Shreerang A. Bhide

Concomitant chemoradiotherapy (CRT) is a standard-of-care treatment for unresectable locally advanced head and neck squamous cell carcinoma (LA-SCCHN), but its immune effects, particularly relative to radiotherapy (RT) alone, remain incompletely understood. Using syngeneic murine models, we integrated Nr4a3-Tocky reporter analysis with single-cell transcriptomics and T-cell receptor clonotyping to comprehensively profile intratumoral CD8+ T-cell responses following RT and CRT. We show that CRT uniquely drives robust clonal expansion of antigen-engaged CD8+ T-cells and biases differentiation toward progenitor-exhausted (TPEX) states, whereas RT favors terminally exhausted (TEX) states. Single-cell analyses further reveal preferential clonal dominance within TPEX populations following CRT. In peripheral blood mononuclear cells from patients treated with CRT, TEX populations predominated early after treatment, followed by delayed expansion of circulating TPEX-like populations at 3 months, suggesting phased recovery of progenitor-like T-cell states. Together, these findings demonstrate that CRT and RT induce distinct CD8+ T-cell differentiation trajectories and clonal architectures, providing insight into treatment-induced immune remodeling in LA-SCCHN.

A, Hematoxylin and eosin images and annotated BaseScope staining for point mutations in PIK3CA (Case C537, top) or KRAS (Case 539, bottom). Scale bars, 5 mm (top) and 1 mm (bottom). Regions circled in green were macrodissected for FUME-TCRseq. B, Bar chart showing the numbers of unique TCR clonotypes detected in the PIK3CA WT and mutant (E545K) subclones of Case C537. C, Bar chart showing the diversity of the TCR repertoire in PIK3CA WT and mutant subclones of Case C537. D, Plot showing the frequencies of the 10 most common clonotypes detected in the PIK3CA E545K mutant subclone and the frequencies at which they appear in the WT subclone. Arrow, TCR sequence CASSYGHPNTEAFF. E, Bar chart showing the numbers of unique TCR clonotypes detected in the KRAS WT and mutant (G12C) subclones of Case C539. F, Bar chart showing the diversity of the TCR repertoire in KRAS WT and mutant subclones of Case C539. G, Plot showing the frequencies of the 10 most common clonotypes detected in the KRAS G12C mutant subclone and the frequencies at which they appear in the WT subclone.

A, Bar plots showing the proportion of each V gene detected by FUME-TCRseq (left column) and Immunoverse (right column) in two representative FFPE colorectal cancer samples (PIK3CA WT, top row; PIK3CA E545K, bottom row). B, Bar chart showing the numbers of unique TCR clonotypes detected in the two samples with FUME-TCRseq and Immunoverse methodologies. C, Plot showing the frequencies of the 10 most common clonotypes detected in the WT sample by FUME-TCRseq and the frequencies at which they appear in the Immunoverse data. D, Plot showing the frequencies of the 10 most common clonotypes detected in E545K sample by FUME-TCRseq and the frequencies at which they appear in the Immunoverse data.

Genomic analysis of the T-cell receptor (TCR) reveals the strength, breadth, and clonal dynamics of the adaptive immune response to pathogens or cancer. The diversity of the TCR repertoire, however, means that sequencing is technically challenging, particularly for samples with low-quality, degraded nucleic acids. Here, we developed and validated FUME-TCRseq, a robust and sensitive RNA-based TCR sequencing methodology that is suitable for formalin-fixed paraffin-embedded samples and low amounts of input material. FUME-TCRseq incorporates unique molecular identifiers into each molecule of cDNA, allowing correction for sequencing errors and PCR bias. Using RNA extracted from colorectal and head and neck cancers to benchmark the accuracy and sensitivity of FUME-TCRseq against existing methods demonstrated excellent concordance between the datasets. Furthermore, FUME-TCRseq detected more clonotypes than a commercial RNA-based alternative, with shorter library preparation time and significantly lower cost. The high sensitivity and the ability to sequence RNA of poor quality and limited amount enabled quantitative analysis of small numbers of cells from archival tissue sections, which is not possible with other methods. Spatially resolved FUME-TCRseq analysis of colorectal cancers using macrodissected archival samples revealed the shifting T-cell landscapes at the transition to an invasive phenotype and between tumor subclones containing distinct driver alterations. In summary, FUME-TCRseq represents an accurate, sensitive, and low-cost tool for the characterization of T-cell repertoires, particularly in samples with low-quality RNA that have not been accessible using existing methodology.Significance:FUME-TCRseq is a TCR sequencing methodology that supports sensitive and spatially resolved detection of TCR clones in archival clinical specimens, which can facilitate longitudinal tracking of immune responses through disease course and treatment.

