Pan-cancer analysis of genomic properties and clinical outcome associated with tumor tertiary lymphoid structure.

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Utilizing the genomic and transcriptome data of solid tumors on TCGA, we quantified TLS based on a previous identified 12-chemokine signature and evaluated its correlation with mutation/neoantigen burden, functional mutation of oncogenes and the presence of viral infection. Clinical data was integrated to decide the prognostic significance of TLS for different cancers after surgical treatment. Publicly available data (clinical and transcriptome data) of immunotherapy clinical trials involving melanoma and lung cancer were also collected to evaluate TLS's association with therapeutic outcome. Mutation burden and predicted neoantigen counts were positively correlated with TLS scoring in multiple cancer types. Mutation in tumor suppressor genes (KEAP1, PBRM1) and genes involved in extrinsic apoptosis (CASP8), antigen-presentation (HLA-A, HLA-B), immune regulation (SMAD4) or DNA repair (BRCA1, BRCA2, TP53BP1) correlated with TLS alteration in multiple tumor types, indicating the interaction between mutation landscape and TLS formation. Epstein-Barr virus (EBV) infection in gastric cancer and human papillomavirus (HPV) infection in Head and Neck squamous cell carcinoma were associated with increased TLS scoring. High TLS scoring predicted favorable prognosis in certain cancer after surgical treatment and improved response to immunotherapy in lung cancer and melanoma. Our findings unraveled the genomic properties associated with TLS formation in different solid tumors and highlighted the prognostic and predictive significance of TLS in surgical treatment and immunotherapy.", "claims": [{"id": "claim-method", "statement": "Spearman rank correlation analysis was performed to evaluate the correlation between TLS abundance and mutation/neo-antigens load.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33299035", "locator": "sections[17] \"Statistical analysis\" paragraph 2", "quote": "Spearman rank correlation analysis was performed to evaluate the correlation between TLS abundance and mutation/neo-antigens load."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Fastq files of RNA-Seq data for 8672 tumor samples and 741 normal samples across 24 cancer types downloaded from TCGA hub (https://portal.gdc.cancer.gov/) were preprocessed with \"Rsubread\" R package", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33299035", "locator": "sections[11] \"Data sets of tumor and normal samples\" paragraph 2", "quote": "Fastq files of RNA-Seq data for 8672 tumor samples and 741 normal samples across 24 cancer types downloaded from TCGA hub (https://portal.gdc.cancer.gov/) were preprocessed with \"Rsubread\" R package"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:33299035"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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