Genomic analysis of T Cell receptors reveals lynch syndrome specific immune signatures

Domain: neuroscience
Published: 2026-04-03
Editorial outcome: major_revision

Abstract
Lynch Syndrome (LS) provides the perfect context to understand DNA mismatch repair deficient carcinogenesis, which is characterized by neoplastic lesions with high rates of shared neoantigens eliciting adaptive immunity through T cell receptor (TCR) recognition. However, the TCR landscape in LS carriers remains unexplored. Here, we perform TCR sequencing of 277 blood samples from LS cancer survivors, previvors, and controls, as well as matching colorectal cancers and pre-cancers. We show that up to 41% of the most expanded TCRβs from colorectal neoplasms are detectable in the blood of LS carriers, while showing minimal expansion in controls. In addition, we develop and validate a classification model that distinguishes LS carrier from controls using circulating TCRβs signatures associated with LS carriers independent of their cancer history and with cancer-free LS previvors. Together, our findings characterize circulating and tissue TCRβs associated with LS representing a step toward identifying blood-based TCR biomarkers for immune surveillance.

Introduction
Lynch Syndrome (LS) is a genetic condition estimated to affect more than one million individuals in the United States1,2. LS carriers inherit a heterozygous germline pathogenic variant in one of four DNA mismatch repair (MMR) genes (MLH1, MSH2, MSH6, and PMS2), which predisposes them to develop different types of cancers, starting at a young age3. LS is the primary cause of hereditary colorectal cancer (CRC), conferring a 50-80% lifetime risk as well as a 40-60% endometrial cancer (EC) risk, and enhances the risk to develop multiple other tumor types4. MMR deficiency occurs when cells acquire a second somatic hit in the alternate allele of the gene that harbors the germline pathogenic alteration. This event leads to the accumulation of hundreds of somatic mutations, primarily insertion- deletions (indels) in microsatellite (MS) loci5.

Therefore, malignant MMR-deficient (MMRd) cells produce high levels of tumor-specific neoantigens (neoAgs), derived primarily from the MS indels, which trigger an anti-cancer immune response via the major histocompatibility complex (MHC)6. Yet, subsequent activation of immune checkpoint regulators enables malignant cells to evade this immune response and continue to proliferate uncontrollably7. Many of these neoAgs are widely shared among LS carriers, as several coding MS (cMS) loci have high mutation propensity, thus resulting in identical mutations occurring at common loci in tumors from different LS individuals8. Similar to viral antigens, these neoAgs are highly immunogenic, eliciting T cell activation and immune memory that enables rapid responses upon re-exposure9. Following the elimination of the neoAgs, proliferated T-cells undergo contraction and continue to exist as a smaller population of memory T-cells10, which may enter the bloodstream and circulate within the body11. As a result, the diversity and abundance of peripheral blood TCR clonotypes reflect the individual’s history of antigen exposure

Public TCRs, defined as clonotypes shared across individuals due to recurring antigen exposure, offer a window into shared immunological memory12. Mapping public TCRs has diagnostic potential13,14. The ‘TCR-Antigen Map’ initiative aimed to catalogue TCRs binding clinically relevant antigens across diseases. As proof of concept, it identified cytomegalovirus (CMV)-specific TCRs15, an approach that ultimately led to FDA approval of the T-Detect COVID test16. Given the unique context of LS as a well-defined, high-risk MMRd cancer model, the extensive sharing of neoantigens among LS individuals across disease stages, and the limited characterization of LS-associated TCR repertoires, there remains a critical gap in understanding how shared, antigen-experienced TCR clonotypes are represented systemically

Here, we show that analysis of T cell receptor landscapes from 277 peripheral blood mononuclear cell (PBMC) samples from 102 LS MMRd cancer survivors, 130 LS carriers without history of cancer (LS previvors), and 45 controls without LS or history of cancer. Additionally, we performed TCR-sequencing (TCRseq) in colorectal tissues of 3 cancers and 11 pre-cancers matching their PBMC. Together, these findings reveal that circulating cancer-associated TCRs can be identified in the peripheral blood of LS carriers and provide insights into the immune-biology mechanisms influencing cancer susceptibility in LS individuals.

Results
Demographic and clinical characteristics of the LS patient cohort. We analyzed 277 PBMC or whole blood samples from 102 LS survivors, 130 LS previvors, and 45 controls collected at MD Anderson Cancer Center (MDACC), Kansas University, and the Catalan Institute of Oncology (ICO-IDIBELL, Spain, Table 1). LS survivors had a mean age of 54 years (range 26–82) and were predominantly female (61%), whereas LS previvors had a mean age of 44 years (range 18–85) with 71% females; controls had a mean age of 59 years (range 28–76) with balanced sex distribution (49% female, 51% male). Most LS survivors carried pathogenic variants in MSH2 (42%), followed by MLH1 (26%), MSH6 (21%), and PMS2 (11%). LS previvors showed a similar pattern: MSH2 (34%), MLH1 (23%), MSH6 (23%), and PMS2 (20%). Among survivors, 72 had colorectal cancer (CRC) and 24 had endometrial cancer (EC), with fewer cases of pancreatic, urothelial, gastric, small bowel, ovarian, and prostate cancers. We also analyzed 14 matching colorectal lesions from LS carriers in our PBMC/whole-blood cohort, including 11 tubular adenomas and three adenocarcinomas (Table S1). Among the 14 lesions, four were MMRd, and three were MMR-proficient (MMRp) by IHC; two lesions had indeterminate IHC due to ambiguous staining. The remaining five samples had been exhausted for TCRseq, leaving MMR status unavailable (Table S2).

Clonality and diversity of circulating TCRs. We assessed diversity of circulating TCRβ chains using Hill numbers, which estimate the effective number of clonotypes with equal abundance; higher values indicate greater diversity. Our analysis revealed that LS survivors exhibited significantly (P=0.0006, two-tailed Mann-Whitney test) lower diversity levels compared to LS previvors (median 97.38 vs. 191.4, respectively) and compared to controls (P=0.076, two-tailed Mann-Whitney test, median controls: 160.2, Figure 1A). This trend persisted when using the Simpson clonality index (SCI), which assesses both clonotype numbers and their degree of clonal expansion; an SCI value closer to 1 indicates lower diversity, whereas a value closer to 0 indicates higher diversity. LS survivors exhibited significantly (P=0.0002, two-tailed Mann-Whitney test) higher clonality in their TCR repertoires than LS previvors (median 0.03500 vs. 0.02200, respectively), and a marginally significant difference (P = 0.094, two-tailed Mann-Whitney test) was also observed between LS Survivors and controls (median 0.02660, Figure 1B). These differences likely reflect a higher frequency of large, hyperexpanded TCRβ clonotypes in LS survivors, whereas LS previvors show greater abundance of rare, minimally expanded clonotype (Figure 1C).

We observed weak positive correlations between the age of individuals and the level of clonality among survivors (Spearman’s correlation, R=0.31), previvors (R=0.31), and controls (R=0.22, Figure S1A). Only among females, a significant difference in SCI between LS survivors and LS previvors was observed (median 0.03655 for LS survivors vs. 0.01900 for LS previvors, P=0.00022, two-tailed Mann-Whitney test, Figure S1B). Similarly, regardless of the originating institution of the sample, LS survivors exhibited more clonal repertoires than LS previvors and controls (Figure S1C). No significant differences were detected in the diversity of the TCRβ repertoires among LS survivors and previvors when analyzed based on the mutated MMR genes (Figure S1D). The effects of disease status and age were further confirmed using a generalized linear model (GLM) analysis, which considered disease status, institution, gender, mutant MMR gene, and HLAs as covariates. Ultimately, only age and disease status were found to be statistically associated with the SCI (Tables S3 and S4).

Public TCRβ repertoires and similarity within cancer status groups. We first focused on circulating TCRβs shared among individuals in our PBMC/blood samples, as these were of particular interest to us. We considered a TCRβ to be "shared" or "public" if it was present in at least two individuals with an identical amino acid sequence match of the CDR3β, Vβ, and Jβ segments. The majority of these public TCRβs (a total of 1,232,112, Jaccard Index of 0.617) were shared by both LS survivors and previvors (Figures 2A and B). To better understand the differences in TCR sharing between the three groups, we then used a rank–size frequency distribution analysis17,18. The data showed that only 3 public TCRs (0.000539%) in the control cohort were shared by 50% of controls. In contrast, 69 public TCRs (0.00454%) in LS previvors and 13 public TCRs (0.00197%) in LS survivors were shared by 50% of samples in each group (Figure 2C). Additionally, only 4,058 public TCRs (0.73%) were shared by the control cohort, while 25,731 TCRs (1.69%) in LS previvors and 12,231 TCRs (1.85%) in LS survivors were shared by 10% of the samples in each group, respectively. The power exponent α value of the power-law distribution equation was 0.307 for controls, 0.368 for previvors, and 0.405 for survivors, thus suggesting that the previvor and survivor groups share more public TCRs than the controls (P=1x10-16 for both previvor and survivor groups compared to controls in the F test, Figure 2C).

TCRβ repertoire overlap between colorectal lesions and peripheral blood. Our primary goal was to identify a circulating TCRβ signature reflecting neoAg exposure in colorectal tissue, thus we investigated whether TCRβ sequences from colorectal cancers and pre-cancers were detectable in matched blood. We conducted bulk TCR-sequencing on 14 colorectal lesions from LS carriers collected during their routine surveillance colonoscopy (Table S1). The TCRβ repertoire from each tissue sample was used to analyze the overlap between the top 100 most expanded clonotypes in each lesion and the matched patient's blood samples. Figures 3A and B show the top 100 most expanded TCRβs in cancer and tubular adenomas. A total of 41 and 28 of the most expanded TCRβs were also identified in the matched blood, respectively, even when the blood was collected at later timepoints after the biopsy (Figure 3C and Table S2). Some of these overlapping TCRβs showed medium and large expansion in the blood and, most importantly, some were identified as public TCRβs shared by different LS individuals (Figures 3A and B, red font). Overall, on average, 18% of the top 100 expanded TCRs in CRCs and pre-cancers were present in blood collected at the same time as the lesions. This overlap slightly decreases to an average of 14% when blood samples are taken 1-10 years after detecting the lesions (Figure S1E). Figure S2 summarizes the overall overlap of the most expanded blood-derived TCRβs between cancer and tubular adenoma samples. Moreover, we observed that 3 to 25% of the top 100 most expanded tissue-infiltrating TCRβs from each colorectal lesion were also present in the circulating public TCRβ repertoire of our patient cohort (Figure 3C). Therefore, our findings suggest that these TCRs may be targeting antigens derived from the colorectal tissue and T-cell responses are detectable both in the lesion microenvironment and in circulation.

