Better pancreatic adenocarcinoma outcomes linked to anti-EBV TCR CDR3 detection via tumor RNAseq files.

10042 Background: Neuroblastoma has variable outcomes across different risk groups. In children with stage 4 neuroblastoma, five-year overall remains around 50% in high-risk children despite the emergence of anti-GD2 antibodies. T- and NK-cell infiltration is prognostic in therapy-resistant neuroblastoma, and higher HLA class I expression is linked to better overall survival (OS). In other cancers, specific HLA alleles and T-cell receptor (TCR) V, J- gene segments have been associated with survival. Thus, we conducted a retrospective study in stage 4S neuroblastoma patients to assess whether specific HLA allele, TCR V- and J-gene segment usage combinations correlated with OS in NBL. Among combinations that were associated with OS, we also identified changes in expression of immune marker genes. Methods: We obtained HLA allele data from exome files of the TARGET-NBL dataset using the xHLA software. The TCR recombination reads were obtained from the TARGET-NBL RNAseq files representing tumor specimens from 99 cases, utilizing a high-stringency search algorithm. The TCR recombination reads were translated, and the complementarity determining region-3 (CDR3) amino acid sequences were obtained. HLA and TCR datasets were integrated to assess OS probabilities, comparing cases with and without specific HLA allele, TCR V- or J-gene usage combinations. Significance was determined only if independent HLA allele or V- and J-gene usage assessments were not statistically significant, but significant in the corresponding HLA allele, TCR V- or J-gene segment usage combinations. HLA allele and TCR usage combinations were grouped by association with better or worse OS probabilities, and immune marker gene expression correlations were assessed via Student’s t-test and Mann-Whitney U test with a Bonferroni-corrected threshold of p = 0.00114. Results: We identified 73 HLA allele, TCR V- and J-gene usage combinations with significant OS distinctions: 20 associated with improved OS and 53 with worse OS. For example, 20 TARGET-NBL cases with the HLA-DQB1*04:02 and TRAJ29 usage combination did not reach the median compared to the 1319-day OS median for all remaining cases (log-rank p = 0.009). Among the cases with at least one HLA allele, TCR V- or J-gene segment usage combination with improved OS, we found that the RNAseq values for the immune markers CD4, CD22, CD38, RPH1 , TNFRSF17 , and TNFRSF13B were upregulated, as assessed via a Mann-Whitney U analysis. Conclusions: Identifying specific HLA allele, TCR V- and J- gene segment usage combinations associated with survival may further indicate patients who could benefit from immunologic-boosting treatments. Studies employing functional assays, immunogenomic profiling, and targeted immune pathway analyses may advance immunotherapeutic strategies and predictive biomarkers for neuroblastoma, particularly in high-risk patients.

691 Background: Pancreatic cancer (PC) is anticipated to become the 2nd leading cause of cancer-related deaths by 2030. Pancreatic adenosquamous carcinoma (PASC) is a rare subtype of PC with histology including squamous adenomatous features (≥30% squamous histology), while pancreatic squamous cell cancer (PSCC) consists purely of squamous cells. We evaluated genomic, transcriptomic and prognostic differences between PASC, PSCC and pancreatic ductal adenocarcinoma (PDAC). Methods: 8951 PC tumors (83 PASC, 22 PSCC & 8846 PDAC) tested at Caris Life Sciences with WTS (Illumina, Novaseq) and NextGen DNA sequencing (NextSeq, 592 genes and NovaSeq, WES) and IHC were analyzed. Immune cell fraction was calculated by QuantiSeq. Overall survival (OS) analysis (time of tissue collection to last contact) was obtained from insurance claims and calculated with KM method. Statistical significance was determined using Chi-square/Fisher-Exact and adjusted for multiple comparisons (q<0.05). Results: No age or sex differences were seen between PDAC (median 68yo) and PASC (median 68.5yo), however PSCC was more prevalent in younger pts (median 62.5yo, q<0.05). Mutation rates of CDKN1B (3% vs 0.2%), SF3B1 (8 vs 2%), PTEN (5 vs 0.8%), BCL9 (4 vs 0.4%), AXIN1 (4 vs 0.2%) and CASP8 (3 vs 0.2%) and amplifications of AKT2 (5 vs 2%), ZNF384 (1 VS 0.1%), HMGA2 (7 vs 0.7%) and ROS1 fusions (1 vs 0.04%) all trended higher in PASC than PDAC (p<0.05). Several YAP signaling and squamous differentiation genes were significantly differentially expressed among histologies (table). PD-L1 IHC was higher in PASC vs PDAC (37 vs 14%, q<0.05); with a trend towards higher MSI-H (6 vs 1%) & TMB-H (6 vs 2%) in PASC vs PDAC (p<0.05). Higher infiltration of CD4+ T cells into the TME was seen in PASC vs PDAC (non-zero %, 73% vs 23%, q<0.05). Several immune genes ( CD274, IDO1, LAG3, & CTLA4) had higher expression in PASC vs PDAC (fold change 1.3-3.1, q<0.05). The MAP kinase pathway activation score (MPAS) was lower in PASC vs PDAC (median score -0.48 vs 0.25), while the IFN gamma signature was higher in PASC vs PDAC (median -0.24 vs -0.33) [both q<0.05]. There was increased OS for PASC compared to PSCC (12.6 vs 4.7 mo, HR=0.43, CI=0.25-0.75, p=0.002); no significant difference in OS was observed between PASC and PDAC. Conclusions: This is the largest molecular profiling analysis of PASC, which is characterized by unique genomic alterations, and is associated with higher PD-L1 expression, immune related gene expression, CD4+ T cell infiltration and IFN gamma signature, and lower MAPK activation. PASC is associated with better OS compared to PSCC. These findings may provide subtype-specific therapeutic opportunities for PASC and PSCC pts.[Table: see text]

