scholarly journals Exploiting convergent evolution to derive a pan-cancer cisplatin sensitivity gene expression signature

2021 ◽  
Author(s):  
Jessica A Scarborough ◽  
Steven A Eschrich ◽  
Javier Torres-Roca ◽  
Andrew Dhawan ◽  
Jacob G Scott
Keyword(s):  
Gene Expression ◽  
Precision Medicine ◽  
Convergent Evolution ◽  
Gene Expression Signature ◽  
Signature Extraction ◽  
Tumor Subtypes ◽  
Mutational Status ◽  
Chemotherapeutic Response ◽  
Cisplatin Sensitivity ◽  
Chemotherapy Agents

Precision medicine offers remarkable potential for the treatment of cancer, but is largely focused on tumors that harbor actionable mutations. Gene expression signatures can expand the scope of precision medicine by predicting response to traditional(cytotoxic) chemotherapy agents without relying on changes in mutational status. We present a novel signature extraction method, inspired by the principle of convergent evolution, which states that tumors with disparate genetic backgrounds may evolve similar phenotypes independently. This evolutionary-informed method can be utilized to produce signatures predictive of response to over 200 chemotherapeutic drugs found in the Genomics of Drug Sensitivity in Cancer Database. Here, we demonstrate its use by extracting the Cisplatin Response Signature, CisSig, for use in predicting a common trait (sensitivity to cisplatin) across disparate tumor subtypes (epithelial-origin tumors). CisSig is predictive of cisplatin response within the cell lines and clinical trends in independent datasets of tumor samples. This novel methodology can be used to produce robust signatures for the prediction of traditional chemotherapeutic response, dramatically increasing the reach of personalized medicine in cancer.

scholarly journals Relation of Gene Expression Phenotype to Immunoglobulin Mutation Genotype in B Cell Chronic Lymphocytic Leukemia

2001 ◽  
Vol 194 (11) ◽  
pp. 1639-1648 ◽  
Author(s):  
Andreas Rosenwald ◽  
Ash A. Alizadeh ◽  
George Widhopf ◽  
Richard Simon ◽  
R. Eric Davis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chronic Lymphocytic Leukemia ◽  
B Cell ◽  
Germ Line ◽  
Gene Expression Signature ◽  
Lymphocytic Leukemia ◽  
Common Mechanism ◽  
Human Leukemia ◽  
Mutational Status ◽  
Cell Chronic Lymphocytic Leukemia

The most common human leukemia is B cell chronic lymphocytic leukemia (CLL), a malignancy of mature B cells with a characteristic clinical presentation but a variable clinical course. The rearranged immunoglobulin (Ig) genes of CLL cells may be either germ-line in sequence or somatically mutated. Lack of Ig mutations defined a distinctly worse prognostic group of CLL patients raising the possibility that CLL comprises two distinct diseases. Using genomic-scale gene expression profiling, we show that CLL is characterized by a common gene expression “signature,” irrespective of Ig mutational status, suggesting that CLL cases share a common mechanism of transformation and/or cell of origin. Nonetheless, the expression of hundreds of other genes correlated with the Ig mutational status, including many genes that are modulated in expression during mitogenic B cell receptor signaling. These genes were used to build a CLL subtype predictor that may help in the clinical classification of patients with this disease.

Use of predictive gene expression signature from advanced-stage serous papillary ovarian cancer to identify biologically relevant molecular targets for chemoresponse

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 5500-5500
Author(s):  
L. Ozbun ◽  
T. Bonome ◽  
M. Radonovich ◽  
C. Pise-Masison ◽  
J. Brady ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cell Lines ◽  
Ovarian Cancer Cell ◽  
Gene Expression Signature ◽  
Advanced Stage ◽  
Expression Signature ◽  
Qrt Pcr ◽  
Ovarian Cancer Cell Lines ◽  
Cisplatin Sensitivity

