Patient Avatars Research Articles

Virtual Patient Technology: Engaging Primary Care in Quality Improvement Innovations.

BackgroundEngaging health care staff in new quality improvement programs is challenging.ObjectiveWe developed 2 virtual patient (VP) avatars in the context of a clinic-level quality improvement program. We sought to determine differences in preferences for VPs and the perceived influence of interacting with the VP on clinical staff engagement with the quality improvement program.MethodsUsing a participatory design approach, we developed an older male smoker VP and a younger female smoker VP. The older male smoker was described as a patient with cardiovascular disease and was ethnically ambiguous. The female patient was younger and was worried about the impact of smoking on her pregnancy. Clinical staff were allowed to choose the VP they preferred, and the more they engaged with the VP, the more likely the VP was to quit smoking and become healthier. We deployed the VP within the context of a quality improvement program designed to encourage clinical staff to refer their patients who smoke to a patient-centered Web-assisted tobacco intervention. To evaluate the VPs, we used quantitative analyses using multivariate models of provider and practice characteristics and VP characteristic preference and analyses of a brief survey of positive deviants (clinical staff in practices with high rates of encouraging patients to use the quit smoking innovation).ResultsA total of 146 clinical staff from 76 primary care practices interacted with the VPs. Clinic staff included medical providers (35/146, 24.0%), nurse professionals (19/146, 13.0%), primary care technicians (5/146, 3.4%), managerial staff (67/146, 45.9%), and receptionists (20/146, 13.7%). Medical staff were mostly male, and other roles were mostly female. Medical providers (OR 0.031; CI 0.003-0.281; P=.002) and younger staff (OR 0.411; CI 0.177-0.952; P=.038) were less likely to choose the younger, female VP when controlling for all other characteristics. VP preference did not influence online patient referrals by staff. In high-performing practices that referred 20 or more smokers to the ePortal (13/76), the majority of clinic staff were motivated by or liked the virtual patient (20/26, 77%).ConclusionsMedical providers are more likely motivated by VPs that are similar to their patient population, while nurses and other staff may prefer avatars that are more similar to them.

Abstract IA22: Clonal evolution and drug resistance in colorectal cancers: The EGFR-RAS signaling pathway

Abstract When metastatic colorectal cancers are challenged with targeted agents almost invariably a subset of cells insensitive to the drug emerges. As a result, in most instances, targeted therapies are only transiently effective in patients. Strategies to prevent or overcome resistance are therefore essential to design the next generation of clinical trials. How can we overcome the near-certainty of disease recurrence following treatment with targeted agents? To address this question a deeper understanding of the evolutive nature of cancer cells is necessary. We used colorectal cancer (CRC) as a model system to test the hypothesis that by understanding tumor's evolution the emergence of drug resistance can be controlled. We find that clonal dynamics can be monitored in real time in the blood of patients, and liquid biopsies can be used to intercept the emergence of resistant clones before relapses are clinically manifest. We discovered that a multistep clonal evolution process driven by progressive increases in drug fitness underlies the development of resistance in cells and patient avatars. To have long-term efficacy, the use of targeted therapies must take into account the continuous evolution of cancer cells, that is to say, therapies must adapt to tumor evolution. One possibility is to anticipate the changes the tumors will make. For example, by knowing in advance how CRC cells overcome resistance to EGFR blockade, we devised further rounds of therapy. Another approach is to unleash the ability of cancer cells to be recognized by the immune system. We tested this possibility in syngeneic mouse models of colorectal tumors. Our findings indicate that inactivation of DNA repair triggers dynamic neoantigen evolution; impairs cancer growth and leads to prolonged therapeutic responses. Citation Format: Alberto Bardelli. Clonal evolution and drug resistance in colorectal cancers: The EGFR-RAS signaling pathway. [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer: From Initiation to Outcomes; 2016 Sep 17-20; Tampa, FL. Philadelphia (PA): AACR; Cancer Res 2017;77(3 Suppl):Abstract nr IA22.

Patient Similarity: Emerging Concepts in Systems and Precision Medicine.

Patient Similarity: Emerging Concepts in Systems and Precision Medicine.