Decision letter: TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract T-cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages experimentally inferred antigen-associated TCRs to form meta-clonotypes – groups of biochemically similar TCRs – that can be used to robustly quantify functionally similar TCRs in bulk repertoires across individuals. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the SARS-CoV-2 antigen-associated TCRs that have strong evidence of restriction to patients with a specific human leukocyte antigen (HLA) genotype. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele (false discovery rate [FDR]<0.01), demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package, tcrdist3, that implements this framework and facilitates flexible workflows for distance-based TCR repertoire analysis. Editor's evaluation This paper introduces and validates a novel concept which will be of great interest to all those interested in T cell immunity and especially the T cell receptor repertoire. The concept builds on the idea that TCRs to the same antigen often share sequence similarities, which they quantify using a bespoke tool tcrdist3. Using this tool they develop the idea of a meta-clone, a set of TCRs sharing biochemical similarities and potentially recognising the same antigen. In this paper they further show that such clonotypes may show increased sharing between HLA-related individuals, and explore the use of such clonotypes in characterising antigen-specific immune response across cohorts of individuals. https://doi.org/10.7554/eLife.68605.sa0 Decision letter Reviews on Sciety eLife's review process Introduction An individual's unique repertoire of T-cell receptors (TCRs) is shaped by antigen exposure and is a critical component of immunological memory (Emerson et al., 2017; Welsh and Selin, 2002). With the advancement of immune repertoire profiling, TCR repertoires are a largely untapped source of biomarkers that could potentially be used to predict immune responses to a wide range of exposures including viral infections (Wolf et al., 2018), tumor neoantigens (Ahmadzadeh et al., 2019; Chiou et al., 2021; Kato et al., 2018), or environmental allergens (Cao et al., 2020). However, the extreme diversity characterizing TCR repertoires, both within and between individuals, presents major hurdles to biomarker development. Using peptide—major histocompatibility complex (pMHC) tetramer sorting to focus on TCRs recognizing individual epitopes, which depends on knowing the peptide antigen and its MHC restriction, typically reveals that many distinct TCRs are able to recognize even a single pMHC (Coles et al., 2020; Meysman et al., 2019). This complicates detection of population-wide signatures of antigen exposure. Modeling (Elhanati et al., 2018) and empirical evidence (Soto et al., 2019) suggest that only 10–15% of single-chain TCRs are public or shared by multiple individuals. Furthermore, only a fraction of the repertoire can be sampled, making it difficult to reproducibly detect relevant TCR clonotypes from an individual, let alone reliably detect public clonotypes in a population; in practice, the problem can be exacerbated by heterogeneous repertoire sequencing depth, which affects the precision with which the frequency of rare TCRs can be estimated. Thus, individual T-cell clonotypes are currently suboptimal and underpowered for population-level investigations of TCR specificity, which limits their application in the development of TCR-based clinical biomarkers. In this study, we describe a framework for engineering 'meta-clonotypes': groupings of TCRs sharing biochemically similar complementarity determining regions (CDRs), which enable population-level biomarker development (Figure 1). Previously, we introduced TCRdist, a biochemically informed distance metric that enabled grouping of paired αβ TCRs by antigen specificity based on their sequence similarity (Dash et al., 2017). TCRdist is correlated with edit distance, but it can vary considerably among TCRs with identical edit distances (Figure 2). While other tools exist to identify statistically anomalous groups of TCRs within a single sample that may be indicative of a polyclonal response to antigenic selection (Glanville et al., 2017; Huang et al., 2020; Pogorelyy et al., 2019; Pogorelyy and Shugay, 2019; Ritvo et al., 2018; Shugay et al., 2015), the meta-clonotype framework has been developed for a different task: leveraging receptor–antigen associations determined from in vitro experiments to create public, antigen-associated meta-clonotypes from otherwise private TCRs. This application is made possible by a new open-source Python3 software package tcrdist3 that brings flexibility to distance-based repertoire analysis, allowing customization of the distance metric, and at-scale computation with sparse data representations and parallelized, byte-compiled code. Figure 1 Download asset Open asset T-cell receptor (TCR) meta-clonotypes. (A) Defining meta-clonotypes from antigen-associated TCRs. Sets of antigen-associated TCRs were used together with synthetic background repertoires to engineer TCR meta-clonotypes that define biochemically similar TCRs based on a centroid TCR and a TCRdist radius. For each antigen-specific clonotype, we used tcrdist3 to evaluate the proportion of TCRs spanned at different TCRdist radii within (i) its antigen-associated TCR set (black) and (ii) a synthetic control V- and J-gene-matched background set (purple). A synthetic background was generated using 