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. 2020 May 8:10:512.
doi: 10.3389/fonc.2020.00512. eCollection 2020.

Measuring Intratumoral Heterogeneity of Immune Repertoires

Affiliations

Measuring Intratumoral Heterogeneity of Immune Repertoires

Diana Vladimirovna Yuzhakova et al. Front Oncol. .

Abstract

There is considerable clinical and fundamental value in measuring the clonal heterogeneity of T and B cell expansions in tumors and tumor-associated lymphoid structures-along with the associated heterogeneity of the tumor neoantigen landscape-but such analyses remain challenging to perform. Here, we propose a straightforward approach to analyze the heterogeneity of immune repertoires between different tissue sections in a quantitative and controlled way, based on a beta-binomial noise model trained on control replicates obtained at the level of single-cell suspensions. This approach allows to identify local clonal expansions with high accuracy. We reveal in situ proliferation of clonal T cells in a mouse model of melanoma, and analyze heterogeneity of immunoglobulin repertoires between sections of a metastatically-infiltrated lymph node in human melanoma and primary human colon tumor. On the latter example, we demonstrate the importance of training the noise model on datasets with depth and content that is comparable to the samples being studied. Altogether, we describe here the crucial basic instrumentarium needed to facilitate proper experimental setup planning in the rapidly evolving field of intratumoral immune repertoires, from the wet lab to bioinformatics analysis.

Keywords: TCR repertoire; clonal expansions; immunoglobulin repertoire; tumour clonality; tumour heterogeneity.

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Figures

Figure 1
Figure 1
Lymphocyte distribution in B16F0 mouse melanoma. (A) Overview image of the tumor and surrounding tissue labeled with H&E staining (left) or multicolor immunofluorescence (right). (B–D) show close-up of rectangles 1, 2, and 3 from panel (A). Green represents CD4+ T cells, cyan represents CD8+ T cells, red represents B220/CD45R+ B cells and blue indicates DAPI-stained nuclei. Yellow dashed curves outline subcutaneous fibrous tissue that constitutes the tumor capsule. Yellow dotted curves outline regions that surround vessel and are enriched in leukocytes. Cyan dotted curves on H&E images show blood vessels and capillaries that have no prominent leukocyte pockets. It should be noted that tissue structures are marked based on H&E images; these marks do not coincide directly with cells in the fluorescence images since these show different slices spaced ~20 μm apart.
Figure 2
Figure 2
Pipeline for measuring the extent of local intratumoral T cell expansions. Control samples are generated by splitting the replicas at the level of single-cell suspension. Experimental samples are split at the level of tissue fragments.
Figure 3
Figure 3
Identification of CD8+ T cell clones heterogeneously distributed within tumor samples from a mouse model of melanoma. (A). The concentration of each TCRβ CDR3 clonotype in the first control sample is plotted against the concentration of the same clonotype in the second control sample. (B). The concentration of each TCRβ CDR3 clonotype in tumor section #3 (y-axis) is plotted against the concentration of the same TCR clonotype in tumor sections #2 and #4 (x-axis). Clonotype variants that were identified as significantly expanded in one of the tumor sections are shown in orange or blue. (C). Numbers of expanded or contracted clones between all pairs of repertoires in four experimental and three control tumor. (D). Pearson's r measurement of correlation between counts of clones present in each pair of control or experimental repertoires.
Figure 4
Figure 4
Alternative pipeline for measuring the extent of local intratumoral expansions. Experimental samples are split at the level tissue fragments. Each experimental fragment is further split into replicas at the level of single-cell suspension.
Figure 5
Figure 5
Estimating immunoglobulin repertoire heterogeneity within a melanoma-infiltrated lymph node. (A) The concentration of each IGH CDR3 clonotype in one part of fragment is plotted against the concentration of the same clonotype in the other part of that fragment after being split at the level of homogenized cells. (B) Pairwise comparison of the four repertoires obtained from two fragments of the tumor. Red circles indicate clonotypes independently identified as expanded in all four comparisons. Blue color shows clonotypes expanded in tumor fragment Y, orange—in fragment X. (C) Number of expanded or contracted clones between pairs of control or experimental repertoires. (D) Pearson's r measurement of correlation between counts of clones present in each pair of control or experimental repertoires. (E) Isotype proportions in replicates of the two analyzed fragments.
Figure 6
Figure 6
Estimating immunoglobulin repertoire heterogeneity between two sections of colon cancer tissue. (A) Frequencies of IGH CDR3 clonotypes in the Y1 and Y2 replicates, which were split at the level of homogenized cells. (B) Pairwise comparison of the four experimental repertoires obtained from two fragments and two replicates. (C) Frequencies of each IGH CDR3 clonotype in X1 and X2 replicates, which were split at the level of homogenized cells. Orange and blue circles show clonotypes that were erroneously identified as expanded between the two replicates, using a beta-binomial noise model trained on the replicates of fragment Y. (D) The number of expanded or contracted clones between control and experimental pairs of repertoires. (E) Pearson's r correlation between counts of clones present in each control and experimental pair of repertoires.

References

    1. Schrama D, Ritter C, Becker JC. T cell receptor repertoire usage in cancer as a surrogate marker for immune responses. Semin Immunopathol. (2017) 39:255–68. 10.1007/s00281-016-0614-9 - DOI - PubMed
    1. Bradley P, Thomas PG. Using T cell receptor repertoires to understand the principles of adaptive immune recognition. Annu Rev Immunol. (2019) 37:547–70. 10.1146/annurev-immunol-042718-041757 - DOI - PubMed
    1. Jiang N, Schonnesen AA, Ma KY. Ushering in integrated T cell repertoire profiling in cancer. Trends Cancer. (2019) 5:85–94. 10.1016/j.trecan.2018.11.005 - DOI - PMC - PubMed
    1. Mose LE, Selitsky SR, Bixby LM, Marron DL, Iglesia MD, Serody JS, et al. . Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V'DJer. Bioinformatics. (2016) 32:3729–34. 10.1093/bioinformatics/btw526 - DOI - PMC - PubMed
    1. Bolotin DA, Poslavsky S, Davydov AN, Frenkel FE, Fanchi L, Zolotareva OI, et al. . Antigen receptor repertoire profiling from RNA-seq data. Nat Biotechnol. (2017) 35:908–11. 10.1038/nbt.3979 - DOI - PMC - PubMed