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Research
Apostolos Zaravinos
Department of Basic Medical Sciences, College of Medicine, Member of QU Health, Qatar University, 2713 Doha, Qatar.
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4625-5562
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Skin melanoma is a highly immunogenic cancer with extensive genetic and transcriptional diversity. Despite the enthusiastic clinical results seen in advanced-stage metastatic melanoma patients treated with immune checkpoint inhibition, a subgroup of them later relapse and develop acquired resistance. The intratumoral immune cytolytic activity (CYT) reflects the ability of cytotoxic T cells and NK cells to eliminate cancer cells, and is associated with improved patient survival. Here, we questioned whether CYT associates with different genomic profiles in skin melanoma.
Methods: We explored the TCGA-SKCM dataset and stratified patients to distinct subgroups of cytolytic activity. The tumor immune contexture, somatic mutations and recurrent copy number aberrations were calculated using quanTIseq, MutSigCV and GISTIC2. Chromothriptic events were explored using CTLPScanner and cancer neoepitopes were predicted with antigen.garnish. Each tumor's immunophenoscore was calculated using Immunophenogram.
Results: Metastatic skin melanomas had significantly higher CYT compared to primary tumors. We also assessed enrichment for immune-related gene sets within CYT-high tumors; whereas, CYT-low tumors were enriched for non-immune related genes sets. In addition, distinct mutational and neoantigenic loads, primarily composed of C>T transitions, along with specific types of copy number aberrations, characterized each cytolytic subgroup. We found a broader pattern of chromothripsis across CYT-low tumors, where non-telomeric chromosomal regions harboring chromothripsis, contained a higher number of cancer genes. CYT-high patients had markedly higher immunophenoscore and should consequently, display an expected clinical benefit compared to CYT-low patients who either received or not, checkpoint inhibition.
Conclusions: Overall, our data highlight the existence of distinct genomic features across cytolytic subgroups in skin melanoma.
Keywords
skin melanoma, cytolytic activity, immune checkpoints, chromothripsis, kataegis, chromosomal instability, neoantigen load, immunophenoscore, cell type fractions
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This is a list of supplementary files associated with this preprint. Click to download.
- TableS4.pdf
Table S4. Cancer genes within chromosomal regions that harbored chromothripsis, with events of genomic gains or losses, across cytolytic high (CYT-high) or low (CYT-low) skin melanomas.
- TableS4.pdf
Table S4. Cancer genes within chromosomal regions that harbored chromothripsis, with events of genomic gains or losses, across cytolytic high (CYT-high) or low (CYT-low) skin melanomas.
- TableS4.pdf
Table S4. Cancer genes within chromosomal regions that harbored chromothripsis, with events of genomic gains or losses, across cytolytic high (CYT-high) or low (CYT-low) skin melanomas.
- TableS3.pdf
Table S3. Summary table of the key statistics for the top differentially expressed genes in primary and metastatic skin melanoma, compared to the normal skin. logFC: estimate of the log2-fold-change corresponding to the effect or contrast; AveExpr: average log2-expression; t: moderated t-statistic; F: moderated F-statistic; P.Value: raw p-value; adj.p: adjusted p-value; B: log-odds that the gene is differentially expressed.
- TableS3.pdf
Table S3. Summary table of the key statistics for the top differentially expressed genes in primary and metastatic skin melanoma, compared to the normal skin. logFC: estimate of the log2-fold-change corresponding to the effect or contrast; AveExpr: average log2-expression; t: moderated t-statistic; F: moderated F-statistic; P.Value: raw p-value; adj.p: adjusted p-value; B: log-odds that the gene is differentially expressed.
- TableS3.pdf
Table S3. Summary table of the key statistics for the top differentially expressed genes in primary and metastatic skin melanoma, compared to the normal skin. logFC: estimate of the log2-fold-change corresponding to the effect or contrast; AveExpr: average log2-expression; t: moderated t-statistic; F: moderated F-statistic; P.Value: raw p-value; adj.p: adjusted p-value; B: log-odds that the gene is differentially expressed.
- Suppl.Table2.xlsx
Table S2. Antibody staining, intensity, quantity and location of GZMA and PRF1 proteins across 12 skin melanomas. Immunohistochemistry data were extracted from the Human Protein Atlas using antibody-based protein profiling. The numbers indicate the skin melanoma patients expressing each gene.
- Suppl.Table2.xlsx
Table S2. Antibody staining, intensity, quantity and location of GZMA and PRF1 proteins across 12 skin melanomas. Immunohistochemistry data were extracted from the Human Protein Atlas using antibody-based protein profiling. The numbers indicate the skin melanoma patients expressing each gene.