Annotation of public TCRβ repertoire to disease-specific TCRs. To assess the specificity of circulating public TCRβs, we annotated them against reference repertoires with known antigen specificities (Figure 4A). For this, we used the McPAS-TCR database, which curates TCR sequences linked to diverse diseases and antigens19. From this database, we extracted two pools: one comprising 24,884 TCRs specific to viral pathogens and another with 2,113 TCRs specific to autoimmune conditions. We also compiled a pool of 14,208 MMRd CRC–specific TCRβs by integrating sequencing data from nine MMRd CRCs previously reported in the literature20. Interestingly, more TCRβs were annotated to the MMRd CRC pool of tissue-resident TCRβs compared to the viral pathogens pool in survivors (15.5% vs. 5.3%, P=1.56x10-13, Chi-Square test), previvors (18.2% vs. 6.1%, P=5.75x10-19, Chi-Square test), and controls (9.4% vs. 2.7%, P=2.31x10-6, Chi-Square test), even though the viral pathogens-specific TCR pool contains nearly twice the number of TCRs compared to the pool of TCRs in MMRd CRC (Figure 4B). Additionally, when we examined the expansion of TCRβs associated with MMRd CRC in the blood and correlated it with their expansion in CRC tissue samples, LS survivors and previvors exhibit significantly higher levels of expansion in the tissue compared to the blood samples (P<0.0001, two-tailed Mann-Whitney test, Figure 4C). Overall, these results indicate that LS survivors harbor MMRd CRC–specific public TCRβs with medium to large expansion in peripheral blood, whereas LS previvors and controls show medium to small expansion.

Circulating TCRβs to distinguish LS carriers from the general population. We next evaluated whether a distinct circulating public TCRβ signature could differentiate LS carriers from controls. The average expansion of this signature of TCRβs in the blood of a given sample is expected to predict whether the sample comes from an LS carrier or a healthy control (LS vs control status).

To identify the TCRβ signature associated with LS, we tested several methods including clustering approaches like GLIPH, Levenshtein distance, and TCRdist, which treat similar TCRs as the same, and an exact match method (Supplementary Data 1). However, the more complex methodologies did not show clear improvement in performance over the exact match approach. Therefore, to streamline the interpretability and simplicity of our method, we used the exact match classifier. To identify the best set of exact match signatures, we considered the following hyperparameters: 1. TCR region: the most optimal region to use among CDR3aa only, CDR3aa plus J segments (CDR3aa+J), CDR3aa plus V segments (CDR3aa+V), or CDR3aa plus both J and V segments (CDR3aa+J+V); 2. Positive presence ratio cutoff: TCRβ clones must be present in a certain percentage of individuals in the positive cohort; 3. Fisher’s test cutoff: a particular TCRβ clone must be significantly enriched (occurrence) in LS individuals compared to controls by passing Fisher’s exact test at a given threshold; 4. Wilcoxon test cutoff: a particular TCRβ clone must show a significant increase (overall level of expansion) in LS individuals compared to controls, as determined by the Wilcoxon test at a given threshold. Then, we randomly assigned 80% of the samples to a training set (Supplementary Data 2) to select the best hyperparameters, and 20% to a validation set. To compensate for the imbalance in the numbers of negative controls, we also included 197 healthy controls from a public dataset21 (Supplementary Data 3). We then used a Leave-One-Out Cross-Validation (LOOCV) strategy to test combinations of the four hyperparameters in the training set: TCR region (CDR3aa, CDR3aa+J, CDR3aa+V, CDR3aa+J+V), positive presence ratio cutoff (10%, 50%, 75%), Fisher’s test cutoff (10⁻¹, 10⁻², 10⁻⁴, 10⁻¹⁰, 10⁻¹⁵), and Wilcoxon test cutoff (10⁻¹, 10⁻², 10⁻⁴, 10⁻¹⁰, 10⁻¹⁵, Figure 5A). The AUROC (Area Under the Receiver Operating Characteristic curve) was used as the performance metric.

We found that using only the CDR3aa region to represent the TCR, requiring TCRs to appear in at least 10% of LS subjects, and applying both Fisher’s and Wilcoxon test cutoffs at 10⁻¹⁰ yielded the best LOOCV performance on the training set (Supplementary Data 1). Using this set of hyperparameters, we identified 13,340 TCRβs associated with the LS phenotype compared to controls (Supplementary Data 4 and Figure S3). These TCRβs exhibited higher expansion and incidence among LS previvors and survivors compared to controls (Figures 5B and 5C). To rule out overfitting, we applied this TCR signature set to the remaining 20% validation set and achieved an AUROC of 0.930, which was slightly lower but comparable to the performance on the training set (AUROC=0.942, Figure 5D). Overall, these findings highlight the capability of TCRβ signatures to effectively distinguish LS carriers from the general population.

Circulating TCRβs classification to distinguish LS previvors from the general population. We next asked whether TCRβs could identify LS carriers without cancer (previvors). Using the same classification approach, we tested whether the TCRβ repertoire distinguished Controls from Previvors within the LS cohort (Figure 6A). When we applied the same strategy to identify the signatures in LS Previvor vs Control, the best LOOCV classification accuracy measured by the AUROC was achieved using the same hyperparameter set as the LS carrier vs Control classifier (TCR region=CDR3aa, Positive Presence Ratio=10%, Fisher’s test cutoff=10-10, Wilcoxon test cutoff =10-10, Supplementary Data 5). Under these parameters, we identified 16,248 TCRβ sequences associated with the LS previvor phenotype (Supplementary Data 6 and Figure S4), resulting from increased expansion and incidence in LS previvors compared to controls (Figures 6B and 6C). In the training dataset, the classifier distinguished LS previvors from controls with high performance (AUROC=0.953), which was also confirmed in the validation dataset (AUROC=0.925, Figure 6D). We also evaluated the classifier after excluding the public dataset and obtained a reasonable LOOCV performance for control vs LS carriers (AUROC = 0.835, Figure S5A), and control vs previvors (AUROC = 0.836, Figure S5B). These results highlight the presence of a TCRβ signature capable of effectively distinguishing LS previvors from controls.

Circulating TCRβs classification to distinguish LS survivors from LS previvors. We then investigated whether LS survivors could be distinguished from LS previvors using a classifier built from TCRβ repertoires (Figure 7A). We focused on the CDR3 aa region when selecting the signature for the previvor vs survivor comparison. The highest classification accuracy measured by AUROC was achieved using a classifier that required the TCRs to be present in >10% of survivors and an unadjusted P-value cutoff of 0.1 for both Fisher’s exact test and the Wilcoxon test (Supplementary Data 7). With these parameters, we identified a total of 581 expanded TCRβ clones associated with the phenotype observed in LS survivors (Supplementary Data 8 and Figure S6). These TCRs demonstrated a greater clonal expansion and higher incidence in survivors compared to previvors (Figures 7B and C). Notably, the previvor vs survivor signature is integrated by 581 TCRs, which is a substantially smaller number when compare the other two groups: control vs LS carriers (13,340 TCR clones) and control vs previvors (16,248 TCR clones), despite being defined under a less stringent statistical cut-off (FDR = 0.1 vs 10⁻¹⁰). The predictive model derived from this signature achieved only moderate discriminative performance (AUROC = 0.732; Figure 7D).

TCRβ signatures distinguishing controls from LS carriers (previvors and survivors combined) showed substantial overlap with the signatures distinguishing controls from LS previvors alone (12,697 out of 16,248; Jaccard Index=0.76, Figure 7E), while only 7 out of 574 signatures (Jaccard Index=0.0005) distinguishing LS survivors from previvors were found in the signatures distinguishing the controls from all LS carriers (previvors and survivors together). We also observed that the mean pairwise Jaccard index was significantly higher between previvors and survivors (0.0128 ± 0.00553) compared with control vs previvor (0.0101 ± 0.00410) and control vs survivor (0.00948 ± 0.00370) comparisons (two-tailed Wilcoxon test, P < 1 × 10⁻²², Fig S7A and S7B). In addition, substantially fewer TCRs clones showed significant differences at different P-value cutoffs in abundance using Wilcoxon test (Fig S7C) or incidence using Fisher test (Fig S7D) between previvors and survivors. Together, these observations indicate a higher overall similarity of TCR clones between previvors and survivors, which likely accounts for the limited classifier performance to distinguish them. The similarity between TCR repertoires suggests that LS previvors may have already been exposed to many of the same antigens before tumor development occurs in LS survivors.

In vitro validation of TCR-seq analysis. We next validated these findings using in vitro immunological assays. We leveraged annotated public TCRβs classified as viral pathogen–specific in the McPAS-TCR database (Figure 4A and 4B), and selected for validation a subset previously reported as EBV-specific for the BMLF1 epitope (GLCTLVAML). An EBV peptide-tetramer complex was used to isolate antigen-specific CD8+ T-cells by flow cytometry for single-cell TCR sequencing (Figure S8A). EBV-tetramer–specific CD8⁺ T cells comprised 3.56% of total cells (Figure S8B) and were compared with EBV-annotated public TCRs in McPAS-TCR. A total of 47 TCRβs overlapped between the EBV-tetramer-specific CD8+ T-cells and our circulating public TCRβ cohort (Supplementary Data 9 and Figure S8C). Of these, five TCRβs were annotated as EBV-BMLF1–specific in McPAS-TCR, thus validating the accuracy of our annotation findings (highlighted by purple arrows, Figure S8D).

Validation of the LS neoAg-specificity from the circulating Lynch-associated TCR signature. After refining our experimental approach using an EBV-tetramer, we then validated our TCRβ LS-phenotype signatures for two highly recurrent neoAg peptides, RNF43 (TQLARFFPI) from two LS carriers (IDs S_ABDL106 and ID S_ABDL165), and MSH3 (FLLALWECSL) from another LS carrier (ID S_ABDL166, Figure 8A) using dextramer staining followed by paired scRNA and scTCR sequencing. We had previously demonstrated that these two neoAg are immunogenic in vitro22. After staining, 2.1% of cells were successfully isolated as activated RNF43-specific CD3+ T-cells from a LS carrier (ID S_ABDL106, Figure 8B and Figure S9A, top). This population included both CD4+ and CD8+ T-cells. We then repeated the RNF43 neoAg staining with the addition of CD8 gating in a second LS carrier (S_ABDL165, Figure S9A, bottom), and also included a second neoAg, MSH3, from another LS carrier (S_ABDL166, Figure S9B). This refined approach revealed that 10.6% of CD8⁺ T-cells were RNF43-specific in a LS carrier (S_ABDL165, Figure 8B and Figure S9A, bottom), while 6.38% of CD8⁺ T-cells were MSH3-specific in another LS carrier (S_ABDL166, Figure 8B and Figure S9B). Figure S9C displays the corresponding UMAP projections of CD3⁺, CD4⁺, and CD8⁺ T-cell populations derived from these single-cell datasets, confirming consistent clustering and annotation across samples (gating strategy shown in Figure S10). Notably, upon examining the intersection between the circulating LS-associated TCRβ signature, which was derived from the classifier distinguishing all LS carriers (survivors and previvors) from controls, and the RNF43-dextramer-positive CD3⁺ and CD8⁺ T-cells, we identified a total of 80 TCRβ sequences that overlapped between the two sets originating from a T-cell exhibiting CD3+ and CD8+ phenotype (Supplementary Data 10 and Figure 8C). These shared TCRβs represent approximately 6.4% of the total unique CDRβ sequences (n=1249) obtained from the two RNF43-specific samples. From the MSH3-specific CD8⁺ T-cell population, we identified 20 TCRs whose CDR3β sequences overlapped with the circulating LS-associated TCRβ signature, thus representing approximately 5.2% of the total unique CDR3β sequences (n=388) in that sample (Supplementary Data 10 and Figure 8D). These results provide direct evidence that a subset of circulating LS-associated TCRβ signatures identified in this study are neoAg-specific and detectable at the single-cell level, thereby validating their relevance to immune surveillance in LS carriers.

