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Robust multi-tissue gene panel for cancer detection.

ABSTRACT: BACKGROUND: We have identified a set of genes whose relative mRNA expression levels in various solid tumors can be used to robustly distinguish cancer from matching normal tissue. Our current feature set consists of 113 gene probes for 104 unique genes, originally identified as differentially expressed in solid primary tumors in microarray data on Affymetrix HG-U133A platform in five tissue types: breast, colon, lung, prostate and ovary. For each dataset, we first identified a set of genes significantly differentially expressed in tumor vs. normal tissue at p-value = 0.05 using an experimentally derived error model. Our common cancer gene panel is the intersection of these sets of significantly dysregulated genes and can distinguish tumors from normal tissue on all these five tissue types. METHODS: Frozen tumor specimens were obtained from two commercial vendors Clinomics (Pittsfield, MA) and Asterand (Detroit, MI). Biotinylated targets were prepared using published methods (Affymetrix, CA) and hybridized to Affymetrix U133A GeneChips (Affymetrix, CA). Expression values for each gene were calculated using Affymetrix GeneChip analysis software MAS 5.0. We then used a software package called Genes@Work for differential expression discovery, and SVM light linear kernel for building classification models. RESULTS: We validate the predictability of this gene list on several publicly available data sets generated on the same platform. Of note, when analysing the lung cancer data set of Spira et al, using an SVM linear kernel classifier, our gene panel had 94.7% leave-one-out accuracy compared to 87.8% using the gene panel in the original paper. In addition, we performed high-throughput validation on the Dana Farber Cancer Institute GCOD database and several GEO datasets. CONCLUSIONS: Our result showed the potential for this panel as a robust classification tool for multiple tumor types on the Affymetrix platform, as well as other whole genome arrays. Apart from possible use in diagnosis of early tumorigenesis, some other potential uses of our methodology and gene panel would be in assisting pathologists in diagnosis of pre-cancerous lesions, determining tumor boundaries, assessing levels of contamination in cell populations in vitro and identifying transformations in cell cultures after multiple passages. Moreover, based on the robustness of this gene panel in identifying normal vs. tumor, mislabelled or misinterpreted samples can be pinpointed with high confidence.

SUBMITTER: Irgon J

PROVIDER: S-EPMC2906482 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Robust multi-tissue gene panel for cancer detection.

Irgon Joseph J Huang C Chris CC Zhang Yi Y Talantov Dmitri D Bhanot Gyan G Szalma Sándor S

BMC cancer 20100622

<h4>Background</h4>We have identified a set of genes whose relative mRNA expression levels in various solid tumors can be used to robustly distinguish cancer from matching normal tissue. Our current feature set consists of 113 gene probes for 104 unique genes, originally identified as differentially expressed in solid primary tumors in microarray data on Affymetrix HG-U133A platform in five tissue types: breast, colon, lung, prostate and ovary. For each dataset, we first identified a set of gene ...[more]

PMID: 20569444

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Project description:BackgroundMicrosatellite instability (MSI) is a key biomarker for cancer immunotherapy and prognosis. Integration of MSI testing into a next-generation-sequencing (NGS) panel could save tissue sample, reduce turn-around time and cost, and provide MSI status and comprehensive genomic profiling in single test. We aimed to develop an MSI calling model to detect MSI status along with the NGS panel-based profiling test using tumor-only samples.MethodsFrom January 2019 to December 2020, a total of 174 colorectal cancer (CRC) patients were enrolled, including 31 MSI-high (MSI-H) and 143 microsatellite stability (MSS) cases. Among them, 56 paired tumor and normal samples (10 MSI-H and 46 MSS) were used for modeling, and another 118 tumor-only samples were used for validation. MSI polymerase chain reaction (MSI-PCR) was performed as the gold standard. A baseline was built for the selected microsatellite loci using the NGS data of 56 normal blood samples. An MSI detection model was constructed by analyzing the NGS data of tissue samples. The performance of the model was compared with the results of MSI-PCR.ResultsWe first intersected the target genomic regions of the NGS panels used in this study to select common microsatellite loci. A total of 42 loci including 23 mononucleotide repeat sites and 19 longer repeat sites were candidates for modeling. As mononucleotide repeat sites are more sensitive and specific for detecting MSI status than sites with longer length motif and the mononucleotide repeat sites performed even better than the total sites, a model containing 23 mononucleotide repeat sites was constructed and named Colorectal Cancer Microsatellite Instability test (CRC-MSI). The model achieved 100% sensitivity and 100% specificity when compared with MSI-PCR in both training and validation sets. Furthermore, the CRC-MSI model was robust with the tumor content as low as 6%. In addition, 8 out of 10 MSI-H samples showed alternations in the four mismatch repair genes ( MLH1 , MSH2 , MSH6 , and PMS2 ).ConclusionMSI status can be accurately determined along the targeted NGS panels using only tumor samples. The performance of mononucleotide repeat sites surpasses loci with longer repeat motif in MSI calling.