Abstract PL04-03: Heterogeneity, drug resistance, and clonal evolution in colorectal cancers

Abstract When metastatic cancers are challenged with targeted agents almost invariably a subset of cells insensitive to the drug emerges. As a result, in most instances, targeted therapies are only transiently effective in patients. Strategies to prevent or overcome resistance are therefore essential to design the next generation of clinical trials. How can we overcome the near-certainty of disease recurrence following treatment with targeted agents? To address this question a deeper understanding of the evolutive nature of cancer cells is necessary. We used colorectal cancer (CRC) as a model system to test the hypothesis that by understanding tumor's evolution the emergence of drug resistance can be controlled. We find that clonal dynamics can be monitored in real time in the blood of patients, and liquid biopsies can be used to intercept the emergence of resistant clones before relapses are clinically manifest. We discovered that a multistep clonal evolution process driven by progressive increases in drug fitness underlies the development of resistance in cells and patient avatars. To have long-term efficacy, the use of targeted therapies must take into account the continuous evolution of cancer cells, that is to say, therapies must adapt to tumor evolution. One possibility is to anticipate the changes the tumors will make. For example, by knowing in advance how CRC cells overcome resistance to EGFR blockade, we devised further rounds of therapy. Another approach is to unleash the ability of the immune system to recognize drug resistant cells. We tested this possibility in syngeneic mouse models of CRC sensitive to targeted therapies. Our findings indicate that manipulation of the mutational loads can trigger prolonged therapeutic responses that are not observed when cancer cells are challenged with targeted drugs alone. We postulate that rationally combined targeted and immuno-therapies can restrain tumor evolution, and can limit the emergence of drug resistance thus leading to long-term responses. Citation Format: Alberto Bardelli. Heterogeneity, drug resistance, and clonal evolution in colorectal cancers. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr PL04-03.

Tumor Organoids Fill the Niche

Organoid technologies have significant potential as effective patient avatars. Fujii etal. (2016) and van de Wetering etal. (2015) derive biobanks of colorectal tumor and matching normal organoids and identify associations between tumor subtype, oncogenic drivers, gene-drug interactions, and varying niche requirements for tumor organoid growth, engraftment, and metastasis.

Using Patient Avatars to Promote Health Data Sharing Applications: Perspectives and Regulatory Challenges.

The potential of ICT to address problems in modern healthcare is considerable, and an ICT-driven revolution in healthcare appears imminent. Such developments maybe viewed largely in positive terms. Thus they should result in enhanced treatment and care options, empowering patients--including by permitting greater self-management of illness outside hospital, while offering economic benefits and costs savings over traditional healthcare provision. However, the new possibilities also present manifold risks, such as of data breaches, encroachments on subject autonomy, as well as of other harms. This article considers some of the key regulatory challenges against the background of the progress of the current EU Commission-sponsored 'MyHealthAvatar' project.

Developing Virtual Reality Simulations for Office-Based Medical Emergencies

Virtual reality simulation may significantly benefit a geographically dispersed learner demographic in the medical outpatient setting. Our research used an immersive virtual reality platform as a novel way to recreate high-risk medical scenarios targeted for office-based emergencies. Using a design-based research approach we designed virtual-reality-based simulation scenarios to prepare interprofessional office personnel for emergencies. Learners were connected using laptop computers, via a browser interface, with learner controlled team member avatars and educator controlled patient avatars. The virtual environment was modeled after a multi-provider healthcare office setting in a large suburban health network. Evaluation occurred via post-event surveys and feedback transcribed from video recordings and debriefings. Three office-based emergency scenarios were created (chest pain, respiratory distress/allergic reaction, and suicidal risk), with progressively smaller changes to the virtual environment with each iterative improvement. In total, 18 individuals representative of a typical outpatient office interprofessional care team participated in the pilot study. Qualitative design-related feedback from participants and faculty improved the educational environment, artifacts, and scenarios. Participant feedback was overwhelmingly positive and enthusiastic about the use of virtual reality-based simulations to explore teamwork, build scope of practice, and rehearse infrequently used clinical skills. We successfully created novel outpatient virtual reality simulations in a first-person-perspective virtual environment. Pilot testing revealed successful rapid development, implementation, and participant orientation, with the ability to present learning opportunities. Future efforts will include assessments and attempt to overcome development barriers by switching to a more versatile platform.