Validation of the CRC-specificity from the circulating LS-associated TCR signature. Finally, we examined the convergence between the LS-associated signatures derived from the LS carriers (previvors and survivors) vs controls (Figure 5B and C) and the MMRd CRC-resident TCR pool, which was previously compiled for annotation (Figure 4A and 4B). This MMRd CRC-resident pool was generated by combining TCRβ repertoires from nine publicly available MMRd CRC20 and two additional MMRd ADCAs obtained from our tissue cohort at MD Anderson. This analysis revealed 783 overlapping TCRβs between these two datasets (Figure 8E and Supplementary Data 11). Of note, 8 of these overlapping TCRβs displayed notable expansion (Frequency>0.5%) within the tumor microenvironment of the annotated MMRd CRC tissue samples (Figure 8F). The observed overlap between LS-associated CDR3β sequences and those identified in RNF43+ scRNA-seq and MMRd CRC data supports the notion that frequent exposure to shared neoAgs, such as RNF43-derived neoepitopes, drives the convergence of TCRβ repertoire and is responsible for the immune surveillance in LS. Taken together, these observations suggest that some TCRβs associated with LS demonstrate specificity towards MMRd CRC and neoAgs presented by these malignancies.

Methods
Ethical approval. Written informed consent was obtained from all participants. The study protocol was approved by the Institutional Review Board of The University of Texas MD Anderson Cancer Center (IRB #Lab-94-032, IRB #PA12-0327S, and IRB #PA13-0178), with approval from the corresponding institutional review boards at participating institutions.

Participants and samples. Bulk TCRseq was performed from whole-blood or PBMCs of 277 individuals (102 LS survivors, 130 LS previvors and 45 controls without history of cancer or LS, Supplementary Data 12) collected at The University of Texas MD Anderson Cancer Center (MDACC), The University of Kansas Cancer Center (KUCC), and the Catalan Institute of Oncology (ICO, Barcelona, Spain). For the MDACC cohort, the median follow-up duration was 62 months calculated from the date of consent to the last recorded contact; three individuals were deceased at last follow-up. For the KUCC cohort, the median follow-up was approximately 72 months with one deceased individual. For the ICO cohort, the median follow-up duration was 138 months; however, vital status was unknown for six individuals. Across all three cohorts, aside from the four known deceased and the six participants with unknown status, all others across the three cohorts were alive at their last follow-up (Supplementary Data 12). LS survivors were defined as LS carriers with a history of an active LS-associated cancer type. LS previvors were LS carriers with no history of any type of cancer and controls were healthy individuals at average-risk for CRC with no history of any type of cancer or family history of LS. Furthermore, we also analyzed 14 colorectal neoplasms (11 pre-cancers and 3 cancers) of the colon and rectum of LS carriers from whom we obtained PBMCs or blood. Three unstained slides (4 μm thick) were obtained from each sample to perform a hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC) of mismatch repair (MMR) proteins, as described previously40. Pathological diagnosis of tissue samples was verified by an expert gastrointestinal pathologist (M.W.T.) Tissue samples were categorized as tubular adenomas (TA) and adenocarcinomas (ADCA) based on pathological assessment and then grouped into three categories: cancers; advanced pre-cancers, which comprised TAs larger than 1 cm in size or containing villous features, or the presence of high- grade dysplasia; and pre-cancers, consisting of TAs without advanced characteristics.

Sequencing, diversity and public TCRβ repertoire Analysis. Genomic DNA was obtained from all PBMCs, whole blood, and tissue samples using the QIAGEN DNeasy Blood & Tissue Kit (Cat. No. 69504), following the manufacturer’s instructions. Library preparation of genomic DNA samples was performed by Adaptive Biotechnologies using the TCRβ ImmunoSEQ Assay. DNA samples from MDACC (N=180) were sequenced at “Deep” resolution and all samples from KUCC and ICO (N=97) were sequenced with Adaptive’s new “Single” resolution, which is comparable to the deep resolution. Downstream analyses, including diversity and clonality indexes as well as the analysis of TCRβ homeostasis and public TCRβs were performed with the Immunarch R41 package and the ImmunoSEQ Analyzer from Adaptive Biotechnologies. Only productive TCRβ rearrangements were considered for the analysis. To evaluate TCR diversity in each sample, we used Hill number, which is a measurement frequently used in ecology to quantify the biodiversity of ecosystems. It allows for measuring the effective number of distinct clonotypes that are equally abundant corresponding to diversity42. The Simpson clonality index was obtained by calculating the square root of the sum over all observed rearrangements of the square fractional abundances of each rearrangement. Productive clonality was obtained by calculating the square root of Simpson’s diversity index for all productive rearrangements in each sample. A public TCRβ was defined as a productive TCRβ shared by at least 2 individuals from our cohort by matching the CDR3β, Vβ, and Jβ segments at the amino acid level. Additionally, a rank–size frequency distribution analysis was used to assess the differences in TCR sharing between the LS Survivors, LS Previvors, and controls, which in different subjects follows a power-law distribution, represented by the equation (f(x) = kx-α)17,18. A larger power exponent (α) indicates that the population shares more public TCRs. Moreover, the Morisita’s index was used to assess levels of similarity among samples by considering the relative frequencies of TCRβ along with the presence of specific TCRβs in more than one sample. Overlap between tissue and blood samples was assessed by matching the CDR3β, Vβ, and Jβ segments at the amino acid level.

TCRβ annotation. Assessment of viral and autoimmune conditions- specificity for all public TCRβs was performed by annotating them against the Pathogens and Autoimmune categories from the McPAS-TCR database19, based on exact matches of the CDR3β, J, and V segments at the amino acid level. For the assessment of MMRd CRC-specific public TCRβs, nine MMRd colorectal adenocarcinomas, from which TCR sequencing was performed using the same TCRβ ImmunoSEQ assay from Adaptive Biotechnologies, were pooled into a single MMRd CRC-specific TCRβ dataset. This dataset included three adenocarcinomas from our tissue cohort (Table S2), three MMRd colorectal adenocarcinomas obtained at MDACC from individuals not included in our PBMCs/Blood sample cohort, and three MMRd colorectal primary tumors from a publicly available dataset accessible through the immuneACCESS platform (https://doi.org/10.21417/B7WW2C) and previously published20. Public TCRβs from the PBMC/Blood sample cohort were annotated against the MMRd CRC-specific TCRβs pool by exactly matching the CDR3β, J, and V segments at the amino acid level.

Phenotype association Analysis. The TCRβ sequence and its abundance in the peripheral blood were used as input features in the classifier to differentiate between LS carriers (previvors N = 130, and survivors N=102, total N=232) and controls (N = 45), between LS previvors and controls, and between LS survivors and previvors (Figure 5A). To balance the number of control samples, we included 197 healthy (control) samples from a public dataset (Supplementary Data 3, https://www.synapse.org/Synapse:syn61987835/). We then randomly selected 20% of the samples as the validation set using the R ‘sample’ function (seed = 1234) and used the remaining 80% as the training/testing set to optimize the parameters (Supplementary Data 2). We considered the following hyperparameters: 1. TCR region: we considered the most optimal region to use CDR3aa only, CDR3aa plus J segments, CDR3aa plus V segments, or CDR3aa plus both J and V segments; 2. Positive presence ratio cutoff: The minimum percentage of positive-cohort individuals in which a TCRβ clone must be present; 3. Fisher’s test cutoff: The significance threshold for a TCRβ clone's enrichment in the positive vs negative group in Fisher’s test; 4. Wilcoxon test cutoff: The significance threshold for a TCRβ clone's increased expansion in the positive vs negative group in Wilcoxon test. We performed a grid search over combinations of these four parameters including TCR region (CDR3aa, CDR3aa+J, CDR3aa+V, CDR3aa+J+V), positive presence ratio cutoff (10%, 50%, 75%), Fisher’s test cutoff (10⁻¹, 10⁻², 10⁻⁴, 10⁻¹⁰, 10⁻¹⁵), and Wilcoxon test cutoff (10⁻¹, 10⁻², 10⁻⁴, 10⁻¹⁰, 10⁻¹⁵, Figure 5A) on the training set using the Leave-One-Out Cross Validation (LOOCV). After signature TCRs were identified, their mean expansion within a sample was used to predict the sample’s phenotype using Gaussian Naïve Bayes classifiers. We assumed that the arcsine square root transformed mean proportions of these signature TCRs in each sample followed a Gaussian distribution with parameters (mean and standard deviation) estimated from both the positive and negative groups. Based on these distributions, we estimated the likelihood of a sample belonging to the positive group using the probability density functions of both the positive and negative groups. We analyzed the trade-off between the true positive rate (TPR) and false positive rate (FPR) across various probability thresholds and reported AUROCs (area under the receiver operating characteristic curve) for the classifiers. Optimal parameters were selected based on the AUROC values from the LOOCV results of the training/testing set. In cases where AUROC values were tied at the second decimal place, we prioritized models with shorter TCR regions and smaller signature sets. The signature TCRs identified from the training/testing set using the optimal hyperparameters were tested on the 20% held-out validation set to assess the potential overfitting.

In vitro validation of TCRs viral and neoAg specificity. As part of the TCRβ annotation process, we identified a subset of TCRβ sequences within our circulating public TCRβ repertoire, which were previously reported in the McPAS TCR database as specific to viral antigens. Among these viral antigens, we focused on an Epstein Barr Virus (EBV) antigenic peptide (BMLF1, amino acid sequence: GLCTLVAML), since we detected that several public TCRβ sequences from the blood samples of survivors, previvors, and controls showed specificity towards this BMLF1 peptide. To validate our annotation results in silico, we utilized the BMLF1 peptide in conjunction with a tetramer to stimulate PBMCs from a healthy human donor, thereby obtaining BMLF1-specific CD8+ T-cells for subsequent single-cell TCR sequencing. We then compared the TCRβ sequences obtained from these cells to our annotated public TCRβ repertoire to assess the degree of overlap and validate the specificity of the identified TCRβ sequences.