100,000 Optimized Likelihood estimate of Immunoglobulin Amino acid sequences (OLGA)-generated TCRs and 100,000 TCRs subsampled from umbilical cord blood; OLGA-generated TCRs were sampled to match the V–J gene frequency in each MIRA receptor set, with weighting to account for the sampling bias (see Methods for details). The objective was to select the largest radius that includes no more than an estimated proportion of 1E−6 TCRs in the background. The subset of antigen-associated TCRs spanned by the selected radius were then used to develop an additional meta-clonotype motif constraint based on conserved residues in the complementarity determining region (CDR)3 (see Methods for details). An example logo plot shows the CDR3 β-chain motif formed from TCRs – activated by a SARS-CoV-2 peptide (MIRA55 ORF1ab amino acids 1316:1330, ALRKVPTDNYITTY) – within a TCRdist radius 16 of this meta-clonotype's centroid TCR. (B) Quantifying meta-clonotype conformant TCRs in bulk repertoires. The definition of each TCR meta-clonotype can be used to quantify the frequency of similar TCRs in bulk repertoires. EXACT sequences match the meta-clonotype centroid at the amino acid level, RADIUS-conformant sequences diverge from the centroid by no more than the radius distance, and RADIUS + MOTIF conformant sequences is the subset of radius-conformant TCRs with a CDR3 sequences matching the meta-clonotype's CDR3 motif. (C) Population-level analysis of TCR meta-clonotype frequency. The frequency of meta-clonotype conformant sequences in multiple bulk repertoires allows comparison across a population. In this study, to test whether meta-clonotypes carry important antigen-specific signals above and beyond individual clonotypes, we searched for meta-clonotype conformant TCRs in COVID-19 patients with repertoires collected 0–30 days after diagnosis. We found stronger associations with predicted HLA restrictions based on counts of meta-clonotype conforming TCRs compared to associations using counts of exact clonotypes. Figure 2 Download asset Open asset TCRdist compared to edit distance. (A) Correspondence between edit distance (x-axis) and TCRdist (y-axis) for MIRA55 T-cell receptors (TCRs) with matching TRBV genes. The grayscale colormap shows the percentage of TCRs with a given TCRdist score within each edit distance category. (B) Examples of complementarity determining region (CDR)3s with TCRdist varying between 6 and 24 units among sequences with edit distance 2 (2 substitutions) from a centroid with matching TRBV genes. TCR distances range based on differential penalties assigned to specific residue substitutions. The framework is based on TCR sequences that have been experimentally enriched for antigen recognition, most commonly by sorting T cells labeled by peptide–MHC multimers or by activation-induced markers upon stimulation (we refer to these as 'antigen-associated' TCRs). Each meta-clonotype is defined by an antigen-associated centroid TCR and a TCRdist radius chosen so that the expected frequency of antigen-naive receptors within the radius is low. A CDR3 'motif' is constructed from the subset of antigen-associated TCRs within the radius to further refine the meta-clonotype's specificity. Together the centroid receptor, radius, and the CDR3 motif can be used to search for conformant TCRs in large bulk-sequenced repertoires and quantify their frequency (Figure 1). As intended, we find that TCR centroids, which are often private, gain publicity as meta-clonotypes. The expanded publicity of meta-clonotypes provides an opportunity to develop population-level biomarkers that may depend on antigen-specific features of the TCR repertoire. Shifting the focus of repertoire analysis from clonotypes to meta-clonotypes increases statistical power; grouping similar clonotypes reduces the sparsity of finite repertoire samples and increases the precision with which antigen-specific cell abundance can be estimated. To demonstrate one potential application of meta-clonotypes and to characterize their ability to estimate the frequency of similar antigen-specific T cells in bulk-sequenced TCR repertoires, we apply the meta-clonotype framework to a large publicly available dataset of SARS-CoV-2 antigen-associated TCRs. The dataset comes from a recent study that sought to elucidate the role of cellular immune responses in acute SARS-CoV-2 infection and examined the TCR repertoires of patients diagnosed with COVID-19 disease. Researchers used an assay based on antigen stimulation and flow cytometric sorting of activated CD8+ T cells to identify SARS-CoV-2 peptide-associated TCR β-chains; the assay is called 'multiplex identification of TCR antigen specificity' or MIRA (Klinger et al., 2015) and the output is a set of predicted antigen-associated TCR sequences. Data from these experiments were released publicly in July 2020 by Adaptive Biotechnologies and Microsoft as part of 'immuneRACE' and their efforts to stimulate science on COVID-19 (Nolan et al., 2020; Snyder et al., 2020). The MIRA antigen stimulation assays identified 253 sets of 6 or more TCR β-chains associated with CD8+ T cells activated by exposure to SARS-CoV-2 peptides, with TCR sets analyzed ranging in size from 6 to 16,607 TCRs (Supplementary file 1b); we refer to these sets as MIRA0 through MIRA252 in rank order by their size. The deposited immuneRACE datasets also included bulk TCR β-chain repertoires from 694 patients within 0–30 days of COVID-19 diagnosis. Our analysis of these data demonstrates how it is possible to define public meta-clonotypes from sets of private antigen-associated TCRs and directly evaluates their ability to