Multi-scale models of ovarian cancer

In ovarian cancer, disease and treatment can be examined across multiple spatial scales including molecules, cells, intra-tumor vasculature, and body-scale dynamics of circulating drugs. Survival of primary tumor cells and their development into disseminated tumors is related to adhesion between the cells, attachment, and invasion. Growth of new tumors depends on the delivery of nutrients, which depends on the tumor diameter and the tumors vasculature. Drug delivery also depends on tumor diameter and vasculature, and molecular- and gross-scale drug processes. A cellular Potts simulation integrated data at these multiple scales to model microscopic residual disease during relapse after a primary surgery. The model generated new hypotheses about tumor cell behavior, and the effectiveness of drug delivery to tumors disseminated in the peritoneal cavity. First, the model required high intra-tumor adhesion in ovarian tumors, the existence of an unknown factor that drew tumor cells to vessels, a threshold of vascular endothelial growth factor (VEGF) for initiation of endothelial sprouting, and constitutive expression of angiogenic chemical messengers by tumor cells prior to needing oxygen. Alteration of the model incorporated drug delivery by the two standard routes, intraperitoneal and intravenous, from tumor vasculature parameterized from real patient data. Delivery of both small- and large-molecular weight therapies was superior during intraperitoneal therapy. Finally, empirical and theoretical distributions of vessel radii were considered. Samples from tumors with each type of vascular morphology were run as though too distant from the peritoneal cavity to receive peritoneal delivery, with three results: first, intravenous delivery was superior to the secondary delivery into the circulatory system from a primary intraperitoneal delivery. Second, small molecules penetrated homogeneously across all cells, regardless of vascular volume or morphology, while antibodies penetrated heterogeneously, particularly in low-vessel-volume samples. Third, when each of the whole tumors was considered, this heterogeneity resulted in a large sub-population of cells that accumulated non-therapeutic levels of antibody, even during the best delivery scenario (IV). Fourth, delivery of antibodies was poorest in the empirical distribution. Finally, hypotheses were generated about the impact of heterogeneity of drug delivery, to be addressed as future questions

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Multi-scale models of ovarian cancer

In ovarian cancer, disease and treatment can be examined across multiple spatial scales including molecules, cells, intra-tumor vasculature, and body-scale dynamics of circulating drugs. Survival of primary tumor cells and their development into disseminated tumors is related to adhesion between the cells, attachment, and invasion. Growth of new tumors depends on the delivery of nutrients, which depends on the tumor diameter and the tumor’s vasculature. Drug delivery also depends on tumor diameter and vasculature, and molecular- and gross-scale drug processes. A cellular Potts simulation integrated data at these multiple scales to model microscopic residual disease during relapse after a primary surgery. The model generated new hypotheses about tumor cell behavior, and the effectiveness of drug delivery to tumors disseminated in the peritoneal cavity. First, the model required high intra-tumor adhesion in ovarian tumors, the existence of an unknown factor that drew tumor cells to vessels, a threshold of vascular endothelial growth factor (VEGF) for initiation of endothelial sprouting, and constitutive expression of angiogenic chemical messengers by tumor cells prior to needing oxygen. Alteration of the model incorporated drug delivery by the two standard routes, intraperitoneal and intravenous, from tumor vasculature parameterized from real patient data. Delivery of both small- and large-molecular weight therapies was superior during intraperitoneal therapy. Finally, empirical and theoretical distributions of vessel radii were considered. Samples from tumors with each type of vascular morphology were run as though too distant from the peritoneal cavity to receive peritoneal delivery, with three results: first, intravenous delivery was superior to the secondary delivery into the circulatory system from a primary intraperitoneal delivery. Second, small molecules penetrated homogeneously across all cells, regardless of vascular volume or morphology, while antibodies penetrated heterogeneously, particularly in low-vessel-volume samples. Third, when each of the whole tumors was considered, this heterogeneity resulted in a large sub-population of cells that accumulated non-therapeutic levels of antibody, even during the best delivery scenario (IV). Fourth, delivery of antibodies was poorest in the empirical distribution. Finally, hypotheses were generated about the impact of heterogeneity of drug delivery, to be addressed as future questions.BiologyDoctoralUniversity of New Mexico. Biology Dept.Moses, MelanieWearing, HelenJiang, YiWilson, Bridget S.Wearing, Hele

Abstract POSTER-TECH-1124: Modeling antibody penetration into metastatic ovarian tumors after intravenous or intraperitoneal delivery

