Analytic approaches to clinical validation of results from preclinical models of glioblastoma: a systematic review

AbstractBackgroundAnalytic approaches to clinical validation of results from preclinical models are important in assessment of their relevance to human disease. This systematic review examined consistency in reporting of glioblastoma cohorts from The Cancer Genome Atlas (TCGA) and assessed whether studies included patient characteristics in their survival analyses.MethodsWe searched Embase and Medline on 02Feb21 for studies using preclinical models of glioblastoma published after Jan2008 that used data from TCGA to validate the association between at least one molecular marker and overall survival in adult patients with glioblastoma. Main data items included cohort characteristics, statistical significance of the survival analysis, and model covariates.ResultsThere were 58 eligible studies from 1,751 non-duplicate records investigating 126 individual molecular markers. In 14 studies published between 2017 and 2020 using TCGA RNA microarray data that should have the same cohort, the median number of patients was 464.5 (interquartile range 220.5-525). Of the 15 molecular markers that underwent more than one univariable or multivariable survival analyses, five had discrepancies between studies. Covariates used in the 17 studies that used multivariable survival analyses were age (76.5%), pre-operative functional status (35.3%), sex (29.4%) MGMT promoter methylation (29.4%), radiotherapy (23.5%), chemotherapy (17.6%), IDH mutation (17.6%) and extent of resection (5.9%).ConclusionsPreclinical glioblastoma studies that used TCGA for validation did not provide sufficient information about their cohort selection and there were inconsistent results. Transparency in reporting and the use of analytic approaches that adjust for clinical variables can improve the reproducibility between studies.Importance of the StudyDespite using the same data from The Cancer Genome Atlas, translational preclinical studies in glioblastoma research included different numbers of patients into their analyses and their results were inconsistent.Fewer than a third of the studies used multivariable survival analysis to adjust for clinical variables but most did not take treatment factors into account.Greater transparency in cohort selection from open access data and integration of clinical variables into analyses will help improve reproducibility in glioblastoma research.

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P13.04 Survival analyses for determining clinical relevance of molecular markers in translational glioblastoma research - a systematic review

Neuro-Oncology ◽

10.1093/neuonc/noab180.114 ◽

2021 ◽

Vol 23 (Supplement_2) ◽

pp. ii33-ii33

Author(s):

B Fitt ◽

G Loy ◽

E Christopher ◽

P M Brennan ◽

M T C Poon

Keyword(s):

Survival Analysis ◽

Molecular Markers ◽

Patient Survival ◽

Genomic Data ◽

The Cancer Genome Atlas ◽

Inclusion Criteria ◽

Survival Analyses ◽

Clinical Variables ◽

Multivariable Analyses ◽

Genome Atlas

Abstract BACKGROUND Pre-clinical glioblastoma studies can assess the relevance of their findings to patient survival using integrated clinical and genomic data. Validity of univariable analyses requires an assumption that molecular markers are randomly distributed across patient characteristics to mitigate the confounding effects of clinical variables. Multivariable survival analyses adjusting for clinical variables do not change the association if this assumption holds. We aimed to assess this by summarising the types of survival analyses and their results in translational glioblastoma research. MATERIAL AND METHODS We systematically searched Medline and Embase Jan 2008 to Feb 2021 for glioblastoma cell line or animal studies validating their molecular markers in The Cancer Genome Atlas (TCGA) or the Chinese Glioma Genome Atlas (CGGA) using survival analyses. Studies that exclusively used genomic data without laboratory findings were excluded. Two reviewers independently assessed study eligibility and extracted data. Data items included patient inclusion criteria, characteristics of survival analyses, and whether molecular markers had statistically significant association with overall survival. RESULTS Of 1,047 potentially eligible studies, we included 59 pre-clinical glioblastoma studies that tested the association between their molecular markers and survival using TCGA or CGGA data. All studies used TCGA data and 2 also used CGGA data. Sixteen (27%) studies specified their patient inclusion criteria from TCGA for survival analysis. Eight studies exclusively investigated sets of molecular markers, leaving 51 studies reporting 126 molecular markers. Eighteen (31%) studies used multivariable survival analysis in addition to univariable analyses. All molecular markers underwent univariable analyses, of which 12 (10%) molecular markers had additional multivariable survival analyses. In the 13 multivariable analyses on 12 molecular markers, four (31%) markers were associated with survival in the univariable analyses but not in the multivariable analyses. CONCLUSION Most pre-clinical studies used univariable survival analyses alone in public genomic repositories to assess the relevance of their results to patient survival. Our findings demonstrated that multivariable analyses are needed to account for confounding effects of clinical variables. Using relevant components from reporting guidelines for observational studies can improve the transparency and quality of translational studies.

