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Immunotherapy response induces divergent tertiary lymphoid structure morphologies in hepatocellular carcinoma

Nature Immunology volume 25, pages 2110–2123 (2024)Cite this article

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Abstract

Tertiary lymphoid structures (TLS) are associated with improved response in solid tumors treated with immune checkpoint blockade, but understanding of the prognostic and predictive value of TLS and the circumstances of their resolution is incomplete. Here we show that in hepatocellular carcinoma treated with neoadjuvant immunotherapy, high intratumoral TLS density at the time of surgery is associated with pathologic response and improved relapse-free survival. In areas of tumor regression, we identify a noncanonical involuted morphology of TLS marked by dispersion of the B cell follicle, persistence of a T cell zone enriched for T cell–mature dendritic cell interactions and increased expression of T cell memory markers. Collectively, these data suggest that TLS can serve as both a prognostic and predictive marker of response to immunotherapy in hepatocellular carcinoma and that late-stage TLS may support T cell memory formation after elimination of a viable tumor.

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Fig. 1: TLS correlate with clinical benefit in HCC treated with neoadjuvant immunotherapy.
Fig. 2: Involuted TLS are present in areas of tumor regression.
Fig. 3: Involuted TLS display persistence of the T cell zone.
Fig. 4: Immune repertoire overlap at TLS is greater for T cells than B cells.
Fig. 5: GZMK+ and GZMB+CD8+ T cells are highly represented in TLS.
Fig. 6: Involuted TLS are found in regression beds of other tumors types treated with neoadjuvant ICB.
Fig. 7: Comparison of mature TLS in areas of viable tumor and involuted TLS in the tumor regression bed.

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Data availability

IMC data files have been deposited and are available via Zenodo at https://doi.org/10.5281/zenodo.10020271 (ref. 69). Processed bulk RNA sequencing and single-cell RNA/TCR/BCR sequencing data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus database under accession code GSE272348. Bulk TCR-β and IGH sequencing data are available from the Adaptive Biotechnologies ImmuneACCESS repository (https://clients.adaptivebiotech.com/pub/shu-2024-ni). All other data are present in the Article and its supplementary files or can be obtained from the corresponding authors upon reasonable request.

Code availability

All custom code used to generate the results in this study has been deposited and is available via GitHub at https://github.com/FertigLab/HCCTLS.

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Acknowledgements

This research was a collaborative effort of the imCORE Network, made possible through support from F. Hoffmann-La Roche. Additional research support was provided by the Johns Hopkins SPORE in Gastrointestinal Cancer and the National Institutes of Health U01CA253403 and U01CA212007 grants to M.Y. and E.J.F.; the Breeden-Adams Foundation and Conquer Cancer to M.Y.; the Johns Hopkins School of Medicine J. Mario Molina Physician Scientist Fund to D.H.S; and the Maryland Cancer Moonshot Research Grant to the Johns Hopkins Medical Institutions to D.J.Z. We thank N. Ruddle and T. Arulraj for reviewing the final manuscript.

Author information

Authors and Affiliations

  1. Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD, USA

    Daniel H. Shu

  2. Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Daniel H. Shu, Won Jin Ho, Luciane T. Kagohara, Alexander Girgis, Sarah M. Shin, Ludmila Danilova, Dimitrios N. Sidiropoulos, Sarah Mitchell, Kabeer Munjal, Kathryn Howe, Kayla J. Bendinelli, Emma Kartalia, Hanfei Qi, Guanglan Mo, Janelle Montagne, James M. Leatherman, Tamara Y. Lopez-Vidal, Amanda L. Huff, Alexei Hernandez, Erin M. Coyne, Neeha Zaidi, Daniel J. Zabransky, Marina Baretti, Daniel Laheru, Jennifer N. Durham, Hao Wang, Elizabeth M. Jaffee, Elana J. Fertig & Mark Yarchoan

  3. Convergence Institute, Johns Hopkins University, Baltimore, MD, USA

    Daniel H. Shu, Won Jin Ho, Luciane T. Kagohara, Alexander Girgis, Dimitrios N. Sidiropoulos, Janelle Montagne, Tamara Y. Lopez-Vidal, Qingfeng Zhu, Amanda L. Huff, Neeha Zaidi, Daniel J. Zabransky, Daniel Laheru, Robert A. Anders, Elizabeth M. Jaffee, Elana J. Fertig & Mark Yarchoan

