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E. Roarty
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MINI 09 - Drug Resistance (ID 107)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 1
- Moderators:L. Villaruz, J. Minna
- Coordinates: 9/07/2015, 16:45 - 18:15, 205+207
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MINI09.10 - Tumor Angiogenesis in LKB1-Mutant Non-Small Cell Lung Cancer (NSCLC) (ID 3059)
16:45 - 18:15 | Author(s): E. Roarty
- Abstract
- Presentation
Background:
LKB1 is a critical regulator of cell growth, metabolism and EMT, and it is mutated in 20-30% of non-small cell lung cancers (NSCLC). LKB1 mutations co-occur with KRAS-activating mutations in 7%-10% of all NSCLC and results in an aggressive phenotype and a worse response to chemotherapy compared to KRAS-mutated tumors. Because LKB1 activates AMPK (AMP-activated protein kinase) which functions as a cellular energy sensor, LKB1-deficient cells are unable to appropriately sense metabolic and energetic stress. LKB1 is also known to regulate angiogenesis, but the mechanism(s) by which this occurs remains unclear. Bevacizumab, the human anti-VEGF antibody approved for the treatment of NSCLC, improves the progression-free and overall survival of NSCLC patients combined with chemotherapy, but often the benefit is transient, and therapeutic resistance occurs. Our laboratory has previously identified phenotypical differences in vasculature patterns in A549 NSCLC tumors resistant to bevacizumab (LKB1 mutant), when compared to H1975 tumors, (LKB1 wild-type). In addition, LKB1 mutant NSCLC cell lines are highly vulnerable to agents acting on energetic pathways. These results may indicate that loss of LKB1 in NSCLC could alter the tumor vasculature and regulate sensitivity to anti-angiogenic therapies. Here, we investigate the hypothesis that combinations of energetic-depleting compounds along with blockade of tumor angiogenesis would be more effective in NSCLC LKB1 mutant tumors.
Methods:
mRNA and protein expression of 584 angiogenesis-related genes were analyzed in wild-type and LKB1 mutant NSCLC (TCGA, RPPA and PROSPECT databases). In vitro validation was performed using qPCR, immunohistochemistry and western blot analysis as well as pairs of isogenic LKB1 mutant cell lines with overexpressed or silenced LKB1. Endothelial cells were incubated with conditioned medium of wild-type and LKB1 mutant NSCLC cell lines, and tube formation matrigel, proliferation and migration (Boyden chamber) assays were performed.
Results:
We identify a group of new and classic angiogenesis-related molecules: VEGFA, VEGFR1, KDR, NRP1, PDGFB, PDGFRA-B, HIF-1A, C-KIT, VCAM1, hypoxia related molecules: HIF1AN, EGLN1, HIF3A, CA12, EPAS1 and immune related molecules: TNFSF11, NFKB1, CD47, PDL1 differentially expressed in LKB1-wild type and LKB1 mutant NSCLC (p<0.05 and fold-change ≥ or ≤1.5). LKB1 mutant cell lines showed higher protein expression of phospho-cKIT, a tyrosine-kinase receptor involve in cell proliferation and angiogenesis, and CA12 (Carbonic anhydrase 12), a known HIF-1α regulated molecule, involved in maintaining cellular pH homeostasis. Also, LKB1 mutant cells exhibit different quantitative vascular patterns in matrigel assays like number of nodes, junctions, length and branching of the endothelial matrix (p<0.05). Human endothelial cells exhibited an increase rate of proliferation and migration when incubated with conditioned medium from LKB1 mutant NSCLC cell lines compared with conditioned media from LKB1-wild type NSCLC cell lines (p<0.05).
Conclusion:
There are biological differences in vasculature patterns in LKB1 mutant NSCLC tumors and in LKB1 mutant cell lines comparing with wild-type LKB1. These differences are translated in biological alterations of human endothelial cells in vitro suggesting an important role of LKB1 in resistance to anti-angiogenic treatments in vivo.
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P2.01 - Poster Session/ Treatment of Advanced Diseases – NSCLC (ID 207)
- Event: WCLC 2015
- Type: Poster
- Track: Treatment of Advanced Diseases - NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P2.01-041 - MD Anderson Oncology Expert Advisor™ System (OEA™): A Cognitive Computing Recommendations Application (App) for Lung Cancer (ID 3106)
09:30 - 17:00 | Author(s): E. Roarty
- Abstract
Background:
The OEA[TM] is a clinical support system with a continuous improvement capability. Its objectives are to enable/empower evidence-based decisions/care by disseminating knowledge and expertise to physicians/users tailored to meet the clinical needs of individual patients as if consulting with an expert. Cognitive computing platforms have the potential to disseminate expert knowledge and tertiary level care to patients. This objective is made possible by making available to physicians/providers cognitive computing generated expert recommendations in diagnosis, staging and treatment. The cognitive computing software was trained by MD Anderson experts using currently available consensus guidelines and an iterative feedback process. Here we test the capability of this cognitive computing software program developed at MD Anderson to generate expert recommendations when patients with advanced-stage NSCLC have a targetable molecular aberration.
