Project description:Purpose: Despite advances in radical surgery and chemotherapy delivery, ovarian cancer is the most lethal gynecologic malignancy. Most of these patients are treated with platinum-based chemotherapies, but there is no biomarker model to guide their responses to these therapeutic agents. We have developed and independently tested our novel multivariate molecular predictors for forecasting patients' responses to individual drugs on a cohort of 58 ovarian cancer patients. Experimental Design: We adapted and applied the previously-published COXEN algorithm to develop molecular predictors for therapeutic responses of patients' tumors based on expression signatures derived from the NCI-60 in vitro drug activities and genomic expression data. Genome-wide candidate biomarkers were first triaged by examining expression patterns of frozen and formalin-fixed paraffin embedded (FFPE) tissue samples. We then identify initial drug sensitivity biomarkers for carboplatin and paclitaxel, respectively. These biomarkers were further narrowed by examining concordant expression patterns between cell lines and a historical set of ovarian cancer patients. Multivariate predictors were obtained from the NCI-60 cell lines and refined using historical patient cohorts. To independent validate these molecular predictors, we performed genome-wide profiling on FFPE samples of 58 ovarian cancer patients obtained prior to adjuvant chemotherapy. Results: Carboplatin predictor significantly stratified platinum sensitive and resistant patients (p = 0.019) with sensitivity = 93%, specificity = 33%, PPV = 65%, and NPV = 78%. Paclitaxel predictor also significantly stratified patients' responses (p = 0.033) with sensitivity = 96%, specificity = 26%, PPV = 61%, and NPV = 86%. The combination predictor for platinum-taxane combination demonstrated a significant survival difference between the predicted responders and nonresponders with median survival of 12.9 months vs. 8.1 months (p = 0.045). Conclusions: COXEN predictors successfully stratified platinum resistance and taxane response in this retrospective cohort, especially based on their FFPE tumor samples. Accurate prediction of chemotherapeutic response, especially to platinum agents is highly clinically relevant and could alter primary management of ovarian cancer. Gene expression data from 58 stage III-IV ovarian cancer patients treated with Carboplatin and Taxol agents
Project description:Purpose: Despite advances in radical surgery and chemotherapy delivery, ovarian cancer is the most lethal gynecologic malignancy. Most of these patients are treated with platinum-based chemotherapies, but there is no biomarker model to guide their responses to these therapeutic agents. We have developed and independently tested our novel multivariate molecular predictors for forecasting patients' responses to individual drugs on a cohort of 58 ovarian cancer patients. Experimental Design: We adapted and applied the previously-published COXEN algorithm to develop molecular predictors for therapeutic responses of patients' tumors based on expression signatures derived from the NCI-60 in vitro drug activities and genomic expression data. Genome-wide candidate biomarkers were first triaged by examining expression patterns of frozen and formalin-fixed paraffin embedded (FFPE) tissue samples. We then identify initial drug sensitivity biomarkers for carboplatin and paclitaxel, respectively. These biomarkers were further narrowed by examining concordant expression patterns between cell lines and a historical set of ovarian cancer patients. Multivariate predictors were obtained from the NCI-60 cell lines and refined using historical patient cohorts. To independent validate these molecular predictors, we performed genome-wide profiling on FFPE samples of 58 ovarian cancer patients obtained prior to adjuvant chemotherapy. Results: Carboplatin predictor significantly stratified platinum sensitive and resistant patients (p = 0.019) with sensitivity = 93%, specificity = 33%, PPV = 65%, and NPV = 78%. Paclitaxel predictor also significantly stratified patients' responses (p = 0.033) with sensitivity = 96%, specificity = 26%, PPV = 61%, and NPV = 86%. The combination predictor for platinum-taxane combination demonstrated a significant survival difference between the predicted responders and nonresponders with median survival of 12.9 months vs. 8.1 months (p = 0.045). Conclusions: COXEN predictors successfully stratified platinum resistance and taxane response in this retrospective cohort, especially based on their FFPE tumor samples. Accurate prediction of chemotherapeutic response, especially to platinum agents is highly clinically relevant and could alter primary management of ovarian cancer.