  4. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Alexander Girgis, Xuan Yuan, Joel C. Sunshine & Elana J. Fertig

  5. Department of Pathology, Johns Hopkins University School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Jae W. Lee, Qingfeng Zhu, Joel C. Sunshine & Robert A. Anders

  6. Department of Dermatology, Johns Hopkins University School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Logan L. Engle, Aleksandra Ogurtsova, Joel C. Sunshine, Julie Stein Deutsch & Janis M. Taube

  7. The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, MD, USA

    Logan L. Engle, Aleksandra Ogurtsova, Joel C. Sunshine, Julie Stein Deutsch & Janis M. Taube

  8. Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA

    Logan L. Engle, Aleksandra Ogurtsova, Joel C. Sunshine, Julie Stein Deutsch, Janis M. Taube, Elizabeth M. Jaffee & Mark Yarchoan

  9. Genentech Inc, South San Francisco, CA, USA

    Robert J. Johnston

  10. Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA

    Elana J. Fertig

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Contributions

D.H.S., E.J.F. and M.Y. conceived and designed this study, reviewed all data and interpreted the results. S.M. and J.M.L. performed sample collection and processing. R.A.A., Q.Z., J.W.L., J.S.D. and J.M.T performed pathologic review. L.L.E., A.O., E.K. and K.M. performed immunohistochemistry and histologic analysis. W.J.H., S.M.S., X.Y., A.H., E.M.C. and D.H.S. collected and analyzed IMC data. L.T.K., G.M., L.D., A.G., D.N.S., T.Y.L.-V., J.M. and K.J.B. assisted with high-throughput sequencing and analysis. H.Q. and H.W. assisted with statistical analysis and J.N.D. assisted with regulatory issues. K.H., A.L.H., N.Z., D.J.Z., M.B., D.L., J.C.S., R.J.J., R.A.A., E.M.J., E.J.F. and M.Y. provided valuable discussion and assisted with manuscript preparation. D.H.S. and M.Y. wrote the manuscript with input from all the authors.

Corresponding authors

Correspondence to Elana J. Fertig or Mark Yarchoan.

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Competing interests

M.Y. reports consulting fees from Astra Zeneca, Exelixis, Genentech, Replimune, Hepion, Lantheus; research funding (to Johns Hopkins) from Bristol-Myers Squibb, Exelixis, Incyte and Genentech; and is a co-founder with equity of Adventris Pharmaceuticals. E.J.F. is on the Scientific Advisory Board of Viosera/Resistance Bio, is a paid consultant for Merck and Mestag Therapeutics, and receives research funds from Abbvie. W.J.H. has received patent royalties from Rodeo/Amgen and is the recipient of grants from Sanofi, NeoTX, and CirclePharma. He has received speaking/travel honoraria from Exelixis and Standard BioTools. E.M.J. reports grant/research support from the Lustgarten Foundation, Break Through Cancer, Roche/Genentech, Bristol-Meyers Squibb; honoraria from Achilles, DragonFly, Parker Institute, Cancer Prevention and Research Institute of Texas, Surge, HDT Bio, Mestag Therapeutics, Medical Home Group; and equity in AbMeta Therapeutics and Adventris Pharmaceuticals. D.J.Z. reports grant/research support from Roche/Genentech. R.J.J. is an employee and stockholder of Roche/Genentech. J.M.T. reports research funding from BMS and Akoya Biosciences; serves as a consultant for BMS, Merck, Astra Zeneca, Genentech, Regeneron, Elephas, Lunaphore, Compugen, and Akoya Biosciences; and holds stock in Akoya Biosciences. The remaining authors declare no competing interests.