In more detail, healthy donor PBMCs were cultured in 12-well plates (1.5x106/well) using R10 media (RPMI 1640 with L-glutamine (Cat #10040CV, Corning), 10% heat-inactivated FBS (Cat# SH30070.03, HyClone), 10 mM Hepes buffer (Cat# 25060-CI, Corning), and 1X pen/strep (Cat#30002CI, Corning) supplemented with recombinant human IL-7 (R&D Systems Biotech, 330U/ml). PBMCs were stimulated with the BMLF1 (GLCTLVAML at 6 µg/ml). On days 3 and 7 of culture, cells were fed with R10 media in the presence of IL-2 (R&D Systems Biotech, 20U/mL). On day 10, cells were boosted with R10 media in the presence of high dose IL-2 (1000U/ml). On day 12, cells were harvested, and CD3+ pan T-cells were isolated for subsequent flow cytometry analysis. Unstimulated T-cells (>96% purity) were isolated from PBMCs of HLA-A*02:01-positive healthy human donors using the Pan T-cell Isolation kit from Miltenyi Biotec (Bergisch Gladbach, Germany). Briefly, non-T-cells were depleted from PBMC using biotin-conjugated Abs against CD14, CD16, CD19, CD36, CD56, and CD123, followed by glycophorin A anti-biotin-labeled magnetic beads and LS columns. To generate peptide-loaded tetramers, we used Flex-T™ HLA-A*02:01 UVX monomers (Biolegend) according to the manufacturer's instructions. In brief, 20 µL of the HLA-A*02:01 UVX monomer was mixed with 20 µL of the respective viral peptide diluted to 400 µM in PBS. Then, the mixture was UV irradiated (366 nm) for 30 min, followed by incubation for an additional 30 min at 37°C in the dark. The irradiated solution was mixed with APC-streptavidin and PE-streptavidin (Biolegend) to generate the tetramers. Flex-T™ monomers were assembled with two different streptavidin conjugates (APC and PE) in separate reactions to ensure the highest specificity. Finally, we added D-biotin to the tetramer solution to block any remaining free APC and PE-streptavidin. The tetramers were always prepared the day before the staining and left at 4°C in the dark overnight. For each sample, the protein kinase inhibitor Dasatinib (Cayman Chemicals) was added to the culture medium to a final concentration of 50 nM to enhance the binding of fluorochrome-conjugated peptide-major histocompatibility complex (pMHC) tetramers and the cells were incubated for 1 hour at 37°C. Then expanded pan T-cells were suspended in Ca2+/Mg2+ free Phosphate Buffered Saline (PBS), supplemented with 0.5% bovine serum albumin (BSA) (wash buffer), and were stained with R-phycoerythrin (PE) and Allophycocyanin (APC)- labeled pMHC-tetramer and Peridinin-Chlorophyll-Protein (PerCP) mouse anti-human CD8 antibody (Cat no. 347314, BD Biosciences, San Jose, CA, USA) to determine the number of viral peptide-specific CD8+ T-cells. Cells were incubated for tetramer staining for 30 min in the dark at 4°C at the manufacturer's recommended concentrations. After the tetramer staining, cells were washed twice with 2 ml of wash buffer, centrifuged at 300 g for 5 min at 4°C, resuspended on the residual volume, and incubated with the anti-CD8 antibody for 30 min on ice. Dead cells were excluded by Sytox Blue staining (1 mM, Molecular Probes, Carlsbad, CA, USA). Unstained pan T-cells were used to detect auto-fluorescence or background staining. Stained cells were analyzed and sorted using a CytoFLEX SRT Flow cytometer (Beckman Coulter, USA) under sterile conditions, and the results were analyzed by FlowJo Software version 10.8.1 (Tree Star, Inc., Ashland, OR, USA).

In vitro validation of the signature TCRs specificity using RNF43 and MSH3 neoAgs. We selected RNF43 (with amino acid sequence: TQLARFFPI), one of the most recurrent peptides in LS colorectal cancers and pre-cancers with validated immunogenicity that has been identified in our previous work1. We used this RNF43_3 peptide in conjunction with a custom dextramer (Immudex) to stimulate PBMCs from 2 LS patients, thereby obtaining RNF43-specific T-cells. Subsequently, we performed single-cell RNA and single-cell TCR sequencing on these RNF43-dextramer-specific CD3+ and CD8+ T-cells. In parallel, a second recurrent neoAg peptide derived from a frameshift mutation in MSH3 (FLLALWECSL), previously described as a shared and immunogenic neoAg in LS carriers8,22, was used to stain PBMCs from an additional LS carrier using an MSH3- specific HLA-A02:01 dextramer. We then compared the TCRβ sequences obtained from these cells to our LS-associated signature TCRβs and separately to our LS-survivor-associated signature TCRβs to assess overlap and validate the neoAg specificity of the identified TCRβ signature sequences.

In more detail, PBMCs from LS patients carrying the HLA-A*02:01 allele were thawed and cultured in 12-well plates at a density of 1.5×10⁶ cells/well in R10 medium (RPMI 1640 supplemented with L-glutamine (Catalog #10040CV, Corning, NY), 10% heat-inactivated FBS (Catalog #SH30070.03, HyClone, Cytiva, MA), 10 mM HEPES buffer (Catalog #25060-CI, Corning), and 1×Penicillin-Streptomycin (Catalog #30002CI, Corning). For some assays, this medium was further enriched with 1000 IU/ml human GM-CSF (Catalog #215-GM-050/CF, R&D Systems, MN), recombinant human IL-4 (Catalog #202-IL-050/CF, R&D Systems, MN), and human Flt-3L (Catalog #308-FKN-025, R&D Systems, MN), and incubated overnight (day 0). On day 1, PBMCs were stimulated with RNF43 or MSH3 peptides (1 μM) without viral peptides. Cultures were supplemented with human recombinant cytokines: IL-2 (10 IU/ml; Catalog #202-IL-050, R&D Systems, MN), IL-7 (10 ng/ml; Catalog #207-IL-025, R&D Systems, MN), and IL-15 (10 ng/ml; Catalog #200-15, PeproTech). After 24 h (day 2), cells were harvested and washed with PBS (without Ca²⁺/Mg²⁺) containing 0.5% BSA. For staining, cells were incubated with HLA-A*02:01 RNF43 (TQLARFFPI) and MSH3 (FLLALWECSL) dextramer-PE reagents (10 μl per 50 μl PBS containing 50% FBS; Immudex) for 30 min at room temperature. Following staining, cells were washed twice, centrifuged at 300 g for 5 min at 4°C, and resuspended in residual buffer. Cells were then stained with anti-CD3-FITC (Catalog #561807, BD Biosciences, CA), anti-CD8-PerCP (Catalog #340693, BD Biosciences, CA), and 4-1BB-APC-Cy7 (Catalog #309830, BioLegend, CA) antibodies for 20 min on ice. Dead cells were excluded using Sytox Blue (1 mM, Molecular Probes, CA). Controls included unstained PBMCs for autofluorescence and HLA-unmatched healthy donor PBMCs as negative controls. Cells were analyzed and sorted on a CytoFLEX SRT flow cytometer (Beckman Coulter, CA), and data were processed using FlowJo v10.8.1 (Tree Star, OR).

Cell preparation for Single-Cell profiling. To maximize cellular viability and recovery, samples were processed according to the 10x Genomics Demonstrated Protocol Cell Preparation Guide (CG00053; 10x Genomics, Pleasanton, CA, USA). Briefly, after removal of the supernatant, T-cells were gently washed twice in 1.5–3 ml of 0.04% BSA in PBS. Additionally, cells were passed through a 35μm strainer (BD Falcon® 5 ml Round-Bottom Tubes with Cell Strainer Cap, Corning, NY, USA) to eliminate cell clumps. Next, cells were stained with 0.4% Trypan blue and quantified and assessed for viability using the automated cell counting machine Countess 3 (Invitrogen, FL, US).

10x Genomics 5′ single cell RNA and V(D)J sequencing. Single-cell RNA-seq (scRNA) and single-cell TCR-seq (scTCR) analyses were performed using the 10x Genomics Single Cell Immune Profiling Solution V1.0 according to the manufacturer’s protocols (10x Genomics V(D)J + 5′ Gene Expression). The scRNA libraries were sequenced on an Illumina HiSeq 3000 system using read lengths of 26 bp read 1, 8 bp i7 index, 98 bp read 2. The scTCR libraries were sequenced on an Illumina HiSeq 3000 using read lengths of 150 bp read 1, 8 bp i7 index, 150 bp read 2. Sequencing data were preprocessed separately using Cell Ranger 2.1.1, which included generating fastq files, aligning reads, and calculating gene-cell expression matrices. TCR reads were aligned to the GRCh38 reference genome, and consensus TCR annotation was performed using the cellranger vdj program. Barcodes with a higher number of Unique Molecular Identifier (UMI) counts than simulated background were identified as cell barcodes. For each barcode, cellranger performed de novo assembly and identified productive contigs along with their corresponding CDR3 regions and V, D, J, C genes.

The single-cell gene expression results were processed using Cell Ranger v8 multi and aligned to the GRCh38 reference genome. The seq counts were then analyzed using the Seurat v5 pipeline. Cells with more than 5% mitochondrial gene content were filtered out. Data normalization was performed using the log-normalization method, and variable features were identified. Principal Component Analysis (PCA) was conducted, and the first 30 principal components (PCs) were used for subsequent analyses, including clustering and visualization with Uniform Manifold Approximation and Projection (UMAP). The top marker genes for each cluster were identified and ranked by the BH-adjusted P value using the Wilcoxon test, and cell types were manually annotated.