Principles for Developing Patient Avatars in Precision and Systems Medicine.

The day is drawing near when each patient can be made into a SuperModel—not a fashion model, but a digital or virtual model (see companion paper Brown, in review). Such representation of a patient can also be termed a patient avatar (The Discipulus Project, 2013). Principles to help guide successful implementation of these SuperModels are addressed in this article, including efficiency and cost-effectiveness (Figure ​(Figure11). Figure 1 Emerging principles for precision and systems medicine. A patient-clinician-technician alliance will be needed to work effectively with patient avatars in precision and systems medicine. The technician will feed data from the clinician and the patient ... Patient-clinician-technician alliance A patient-clinician-technician alliance will be needed for optimal care. The patient will interact with the clinician and technician, while the technician guides input of information into the SuperModel. Upon availability of model results, the technician will help interpret output for the clinician and the patient (The Discipulus Project, 2013). Multiple levels of support among the patient's health care team will be needed for shared clinical decision-making, buttressed by patient engagement and involvement of kinship and social networks. Thus, the health care team, patient, and kinship and social networks should be educated about the probabilistic (as opposed to deterministic) nature of SuperModel output. This would be facilitated by systems medicine counseling (Brown, in review). Various clinical decision support (CDS) tools are being developed for research and clinical use (e.g., http://www.myhealthavatar.eu, http://www.myresults.org), which help physicians decipher results and communicate with patients and their networks.

Building SuperModels: emerging patient avatars for use in precision and systems medicine.

The era is approaching when each individual can be mapped to a patient avatar—not a life-sized 3D blue form of the patient filled with physical substance (as in the movie “Avatar”), but a hologram of the patient that simulates key medical components. Patient avatars will be composed of computational models combined with various data types and analytics to form what might be called SuperModels (Figure ​(Figure1).1). These SuperModels (comprehensive virtual representations of the patient, not fashion models) will be important to help realize visionary precision medicine initiatives that have recently been announced (Collins and Varmus, 2015; Nature Biotechnology, 2015). Figure 1 Building SuperModels for precision and systems medicine, with incorporation of computational avatars. Input: A feedforward loop (top left panel X connects to patient avatar Z both indirectly via bottom left panel Y and also directly, by large blue arrows) ... Precision medicine tailors prevention, diagnosis, therapeutics, and prognosis for each patient (Garay and Gray, 2012; Highnam and Mittelman, 2012; Mirnezami et al., 2012). Related to precision medicine is systems medicine (Auffray et al., 2009; Capobianco, 2012; Emmert-Streib and Dehmer, 2013; Wolkenhauer, 2013), which leverages systems biology (Noble, 2008) for clinical application, with resulting data termed “systems medicine data” (Brown et al., 2015b). Systems biology studies the behavior of organisms or cells as whole systems, and uses various advances in biotechnology, including genomics, transcriptomics, proteomics, metabolomics, methylomics, microbiomics, and elucidation of cellular interaction networks by network biology (Figure ​(Figure1,1, top left panel labeled X). Often, systems medicine data from these various advances can be modeled and simulated with complementary computer science, mathematics, chemistry, physics, and engineering concepts in computational biology. A variety of fields have used computational models as virtual surrogates of specific portions of patient physiology. These individual models can be considered computational avatars of a subset of the patient's organic identity. This is akin to cancer avatars in mice, which involve mouse models mapped to individual patients, for example, by injection of tumor cells from a particular individual. These cancer avatars facilitate personalized study of the pathophysiology and response to drugs of a particular patient's cancer cells. Similarly, biomathematical or computational cancer avatars simulate the micro-environment of individualized cancer cells. Beyond such in silico exemplars, a computational avatar may also be thought of as any finite representation of a specific portion of the patient, that harnesses computing power. This includes electronic health records (EHR), patient portals, and a variety of other precision medicine tools. However, this paper focuses on individual biomathematical models as computational avatars that can be incorporated into comprehensive patient avatars for use in precision and systems medicine. The following section describes a non-exhaustive sample of biomathematical models, including genome-scale metabolic models (GEMs) that use computational approaches to integrate omics data (Yizhak et al., 2015). These computational avatars can serve as ingredients for SuperModels, forming the first portion (X → Z) of a positive feedback loop in Figure ​Figure11.