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Nature Immunology thanks Dan Duda, Göran Jönsson and Thomas Marron for their contribution to the peer review of this work. Primary Handling Editor: Nick Bernard was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 TLS density in HCC tumors treated with neoadjuvant ICB compared to untreated controls.

a, TLS composition according to ___location in untreated (n = 52) and neoadjuvant ICB (n = 19) treated HCC tumors. Tumors found to have no TLS are not shown. b, Box-and-whisker plots showing total, peritumoral, and intratumoral TLS density in patients with locally advanced HCC treated with neoadjuvant ICB and untreated controls. c, Representative images of a single TLS in formalin-fixed, paraffin embedded (FFPE) HCC tumor stained with hematoxylin and eosin (H&E) and anti-CD3, CD8, CD20, CD56, and CXCL13. Scale bar, 250 μm. d, Comparison of cell density of CD3+, CD8+, CD20+, CD56+ positive cells and percent area of CXCL13+ tissue in untreated and neoadjuvant ICB-treated HCC tumors. e, Proportion of untreated and neoadjuvant ICB-treated HCC tumors found to have TLS according to non-viral or viral HCC etiology. f, Total, peritumoral, and intratumoral TLS density in untreated and neoadjuvant ICB-treated HCC tumors according to non-viral or viral HCC etiology. g-h, Box-and-whisker plots showing total (g) and peritumoral TLS density (h) according to pathologic response. NR, non-response. pPR, partial pathologic response. MPR/pCR, major pathologic response or complete response. Statistical significance was determined by two-tailed t-test (b, d, and f), Fisher’s exact test (e), and one-way ANOVA followed by Tukey’s honest significant difference (HSD) test (g and h). For each box-and-whisker plot, the horizontal bar indicates the median, the upper and lower limits of the boxes the interquartile range, and the ends of the whiskers 1.5 times the interquartile range.

Extended Data Fig. 2 Relapse free survival and overall survival in HCC treated with neoadjuvant ICB, according to clinical covariates.

a-l, Kaplan-Meier curves showing relapse free survival and overall survival after surgical resection for HCC patients treated with neoadjuvant ICB, according to total TLS density (a and b), peritumoral TLS density (c and d), pathologic response (e and f), prior hepatitis C (HCV) infection (g and h), prior hepatitis B (HBV) infection (i and j), and presence or absence of CD20+CXCL13+ lymphoid aggregates in pre-treatment biopsy (k and l). Statistical significance was determined by log-rank test.

Extended Data Fig. 3 Pretreatment single marker immunohistochemistry compared to posttreatment cell density and TLS density.

a, Line plots showing density of CD3, CD8, CD20, CD56 positive cells, and percent area of tissue positive for CXCL13 in pretreatment biopsy and post-neoadjuvant ICB HCC tumors. b, Dotplots showing correlation between post-treatment TLS density and pretreatment density of CD3, CD8, CD20, CD56 positive cells, and percent area of tissue positive for CXCL13 tissue. Statistical significance was determined by two-tailed t-test (a) and Pearson’s correlation (b).

Extended Data Fig. 4 High TLS density is associated with increased T and B cell activation.

a, Principal component analysis of bulk RNA sequencing data from HCC tumors resected after neoadjuvant ICB (n = 12) assigned to high (n = 5) and low (n = 7) TLS density groups. b, Heatmap showing differentially expressed genes (DEGs) with a log2 fold change (log2FC) > 1 and adjusted P value < 0.05 between tumors with TLS high and low groups. Annotation rows indicate TLS density group, HCC etiology, neoadjuvant treatment, pathologic response, relapse status, and TLS density. Annotation columns at right identify DEGs belonging to Gene Oncology Biological Pathways gene sets for T cell activation, B cell activation, Cytokine production, and Dendritic Cell Antigen Processing and Presentation. c, Volcano plot showing differentially expressed genes between tumors with high and low TLS density. Vertical dotted lines represent log2FC greater than or less than 1. Horizontal dotted line indicates adjusted P value of 0.05. 4 outlier genes with the lowest log2FC are excluded from the plot for the purposes of visualization. d, Gene set enrichment analysis showing differentially enriched gene sets from the HALLMARK database between tumors with high and low TLS density. e, Heatmap showing expression of the 12-chemokine TLS gene signature in TLS high and low tumors.