carry population-level antigen-specific signals in comparison with individual clonotypes. Results Experimental enrichment of antigen-specific T cells allows discovery of TCRs with biochemically similar neighbors Searching for identical TCRs within a repertoire – arising either from clonal expansion or convergent nucleotide encoding of amino acids in the CDR3 – is a common strategy for identifying functionally important receptors. However, in the absence of experimental enrichment procedures, observing T cells with the same amino acid TCR sequence in a bulk sample is rare. For example, in 10,000 β-chain TCRs from an umbilical cord blood sample, less than 1 % of TCR amino acid sequences were observed more than once, inclusive of possible clonal expansions (Figure 3A). By contrast, a valuable feature of antigen-associated TCRs is the presence of multiple receptors with identical or highly similar amino acid sequences (Figure 3A). For instance, 45% of amino acid TCR sequences were observed more than once (excluding clonal expansions) in a set of influenza M1(GILGFVFTL)-A*02:01 peptide–MHC tetramer-sorted subrepertoires from 15 subjects (Dash et al., 2017). Enrichment was evident compared to cord blood for additional peptide–MHC tetramer-sorted subrepertoires obtained from VDJdb (Shugay et al., 2018), though the proportion of TCRs with an identical or similar TCR in each set was heterogeneous. Figure 3 Download asset Open asset Experimental enrichment of antigen-associated T-cell receptors (TCRs) increases neighbor density. (A) TCR repertoire subsets obtained by single-cell sorting with peptide–major histocompatibility complex (MHC) tetramers (green), MIRA peptide stimulation enrichment (MIRA55, MIRA48; purple), or random subsampling of umbilical cord blood (1000 or 10,000 TCRs; blue). Biochemical distances were computed among all pairs of TCRs in each subset using the TCRdist metric. Neighborhoods were formed around each TCR using a variable radius (x-axis) and the percent of TCRs in the set with at least one other TCR within its neighborhood was computed; notably the line represents a summary of TCRs in each set and is therefore more precise for larger TCR sets. A radius of zero indicates the proportion of TCRs that have at least one TCR with an identical amino acid sequence (solid square). Dash BMLF (Epstein–Barr Virus), M1 (Influenza), and pp65 (Cytomegalovirus) refer to epitopes from Dash et al., 2017. ELAGIGILTV (Human Mart-1 antigen) and LLLGIFILV (HM1.24 antigen in multiple myeloma) downloaded from VDJdb (Shugay et al., 2018), which were submitted by Andrew Sewell et al. (B) Analysis of MIRA sets for which the participants contributing the TCRs were significantly enriched with a specific class I HLA allele Supplementary file 1c. Colors are assigned based on the vertical ranking of the lines along the right y-axis and match the order in the color legend. We investigated the degree to which the MIRA assay employed by Nolan et al., 2020 identified TCRs with identical or similar amino acid sequences. In general, across sets of MIRA-identified β-chain TCRs, each associated with a different antigen, the proportion of amino acid sequences observed more than once was generally lower than in the tetramer-enriched repertoires and varied considerably across the sets; some MIRA sets resembled tetramer-sorted subrepertoires (Figure 3B; see MIRA133), while others were more similar to unenriched repertoires (Figure 3B; see MIRA90). The increased diversity in MIRA-enriched TCR sets versus tetramer-enriched TCR sets may, in part, be explained by: (1) peptides being presented by the full complement of the native host's MHC molecules compared to a single defined peptide–MHC complex, (2) recruitment of lower affinity receptors, (3) antigen specificity conferred primarily by the alpha rather than the sequenced beta chain, or (4) nonspecific 'bystander' activation in the MIRA stimulation assay. From an experimental standpoint, MIRA offers the benefit of being able to identify TCRs associated with an antigen before a specific pMHC has been identified; however, the resultant diversity in antigen-associated TCRs recovered by MIRA poses a challenge for identifying relevant TCR motifs associated with multiple possible TCR:pMHC interactions. TCR biochemical neighborhood density is heterogeneous among set of antigen-associated TCRs We next investigated the proportion of unique TCRs with at least one biochemically similar neighbor among TCRs with the same putative antigen specificity. We and others have shown that a single peptide–MHC epitope is often recognized by many distinct TCRs with closely related amino acid sequences Dash et al., 2017; in fact, the detection of such clusters in bulk-sequenced repertoires is the basis of several existing tools: GLIPH (Glanville et al., 2017; Huang et al., 2020), ALICE (Pogorelyy et al., 2019), TCRNET (Ritvo et al., 2018), and RepAn (Yohannes et al., 2021). Therefore, to better understand sets of antigen-associated TCRs, like the SARS-CoV-2 MIRA data, we evaluated the neighborhood surrounding each TCR, defined as the set of similar TCRs whose sequence divergence is within a specified radius. The radius was measured using TCRdist, a position weighted, multi-CDR distance metric. Briefly, differences in the amino acid sequences of the CDRs are totaled based on the number of gaps (−4) and their BLOSUM62 substitution penalties (ranging from 0 to −4) with a default threefold weighting on CDR3 substitutions (see Methods for details of tcrdist3 reimplementation of TCRdist); a one amino acid mismatch in the CDR3 results in a maximal distance of 12 TCRdist