Computational Patient Avatars for Surgery Planning.

In this paper a new method is described for the generation of computational patient avatars for surgery planning. By "patient avatar" a computational, patient-specific, model of the patient is meant, that should be able to provide the surgeon with an adequate response under real-time restrictions, possibly including haptic response. The method is based on the use of computational vademecums (F. Chinesta et al., PGD-based computational vademecum for efficient design, optimization and control. Arch. Comput. Methods Eng. 20(1):31-59, 2013), that are properly interpolated so as to generate a patient-specific model. It is highlighted how the interpolation of shapes needs for a specialized technique, since a direct interpolation of biological shapes would produce, in general, non-physiological shapes. To this end a manifold learning technique is employed, that allows for a proper interpolation that provides very accurate results in describing patient-specific organ geometries. These interpolated vademecums thus give rise to very accurate patient avatars able to run at kHz feedback rates, enabling not only visual, but also haptic response to the surgeon.

Authoring, deploying, and managing dynamic Virtual Patients in Virtual Clinical Environments

Abstract Following their introduction at the beginning of the 21st century, interactive or dynamic Virtual Patients are beginning to be used more widely in clinical education. They can be seen as being at the end of a continuum of simulation technical complexity, having been earlier developed on a wide range of “media”: human actors, paper, video, physical mannequins, etc. This paper focuses on the current emergent more complex Virtual Patients in three-dimensional (3D) immersive clinical environments. In these environments, in silico 3D patient avatars interact directly in response to virtual clinical interventions undertaken by avatars, each of which is controlled by one or more users. The paper explores the issues of authoring, deploying, and managing these real-time, dynamic Virtual Patients using as an example the immersive clinical environment CliniSpace. As clinician-accessible Virtual Patient authoring is now becoming available in immersive clinical environments, so these wider clinical and managerial non-technical issues are coming rapidly to the fore.

A Novel Simulation Method for Mapping Dysregulated Pathways and Predicting Effective Therapeutics in the Myelodysplastic Syndromes

