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Accurate Identification of Sites of Origin in Carcinoma of Unknown Primary (CUP) Tumors Using DNA Methylation II

ABSTRACT: 53 tumors used in the clinical training and validation of a CUP machine learning classifier using methylation profiling

ORGANISM(S): Homo sapiens

PROVIDER: GSE249711 | GEO | 2025/01/15

REPOSITORIES: GEO

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Accurate Identification of Sites of Origin in Carcinoma of Unknown Primary (CUP) Tumors Using DNA Methylation I

Project description:53 tumors used in the clinical training and validation of a CUP machine learning classifier using methylation profiling

2025-01-15 | GSE249688 | GEO

Cancers of unknown primary (CUP) are characterized by chromosomal instability (CIN) compared to metastasis of know origin.

Project description:Employing genome wide transcriptome analysis,Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), we created a successful classification model to identify the putative origin of a CUP. Using the classifications of the CUPs enable the comparison of transcriptome profiles from CUPs to the equivalent normal metastasis across the different carcinoma types. Gene Set Enrichment Analysis (GSEA) and Pathway analysis were used to reveal the common biological features characterizing CUPs. The classifier was build using 2208 samples of normal tissue, known primary tumors, metastasis of known origin and tested on 60 CUP samples. 130 of these samples were collected and analyzed as part of the project and submitted to ArrayExpress. The analyses show that CUPs are distinct from metastases of known origin. CUPs exhibit inconsistent expression of conventional cancer biomarkers and QDA derived outlier scores show that CUPs are more distantly related to their primary tumor class than corresponding metastases of known origin. Gene set enrichment analysis showed that CUPs display increased expression of genes involved in DNA damage repair and by employing signatures of chromosome instability (CIN), we found that CUPs are chromosome unstable compared to metastases of known origin.

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Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

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Expression analysis of cancer of unknown primary (CUP) 1

Project description:Biopsies (lymph nodes, ascites or hydrothorax) from 60 patients with cancer of unknown primary origin were analyzed. Results provide insight into the molecular pathogenesis of CUP.

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Expression analysis of cancer of unknown primary (CUP) 2

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Expression analysis of cancer of unknown primary (CUP)

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Accurate Identification of Sites of Origin in Carcinoma of Unknown Primary (CUP) Tumors Using DNA Methylation

Project description:This SuperSeries is composed of the SubSeries listed below.

2025-01-15 | GSE249712 | GEO

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

2013-01-01 | GSE29210 | GEO

Accurate Identification of Sites of Origin in Carcinoma of Unknown Primary (CUP) Tumors Using DNA Methylation

Project description:Accurate Identification of Sites of Origin in Carcinoma of Unknown Primary (CUP) Tumors Using DNA Methylation

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CUPiD, a cfDNA methylation-based tissue-of-origin classifier for Cancers of Unknown Primary

Project description:Cancers of Unknown Primary (CUP) remains a diagnostic and therapeutic challenge due to biological heterogeneity and poor responses to standard chemotherapy. Predicting tissue-of-origin (TOO) molecularly could help refine this diagnosis, with tissue acquisition barriers mitigated via liquid biopsies. However, TOO liquid biopsies have yet to be explored in CUP cohorts. Using publicly available DNA methylation data, we developed a machine learning classifier termed CUPiD with accurate TOO predictions across 29 tumour classes. We tested CUPiD on 143 cfDNA samples from patients with 13 cancer types alongside 27 non-cancer controls, with overall sensitivity of 87.1% and specificity of 97.9%. CUPiD predictions for a further 41 patients with CUP were made for 78.0% of cases, of which 71.9% were clinically consistent with a subsequent or suspected primary tumour diagnosis. Combining CUPiD with cfDNA mutation data demonstrated potential diagnosis re-classification and/or treatment change in this hard-to-treat cancer group.

| EGAS00001007445 | EGA

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