Statistical Analyses. PRISM8 was used to perform all statistical analyses. The Mann-Whitney test was used to assess statistical significance of the differences among LS survivors, LS previvors and controls in the context of clonality, diversity, Morisita’s index, the occupation of the repertoire space of hyper-expanded, large, medium, and small TCRβs, the gender of the individuals, the institution from which the samples were collected and the MMR gene with the germline pathogenic variant. A Chi-square test was used to assess the statistical significance of the differences among the percentage of TCRβs from each disease-specific pool that annotate to LS cohort of survivors and previvors, and controls. Moreover, the power law distribution of the TCRs present in subjects was estimated using a linear model. This was done after log-transforming the percentage of positive subjects and the percentile rank of the TCRs (log10(y) = log10(k) + log(C) *α ). Additionally, to assess the effects of the LS disease status (survivor, previvor, or control) in combination with age, the institution from which the samples were obtained, the gender of the individuals, the MMR gene with the germline pathogenic variant and the input material (blood vs PBMCs), on the Simpson clonality index (dependent variable), a generalized linear model for gamma distributed data was applied using the Stats (version 3.6.2) package in R, given the gamma distribution of the Simpson clonality index variable. In more detail, the Simpson Clonality index was established as the response variable, and disease status, the institution from which the samples were collected, the gender, the MMR gene carrying the germline pathogenic variant, the input material (PBMCs or Whole blood), as well as the age of the individuals were used as the predictor variables. To infer the significance of correlation between the individual’s age and the Simpson clonality index, the non-parametric Spearman’s rank correlation coefficient was used. For every test, significance was defined by a P-value<0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The reviewer acknowledges that the analysis is novel, as this type of investigation has not previously been conducted in the\ncontext of Lynch Syndrome. The manuscript is well-written, and the topic serves as a logical extension of the authors’ prior\nwork.\nHowever, the reviewer has several concerns that limit the overall impact of the study:\n1- The correlation between TCR repertoire in tissue and blood is weakened by the inclusion of tissue samples that appear to\nlack proper molecular characterization. As stated in the manuscript: “we also analyzed 14 colorectal tissue samples\nobtained from polyps or cancers in the lower gastrointestinal tract of LS individuals from whom we obtained PBMCs or\nblood” and “14 colorectal lesions from LS carriers collected during their routine surveillance colonoscopy.”\nHowever, not all colorectal lesions detected during surveillance colonoscopy in Lynch Syndrome (LS) patients are\nmismatch repair deficient (MMRd). Including these samples in the analysis without confirming MMR status post-collection is\nproblematic (PMID: 39660603, 37262391). The manuscript does not specify whether the tissues were tested for MMR\ndeficiency or if the molecular alterations aligned with the individual’s germline variant. This lack of validation represents a\ncritical limitation in the study design.\n2- The stated aim of the journal is to “represent important advances of significance to specialists within each field.” In their\nmanuscript, the authors acknowledge: “However, our algorithm produced only moderate performance in distinguishing LS\nprevivors from survivors, and the number of markers identified was limited.” While the study appears sufficiently powered to\ndifferentiate between cases and controls, this alone does not constitute a meaningful\nadvancement, as germline testing is\nalready available to identify unaffected individuals.\nThe authors do highlight a potentially valuable clinical utility, noting that “our LS-associated and LS previvors-associated\nsignatures could offer a solution in instances in which individuals meet clinical criteria for LS (e.g., Amsterdam criteria) but\nhave negative germline testing for pathogenic variants in MMR genes, thus leaving these cases unresolved.” However, this\nspecific application was not evaluated in the current study.\nAdditionally, the authors acknowledge: “Further analysis with a larger LS survivor cohort and diverse neoAg is necessary for\ndeeper insights”. The authors performed an in-vitro validation based on testing one specific neoAg (RNF43) in cells from\none LS. How representative is this experiment of the overall population of neoAg among LS patients?\nGiven these limitations, the reviewer concludes that currently, the findings do not represent a significant advance likely to\nimpact specialists within the field.", "reviewer_2": "This study provides a genomic analysis of T-cell receptors that provides new insights into cancer immunology in Lynch\nsyndrome carriers. While very interesting, there are several concerns that should be discussed.\nStudy design: deaths should be included.\nSmall sample of colorectal cancer and precancerous lesions.\nSmall number of healthy subjects.\nRESULTS:\nWhy did “cancer status” and “age” mainly have a significant impact on TCR clonality?\nIt is appreciable that up to 41% of the TCRβ clones that were most extended in colorectal cancer/precancerous lesions were\ndetectable in the blood of LS carriers. However, the sample was small, and a maximum of 41% may not be a biomarker.\nConclusion: Can TCRβ clones be easily collected from blood as patient samples in the future?", "reviewer_3": "The manuscript by Bolivar et al. describes a large-scale analysis of the T cell receptor repertoire (as measured by the TCR-\nbeta sequence) in lynch syndrome (LS) carriers. From a cohort of 102 LS survivors, 130 LS previvors and 45 controls, the\nauthors make a number of interesting observations, including the level of clonotype expansion and diversity in the 3 groups,\nthe degree of TCR overlap between groups, and the extent of detectability of expanded tumor T cell clones in the blood.\nThey then evaluate the potential utility of TCRs as biomarkers of disease by training prediction models to distinguish\nbetween LS carriers and controls and survivors from previvors.\nIn general, this is an interesting study and a very valuable dataset to study TCR repertoires. However, some of the\nconclusions are not sufficiently supported by the data, and a number of aspects should be clarified as detailed below.\nMajor comments:\n1. Reporting performance of the classifiers on the training set is misleading. Training and testing machine learning models\non the same data, especially when the parameter space is large (e.g. results in Figure 5 and 6), leads to overfitting and to\nunrealistic AUC values of 0.99. This is clear from the LOO experiments, where we see that by simply removing one of the\npatients at a time, the performance drops drastically.\n2. Leave-one-out experiments are less biased, but they still suffer from some level of overfitting. From the text, it appears that\nseveral hyperparameters (such as the threshold for TCRb inclusion, threshold for Fischer test, level of expansion and types\nof segments included in the model) are chosen from the full dataset – including the data used for testing. To avoid overfitting,\nthe signatures of TCRs used for the model should only be chosen on each training set, without using information from the\ntest set. Finally, without performance evaluation in a completely independent cohort, the strength of the evidence will remain\nweak. These limitations should be discussed and conclusions adapted accordingly.\n3. Multiple factors other than disease contribute to TCR repertoire. Indeed, the authors evaluated gender, MMR gene, age\nand institution/batch as covariates, and found that age significantly explained TCR repertoire. Genetic background, prior\ninfections, geographical region, and especially HLA type should be investigated carefully as likely confounders.\n4. The analysis presented in Figure 4 shows that the TCRs in the PBMCs from this study have an overlap with TCRs\npreviously reported in other studies and databases (associated with tumors, viruses or autoimmunity). As expected, a larger\nproportion of tumor-associated TCRs are seen compared to the viral and autoimmune pools. However, it seems that the\nsame pattern is observed in controls, previvors and survivors in the three pools (Figure 4B), indicating that, contrary to the\nconclusions, these tumor-associated TCRs are not specific to survivors or previvors. Additionally, this plot should take into\naccount the size of each group – there are less than half controls than the other two categories, and this imbalance mirrors\nthe % TCR from each pool in dMMR, pathogens and autoimmune pool (Figure 4B).\n5. A similar effect of imbalance in the number of subjects per group is seen in Figure 2A. Intersection of TCRs between any\npair of subjects in the control group is bound to be combinatorially much lower simply because the control group is smaller.\n6. Throughout the paper, the authors claim that identified TCRs are colorectal cancer specific, e.g. “shared TCRs between\ntissue and blood represent an immune response against colorectal neoplasms”, “CRC and precancer-specific public TCRs\nare detectable in circulation”, “This study is the first to describe circulating TCRβs that recognize neoantigens derived from\ncolorectal cancers and pre-cancers in LS”.\nHowever, this is not supported by the data. TCRs tumor reactivity is not shown, and the suggesting TCRβs enrichment\nwithin tumor-infiltrating TCRs only provides weak evidence and is inconclusive (as discussed in comment #4).\n7. The authors conduct an experimental validation to test if some of the LS-associated TCRβs are reactive to a previously\nidentified LS-associated MHC-I restricted epitope (recurrent neoantigen caused by frameshift mutation in RNF43). RNF43-\n(HLA-A∗02:01)-dextramer-positive CD3+ T cells are isolated from a single LS donor and subjected to scRNAseq +\nscTCRseq. 6 out of 1400 TCRβs were detected among the 1100 LS-associated TCRβs. Strangely, most RNF43-HLA-\nA∗02:01-dextramer-positive CD3+ T cells were CD4+ contrary to the expected. Although this unexpected results is\ndiscussed in the paper, the experiment seems to be inconclusive and should be replicated in other samples/donors.\nMinor comments\n1. The observations on the TCR repertoire overlap between colorectal lesions and blood among the top 100 expanded\nclones is interesting and informative. It would be useful to extend this analysis and calculate how the overlap changes\ndepending on the N most expanded clones considered (e.g. top 10, top 50, top 100, top 1000…).\n2. In this work, analysis of TCR repertoires is performed based on exact matches of CDR3 and V(D)J segments of the TCR\nbeta chain. However, variants in VDJ segments or CDR3 positions that are not in contact with the pMHC are likely to be\npermissive in terms of recognizing the same antigen. It would be interesting to see whether there are TCR motifs in the\nrepertoires, i.e. groups of TCRs with similar sequences that may recognize the same antigen. Several algorithms such\nGLIPH (10.1038/nature22976 ) or GIANA (https://doi.org/10.1038/s41467-021-25006-7) may be helpful in this kind of\nanalysis.\n3. Following on the previous point, including TCR similarity measures in the encoding of TCR sequences (as opposed to\nexact matches) may be beneficial towards the building of the classifiers. Did the authors consider this kind of approach?\n4. the use of “one-tailed” Mann Whitney tests should be justified (eg page 9)\n5. x-axis labels of Figure 8 C are misaligned", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Point by point responses - NCOMMS-25-07866-T\nReviewer #1 The reviewer acknowledges that the analysis is novel, as this type of investigation\nhas not previously been conducted in the context of Lynch Syndrome. The manuscript is well-\nwritten, and the topic serves as a logical extension of the authors’ prior work. However, the\nreviewer has several concerns that limit the overall impact of the study.\nQuestion #1: The correlation between TCR repertoire in tissue and blood is weakened by the\ninclusion of tissue samples that appear to lack proper molecular characterization. However, not all\ncolorectal lesions detected during surveillance colonoscopy in Lynch Syndrome (LS) patients are\nmismatch repair deficient (MMRd). Including these samples in the analysis without confirming\nMMR status post-collection is problematic (PMID: 39660603, 37262391). The manuscript does\nnot specify whether the tissues were tested for MMR deficiency or if the molecular alterations\naligned with the individual’s germline variant. This lack of validation represents a critical\nlimitation in the study design.\nResponse: We thank the Reviewer for highlighting this very important point and for the\nopportunity to provide additional information on the tissue samples. We have performed\nimmunohistochemistry (IHC) of the MMR proteins based on the known germline mutations of\nthese LS patients to assess the MMR status. Please note that despite our efforts to complete the\nIHC in all the samples, we did not have available residual material for all of them. As the Reviewer\ncan see we had MMRd and MMRp samples with no clear distinctive patters in the sharing of TCR\nclones between blood and tissue. We have now included these results in the Results section under\nDemographic and Clinical Characteristics of the LS Patient Cohort, which reads as follows:\n“Among the 14 lesions, four were MMRd, and three were MMR-proficient (MMRp) by\nimmunohistochemistry (IHC) analysis. In two lesions, the IHC results were intermediate due to\nambiguous or weak staining. The remaining five samples had been exhausted for TCRseq, so MMR\nstatus was unavailable” (lines 172-175) with corresponding details also added to the Materials\nand Methods section (lines 538–540). IHC results are also included in Table S2.