- Suppl.Table2.xlsx
Table S2. Antibody staining, intensity, quantity and location of GZMA and PRF1 proteins across 12 skin melanomas. Immunohistochemistry data were extracted from the Human Protein Atlas using antibody-based protein profiling. The numbers indicate the skin melanoma patients expressing each gene.
- TableS1.pdf
Table S1. Clinical information of the patients in the TCGA-SKCM dataset.
- TableS1.pdf
Table S1. Clinical information of the patients in the TCGA-SKCM dataset.
- TableS1.pdf
Table S1. Clinical information of the patients in the TCGA-SKCM dataset.
- SupplFigure8.pdf
Figure S8. BRAF, NRAS, TP53 and NF1 mutations across the SKCM dataset and association with CYT, showing no preference for these four gene mutations in any cytolytic subgroup.
- SupplFigure8.pdf
Figure S8. BRAF, NRAS, TP53 and NF1 mutations across the SKCM dataset and association with CYT, showing no preference for these four gene mutations in any cytolytic subgroup.
- SupplFigure8.pdf
Figure S8. BRAF, NRAS, TP53 and NF1 mutations across the SKCM dataset and association with CYT, showing no preference for these four gene mutations in any cytolytic subgroup.
- SupplFigure7.pdf
Figure S7. CIBERSORT analysis revealed that the majority of (primary and metastatic) CYT-high tumors were significantly enriched in γδT cells, follicular helper T cells (Tfh) and Tregs, compared to their CYT-low counterparts.
- SupplFigure7.pdf
Figure S7. CIBERSORT analysis revealed that the majority of (primary and metastatic) CYT-high tumors were significantly enriched in γδT cells, follicular helper T cells (Tfh) and Tregs, compared to their CYT-low counterparts.
- SupplFigure7.pdf
Figure S7. CIBERSORT analysis revealed that the majority of (primary and metastatic) CYT-high tumors were significantly enriched in γδT cells, follicular helper T cells (Tfh) and Tregs, compared to their CYT-low counterparts.
- Suppl.Figure6.pdf
Figure S6. The expression of the immunoinhibitor LAG3, immunostimulators (CD70, LTA, NT5E and ENTPD1), markers for activated CD8+ T cells (NKG7, CD3E, GZMA, GZMH, GZMK), MDSC (CD2), activated dendritic cells (UBD, C1QB, C1QC), NK cells (FASLG and FAS) and chemokines the CXCL9, CXCL10, CXCL11 and CXCL13, was significantly higher across CYT-high (primary and metastatic) cutaneous melanomas.
- Suppl.Figure6.pdf
Figure S6. The expression of the immunoinhibitor LAG3, immunostimulators (CD70, LTA, NT5E and ENTPD1), markers for activated CD8+ T cells (NKG7, CD3E, GZMA, GZMH, GZMK), MDSC (CD2), activated dendritic cells (UBD, C1QB, C1QC), NK cells (FASLG and FAS) and chemokines the CXCL9, CXCL10, CXCL11 and CXCL13, was significantly higher across CYT-high (primary and metastatic) cutaneous melanomas.
- Suppl.Figure6.pdf
Figure S6. The expression of the immunoinhibitor LAG3, immunostimulators (CD70, LTA, NT5E and ENTPD1), markers for activated CD8+ T cells (NKG7, CD3E, GZMA, GZMH, GZMK), MDSC (CD2), activated dendritic cells (UBD, C1QB, C1QC), NK cells (FASLG and FAS) and chemokines the CXCL9, CXCL10, CXCL11 and CXCL13, was significantly higher across CYT-high (primary and metastatic) cutaneous melanomas.
- Suppl.Figure5.pdf
Figure S5. The unsupervised hierarchical clustering of the two cytolytic subgroups using the two IFN-γ-related gene signatures proposed by Ayers, et al. (2017). These are the “IFN-γ signature” composed of IDO1, CXCL10, CXCL9, HLA-DRA, STAT1 and IFNG (a), and the “expanded IFN-γ signature” composed of CD30, IDO1, CIITA, CD3E, CCL5, GZMK, CD2, HLA-DRA, CXCL13, IL2RG, NKG7, HLA-E, CXCR6, LAG3, TAGAP, CXCL10, STAT1 and GZMB (b). Both signatures shows are enriched in CYT-high skin melanomas. (c) Boxplots depict the individual gene expression levels of each signature gene in the two cytolytic subsets of skin melanoma (SKCM).