Project description:With the progress of sequencing technologies, an ever-increasing number of variants of unknown functional and clinical significance (VUS) have been identified in both coding and non-coding regions of the main Breast Cancer (BC) predisposition genes. The aim of this study is to identify a mutational profile of coding and intron-exon junction regions of 12 moderate penetrance genes (ATM, BRIP1, CDH1, CHEK2, NBN, PALB2, PTEN, RAD50, RAD51C, RAD51D, STK11, TP53) in a cohort of 450 Italian patients with Hereditary Breast/Ovarian Cancer Syndrome, wild type for germline mutation in BRCA1/2 genes. The analysis was extended to 5'UTR and 3'UTR of all the genes listed above and to the BRCA1 and BRCA2 known regulatory regions in a subset of 120 patients. The screening was performed through NGS target resequencing on the Illumina platform MiSeq. 8.7% of the patients analyzed is carriers of class 5/4 coding variants in the ATM (3.6%), BRIP1 (1.6%), CHEK2 (1.8%), PALB2 (0.7%), RAD51C (0.4%), RAD51D (0.4%), and TP53 (0.2%) genes, while variants of uncertain pathological significance (VUSs)/class 3 were identified in 9.1% of the samples. In intron-exon junctions and in regulatory regions, variants were detected respectively in 5.1% and in 32.5% of the cases analyzed. The average age of disease onset of 44.4 in non-coding variant carriers is absolutely similar to the average age of disease onset in coding variant carriers for each proband's group with the same cancer type. Furthermore, there is not a statistically significant difference in the proportion of cases with a tumor onset under age of 40 between the two groups, but the presence of multiple non-coding variants in the same patient may affect the aggressiveness of the tumor and it is worth underlining that 25% of patients with an aggressive tumor are carriers of a PTEN 3'UTR-variant. This data provides initial information on how important it might be to extend mutational screening to the regulatory regions in clinical practice.

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Project description:Background: New biomarker combinations have been increasingly developed to improve the precision of current diagnostic and therapeutic modalities. Recently, researchers have found that tumor cells are more vulnerable to ferroptosis. Furthermore, ferroptosis-related genes (FRG) are promising therapeutic targets in breast cancer patients. Therefore, this study aimed to identify FRG that could predict disease-specific survival (DSS) in breast cancer patients. Methods: Gene expression matrix and clinical data were downloaded from public databases. We included 960, 1,900, and 234 patients from the TCGA, METABRIC, and GSE3494 cohorts, respectively. Data for FRG were downloaded from the FerrDb website. Differential expression of FRG was analyzed by comparing the tumors with adjacent normal tissues. Univariate Cox analysis of DSS was performed to identify prognostic FRG. The TCGA-BRCA cohort was used to generate a nine-gene panel with the LASSO cox regression. The METABRIC and GSE3494 cohorts were used to validate the panel. The panel's median cut-off value was used to divide the patients into high- or low-risk subgroups. Analyses of immune microenvironment, functional pathways, and clinical correlation were conducted via GO and KEGG analyses to determine the differences between the two subgroups. Results: The DSS of the low-risk subgroup was longer than that of the high-risk subgroup. The panel's predictive ability was confirmed by ROC curves (TCGA cohort AUC values were 0.806, 0.695, and 0.669 for 2, 3, and 5 years respectively, and the METABRIC cohort AUC values were 0.706, 0.734, and 0.7, respectively for the same periods). The panel was an independent DSS prognostic indicator in the Cox regression analyses. (TCGA cohort: HR = 3.51, 95% CI = 1.792-6.875, p < 0.001; METABRIC cohort: HR = 1.76, 95% CI = 1.283-2.413, p < 0.001). Immune-related pathways were enriched in the high-risk subgroup. The two subgroups that were stratified by the nine-gene panel were also associated with histology type, tumor grade, TNM stage, and Her2-positive and TNBC subtypes. The patients in the high-risk subgroup, whose CTLA4 and PD-1 statuses were both positive or negative, demonstrated a substantial clinical benefit from combination therapy with anti-CTLA4 and anti-PD-1. Conclusion: The new gene panel consisting of nine FRG may be used to assess the prognosis and immune status of patients with breast cancer. A precise therapeutic approach can also be possible with risk stratification.

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Robust multi-tissue gene panel for cancer detection.

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Robust multi-tissue gene panel for cancer detection.

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