scholarly journals Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features

2021 ◽  
Author(s):  
Luan Nguyen ◽  
Arne van Hoeck ◽  
Edwin Cuppen
Keyword(s):  
Metastatic Cancer ◽  
Cancer Of Unknown Primary ◽  
Unknown Primary ◽  
Whole Genome ◽  
Routine Diagnostics ◽  
Genome Wide ◽  
Primary Diagnostics ◽  
Primary Location ◽  
Tissue Of Origin ◽  
Increasing Demand

AbstractTumor tissue of origin (TOO) is an important factor for guiding treatment decisions. However, TOO cannot be determined for ~3% of metastatic cancer patients and are categorized as cancers of unknown primary (CUP). As whole genome sequencing (WGS) of tumors is now transitioning from the research domain to diagnostic practice in order to address the increasing demand for biomarker detection, its use for detection of TOO in routine diagnostics also starts becoming within reach. While proof of concept for the use of genome-wide features has been demonstrated before, more complex WGS mutation features, including structural variant (SV) driver and passenger events, have never been integrated into TOO-classifiers even though they bear highly characteristic links with tumor TOO. Using a uniformly processed dataset containing 6820 whole-genome sequenced primary and metastatic tumors, we have developed Cancer of Unknown Primary Location Resolver (CUPLR), a random forest based TOO classifier that employs 502 features based on simple and complex somatic driver and passenger mutations. Our model is able to distinguish 33 cancer (sub)types with an overall accuracy of 91% and 89% based on cross-validation (n=6139) and hold out set (n=681) predictions respectively. We found that SV derived features increase the accuracy and utility of TOO classification for specific cancer types. To ensure that predictions are human-interpretable and suited for use in routine diagnostics, CUPLR reports the top contributing features and their values compared to cohort averages. The comprehensive output of CUPLR is complementary to existing histopathological procedures and may thus improve diagnostics for patients with CUP.

scholarly journals Cancer of Unknown Primary: Challenges and Progress in Clinical Management

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 451
Author(s):  
Noemi Laprovitera ◽  
Mattia Riefolo ◽  
Elisa Ambrosini ◽  
Christiane Klec ◽  
Martin Pichler ◽  
...  
Keyword(s):  
Current Knowledge ◽  
Distant Metastases ◽  
Cancer Of Unknown Primary ◽  
Unknown Primary ◽  
Diagnostic Strategies ◽  
Molecular Features ◽  
Tissue Of Origin ◽  
Novel Therapeutic Strategies ◽  
Aggressive Clinical Behavior

Distant metastases are the main cause of cancer-related deaths in patients with advanced tumors. A standard diagnostic workup usually contains the identification of the tissue-of-origin of metastatic tumors, although under certain circumstances, it remains elusive. This disease setting is defined as cancer of unknown primary (CUP). Accounting for approximately 3–5% of all cancer diagnoses, CUPs are characterized by an aggressive clinical behavior and represent a real therapeutic challenge. The lack of determination of a tissue of origin precludes CUP patients from specific evidence-based therapeutic options or access to clinical trial, which significantly impacts their life expectancy. In the era of precision medicine, it is essential to characterize CUP molecular features, including the expression profile of non-coding RNAs, to improve our understanding of CUP biology and identify novel therapeutic strategies. This review article sheds light on this enigmatic disease by summarizing the current knowledge on CUPs focusing on recent discoveries and emerging diagnostic strategies.

scholarly journals Expression of Glycolysis-Related Proteins in Cancer of Unknown Primary Origin

2021 ◽  
Vol 11 ◽  
Author(s):  
Murilo Bonatelli ◽  
Isabella Fernandes Fornari ◽  
Priscila Neves Bernécule ◽  
Lara Esquiapatti Pinheiro ◽  
Ricardo Filipe Alves Costa ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lymph Nodes ◽  
Glucose Transporter ◽  
Metastatic Cancer ◽  
Multivariable Analysis ◽  
Cancer Of Unknown Primary ◽  
Unknown Primary ◽  
Primary Origin ◽  
Related Proteins ◽  
Unknown Primary Origin

