Molecular identification of carcinoma of unknown primary (CUP) with gene expression profiling

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 21024-21024 ◽  
Author(s):  
K. Z. Qu ◽  
H. Li ◽  
J. D. Whetstone ◽  
A. D. Sferruzza ◽  
R. A. Bender
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Nearest Neighbor ◽  
Gene Profiling ◽  
High Likelihood ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Analysis ◽  
Tumor Origin ◽  
Profiling Analysis

21024 Background: We previously reported a method for determining the site of tumor origin for CUP by comparing a 92- gene expression profile to that in a database created from 600 primary and metastatic tumor bank specimens of known origin. K-nearest neighbor analysis was used to determine the likelihood of an unknown patient specimen originating from a particular site with the likelihood assigned as a confidence level and reported as high, medium, low, or unclassified. Herein, we report the analysis of the gene expression profiling results from our initial series of clinical CUP specimens. Methods: We reviewed the results of 76 consecutive de-identified patient samples submitted to our laboratory for routine CUP testing. RNA was extracted from the formalin-fixed, paraffin-embedded (FFPE) tissue blocks and cDNA products used in a semi-quantitative real-time PCR to detect 87 tumor-associated genes and 5 reference genes. Gene expression data were then compared with our database and k-nearest neighbor analysis used to identify the 5 closest neighbors. If all 5 or 4/5 were the same, the result was classified as “high likelihood”, 3/5 = “moderate likelihood”, 2/5 = “low likelihood” and none matching was “unclassifiable”. Results: For the 76 clinical CUP samples tested, gene profiling analysis yielded high-likelihood predictions for 34 (45%), moderate for 12 (16%), low for 12 (16%), and unclassified for 14 (18%); amplification was inadequate for 4 (5%) samples. Overall, gene profiling analysis yielded classifiable predictions in 58 (76%) of clinical CUP samples. An occult carcinoid, metastatic melanoma and adenocarcinoma of the endocervix were identified and then found clinically using this assay. Conclusions: Our previous findings indicate that gene expression profiling can correctly identify the site of tumor origin in a high percentage of tumor bank samples. Data from the present study suggests that this approach can identify a primary site of tumor origin in 76% of actual clinical specimens from pathologist-submitted CUP cases. No significant financial relationships to disclose.

Preparation of Salmonella enterica Serovar Typhi Genomic DNA Microarrays for Gene Expression Profiling Analysis*

2009 ◽  
Vol 2009 (2) ◽  
pp. 206-212 ◽  
Author(s):  
Xiu-Mei SHENG ◽  
Xin-Xiang HUANG ◽  
Ling-Xiang MAO ◽  
Chao-Wang ZHU ◽  
Shun-Gao XU ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Salmonella Enterica ◽  
Genomic Dna ◽  
Dna Microarrays ◽  
Profiling Analysis

Gene expression profiling analysis reveals a crucial gene regulating metabolism in adventitious roots of neem (Azadirachta indica)

RSC Advances ◽  
2016 ◽  
Vol 6 (115) ◽  
pp. 114889-114898 ◽  
Author(s):  
Shanying Wang ◽  
Hao Zhang ◽  
Xinglin Li ◽  
Jian Zhang
Keyword(s):  
Gene Expression ◽  
Secondary Metabolites ◽  
Gene Expression Profiling ◽  
Azadirachta Indica ◽  
Expression Profiling ◽  
Adventitious Roots ◽  
Bioactive Secondary Metabolites ◽  
Profiling Analysis

Neem is a widely used traditional plant containing bioactive secondary metabolites, especially azadirachtin.