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Special Report: The Cancer Genome Atlas Begins with 3-Year, $100 Million Pilot

PsycEXTRA Dataset ◽

10.1037/e481292006-009 ◽

2005 ◽

Author(s):

Keyword(s):

Cancer Genome ◽

The Cancer Genome Atlas ◽

Special Report ◽

Cancer Genome Atlas ◽

Genome Atlas

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Differences in endometrial cancer molecular portraits based on ethnicity in The Cancer Genome Atlas

10.26226/morressier.59ba7298d462b80296ca20c6 ◽

2017 ◽

Author(s):

David Guttery

Keyword(s):

Endometrial Cancer ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Cancer Genome Atlas ◽

Genome Atlas

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Comprehensive assessment of PD-L1 and PD-L2 dysregulation in gastrointestinal cancers

Epigenomics ◽

10.2217/epi-2020-0093 ◽

2020 ◽

Author(s):

Qijie Zhao ◽

Jinan Guo ◽

Yueshui Zhao ◽

Jing Shen ◽

Parham Jabbarzadeh Kaboli ◽

...

Keyword(s):

Transcription Factor ◽

Signaling Pathways ◽

Underlying Mechanism ◽

Comprehensive Analysis ◽

The Cancer Genome Atlas ◽

Gastrointestinal Cancers ◽

Tumor Mutation Burden ◽

Mutation Burden ◽

Cancer Genome Atlas ◽

Genome Atlas

Background: PD-L1 and PD-L2 are ligands of PD-1. Their overexpression has been reported in different cancers. However, the underlying mechanism of PD-L1 and PD-L2 dysregulation and their related signaling pathways are still unclear in gastrointestinal cancers. Materials & methods: The expression of PD-L1 and PD-L2 were studied in The Cancer Genome Atlas and Genotype-Tissue Expression databases. The gene and protein alteration of PD-L1 and PD-L2 were analyzed in cBioportal. The direct transcription factor regulating PD-L1/ PD-L2 was determined with ChIP-seq data. The association of PD-L1/PD-L2 expression with clinicopathological parameters, survival, immune infiltration and tumor mutation burden were investigated with data from The Cancer Genome Atlas. Potential targets and pathways of PD-L1 and PD-L2 were determined by protein enrichment, WebGestalt and gene ontology. Results: Comprehensive analysis revealed that PD-L1 and PD-L2 were significantly upregulated in most types of gastrointestinal cancers and their expressions were positively correlated. SP1 was a key transcription factor regulating the expression of PD-L1. Conclusion: Higher PD-L1 or PD-L2 expression was significantly associated with poor overall survival, higher tumor mutation burden and more immune and stromal cell populations. Finally, HIF-1, ERBB and mTOR signaling pathways were most significantly affected by PD-L1 and PD-L2 dysregulation. Altogether, this study provided comprehensive analysis of the dysregulation of PD-L1 and PD-L2, its underlying mechanism and downstream pathways, which add to the knowledge of manipulating PD-L1/PD-L2 for cancer immunotherapy.

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RUNX1 and REXO2 are associated with the heterogeneity and prognosis of IDH wild type lower grade glioma

Scientific Reports ◽

10.1038/s41598-021-91382-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Haiwei Wang ◽

Xinrui Wang ◽

Liangpu Xu ◽

Ji Zhang ◽

Hua Cao

Keyword(s):

Transcription Factor ◽

Low Frequency ◽

Tumor Grade ◽

The Cancer Genome Atlas ◽

Lower Grade ◽

Wild Type ◽

Lower Grade Glioma ◽

Cancer Genome Atlas ◽

Worse Prognosis ◽

Genome Atlas

AbstractBased on isocitrate dehydrogenase (IDH) alterations, lower grade glioma (LGG) is divided into IDH mutant and wild type subgroups. However, the further classification of IDH wild type LGG was unclear. Here, IDH wild type LGG patients in The Cancer Genome Atlas and Chinese Glioma Genome Atlas were divided into two sub-clusters using non-negative matrix factorization. IDH wild type LGG patients in sub-cluster2 had prolonged overall survival and low frequency of CDKN2A alterations and low immune infiltrations. Differentially expressed genes in sub-cluster1 were positively correlated with RUNX1 transcription factor. Moreover, IDH wild type LGG patients with higher stromal score or immune score were positively correlated with RUNX1 transcription factor. RUNX1 and its target gene REXO2 were up-regulated in sub-cluster1 and associated with the worse prognosis of IDH wild type LGG. RUNX1 and REXO2 were associated with the higher immune infiltrations. Furthermore, RUNX1 and REXO2 were correlated with the worse prognosis of LGG or glioma. IDH wild type LGG in sub-cluster2 was hyper-methylated. REXO2 hyper-methylation was associated with the favorable prognosis of LGG or glioma. At last, we showed that, age, tumor grade and REXO2 expression were independent prognostic factors in IDH wild type LGG.

