scholarly journals TRANSCUP: a scalable workflow for predicting cancer of unknown primary based on next-generation transcriptome profiling

2019 ◽  
Author(s):  
Peng Li
Keyword(s):  
Clinical Application ◽  
High Throughput Sequencing ◽  
Model Building ◽  
Transcriptome Profiling ◽  
Cancer Of Unknown Primary ◽  
Cancer Site ◽  
Unknown Primary ◽  
Cancer Type ◽  
Tumor Type ◽  
Type Classification

AbstractSummaryCancer of unknown primary site (CUP) accounts for 5% of all cancer diagnoses. These patients may benefit from more precise treatment when primary cancer site was identified. Advances in high-throughput sequencing have enabled cost-effective sequencing the transcriptome for clinical application. Here, we present a free, scalable and extendable software for CUP predication called TRANSCUP, which enables (1) raw data processing, (2) read mapping, (3) quality re-port, (4) gene expression quantification, (5) random forest machine learning model building for cancer type classification. TRANSCUP achieved high accuracy, sensitivity and specificity for tumor type classification based on external RNA-seq datasets. It has potential for broad clinical application for solving the CUP problem.AvailabilityTRANSCUP is open-source and freely available at https://github.com/plsysu/[email protected]

scholarly journals Management of cancer of unknown primary

2020 ◽  
Vol 48 (2-3) ◽  
pp. 85-88
Author(s):  
Iva Andrašek ◽  
◽  
Mirna Ravlić ◽  
Martina Mikulandra ◽  
Franjo Cmrečak ◽  
...  
Keyword(s):  
Metastatic Tumor ◽  
Cancer Of Unknown Primary ◽  
Primary Site ◽  
Unknown Primary ◽  
Combined Modality Treatment ◽  
Tumor Type ◽  
Patients With Cancer ◽  
Poorly Differentiated ◽  
Primary Origin ◽  
Chemotherapy Agents

Cancer of an unknown primary site is most commonly an aggressive metastatic tumor with a median patient survival of 6 to 9 months. Histologically, it is predominantly adenocarcinoma, and if the primary site is subsequently diagnosed, it is usually the pancreas or lung. Biopsy should be performed whenever possible to classify a tumor of unknown primary origin into one of the following entities: adenocarcinoma, poorly differentiated carcinoma with characteristics similar to adenocarcinoma, squamous cell carcinoma, neuroendocrine carcinoma, poorly differentiated neoplasm. After determining the primary tumor type, the subtype is determined by immunohistochemical staining. In oligometastatic disease, there is a possibility of surgical treatment. Radiotherapy is used as a part of combined modality treatment. Most patients with cancer of unknown primary have an unfavorable prognosis despite multiple chemotherapy agents, and no protocol can be recommended as standard therapy.

scholarly journals Deep Discriminative Fine-Tuning for Cancer Type Classification

2019 ◽  
Author(s):  
Alena Harley
Keyword(s):  
Classification Accuracy ◽  
State Of The Art ◽  
Point Mutations ◽  
Primary Site ◽  
Tumor Classification ◽  
Fine Tuning ◽  
Cancer Tissue ◽  
Cancer Type ◽  
Tumor Type ◽  
Type Classification

AbstractDetermining the primary site of origin for metastatic tumors is one of the open problems in cancer care because the efficacy of treatment often depends on the cancer tissue of origin. Classification methods that can leverage tumor genomic data and predict the site of origin are therefore of great value.Because tumor DNA point mutation data is very sparse, only limited accuracy (64.5% for 12 tumor classes) was previously demonstrated by methods that rely on point mutations as features (1). Tumor classification accuracy can be greatly improved (to over 90% for 33 classes) by relying on gene expression data (2). However, this additional data is often not readily available in clinical setting, because point mutations are better profiled and targeted by clinical mutational profiling.Here we sought to develop an accurate deep transfer learning and fine-tuning method for tumor sub-type classification, where predicted class is indicative of the primary site of origin. Our method significantly outperforms the state-of-the-art for tumor classification using DNA point mutations, reducing the error by more than 30% at the same time discriminating over many more classes on The Cancer Genome Atlas (TCGA) dataset. Using our method, we achieve state-of-the-art tumor type classification accuracy of 78.3% for 29 tumor classes relying on DNA point mutations in the tumor only.

