Multigene Signature Panels and Breast Cancer Therapy: Patterns of Use and Impact on Clinical Decision Making

Bhutiani, Neal; Egger, Michael E.; Ajkay, Nicolás; Scoggins, Charles R.; Martin, Robert C.G.; McMasters, Kelly M.

doi:10.1016/j.jamcollsurg.2017.12.043

Cited by 28 publications

(16 citation statements)

References 24 publications

(37 reference statements)

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“…Gene signatures are currently being used in clinical practice. Two examples are Oncotype DX [13][14][15], which provides a breast cancer recurrence score based on expression of 21 genes, and Coloprint, which provides a colon cancer recurrence score based on expression of 18 genes [16][17][18]. Results obtained with these assays have shown that screening new prognostic cancer markers based on gene expression profiles is a promising high-throughput molecular identification method.…”

Section: Discussionmentioning

confidence: 99%

Six-gene signature for predicting survival in patients with head and neck squamous cell carcinoma

Wang

Chen

Tian

et al. 2020

Aging

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The prognosis of head and neck squamous cell carcinoma (HNSCC) patients remains poor. High-throughput sequencing data have laid a solid foundation for identifying genes related to cancer prognosis, but a gene marker is needed to predict clinical outcomes in HNSCC. In our study, we downloaded RNA Seq, single nucleotide polymorphism, copy number variation, and clinical follow-up data from TCGA. The samples were randomly divided into training and test. In the training set, we screened genes and used random forests for feature selection. Gene-related prognostic models were established and validated in a test set and GEO verification set. Six genes (PEX11A, NLRP2, SERPINE1, UPK, CTTN, D2HGDH) were ultimately obtained through random forest feature selection. Cox regression analysis confirmed the 6-gene signature is an independent prognostic factor in HNSCC patients. This signature effectively stratified samples in the training, test, and external verification sets (P < 0.01). The 5-year survival AUC in the training and verification sets was greater than 0.74. Thus, we have constructed a 6-gene signature as a new prognostic marker for predicting survival of HNSCC patients.

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Section: Discussionmentioning

confidence: 99%

Six-gene signature for predicting survival in patients with head and neck squamous cell carcinoma

Wang

Chen

Tian

et al. 2020

Aging

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“…To date, many studies have explored the prognostic role of gene signatures in predicting the outcome of HCC. The effect of clinical practice based on gene expression profiles has shown that gene signatures might be a promising high-throughput molecular identification method [22][23][24]. In that regard, Liu et al [25] identified a four-gene metabolic signature and Xiang et al [26] identified seven-senescence-associated gene signature for predicting OS in HCC.…”

Section: Discussionmentioning

confidence: 99%

A robust twelve-gene signature for prognosis prediction of hepatocellular carcinoma

Ouyang

Pan

et al. 2020

Cancer Cell Int

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Background: The prognosis of hepatocellular carcinoma (HCC) patients remains poor. Identifying prognostic markers to stratify HCC patients might help to improve their outcomes. Methods: Six gene expression profiles (GSE121248, GSE84402, GSE65372, GSE51401, GSE45267 and GSE14520) were obtained for differentially expressed genes (DEGs) analysis between HCC tissues and non-tumor tissues. To identify the prognostic genes and establish risk score model, univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed based on the integrated DEGs by robust rank aggregation method. Then Kaplan-Meier and time-dependent receiver operating characteristic (ROC) curves were generated to validate the prognostic performance of risk score in training datasets and validation datasets. Multivariable Cox regression analysis was used to identify independent prognostic factors in liver cancer. A prognostic nomogram was constructed based on The Cancer Genome Atlas (TCGA) dataset. Finally, the correlation between DNA methylation and prognosis-related genes was analyzed. Results: A twelve-gene signature including SPP1, KIF20A, HMMR, TPX2, LAPTM4B, TTK, MAGEA6, ANX10, LECT2, CYP2C9, RDH16 and LCAT was identified, and risk score was calculated by corresponding coefficients. The risk score model showed a strong diagnosis performance to distinguish HCC from normal samples. The HCC patients were stratified into high-risk and low-risk group based on the cutoff value of risk score. The Kaplan-Meier survival curves revealed significantly favorable overall survival in groups with lower risk score (P < 0.0001). Time-dependent ROC analysis showed well prognostic performance of the twelve-gene signature, which was comparable or superior to AJCC stage at predicting 1-, 3-, and 5-year overall survival. In addition, the twelve-gene signature was independent with other clinical factors and performed better in predicting overall survival after combining with age and AJCC stage by nomogram. Moreover, most of the prognostic twelve genes were negatively correlated with DNA methylation in HCC tissues, which SPP1 and LCAT were identified as the DNA methylation-driven genes. Conclusions: We identified a twelve-gene signature as a robust marker with great potential for clinical application in risk stratification and overall survival prediction in HCC patients.

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“…At present, some gene signatures have been used in clinics, such as multigene signature panels (MSPs) to evaluate breast cancer recurrence [19], which reveals that it is a reliable molecular identi cation method to screen gene prognostic biomarkers by gene expression pro ling. Lin et al constructed a prognostic model for CC patients undergoing radiotherapy and chemotherapy with stage IB-IV based on magnetic resonance imaging (MRI), whole-tumor apparent diffusion coe cient (ADC), human papillomavirus (HPV) genotyping [20].…”

Section: Discussionmentioning

confidence: 99%

Identification of a Six-gene Signature Predicting Overall Survival for Cervical Cancer

Sun

Zhou

Peng

et al. 2020

Preprint

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Background: Although the incidence of cervical cancer has decreased in decades with the development of human papillomavirus vaccines and cancer screening, cervical cancer remains one of the leading causes of cancer-related deaths worldwide. Identify the potential biomarker for treatment and prognosis of cervical cancer is necessary.Methods: Samples with mRNA-seq, copy number variant, single single nucleotide polymorphism data and clinical follow-up information were download from TCGA database, which were randomly divided into training datasets (N=146) and test datasets (N=147). We selected and identified prognostic gene set and genomic mutated gene set and then integrated the two set of data with random survival forest algorithm and constructed a prognostic signature. External validation datasets and immunohistochemical staining were also evaluated.Results: We obtained 1,416 different expression prognostic-related genes, 624 genes with copy number amplification, 1,038 genes with copy number deletion, and 163 significantly mutated gene. A total of 75 candidate genes were obtained after overlap of the different expression genes and genomic variations. Subsequently we obtained six characteristic genes through random survival forest algorithm. The results showed that high expression of SLC19A3, FURIN, SLC22A3, DPAGT1 and low expression of CCL17, DES were associated with poor prognosis in cervical cancer patients. We constructed a six-gene signature which can separate cervical cancer samples associated with different overall survival and showed robust performance for predicting survival (Training set: p ˂ 0.001, AUC = 0.82; Testing set: p ˂ 0.01, AUC = 0.59). Conclusions: Our study identified a novel six-gene signature and nomogram to predict overall survival of cervical cancer, which may be beneficial to clinical decision making for individual treatment.

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Multigene Signature Panels and Breast Cancer Therapy: Patterns of Use and Impact on Clinical Decision Making

Cited by 28 publications

References 24 publications

Six-gene signature for predicting survival in patients with head and neck squamous cell carcinoma

Six-gene signature for predicting survival in patients with head and neck squamous cell carcinoma

A robust twelve-gene signature for prognosis prediction of hepatocellular carcinoma

Identification of a Six-gene Signature Predicting Overall Survival for Cervical Cancer

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