5500 Background: The aim of our study was to develop and validate a gene expression signature predictive for chemoresponse in advanced stage serous papillary ovarian cancer. Methods: Gene expression profiling was performed on 52 chemonaive, microdissected advanced stage, high-grade papillary serous ovarian cancers using Affymetrix whole-genome microarrays. Patient samples were grouped based on chemoresponse. 19 nonresponders were refractory to chemotherapy, 14 responders relapsing 6 months were considered chemosensitive. Each group was divided into training/validation sets. To generate a predictive gene signature, class prediction algorithms were applied to genes differentially expressed between chemosensitive/resistant or chemosensitive/refractory tumors (p<0.001) using leave-one-out cross-validation. Array validation was performed by qRT-PCR. Select genes underwent biological validation in a series of ovarian cancer cell lines. Results: 31 genes predictive for resistance and 105 genes predictive for refractory to chemotherapy were identified. Percentages of arrays accurately predicted in independent validation sets were 90% (9/10) for resistant and 92% (12/13) for refractory gene signatures. Correlations between microarray/qRT-PCR data were robust for both resistant (17/23 genes) and refractory gene signatures (25/34 genes). Data mining of the predictive signatures using PathwayStudio software identified several biological processes (collagen regulation, apoptosis, cell survival, and DNA repair) implicated in conferring resistance to chemotherapy. We transiently transfected RNAi molecules to silence several signature genes and determine their contribution to taxol/cisplatin sensitivity in a series ofl ovarian cancer cell lines. Preliminary data showed DUSP1 gene expression knockdown potentiated cisplatin sensitivity in SKOV3/OVCA429 cell lines, while POLH knockdown potentiated cisplatin sensitivity in OVCA429/OVCA420 cell lines. Conclusions: A gene expression signature predicts for chemoresponse in ovarian cancers, and has identified novel targets of biological/therapeutic interest. No significant financial relationships to disclose.

Association of YAP1 activation with poor patient prognosis and effect on chemoresistance in gastric cancer.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 4113-4113
Author(s):  
Sung Sook Lee ◽  
Sang Cheul Oh ◽  
Woojin Jeong ◽  
Sang Ho Lee ◽  
Sang-Bae Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Adjuvant Chemotherapy ◽  
Therapeutic Target ◽  
Notch Pathway ◽  
Gene Expression Signature ◽  
Expression Signature ◽  
Tumor Subtypes ◽  
Attractive Therapeutic Target ◽  
Patient Prognosis

4113 Background: Clinical heterogeneity in gastric cancer is likely due to biological differences among patients. Molecular subtypes and their associated biomarkers need to be established to improve treatment of this disease. We aimed to uncover subgroups of gastric cancer that have distinct biological characteristics associated with clinical outcome and to identify potential best treatments or therapeutic targets for each subgroup. Methods: We analyzed gene expression profiling data from gastric cancer cell lines and 267 patients with gastric cancer to uncover tumor subtypes and identify a gene expression signature associated with prognosis and response to adjuvant chemotherapy. The association of the signature with prognosis was validated in an independent cohort of 200 patients, and its association with response to adjuvant therapy was validated by cell culture experiments. Results: We identified an expression signature of 88 genes that specifically reflected activation of the oncogene YAP1. Compared with patients without this signature, patients with the YAP1 signature had significantly poorer prognosis. In multivariate analysis, the signature was the strongest indicator of overall survival among all demographic and clinical variables examined together (hazard ratio, 2.1; 95% confidence interval, 1.3-3.3;P = .002). Activation of YAP1 was significantly associated with resistance to adjuvant chemotherapy. We also demonstrated that the Notch pathway is a potential therapeutic target for overcoming chemoresistance mediated by YAP1. Conclusions: Activation of the oncogene YAP1 is significantly associated with poorer survival of patients with gastric cancer and induces chemoresistance to this disease. Therefore, YAP1 may be highly attractive therapeutic target for patients with gastric cancer resistant to standard chemotherapy.

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