Background: The myelodysplastic syndromes (MDS) are a heterogeneous group of malignancies characterized by multiple genomic abnormalities. New technologies are needed that bridge the complex molecular biology of MDS and available therapeutics.Methods: Therefore, we developed a predictive simulation software program to create MDS patient avatars based on disease-specific genomic profile. The avatar technology has three layers: the first layer is a proprietary mathematics solver engine architected to handle millions of differential equations representing biological cross-talk reactions needed for modeling MDS physiology; the middle layer is the comprehensive functional proteomics representation of disease physiology network; and the topmost layer is a semiconductor engineering-based automaton engine which allows high-throughput simulation of multi-million interventions through assembly code language. The MDS avatars were intended to map the complex interplay of dysregulated pathways and predict potential therapeutics. The human MDS-L cell line is one of few bona fide MDS cell lines and was analyzed by conventional G-banding karyotyping, FISH, array CGH and Sanger sequencing of MDS-relevant genes. Using this genomic information, a simulation patient avatar was created. Next, a library of over 80 FDA approved agents was simulated against the dysregulated pathways. A list of predicted drugs where then prospectively validated using in vitro culture of MDS-L cells.Results: The MDS-L cells harbored an activating NRAS mutation (c.35G>C, p.G12A) and complex chromosome abnormalities. The simulation MDS-L patient avatar predicted activation of the RAF-ERK pathway due to NRAS mutation and various copy number variations (CNVs) including high CN of IGFR, AURKA, PAR5 that predicted high AKT activation, high CN of mTOR, high CN of IL6 and JAK3 with predicted activation of STAT3 and STAT5, high CN of MDM4 with predicted lower levels of TP53, high CN of RCE1, MAPK1 and MAP2K1, and low CN of RASA1 and DUSP1 (Figure 1). Given the strong dominance of AKT and ERK loops in this profile (Figure 1), we simulated potential drugs individually and in combinations to predict impact on these pathways. Iterative simulations identified four FDA-approved drugs: (A) nelfinavir and celecoxib acting on AKT, (B) sorafenib and trametinib that inhibit RAF-ERK targets, and (C) statins that inhibit the RAS-RAF-ERK pathway through inhibition of prenylation. In vitro validation experiments showed statistically significant reductions in MDS-L cell viability when treating with nelfinavir alone, celecoxib alone, and enhanced additive reduction in viability with the combination of nelfinavir and celecoxib (Table 1).Conclusions: This study shows how a novel simulation method can be employed to use patient-specific MDS genomic profiling to map dysregulated pathways and predict potentially therapeutic agents. The MDS case presented led to the discovery of a novel combination of nelfinavir and celecoxib. Results from this study serve as the basis for MDS clinical trials that assign treatment based on genetic mutations. [Display omitted] Table 1MDS-L Cell In Vitro Validation Experiments of Drug Treatments.NelfinavirDrug Dose0μM5μM10μMCelecoxib0 μM100.00%81.45% ± 7.8%53.49% ± 6.9%30 μM71.34% ± 4.89%60.58% ± 3.25%35.78% ± 3.59%MDS-L cells were treated with vehicle control, nelfinavir alone (incremental dose increases), celecoxib alone (incremental dose increases), and combination nelfinavir and celecoxib. MDS-L cell viability was quantified and compared among treatment groups. Both nelfinavir alone and celecoxib alone reduce MDS-L cell viability in a dose-dependent manner. The combination of nelfinavir and celecoxib showed enhanced additive reduction of MDS-L cell viability, again in a dose-dependent manner. DisclosuresCogle:PHAR: Consultancy. Vali:CellWorks: Employment. Kumar:CellWorks: Employment. Singh:CellWorks: Employment. Tyagi:CellWorks: Employment. Abbasi:CellWorks: Employment, Equity Ownership.

Therapy Personalization Using Predictive Simulation Approach with Ex-Vivo Clinical Validations

BackgroundThe epitome of cancer treatment personalization is N=1 segmentation where a custom therapy is designed for every patient. Because most cancer aberrations are not actionable mutations and tumors can have more than one actionable mutation, this one biomarker/one drug approach to cancer personalization has inherent limitations due to its over simplification.Personalization 2.0 methodology creates a patient simulation avatar incorporating a patient’s genomic profile information holistically.MethodsBone marrow samples from two myeloma patients (P1 and P2) refractory to most recent treatment was collected, and P1’s sample was sorted into CD138+ and CD138- cells. The patient cells were analyzed for chromosomal alterations using Comparative Genomic Hybridization (aCGH) arrays by GenPath Diagnostics and cytogenetic chromosome analysis by Washington University School of Medicine and New York University (NYU), respectively.Using this information, a predictive simulation avatar model of each patient was created by Cellworks based on genomic profile of patients. A digital functional library of over 80 FDA-approved drugs and agents currently in clinical trials were simulated individually and in combination using the two patient avatars to create a personalized treatment for each patient. The findings were prospectively validated using patient cells ex vivo as assessed by MTT assay at New York University.ResultsP1 aberrations included trisomy of CCND1 and deletion of TP53 along with single copy losses in different arms of chromosomes 1, 6, 8, 12, 13, 14, 16, 17 and 22 and gains in different arms and regions of chromosomes X, 1, 4, 7, 9, 17, 3, 5, 11, 15 and 19, indicating the presence of hyperdiploid clones. Using this information, 897 gene perturbations were included to model this patient simulation avatar. Simulation predicted high beta-catenin (CTNNB1) activity with increased hedgehog and NOTCH pathways that were inherent causes of Bortezomib resistance. Significant activation of STAT3 and STAT5 due to amplification of IL6 pathway, JAK2 and JAK3 was noted. Amplifications of MET, IGFR and FGFR converged at ERK and AKT signaling loops. Along with deletion of TP53, this profile had amplification of many anti-apoptotic genes including survivin, MCL1 and XIAP. Modeling predicted sensitivity to the JAK inhibitor Tofacitinib, a drug approved for rheumatoid arthritis. This was prospectively validated ex vivo, and the experimental data correlated with the prediction showing a reduction in viability.P2 aberrations include losses in chromosomes X and 9 and a chromosome 11:14 translocation that is a common occurrence in MM. This translocation results in an amplification of CCND1 expression. The genomic aberrations reported include knockdown of tumor suppressors RXRA, TGFBR1, TJP2 and TSC1. TSC1 regulates the mTOR pathway, and its deletion causes an aberrant activation of mTOR and its downstream targets. Reduced expression of RXRA and TJP2 both in different manners leads to increase in AP1 activation. NFkB is also activated due to RXRA reduction. TGFBR1 reduction decreases the expression of cell cycle inhibitors via SMAD2/3 down-regulation. In this patient avatar, modeling predicted sensitivity to a combination of Sirolimus and Trametinib. Ex vivo validation confirmed this prediction of additive synergy of these two drug agents in the context of this patient.ConclusionsThis study demonstrates and validates the personalization of treatment through two patient cases based on creating predictive simulation avatar models using genomic profile information. This modeling holistically incorporates all genomic aberration information and is not limited to associating drugs to actionable mutations. DisclosuresDoudican:Cellworks: Research Funding. Vali:Cellworks: Employment. Basu:Cellworks: Employment. Kumar:cellworks: Employment. Singh:Cellworks: Employment. Sultana:Cellworks: Employment. Abbasi:Cellworks: Employment, Equity Ownership.