- Suppl.Figure5.pdf
Figure S5. The unsupervised hierarchical clustering of the two cytolytic subgroups using the two IFN-γ-related gene signatures proposed by Ayers, et al. (2017). These are the “IFN-γ signature” composed of IDO1, CXCL10, CXCL9, HLA-DRA, STAT1 and IFNG (a), and the “expanded IFN-γ signature” composed of CD30, IDO1, CIITA, CD3E, CCL5, GZMK, CD2, HLA-DRA, CXCL13, IL2RG, NKG7, HLA-E, CXCR6, LAG3, TAGAP, CXCL10, STAT1 and GZMB (b). Both signatures shows are enriched in CYT-high skin melanomas. (c) Boxplots depict the individual gene expression levels of each signature gene in the two cytolytic subsets of skin melanoma (SKCM).
- Suppl.Figure5.pdf
Figure S5. The unsupervised hierarchical clustering of the two cytolytic subgroups using the two IFN-γ-related gene signatures proposed by Ayers, et al. (2017). These are the “IFN-γ signature” composed of IDO1, CXCL10, CXCL9, HLA-DRA, STAT1 and IFNG (a), and the “expanded IFN-γ signature” composed of CD30, IDO1, CIITA, CD3E, CCL5, GZMK, CD2, HLA-DRA, CXCL13, IL2RG, NKG7, HLA-E, CXCR6, LAG3, TAGAP, CXCL10, STAT1 and GZMB (b). Both signatures shows are enriched in CYT-high skin melanomas. (c) Boxplots depict the individual gene expression levels of each signature gene in the two cytolytic subsets of skin melanoma (SKCM).
- SupplFigure4.pdf
Figure S4. Purity (a) and ploidy (b) scores across the two cytolytic subsets of primary and metastatic skin melanomas, using ABSOLUTE.
- SupplFigure4.pdf
Figure S4. Purity (a) and ploidy (b) scores across the two cytolytic subsets of primary and metastatic skin melanomas, using ABSOLUTE.
- SupplFigure4.pdf
Figure S4. Purity (a) and ploidy (b) scores across the two cytolytic subsets of primary and metastatic skin melanomas, using ABSOLUTE.
- SupplFigure3.pdf
Figure S3. (a) The plots depict the voom, mean-variance trend for primary and metastatic skin melanomas, respectively. (b) Principal-components analysis (PCA) for the two cytolytic subgroups of primary and metastatic skin melanomas. (c) Volcano plot depicting the significantly differentially expressed genes in primary (left) and metastatic (right) skin melanomas. The top 50 differentially upregulated genes are highlighted in blue. (d) The Venn-diagram depicts the unique differential genes within primary (13 genes) and metastatic skin melanomas (8,121 genes), as well as the co-deregulated genes (1,054 genes) between the two tumor types.
- SupplFigure3.pdf
Figure S3. (a) The plots depict the voom, mean-variance trend for primary and metastatic skin melanomas, respectively. (b) Principal-components analysis (PCA) for the two cytolytic subgroups of primary and metastatic skin melanomas. (c) Volcano plot depicting the significantly differentially expressed genes in primary (left) and metastatic (right) skin melanomas. The top 50 differentially upregulated genes are highlighted in blue. (d) The Venn-diagram depicts the unique differential genes within primary (13 genes) and metastatic skin melanomas (8,121 genes), as well as the co-deregulated genes (1,054 genes) between the two tumor types.
- SupplFigure3.pdf
Figure S3. (a) The plots depict the voom, mean-variance trend for primary and metastatic skin melanomas, respectively. (b) Principal-components analysis (PCA) for the two cytolytic subgroups of primary and metastatic skin melanomas. (c) Volcano plot depicting the significantly differentially expressed genes in primary (left) and metastatic (right) skin melanomas. The top 50 differentially upregulated genes are highlighted in blue. (d) The Venn-diagram depicts the unique differential genes within primary (13 genes) and metastatic skin melanomas (8,121 genes), as well as the co-deregulated genes (1,054 genes) between the two tumor types.
- SupplFigure2.pdf
Figure S2. Data normalization and filtering on the RNA-seq read counts. Read counts were converted to log2-counts-per-million (logCPM) and voom transformed using the limma package. (a) Un-normalized logCPM, (b) Voom transformed logCPM.
- SupplFigure2.pdf
Figure S2. Data normalization and filtering on the RNA-seq read counts. Read counts were converted to log2-counts-per-million (logCPM) and voom transformed using the limma package. (a) Un-normalized logCPM, (b) Voom transformed logCPM.
- SupplFigure2.pdf
Figure S2. Data normalization and filtering on the RNA-seq read counts. Read counts were converted to log2-counts-per-million (logCPM) and voom transformed using the limma package. (a) Un-normalized logCPM, (b) Voom transformed logCPM.
