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Molecular analysis of primary melanoma T cells identifies patients at risk for metastatic recurrence

A Publisher Correction to this article was published on 17 March 2020

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Primary melanomas >1 mm thickness are potentially curable by resection, but can recur metastatically. We assessed the prognostic value of the T-cell fraction (TCFr) and repertoire T-cell clonality, measured by high-throughput sequencing of the T-cell receptor β-chain in T2–T4 primary melanomas (n = 199). TCFr accurately predicted progression-free survival and was independent of thickness, ulceration, mitotic rate and age. TCFr was second only to tumor thickness in its predictive value, using a gradient-boosted model. For accurate progression-free survival prediction, adding TCFr to tumor thickness was superior to adding any other histopathological variable. Furthermore, a TCFr >20% was protective regardless of tumor ulceration status, mitotic rate or presence of nodal disease. TCFr is a quantitative molecular assessment that predicts metastatic recurrence in primary melanoma patients whose disease has been resected surgically. The present study suggests that a successful T-cell-mediated, antitumour response can be present in primary melanomas.

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Fig. 1: Study design.
Fig. 2: TCRB CDR3 repertoire sharing and distribution of insertions and deletions.
Fig. 3: TCFr is not associated with other clinical or histopathological factors.
Fig. 4: TCFr cut-off optimization and validation.
Fig. 5: Primary melanoma patients with low TCFr are at greater risk of progression.
Fig. 6: PFS prediction accuracy improves when TCFr is added to a histopathological feature.
Fig. 7: Comparison of TCFr by HTS to histopathological TIL gradings.
Fig. 8: Composition of the T-cell infiltrate is highly variable in primary melanomas.

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Data availability

TCRB-sequencing data that support the findings of this study have been deposited at the publicly available immuneACCESS platform at and Previously published data that were reanalyzed here can be found at (, and Imaging source data are available on request from the corresponding author. Numerical source data for Figs. 3c, 7a–d and 8 are presented with the paper. All other data supporting the findings of the present study are available from the corresponding author upon reasonable request.

Code availability

All code was performed using R v.3.5.1 and publicly available packages. Cox’s regressions were performed using the survival package v.2.43. Plots were generated using ggplot2 v.3.2.0. Heatmaps of V-family and J-gene usage were generated using the LymphoSeq package ( v.3.10. No custom packages were written for the analysis. Code underlying the analysis is available upon request from the corresponding author.

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This work was supported by National Institutes of Health grant nos. R01 AI127654 (to T.S.K.) and R01 CA203721 (to R.A.C. and T.S.K.). W.P. was supported by a grant of the German Research Foundation (grant no. PR 1621/1-1), B.D.-A. received grant support from the Novo Nordisk Foundation and the Lundbeck Foundation. Laboratory services were provided at no cost by Adaptive Biotechnologies. We thank Dana-Farber/Harvard Cancer Center in Boston, MA, for the use of the Pathology Specimen Locator Core, which is supported in part by a National Cancer Institute Cancer Center support grant (no. NIH 5 P30 CA06516). We thank J. Lock Andersen for help providing clinical data related to the collected archival melanoma samples. Support from colleagues of the MIA and the Australian National Health and Medical Research Council is also gratefully acknowledged. J.S.W. and R.A.S. are supported by Australian National Health and Medical Research Council Fellowships. J.F.T. is supported by the Medical Foundation of the University of Sydney. G.A. is supported by a scholarship from the University of Sydney. immunoSEQ, immuneACCESS and their associated designs are trademarks of Adaptive Biotechnologies. Other trademarks are the property of their respective owners. immunoSEQ Assays are for research use only and not for use in diagnostic procedures.

Author information

Authors and Affiliations



W.P. and T.S.K. searched the literature and designed the study, and performed the data analysis and interpretation. J.R. performed the statistical analysis, data analysis and interpretation of high-throughput sequencing. W.P., P.F., E.Y. and H.R. contributed to the high-throughput sequencing. J.W., B.D-A., J.F.T., P.M.F., K.K., R.A.C. and R.A.S. acquired the clinical samples and patient data, and provided clinical interpretation. W.P. and B.D-A. processed the samples and performed laboratory experiments. M.C.M. histologically reassessed the TIL infiltration. Q.Z. helped with immunofluorescence staining. G.H.A. performed the mIHC and multispectral image analysis. A.J.C and J.W. reviewed the mIHC and multispectral image analysis. J.R. and W.P. prepared the figures. W.P., J.R. and T.S.K. wrote the manuscript. All the co-authors reviewed the manuscript.

Corresponding author

Correspondence to Thomas S. Kupper.

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Competing interests

J.R., P.F., E.Y. and H.R. are or were employed by and have financial interest in Adaptive Biotechnologies. H.R. owns intellectual property associated with Adaptive Biotechnologies. T.S.K. serves on the Scientific Advisory Board (Hematology) of Adaptive Biotechnologies but does not own stock or receive compensation.

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Extended data

Extended Data Fig. 1 Effect of FFPE age on TCR sequencing.

