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Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics.

ABSTRACT: The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.

SUBMITTER: Liu P

PROVIDER: S-EPMC8770539 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMetastatic adenocarcinoma of unknown primary site (MACUP) is the most common cancer of unknown primary site, and shows worse prognosis. Prediction of its tumor site origin attracts a growing attention. However, the site determined by gene expression profiling does not have a significant impact on the survival. Some other special method might need to be found out.MethodsWe reviewed 1011 MACUP patients diagnosed by pathological examination and immunohistochemistry based on the Surveillance, Epidemiology, and End Results (SEER) database during 2010-2016. Kaplan-Meier curves and Cox proportional hazard model were analyzed to compare the survival. Logistic regression models and relevant nomograms were performed to predicting the probability of the primary site which including digestive system, respiratory system, and female breast. The validation and clinical utility of models were measured with relevant statistical approaches.ResultsAbout 324 (32.1%), 299 (29.6%), and 203 (20.1%) of MACUP patients were identified as the primary sites of digestive system, respiratory system, and female breast, respectively. Patients derived from digestive system and respiratory system showed poorer survival than these with other sites. Digestive system was significantly associated with liver (Odds ratio =13.21 [95% confidence interval =8.48-21.02]) or lung (2.36 [1.40-3.97]) metastasis, while respiratory system was linked to brain (11.68 [6.68-21.26]) or lymph node (3.39 [2.26-5.13]) metastasis. Patients identified as female breast were prone to occur bone metastasis (5.85 [3.68-9.45]). Logistic regression nomograms were developed to help clinicians intuitively predict the probabilities of tumor site origin with 0.867, 0.824, and 0.753 of the C-index, respectively. Decision curve analysis and clinical impact curves both revealed the clinical effectiveness.ConclusionsWe profiled different tumor site origin of MACUP patients and established prediction models. These features might be significant for clinicians to improve the probabilities of predicting the primary sites, and to decide subsequent treatment strategy.

Project description:Carcinoma of unknown primary (CUP) is a difficult-to-manage malignancy. Multi-omic profiles and treatment outcome vs. degree of precision matching were assessed. Tumours underwent next-generation sequencing (NGS) [tissue and/or blood-derived cell-free DNA (cfDNA)]. Selected patients had transcriptome-based immune profiling and/or programmed cell death 1 ligand 1 (PD-L1) immunohistochemistry analysis. Patients could be reviewed by a Molecular Tumor Board, but physicians chose the therapy. Of 6497 patients in the precision database, 97 had CUP. The median number of pathogenic tissue genomic alterations was 4 (range, 0-25), and for cfDNA, was 2 (range, 0-9). Each patient had a distinct molecular landscape. Food and Drug Administration (FDA)-approved biomarkers included the following: PD-L1+ ≥ 1%, 30.9% of CUPs tested; microsatellite instability, 3.6%; tumour mutational burden ≥ 10 mutations·Mb-1, 23%; and neurotrophic receptor tyrosine kinase (NTRK) fusions, 0%. RNA-based immunograms showed theoretically druggable targets: lymphocyte activation gene 3 protein (LAG-3), macrophage colony-stimulating factor 1 receptor (CSF1R), adenosine receptor A2 (ADORA2) and indoleamine 2,3-dioxygenase 1 (IDO1). Overall, 56% of patients had ≥ 1 actionable biomarker (OncoKB database). To quantify the degree of matching (tumours to drugs), a Matching Score (MS; roughly equivalent to number of alterations targeted/total number of deleterious alterations) was calculated post hoc. Comparing evaluable treated patients [MS high, > 50% (N = 15) vs. low ≤ 50% (N = 47)], median progression-free survival was 10.4 vs. 2.8 months (95% CI 0.11-0.64; HR 0.27; P = 0.002); survival, 15.8 vs. 6.9 months (95% CI 0.17-1.16; HR 0.45; P = 0.09); and clinical benefit rate (stable disease ≥ 6 months/partial/complete response), 71% vs. 24% (P = 0.003). Higher MS was the only factor that predicted improvement in outcome variables after multivariate analysis. In conclusion, CUPs are molecularly complex. Treatments with high degrees of matching to molecular alterations (generally achieved by individualized combinations) correlated with improved outcomes.

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Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics.

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