Project description:YAP1 plays importance roles in development of colorectal cancer as evidenced by their overexpression in colorectal cancer and their expression promoted cell proliferation and survival of colorectal cancer cells. In order to understand potential roles of YAP1 in colorectal cancer, we over-expressed constitutively active YAP1 mutant in NCI-H716 colorectal cancer cells and identified and analyzed genes whose expression is activated by YAP1 activation in colorectal cancer. Pre-clinical study
Project description:YAP1 plays importance roles in development of colorectal cancer as evidenced by their overexpression in colorectal cancer and their expression promoted cell proliferation and survival of colorectal cancer cells. In order to understand potential roles of YAP1 in colorectal cancer, we over-expressed constitutively active YAP1 mutant in NCI-H716 colorectal cancer cells and identified and analyzed genes whose expression is activated by YAP1 activation in colorectal cancer.
Project description:Claret2009 - Predicting phase III overall survival in colorectal cancer
This model is described in the article:
Model-based prediction of
phase III overall survival in colorectal cancer on the basis of
phase II tumor dynamics.
Claret L, Girard P, Hoff PM, Van
Cutsem E, Zuideveld KP, Jorga K, Fagerberg J, Bruno R.
J. Clin. Oncol. 2009 Sep; 27(25):
4103-4108
Abstract:
PURPOSE: We developed a drug-disease simulation model to
predict antitumor response and overall survival in phase III
studies from longitudinal tumor size data in phase II trials.
METHODS: We developed a longitudinal exposure-response
tumor-growth inhibition (TGI) model of drug effect (and
resistance) using phase II data of capecitabine (n = 34) and
historical phase III data of fluorouracil (FU; n = 252) in
colorectal cancer (CRC); and we developed a parametric survival
model that related change in tumor size and patient
characteristics to survival time using historical phase III
data (n = 245). The models were validated in simulation of
antitumor response and survival in an independent phase III
study (n = 1,000 replicates) of capecitabine versus FU in CRC.
RESULTS: The TGI model provided a good fit of longitudinal
tumor size data. A lognormal distribution best described the
survival time, and baseline tumor size and change in tumor size
from baseline at week 7 were predictors (P < .00001).
Predicted change of tumor size and survival time distributions
in the phase III study for both capecitabine and FU were
consistent with observed values, for example, 431 days (90%
prediction interval, 362 to 514 days) versus 401 days observed
for survival in the capecitabine arm. A modest survival
improvement of 39 days (90% prediction interval, -21 to 110
days) versus 35 days observed was predicted for capecitabine.
CONCLUSION: The modeling framework successfully predicted
survival in a phase III trial on the basis of capecitabine
phase II data in CRC. It is a useful tool to support
end-of-phase II decisions and design of phase III studies.
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Project description:Kynureninase is a member of a large family of catalytically diverse but structurally homologous pyridoxal 5'-phosphate (PLP) dependent enzymes known as the aspartate aminotransferase superfamily or alpha-family. The Homo sapiens and other eukaryotic constitutive kynureninases preferentially catalyze the hydrolytic cleavage of 3-hydroxy-l-kynurenine to produce 3-hydroxyanthranilate and l-alanine, while l-kynurenine is the substrate of many prokaryotic inducible kynureninases. The human enzyme was cloned with an N-terminal hexahistidine tag, expressed, and purified from a bacterial expression system using Ni metal ion affinity chromatography. Kinetic characterization of the recombinant enzyme reveals classic Michaelis-Menten behavior, with a Km of 28.3 +/- 1.9 microM and a specific activity of 1.75 micromol min-1 mg-1 for 3-hydroxy-dl-kynurenine. Crystals of recombinant kynureninase that diffracted to 2.0 A were obtained, and the atomic structure of the PLP-bound holoenzyme was determined by molecular replacement using the Pseudomonas fluorescens kynureninase structure (PDB entry 1qz9) as the phasing model. A structural superposition with the P. fluorescens kynureninase revealed that these two structures resemble the "open" and "closed" conformations of aspartate aminotransferase. The comparison illustrates the dynamic nature of these proteins' small domains and reveals a role for Arg-434 similar to its role in other AAT alpha-family members. Docking of 3-hydroxy-l-kynurenine into the human kynureninase active site suggests that Asn-333 and His-102 are involved in substrate binding and molecular discrimination between inducible and constitutive kynureninase substrates.