A Novel Method of Using Molecular Profiling in Myelodysplastic Syndromes to Predict Patient-Specific Potential Therapeutics

Background: The myelodysplastic syndromes (MDS) are a heterogeneous group of malignancies characterized by multiple genomic abnormalities. Precise characterization of MDS molecular abnormalities in individual patients and treatment with drugs that target the specific constellation of molecular abnormalities may provide a practical approach in this complex disease.Methods: Therefore, bone marrow cells from 90 MDS patients were molecularly profiled by testing for mutations in the following 14 MDS-specific genes: ASXL1, ETV6, EZH2, IDH1, IDH2, NRAS, CBL, RUNX1, SF3B1, SRSF2, TET2, TP53, U2AF1 and ZRSR2. We used these profiles in a novel predictive simulation software program to create MDS patient avatars. The avatar technology has three layers: the first layer is a proprietary mathematics solver engine architected to handle millions of differential equations representing biological cross-talk reactions needed for modeling MDS physiology; the middle layer is the comprehensive functional proteomics representation of disease physiology network; and the topmost layer is a semiconductor engineering-based automaton engine which allows high-throughput simulation of multi-million interventions through assembly code language. Bone marrow conventional cytogenetics, FISH and molecular profiling data from 90 patients were used in the simulation. A library of over 80 FDA-approved agents was simulated against the predicted dysregulated pathways. A list of predicted drugs where then prospectively validated using in vitro culture of MDS-L cells.Results: Of the 90 MDS patients, the IPSS-R distribution was 25% Very Low, 58% Low, 9% Intermediate, 7% High, and 1% Very High. Median BM blast was 2% (range 0-16%). All 90 patients had detectable genomic abnormalities. 86/90 (96%) of patients had detectable genetic mutations. The 4 patients with undetectable genetic mutations all had structural chromosome abnormalities (e.g., del(11q), +8, del(20q)). Among the 90 patients, recurrent constellations of genomic mutations were identified, which permitted clustering into 63 unique MDS genomic profiles. Using these unique MDS genomic profiles, simulation avatars of dysregulated pathways were created for each MDS patient (Figure 1 as example). Next, we used the MDS avatars to simulate FDA-approved drugs individually and in combinations to predict impact on these dysregulated pathways. Iterative simulations identified 12 new drug regimens depending on the MDS patient’s constellation of genomic abnormalities: (1) nelfinavir, (2) bortezomib, (3) metformin + simvastatin, (4) metformin + seliciclib, (5) nelfinavir + bortezomib, (6) nelfinavir + ruxolitinib, (7) nelfinavir + simvastatin, (8) nelfinavir + sorafenib, (9) nelfinavir + trametinib, (10) ponatinib + bortezomib, (11) nelfinavir + ponatinib, and (12) trametinib + simvastatin. In vitro validation experiments of drug regimens with MDS-L cells showed significant, dose-dependent reductions of MDS-L cell viability.Conclusions: This study shows how molecular profiling using 14 genes and a novel simulation method can be used to map dysregulated pathways and predict potentially therapeutic agents for patients with MDS. Results from this study serve as the basis for an MDS clinical study that assigns treatment based on genomic abnormalities. [Display omitted] DisclosuresCogle:Leukemia & Lymphoma Society: Research Funding. Off Label Use: Treating MDS with one of these regimens: (1) nelfinavir, (2) bortezomib, (3) metformin + simvastatin, (4) metformin + seliciclib, (5) nelfinavir + bortezomib, (6) nelfinavir + ruxolitinib, (7) nelfinavir + simvastatin, (8) nelfinavir + sorafenib, (9) nelfinavir + trametinib, (10) ponatinib + bortezomib, (11) nelfinavir + ponatinib, and (12) trametinib + simvastatin.. Vali:Cellworks: Employment. Kumar:Cellworks: Employment. Singh:Cellworks: Employment. Nair:Cellworks: Employment. Lala:Cellworks: Employment. Basu:Cellworks: Employment. Abbasi:Cellworks: Employment. Albitar:Neogenomics: Employment.

