bioRxivImmunologyPreprint
Tumor γδ T-cell abundance is associated with favorable cancer treatment outcomes
Xingzhi NiuiD, Deepali L. KundnaniiD, Mikaela DicomeiD, Luis E. Tafoya, Li SongiD, Murad R. MamedoviD, X. Shirley LiuiD, Avinash D SahuiD
Abstract
Purpose: Clinical response to immune checkpoint blockade (ICB) remains variable. We asked whether immune-cell populations in the tumor microenvironment (TME) are associated with benefit across treatments and tumor types. Experimental Design: We analyzed pretreatment bulk tumor RNA-seq from ICB cohorts and TCGA. Gene-level effects associated with ICB response or TCGA survival were projected onto Human Primary Cell Atlas profiles of 157 cell types. Cox and mixed-effects models accounted for cancer type, cohort, and therapy, as appropriate. After {gamma}{delta} T cells emerged as a leading population, we adjusted their associations for eight CD8 estimators and evaluated them using TRUST4-based TRG/TRD reconstruction and single-cell RNA-seq. Results: {gamma}{delta} T-cell programs were among the signatures consistently associated with ICB response and favorable TCGA survival. Across ICB cohorts, {gamma}{delta} T-cell abundance was associated with response (n=1,356; OR, 1.38; 95% CI, 1.23-1.56) and overall survival (n=1,074; HR, 0.82; 95% CI, 0.76-0.88), with associations persisting after CD8 adjustment. ICB-response-associated cell-type profiles were strongly concordant with chemotherapy response (r=0.92) and moderately concordant with radiation response (r=0.58); targeted and hormone therapy analyses were underpowered. TRUST4 reconstruction and single-cell RNA-seq provided orthogonal support for the {gamma}{delta} signal. Conclusions: Pretreatment {gamma}{delta} T-cell abundance was associated with favorable ICB outcomes and survival across cancers, while related cell-type programs extended to selected non-immunotherapy response settings. Although associative and context dependent, these findings support prospective evaluation of {gamma}{delta} T-cell abundance as a candidate tumor-immune biomarker.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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