- Suppl.Figure1.pdf
Figure S1. (a, b) The reconstructed mutational profiles and COSMIC signatures contributions did not differ between CYT-high and CYT-low (primary and metastatic) SKCM tumors (C>T relative contribution). The residual sum of squares (RSS) is a measure of the efficiency of the original mutational profile reconstruction. The cosine similarity between profiles presents a direct measure of the correspondence between the two depicted profiles in a 0–1 range. Analysis was performed using MuSiCa. (c) Rainfall plots depicting the log10 of the intermutation distance versus genomic position for two representative skin melanoma samples with evidence of kataegis: one CYT-high (TCGA-3N-A9WC-06A-11D-A38G-08) and onw CYT-low (TCGA-D3-A8GL-06A-11D-A372-08) skin tumor. The dots represent the mutations and the different colors represent each of the 6 subtypes of base substitutions: C>A, C>G, C>T, T>A, T>C, and T>G. Arrowheads depict clusters of mutations in kataegis.
- Suppl.Figure1.pdf
Figure S1. (a, b) The reconstructed mutational profiles and COSMIC signatures contributions did not differ between CYT-high and CYT-low (primary and metastatic) SKCM tumors (C>T relative contribution). The residual sum of squares (RSS) is a measure of the efficiency of the original mutational profile reconstruction. The cosine similarity between profiles presents a direct measure of the correspondence between the two depicted profiles in a 0–1 range. Analysis was performed using MuSiCa. (c) Rainfall plots depicting the log10 of the intermutation distance versus genomic position for two representative skin melanoma samples with evidence of kataegis: one CYT-high (TCGA-3N-A9WC-06A-11D-A38G-08) and onw CYT-low (TCGA-D3-A8GL-06A-11D-A372-08) skin tumor. The dots represent the mutations and the different colors represent each of the 6 subtypes of base substitutions: C>A, C>G, C>T, T>A, T>C, and T>G. Arrowheads depict clusters of mutations in kataegis.
- Suppl.Figure1.pdf
Figure S1. (a, b) The reconstructed mutational profiles and COSMIC signatures contributions did not differ between CYT-high and CYT-low (primary and metastatic) SKCM tumors (C>T relative contribution). The residual sum of squares (RSS) is a measure of the efficiency of the original mutational profile reconstruction. The cosine similarity between profiles presents a direct measure of the correspondence between the two depicted profiles in a 0–1 range. Analysis was performed using MuSiCa. (c) Rainfall plots depicting the log10 of the intermutation distance versus genomic position for two representative skin melanoma samples with evidence of kataegis: one CYT-high (TCGA-3N-A9WC-06A-11D-A38G-08) and onw CYT-low (TCGA-D3-A8GL-06A-11D-A372-08) skin tumor. The dots represent the mutations and the different colors represent each of the 6 subtypes of base substitutions: C>A, C>G, C>T, T>A, T>C, and T>G. Arrowheads depict clusters of mutations in kataegis.
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Research
Apostolos Zaravinos
Department of Basic Medical Sciences, College of Medicine, Member of QU Health, Qatar University, 2713 Doha, Qatar.
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4625-5562
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 15 Sep, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Skin melanoma is a highly immunogenic cancer with extensive genetic and transcriptional diversity. Despite the enthusiastic clinical results seen in advanced-stage metastatic melanoma patients treated with immune checkpoint inhibition, a subgroup of them later relapse and develop acquired resistance. The intratumoral immune cytolytic activity (CYT) reflects the ability of cytotoxic T cells and NK cells to eliminate cancer cells, and is associated with improved patient survival. Here, we questioned whether CYT associates with different genomic profiles in skin melanoma.
Methods: We explored the TCGA-SKCM dataset and stratified patients to distinct subgroups of cytolytic activity. The tumor immune contexture, somatic mutations and recurrent copy number aberrations were calculated using quanTIseq, MutSigCV and GISTIC2. Chromothriptic events were explored using CTLPScanner and cancer neoepitopes were predicted with antigen.garnish. Each tumor's immunophenoscore was calculated using Immunophenogram.
Results: Metastatic skin melanomas had significantly higher CYT compared to primary tumors. We also assessed enrichment for immune-related gene sets within CYT-high tumors; whereas, CYT-low tumors were enriched for non-immune related genes sets. In addition, distinct mutational and neoantigenic loads, primarily composed of C>T transitions, along with specific types of copy number aberrations, characterized each cytolytic subgroup. We found a broader pattern of chromothripsis across CYT-low tumors, where non-telomeric chromosomal regions harboring chromothripsis, contained a higher number of cancer genes. CYT-high patients had markedly higher immunophenoscore and should consequently, display an expected clinical benefit compared to CYT-low patients who either received or not, checkpoint inhibition.
Conclusions: Overall, our data highlight the existence of distinct genomic features across cytolytic subgroups in skin melanoma.
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