Abstract 1196: Overexpression of IGF-2 in primary patient ovarian cancer tumorgrafts increases bowel and thoracic metastasis

Abstract Bowel obstruction is common sequelae of advanced ovarian cancer. Reports suggest that from 5.5 percent to 51 percent of ovarian cancer patients with Stage III or Stage IV disease develop a malignant bowel obstruction. During the course of the disease, one-third of all ovarian cancer patients also will develop pleural effusions, and three-quarters of these contain malignant cells. Such complications adversely affect both survival and quality of life. Our murine primary patient ovarian cancer intraperitoneal tumorgrafts (Avatars) maintain high fidelity of genetic aberrations from the original patient tumor, thus representing a clinically relevant model system. Expression microarray data of Avatars with preferential metastasis to the bowel has revealed upregulation of IGF2. To determine if IGF2 overexpression was sufficient for metastasis, four patient Avatars were transfected with IGF2/Gluc. In order to minimize the potential for genetic drift sometimes seen with in vitro culture, pre-transplantation tumors were minced and incubated with lentiviruses (Control vector or IGF-2/Gluc expression vector) ex-in vivo for a short time (2 hours), then directly transplanted intraperitoneally into immunodeficient mice (five or six mice in each Avatar model). Total 32 animals were used in this study. Stable transfection and overexpression of human IGF-2 was achieved and maintained after three passages as determined by ELISA on tumor tissue lysates. Only one out of 32 mice did not engraft tumor after three months, and tumor taking rate was 96.875%, similar to our previous passage successful rate at 98% and indicate that our ex-in vivo transfection method and overexpression of human IGF-2 did not substantially affect the tumor taking rate. By using Xenogen IVIS200 imaging system (Xenogen Corp.), pleural fluid was observed in all the Avatar mice with tumors overexpressing IGF-2 or not, and the retrieved pleural fluids were positive for malignant cells. However higher pleural nodules forming rate was more common in mice with IGF-2 overexpressing tumors 77% (n=9) vs 40% (n=6). In model PH003/IGF-2, the tumor had 100% bowel metastasis (n=9) compare to 60% in the control group (n=6). Taken together, our Avatar models not only effectively recapitulate ovarian cancer abdominal peritoneum metastasis, but also can mimic advanced ovarian cancer with malignant pleural fluid. The IGF-2 overexpression enhanced metastatic potential relative to control tumor. These results also indicate that the ex-in vivo transfection method developed in this study may be a viable approach for biological research and therapies. This work is funded by the Mayo Ovarian SPORE (CA136393). Citation Format: Xiaonan Hou, Marc A Becker, Saravut J Weroha, Kristina A Butler, Suzanne M Greiner, Paul Haluksa. Overexpression of IGF-2 in primary patient ovarian cancer tumorgrafts increases bowel and thoracic metastasis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1196. doi:10.1158/1538-7445.AM2014-1196

