 patients (11 on Arm A, and 8 on Arm B). On Arm A, 3 patients had a higher cell density of CD3⁺CD8⁻ T cells and CD3⁺CD8⁺ T cells in their on-treatment biopsy than in their baseline biopsy including 1 patient who had a best response of PR (shown in blue on Fig. 3A). On Arm B, the density of CD3⁺CD8⁻ T cells was higher on-treatment in 6 of 8 patients including 2 patients who achieved SD as their best response (shown in yellow on Fig. 3B), while CD3⁺CD8⁺ T cell and CD3⁺CD8⁻FOXP3⁺ T-cell density on treatment was increased in 5 of 8 patients compared with their baseline. The patient on Arm A who achieved PR also had increased CD3⁺CD8⁻FOXP3⁺ T cells and CD68⁺ macrophages in their on-treatment biopsy (Fig. 3C). An increase in CD68⁺ macrophages was observed in 3 patients on Arm B, including 1 of the 2 patients with a best response of SD (Fig. 3B and C).

RNA-seq was successfully performed on baseline tumor samples obtained from 17 patients (11 on Arm A and 6 on Arm B). Using the median CIBERSORT score as cutoff, patients with high M1 macrophage levels in the TME at baseline had significantly longer OS (P = 0.0019), but not PFS (Fig. 4A). High M1/M2 macrophage ratio score was also associated with longer OS but not PFS (Fig. 4B). With a median-based dichotomization of clinical outcomes to compare patients with OS > 14 months (n = 9), PFS > 3 months (n = 8), or SD for ≥ 6 months (n = 5) to those without, there was no significant differences observed by CIBERSORT or ESTIMATE score analyses (data not shown). Gene set enrichment analysis identified 6 immune-related signatures and 1 angiogenesis-related signature were significantly associated with improved OS among patients in the entire DAPPER leiomyosarcoma cohort (Fig. 4C). After FDR correction for multiple hypothesis testing, 1 transcriptomic signature comprising genes that are reflective of high overall B-cell activity remained significant (Fig. 4D). None of the 7 transcriptomic signatures were associated with OS benefit in the TCGA leiomyosarcoma cohort (n = 104) where patients did not receive ICB (Supplementary Fig. S1).

Intestinal microbiome analysis was performed on baseline stool samples from all 30 patients in the cohort. Shannon Diversity Index (SDI) was negatively correlated with relative abundance of Bacteroides vulgatus (r = −0.74; P_adj = 0.0012), Bacteroides caccae (r = −0.60; P_adj = 0.0495), and Lachinospira pectinoschiza (r = −0.66; P_adj = 0.0131). Unadjusted univariate Cox modeling showed that higher abundance of Bacteroides ovatus and Bacteroides vulgatus were associated with worse OS, while a higher abundance of Roseburia faecis was associated with improved OS. After correction for multiple testing however, none of the microbiome features evaluated was found to have significant association with PFS or OS. Similarly, none of the principal components from radiomic analysis correlated with PFS or OS. Principal component 8, which characterizes changes in tumor spatial heterogeneity and texture and has previously been associated with poor outcome was associated with increased hazard for OS (HR, 1.6; 95% CI, 0.99–2.7) but did not reach statistical significance (P = 0.054).

In the DAPPER study, durvalumab in combination with olaparib or cediranib was safe and well-tolerated but had limited clinical efficacy. Overall, the toxicity profile with mostly grade 1 and 2 TRAEs was comparable to reports from previous studies that evaluated these combinations in solid tumor patients (26, 27). A pulmonary embolism was suspected in a patient who had a grade 5 AE, and attribution to cediranib could not be ruled out because the event happened while the patient was on a trip abroad in circumstances that precluded radiologic or autopsy confirmation of the cause of death. No other treatment-related SAEs or unexpected toxicity was observed. The clinical activity of the treatment combinations in this heavily pretreated cohort of patients with leiomyosarcoma was limited with ORR 4% and CBR 18% in the overall cohort. PR and SD ≥ 6 months were limited to patients on Arm A (n = 5/15, 33%), who also had a significant reduction in the median TGR after commencing treatment. There was, however, no significant difference between the treatment arms in terms of PFS or OS, although this was not a formal comparison due to study design and limited sample size.

No prior studies have reported clinical activity of PARP inhibitors in combination with ICB in leiomyosarcoma, although promising results have been seen in other solid malignancies that harbor DDR defects (26–29). PARP inhibitors in combination with temozolomide chemotherapy showed very promising activity in a phase II clinical trial of uterine patients with leiomyosarcoma (30), suggesting that DDR defects shown in preclinical studies of leiomyosarcoma (31) may be exploited for patient benefit. Similarly, the phase Ib TOMAS study evaluating olaparib with trabectedin in bone and soft-tissue sarcoma observed PRs in 3 patients with leiomyosarcoma (32, 33). Correlative studies perfomed in the TOMAS study showed that high expression of PARP1 in baseline tumor samples was associated with disease control, but did not show correlation between BRCA1 or BRCA2 mutation status and clinical benefit. In a follow-up multicenter phase II study however, no objective responses were seen among the cohort of 15 patients with leiomyosarcoma treated with olaparib and trabectedin (34) suggesting that further translational studies to develop suitable predictive biomarkers and optimal combinatorial strategies to exploit DDR in leiomyosarcoma are needed.