\nQuestion #2: The stated aim of the journal is to “represent important advances of significance to\nspecialists within each field.” In their manuscript, the authors acknowledge: “However, our\nalgorithm produced only moderate performance in distinguishing LS previvors from survivors,\nand the number of markers identified was limited.” While the study appears sufficiently powered\nto differentiate between cases and controls, this alone does not constitute a meaningful\nadvancement, as germline testing is already available to identify unaffected individuals.\nResponse: We appreciate the Reviewer’s thoughtful comment. We agree that germline\ntesting remains the gold standard for diagnosing LS carriers and detecting their pathogenic\ngermline MMR mutations. However, our findings provide a novel concept on how circulating\nTCRβ repertoires reflect ongoing and historical cancer-associated (and pre-cancer) immune\nsurveillance responses in LS carriers. In response to this Reviewer and Reviewer #3, and to ensure\na more balanced comparison group, we incorporated a large number of healthy controls from a\npublic dataset. In this context, we observed similar performance in distinguishing LS carriers from\naverage-risk individuals, further supporting the potential of circulating TCRβ signatures as\ncomplementary immunologic biomarkers of LS-associated cancer risk. We agree with Reviewer\n#1 that proposing the use of TCR signatures to diagnose LS is not advantageous or even cost-\nefficient, but it is within our reach to utilize these signatures in the near future to identify previvor\nand survivor status, and to further refine cancer surveillance and screening recommendations in\nLS carriers. In agreement with Reviewer #1 statements, we acknowledge that our model showed\nonly moderate accuracy in distinguishing LS survivors from previvors. As discussed in the\nmanuscript, this is likely due to overlapping neoAg exposure between these groups, combined with\nthe use of the full circulating TCR repertoire, which includes many non-tumor-related clonotypes.\nWe also added within the manuscript: ‘isolating and profiling neoAg-specific T cells remains\ntechnically and logistically challenging to apply at scale, especially in large patient cohorts’ (see\nlines 490-491). Thus, while our classifier does not yet enable precise stratification between\nsurvivors and previvors, we believe the demonstration that tumor- and neoAg-associated public\nTCRβs are detectable in the circulation of LS carriers represents a useful step toward developing\nblood-based immune biomarkers. Following Reviewer #1’s comment, we have now introduced a\nclear statement in the revised manuscript to clarify and provide better context on the outcomes of\nour findings. The sentence reads as ‘However, and despite this limitation, our findings suggest that\nthese circulating TCRβ repertoires might prove to be potential TCR biomarkers for assessing\nimmune surveillance status in LS carriers.’ (Discussion, lines 477-479).\nQuestion #3: The authors do highlight a potentially valuable clinical utility, noting that “our LS-\nassociated and LS previvors-associated signatures could offer a solution in instances in which\nindividuals meet clinical criteria for LS (e.g., Amsterdam criteria) but have negative germline\ntesting for pathogenic variants in MMR genes, thus leaving these cases unresolved.” However,\nthis specific application was not evaluated in the current study.\nResponse: We thank the Reviewer for this observation and agree that this potential clinical\napplication was not directly evaluated and explored in our current study. Our conclusion was not\nto suggest using TCRβ signatures as a diagnostic tool at this moment without having them\nvalidated. The scope of the present study is exploratory; however, this data suggests that these\nsignatures may offer complementary immune profiling information in cases where germline\ntesting results are inconclusive (e.g., VUS). In fact, we have an ongoing project open in our group\nto reclassify VUS in MMR genes based on TCRseq profiles. To avoid misinterpretations of our\nstatement, we have revised the text that now reads: ‘These TCRβ repertoires have potential as\nimmune biomarkers; however, further validation in independent cohorts is necessary to assess\ntheir clinical utility and tumor reactivity’ (see in Discussion lines, 509-511).\nQuestion #4: Additionally, the authors acknowledge: “Further analysis with a larger LS survivor\ncohort and diverse neoAg is necessary for deeper insights”. The authors performed an in-vitro\nvalidation based on testing one specific neoAg (RNF43) in cells from one LS. How representative\nis this experiment of the overall population of neoAg among LS patients?\nGiven these limitations,\nthe reviewer concludes that currently, the findings do not represent a significant advance likely to\nimpact specialists within the field.\nResponse. We thank the Reviewer for this insightful comment. We acknowledge that the\nin vitro validation initially focused on a single neoAg, RNF43, and we used samples from one LS\ncarrier to test the proof of concept. However, the RNF43 neoAg was specifically selected because\nits recurrence and immunogenicity were observed among LS-associated colorectal neoplasia. This\nhas been demonstrated in our previous work (Bolivar et al 2024, Ref #21 PMID: 38244726), where\nRNF43 neoAg was ranked among the top shared neoepitopes across LS carriers. In addition,\nRNF43 has also been reported in microsatellite instability (MSI) tumors more broadly and elicited\nrobust CD8+ T cell responses across diverse HLA backgrounds (see Roudko, V. et al. 2020, Ref#8\nPMID:33259803). To address the Reviewer’s concern and further support the conclusion, we have\nnow included new data from two additional neoAg-specific TCRs: (i) a second RNF43-specific\nTCR clones isolated from an independent LS patient; (ii) an MSH3-specific TCR, which is another\nrecurrent and immunogenic neoAg described in both our group's prior studies and the literature\n(Bolivar et al 2024, Ref #21 PMID: 38244726, Roudko, V. et al. 2020, Ref#8 PMID:33259803).\nThese TCRs were derived from LS patients within the same bulk sequencing cohort to maintain\ninternal consistency. As described in the revised Results section (lines 382 to 387), we used a\nrefined gating strategy in the follow-up experiments. In these validations, we detected 10.6%\nRNF43-specific CD8⁺ T cells in the second donor and 6.38% in the MSH3-specific sample (revised\nFigure S6A, Figure S6B, lines 387-390). Notably, upon intersecting the circulating LS-associated\nTCRβ signature with TCRs from RNF43-dextramer-positive CD3⁺ and CD8⁺ T cells, we identified\n80 overlapping CDR3β sequences (Table S14 and Figure 8C). The LS-associated TCRβ\nsignature was derived from a classifier trained to distinguish all LS carriers (both survivors and\nprevivors) from controls, thereby capturing the shared immune repertoire features of LS-associated\nimmune surveillance. These shared TCRβs represent approximately 6.4% of the total unique\nCDRβ sequences (n = 1249) obtained from the two RNF43-specific samples. From the MSH3-\nspecific CD8⁺ T cell population, we identified 20 TCRs whose CDR3β sequences overlapped with\nthe circulating LS-associated TCRβ signature, representing approximately 5.2% of the total unique\nCDR3β sequences (n = 388) in that sample (Table S14 and Figure 8D, lines 394-399). In addition,\nwe observed 783 CDR3 sequences overlapping with previously reported dMMR CRC-resident\nTCRs, including 8 that were expanded (Frequency > 0.05, Figure 8E) in the colorectal tumor\nsamples (lines 406-411). Together, these new validation results further support the relevance of\ncirculating neoAg-specific T cell receptors as biomarkers of immune recognition in LS.\nReviewer #2 This study provides a genomic analysis of T-cell receptors that provides new insights\ninto cancer immunology in Lynch syndrome carriers. While very interesting, there are several\nconcerns that should be discussed. Study design: deaths should be included. Small sample of\ncolorectal cancer and precancerous lesions. Small number of healthy subjects.\nResponse: We thank the Reviewer for these important comments. Regarding clinical\noutcomes, available death information from all three contributing sites has now been incorporated\ninto the clinical annotation table (Table S16 “Vital Status”, Column K) for completeness and\ntransparency. We added the text, which reads as follows: ‘For the MDACC cohort, the median\nfollow-up duration was 62 months calculated from the date of consent to the last recorded contact;\nthree individuals were deceased at last follow-up. For the KUCC cohort, the median follow-up\nwas approximately 72 months with one deceased individual. For the ICO cohort, the median\nfollow-up duration was 138 months; however, vital status was unknown for six individuals. Across\nall three cohorts, aside from the four known deceased and the six with unknown status, all\nremaining participants across the three cohorts were alive at last follow-up’ (Table S16). These\ndetails have been added to the Methods section under ‘Participants and samples’ (lines 521–533).\nRegarding the limited number of colorectal cancer and precancerous lesions included in our\nanalysis, we fully agree and acknowledge the reviewer’s concern that we had insufficient number\nof available tissue samples analyzed in this study. We agree that expanding the number of matched\ntissue samples in future studies will improve statistical power and generalizability. However, it is\nimportant to emphasize that all tissue samples were matched to individuals from our broader blood\nTCR repertoire cohort, which included 277 participants (102 LS survivors, 130 LS previvors, and\n45 controls). The main focus of this study is to identify circulating TCR repertoires from LS\ncohorts and then test the overlap between circulating TCRβ repertoires with tumor-infiltrating\nTCRs in available tissues. Notably, we observed that up to 41% of the top 100 most expanded\nTCRs in colorectal lesions were detectable in matched peripheral blood, and on average, 18% of\nthese expanded tissue TCRs overlapped with the blood repertoire when collected concurrently.\nMoreover, several overlapping TCRs were public and detectable across multiple LS individuals,\nfurther strengthening the biological relevance of these findings despite the modest number of\ntissues. We have now modified the sentence which reads as: ‘Therefore, our findings suggest that\nthese TCRs may be targeting antigens derived from the colorectal tissue and T cell responses are\ndetectable both in the lesion microenvironment and also in circulation.’(lines 243-245). This\noverlap supports the core hypothesis that precancerous and cancerous colorectal lesions in LS\ncarriers are immunologically recognized, and that these TCR responses can be captured in\ncirculation. However, given the logistical and ethical constraints of collecting fresh colorectal\nlesions from LS carriers undergoing routine surveillance, we believe our matched blood–tissue\ndataset provides valuable, novel insight into immune surveillance in LS.\nFurthermore, Reviewer #1 and #3 also raised a similar point regarding the imbalance in the lower\nnumber of control subjects. We have addressed this issue, specifically focusing on the limited\nnumber of healthy individuals. We added 197 healthy controls from public datasets\n(https://www.synapse.org/Synapse:syn61987835) to compensate for the imbalance of negative\ncontrol (lines 290-291). The detailed explanation is included in the 'Phenotype Association\nAnalysis' section under the Methods, lines 591-593 (Table S5).\nQuestion #1: Why did “cancer status” and “age” mainly have a significant impact on TCR\nclonality?\nResponse: We appreciate the Reviewer’s questions and agree that understanding the\ninfluence of clinical variables, including ‘cancer status and age’, on TCR clonality is essential for\ninterpreting repertoire dynamics. Both cancer status and age are well-established factors known to\naffect the TCR repertoire. In fact, age has been consistently associated with reduced TCR diversity\nand increased clonality due to thymic involution and the accumulation of memory T cells from\nlifelong antigenic exposures. This age-related reduction in the TCR repertoire has been described\nacross multiple studies [Britanova et al., Nat Commun (2020), 11:4046; Sun et al., J Clin Invest\n(2022), 132(17):e158122]. This association was reflected in our dataset, where we observed weak\nbut consistent positive correlations between age and clonality across all groups: LS survivors\n(Spearman’s R = 0.31), LS previvors (R = 0.31), and controls (R = 0.22), as shown in Figure S1A\n(lines 194-196). We have now incorporated the age-related literature and discussion into the\nDiscussion section which reads as follows: ‘In our own dataset, we also observed weak but\nconsistent positive correlations between age and TCR clonality across all groups, including LS\nsurvivors, LS previvors and controls, and confirmed age as an independent factor associated with\nclonality through multivariable modeling’ (lines 436-439).