Supplementary Tables S1-S12 from Comprehensive Genetic Characterization of Human Thyroid Cancer Cell Lines: A Validated Panel for Preclinical Studies

dataset

posted on 2023-03-31, 20:23 authored by Iñigo Landa, Nikita Pozdeyev, Christopher Korch, Laura A. Marlow, Robert C. Smallridge, John A. Copland, Ying C. Henderson, Stephen Y. Lai, Gary L. Clayman, Naoyoshi Onoda, Aik Choon Tan, Maria E.R. Garcia-Rendueles, Jeffrey A. Knauf, Bryan R. Haugen, James A. Fagin, Rebecca E. Schweppe

Supplementary Table S1. Cell line authentication by Short Tandem Repeat Profiling (STR) Supplementary Table S2. Full list of genetic variants identified by MSK-IMPACT sequencing Supplementary Table S3. Details for identified high-confidence gene rearrangements resulting in in-frame fusion proteins Supplementary Table S4. Recurrent copy number alterations in thyroid cancer cell lines. Supplementary Table S5. Genes located within the recurrent copy number alteration regions. Supplementary Table S6. Differentially expressed genes, which are located within CNA regions. "Matching_CNA_type" column indicates whether the direction of the expression change corresponds to the CNA type (133 out of 134, overexpression for gene amplification and underexpression for gene deletions). Supplementary Table S7. Thyroid cancer cell lines gene expression data. Normalized gene expression values for all genes in the 44 cell lines assessed by Affymetrix expression microarray in this study ("CU" prefix, University of Colorado). Supplementary Table S8. Genes with differential expression in PTC vs. ATC cell lines. List of differentially expressed genes between PTC-derived vs. ATC-derived cell lines (limma, adjusted p-value<0.05). Supplementary Table S9. Genes with differential expression in PTC vs. ATC tumors. List of top 1000 differentially expressed genes when comparing PTC vs. ATC tumors from other studies (19,20), assessed with the same platform used for cell line transcriptomic profiling. Supplementary Table S10. Thyroid differentiation score (TDS) in thyroid cancer cell lines and tissues. TDS values are calculated for 44 cell lines from this study ("CU" prefix, University of Colorado) and thyroid tissues from other studies, assessed with the same platform: 9 PTC-normal pairs ("He", (19)) 17 PDTCs and 20 ATCs ("Landa", (20)). Supplementary Table S11. Association of TDS16 and TDS13 with clinical and histopathologic characteristics of PTC tumors from the TCGA. P-values calculated with Kruskal-Wallis rank sum test are shown. Supplementary Table S12. BRAFV600E-RAS scores (BRS) and the sensitivity of thyroid cancer cell lines to trametinib and PD-0325901. Drug sensitivity is expressed as a relative area under the dose response curve.

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NCI

Mary Rossick Kern and JeromeHKern Endowment

Alfred D. and Audrey M. Petersen Endowment

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ARTICLE ABSTRACT

Thyroid cancer cell lines are valuable models but have been neglected in pancancer genomic studies. Moreover, their misidentification has been a significant problem. We aim to provide a validated dataset for thyroid cancer researchers. We performed next-generation sequencing (NGS) and analyzed the transcriptome of 60 authenticated thyroid cell lines and compared our findings with the known genomic defects in human thyroid cancers. Unsupervised transcriptomic analysis showed that 94% of thyroid cell lines clustered distinctly from other lineages. Thyroid cancer cell line mutations recapitulate those found in primary tumors (e.g., BRAF, RAS, or gene fusions). Mutations in the TERT promoter (83%) and TP53 (71%) were highly prevalent. There were frequent alterations in PTEN, PIK3CA, and of members of the SWI/SNF chromatin remodeling complex, mismatch repair, cell-cycle checkpoint, and histone methyl- and acetyltransferase functional groups. Copy number alterations (CNA) were more prevalent in cell lines derived from advanced versus differentiated cancers, as reported in primary tumors, although the precise CNAs were only partially recapitulated. Transcriptomic analysis showed that all cell lines were profoundly dedifferentiated, regardless of their derivation, making them good models for advanced disease. However, they maintained the BRAFV600E versus RAS-dependent consequences on MAPK transcriptional output, which correlated with differential sensitivity to MEK inhibitors. Paired primary tumor-cell line samples showed high concordance of mutations. Complete loss of p53 function in TP53 heterozygous tumors was the most prominent event selected during in vitro immortalization. This cell line resource will help inform future preclinical studies exploring tumor-specific dependencies.

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Keywords

Computational Methods Gene expression profiling Endocrine-Related Cancers Gene Technologies Mutation detection methods Genome Biology Preclinical Models Xenograft models Translational Research

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