units (tdus). As the radius about a TCR centroid expands, the number of TCRs it encompasses naturally increases. The increase was greater among the sets of antigen-associated TCRs compared to the 'background' repertoires that were not experimentally enriched for antigen-specific T cells (Figure 3). To better understand the relationship between the TCR distance radius and the density of proximal TCRs, we constructed empirical cumulative distribution functions (ECDFs) for each unique TCR (Figure 4). The ECDF for each unique TCR (each represented by one line in Figure 4) shows the proportion of all TCRs within the indicated radius; those with sparse neighborhoods appear as lines that remain low and do not increase along the y-axis even as the search radius expands (lines are hidden by the x-axis). The proportion of these TCRs with sparse or empty neighborhoods (ECDF proportion <0.001) is indicated by the height of the gray area plotted below the ECDF (Figure 4). We observed the highest density neighborhoods within repertoires that were sorted based on binding to a single peptide–MHC tetramer. For instance, with the influenza M1(GILGFVFTL)-A*02:01 tetramer-enriched repertoire from 15 subjects, we observed that many TCRs were concentrated in dense neighborhoods, which included as much as 30 % of the other influenza M1-recognizing TCRs within a radius of 12 tdus (Figure 4A). Notably there were also many TCRs with empty or sparse neighborhoods using a radius of 12 tdus (111/247, 44%) or 24 tdus (83/247, 34%). Based on previous work (Dash et al., 2017), we assume that the majority of these tetramer-sorted CD8+ T cells with few proximal neighbors do indeed bind the influenza M1:A*02:01 tetramer. This suggests that TCRs within sparse neighborhoods represent uncommon modes of antigen recognition and highlights the broad heterogeneity of neighborhood densities even among TCRs recognizing a single peptide–MHC. Figure 4 Download asset Open asset T-cell receptor (TCR) neighborhoods have higher density among TCRs that have been experimentally enriched for antigen-specific T cells compare to unenriched repertoires. TCR β-chains from (A) a peptide–major histocompatibility complex (MHC) tetramer-enriched subrepertoire (n = 247), (B) a MIRA peptide stimulation-enriched subrepertoire (n = 497), or (C) an umbilical cord blood unenriched repertoire (n = 9966), and (D) synthetically generated sequences using Optimized Likelihood estimate of Immunoglobulin Amino acid sequences (OLGA; n = 10,000; Sethna et al., 2019). Within each subrepertoire, an empirical cumulative distribution was estimated for each TCR as the centroid of a neighborhood a range of distance radii Each ECDF shows the proportion of TCRs within the set with a distance to the centroid less than the indicated radius. ECDF color to the of the complementarity determining region ECDF were by along the to ECDF lines at no similar TCRs at or below that radius. of TCRs with an ECDF proportion indicates the percentage of TCRs or with few biochemically similar neighbors at the given radius. densities for individual TCRs within the MIRA sets were highly heterogeneous. for an MIRA set are shown in Figure peptide Within this antigen-associated at 24 tdus % of TCR neighborhoods included of the other CD8+ TCRs (Figure As TCR neighborhoods in the umbilical cord blood repertoire were (Figure the neighborhood included only % of the repertoire at 24 We also that TCRs with sparse or empty neighborhoods to have CDR3 (Figure and lower of Figure This is with that shows that TCRs with CDR3 have a lower of the TCR et al., 2018; et al., Sethna et al., 2019). strong selection for antigen recognition, TCRs with lower are to have less dense biochemical these suggest that biochemical neighborhood density is highly heterogeneous among TCRs and that it may depend on of antigen recognition as as receptor and 2019). Figure Download asset Open asset neighborhood densities within an antigen-associated and a synthetic background repertoire. (A) Each T-cell receptor (TCR) n = in the MIRA55 antigen-associated set as the centroid of a neighborhood and an empirical cumulative distribution is estimated a range of distance radii Each ECDF shows the proportion of TCRs within the MIRA set a distance to the centroid less than the indicated radius. The ECDF line color to the TCR of estimated using Optimized Likelihood estimate of Immunoglobulin Amino acid sequences (OLGA; Sethna et al., 2019). The ECDF are by along the to The shows the percentage of TCRs with an ECDF proportion (B) ECDF for each MIRA55 TCR based on the proportion of TCRs in a synthetic background repertoire that are within the indicated radius A synthetic background was generated using 100,000 OLGA-generated TCRs and 100,000 TCRs subsampled from umbilical cord blood; OLGA-generated TCRs were sampled to match the V–J gene frequency in the MIRA receptor set, with weighting to account for the sampling bias (see Methods for details). (C) ECDF (y-axis) of one example neighborhood plotted ECDF within the synthetic background TCR neighborhood is the same indicated by the line in (A) and The gray line indicates neighborhoods that are dense with TCRs from the antigen-associated and background the meta-clonotype radius for each data in TCRdist radius can be to and specificity The utility of a TCR-based biomarker depends on the antigen specificity of the TCRs. Therefore, a of distance-based is the presence of similar TCR sequences that the ability to recognize the antigen. To be a meta-clonotype definition be broad to multiple biochemically similar TCRs with shared antigen recognition, but not broad as to a