\nRegarding cancer status, particularly in the context of microsatellite instability and neoAg\nburden as seen in LS-associated tumors, this factor can lead to the selective expansion of tumor-\nreactive T cell clones. This antigen-driven expansion results in increased clonality, which has been\nreported in both tissue and peripheral TCR repertoires from patients with solid tumors [Yossef R\net al, Cancer Cell (2023), Vol. 41(12), 2154-2165; Mehra and Taylor, Haematologica (2024),\n109(7), 2038-2040; Matsuda T et al., Oncoimmunology (2019), 8(6), e1588085; He J et al., Cell\nRes (2022), 32(6), 530-542]. In our study, LS survivors exhibited significantly more clonal TCRβ\nrepertoires compared to LS previvors and controls (Figure S1D), likely reflecting immune\nmemory from prior tumor exposure. We also found that this effect was especially pronounced in\nfemales (median SCI: 0.03655 for LS survivors vs 0.01900 for LS previvors, P = 0.00011;\nFigure\nS1B), and that TCR clonality did not significantly differ by institution or mutated MMR gene\n(Figure S1C). To further confirm these associations, we performed a generalized linear model\n(GLM) analysis including disease status, institution, gender, mutant MMR gene and HLAs as\ncovariates. Only age and disease status remained statistically associated with clonality (SCI), as\nshown in Table S3 (lines 204-205). Thus, both aging and antigen exposure from cancer likely\ncontribute to the observed effects on TCR clonality in our cohort.\nQuestion #2: It is appreciable that up to 41% of the TCRβ clones that were most extended in\ncolorectal cancer/precancerous lesions were detectable in the blood of LS carriers. However, the\nsample was small, and a maximum of 41% may not be a biomarker.\nResponse: We thank the Reviewer for making this observation. We agree that the tissue\nsample size is limited, and we have now emphasized this limitation more clearly in the revised text\nof the Discussion. Nonetheless, we believe that the detectable overlap up to 41% of expanded\ntissue TCRβ clonotypes also found in blood provides preliminary evidence of a circulating tumor-\nreactive T cell pool in LS carriers. This finding supports the feasibility of monitoring tumor-\nassociated immune responses through blood-based profiling. We agree that the 41% overlap, while\npromising, may not yet fulfill the criteria for a clinically actionable biomarker. However, we view\nthis observation as a biologically meaningful signal that warrants further investigation in a larger\nand longitudinally followed cohort. As described in our earlier work (PMID: 38244726) and now\nsupported by our current data, recurrent neoAg-specific clones can circulate in the blood and may\nreflect durable immune memory. We have now included a sentence in Discussion which reads as\n‘However, and despite this limitation, our findings suggest that these circulating TCRβ repertoires\nmight prove to be potential TCR biomarkers for assessing immune surveillance status in LS\ncarriers’ (lines, 477-479).\nQuestion #3: Can TCRβ clones be easily collected from blood as patient samples in the future?\nResponse: We thank the Reviewer for this important question. As noted in the Introduction section\n(lines 141-145) of the manuscript, TCR-based tests have already entered clinical use for example,\nin the detection of CMV-specific TCRs in immunocompromised individuals using platforms such\nas Adaptive Biotechnologies’ immunoSEQ and T-Detect™. Although our study did not utilize\nthese clinical platforms, their successful application supports the feasibility of developing similar\nblood-based immune monitoring tools in the context of Lynch Syndrome. We believe this\nhighlights the broader translational potential of circulating TCRβ profiling in high-risk cancer\npredisposition syndromes.\nReviewer #3 The manuscript by Bolivar et al. describes a large-scale analysis of the T cell receptor\nrepertoire (as measured by the TCR-beta sequence) in lynch syndrome (LS) carriers. From a cohort\nof 102 LS survivors, 130 LS previvors and 45 controls, the authors make a number of interesting\nobservations, including the level of clonotype expansion and diversity in the 3 groups, the degree\nof TCR overlap between groups, and the extent of detectability of expanded tumor T cell clones\nin the blood. They then evaluate the potential utility of TCRs as biomarkers of disease by training\nprediction models to distinguish between LS carriers and controls and survivors from previvors.\nIn general, this is an interesting study and a very valuable dataset to study TCR repertoires.\nHowever, some of the conclusions are not sufficiently supported by the data, and a number of\naspects should be clarified as detailed below.\nQuestion #1: Reporting performance of the classifiers on the training set is misleading. Training\nand testing machine learning models on the same data, especially when the parameter space is\nlarge (e.g. results in Figure 5 and 6), leads to overfitting and to unrealistic AUC values of 0.99.\nThis is clear from the LOO experiments, where we see that by simply removing one of the patients\nat a time, the performance drops drastically.\nQuestion #2: Leave-one-out experiments are less biased, but they still suffer from some level of\noverfitting. From the text, it appears that several hyperparameters (such as the threshold for TCRβ\ninclusion, threshold for Fischer test, level of expansion and types of segments included in the\nmodel) are chosen from the full dataset – including the data used for testing. To avoid overfitting,\nthe signatures of TCRs used for the model should only be chosen on each training set, without\nusing information from the test set. Finally, without performance evaluation in a completely\nindependent cohort, the strength of the evidence will remain weak. These limitations should be\ndiscussed and conclusions adapted accordingly.\nResponse to Question #1 and #2: We thank the Reviewer for both comments and\napologize for providing misleading statements in the manuscript. To address these comments, we\nhave now split our initial dataset and used a subset as a validation set (20% of the total initial\ncohort). For training and testing purposes, we now perform leave-one-out (LOO) validation solely\nto identify the optimal parameters for determining the signature TCRs on the training/testing set.\nWe have revised the methods section and clarified our design and procedure in the revised version\nof the manuscript (lines 291-297). The detailed explanation is included in the 'Phenotype\nAssociation Analysis' section under the Methods, lines 591–620 (Table S6).\nQuestion #3: Multiple factors other than disease contribute to TCR repertoire. Indeed, the authors\nevaluated gender, MMR gene, age and institution/batch as covariates, and found that age\nsignificantly explained TCR repertoire. Genetic background, prior infections, geographical region,\nand especially HLA type should be investigated carefully as likely confounders.\nResponse: Thank you for the Reviewer’s suggestion regarding the potential impact of\nHLA. We obtained HLA information for the samples from MDACC and incorporated it into Table\nS16. We also examined the top 10 most frequent HLA alleles in our cohort and found no significant\ndifferences in clonality (Simpson’s index) among individuals carrying these alleles. This updated\nanalysis is included in Table S4.\nQuestion #4: The analysis presented in Figure 4 shows that the TCRs in the PBMCs from this\nstudy have an overlap with TCRs previously reported in other studies and databases (associated\nwith tumors, viruses or autoimmunity). As expected, a larger proportion of tumor-associated TCRs\nare seen compared to the viral and autoimmune pools. However, it seems that the same pattern is\nobserved in controls, previvors and survivors in the three pools (Figure 4B), indicating that,\ncontrary to the conclusions, these tumor-associated TCRs are not specific to survivors or previvors.\nAdditionally, this plot should take into account the size of each group – there are less than half\ncontrols than the other two categories, and this imbalance mirrors the % TCR from each pool in\ndMMR, pathogens and autoimmune pool (Figure 4B).\nResponse: Thank you for your comments. We observe similar patterns among previvors and\nsurvivors across different pools, thus suggesting that LS carriers as a whole consistently show a\nhigher number of annotated TCRs across all categories. In the current version of the manuscript,\nthe percentage of TCRs has been normalized by the size of each category, thus making the\ncomparisons meaningful. However, we currently do not have a clear biological explanation for\nwhy LS patients exhibit more annotated TCRs associated with autoimmune conditions and\ninfections. Nonetheless, studies (Chiou et al, 2021, PMID: 33691136) have shown that T cells\nspecific for viral antigens like EBV and influenza can be found at high frequencies in tumors and\ncirculation even in individuals without virus-associated cancers. This has been attributed to TCR\ncross-reactivity between pathogen-derived and tumor or self-antigens, which may be more\npronounced in immune surveillance contexts such as LS.\nQuestion #5: A similar effect of imbalance in the number of subjects per group is seen in Figure\n2A. Intersection of TCRs between any pair of subjects in the control group is bound to be\ncombinatorially much lower simply because the control group is smaller.\nResponse: Thanks for your suggestion regarding the imbalance issue. To address this, we\nhave included the Jaccard Index to normalize for population size differences and to better illustrate\nthe overlap effect (New Figure 2B). The Jaccard index between Control and Previvor groups was\nlower than that between Control and Survivor or Previvor and Survivor groups.\nQuestion #6: Throughout the paper, the authors claim that identified TCRs are colorectal cancer\nspecific, e.g. “shared TCRs between tissue and blood represent an immune response against\ncolorectal neoplasms”, “CRC and precancer-specific public TCRs are detectable in circulation”,\n“This study is the first to describe circulating TCRβs that recognize neoAgs derived from\ncolorectal cancers and pre-cancers in LS”. However, this is not supported by the data. TCRs tumor\nreactivity is not shown, and the suggesting TCRβs enrichment within tumor-infiltrating TCRs only\nprovides weak evidence and is inconclusive (as discussed in comment #4).\nResponse: We thank the Reviewer for this important comment and completely agree that\ndefinitive tumor-specific-TCR cannot be assigned to the majority of TCRβs identified in our study,\nas most were not functionally validated. Our interpretation was based on: (i) the significant\nenrichment of these TCRs in tumor-infiltrating lymphocytes compared to matched blood samples;\n(ii) their expansion patterns; (iii) overlap with previously reported TCRs associated with MMRd\ncolorectal cancer; (iv) our targeted validation of one RNF43-specific TCR. However, we\nacknowledge that these features are supportive but not conclusive of tumor specificity for TCR\ncandidates. A future study is warranted to test the tumor reactivity for some of the TCR candidates.\nTo address this concern, we have carefully revised the language throughout the manuscript to avoid\nany overstatements. Specifically, the sentence: ‘Given this high ratio of shared TCRs between the\nlesion’s microenvironment and PBMCs, we concluded that the immune response against\ncolorectal neoplasms in LS patients is not confined to the lesion’s microenvironment but is also\npresent in circulation, and that precancerous lesions are already being recognized by T-cells’ has\nbeen revised to: ‘Therefore, our findings suggest that these TCRs may be targeting antigens\nderived from the colorectal tissue and T cell responses are detectable both in the lesion\nmicroenvironment and also in circulation.’ (lines 243-245).\nSimilarly, the statement: ‘According to our findings, MMRd CRC and precancer-specific\npublic TCRβs are detectable in circulation in LS carriers’ has been revised to: ‘Based on above\nfindings that public TCRβs associated with MMRd CRC and infiltrating precancerous lesions are\ndetectable in the circulation of LS carriers (combining both previvors and survivors), we aimed to\n……’ (lines 267-273).\nAnd finally, this statement: ‘This study is the first to describe circulating TCRβs that\nrecognize neoAgs derived from colorectal cancers and pre-cancers in LS carriers, moving the field\ntowards the identification of a blood based TCRβ cancer biomarker.’ has been revised to: ‘This\nstudy is the first to characterize circulating TCRβs in LS carriers associated with CRC and pre-\ncancers, thus representing a step towards the identification of potential blood-based TCRβ\nbiomarkers for immune surveillance’ (lines 112-114).\nQuestion #7: The authors conduct an experimental validation to test if some of the LS-associated\nTCRβs are reactive to a previously identified LS-associated MHC-I restricted epitope (recurrent\nneoAg caused by frameshift mutation in RNF43). RNF43-(HLA-A∗02:01)-dextramer-positive\nCD3+ T cells are isolated from a single LS donor and subjected to scRNAseq + scTCRseq. 6 out\nof 1400 TCRβs were detected among the 1100 LS-associated TCRβs. Strangely, most RNF43-\nHLA-A∗02:01-dextramer-positive CD3+ T cells were CD4+ contrary to the expected. Although\nthis unexpected result is discussed in the paper, the experiment seems to be inconclusive and\nshould be replicated in other samples/donors.\nResponse: We thank the Reviewer for this thoughtful comment. We agree that the initial\nRNF43-HLA-A∗02:01 dextramer validation was conducted in a single LS donor and, therefore\nwas limited in scope. In the previous version of the manuscript, we proposed multiple possible\nexplanations for CD4+ dextramer+ T cells, including noncanonical or low-affinity binding of the\ndextramer in CD4+ T cells, underdetection of CD8+ T cells due to sorting limitations, and cross-\npresentation of the RNF43 peptide on MHC-II molecules. The latter explanation is further\nsupported by our previous work (PMID: 38244726, Figure S4A), which showed predicted MHC-\nII binding overlap for the MHC-I restricted RNF43 neoAg peptide.