Dataset Information

Risk factors and predictors for tumor site origin in metastatic adenocarcinoma of unknown primary site.

ABSTRACT:

Background

Metastatic 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.

Methods

We 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.

Results

About 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.

Conclusions

We 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.

SUBMITTER: Li X

PROVIDER: S-EPMC7897950 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Risk factors and predictors for tumor site origin in metastatic adenocarcinoma of unknown primary site.

Li Xinrong X Shao Yan Y Sheng Liqiang L Zhu Junquan J Wang Zeming Z Guo Kaibo K Sun Leitao L

Cancer medicine 20210106 3

<h4>Background</h4>Metastatic 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.<h4>Methods</h4>We reviewed 1011 MACUP patients diagnosed by pathological examination and immunohistochemistry based ...[more]

PMID: 33405390

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Project description: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.

Project description:Cancer of unknown primary (CUP) is an umbrella term used to classify a heterogeneous group of metastatic cancers based on the absence of an identifiable primary tumor. Clinically, CUPs are characterized by a set of distinct features comprising early metastatic dissemination in an atypical pattern, an aggressive clinical course, poor response to empiric chemotherapy and, consequently, a short life expectancy. Two opposing strategies to change the dismal prognosis for the better are pursued. On the one hand, following the traditional tissue-gnostic approach, more and more sophisticated tissue-of-origin (TOO) classifier assays are employed to push identification of the putative primary to its limits with the clear intent of allowing tumor-site specific treatment. However, robust evidence supporting its routine clinical use is still lacking, notably with two recent randomized clinical trials failing to show a patient benefit of TOO-prediction based site-specific treatment over empiric chemotherapy in CUP. On the other hand, with regards to a tissue-agnostic strategy, precision medicine approaches targeting actionable genomic alterations have already transformed the treatment for many known tumor types. Yet, an unmet need remains for well-designed clinical trials to scrutinize its potential role in CUP beyond anecdotal case reports. In the absence of practice changing results, we believe that the emphasis on finding the presumed unknown primary tumor at all costs, implicit in the term CUP, has biased recent research in the field. Focusing on the distinct clinical features shared by all CUPs, we advocate adopting the term primary metastatic cancer (PMC) to denominate a distinct cancer entity instead. In our view, PMC should be considered the archetype of metastatic disease and as such, despite accounting for a mere 2-3% of malignancies, unraveling the mechanisms at play goes beyond improving the prognosis of patients with PMC and promises to greatly enhance our understanding of the metastatic process and carcinogenesis across all cancer types.

Dataset Information

Risk factors and predictors for tumor site origin in metastatic adenocarcinoma of unknown primary site.

Background

Methods

Results

Conclusions

Publications

Risk factors and predictors for tumor site origin in metastatic adenocarcinoma of unknown primary site.

Similar Datasets

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