Los Angeles, CA – June 15, 2015 – NantOmics, a leading next generation molecular diagnostic company and wholly owned subsidiary of NantWorks, today announced data supporting the use of comprehensive genomics and transcriptomic (panomics) analysis to guide and inform clinical treatment decisions to be presented at the American Association for Cancer Research (AACR) Precision Medicine Meeting in Salt Lake City on Monday, June 15, 2015. In addition data will be presented demonstrating for the first time Nantomic’s novel bioinformatic predictive modeling, machine learning engine capable of predicting drug sensitivity or resistance based on genomic and transcriptomic data.
Poster #25
Title:
- “Whole genome sequencing and quantitative proteomics reveal HPV integration and HER2 overexpression in a patient with cervical cancer: Comprehensive omics analysis driving clinical treatment decisions,” will be presented by NantOmics, Monday, June 15, from 12-3 p.m. in the Grand Ballroom at the Grand America Hotel.
Poster #44
Title:
- “Building patient-specific predictors of drug responses from cell line genomics,” will be presented by NantOmics Monday, June 15, from 12-3 p.m. in the Grand Ballroom at the Grand America Hotel.
“In our study, whole genome sequencing (WGS) combined with proteomic profiling of tumors identified, confirmed and quantified an appropriate target for pharmaceutical intervention,” said Patrick Soon-Shiong, M.D., founder and CEO of NantOmics. “Our data supports the use of comprehensive omics analysis to guide clinical decisions for personalized management of cancer care with therapies determined based on a quantitative proteomic signature, independent of anatomical tumor type.
“The accuracy of predicting therapy outcomes may be enhanced using cell line genomics and drug-response data,” said Steve Benz, President of Genomics at Nantomics. “Combining best-in-class genome-wide assays, pathway analysis (Paradigm) and machine learning techniques, resulted in a rational, data-driven method for stratifying individual patients into responders and non-responders to oncotherapeutics. This expertise may prove invaluable in clinical drug development.”
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About NantOmics
NantOmics, a member of the NantWorks ecosystem of companies, delivers molecular diagnostic capabilities with the intent of providing actionable intelligence and molecularly driven decision support for cancer patients and their providers at the point of care. NantOmics is the first molecular diagnostics company to pioneer an integrated approach to unearthing the genomic and proteomic variances that initiate and drive cancer, by analyzing both normal and tumor cells from the same patient and following identified variances through from DNA to RNA to protein to drug. NantOmics has a highly scalable cloud-based infrastructure capable of storing and processing thousands of genomes a day, computing genomic variances in near real-time, and correlating proteomic pathway analysis with quantitative multiplexed protein expression analysis from the same micro-dissected tumor sample used for genomic analysis. For more information please visit www.nantomics.com and follow Dr. Soon-Shiong on Twitter@solvehealthcare.
About NantTransporter
Data transfer is one of the largest challenges associated with the analysis of sequencing data. NantOmics has designed an infrastructure capable of storing and processing thousands of genomes a day quickly and securely. Our NantTransporter software application enables secure transport of data directly from sequencing machines to NantOmics’s secure private genome processing cloud. Data streams are encrypted using 128-bit Advanced Encryption Standard (AES-128), the same algorithms designated for Top Secret government documents. Initiating a transfer is as easy as obtaining a transfer code from our web site, and specifying the files to be sent.
NantWorks has built a dark fiber network capable of Tb/sec transfers between hospitals, academic institutions and sequencing centers. In July 2012 we demonstrated the capabilities of NantTransporter by transferring 94 TB of genomic data and achieved speeds of up to 9.55 Gb/sec with a sustained rate of over 8.232 Gb/sec, the fastest and largest documented transfer of genomic data to date. To date we have transported over 15,000 cancer genomes via NantTransporter.
About NantContraster
Accurately assessing the state of a patient’s genome is one of the most powerful tools in the emerging field of personalized medicine. NantOmics applies its leading, novel genomic analyses to rapidly discover variants in a patient’s raw genomics data using the latest sequencing technologies combined with advanced statistics and machine learning techniques.
NantContraster annotates all variants against a knowledge database comprising all known and probable disease-associated genes to rank the genomic alterations that have the highest clinical relevance. When applied to cancer tumor/normal samples, our analysis is capable of quickly identifying genomic alterations that could lead to tumorigenesis for a fraction of the cost of conventional approaches. NantContraster is capable of concurrently processing many samples to handle large workflows from researchers and hospital settings.
About NantCancer Genome Browser
NantOmics, NantCancer Genome Browser enables clinicians for the first time to investigate a tumor genome from the full three billion bases down to the single-base level in real-time, thanks to the power of the NantOmics supercomputing and secure mobile infrastructure. The Cancer Genome Browser integrates with NantHealth’s treatment recommendation engine, Eviti, to personalize treatment protocols and clinical trail selection to individual patients based on their genomic and transcriptomic signature. The NantCancer Genome Browser is fully encrypted to allow deployment in a HIPAA secured environment, enabling clinicians to securely access patient data as soon as it’s available, wherever they are.
About Paradigm
PARADIGM (Pathway Recognition Algorithm using Data Integration on Genomic Models) is a proprietary algorithm that uses a probabilistic graphical model to integrate multiple genomic data types on curated pathway databases and is unique for its per-sample approach that allows individual samples to be assessed alone or within the context of a cohort of interest.