\nIn addition, to further address this concern we have now expanded our validation to include:\n(i) performing additional dextramer staining to isolate RNF43-specific T cells from an independent\nLS patient, and (ii) conducting a parallel dextramer-based isolation of neoAg-specific T cells\nrecognizing another frameshift mutation in MSH3, which is another recurrent and immunogenic\nneoAg described in both our group’s prior studies and prior literature (Bolivar et al 2024, Ref #21\nPMID: 38244726, Roudko, V. et al. 2020, Ref#8 PMID:33259803). In these follow-up\nexperiments, we refined our approach by specifically isolating dextramer-positive T cells with\nhigh CD8+ expression. This strategy allowed us to enrich for a more functionally pure population\nof neoAg-specific CD8+ T cells and helped avoid ambiguity arising from potential MHC-II\npresentation, which complicated interpretation in our earlier analysis. As a result of this improved\nspecificity, the second RNF43 validation and the MSH3 validation both yielded CD8-enriched T\ncell populations (Now in Figure S6A, Figure S6B, and Figure 8B). Using this strategy, we\nidentified 80 TCRβs from RNF43-specific CD8⁺ and CD4⁺ T cells, and 20 TCRβs from MSH3-\nspecific CD8⁺ T cells, that overlapped with the circulating LS-associated TCRβ signature (Now in\nTable S14, Figure 8C, and Figure 8D). The data is now outlined in the revised text (lines 387-\n401). We believe that these additional validations and the updated classifier together provide\nstronger support for the presence of circulating, neoAg-specific CD8+ T cells in LS carriers.\nMinor comments\nQuestion #1: The observations on the TCR repertoire overlap between colorectal lesions and\nblood among the top 100 expanded clones is interesting and informative. It would be useful to\nextend this analysis and calculate how the overlap changes depending on the N most expanded\nclones considered (e.g. top 10, top 50, top 100, top 1000…).\nResponse: Thank you for your suggestion. We have incorporated this data into Figure S7,\nwhich illustrates the overlap between the top N tissue-derived TCRs and circulating TCRs and\ntheir corresponding expansion levels.\nQuestion #2: In this work, analysis of TCR repertoires is performed based on exact matches of\nCDR3 and V(D)J segments of the TCR beta chain. However, variants in VDJ segments or CDR3\npositions that are not in contact with the pMHC are likely to be permissive in terms of recognizing\nthe same antigen. It would be interesting to see whether there are TCR motifs in the repertoires,\ni.e. groups of TCRs with similar sequences that may recognize the same antigen. Several\nalgorithms such as GLIPH (10.1038/nature22976 ) or GIANA (https://doi.org/10.1038/s41467-\n021-25006-7) may be helpful in this kind of analysis.\nQuestion #3: Following on the previous point, including TCR similarity measures in the encoding\nof TCR sequences (as opposed to exact matches) may be beneficial towards the building of the\nclassifiers. Did the authors consider this kind of approach?\nResponse to Questions #2 and #3: Thank you for your comment. We have tested various\nmethods, but they did not perform better than the relatively “naïve” exact matching approach. For\nreasons of explainability and interpretability, we chose to use the exact match method for the final\nresults. However, in response to the Reviewer’s suggestions, we have now included results based\non GLIPH, Levenshtein distance, and TCRdist (See Table S7). The corresponding findings have\nbeen incorporated into the revised manuscript between lines 275 and 277.\nQuestion #4: the use of “one-tailed” Mann Whitney tests should be justified (eg page 9), and x-\naxis labels of Figure 8 C are misaligned\nResponse: Thank you for your comment. We have addressed this comment and corrected\nthe Figure as requested (see lines 180, 181, 187, and Figures 1 and 8).\nPoint by point responses to Reviewers\nReviewer #2 (Remarks to the Author):\nYou replied well, so the manuscript is suitable for publication.\nResponse: Thanks!\nReviewer #3 (Remarks to the Author):\nThank you for considering our comments and suggestions. One important issue remains with\nregard to code sharing and reproducibility. While a GitHub repository has been provided, it is\ninsufficient to reproduce the analyses and lacks essential information: 1) there is no code for\ntraining the classification model, including parameters and train/test design; 2) a “functions.R”\nfile is loaded by the scripts but not provided in the repository; 3) the scripts are bare of comments\nand contain no information on data input formats; 4) there is information on R and package\nversions (e.g the “renv” system is recommended). In general, providing usable code and\nsufficiently detailed methodological descriptions is a minimum standard in bioinformatics. As it\nstands, the current methods fall below these standards, making it difficult for users or reviewers\nto reproduce and evaluate the work.\nResponse: We appreciate your comments and suggestions to improve the transparency of our\nwork and access to our methods. The requested code and the renv.lock file specifying the R\nenvironment have been added to ensure reproducibility. We have provided detailed comments\nand step-by-step instructions for executing it. All the new information can be found at\nhttps://github.com/Vilarlab-MDACC/LS_TCR\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: Thanks!\nReviewer #5 (Remarks to the Author):\nThis manuscript investigates the important topic of the changes and evolution of T-cell receptors\n(TCRs) in Lynch Syndrome (LS) carriers in response to the common indels frequently observed\nin LS tumors. This is a novel and promising analysis that yields several fascinating findings. In\nmy opinion, the most interesting aspect is the clearly observable phenotype in LS carriers even\nbefore a tumor becomes detectable—and perhaps even before it develops.\nHowever, while the biological claim is important, the article focuses less on exploring the\nbiological mechanisms and more on using the observation as a classifier. As a classifier, the\nresults in the revised version are improved compared to the previous one but remain borderline.\nThe inclusion of large control cohorts strengthens the analysis, yet the distinction between\nPrevivors and Survivors is still not sharp enough. Moreover, regarding Q2, the large discrepancy\nin Figure 7D between the AUC scores of the training set (0.991) and the test set (0.732) suggests\npotential overfitting in the model.\nResponse: Thank you for your comments and suggestions. We agree that the classifier\ndistinguishing LS previvors from LS survivors has suboptimal performance. This likely reflects\nboth the limited sample size and, more importantly, the intrinsic biological similarity between\nthese two groups of carriers (survivors and previvors), despite being in a different stage of their\ndisease.\nIn our previous study, we found that individuals with LS may carry a high burden of somatic\nmutations and corresponding neoantigens generated from indels even in pre-cancer samples in\nLS carriers (PMID: 38244726). As a result, LS previvors may already develop specific TCR\nrepertoires toward these neoantigens in pre-cancer tissues, which persist in circulation even\nafter tumors develops, thus making their repertoires similar to those in survivors. However, some\nof these TCR clones may have clonally expanded in tumors. The dynamics of the TCR repertoire\nevolution during the transition from normal to precancer and later to cancer are not well\nunderstood yet. One possibility is that novel neoantigens associated with driver mutations arise\nduring the precancer-to-cancer transition, leading to additional tumor-specific TCRs. Another\npossibility is that the TCR repertoire remains largely unchanged, either because driver mutations\nare already present at the precancer stage or because cancer progresses through immune escape\nrather than the generation of new antigens. There is also the possibility that these two dynamics\nare both present in the LS population but occur in different patients.\nIn our dataset, we detected very few differentially abundant or incident TCRs between previvors\nand survivors, which is more consistent with the second possibility. However, due to the modest\nsample size, we avoided over-interpret this finding. Future exploration of this concept is\nwarranted. To achieve this goal, larger cohorts and longitudinal sampling will be required to\nclarify the dynamics of the TCR repertoire during early stages of pre-cancer and cancer\ninitiation. Therefore, it is beyond the scope of the current study.\nIn response to Reviewer’s #5 astute comment, we have now added new analysis that accounts for\nlimited performance of classifier signatures between these two groups (see new Figure S7). In\naddition, we have modified the text and added clarifications to address these points in the revised\nmanuscript (lines 331-357 and 457-482)."}, {"round": 2, "reviewer_comments": {"reviewer_2": "You replied well, so the manuscript is suitable for publication.", "reviewer_3": "Thank you for considering our comments and suggestions. One important issue remains with regard to code sharing and reproducibility. While a GitHub repository has been provided, it is insufficient to reproduce the analyses and lacks essential information: 1) there is no code for training the classification model, including parameters and train/test design; 2) a “functions.R” file is loaded by the scripts but not provided in the repository; 3) the scripts are bare of comments and contain no information on data input formats; 4) there is information on R and package versions (e.g the “renv” system is recommended). In general, providing usable code and sufficiently detailed methodological descriptions is a minimum standard in bioinformatics. As it stands, the current methods fall below these standards, making it difficult for users or reviewers to reproduce and evaluate the work.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "his manuscript investigates the important topic of the changes and evolution of T-cell receptors (TCRs) in Lynch Syndrome (LS) carriers in response to the common indels frequently observed in LS tumors. This is a novel and promising analysis that yields several fascinating findings. In my opinion, the most interesting aspect is the clearly observable phenotype in LS carriers even before a tumor becomes detectable—and perhaps even before it develops.\nHowever, while the biological claim is important, the article focuses less on exploring the biological mechanisms and more on using the observation as a classifier. As a classifier, the results in the revised version are improved compared to the previous one but remain borderline. The inclusion of large control cohorts strengthens the analysis, yet the distinction between Previvors and Survivors is still not sharp enough. Moreover, regarding Q2, the large discrepancy in Figure 7D between the AUC scores of the training set (0.991) and the test set (0.732) suggests potential overfitting in the model."}, "author_response": "REVIEWERS' COMMENTS\nReviewer #3 (Remarks to the Author):\nNo further comments\nThank you.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nThank you.\nReviewer #5 (Remarks to the Author):\nThe authors addressed the moderate classification results, suggesting that they are due to the\nsmall sample size. While this is possible, the results may actually stem from the inherent\nsimilarity between survivors and previvors. If the latter is the case, even a larger dataset would\nfail to clearly distinguish between the two groups.\nAlthough their analysis discusses these issues-and their main claim appears to focus on\ndistinguishing previvors from controls-the explanation provided to the reviewers must still be\nclearly stated in the main text. Specifically, given these two possibilities, it remains unclear\nwhether a larger sample size would resolve the issue.\nThanks to Reviewer #5 for this important suggestion. We have revised the discussion (Lines 465-\n486) to address this point more explicitly. We are now incorporating our previous findings\n(Reference #5) which indicate that survivors may experience a high neoantigen load. We have\nclarified that the observed TCR overlap supports the hypothesis that the adaptive immune\nresponse is established early during the precancerous stage and then persists over time, thus\nrendering two groups immunologically indistinguishable. Thus, the classifier’s performance may\nreflect this shared biological history rather than being solely attributable to limitations in\nsample size as pointed by Reviewer #5."}, {"round": 3, "reviewer_comments": {"reviewer_3": "No further comments", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "The authors addressed the moderate classification results, suggesting that they are due to the small sample size. While this is possible, the results may actually stem from the inherent similarity between survivors and previvors. If the latter is the case, even a larger dataset would fail to clearly distinguish between the two groups.\nAlthough their analysis discusses these issues-and their main claim appears to focus on distinguishing previvors from controls-the explanation provided to the reviewers must still be clearly stated in the main text. Specifically, given these two possibilities, it remains unclear whether a larger sample size would resolve the issue."}, "author_response": ""}]

Decision letter