A, Hematoxylin and eosin images and annotated BaseScope staining for point mutations in PIK3CA (Case C537, top) or KRAS (Case 539, bottom). Scale bars, 5 mm (top) and 1 mm (bottom). Regions circled in green were macrodissected for FUME-TCRseq. B, Bar chart showing the numbers of unique TCR clonotypes detected in the PIK3CA WT and mutant (E545K) subclones of Case C537. C, Bar chart showing the diversity of the TCR repertoire in PIK3CA WT and mutant subclones of Case C537. D, Plot showing the frequencies of the 10 most common clonotypes detected in the PIK3CA E545K mutant subclone and the frequencies at which they appear in the WT subclone. Arrow, TCR sequence CASSYGHPNTEAFF. E, Bar chart showing the numbers of unique TCR clonotypes detected in the KRAS WT and mutant (G12C) subclones of Case C539. F, Bar chart showing the diversity of the TCR repertoire in KRAS WT and mutant subclones of Case C539. G, Plot showing the frequencies of the 10 most common clonotypes detected in the KRAS G12C mutant subclone and the frequencies at which they appear in the WT subclone.

A, Scatter plot showing V gene usage data from FUME-TCRseq and 5′-RACE ligation (“5′-RACE TCRseq”) methodologies, performed on RNA extracted from a representative whole blood sample. B, Bar plot showing the proportion of each V gene detected by FUME-TCRseq and 5′-RACE ligation. Red bars, highly frequent V genes; blue, less frequent V genes. C, Scatterplot showing overlapping clonotype abundances for the FUME-TCRseq and 5′RACE TCRseq data. Point size is scaled to the geometric mean of clonotype frequency in both samples and axes represent log10 clonotype frequencies in each sample. D, Plot showing the 10 most common clonotypes identified by FUME-TCRseq and the frequencies at which they were detected using 5′-RACE ligation methodology. E, Scatterplot showing overlapping clonotype abundances for technical repeats. Point size is scaled to the geometric mean of clonotype frequency in both samples and axes represent log10 clonotype frequencies in each sample. F, Plot showing the 10 most common clonotypes detected in technical repeats of FUME-TCRseq performed on RNA extracted from an inflammatory polyp.

A, Hematoxylin and eosin showing the regions of FFPE adenoma and carcinoma that were macrodissected for RNA extraction and FUME-TCRseq. Scale bar, 5 mm. B, Bar chart showing the numbers of unique TCR clonotypes detected in carcinoma and adenoma regions. C, Bar chart showing the diversity of the TCR repertoire in carcinoma and adenoma regions. D, Plot showing the frequencies of the 10 most common clonotypes detected in the adenoma region and the frequencies at which they appear in the carcinoma.

Genomic analysis of the T-cell receptor (TCR) reveals the strength, breadth, and clonal dynamics of the adaptive immune response to pathogens or cancer. The diversity of the TCR repertoire, however, means that sequencing is technically challenging, particularly for samples with low-quality, degraded nucleic acids. Here, we developed and validated FUME-TCRseq, a robust and sensitive RNA-based TCR sequencing methodology that is suitable for formalin-fixed paraffin-embedded samples and low amounts of input material. FUME-TCRseq incorporates unique molecular identifiers into each molecule of cDNA, allowing correction for sequencing errors and PCR bias. Using RNA extracted from colorectal and head and neck cancers to benchmark the accuracy and sensitivity of FUME-TCRseq against existing methods demonstrated excellent concordance between the datasets. Furthermore, FUME-TCRseq detected more clonotypes than a commercial RNA-based alternative, with shorter library preparation time and significantly lower cost. The high sensitivity and the ability to sequence RNA of poor quality and limited amount enabled quantitative analysis of small numbers of cells from archival tissue sections, which is not possible with other methods. Spatially resolved FUME-TCRseq analysis of colorectal cancers using macrodissected archival samples revealed the shifting T-cell landscapes at the transition to an invasive phenotype and between tumor subclones containing distinct driver alterations. In summary, FUME-TCRseq represents an accurate, sensitive, and low-cost tool for the characterization of T-cell repertoires, particularly in samples with low-quality RNA that have not been accessible using existing methodology.Significance:FUME-TCRseq is a TCR sequencing methodology that supports sensitive and spatially resolved detection of TCR clones in archival clinical specimens, which can facilitate longitudinal tracking of immune responses through disease course and treatment.

A, Bar plots showing the proportion of each V gene detected by FUME-TCRseq (right column) and immunoSEQ (left column) in three representative FFPE head and neck cancer samples (HN1, HN2, HN3). B, Bar chart showing the numbers of unique TCR clonotypes detected in the three samples with FUME-TCRseq and immunoSEQ methodologies. C, Heat map showing the Morisita overlap between each sample. D, Plots showing the frequencies of the 10 most common clonotypes detected in each sample by immunoSEQ and the frequencies at which they appear in the FUME-TCRseq data.

Supplementary Tables 1 and 2, and Supplementary Figure 1