Extended Data Fig. 5 Identification of divergent TLS morphologies in viable tumor and tumor regression bed by immunohistochemistry and imaging mass cytometry.

a, Serial FFPE tissue sections from the tumor of patient OT7 showing multiple involuted TLS. Images show staining with hematoxylin and eosin (left) and immunohistochemistry staining for CD20 and Ki67 (top right), CD3 and CD21 (middle right), and CD4 and CD8 (bottom right). Scale bar, 500 μm. b, Serial FFPE sections of a single involuted TLS from the tumor of patient OT7 stained with anti-CD20 antibody (brown). Numbers indicate the order in which the sections were cut from the tissue block. Scale bar, 250 μm. c-d, Dot plots showing representative mature (c) and involuted (d) TLS, colored according to cluster assignment of individual cells after cell segmentation. e, Box-and-whisker plots showing cell cluster density in mature versus involuted TLS for CXCR3low CD4 T cells, CD57+ CD4 T cells, Macrophages, and Stroma. f, Box-and-whisker plots showing comparison of cell cluster density in mature TLS by ___location (intratumoral versus peritumoral). g, HLA-DR expression in the tumor cluster, by TLS morphology. Statistical significance was determined by pairwise two sample Wilcoxon test (f-g). For each box-and-whisker plot, the horizontal bar indicates the median, the upper and lower limits of the boxes the interquartile range, and the ends of the whiskers 1.5 times the interquartile range.

Extended Data Fig. 6 T and B cell clonal dynamics of TLS.

a, Representative images showing method of identification and microdissection of individual TLS. Image on left shows HCC tumor stained with hematoxylin and eosin (H&E) at low magnification. Insets show higher magnification of staining with H&E, anti-CD20 (magenta) and anti-Ki67 (brown), anti-CD3 (magenta) and anti-CD21 (brown), anti-CD4 and anti-CD8 (bottom right), and corresponding pre- and post-microdissection images. Scale bar, 1 mm. b-c, Total number of T cell receptor beta chain (TCRβ) (b) and immunoglobulin heavy chain (IGH) (c) clones identified in microdissected TLS. d, Box-and-whisker plot comparing the percentage of the TCRβ or IGH repertoire of each TLS that is shared with other TLS from the same tumor, by ___location. e-f, Dotplots showing TCRβ (e) and IGH (f) repertoire clonality (as determined by Normalized Shannon Entropy) for matched mature and involuted TLS. g, Violin plots comparing number of IGH V gene substitutions in mature and involuted TLS, by patient. Individual data points (not shown) represent individual IGH sequences. Statistical significance was determined by one-way ANOVA followed by Tukey’s honest significant difference (HSD) test (d) and two-tailed t test (e-g). For each box-and-whisker plot, the horizontal bar indicates the median, the upper and lower limits of the boxes the interquartile range, and the ends of the whiskers 1.5 times the interquartile range. For each violin plot, the horizontal bar indicates the median and the upper and lower limits of the boxes the interquartile range.

Extended Data Fig. 7 T and B cell clonal dynamics of TLS, peripheral blood, and tumor draining lymph nodes.

a-b, Proportion of unique T cell receptor beta chain (TCRβ) clonotypes at each TLS that were also detected in matched post-treatment (a) and pre-treatment (b) peripheral blood from patients P02, P03, P07, P08, and P12. c-d, Proportion of unique TCRβ (c) and Immunoglobulin Heavy Chain (IGH) (d) clonotypes at each TLS that were identified in the TDLN of patients OT1 and OT6. e-f, Representative upset plots showing overlap in unique TCRβ (e) and IGH (f) between tumor draining lymph node (TDLN) and microdissected TLS for patient OT1. Bottom barplots and annotation row indicate number of overlapping clonotypes between different TLS repertoires. Top stacked barplots indicate clonal composition. Bottom right stacked barplots indicate total number of unique TCRβ or IGH clonotypes identified and overall clonal composition.