Disparities between high-impact clinical trials in oncology and disease burden.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e17591-e17591
Author(s):  
Nicklas Pfanzelter ◽  
Neha Jeurkar ◽  
Brian S. Finkelman ◽  
Ronac Mamtani
Keyword(s):  
Breast Cancer ◽  
Clinical Trials ◽  
Disease Burden ◽  
High Impact ◽  
World Health ◽  
Cancer Site ◽  
Years Of Life Lost ◽  
Cancer Type ◽  
Tumor Type ◽  
Goodness Of Fit Test

e17591 Background: Recent research has identified a discrepancy between NIH funding in oncology and various measures of disease burden by tumor type. We sought to identify whether this disparity exists in recent high impact publications. Methods: 833 clinical trials published in five high impact general medicine and clinical oncology journals between January, 2009 and October, 2012 were reviewed. 692 trials were included in this analysis after excluding those that studied >1 tumor type. Disease burden was measured as person-years of life lost (YLLs), reported in the Surveillance, Epidemiology, and End Results database, and disability adjusted life years (DALYs), reported by the World Health Organization. We used a chi square goodness of fit test to compare the overall distribution of trials by tumor type to the distribution of annual YLLs and DALYs. Results: Breast cancer was the most published tumor, accounting for 14% of all trials, followed by lung (13%) and colorectal (7%) cancers. More than half of the trials (56%) were for patients with metastatic disease, and most (81%) were phase 2 and 3 clinical trials. Nearly half of all publications studied targeted therapies (45%), and the majority received industry support (61%). 67% of trials with a comparator arm met their primary endpoint. The distribution of trials by cancer site differed significantly from the distribution of both measures of disease burden (YLLs and DALYs) (both p<0.001). The findings were unchanged in analyses that accounted for the total number of subjects enrolled in the trials (both p<0.001). The most underrepresented malignancies based on burden of disease were lung and pancreatic cancers, while the most overrepresented were breast cancer, leukemia, and melanoma. Conclusions: The number of trials published by tumor type does not directly reflect the burden of these diseases in the population as assessed by YLL or DALY. Future studies examining potential confounders such as funding availability by cancer type or number of unpublished clinical trials may further clarify the observed disparities.

Identification of cancer of unknown primary with gene expression profiling

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 10052-10052
Author(s):  
H. Li ◽  
K. Qu ◽  
K. Tokoro ◽  
Y. Ren ◽  
J. Y. Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Real Time Pcr ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Of Unknown Primary ◽  
Unknown Primary ◽  
Tumor Type ◽  
Tumor Origin ◽  
Tumor Types

10052 Background: Patients with metastatic cancer of unknown primary (CUP) generally have a poor prognosis, with a median survival of 2–10 months. Conventional diagnostic approaches for identifying the primary tumor site are successful in only 20%-30% of cases; however, such identification provides prognostic information and helps with selection of tumor-specific therapy, leading to improved survival. Recent studies indicate that gene expression-based classification of CUP is highly successful in predicting the site of origin. We report herein development and validation of a method that determines the site of tumor origin by comparing the gene expression profiles of CUP cases to those in a database created from known tumor types. Methods: RNA extracted from frozen and formalin-fixed, paraffin-embedded (FFPE) tissue wasis purified and amplified using the Paradise Reagent System System (Arcturus, Mountain View, CA). Following reverse-transcription, cDNA products wereare used in a semi-quantitative real-time PCR to detect 87 tumor-associated genes and 5 reference genes in an ABI PRISM 7900HT Detection System (Applied Biosystems, Foster City, CA). Gene expression data wereare then compared to those in a database, composed of gene expression profiles of 571 samples from 39 different tumor types, using k-nearest neighbor analysis to predict the most likely site of tumor origin. Intra- and interassay reproducibility was determined. Frozen and FFPE tissues (n=57) from a well-characterized, independent sample set were also tested in a blinded manner to further validate the method. Results: Based on the real-time PCR cycle threshold, the intra- and interassay reproducibility ranged from 0.1%-4.3% and 0.5%-8.2%, respectively. The primary tumor type was identified in 77% of cases. The assay determined the correct tumor type in 88% (44/50) of the samples. Seven samples were not reported: 3 failed to amplify adequately and 4 had an unacceptably low confidence level. Conclusions: We have shown that gene expression profiling can determine the most likely site of tumor origin. Our data suggest that this new method is able tomay identify the primary site of tumor origin in 77% of CUP cases. No significant financial relationships to disclose.

Transcriptome-based cancer type prediction for tumors of unknown origin.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3081-3081 ◽  
Author(s):  
Jack Michuda ◽  
Catherine Igartua ◽  
Tim Taxter ◽  
Joshua SK Bell ◽  
Raphael Pelossof ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Unknown Origin ◽  
Tissue Type ◽  
Cancer Site ◽  
Rna Expression ◽  
Cancer Type ◽  
Tumor Type ◽  
Rna Seq ◽  
Metastatic Tumors ◽  
Primary Tumors