Abstract Although targeted therapy with monoclonal antibodies has been successful against certain types of cancer, these treatments have had little success in metastatic ovarian cancer. Limited response may in part be due to suboptimal delivery of antibodies to tumors within the peritoneal cavity, as well as heterogeneous expression of surface receptors. Recent studies have shown that intraperitonal (i.p.) delivery of cisplatin has a survival advantage over intravenous (i.v.) delivery, yet few studies have examined how route of delivery influences the efficacy of antibody treatment. We have combined experimental and mathematical models to explore whether between-patient variation in tumor vascular density can predict which route of delivery is optimal for targeted treatment of metastatic ovarian tumors. The vascular density of paired bowel and omental metastatic ovarian tumors from ten patients was calculated and found to be uniform for tumors from the same patient, but vascular density among patients ranged from 1% to 10% indicating that vascular density may influence a patient’s response to treatment. Antibody penetration was measured in 3-D spheroid cultures and in vivo in an orthotopic mouse model of ovarian cancer metastasis. For our spheroid model, SKOV3.ip-GFP human ovarian cancer cells that overexpress erbB2 were allowed to form spheroids for 48 hours and then incubated with a fluorescently tagged anti-erbB2 therapeutic antibody, pertuzumab. Pertuzumab bound to the surface of the spheroids within one hour of treatment, but penetration into the center of the spheroid was slow, only reaching the center of the spheroid by 24 hours. In contrast, the chemotherapeutic agent doxorubicin completely penetrated the spheroids within 90 minutes. In our mouse model, SKOV3.ip cells were injected directly into the peritoneal cavity. Fluorescently-tagged pertuzumab was delivered via intravenous or intraperitoneal injection two weeks post-injection by which time tumors were seeded throughout the peritoneal cavity. Excised tumors were imaged on a two-photon microscope to observe antibody penetration. By 24 hours post-injection, antibody had fully penetrated into peritoneal tumors regardless of delivery route, suggesting that any advantage for i.p. delivery will likely occur at early time points. Using parameters derived from our experimental data, we have built a 3-D mathematical model, the OvTM (Ovarian Tumor Model), which simulates cell-cell interactions within an ovarian tumor. The simulated tumor can be avascular to simulate penetration of drugs into spheroids within the ascites fluid, or it can be populated with vessels that are matched to the vascular density of each patient’s tumors. We can use this individualized model to investigate the effect of different modes of drug delivery on drug distribution. Ultimately, we hope that our model will provide improved methods for stratifying patients by treatment route in order to optimize antibody penetration into metastatic ovarian tumors. Citation Format: Mara P. Steinkamp, Kimberly Kanigel Winner, Melanie Moses, Yi Jiang, Bridget S. Wilso. Modeling antibody penetration into metastatic ovarian tumors after intravenous or intraperitoneal delivery [abstract]. In: Proceedings of the 10th Biennial Ovarian Cancer Research Symposium; Sep 8-9, 2014; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(16 Suppl):Abstract nr POSTER-TECH-1124.</jats:p

Supplementary Material; Supplementary Figures 1 and 2 and Supplementary Table 1 from Spatial Modeling of Drug Delivery Routes for Treatment of Disseminated Ovarian Cancer

&lt;p&gt;Further details on model assumptions, statistical analysis, and model parameters. Supplementary Figure 1: Vascular Densities in Bowel and Omentum Metastatic Sites for Individual Patients Do Not Correlate. Supplementary Figure 2: Box Plots of Vascular Density in Patient Tumor Samples. Supplementary Table 1: Concentration Fits for IP and IV Compartments.&lt;/p&gt;</jats:p

Use of crowdsourced research to develop a prognostic model for first-line metastatic castrate resistant prostate cancer (mCRPC).

180 Background: Project Data Sphere, LLC (PDS) and Sage Bionetworks/DREAM have completed the “Prostate Cancer DREAM Challenge” (Challenge), a crowdsourced competition, using historical prostate cancer clinical trial data from PDS. The Challenge aimed to improve prognostic models for overall survival (OS) and to explore predictive models for treatment toxicity in mCRPC patients. Methods: Control arms of 4 randomized phase III trials (total 2,070 patients) were used as training and validation data sets for the Challenge: ASCENT2, MAINSAIL, VENICE and ENTHUSE33. All subjects were first line mCRPC patients receiving docetaxel treatment. Curated baseline clinical covariates (demographics, comorbidity, prior treatment, laboratory, lesion and vital signs) were modeled along with raw clinical data tables. The primary purpose of the Challenge was to develop a prognostic model for OS (SubChallenge 1). The models were scored using concordance index and integrated area under receiver operator curve (iAUC) from 6-30 months. The published mCRPC OS model of Halabi, et al., JCO, 2014, was used as the benchmark. Results: The Challenge attracted over 160 active participants who formed 50 teams that submitted final models for SubChallenge 1. Median iAUC was 0.76 (0.67-0.78) with a maximum score of 0.792. Over half (n = 35) of these models exceeded the published benchmark (0.743 iAUC). Teams explored new methodologies such as model-based imputation and machine learning techniques to develop the best performing models. Many leveraged raw clinical data sets to create their own covariates and expanded beyond existing prognostic models. Conclusions: The Challenge externally validated Halabi’s first line prognostic model. New prognostic models were proposed and validated with significant improvements over the benchmark. Further analyses are needed to examine the winning models for new prognostic factors and to validate them using additional trial data from PDS. The Challenge drove interest from cross-disciplinary teams of global experts to explore and enhance their technical abilities using real clinical data whilst serving as a vehicle to accelerate medical innovation. </jats:p

Supplementary Material; Supplementary Figures 1 and 2 and Supplementary Table 1 from Spatial Modeling of Drug Delivery Routes for Treatment of Disseminated Ovarian Cancer

Further details on model assumptions, statistical analysis, and model parameters. Supplementary Figure 1: Vascular Densities in Bowel and Omentum Metastatic Sites for Individual Patients Do Not Correlate. Supplementary Figure 2: Box Plots of Vascular Density in Patient Tumor Samples. Supplementary Table 1: Concentration Fits for IP and IV Compartments.</p