Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: An exploratory multi-omic analysis

Abstract

BackgroundInhibition of programmed death-ligand 1 (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens, and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance.Methods and findingsThe goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pretreatment tumor samples as well as TCR-seq of matched, serially collected peripheral blood, collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression-free survival [PFS] >6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state.Patients with DCB displayed a higher proportion of tumor-infiltrating T lymphocytes (TIL) (n = 24, Mann-Whitney p = 0.047). Pretreatment peripheral blood TCR clonality below the median was associated with improved PFS (n = 29, log-rank p = 0.048) and OS (n = 29, log-rank p = 0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n = 22, Mann-Whitney p = 0.022). The combination of high pretreatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n = 10, hazard ratio (HR) (mean) = 89.88, HR (median) = 23.41, 95% CI [2.43, 506.94], p(HR > 1) = 0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which, in turn, impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load, and expressed neoantigen load did not demonstrate significant association with DCB (n = 25, Mann-Whitney p = 0.22, n = 25, Mann-Whitney p = 0.55, and n = 25, Mann-Whitney p = 0.29, respectively). Instead, we found evidence of time-varying effects of somatic mutation load on PFS in this cohort (...

ALT-803, an IL-15 superagonist, in combination with nivolumab in patients with metastatic non-small cell lung cancer: a non-randomised, open-label, phase 1b trial

“…Genomic DNA was prepared from peripheral blood mononuclear cells and sent to Adaptive Biotechnologies (Seattle, WA, USA) for T-cell receptor (TCR) sequencing and analysis using the immunoSEQ Assay in view of the findings of previous studies showing an association between response to PD-1 blockade and changes in the TCR β-chain repertoire. 17,18 Additional details of the procedure are included in the appendix (p 19). …”

Predicting the spectrum of TCR repertoire sharing with a data‐driven model of recombination

“…Bladder cancer data came from a published immunotherapy study 54. We used the blood samples from the first time point of each patient in the study.…”

“…The T‐cell repertoire of tumor‐infiltrating cells has been studied to look for signatures of immunogenicity,52, 53, 54 and the overlap between the tumor and blood repertoires was shown to predict survival in glioblastoma patients 55. In addition, the tumor‐specific TCRs have been reported to be shared in the tumor‐infiltrating and blood T‐cell repertoires of breast cancer 56…”

“…We performed the sharing analysis on 30 patients with bladder cancer, on TCR repertoires sequenced from blood samples 54. We compared it with 30 healthy individuals, chosen at random among the individuals studied in 30 to have similar sample sizes.…”

iReceptor: A platform for querying and analyzing antibody/B‐cell and T‐cell receptor repertoire data across federated repositories

“…One recent example of using TCR repertoires to monitor and predict response to anti-checkpoint therapy is provided by Snyder et al (23), who studied urothelial cancer patients treated with atezolizumab. These researchers concentrated on the clonality of the TCRβ repertoire, the set of sequences making up one of the two chains in a T-cell receptor.…”