Other studies that evaluated immune checkpoint inhibition in combination with angiogenic targeted agents have also reported few objective responses in leiomyosarcoma. For example, Wilky and colleagues observed PR in only 1 of 6 leiomyosarcoma cases treated with pembrolizumab and axitinib (8), while Liu and colleagues reported a phase II trial of a PD-L1 inhibitor (TQB2450) in combination with anlotinib and observed 1 PR among 4 patients with leiomyosarcoma treated (35). In a real-world study, no objective responses were seen among 15 patients with leiomyosarcoma treated with ICB combined with VEGF inhibition (36). Given that patients with leiomyosarcoma occasionally appear to derive a clinical benefit from this combination, however, identification of biomarkers suitable for treatment selection would be useful.

Correlative analyses performed in the DAPPER trial were focused primarily on immune biomarkers. mIHC showed that ICB in combination with olaparib or cediranib resulted in an increase in the density of CD8⁺ T cells, CD8⁻FOXP3⁺ T cells, and CD68⁺ macrophages in the tumor biopsies of several patients with the patient in arm A who achieved a PR having the greatest increase in CD8⁺ T cells and macrophages after treatment. Antigen-specific cytotoxic CD8⁺ T cells kill tumor cells and are associated with good prognosis in several cancers (37), while classical M1-like macrophages promote inflammation and cytotoxic T-cell activity (38). ICB has been shown to enhance CD8⁺ T-cell activation and production of IFNγ (39) which promotes a pro-inflammatory macrophage phenotype (40). Further characterization of the CD8⁺ T cells and CD68⁺ macrophages found in the leiomyosarcoma tumor biopsies is required to describe their phenotype and to evaluate their role in the antitumor response.

Transcriptomic analysis of baseline tumor samples showed that patients with higher levels of M1 macrophages or higher ratio of M1/M2 macrophages in the TME had significantly longer OS. Tumor-associated macrophages (TAM) first exist as uncommitted M0 macrophages and are polarized in response to cytokine stimuli to become either of two simplified subgroups: M1-like which are pro-inflammatory, with tumor killing properties, or M2-like which have pro-tumor properties and have been linked with resistance to ICB (38, 40, 41). M1 macrophage levels and M1/M2 macrophage ratio may therefore be biomarkers with prognostic and/or predictive potential in patients with leiomyosarcoma treated with immunotherapy and should be further evaluated. TAMs have also been shown to switch polarization in response to further external stimuli including IFNγ and IL10 (42, 43), making this a potential strategy for overcoming ICB resistance (44, 45) that may be exploited in leiomyosarcoma.

Association of the gene expression signature for high B-cell activity signature with longer OS in patients with leiomyosarcoma on the DAPPER trial, but not in the ICB treatment-naïve TCGA cohort, suggests that high B-cell activity may identify patients who are more likely to have favorable outcomes with ICB. This signature has previously been shown to be predictive of benefit from ICB in lung adenocarcinoma (46). Importantly, however, this observation supports translational studies by Petitprez and colleagues, which found that high B-cell activity in baseline tumor samples was associated with higher ORR and longer PFS in sarcoma patients treated with pembrolizumab (47). Put together, our results support ongoing further evaluation and integration of these biomarkers in leiomyosarcoma.

Intestinal microbiome analysis showed that relative abundance of Bacteroides vulgatus was negatively correlated with SDI suggesting a higher abundance of this taxon is associated with lower alpha-diversity, which has been associated with poor outcomes in solid tumor patients receiving ICB (48, 49). However, SDI was not associated with OS or PFS in this study. While these analyses are limited by the small sample size, the data may lead one to hypothesize that a higher abundance of Bacteroides vulgatus may be a prognostic biomarker in individuals with leiomyosarcoma, and that the organism may be targeted by antibiotics to improve survival. Additional studies with greater power are needed to confirm or refute these findings. Other exploratory analyses of tumor radiomic features with clinical outcome and intestinal microbiome showed no significant associations.

The DAPPER trial has demonstrated limited clinical benefit of durvalumab with cediranib in patients with leiomyosarcoma. However, clinical benefit was seen in up to a third of patients in the durvalumab and olaparib arm. While the robustness of the correlative analyses performed was limited by the small sample size and low objective response rate, the results provide insights into immune cell dynamics within the TME with treatment. Further exploration of the tumor immune microenvironment in larger studies is warranted to identify patients with leiomyosarcoma for whom specific immune-oncology treatments will be beneficial.