proportion of nonspecific TCRs. we of a meta-clonotype definition as a to and specificity, the ability to TCRs and nonspecific TCRs. the density of neighborhoods around TCRs are we that the radius a meta-clonotype may for each TCR. To find the radius we the density of a neighborhood within a set of antigen-associated TCRs (Figure to the density of the neighborhood within a background TCR repertoire (Figure the background repertoire can be set of TCRs from antigen-naive repertoires. this background antigen-specific TCRs, we can use a repertoire as a background the frequency of antigen-specific T cells in a large background from antigen-naive is to be low. a background is a relevant it provides an estimate of the number of we each meta-clonotype is used to search for and quantify antigen-specific sequences in bulk repertoires. An radius define a meta-clonotype with a density of conformant sequences within a set of antigen-associated TCRs and a low density among a set of background TCRs. We demonstrate an for an radius for each TCR in the which includes TCRs from 15 COVID-19 diagnosed subjects (see Methods for details about MIRA and the immuneRACE an ECDF is constructed for each centroid TCR the relationship between the meta-clonotype radius and its its of similar TCRs, by the proportion of TCRs in the antigen-associated MIRA set that are within the radius (Figure an ECDF is constructed for each TCR the relationship between the meta-clonotype radius and its its of TCRs with antigen recognition, by the proportion of TCRs in a background repertoire within the radius (Figure The objective is to select the largest radius that includes no more than one in background TCRs; while this is it that in a sampled repertoire we to only a few TCRs within the radius and that from this may antigenic to estimate the frequency of a rare one to many such and use the this a background set of many of TCRs that be used to evaluate potential radius for each TCR a We based on residues much of the TCR background is from a single TCR to be relevant and therefore that could be by on TCRs that share the same TRBV and genes. Therefore, for each set of antigen-associated TCRs identified using we a part background. part of 100,000 synthetic TCRs whose and those of the antigen-associated TCRs; TCRs were generated using the software with to V–J gene sequence et al., 2018; Sethna et al., 2019). The other part of 100,000 umbilical cord blood TCRs sampled from subjects et al., This sampling of sequences the meta-clonotype with broad sampling of TCRs from an antigen-naive repertoire. The dense sampling of TCRs with similar V–J to the antigen-associated TCRs for of the frequency of meta-clonotype neighbors in the background below 1 in this is we the TCRs that were more to be within the meta-clonotype radius, therefore the statistical and precision with which we could estimate the frequency of meta-clonotype neighbors in the background. This idea is an of methods that are commonly used to results the sampling has is to demonstrate the concept with an we background TCRs from one V–J gene we find that TCRs are within a radius, but that they are sampled from a V–J gene with the estimated frequency in the full background be 1 in 1 = all V–J gene defined in the synthetic the frequency can be estimated as a with V–J gene from the full background as the is the number of TCRs within the radius of the centroid in the V–J gene defined and is the number of

Decision letter: Neutrophil-mediated fibroblast-tumor cell il-6/stat-3 signaling underlies the association between neutrophil-to-lymphocyte ratio dynamics and chemotherapy response in localized pancreatic cancer: A hybrid clinical-preclinical study

Simple SummaryThe chemical complementarity of glioblastoma, tumor-resident T-cell receptors and cancer testis antigens were associated with a worse outcome. Additionally, the high expression of immune marker and low expression of apoptosis genes were associated with a high T-cell receptor–cancer testis antigen chemical complementarity and a worse outcome. In sum, T-cell receptor recombination reads from exome files have the potential to aid in glioblastoma prognoses and may provide opportunities to detect unproductive immune responses. Introduction. Glioblastoma (GBM) is the most aggressive primary brain tumor in adults. Despite a growing understanding of glioblastoma pathology, the prognosis remains poor. Methods. In this study, we used a previously extensively benchmarked algorithm to retrieve immune receptor (IR) recombination reads from GBM exome files available from the cancer genome atlas. The T-cell receptor complementarity determining region-3 (CDR3) amino acid sequences that represent the IR recombination reads were assessed and used for the generation of chemical complementarity scores (CSs) that represent potential binding interactions with cancer testis antigens (CTAs), which is an approach particularly suited to a big data setting. Results. The electrostatic CSs representing the TRA and TRB CDR3s and the CTAs, SPAG9, GAGE12E, and GAGE12F, indicated that an increased electrostatic CS was associated with worse disease-free survival (DFS). We also assessed the RNA expression of immune marker genes, which indicated that a high-level expression of SPHK2 and CIITA genes also correlated with high CSs and worse DFS. Furthermore, apoptosis-related gene expression was revealed to be lower when the TCR CDR3-CTA electrostatic CSs were high. Conclusion. Adaptive IR recombination reads from exome files have the potential to aid in GBM prognoses and may provide opportunities to detect unproductive immune responses.

