The impact of the 21-gene recurrence score (Oncotype DX) on concordance of adjuvant therapy decision making as measured by the Liverpool Systemic Therapy Adjuvant Decision Tool

Olsson‐Brown, Anna; Piskilidis, P.; O'Hagan, J; Thorp, Nicky; Robson, Peter; Innes, Helen; Wong, Helen; Cicconi, Silvia; Jackson, Richard; Kiernan, Tamara; Holcombe, Christopher; O’Reilly, Susan; Palmieri, Carlo

doi:10.1016/j.breast.2019.01.005

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…In this paper, the expression pro les of HCC samples from GEO and TCGA were analyzed to identify the stable and reliable 5-gene signature related to OS, which was independent of clinical factors. So far, some gene signatures have been applied clinically, such as the Oncotype DX for disease recurrence grading constructed based on the expression of 21 genes in breast cancer [32], and the Coloprint constructed based on the expression of 18 genes in colon cancer [33]. Thus, it is suggested that screening novel prognostic markers in cancer through gene expression pro les has become the most promising high-throughput molecular identi cation method.…”

Section: Discussionmentioning

confidence: 99%

Using integrated multi-omics data analysis to identify 5-gene signature for predicting survival of patients with hepatocellular carcinoma

Zhang

Zhao

et al. 2022

Preprint

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Due to the poor prognosis for hepatocellular carcinoma (HCC) presently, a systemic analysis supported by the multi-omics data is extremely necessary to search for gene markers for the clinical prognostic prediction of HCC. The data on RNA-seq, single nucleotide polymorphism (SNP), and copy number variation (CNV), etc. were downloaded from TCGA, leading to a final of 367 samples, which were divided into training set and testing set randomly. In the training set, both prognosis-related genes and those with SNP or CNV were screened, which were incorporated for feature selection using the random forest method. The testing and GEO verification sets (N=265) were used to verify the constructed gene-related prognosis model. qPCR was used to detect the expression of 5 genes in clinical specimens. After including genomic variant and prognosis-related genes, we got 78 candidate genes and 5 feature genes (CISH, LHPP, MGMT, PDRG1, and LCAT) eventually through random forest feature selection. The 5-gene signature is an independent prognostic risk factor for HCC patients. In addition, the signature shows good predicting performance and clinical practicality in train- ing set, testing set and external verification set. The results of qPCR based on clinical samples showed that the expression of PDRG1 was increased in colon cancer tissues and the expression of CISH, LHPP, MGMT and LCAT were decreased in colon cancer tissues. We identify the ability of 5-gene signature to serve as an innovative marker of survival prediction for patients with HCC.

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Section: Discussionmentioning

confidence: 99%

Using integrated multi-omics data analysis to identify 5-gene signature for predicting survival of patients with hepatocellular carcinoma

Zhang

Zhao

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…So far, some gene signatures have been applied clinically, such as the Oncotype DX for disease recurrence grading constructed based on the expression of 21 genes in breast cancer [32], and the Coloprint constructed based on the expression of 18 genes in colon cancer [33]. Thus, it is suggested that screening novel prognostic markers in cancer through gene expression pro les has become the most promising high-throughput molecular identi cation method.…”

Section: Discussionmentioning

confidence: 99%

Using Integrated Multi-Omics Data Analysis to Identify 5-gene Signature for Predicting Survival of Patients with Hepatocellular Carcinoma

Zhang

Zhao

et al. 2021

Preprint

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Due to the poor prognosis for hepatocellular carcinoma (HCC) presently, a systemic analysis supported by the multi-omics data is extremely necessary to search for gene markers for the clinical prognostic prediction of HCC. The data on RNA-seq, single nucleotide polymorphism (SNP), and copy number variation (CNV), etc. were downloaded from TCGA, leading to a final of 367 samples, which were divided into training set and testing set randomly. In the training set, both prognosis-related genes and those with SNP or CNV were screened, which were incorporated for feature selection using the random forest method. The testing and GEO verification sets (N = 265) were used to verify the constructed gene-related prognosis model. qPCR was used to detect the expression of 5 genes in clinical specimens. After including genomic variant and prognosis-related genes, we got 78 candidate genes and 5 feature genes (CISH, LHPP, MGMT, PDRG1, and LCAT) eventually through random forest feature selection. The 5-gene signature is an independent prognostic risk factor for HCC patients. In addition, the signature shows good predicting performance and clinical practicality in train- ing set, testing set and external verification set. The results of qPCR based on clinical samples showed that the expression of PDRG1 was increased in colon cancer tissues and the expression of CISH, LHPP, MGMT and LCAT were decreased in colon cancer tissues. We identify the ability of 5-gene signature to serve as an innovative marker of survival prediction for patients with HCC.

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“…Among these, Oncotype DX is a validated multigene (21 genes) recurrence score (RS) assay that serves as a reliable predictor of prognosis, i.e., recurrence, survival [9], treatment, and response to NACT [10][11][12]. Oncotype DX can impact decision making for surgery [13] and adjuvant therapy [14,15] in early-stage breast cancer. The clinical application of Oncotype DX RS has provided improved survival [16,17] and decreased rates of receiving adjuvant chemotherapy in breast cancer patients [18].…”

Section: Introductionmentioning

confidence: 99%

Radiogenomic Signatures of Oncotype DX Recurrence Score Enable Prediction of Survival in Estrogen Receptor–Positive Breast Cancer: A Multicohort Study

Fan¹,

Cui²,

You³

et al. 2022

Radiology

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Results: Radiogenomic signatures were identified to generate the predicted Oncotype RS (pRS) (R square 0.332). High pRS greater than 29.9 was significantly associated with both RFS and OS (p=0.0012 and 0.0014, respectively), and the association results remained significant after controlling for histological information. A high pRS was significantly correlated with a good response to NACT (p=0.001). Multivariate analysis by combining the pRS and complementary imaging features achieved improved performance in both the prediction of response to NACT and survival.Conclusions: Radiogenomic signatures associated with genomic assays were identified and validated across multiple cohorts, which indicate promising, noninvasive biomarkers of prognosis and treatment in breast cancer.

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The impact of the 21-gene recurrence score (Oncotype DX) on concordance of adjuvant therapy decision making as measured by the Liverpool Systemic Therapy Adjuvant Decision Tool

Cited by 4 publications

References 31 publications

Using integrated multi-omics data analysis to identify 5-gene signature for predicting survival of patients with hepatocellular carcinoma

Using integrated multi-omics data analysis to identify 5-gene signature for predicting survival of patients with hepatocellular carcinoma

Using Integrated Multi-Omics Data Analysis to Identify 5-gene Signature for Predicting Survival of Patients with Hepatocellular Carcinoma

Radiogenomic Signatures of Oncotype DX Recurrence Score Enable Prediction of Survival in Estrogen Receptor–Positive Breast Cancer: A Multicohort Study

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