Re-analysis of the public dataset:
comparison and evaluation of T cells in paired tumor and normal lung single cell samples
Student:
Fedor Grigoryev
Moscow Institute of Physics and Technology
Supervisor:
Ekaterina Esaulova,
BioNTech US
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Introduction
Due to numerous mutations in the tumor genome, the neoplasm expresses mutation-associated neoantigens (MANA) that can trigger an immune response against cancer in patients. However, as the tumor evolves, malignant cells often acquire the ability to evade the immune response by expressing checkpoint proteins. Immunotherapy using antibodies against these checkpoints has been developed, but a significant number of patients do not respond to this treatment. Biomarkers, such as the expression of checkpoint proteins, tumor mutational load, and neoantigenic load, are used to determine the suitability of immunotherapy. These biomarkers are particularly relevant for highly mutated cancers like melanoma and lung cancer. However, they are not comprehensive, highlighting the need for further research on the immune component of tumors and also dysfunctional programs in MANA specific tumor-infiltrating lymphocytes (TILs).
Our project is based on open data from the article “Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers” (Caushi J. X. et al., 2021. doi: 10.1038/s41586-021-03752-4).
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Aim and objectives
Aim
Re-processing and replication of the analysis for published single-cell RNAseq and matched TCRseq data: T cells from normal and cancerous tissues from multiple donors
Objectives
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Approaches
+ TCRseq
single-cell
RNAseq
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Results
Low-quality cells were filtered out if:
Mitochondrial genes, and genes associated with poorly supported transcriptional patterns were also removed from the analysis
Patient | Type of tissue | Number sequencing cell | Number sequencing cell after filtration (%) |
MD043-006 | normal | 10316 | 7049 (80.5) |
tumor | 11958 | 9633 (82.0) | |
MD043-011 | normal | 3633 | 2816 (77.5) |
tumor | 7264 | 4373 (65.3) | |
MD01-010 | normal | 3502 | 2901 (82.8) |
tumor | 6327 | 4373 (69.1) | |
MD043-008 | normal | 2854 | 2130 (77.5) |
tumor | 2115 | 1735 (65.3) | |
Total | | 47969 | 35385 (73.8) |
Table 1. Number sequencing cell before and after filtration
/ QC and filtration
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Results
/ Need for batch correction
Fig. 1 UMAP after QC filtration
TCR, immunoglobulin and mitochondrial genes, as well as features that constitute Interferon I mediated pathway, were excluded from clustering to make sure that clustering result will not be influenced by their variability.
PCA was performed based on the 3,000 most variable genes. UMAP on PCA results have shown that cells group by samples of their origin, indicating the need for a batch effect correction.
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Results
/ UMAP after batch correction
After harmonization, cell clusters are more evenly distributed among patients, however, the biological difference of the tumor/normal immune environment is not lost
Fig. 2 UMAP after batch correction
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Results
/ Clustering and annotation
Leiden clustering resulted in 14 separate clusters, that were annotated using combination of general CD4/CD8 markers with common subset specific markers (as FOXP3 for Tregs; MKI67 for proliferating cells; CXCL13 for Follicular helpers; GZMA, GZMB, GZMK for effector cells; ZNF683 and ITGAE for memory cells; KLRC1 for NK cells; SLC4A10 for MAIT cells and so on)
Fig. 3 Clustering results
Fig. 4 Cell subtype marker expression
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Results
Fig. 5. Dotplot of cell markers, differentially expressed genes and T cell checkpoint associated genes
Differentially expressed genes were found using wilcoxon test for each cell type vs all other cells
Fig. 3 Clustering results
/ Cell annotation
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Results
Fig. 6. Cell type composition by type of tissue
As expected, cellular distribution in tumor and normal sample is highly different.
/ Cell type enrichment by tissue
Different subsets of CD8+ cells are expanded to different pathogenic sources. Whereas influenza-specific cells should be the most abundant in normal lung, MANA-specific CD8 cells should be more numerous in the tumour.
Indeed, query to VDJdb showed that proportion of CD8+ TCRs specific to common lung pathogens (InfluenzaA, SARS-CoV-2, M.tuberculosis) was 1.4 fold higher in normal subset.
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Results
/ Matched TCR seq merge
Fig. 7. Fraction of cells with matched TCR by sample
Fig. 8. Fraction of cells with matched TCR by patient
Fig. 9. UMAP for matched TCRs
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Results
Fig. 10. Clonal expansion per sample
/ Matched TCR seq merge
Fig. 11. Clonal expansion per cell type
CD8+ cell clones are highly more expanded than CD4+
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Results
/ Matched TCR seq merge
Fig. 12. Cell type composition across 10 most populated clonotypes in Tumor and Normal
Tumor and Normal samples do not share clonal compositions
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Results
Construction of a clonotype network by computing distances between CDR3 sequences reveals a single public clonotype - clonotype cluster shared by all of the patients, illustrating possible specificity to a common pathogen
The query to VDJdb has shown that TCRs from one of public clonotype – 1238 – networks were indeed specific for antigens from single pathogenic species - CMV.
/ Clonotype network construction
Fig. 13. Clonotype networks
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Results
Query to VDJdb has revealed specificity of some TCRs to epitopes of common pathogens. However, as the neoantigens are tumor specific, it is impossible to mine TCR specificity from public databases. In fact, exploring transcriptional programs of MANA specific cells would be of greatest interest.
/ Epitope specificity
Fig. 14. Map by TCR sequence specificity to corresponding pathogenic species queried from VDJdb
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Summary and Further plans
Conclusion:
We have explored the possibilities of paired RNAseq+TCRseq on a public dataset of matched tumor and normal tissues in resectable non-small cell lung cancer.
It would be of great interest to explore and compare transcriptional programs of tumor associated antigen specific clones in the subsequent works.
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GitHub
to be announced!