5435 - TCR-beta repertoire convergence and evenness are associated with response to immune checkpoint inhibitors

Background

Immune checkpoint inhibitors (ICI) significantly improve clinical outcome of advanced non-small cell lung cancer (NSCLC) patients. However, as only a subset of patients responds to treatment, there is an urgent need for predictive biomarkers. Here we investigated the association of TCR-beta (TCRB) clonal expansion and convergence with treatment response. We assessed these features within the tumor microenvironment of treatment naïve patients and compared their predictive value with other biomarkers such as tumor mutational burden (TMB) and PD-L1 status.

Methods

Total RNA was extracted from NSCLC FFPE pretreatment tissue biopsies of patients receiving ICI therapy (n = 45). TCRB repertoire NGS libraries were prepared with the Oncomine TCRB-SR assay and sequenced on the Ion Torrent instrument. TCR convergence (=frequency of clonotypes identical in amino acid but different in nucleotide space) and clonal evenness (=measurement of the similarity of clone sizes) were evaluated independently using Fisher’s test. TMB values from the same biopsies were assessed from extracted genomic DNA via the Oncomine Tumor Mutation Load Assay. PD-L1 status was determined by immunohistochemical staining.

Results

Durable clinical benefit from ICI therapy was associated with increased TCR convergence (p = 0.12) and decreased clonal evenness (p = 0.01) independently. The TCR-based patient classification was able to identify responders who otherwise had low to intermediate (<9 Mutations per Mb) TMB or negative (<1%) PD-L1 status. Adding TCR evenness to TMB and PD-L1-based stratification allowed for the identification of 82% of responders, compared to 47% for TMB alone and to the identification of 94% of responders, compared to 59% for PD-L1 alone.

Conclusions

Our results show that evaluation of the TCR-beta repertoire in NSCLC specimens is an effective tool to stratify patients according to their response to ICI therapy. In particular, TCR assessment identifies subpopulations of responding patients that would otherwise be misclassified by either TMB or PD-L1 status. Thus, combinatorial use of several biomarkers may yield the highest clinical accuracy for ICI therapy selection.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Philip Jermann.

Funding

Thermo Fisher Scientific.

Disclosure

P. Jermann: Honoraria (self), Research grant / Funding (institution), Travel / Accommodation / Expenses: Thermo Fisher Scientific; Research grant / Funding (institution): BMS. K. Leonards: Research grant / Funding (institution): Thermo Fisher Scientific; Research grant / Funding (institution): Bristol-Myers Squibb. T. Looney: Full / Part-time employment: Thermo Fisher Scientific. I. Alborelli: Research grant / Funding (institution), Travel / Accommodation / Expenses: Thermo Fisher Scientific; Research grant / Funding (institution): Bristol-Myers Squibb. S.I. Rothschild: Honoraria (self), Research grant / Funding (institution): AstraZeneca; Honoraria (self), Research grant / Funding (institution): BMS; Research grant / Funding (institution): Merck Serono; Honoraria (self): MSD; Honoraria (self): Roche; Honoraria (self): Novartis. S. Savic Prince: Honoraria (self), Advisory / Consultancy: MSD; Advisory / Consultancy: AstraZeneca; Honoraria (self): Roche. A. Zippelius: Advisory / Consultancy: BMS; Advisory / Consultancy, Research grant / Funding (institution): Roche; Advisory / Consultancy: MSD; Advisory / Consultancy: NBE Therapeutics; Research grant / Funding (institution): Secarna; Research grant / Funding (institution): Beyondsprings; Research grant / Funding (institution): Crescendo; Research grant / Funding (institution): Hookipa. L. Bubendorf: Honoraria (self), Research grant / Funding (institution): Roche; Honoraria (self), Research grant / Funding (institution): MSD; Honoraria (self): BMS; Honoraria (self): AstraZeneca. All other authors have declared no conflicts of interest.