(a) Data quality of immunosequenced FFPE samples by sample age; bin-width = 1 year. Samples with < 500 nucleated cells (orange) were considered to have insufficient cellularity and/or DNA degradation and were excluded from analysis. All samples with ≥ 500 nucleated cells were analyzed for T cell fraction (green and yellow); only samples with ≥ 500 nucleated cells and ≥ 100 T cells (green) were analyzed for Simpson’s clonality. (b) Fitted LOESS curve showing the fraction of analyzable samples (green and yellow samples from figure (a) by FFPE sample age.

Extended Data Fig. 2 Deletions and insertions during somatic TCRB recombination.

Frequency distribution of (a) sum of deletions (Vdels, D5dels, D3dels and Jdels) and (b) insertions (N1ins, N2ins). Progressors and Non-Progressors do not differ. Non-Progressors, n = 107 primary melanoma samples; Progressors, n = 92. For all box plots, the bold line indicates the median; box illustrates lower and upper quartiles; whiskers show the lowest and highest data point still within 1.5x of interquartile range from lower or upper quartile, respectively; dots are outliers.

Extended Data Fig. 3 TCRB-V-family and TCRB-J-gene usage in progressors versus non-progressors.

Non-clustered heatmaps illustrating the usage of TCRB-V-family (a) or TCRB-J-gene (b) generated with the R package LymphoSeq. Gene usage does not differ between progressors and non-progressors. NA = patients with unknown recurrence status.

Extended Data Fig. 4 Simpson’s clonality and associations between clinicopathological variables.

(a) Simpson’s clonality is comparable between all T stages. Kruskal-Wallis Test; 1-2 mm, n = 42 independent samples; 2-4 mm, n = 59; > 4 mm, n = 57. Box plots: bold line indicates the median; box illustrates lower and upper quartiles; whiskers show the lowest and highest data point still within 1.5x of interquartile range from lower or upper quartile, respectively; all dots are data points. (b) Simpson’s clonality is not correlated with Breslow thickness. Spearman’s correlation test; n = 158 primary melanoma samples. (c) Spearman’s Rho calculated for correlations between continuous variables; Spearman’s correlation test; n = 158 primary melanoma samples.

Extended Data Fig. 5 TCFr cut-off benchmarking and selection.

(a) Fraction below cut-off and (b) true negative rate (TNR: proportion of patients above the cut-off who remain disease-free) for seven different TCFr cut-off values as measured by bootstrapping the training group 100 times. Box plots (n = 100 bootstraps): the bold line indicates the median; box illustrates lower and upper quartiles; whiskers show the lowest and highest data point still within 1.5x of interquartile range from lower or upper quartile, respectively. Each dot represents one bootstrap per TCFr value. (c) Precision-Recall Curves of 100 training cohort bootstraps colorized by TCFr cut-off. (d) First order derivate of F-score versus TCFr cut-off colorized by the TPR; each dot represents one bootstrap per TCFr value, which were fitted with a LOESS curve.

Extended Data Fig. 6 Histological TIL grading.

TIL regrading of samples, using two histological grading systems (n = 153 primary melanoma samples). (a,b) Shown are T cell fraction per TIL grade for each thickness group. Dots for samples with low ( < 20%) TCFr are colorized orange, with high ( ≥ 20%) TCFr in green. Number of independent samples listed in plots. For all box plots: the bold line indicates the median; box illustrates lower and upper quartiles; whiskers show the lowest and highest data point still within 1.5x of interquartile range from lower or upper quartile, respectively; dots are data points. (c,d) Kaplan-Meier PFS curves according to (c) conventional TIL briskness assessment and (d) MIA TIL scores. Both grading systems are predictive of PFS; Cox regression with two-sided Z-test and Likelihood ratio test (LRT) (briskness: LRT p = 3E-5; MIA: LRT p = 0.001).

Extended Data Fig. 7 Composition of T cell infiltrate.

(a) Boxplot illustrating the higher median TCFr measured by TCRB sequencing in comparison to multiplex immunohistochemistry (mIHC), medianHTS = 0.21 vs medianmIHC = 0.05. Two-sided Wilcoxon Rank Sum Test; TCRseq, n = 199 primary melanoma patients; mIHC, n = 57. Box plots: the bold line indicates the median; box illustrates lower and upper quartiles; whiskers show the lowest and highest data point still within 1.5x of interquartile range from lower or upper quartile, respectively; dots are data points. (b) Overall, the TCFr determined by TCRB sequencing correlates with the fraction of CD3+/DAPI (all) cells by mIHC. Line = regression line, grey shading = 95% confidence interval, Spearman’s correlation test; n = 57 primary melanoma samples. (c) The percentage of CD3+ cells as well as of CD8+, CD4+, Tregs and tumor antigen specific CD8+ T cells (CD8+ CD39+ and CD8+ CD39+ CD103+) varies greatly if multiple (sequential) sections of one tumor are analyzed.

Extended Data Fig. 8 Power calculation.

Estimation of statistical power given 𝑥 number of subjects in a cohort and a range of effect sizes (i.e., hazard ratios), assuming a type I error rate of 5% and the typical variance observed for tumor T cell fractions.

Supplementary information

Source data

Source Data Fig. 3

Correlation statistics.

Source Data Fig. 7

Histological TIL grading data.

Source Data Fig. 8

Multiplex IHC data.

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Pruessmann, W., Rytlewski, J., Wilmott, J. et al. Molecular analysis of primary melanoma T cells identifies patients at risk for metastatic recurrence. Nat Cancer 1, 197–209 (2020).

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