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Mutational Signature and Transcriptomic Classification Analyses as the Decisive Diagnostic Tools for a Cancer of Unknown Primary with Neuroendocrine Differentiation

ABSTRACT: PURPOSE: Cancer of unknown primary (CUP) is a group of metastatic tumors in which the standard diagnostic work-up fails to identify the site of origin of the tumor. The potential impact of precision oncology on this group of patients is large since their tumors might have actionable driver mutations that can provide treatment options otherwise not available for patients with these fatal cancers. This study investigated if comprehensive genomics analyses could inform on the origin of the tumor. PATIENT AND METHODS: Here we describe a patient whose tumor was misdiagnosed at least three times. Next-generation sequencing, a PDX mouse model and bioinformatics was used to identify an actionable mutation, predict resistance development to the targeted therapy, and to correctly diagnose the origin of the tumor. The Cancer Genome Atlas was used to benchmark the bioinformatics workflow. RESULTS: Despite the lack of a known primary tumor site and the absence of diagnostic immunohistochemical markers, the origin of the patient's tumor was established using the novel bioinformatics workflow. This included a mutational signature analysis of the sequenced metastases and comparison of their transcriptomic profiles to a pan-cancer panel of tumors from The Cancer Genome Atlas. We further discuss the strengths and limitations of the latter approaches in the context of three potentially incorrectly diagnosed TCGA lung tumors. CONCLUSION: Comprehensive genomics analyses could inform on the origin of tumors in patients suffering from CUP.

PROVIDER: EGAS00001003026 | EGA |

REPOSITORIES: EGA

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Project description:Aberrant DNA methylation is frequently observed in breast cancer. However, the relationship between methylation patterns and the heterogeneity of breast cancer has not been comprehensively characterized. Whole-genome DNA methylation analysis using 450K Illumina BeadArrays was performed on 188 human breast tumors. Unsupervised bootstrap consensus clustering was performed to identify DNA methylation epigenetic subgroups (epitypes). The Cancer Genome Atlas data, incluing methylation profiles of 669 human breast tumors, was utilized for validation. The identified epitypes were characterized by integration with publicly available genome-wide data, including gene expression levels, DNA copy numbers, whole-exome sequencing data, and chromatin states. We identified seven breast cancer epitypes. One epitype was distinctly associated with basal-like tumors and with BRCA1 mutations, one epitype contained a subset of ERBB2-amplified tumors characterized by multiple additional amplifications and the most complex genomes, and one epitype displayed a methylation profile similar to normal epithelial cells. Luminal tumors were stratified into the remaining four epitypes, with differences in promoter hypermethylation, global hypomethylation, proliferative rates and genomic instability. We observed two dominant patterns of aberrant methylation in breast cancer. One pattern, constitutively methylated in both basal-like and luminal breast cancer, was linked to genes with promoters in a Polycomb-repressed state in normal epithelial cells and displayed no correlation to gene expression levels. The second pattern correlated with gene expression levels and was associated with methylation in luminal tumors and genes with active promoters in normal epithelial cells. Our results suggest that hypermethylation patterns in basal-like breast cancer may have limited influence on tumor progression and instead reflects the repressed chromatin state of the tissue of origin. On the contrary, hypermethylation patterns specific to luminal breast cancer influence gene expression, may contribute to tumor progression, and may present an actionable epigenetic alteration in some luminal breast cancers. Genome-wide DNA methylation analysis of 188 breast cancers using Illumina Human Methylation 450K Beadchips.

Project description:Background: Fatal cancer is often the result of spread, or metastasis, of a cancer cell from the site of its origin to a distant anatomic site. While the metastatic process and the foreign environment of the metastatic site impact a tumorâ??s biology, we continue to determine therapy for patients based upon their cancerâ??s site of origin. We have performed an unbiased analysis across metastatic solid tumors from common primary sites to determine the molecular impact of the metastatic process on site-specific biology and to identify novel therapeutic strategies. Methods: Global gene expression was used as a biological phenotype to perform a top-down analysis of 96 metastatic human tumors. Laser capture microdissection, RNA amplification, and microarray analysis were used to measure the transcription patterns of malignant epithelial cells. Genes, multi-gene expression â??signaturesâ??, and pathways associated with site of origin (SOO) and site of metastases (SOM) were identified using established computational approaches. SOO and SOM expression signatures were validated on multiple, independent datasets comprising 1217 samples (1104 samples from GSE2109 (Expression Project for Oncology) and 113 samples from GSE12630 (Monzon FA, Lyons-Weiler M, Buturovic LJ, Rigl CT et al. Multicenter validation of a 1,550-gene expression profile for identification of tumor tissue of origin. J Clin Oncol 2009 May 20;27(15):2503-8. PMID: 19332734). Reverse phase proteomics and in vitro tissue culture were used to validate associations between biological pathways, site of primary, and implicated therapeutic combinations. Findings: SOO has the dominant influence on solid tumor biology as samples segregate based upon their primary site during unsupervised hierarchical clustering. In addition, statistically significant associations are identified between single genes and pathways and each primary site investigated and SOO signatures for colon, breast, ovary, lung, and prostate cancers accurately identify primary site for independent samples of both local and metastatic tumors independent of degree of histological differentiation. The impact of SOM on tumor biology is evident as genes and pathways are significantly associated with metastatic site and SOM signatures can be generated but they are not strongly predictive when applied to localized tumors. Pathway analysis identified relatively increased expression of MYC, beta-catenin, and SRC gene sets in metastatic colorectal cancers which was confirmed with proteomic analysis of a sub-set of the original tumors. Within colorectal cancers, high SRC expression also correlates with predicted oxaliplatin sensitivity and the combination of an SRC inhibitor with oxaliplatin demonstrated synergy in three independent colorectal cancer cell lines. Interpretation: Our findings suggest that the complex alterations required for metastasis do not obscure the impact of a cancer cellâ??s origin. SOO signatures have the potential to be highly accurate diagnostic tools and the underlying site-specific biology can be used to identify novel therapeutic targets for advanced cancers. Keywords: Gene expression analysis Ninety-six laser capture microdissected adenocarcinoma patient tumor samples of various primary and metastatic sites were processed for Total RNA. Our 96-sample datatset was enriched by inclusion of previously deposited microarray data in GEO (reprocessed for this study): A total of 1217 samples (1104 samples from GSE2109, 113 samples from GSE12630) were reprocessed from the CEL files using RMA. Supplementary files: The reprocessed data matrices. A list of the 1217 Samples' GSM accession numbers and the corresponding reprocessed sample IDs.

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Mutational Signature and Transcriptomic Classification Analyses as the Decisive Diagnostic Tools for a Cancer of Unknown Primary with Neuroendocrine Differentiation

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