3081 Background: Tumors of unknown origin occur in approximately 5% of newly diagnosed cancers and are difficult to treat without establishing the tissue type from which they derive. Establishing tumor origin guides standard of care treatment for several NCCN targeted therapy guidelines. Leveraging tissue specificity in gene expression profiles, classification models based on RNA expression offer a promising approach to identify the likely primary cancer site in tumors of unknown origin. Methods: In this study, we developed a transcriptome-based cancer type classifier trained on over 10,000 tissue samples annotated by pathologists and sequenced for RNA expression to identify conserved patterns of expression characteristic of 30 tumor types across primary and metastatic tissue sites. The classifier probabilistically ranks cancer of origin. Results: Overall, the accuracy of the most probable cancer prediction was 85%, 88% within primary tumors and 77% within metastatic tumors. The top three cancers types with the highest accuracy were colorectal (accuracy in metastatic: 93%, accuracy in primary tumors: 99%), breast (95%, 96%) and lung (87%, 94%). Classifier performance was lower in low-purity metastatic tumors where the surrounding normal tissue obscures the tumor transcriptional profile, though the classifier still achieves 71% accuracy on metastatic tumors with less than 50% purity. Conclusions: We present a novel method to probabilistically predict tumor type for cancers of unknown origin using RNA-Seq. Our method achieves robust classification that is applicable to primary and metastatic tumors and demonstrates the value of utilizing RNA-Seq to aid cancer diagnosis and treatment decisions.

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 Pan-cancer analysis of CD274 (PD-L1) mutations in 314,631 patient samples and subset correlation with PD-L1 protein expression

2021 ◽  
Vol 9 (6) ◽  
pp. e002558
Author(s):  
Richard S.P. Huang ◽  
Brennan Decker ◽  
Karthikeyan Murugesan ◽  
Matthew Hiemenz ◽  
Douglas A. Mata ◽  
...  
Keyword(s):  
Protein Expression ◽  
Checkpoint Inhibitors ◽  
Endometrial Adenocarcinoma ◽  
Primary Melanoma ◽  
B Cell Lymphoma ◽  
Unknown Primary ◽  
Future Research ◽  
Tumor Type ◽  
Missense Mutations ◽  
L1 Protein

BackgroundThe effects of non-amplification short variant (SV) mutations in CD274 (programmed death-ligand 1 (PD-L1)) on PD-L1 protein expression and immune checkpoint inhibitors (ICPIs) therapy are unknown. Here, we present a retrospective analysis of CD274 mutations detected by comprehensive genomic profiling (CGP) and correlate these results with tumor-cell PD-L1 immunohistochemistry (IHC)-based expression assessment to better understand the relationship between mutations and protein expression of PD-L1.MethodsCGP was performed on hybridization-captured, adaptor ligation-based libraries using DNA and/or RNA extracted from 314,631 tumor samples that were sequenced for up to 406 cancer-related genes and select gene rearrangements. PD-L1 IHC was performed on a subset of cases (n=58,341) using the DAKO 22C3 PD-L1 IHC assay and scored with the tumor proportion score (TPS).ResultsOverall, the prevalence of CD274 SV mutations was low (0.3%, 1081/314,631) with 577 unique variants. The most common CD274 SV mutations were R260H (n=51), R260C (n=18), R125Q (n=12), C272fs*13 (n=11), R86W (n=10), and R113H (n=10). The prevalence of CD274 mutations varied depending on tumor type with diffuse large B-cell lymphoma (1.9%, 19/997), cutaneous squamous cell carcinoma (1.6%, 14/868), endometrial adenocarcinoma (1.0%, 36/3740), unknown primary melanoma (0.9%, 33/3679), and cutaneous melanoma (0.8%, 32/3874) having the highest frequency of mutations. Of the R260H cases concurrently tested with PD-L1 IHC, most (81.8%, 9/11) had no PD-L1 expression, which contrasts to the five E237K cases where most (80%, 4/5) had PD-L1 expression. In addition, we saw a significantly lower level of PD-L1 expression in samples with a clonal truncating variant (nonsense or frameshift indel) when compared with samples with a subclonal truncating variants (mean: TPS=1 vs TPS=38; p<0.001), and also in clonal versus subclonal missense mutations (mean: TPS=11 vs TPS=22, respectively; p=0.049)ConclusionsWe defined the landscape of CD274 mutations in a large cohort of tumor types that can be used as a reference for examining CD274 mutations as potential resistance biomarkers for ICPI. Furthermore, we presented novel data on the correlation of CD274 mutations and PD-L1 protein expression, providing important new information on the potential functionality of these mutations and can serve as a basis for future research.

scholarly journals Cancer of unknown primary

Medicine ◽  
2017 ◽  
Vol 96 (16) ◽  
pp. e6693 ◽  
Author(s):  
Anne-Kirstine Dyrvig ◽  
Knud Bonnet Yderstræde ◽  
Oke Gerke ◽  
Peter Bjødstrup Jensen ◽  
Søren Hess ◽  
...  
Keyword(s):  
Cancer Of Unknown Primary ◽  
Unknown Primary
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