CGRP signaling Links Tumor-Associated Pain to Immune Evasion in Oral Squamous Cell Carcinoma
Peripheral sensory nerves are thought to contribute to solid tumor growth, particularly in oral squamous cell carcinoma (OSCC); however, the link between pain and immunosuppression remains unresolved. Here, using a prospective observational study, we demonstrate an inverse relationship between OSCC-induced pain mediated by calcitonin gene-related peptide (CGRP)-expressing nerves and tumor-associated immunity. Bulk RNA sequencing of tumor-innervating sensory neurons from mice revealed differential regulation of genes associated with excitability, neurotransmission, and axonal sprouting. Using a gain-of-function approach with persistent stimulation of peptidergic afferents, we show that sensory neurons promote oral tongue tumor growth and limit the activation of effective anti-tumor immune responses via efferent CGRP release. Conversely, loss-of-function approaches—including local ablation of nociceptive nerves and systemic CGRP receptor antagonism—slowed tumor growth and improved anti-tumor immunity. Targeting CGRP may therefore represent a therapeutic strategy in OSCC to reduce pain and improve treatment response. Overall design: Retrogradely labeled trigeminal ganglion (TG) neurons were isolated from sham and tumor-bearing mice by fluorescence-activated cell sorting. Briefly, bilateral TGs were harvested, dissociated, and cell viability was assessed by microscopy. DiI? neurons were sorted directly into 500 µL TRIzol reagent and stored at -80 °C until RNA extraction. Gating was established using TG neurons from naïve mice and DiI-injected sham controls.  Bulk RNA sequencing was performed to compare gene expression between MOC1 tumor-bearing and sham control mice (n = 11 total; 6 MOC1 and 5 sham). RNA extraction, library preparation, and sequencing were carried out at the IRIC Genomics Platform (Université de Montréal). RNA integrity was verified using an Agilent Bioanalyzer. Poly(A)? RNA was enriched from total RNA, and libraries were prepared using the KAPA RNA HyperPrep Kit with poly(A) selection according to the manufacturer's instructions. Libraries were sequenced on an Illumina NextSeq 500 to generate single-end reads over 75 sequencing cycles (maximum read length 1 × 85 nt).  Reads were trimmed with Trimmomatic v0.35 and aligned to the mouse reference genome GRCm38 (mm10) using STAR v2.7.1a with strand-specific parameters. Gene-level quantification was performed using STAR and RSEM.  Differential expression analysis was conducted in R using DESeq2, with significance determined using Benjamini–Hochberg adjusted p-values (padj < 0.05). Significant genes were annotated with gene symbols and exported for downstream analyses. Data visualization included volcano plots (ggplot2, ggrepel) and heatmaps of z-score–normalized expression for significant genes, with rows annotated by -log10(padj) and columns labeled by condition. Expression of selected genes (e.g., Trpv1, Calca, and Ngfr) was compared between groups using box plots; statistical testing used Wilcoxon rank-sum tests with Benjamini–Hochberg correction. Principal component analysis (PCA) was performed on rlog-transformed counts to assess sample clustering and overall variance, and visualized using ggplot2 and plotly. All analyses were performed in R to support reproducibility.