Intratumoral T-cell receptor repertoire composition predicts overall survival in patients with pancreatic ductal adenocarcinoma

Figures & data

Table 1. Clinical and pathologic characteristics of pancreatic ductal adenocarcinoma cases according to patient cohort.

		Patient Cohort
Characteristics^a	TCGA (n = 150)	UW (n = 51)	WashU (n = 152)	P^b
Sex				0.652
Female	67 (45%)	19 (37%)	65 (43%)
Male	83 (55%)	32 (63%)	87 (57%)
Mean age ± SD (years)	65.5 ± 11.1	63 ± 9.9	66.5 ± 10.3	0.116
Nodal Status				0.002
Negative	40 (27%)	26 (51%)	41 (27%)
Positive	110 (73%)	25 (49%)	111 (73%)
Mean Survival± SD (days)	521.4 ± 424.4	1421. 1 ± 1057.3	852.5 ± 699.4	<0.001
Survival Group				<0.001
PO	4 (3%)	0 (0%)	5 (3%)
ST	29 (19%)	1 (2%)	24 (16%)
MT	39 (26%)	25 (49%)	47 (31%)
LT	13 (9%)	25 (49%)	37 (24%)
s	65 (43%)	0 (0%)	39 (26%)

^aPercentage indicates the proportion of patients with a specific clinical or pathologic characteristic among patients in each cohort. ^bChi-squared test used for categorical variables (with continuity correction) and regular ANOVA used for continuous variables. PO perioperative (≤3 months), ST short-term (>3 months and <1 year), MT medium-term (≥1 year and ≤3 years), LT long-term, S surviving (surviving, but <3 years). SD Standard Deviation.

Figure 1. TCR Repertoire Diversity Metrics in the WashU Cohort. A-C: comparison of matched tissue and PBMC samples (n = 108) by (a) True entropy, (b) Shannon Weiner index, (c) Inverse Simpson index. D-F: comparison of survival groups in tissue samples by (d) True entropy, (e) Shannon Weiner index, (f) Inverse Simpson index (D: LT n = 27, MT n = 39, ST n = 15; E/F: LT n = 26, MT n = 33, ST n = 15). G-I: comparison of survival groups in PBMC samples by (g) True entropy, (h) Shannon Weiner index, (i) Inverse Simpson index (LT n = 37, MT n = 47, ST n = 24).

*7 patients had clonotype counts below downsampling threshold and were excluded from E/F (A-C) ****p < 0.0001 as calculated by paired t-test. (D-I) *p < 0.05, **p < 0.01 as calculated by ANOVA for overall differences and t-test with false discovery rate correction for multiple pairwise comparisons. ns = not significant

Figure 2. Multivariate cox proportional-hazards models for the UW and WashU – tissue, WashU – PBMC and TCGA – tissue cohorts. (a,b) Multivariate cox proportional-hazards model for UW and Wash U – tissue cohort (N = 167), using the variables: age, nodal status, neoadjuvant therapy, TCR diversity (true entropy) quartile group and fraction T-cell group. Impact of upper quartile compared to lower 3 quartile TCR diversity (a) and impact of above median compared to below median fraction T-cells (b) on overall survival. (c,d) Multivariate cox proportional-hazards model for Wash U-PBMC cohort (N = 152), using the variables: age, nodal status, neoadjuvant therapy, TCR diversity (true entropy) quartile group and fraction T-cell group. Impact of upper quartile compared to lower 3 quartile TCR diversity (c) and impact of above median compared to below median fraction T-cells (d) on overall survival. (e) Multivariate cox proportional-hazards model for TCGA-Tissue cohort (N = 150), using the variables: age, nodal status, and TCR diversity (true entropy) quartile group. Impact of upper quartile compared to lower 3 quartile TCR diversity (e) on overall survival. F: hazard ratios for variables in the three models with 95% confidence intervals calculated with multivariate cox regression.

(a–f) *p < 0.05 as calculated by multivariate Cox proportional-hazards model. Likelihood ratio test p value < 0.05 for all three models.

Figure 3. Clustering of WashU tissue patients by shared TCR specificity. (a) Unsupervised clustering of WashU tissue patients into four distinct patient clusters. (b) Kaplan-Meier curve of overall survival by individual cluster. (c) Kaplan-Meier curve of overall survival comparing cluster 3 to pooled clusters 1,2 and 4. D-E: comparison of patient clusters by (d) TCR diversity and (e) estimated T-cell fraction. F: relative abundance of TCR Metaclusters in patient cluster 3 compared to pooled clusters 1,2 and 4.

(b) *p < 0.05 as calculated by log-rank test. (c) *p < 0.05 as calculated by log-rank test. HR (95% CI) calculated by univariate cox proportional hazards model. (d-e) *p < 0.05 as calculated by ANOVA for overall differences and t-test with false discovery rate correction for multiple pairwise comparisons. (f) Calculated by beta-binomial regression model with significance cutoff of FDR < 0.25.

Figure 4. Clustering of WashU tissue TCRs based on amino acid properties (TESSA). A-B: UMAP dimensional reduction of TESSA embedding on all tissue CDR3b sequences from the WashU tissue data set with TCRs unassigned to a cluster removed (a) and with CDR3b sequences belonging to cohort 3 enriched metaclusters highlighted (b). (c) Highlighted TCRs in (b) belonging to a given metacluster within the same TESSA subset were collapsed into vertices and those belonging to a matched metacluster in different TESSA subsets were connected by gray lines (vertex size corresponds to TCR count). TCRs not present in metaclusters enriched in patient cluster 3 (highlighted in b) are excluded from this panel and subsequent analysis. (d) Metaclusters from (b) and (c) were separated based on TESSA subset and tested as a correlate of overall survival in the WashU tissue cohort. (e) Kaplan-Meier curve of overall survival comparing patients with high frequency of TESSA 12 metaclusters (above median) vs. low frequency (below median) in the WashU PBMC cohort. (f) Kaplan-Meier curve of overall survival comparing patients with high frequency of TESSA 12 metaclusters (upper quartile) vs. patients with low frequency (lower 3 quartiles) in the UW tissue cohort as determined by TCR distances.

(d) *p < 0.05 as calculated by log rank or Spearman correlation with Bonferroni correction for multiple comparisons. ns = not significant (e,f) *p < 0.05 as calculated by log-rank test. HR (95% CI) calculated by univariate Cox proportional hazards model.

Figure 5. T-cell identity and TCR specificity in metaclusters of interest. (a) Euler plot depicting overlap of TESSA 12 metaclusters between patient cohorts. (b) consensus amino acid sequence of metaclusters shared by WashU tissue, UW tissue and MDA. (c,d) Inference of T-cell identities in MDA dataset based on mapping of scRNA-seq transcriptomes to a reference dataset (c) with T-cells bearing TCRs from TESSA 12 metaclusters highlighted (d). (e) Comparison of T-cell subset frequencies in all tumor-infiltrating T-cells (left, n = 7 patients) and T-cells from TESSA 12 metaclusters (right). (f) predicted specificity of two of the six shared metaclusters from (a) determined with TCRmatch and the IEDB database with a significance cutoff of 0.97.

(e) *p < 0.05, **p < 0.01 as calculated by Fisher’s Exact Test

Data availability statement

All software packages in this study are publicly available. All code used to generate figures is available at this location (https://github.com/WashU-TCR-PDAC), and de-identified patient data can be made available upon request. TCRb CDR3 sequences will be made publicly available on Adaptive Technologies Immunoseq Analyzer Portal.