Association of immune gene expression profiling with vinflunine clinical benefit in metastatic urothelial cancer (mUC).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e16034-e16034
Author(s):  
Guillermo de Velasco ◽  
Marta Dueñas ◽  
Alejandra Bernardini ◽  
Alvaro Pinto ◽  
Teresa Alonso-Gordoa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Clinical Benefit ◽  
Expression Profiling ◽  
Checkpoint Inhibitors ◽  
Gene Profiling ◽  
Immune Gene ◽  
Similar Frequency ◽  
Metastatic Urothelial Cancer ◽  
Platinum Based Chemotherapy

e16034 Background: There are limited treatment options for mUC after platinum-based chemotherapy failure. Immune checkpoint inhibitors (ICI) have shown a durable benefit but only in a minority of patients (20-25%). Vinflunine remains as a therapeutic option without validated biomarkers. In this study, we sought to analyze the molecular determinants of vinflunine response in mUC. Methods: mUC patients from 4 University Hospitals in Spain who received second-line vinflunine after platinum-based chemotherapy were classified in non-responders (NR: progressive disease ≤3 months; N = 10) or responders (R: response ≥ 6 months, N = 14). Targeted- sequencing of 275 cancer-related genes and a PanCancer Immune Profiling Panel were performed on pre-treatment tumors. Selected genes were evaluated by RT-qPCR and protein expression was detected by immunohistochemistry. Results: The most common alteration, TP53 mutations, had a similar frequency in R (7/14, 50%) and NR (4/10, 40%). Mutations in 5 genes: ERBB3 (4/14; 28,6%), KTM2C (4/14; 28,6%), PI3KCA (4/14; 28,6%), ARID2 (3/14; 21,4%) and FGFR3 (3/14; 21,4%) were identified only in R. Mutations in ERBB4 (3/10, 33,3%) and BCOR (2/10, 20%) were identified only in NR. Estimated TMBs were not significantly different among the R (13 per Mb) and NR (9 per Mb) samples. According to gene expression profiling, NR had high cytotoxic cells infiltrate and T cells as well as high counts of TILs compared to R. In addition, expression of IDO, MAGE A4, and SOCS1, that has been associated with response to ICIs, were down-regulated in R compared with NR. Conclusions: Gene profiling showed that low-expression levels of immune-related genes are significantly associated with clinical benefit from vinflunine. Validation and complementary studies are ongoing in patients treated with ICIs.

scholarly journals Trends in Gene Expression Profiling for Prostate Cancer Risk Assessment: A Systematic Review

2017 ◽  
Vol 2 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Zhaoyi Chen ◽  
Travis Gerke ◽  
Victoria Bird ◽  
Mattia Prosperi
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
High Throughput Sequencing ◽  
Prostate Cancer Risk ◽  
Gene Profiling ◽  
Cancer Risk Assessment ◽  
Meta Analyses ◽  
Study Designs

Objectives: The aim of the study is to review biotechnology advances in gene expression profiling on prostate cancer (PCa), focusing on experimental platform development and gene discovery, in relation to different study designs and outcomes in order to understand how they can be exploited to improve PCa diagnosis and clinical management. Methods: We conducted a systematic literature review on gene expression profiling studies through PubMed/MEDLINE and Web of Science between 2000 and 2016. Tissue biopsy and clinical gene profiling studies with different outcomes (e.g., recurrence, survival) were included. Results: Over 3,000 papers were screened and 137 full-text articles were selected. In terms of technology used, microarray is still the most popular technique, increasing from 50 to 70% between 2010 and 2015, but there has been a rise in the number of studies using RNA sequencing (13% in 2015). Sample sizes have increased, as well as the number of genes that can be screened all at once, but we have also observed more focused targeting in more recent studies. Qualitative analysis on the specific genes found associated with PCa risk or clinical outcomes revealed a large variety of gene candidates, with a few consistent cross-studies. Conclusions: The last 15 years of research in gene expression in PCa have brought a large volume of data and information that has been decoded only in part, but advancements in high-throughput sequencing technology are increasing the amount of data that can be generated. The variety of findings warrants the execution of both validation studies and meta-analyses. Genetic biomarkers have tremendous potential for early diagnosis of PCa and, if coupled with other diagnostics (e.g., imaging), can effectively be used to concretize less-invasive, personalized prediction of PCa risk and progression.

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