Peripheral blood immune cell dynamics reflect antitumor immune responses and predict clinical response to immunotherapy

Michael Hwang, Jenna Vanliere Canzoniero, Samuel Rosner, Guangfan Zhang, James R. White, Zineb Belcaid, Christopher Cherry, Archana Balan, Gavin Pereira, Alexandria Curry, Noushin Niknafs, Jiajia Zhang, Kellie N. Smith, Lavanya Sivapalan, Jamie E. Chaft, Joshua E. Reuss, Kristen Marrone, Joseph C. Murray, Qing Kay Li, Vincent LamBenjamin P. Levy, Christine Hann, Victor E. Velculescu, Julie R. Brahmer, Patrick M. Forde, Tanguy Seiwert, Valsamo Anagnostou

Research output: Contribution to journalArticlepeer-review

Abstract

Background Despite treatment advancements with immunotherapy, our understanding of response relies on tissue-based, static tumor features such as tumor mutation burden (TMB) and programmed death-ligand 1 (PD-L1) expression. These approaches are limited in capturing the plasticity of tumor-immune system interactions under selective pressure of immune checkpoint blockade and predicting therapeutic response and long-term outcomes. Here, we investigate the relationship between serial assessment of peripheral blood cell counts and tumor burden dynamics in the context of an evolving tumor ecosystem during immune checkpoint blockade. Methods Using machine learning, we integrated dynamics in peripheral blood immune cell subsets, including neutrophil-lymphocyte ratio (NLR), from 239 patients with metastatic non-small cell lung cancer (NSCLC) and predicted clinical outcome with immune checkpoint blockade. We then sought to interpret NLR dynamics in the context of transcriptomic and T cell repertoire trajectories for 26 patients with early stage NSCLC who received neoadjuvant immune checkpoint blockade. We further determined the relationship between NLR dynamics, pathologic response and circulating tumor DNA (ctDNA) clearance. Results Integrated dynamics of peripheral blood cell counts, predominantly NLR dynamics and changes in eosinophil levels, predicted clinical outcome, outperforming both TMB and PD-L1 expression. As early changes in NLR were a key predictor of response, we linked NLR dynamics with serial RNA sequencing deconvolution and T cell receptor sequencing to investigate differential tumor microenvironment reshaping during therapy for patients with reduction in peripheral NLR. Reductions in NLR were associated with induction of interferon-γresponses driving the expression of antigen presentation and proinflammatory gene sets coupled with reshaping of the intratumoral T cell repertoire. In addition, NLR dynamics reflected tumor regression assessed by pathological responses and complemented ctDNA kinetics in predicting long-term outcome. Elevated peripheral eosinophil levels during immune checkpoint blockade were correlated with therapeutic response in both metastatic and early stage cohorts. Conclusions Our findings suggest that early dynamics in peripheral blood immune cell subsets reflect changes in the tumor microenvironment and capture antitumor immune responses, ultimately reflecting clinical outcomes with immune checkpoint blockade.

Original languageEnglish (US)
Article numbere004688
JournalJournal for immunotherapy of cancer
Volume10
Issue number6
DOIs
StatePublished - Jun 10 2022

Keywords

  • immunotherapy
  • translational medical research
  • tumor biomarkers

ASJC Scopus subject areas

  • Molecular Medicine
  • Oncology
  • Cancer Research
  • Immunology and Allergy
  • Pharmacology
  • Immunology

Fingerprint

Dive into the research topics of 'Peripheral blood immune cell dynamics reflect antitumor immune responses and predict clinical response to immunotherapy'. Together they form a unique fingerprint.

Cite this