IntroductionCancer of unknown primary origin (CUP) is defined as metastatic cancer without identification of the primary site. Considering that only 15–20% of patients with CUP show a favorable outcome, identifying biomarkers may help improve the clinical management of patients who do not respond well to conventional therapies. In this context, the study of the metabolic profile of CUP may pave the way to establish new biomarkers and/or therapeutic targets; therefore, this study aimed to characterize the expression of metabolism-related proteins in CUP.Materials and MethodsThe expression of monocarboxylate transporters MCT1, MCT2 and MCT4, their chaperone CD147, the glucose transporter GLUT1 and the pH regulator CAIX was evaluated by immunohistochemistry in a series of 118 CUP patients, and the results were associated with the available clinicopathological information.ResultsThe metabolism-related proteins MCT1, MCT4, CD147, GLUT1 and CAIX were expressed in a critical portion of the CUP (approximately 20 to 70%). MCT1 and CD147 were both more frequently expressed in cases with lymph nodes as metastasis dominant sites (p = 0.001) as well as in samples from lymph nodes (p <0.001 and p = 0.002, respectively), while MCT1 expression was more frequently expressed in squamous cell carcinomas (p = 0.045). A higher overall survival was observed in patients with tumors positive for GLUT1 and CAIX expression (p = 0.011 and p = 0.041, respectively), but none of the proteins was an independent prognostic factor for overall survival in multivariable analysis.ConclusionThe results suggest that a portion of CUPs present a hyperglycolytic phenotype, which is associated with higher overall survival.

scholarly journals 90-Gene Expression Profiling for Tissue Origin Diagnosis of Cancer of Unknown Primary

2021 ◽  
Vol 11 ◽  
Author(s):  
Yi Zhang ◽  
Lei Xia ◽  
Dawei Ma ◽  
Jing Wu ◽  
Xinyu Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Cancer Of Unknown Primary ◽  
The Cancer Genome Atlas ◽  
Unknown Primary ◽  
Gene Expression Assay ◽  
Primary Location ◽  
Cancer Genome Atlas ◽  
Expression Characteristics

Cancer of unknown primary (CUP), in which metastatic diseases exist without an identifiable primary location, accounts for about 3–5% of all cancer diagnoses. Successful diagnosis and treatment of such patients are difficult. This study aimed to assess the expression characteristics of 90 genes as a method of identifying the primary site from CUP samples. We validated a 90-gene expression assay and explored its potential diagnostic utility in 44 patients at Jiangsu Cancer Hospital. For each specimen, the expression of 90 tumor-specific genes in malignant tumors was analyzed, and similarity scores were obtained. The types of malignant tumors predicted were compared with the reference diagnosis to calculate the accuracy. In addition, we verified the consistency of the expression profiles of the 90 genes in CUP secondary malignancies and metastatic malignancies in The Cancer Genome Atlas. We also reported a detailed description of the next-generation coding sequences for CUP patients. For each clinical medical specimen collected, the type of malignant tumor predicted and analyzed by the 90-gene expression assay was compared with its reference diagnosis, and the overall accuracy was 95.4%. In addition, the 90-gene expression profile generally accurately classified CUP into the cluster of its primary tumor. Sequencing of the exome transcriptome containing 556 high-frequency gene mutation oncogenes was not significantly related to the 90 genes analysis. Our results demonstrate that the expression characteristics of these 90 genes can be used as a powerful tool to accurately identify the primary sites of CUP. In the future, the inclusion of the 90-gene expression assay in pathological diagnosis will help oncologists use precise treatments, thereby improving the care and outcomes of CUP patients.

Cancer of unknown primary site

2016 ◽  
pp. 965-974
Author(s):  
Nicholas Pavlidis ◽  
George Pentheroudakis
Keyword(s):  
Expression Profiling ◽  
Cancer Of Unknown Primary ◽  
Primary Site ◽  
Unknown Primary ◽  
Unknown Primary Site ◽  
Clinical Intuition ◽  
Genome Wide ◽  
Genetic Signatures ◽  
Genome Wide Expression ◽  
Tumour Origin

This chapter covers cancer of unknown primary site (CUP), and includes information on epidemiology, molecular biology, pathology, and multidisciplinary management of clinicopathological subsets. Previously, these tumours were diagnosed and treated based on clinical presentation, light microscopy and clinical intuition. Today, the majority of cancers of unknown primary site are becoming less unknown, more accurately classified, and appropriately treated by the use of multiplex or genome-wide expression profiling platforms. These techniques allow for precise and correct knowledge of the true tumour origin, leading to more rational and effective treatment. However, there also may be genetic signatures that are primary-independent, pro-metastatic, and possibly CUP-specific.

