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Principal Investigator: Samir Parekh
Organization: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
Fiscal Year: 2024
Award: $520,055
Funding agency: National Cancer Institute
PROJECT SUMMARY
The era of “big data” has opened the door for genomic and systems biology approaches to be applied to
current challenges in life sciences and precision medicine. One critical challenge in these areas is how to
prioritize research findings to validate and identify actionable insights that can translate into better outcomes
for patients. In this regard, we have assembled a multidisciplinary group of scientists and physicians from
academia and industry with a focus on creating discovery pipelines that combine high-throughput profiling
technologies with advanced statistical and machine learning approaches to generate predictive tools that
enable us to move rapidly from big data to better diagnoses and treatment. In this regard, we propose to apply
these approaches to develop a computational clinical decision tool that will improve disease forecasting and
treatment plans for Multiple Myeloma (MM), an incurable cancer that originates in bone marrow plasma cells
and affects more than 30,000 patients a year. Though there have been some advances in the number and
diversity of available therapeutic options for these patients, relapse remains inevitable, and MM ultimately
remains a terminal diagnosis. The clinical assay and computational pipeline developed in this project will
combine a targeted sequencing panel specific to myeloma patients and clonality estimates with RNA-
sequencing and drug repurposing to expand therapeutic options for MM patients. We will develop this unique
tool with the following specific aims: (1) Develop an integrated genomic clinical decision tool to guide precision
treatment of MM and validate therapy recommendations using PDX profiling, and (2) Validate MM precision
medicine platform in a prospective clinical trial and generate clone-specific treatment recommendations. To
achieve these objectives, we will integrate a Cancer Genetic, Inc.'s FOCUS::Myeloma panel, a targeted panel
designed to specifically interrogateall the genes and copy number alterations commonly altered in myeloma,
and into a computational drug selection pipeline that utilizes RNA-sequencing data
and drug repurposing algorithms to generate therapeutic recommendations matched to a patient's unique
disease profile. These recommendations will be validated in mouse avatars of myeloma to confirm and refine
drug predictions. We will implement our assay in a prospective clinical trial of 100 patients to determine if the
treatment decisions generated by our pipeline achieves an improvement in standard-of-care. Finally, we will
perform clonal modeling on relapsed patients to retrospectively evaluate clone-specific treatment responses.
Completion of these studies will result in a clinic-ready assay and computational tool that will guide MM
precision treatment decisions and inform new therapeutic strategies based on a patient's unique cancer profile.
genomic
clonal
modeling
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Tumor><Hematopoietic and Lymphoid Cell Neoplasm><Hematopoietic and Lymphoid Neoplasms><Heterogeneity><Industry><Inflammatory Bowel Diseases><Inflammatory Bowel Disorder><Intake><Learning><Life Sciences><Machine Learning><Malignant Hematopoietic Neoplasm><Malignant Neoplasms><Malignant Tumor><Measurement><Medication><Mice><Mice Mammals><Modeling><Monitor><Multiple Myeloma><Murine><Mus><Non-Polyadenylated RNA><PDX model><Patient derived xenograft><Patient outcome><Patient-Centered Outcomes><Patient-Focused Outcomes><Patients><Peripheral arterial disease><Persons><Pharmaceutical Preparations><Pharmacotherapy><Physicians><Plasma Cells><Plasma-Cell Myeloma><Plasmacytes><Precision therapeutics><Prediction of Response to Therapy><Primary Neoplasm><Primary Senile Degenerative Dementia><Primary Tumor><RNA><RNA Gene Products><RNA Seq><RNA sequencing><RNAseq><Recommendation><Refractory><Relapse><Research><Research Priority><Ribonucleic Acid><Sampling><Schizophrenia><Schizophrenic Disorders><Scientist><Selection for Treatments><Sequence Alteration><Solid Neoplasm><Solid Tumor><Stream><Suggestion><Systematics><Systems Biology><Systems Integration><Technology><Testing><Therapeutic><Time><Translating><blood cancer><cancer genetics><cancer of blood><cancer of the blood><clinic ready><clinical decision support><clinical ready><clinically actionable><computational modeling><computational models><computational pipelines><computational toolbox><computational tools><computational toolset><computer based models><computerized modeling><computerized tools><data integration><dementia praecox><design><designing><developmental><disease subgroups><disease subtype><disorder subtype><drug repositioning><drug repurposing><drug resistant><drug treatment><drug/agent><efficacy testing><genetic condition><genetic disorder><genomic alteration><genomic data><genomic data-set><genomic dataset><genomic profiles><improved><improved outcome><in vivo><individual patient><individualized therapeutic><inflammatory disease of the intestine><inflammatory disorder of the intestine><insight><integrated system><intestinal autoinflammation><longitudinal design><machine based learning><machine statistical learning><malignancy><medical college><medical schools><model of animal><mouse model><multidisciplinary><multiomics><multiple omics><murine model><myeloma><myelomatosis><neoplasm/cancer><new drug treatments><new drugs><new pharmacological therapeutic><new therapeutic approach><new therapeutic intervention><new therapeutic strategies><new therapeutics><new therapy><new therapy approaches><new treatment approach><new treatment strategy><next generation><next generation therapeutics><novel><novel drug treatments><novel drugs><novel pharmaco-therapeutic><novel pharmacological therapeutic><novel therapeutic approach><novel therapeutic intervention><novel therapeutic strategies><novel therapeutics><novel therapy><novel therapy approach><panomics><patient derived xenograft model><patient oriented outcomes><patient profile><peripheral artery disease><personalization of treatment><personalized medicine><personalized therapeutic><personalized therapy><personalized treatment><pilot trial><plasmocyte><point of care><precision medicine><precision therapies><precision treatment><precision-based medicine><predict therapeutic response><predict therapy response><predictive tools><primary degenerative dementia><profiles in patients><prospective><relapse patients><repurposing agent><repurposing medication><resistance to Drug><resistant to Drug><response to therapy><response to treatment><risk stratification><schizophrenic><school of medicine><selection of treatment><senile dementia of the Alzheimer type><standard of care><statistical and machine learning><stratify risk><support tools><system integration><targeted sequencing><therapeutic response><therapy prediction><therapy response><therapy selection><tool><transcriptome sequencing><transcriptomic sequencing><transcriptomics><treatment planning><treatment prediction><treatment response><treatment response prediction><treatment responsiveness><treatment selection><tumor><tumor xenograft>