A. Salawu reports personal fees from Boehringer Ingelheim, Knight Therapeutics, and Taiho Pharmaceutical outside the submitted work. B.X. Wang reports personal fees from AstraZeneca, Tessa Therapeutics, and Providence Therapeutics outside the submitted work. A. Hernando-Calvo reports other support from Kyowa Kirin and Merck Serono outside the submitted work. A.R. Hansen reports grants and other support from Merck, GSK, and Pfizer, as well as grants from Janssen, BMS, Roche, AstraZeneca, Tyra Biosciences, Genentech, and MacroGenics outside the submitted work. A. Spreafico reports grants from AstraZeneca during the conduct of the study, as well as grants from Novartis, BMS, Merck, Symphogen, AstraZeneca/Medimmune, Surface Oncology, Janssen Oncology, Alkermes, Array Biopharma/Pfizer, GSK, Genentech, Roche, NuBiyota, Seagen, ALX Oncology, Treadwell, Oncorus, and Surface Oncology outside the submitted work. P.L. Bedard reports grants from BMS, Sanofi, AstraZeneca, Genentech/Roche, GlaxoSmithKline, Novartis, Nektar, Merck, Immunomedics, Seagen, Lilly, Amgen, Bicara Therapeutics, Zymeworks, and Medicenna outside the submitted work, as well as uncompensated advisory role for Gilead, Zymeworks, Lilly, Seagen, Merck, and Pfizer. M.O. Butler reports grants and personal fees from Merck and Novartis, as well as personal fees from BMS, Adaptimmune, GSK, Sanofi, LaRoche Possey, Iovance, Pfizer, Medison, IDEAYA Bio, and Regeneron outside the submitted work. B. Coburn reports grants from Canadian Institutes for Health Research, Weston Foundation, and Ontario Institutes for Cancer Research; nonfinancial support from Nubiyota Inc.; and other support from Sanofi Inc. outside the submitted work. B. Haibe-Kains reports personal fees from Code Ocean during the conduct of the study. L.L. Siu reports personal fees from AstraZeneca, as well as grants from AstraZeneca during the conduct of the study. L.L. Siu also reports personal fees from Merck, Pfizer, Roche, GlaxoSmithKline, Voronoi, Arvinas, Tessa, Navire, Relay, Daiichi Sankyo, Coherus, Amgen, Marengo, InteRNA, Medicenna, Tubulis, and LTZ Therapeutics; grants from Novartis, Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, GlaxoSmithKline, Roche/Genentech, Merck, Bayer, AbbVie, Amgen, Symphogen, Intensity Therapeutics, Mirati, Shattucks Lab, BioNTech, 23&Me, and EMD Serono; and other support from Agios and Treadwell Therapeutics outside the submitted work. A.R. Abdul Razak reports grants from AstraZeneca, Deciphera, Karyopharm Therapeutics, Pfizer, Roche/Genentech, Bristol Myers Squibb, MedImmune, Amgen, GlaxoSmithKline, Blueprint Medicines, Merck, AbbVie, Adaptimmune, Iterion Therapeutics, 23&Me, Rain Therapeutics, Neoleukin Therapeutics, Daiichi Sankyo, Symphogen, and Frontier Therapeutics, as well as personal fees from Adaptimmune, Bayer, GlaxoSmithKline, Medison, Inhibrx outside the submitted work. No disclosures were reported by the other authors.

A. Salawu: Data curation, formal analysis, methodology, writing–original draft, writing–review and editing. B.X. Wang: Conceptualization, formal analysis, investigation, methodology, writing–review and editing. M. Han: Data curation, formal analysis, investigation, methodology, writing–original draft, writing–review and editing. C. Geady: Data curation, formal analysis, methodology, writing–original draft, writing–review and editing. A. Heirali: Data curation, formal analysis, investigation, methodology, writing–original draft. H.K. Berman: Resources, investigation, writing–review and editing. T.D. Pfister: Formal analysis, investigation, writing–review and editing. A. Hernando-Calvo: Data curation, writing–original draft, writing–review and editing. E.M. Al-Ezzi: Investigation, writing–review and editing. L.-A. Stayner: Investigation. A.A. Gupta: Investigation, writing–review and editing. O. Ayodele: Formal analysis, investigation, visualization, writing–original draft. B. Lam: Resources, formal analysis, investigation, writing–review and editing. A.R. Hansen: Conceptualization, investigation, writing–review and editing. A. Spreafico: Conceptualization, investigation, writing–review and editing. P.L. Bedard: Conceptualization, formal analysis, investigation, methodology, writing–review and editing. M.O. Butler: Investigation, writing–review and editing. L. Avery: Conceptualization, formal analysis, visualization, methodology, writing–original draft, writing–review and editing. B. Coburn: Conceptualization, formal analysis, methodology, writing–review and editing. B. Haibe-Kains: Conceptualization, formal analysis, writing–review and editing. L.L. Siu: Conceptualization, resources, formal analysis, supervision, investigation, writing–review and editing. A.R. Abdul Razak: Data curation, formal analysis, methodology, writing–original draft, writing–review and editing.

The authors thank all the patients who participated and their families.

AstraZeneca and the Tumor Immunotherapy Program (TIP) at the University Health Network, Toronto, Canada.

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