A new, palliative care led, pathway approach to care in metastatic cancer of unknown primary origin

2012 ◽  
Vol 2 (Suppl 1) ◽  
pp. A46.1-A46
Author(s):  
Philippa Hughes ◽  
Clare Farrington ◽  
David Brooks ◽  
Peter Bath ◽  
Bill Noble
Keyword(s):  
Palliative Care ◽  
Metastatic Cancer ◽  
Cancer Of Unknown Primary ◽  
Unknown Primary ◽  
Primary Origin ◽  
Unknown Primary Origin

scholarly journals The yield and cost of colonoscopy in patients with metastatic cancer of unknown primary

2013 ◽  
Vol 38 (6) ◽  
pp. 628-633 ◽  
Author(s):  
M. Saliminejad ◽  
S. Bemanian ◽  
A. Ho ◽  
B. Spiegel ◽  
L. Laine
Keyword(s):  
Metastatic Cancer ◽  
Cancer Of Unknown Primary ◽  
Unknown Primary

Prediction of Tissue of Origin and Molecular Subtypes for Cancer of Unknown Primary Using Machine Learning

2020 ◽  
Author(s):  
Yue Zhao ◽  
Sandeep Namburi ◽  
Ziwei Pan ◽  
Carolyn A. Paisie ◽  
Honey V. Reddi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Subtypes ◽  
Cancer Of Unknown Primary ◽  
Unknown Primary ◽  
Tissue Of Origin

Diagnostic accuracy of molecular tumor profiling in determining the tissue of origin in adult patients with cancer of unknown primary: A meta-analysis.

2017 ◽  
Vol 35 (7_suppl) ◽  
pp. 20-20
Author(s):  
Rowena Torres Inocencio ◽  
Kavitha Kesari ◽  
Susan Smith
Keyword(s):  
Diagnostic Accuracy ◽  
Diagnostic Test ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Cancer Of Unknown Primary ◽  
Adult Patients ◽  
Unknown Primary ◽  
Test Accuracy ◽  
Tissue Of Origin ◽  
Tumor Profiling

20 Background: Appropriate cancer treatment requires determination of the primary site of origin. The standard of care for diagnosing Cancer of unknown primary (CUP) based on the 2016 NCCN Guidelines is a comprehensive clinical work-up including biopsy and immunohistochemistry (IHC). However, IHC was able to identify a primary tumor in only 25% of CUP. Molecular tumor profiling (MTP) validation studies demonstrated sensitivities of 74-89% and specificities of 95-99%. Only a few studies with small sample sizes using MTP to diagnose the primary in CUP have been performed yielding variable results. This study aims to evaluate the diagnostic accuracy of MTP in determining the tissue of origin in adult patients with CUP. Methods: Literature search included articles published in any language indexed in MEDLINE and Cochrane. A Google Scholar search and a review of all published articles’ references were performed. Unpublished studies and abstracts from conference proceedings were also reviewed. Studies involving adult patients with CUP who underwent both MTP and clinical evaluation with IHC were identified. Articles were included if they had primary data sufficient to calculate both sensitivity and specificity. Methodological qualities of the included studies were evaluated using the QUADAS-2 tool. Data was analyzed using Review Manager 5.3 and MetaDiSc 1.4. Results: Seven studies with 549 patients were analyzed. MTP was found to have a pooled sensitivity of 89% (95% CI 0.85-0.92)and pooled specificity of 74% (95% CI 0.67-0.80)with pooled positive and negative likelihood ratios of 2.97 (95% CI 1.17-7.63) and 0.20 (95% CI 0.07-0.51), respectively. Area under the curve was measured to be 0.9107. Conclusions: This meta-analysis suggests that MTP could be useful in determining the tissue of origin in adult patients with CUP. With an area under the curve of 0.9107, it is an excellent diagnostic test. The results showed considerable heterogeneity as expected in meta-analyses of diagnostic test accuracy. In this case, it may be due to variability among the MTP assays used which may reflect different cutoffs.

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