Extended Data Fig. 8 Single cell gene expression and T cell receptor (TCR) repertoire characteristics of post-treatment peripheral blood.

a, UMAPs showing gene expression of CD3E, CD4, CD8A, CCR7, SELL, GZMK, PDCD1, CXCL13, TOX, and ZNF683 across all single cells sequenced from post-treatment peripheral blood of 7 HCC patients treated with neoadjuvant ICB. b, Heatmap showing gene expression of the top 3 differentially expressed genes per cluster. Rows represent single genes and columns represent individual cells. Annotation bar indicates cluster identity, whether each cell had a sequenced TCR, the clonality of the TCR, and whether the TCR was identified in microdissected TLS from the same patient. Clusters were downsampled to 75 cells per cluster for visualization. c-e, Volcano plots showing differentially expressed genes in the CD8 TEM_GZMK (c), CD8 TEM_GZMB (d), and CD4 Tph (e) clusters compared to all other cells. Vertical dotted lines indicate a fold change of greater or less than 1.4 and horizontal line indicates a P value of 0.05. Labeled genes in c and d indicate genes with the highest differential expression. Labeled genes in e indicate genes known to be highly expressed in CD4 Tph.

Extended Data Fig. 9 Single cell gene expression and T cell receptor (TCR) repertoire characteristics of post-treatment tumor infiltrating lymphocytes from patient OT6.

a, Uniform Manifold Approximation and Projection (UMAP) for 562 T cells identified by single-cell sequencing of CD3+CD19+ FACS-sorted tumor infiltrating lymphocytes. B cells excluded from analysis. b, Barplot showing number of single cells per cluster. c, Violin plots showing expression of subset specific marker genes across clusters. d, UMAP showing clonality of tumor infiltrating lymphocytes according to T cell receptor beta chain (TCRβ) sequence. e, Heatmap showing gene expression of the top 3 differentially expressed genes per cluster. Rows represent single genes and columns represent individual cells. Annotation bar indicates cluster identity, whether each cell had a sequenced TCR, the clonality of the TCR, and whether the TCR was identified in microdissected TLS from the same patient. f-h, Volcano plots showing differentially expressed genes in the CD8 TEM_GZMK (f), CD8 TEM_GZMB (g), and CD4 Tph (h) clusters compared to all other cells. Vertical dotted lines indicate a fold change of greater or less than 1.4 and horizontal line indicates a P value of 0.05.

Extended Data Fig. 10 Single cell gene expression and T cell receptor (TCR) repertoire characteristics of tumor infiltrating lymphocytes identified in TLS of patient OT6.

a, UMAP showing T cells identified by single cell sequencing whose T cell receptor beta chain (TCRβ) sequence was also identified in microdissected TLS. b, Stacked barplot showing proportion of each single cell cluster identified in TLS. c, Inferred transcriptional phenotype of the top 15 TCRβ clonotypes in mature and involuted TLS of patient OT6. d, Single cell cluster identities of shared TCRβ according to unique CDR3 and compartment where the TCR was identified. Piecharts are colored according to the cluster identities of all cells with the same TCRβ. The radius of each piechart is proportional to the total number of cells in which each TCRβ was identified (square root of n cells divided by eight).

Supplementary information

Supplementary Information

Supplementary Tables 1–14.

Reporting Summary

Supplementary Table 1

DEGs in TLS high and TLS low tumors. Each row represents a single gene, and columns provide mean of normalized counts for all samples, log2 fold change in messenger RNA expression, Wald statistic, Wald test P value and Benjamini–Hochberg-adjusted P values.

Supplementary Table 2

Gene set enrichment results showing enriched pathways in TLS high tumors compared with TLS low tumors. Each tab corresponds to a different gene set in the human MSigDB. Each row represents a single pathway, and columns provide the name of the pathway, enrichment P values, a Benjamini Hochberg adjusted P value, the expected error for the s.d. of the P value logarithm, enrichment score, enrichment score normalized to mean enrichment of random samples of the same size, size of the pathway after removing genes not present and a vector with indexes of leading-edge genes that drive the enrichment.

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Shu, D.H., Ho, W.J., Kagohara, L.T. et al. Immunotherapy response induces divergent tertiary lymphoid structure morphologies in hepatocellular carcinoma. Nat Immunol 25, 2110–2123 (2024). https://doi.org/10.1038/s41590-024-01992-w

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