EBV-Negative Post-Transplant Lymphoproliferative Disorder (PTLD): A Retrospective Case-Control Study of Clinical and Pathological Characteristics, Response to Treatment and Survival

Krüppel-Like Factor 10 Expression as a Prognostic Indicator for Pancreatic Adenocarcinoma

Purpose: Pancreatic ductal adenocarcinoma (PDAC) remains the fourth leading cause of cancer-related deaths in the United States, with an overall 5-year survival rates of ~8%. The currently used clinicopathological factors (e.g. tumor size and grade, lymph node etc.) for determining patient prognosis are suboptimal. The only FDA-approved molecular non-invasive prognostic biomarker for PDAC patients is CA-19-9, which suffers from inadequate sensitivity and specificity. Nonetheless, the clinical use of CA-19-9, along with the growing evidence that altered glycosylation plays an important role in PDAC pathogenesis, provided us with a rationale to explore the clinical significance of glycan-related genes as potential prognostic biomarkers in PDAC. Experimental design: We performed a comprehensive biomarker discovery (TCGA cohort, n=182), and validation (ICGC, n=80; GSE62452, n=65) to identify a glycan gene signature for predicting overall survival (OS) in PDAC patients. Subsequently, this gene signature was validated in two, independent, clinical cohorts (test cohort, n=103; validation cohort, n=227). The performance of this signature was further evaluated by univariate and multivariate CoxPH analyses. Lastly, using a logistic-regression model, we explored the feasibility of our glycan signature in identifying various molecular subtypes of PDAC. Results: A comprehensive analysis of 411 glycan genes using Cox-LASSO regression modelling led to the identification of a 12-glycan gene signature, which robustly predicted overall survival (OS) of PDAC patients in the discovery (AUC=0.78), and two validation cohorts (ICGC, AUC=0.72; and GSE62452, AUC=0.70). Subsequent qRT-PCR validation in two in-house clinical cohorts revealed that a 9-gene signature was a robust predictor of OS (Test Cohort, HR: 1.81, 95% CI, 1.22-2.69, p=0.003; Validation Cohort, HR: 2.72, 95% CI, 2.00-3.69, p&amp;lt;0.0001). In univariate analysis, in addition to the 9-gene signature, the nodal status and CA-19-9 levels were significant predictors; while in the multivariate analyses, the gene signature emerged as an independent predictor of OS. A risk-assessment model including the 9-gene signature and the two clinical factors further improved the OS prediction (Test Cohort, HR: 2.71, 95% CI, 1.85-3.99, p&amp;lt;0.0001; Validation Cohort, HR: 2.72, 95% CI, 2.03-3.63, p&amp;lt;0.0001). Intriguingly, our signature was also highly accurate in identifying PDAC subtype with poor survival (i.e. squamous subtype) in the TCGA (AUC=0.87, P&amp;lt;0.0001) and the ICGC cohorts (AUC=0.89, P&amp;lt;0.0001). Conclusion: Our systematic biomarker discovery and validation efforts led to the identification and establishment of a 9-gene glycan signature that robustly predicts survival in PDAC patients, and can accurately identify poor PDAC subtypes - highlighting its potential clinical significance for the personalized management of PDAC patients. Citation Format: Priyanka Sharma, Raju Kandimalla, Jasjit K. Banwait, Masayuki Sho, Yasuhiro Kodera, Ajay Goel. A glycan gene signature that robustly predicts prognosis in patients with pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4883.

Immunogenomics approaches to the characterization of renal cell carcinoma (RCC) have helped to better our understanding of the features of RCC immune dysfunction. However, much is still unknown with regard to specific immune interactions and their impact in the tumor microenvironment. This study applied chemical complementarity scoring for the TRB complementarity determining region-3 (CDR3) amino acid sequences and cancer testis antigens (CTAs) to determine whether such complementarity correlated with survival and the expression of immune marker genes. TRB recombination reads from RCC tumor samples from RNAseq files obtained from two separate databases, Moffitt Cancer Center and The Cancer Genome Atlas (TCGA), were evaluated. Chemical complementarity scores (CSs) were calculated for TRB CDR3-CTA pairs and survival assessments based on those CSs were performed. Moffitt Cancer Center and TCGA cases representing the upper 50th percentile of chemical CSs for TRB CDR3 amino acid sequences and the CTA POTEA were found to be associated with a better overall survival (OS) Also, greater tumor RNA expression of multiple immune signature genes, including granzyme A, granzyme B, and interferon-gamma were correlated with the higher chemical CSs. These results indicate that TRB CDR3-CTA chemical complementarity scoring may be useful in distinguishing RCC cases with a productive, anti-tumor immune response from cases where basic immune parameter assessments are inconsistent with a productive immune response.

This study aimed to evaluate Keratin 19 (KRT19) as a potential prognostic biomarker for the diagnosis and prognosis of pancreatic adenocarcinoma (PAAD). Using data from The Cancer Genome Atlas and the Gene Expression Omnibus, we analyzed KRT19 expression in PAAD tissues and identified differentially expressed genes associated with KRT19. Gene ontology (GO) and gene set enrichment analysis were performed to explore the underlying mechanisms of KRT19 in PAAD progression. Spearman correlation analysis was used to assess the relationships between KRT19 expression and immune cell infiltration, immune checkpoint genes, and TP53 expression. Logistic regression was employed to examine the association between KRT19 expression and clinicopathological features. Kaplan–Meier survival curves, receiver operating characteristic curves, a nomogram model, and Cox regression analyses were used to evaluate the diagnostic and prognostic value of KRT19. KRT19 expression was significantly higher in tumor tissues than in adjacent non-tumor tissues and was closely associated with immune cell infiltration, HAVCR2 expression, and TP53 status. KRT19 levels correlated with T stage, overall survival (OS), disease-specific survival, and histologic grade. Cox regression and receiver operating characteristic analysis further indicated that KRT19 expression is an independent risk factor for OS, disease-specific survival, and progression-free interval in PAAD patients and can effectively distinguish tumor from normal tissue. In conclusion, the findings suggest that KRT19 could serve as a potential biomarker for the diagnosis and prognosis of PAAD, and it may be implicated in the regulation of the immune microenvironment.