Methods:
We developed a web based prototype of MD Anderson’s Oncology Expert Advisor (OEA[TM]), a cognitive clinical decision support tool powered by IBM Watson. The Watson technology is IBM’s third generation cognitive computing system based on its unique capabilities in natural language processing and deep QA (question-answer). We trained OEA[TM] by loading historical patient cases and assessed the accuracy of targeted treatment suggestions using MD Anderson’s physicians’ decisions as benchmark. A false positive result was defined as a treatment recommendation rendered with high confidence that was non-correct (less optimal), whereas false negative was defined as a correct or more optimal treatment suggestion listed as a low confidence recommendation.
Results:
In our preliminary analyses, OEA[TM] demonstrated four core capabilities: 1) Patient Evaluation through interpretation of structured and unstructured clinical data to create a dynamic case summary with longitudinal view of the pertinent events 2) Treatment and management suggestions based on patient profile weighed against consensus guidelines, relevant literature, and MD Anderson expertise, which included approved therapies, genomic based therapies as well as automated matching to appropriate clinical trials at MD Anderson, 3) Care pathway advisory that alerts the user for anticipated toxicities and its early identification and proactive management, and 4) Patient-oriented research functionalities for identification of patient cohorts and hypothesis generation for future potential clinical investigations. Detailed testing continues and the accuracy of standard-of-care (SOC) treatment recommendations of OEA[TM], as well as false positivity and negativity rates will be presented in detail at the meeting.
Conclusion:
OEA[TM] is able to generate dynamic patient case summary by interpreting structured and unstructured clinical data and suggest personalized treatment options. Live system evaluation of OEA[TM] is ongoing and the application of OEA[TM] in clinical practice is expected to be piloted at our institution.
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P3.03 - Poster Session/ Treatment of Locoregional Disease – NSCLC (ID 214)
- Event: WCLC 2015
- Type: Poster
- Track: Treatment of Locoregional Disease – NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P3.03-032 - MD Anderson Oncology Expert Advisor™: A Cognitive Clinical Decision Support Tool for Evidence-Based Multi-Disciplinary Lung Cancer Care (ID 3039)
09:30 - 17:00 | Author(s): E. Roarty
- Abstract
Background:
The majority of patients diagnosed with non-small cell lung cancer (NSCLC) receive care in the community setting with limited access to multidisciplinary management common in tertiary care centers. The availability of genomics allows tailored treatments for patients; and with novel, rapidly emerging therapeutic options, it is challenging for busy clinicians to maintain familiarity with current therapy recommendations. Therefore, to empower practicing oncologists in community settings to offer the optimal management at the first intervention, we have developed the MD Anderson Oncology Expert Advisor™ (OEA) application for multi-disciplinary management of lung cancer patients. As the first multi-disciplinary solution for providing comprehensive management of lung cancer, the objective of OEA™ Lung is to leverage cognitive analytics on vast and ever evolving clinical care information and patient big data to disseminate knowledge and expertise, thus enabling physicians to provide evidence-based care and management tailored for the individual patient, similar to consulting an expert. Further, we aimed to create a system for sharing knowledge from more experienced experts to provide care pathways and management recommendations for physicians globally.
Methods:
Using cognitive computing, our cancer center partnered with IBM Watson to develop an expert system designed to provide physicians with the tools needed to process high-volume patient and medical information and to stay up-to-date with the latest treatment and management options, so that they can make the best evidence-based treatment decisions for their lung cancer patients. The OEA™ application for lung was built upon core capabilities of the OEA™ applications for leukemia and molecular/targeted therapies. Experts in multiple disciplines including thoracic surgery, medical oncology, and radiation oncology met regularly to design and provide specialized input to the IBM technical team in an agile development cycle. This system was powered to utilize both structured and unstructured data from validated sources; to thoroughly evaluate and stage patients; and to offer eligible clinical trials and personalized therapeutic options. In addition to delivering evidence-based, weighted therapy recommendations, OEA™ Lung provides care pathways for management of toxicities for each treatment modality (surgery, radiation, and medical oncology).
Results:
The OEA™ Lung application supports three core functions: 1) dynamic patient summary assimilating complete (structured and unstructured) data to show demographics, labs, genotype, treatment history, and previous treatment responses; 2) weighted evidence-based, multimodality treatment options, with recommendations based on literature support which is provided, along with screening for relevant trials; 3) care pathway advisories, to manage treatment related toxicities for each modality. Further, this product improves quality of care by optimizing outcomes with access to trials and care pathways.
Conclusion:
The OEA™ application for lung is a cognitive expert system designed to assimilate multidisciplinary recommendations for care and management of lung cancer patients based on current consensus guidelines and expert recommendations from a quaternary referral cancer center to the community practice setting. By democratizing knowledge from our specialty cancer center, we have taken steps toward achieving an important goal of ending cancer for all, by providing global access to optimal cancer care for patients with this disease. Further evaluation of outcomes following implementation are warranted.
