Immune awakening revealed by peripheral T cell dynamics after one cycle of immunotherapy

Abstract

Data Availability Sample metadata file are available from GitLab (https://gitlab.com/cruk-mi/tcell-immune-awakening). The data from all TCR sequencing performed for this study are deposited in ImmunoSEQ® Immune ACCESS repository (https://clients.adaptivebiotech.com/ immuneaccess). The RNA-Seq data for patient #12 can be downloaded from EGA (accession code EGAS00001004043). TCR sequencing data for matched pre-treatment and week 3 melanoma biopsy and PBMC samples of locally-advanced melanoma patients 18 re-analyzed here were downloaded from referenced accession EGAS00001003178 EGA study accession dataset EGAD00010001608. TCR sequencing data of matched pre-treatment and week 3 melanoma patient PBMC from AC Huang et al. 7 reanalyzed here were kindly made available by the Authors. TCR sequencing data of matched pre-treatment and week 3 PBMC for the cohort of locally-advanced treatment naive melanoma patients from referenced accession Amaria et al. 18 re-analyzed here were downloaded from EGAS00001003178 EGA study accession dataset EGAD00010001608, patient clinical history metadata file from EGAD00001004352. PBMC and biopsy CyTOF data from Krieg et al. 8 and Greenplate et al. 24 re-analyzed here were downloaded from referenced accession https://flowrepository.org/experiments/1124 and http://flowrepository.org/id/FR-FCM-ZZMC, respectively. PBMC REAP-Seq data from Peterson et al. 27 re-analyzed here were downloaded from referenced accession https:// www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100501. The authors confirm that, for approved reasons (UK Data Protection Act 2018), some access restrictions apply to data containing patient medical records (date of birth). Source data for Fig. 1-7 and Extended Data Fig. 3-6 are provided as Source Data files Fig. 1-7 and Extended Data Fig. 3-6. Additional data that support the findings of this study are available from the corresponding author on reasonable request.

Tumor Infiltrating Effector Memory Antigen-Specific CD8+ T Cells Predict Response to Immune Checkpoint Therapy

“…Successful ICT responses were associated with reduced tumor TCRb diversity in our study, similar to other murine studies (38,62,63). Cancer patients with improved survival exhibit a greater expansion of TCRb clones after ICT in their tumors and peripheral blood, compared to non-responders (16)(17)(18)(19)64), suggesting that effective therapy requires expansion of tumor antigen-specific CTLs. Although we were able to track the expansion of a single antigen-specific CTL clone, the dominance of this clone prevented us from studying the breadth of the anti-tumor T cell response or identifying expansion of other TCRb clones in the endogenous CD8 + T cell compartment in relation to ICT outcomes.…”

“…ICT drives dynamic changes in CTL frequency (7,9), phenotype (10)(11)(12), proliferation (13,14), and cytotoxic function (6,15). T cell receptor (TCR) sequencing studies further suggest that ICT causes clonal proliferation of CTLs within the tumor (7,16,17) and the periphery (16,18,19). As antigen-specificity is crucial for a successful anti-tumor response, we reasoned that dynamic changes in tumor antigen-specific CTLs could inform ICT responses.…”

PD-1 and TIGIT coexpression identifies a circulating CD8 T cell subset predictive of response to anti-PD-1 therapy

“…Indeed, recent findings demonstrated T cell clonotypic expansion at the periphery and its association with clinical response to immune checkpoint blockade. [78][79][80] Globally, the frequency of the DPOS subset only slightly increased on PD-1 blockade, favoring the hypothesis of the priming of new highly reactive clonotypes on PD-1 therapy, replacing exhausted clonotypes. The higher frequencies of DPOS T cells at baseline in responding patients (significant in the MCC) is probably the reflection of an immunogenic and inflammatory tumor microenvironment, leading to the activation of both nearly exhausted T cells (CD39 + , TOX + ) and Tfc-like T cells (CXCR5 + , CXCL13 + , PD-1 high ), whose function is impaired by PD-1 expression.…”

Systemic Blood Immune Cell Populations as Biomarkers for the Outcome of Immune Checkpoint Inhibitor Therapies

“…It was speculated that having a high TCR clonal diversity could correlate with higher probabilities to establish efficacious anti-tumor immune responses. Consequently, several studies have indeed associated a better response to a more diverse TCR repertoire in peripheral T cells [60][61][62][63].…”