Neoadjuvant chemotherapy improves outcomes in resectable pancreatic adenocarcinoma: an updated national cancer database analysis

Introduction: While the role of anti-cytomegalovirus (CMV) T-cell responses in the cancer setting is becoming increasingly recognized, the association between CMV and survival outcomes has not been subject to study through more recent immunogenomic approaches. This study aimed to investigate the association between anti-CMV T-cell receptor (TCR) sequences and improved survival outcomes in two types of cancers where the role of CMV remains currently underexplored, stomach adenocarcinoma (STAD) and soft tissue sarcoma (SARC). The significance of this study is that, to our knowledge, it is the first to identify a potential prognostic role of CMV in both STAD and SARC and highlights the potential for future development of anti-CMV T-cell therapies. Methods: We extracted TCR recombination reads from whole exome sequencing (WXS) and RNA sequencing (RNASeq) files of STAD and SARC samples from the Cancer Genome Atlas (TCGA) and matched the corresponding TCR complementarity determining region-3 (CDR3) AA sequences represented by these reads to known anti-CMV CDR3 sequences. Results: Results indicated that, in STAD cases, the recovery of both TRA and TRB recombination reads from WXS files, from either normal blood or tumor samples, which corresponded to AA sequences matching anti-CMV CDR3s, correlated with improved disease-free survival (DFS) (logrank p=0.025) and improved overall (p=0.026), progression-free (p=0.015), and disease specific (p=0.026) survival on an STP comparison (i.e., a comparison at 45 months). Within the SARC WXS dataset, these were associated with better overall survival (OS) (logrank p=0.021) and disease-specific survival (DSS) (logrank p=0.008). However, TCR CDR3s represented by recombination reads recovered from RNAseq files and matching anti-CMV TCR CDR3 AA sequences correlated with lower DFS probabilities (i.e., STAD STP comparison at 45 months, p=0.035, SARC STP comparison at 65 months, p=0.032). Conclusion: This study reveals a novel association between anti-CMV TCR sequences and survival probability in STAD and SARC. This suggests that TCR CDR3s may be possible indicator of prognosis for STAD and SARC and supports further research into anti-CMV CDR3s as a tool for the development of immunotherapy for these cancers. Additionally, the contrasting results between WXS and RNASeq-derived sequences warrant further investigation into the timing and nature of anti-CMV immune responses in cancer progression. One explanation for these results include the possibility that exposure to CMV prior to onset or progression of cancer may lead to better survival probability, while a potentially active T-cell response to CMV during cancer onset or progression could represent an ongoing comorbidity. Citation Format: Utsav Kapoor, Michael T. Aboujaoude, Konrad Cios, Andrea Chobrutskiy, Boris I. Chobrutskiy, George Blanck. Detection of anti-CMV TCR CDR3s correlates with increased survival for soft tissue sarcoma and stomach adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6254.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is refractory to immune checkpoint inhibitor therapy. However, intratumoral T-cell infiltration correlates with improved overall survival (OS). Herein, we characterized the diversity and antigen specificity of the PDAC T-cell receptor (TCR) repertoire to identify novel immune-relevant biomarkers. Demographic, clinical, and TCR-beta sequencing data were collated from 353 patients across three cohorts that underwent surgical resection for PDAC. TCR diversity was calculated using Shannon Wiener index, Inverse Simpson index, and “True entropy.” Patients were clustered by shared repertoire specificity. TCRs predictive of OS were identified and their associated transcriptional states were characterized by single-cell RNAseq. In multivariate Cox regression models controlling for relevant covariates, high intratumoral TCR diversity predicted OS across multiple cohorts. Conversely, in peripheral blood, high abundance of T-cells, but not high diversity, predicted OS. Clustering patients based on TCR specificity revealed a subset of TCRs that predicts OS. Interestingly, these TCR sequences were more likely to encode CD8+ effector memory and CD4+ T-regulatory (Tregs) T-cells, all with the capacity to recognize beta islet-derived autoantigens. As opposed to T-cell abundance, intratumoral TCR diversity was predictive of OS in multiple PDAC cohorts, and a subset of TCRs enriched in high-diversity patients independently correlated with OS. These findings emphasize the importance of evaluating peripheral and intratumoral TCR repertoires as distinct and relevant biomarkers in PDAC.

Five-year Results of the Phase III Intergroup Trial (RTOG 97-04) of Adjuvant Pre- and Postchemoradiation (CRT) 5-FU vs. Gemcitabine (G) For Resected Pancreatic Adenocarcinoma: Implications for Future International Trial Design