P309 Serious Games As A New Medium For Patients Guidelines Dissemination

BackgroundPatient education documents are faced with a number of issues that limit their dissemination, from intrinsic readability to how they appeal to patients. Previous studies have demonstrated a positive impact...

Abstract 2731: Classification of HCC patient derived xenograft (PDX) models by transcriptome and microRNome profiling, and their resemblance to patients.

Abstract Hepatocellular carcinoma (HCC) is a disease of diverse types. Transcriptome analysis recently classified HCC into three major categories with distinct clinical parameters as well as cellular differentiation1. They are S1 (stem-like group with activation of the WNT pathway and TGF-β), S2 (activation of MYC and AKT), and S3 (hepatocyte differentiation). Patient derived xenografts (PDXs) have been suggested to be predictive experimental models (“Patient avatars”) because they have not been in vitro manipulated, and have the original patients’ histopathological and genetic profiles2,3. In order to assess the resemblance of these “avatar” to patients, we performed transcriptome profiling on a collection of HCC-PDXs using the same algorithm1. Twenty-two HCC-PDX models were classified into 3 groups, among which S1 and S2 are more closely related. Using the 572 genes generated from the algorithm, the same classifications were obtained by hierarchical clustering and principal component analysis (PCA), except for two outliers. The results demonstrate that our cohort of HCC-PDXs can be divided into the same three subclasses as those from patient samples1. In addition, Luk et al., recently classified HCC using expression levels of microRNA cluster at 14q32.2 locus, in which a stem-like HCC phenotype was associated with high expression4. We thus also used this criterion to classify our collection of HCC PDXs. The two resulting categories, 14q32.2-hi and 14q32.2-lo, can be compared to the S1, S2, S3 subclasses determined by mRNA profiling described above, in which S1 belongs to 14q32.2-lo and S2/3 belong to 14q32.2-hi (except LI1025, LI1078). The serum α-fetoprotein (AFP) and tumor AFP-mRNA levels were found to be strongly and positively correlated, and also associated with S21 and 14q32.2-hi4, consistent with previous reports1. Six stem-like markers were found not to be associated with S11, nor with 14q32.2-hi4, thus different from the report by Luk et al4. Activation of c-MET was found exclusively in S1 models, consistent with one of previous observations5, but not the other4. When all these models were treated with Sorafenib, a multi-kinase inhibitor, there seemed to be no correlation in the tumor responses between the subclasses. In summary, the data provided insights into the classifications of HCC by mRNA expression1 and 14q32.2 microRNA expression4, partially consistent with existing reports1,2. The results also show distinctive characteristics of our HCC-PDXs that merit further studies. Citation Format: Sheng Guo, Wubin Qian, Jie Cai, Dawei Chen, Jie Yang, Zhun Wang, Xiaoming Song, Taiping Chen, Jean Pierre Wery, Yiyou Chen, Henry QX Li. Classification of HCC patient derived xenograft (PDX) models by transcriptome and microRNome profiling, and their resemblance to patients. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2731. doi:10.1158/1538-7445.AM2013-2731

Medical students' attitudes toward obese patient avatars of different skin color.

Physicians' biases for skin color and obesity may negatively affect health-care outcomes. Identification of these biases is the first step to address the problem. We randomized 128 U.S medical students into one of four animated videos of avatar physician-patient counseling sessions, varying the weight and skin color of an elderly patient avatar: white-thin, black-thin, white-obese and black-obese. Medical students viewed white obese avatars as unattractive, ugly, noncompliant, lazy, and sloppy. Medical students' comments suggested a paternalistic attitude toward avatar patients. Avatar-mediated experiences can elicit medical students' bias potentially enabling medical educators to implement bias reduction interventions.