npj Precision Oncology (Aug 2023)

Chemokine expression predicts T cell-inflammation and improved survival with checkpoint inhibition across solid cancers

  • Joan Miguel Romero,
  • Emma Titmuss,
  • Yifan Wang,
  • James Vafiadis,
  • Alain Pacis,
  • Gun Ho Jang,
  • Amy Zhang,
  • Bryn Golesworthy,
  • Tatiana Lenko,
  • Laura M. Williamson,
  • Barbara Grünwald,
  • Grainne M. O’Kane,
  • Steven J. M. Jones,
  • Marco. A. Marra,
  • Julie M. Wilson,
  • Steven Gallinger,
  • Janessa Laskin,
  • George Zogopoulos

DOI
https://doi.org/10.1038/s41698-023-00428-2
Journal volume & issue
Vol. 7, no. 1
pp. 1 – 14

Abstract

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Abstract Immune checkpoint inhibitors (ICI) are highly effective in specific cancers where canonical markers of antitumor immunity are used for patient selection. Improved predictors of T cell-inflammation are needed to identify ICI-responsive tumor subsets in additional cancer types. We investigated associations of a 4-chemokine expression signature (c-Score: CCL4, CCL5, CXCL9, CXCL10) with metrics of antitumor immunity across tumor types. Across cancer entities from The Cancer Genome Atlas, subgroups of tumors displayed high expression of the c-Score (c-Scorehi) with increased expression of immune checkpoint (IC) genes and transcriptional hallmarks of the cancer-immunity cycle. There was an incomplete association of the c-Score with high tumor mutation burden (TMB), with only 15% of c-Scorehi tumors displaying ≥10 mutations per megabase. In a heterogeneous pan-cancer cohort of 82 patients, with advanced and previously treated solid cancers, c-Scorehi tumors had a longer median time to progression (103 versus 72 days, P = 0.012) and overall survival (382 versus 196 days, P = 0.038) following ICI therapy initiation, compared to patients with low c-Score expression. We also found c-Score stratification to outperform TMB assignment for overall survival prediction (HR = 0.42 [0.22–0.79], P = 0.008 versus HR = 0.60 [0.29-1.27], P = 0.18, respectively). Assessment of the c-Score using the TIDE and PredictIO databases, which include ICI treatment outcomes from 10 tumor types, provided further support for the c-Score as a predictive ICI therapeutic biomarker. In summary, the c-Score identifies patients with hallmarks of T cell-inflammation and potential response to ICI treatment across cancer types, which is missed by TMB assignment.