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Mining The Cancer Genome Atlas gene expression data for lineage markers in distinguishing bladder urothelial carcinoma and prostate adenocarcinoma

Scientific Reports ◽

10.1038/s41598-021-85993-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ewe Seng Ch’ng

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

The Cancer Genome Atlas ◽

Relative Importance ◽

Expression Data ◽

Gene Expressions ◽

Urothelial Carcinomas ◽

Cancer Genome Atlas ◽

Lineage Markers ◽

Genome Atlas

AbstractDistinguishing bladder urothelial carcinomas from prostate adenocarcinomas for poorly differentiated carcinomas derived from the bladder neck entails the use of a panel of lineage markers to help make this distinction. Publicly available The Cancer Genome Atlas (TCGA) gene expression data provides an avenue to examine utilities of these markers. This study aimed to verify expressions of urothelial and prostate lineage markers in the respective carcinomas and to seek the relative importance of these markers in making this distinction. Gene expressions of these markers were downloaded from TCGA Pan-Cancer database for bladder and prostate carcinomas. Differential gene expressions of these markers were analyzed. Standard linear discriminant analyses were applied to establish the relative importance of these markers in lineage determination and to construct the model best in making the distinction. This study shows that all urothelial lineage genes except for the gene for uroplakin III were significantly expressed in bladder urothelial carcinomas (p < 0.001). In descending order of importance to distinguish from prostate adenocarcinomas, genes for uroplakin II, S100P, GATA3 and thrombomodulin had high discriminant loadings (> 0.3). All prostate lineage genes were significantly expressed in prostate adenocarcinomas(p < 0.001). In descending order of importance to distinguish from bladder urothelial carcinomas, genes for NKX3.1, prostate specific antigen (PSA), prostate-specific acid phosphatase, prostein, and prostate-specific membrane antigen had high discriminant loadings (> 0.3). Combination of gene expressions for uroplakin II, S100P, NKX3.1 and PSA approached 100% accuracy in tumor classification both in the training and validation sets. Mining gene expression data, a combination of four lineage markers helps distinguish between bladder urothelial carcinomas and prostate adenocarcinomas.

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Exceptional Chemotherapy Response in Metastatic Colorectal Cancer Associated With Hyper-Indel–Hypermutated Cancer Genome and Comutation of POLD1 and MLH1

JCO Precision Oncology ◽

10.1200/po.16.00015 ◽

2017 ◽

pp. 1-12

Author(s):

Manish R. Sharma ◽

James T. Auman ◽

Nirali M. Patel ◽

Juneko E. Grilley-Olson ◽

Xiaobei Zhao ◽

...

Keyword(s):

Colorectal Cancer ◽

Colon Cancer ◽

Somatic Mutation ◽

Mutation Rate ◽

Germ Line ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Response To Chemotherapy ◽

Cancer Genome Atlas ◽

Genome Atlas

Purpose A 73-year-old woman with metastatic colon cancer experienced a complete response to chemotherapy with dose-intensified irinotecan that has been durable for 5 years. We sequenced her tumor and germ line DNA and looked for similar patterns in publicly available genomic data from patients with colorectal cancer. Patients and Methods Tumor DNA was obtained from a biopsy before therapy, and germ line DNA was obtained from blood. Tumor and germline DNA were sequenced using a commercial panel with approximately 250 genes. Whole-genome amplification and exome sequencing were performed for POLE and POLD1. A POLD1 mutation was confirmed by Sanger sequencing. The somatic mutation and clinical annotation data files from the colon (n = 461) and rectal (n = 171) adenocarcinoma data sets were downloaded from The Cancer Genome Atlas data portal and analyzed for patterns of mutations and clinical outcomes in patients with POLE- and/or POLD1-mutated tumors. Results The pattern of alterations included APC biallelic inactivation and microsatellite instability high (MSI-H) phenotype, with somatic inactivation of MLH1 and hypermutation (estimated mutation rate > 200 per megabase). The extremely high mutation rate led us to investigate additional mechanisms for hypermutation, including loss of function of POLE. POLE was unaltered, but a related gene not typically associated with somatic mutation in colon cancer, POLD1, had a somatic mutation c.2171G>A [p.Gly724Glu]. Additionally, we noted that the high mutation rate was largely composed of dinucleotide deletions. A similar pattern of hypermutation (dinucleotide deletions, POLD1 mutations, MSI-H) was found in tumors from The Cancer Genome Atlas. Conclusion POLD1 mutation with associated MSI-H and hyper-indel–hypermutated cancer genome characterizes a previously unrecognized variant of colon cancer that was found in this patient with an exceptional response to chemotherapy.

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