genomic expression presentation at oxford 20141208
TRANSCRIPT
By Morten Middelfart, CIO Genomic Expression: Big Data Solutions for Tumor RNA Sequencing”
Source: Brian B. Spear, Margo Heath-Chiozzi, Jeffrey Huff, “Clinical Trends in Molecular Medicine,” Volume 7, Issue 5, 1 May 2001, Pages 201-204.
Only 25% on standard of care
Lives Longer
What Could Save People in The Remaining 75% is
An Algorithm
1990sAccess
2000sSpeed
2010+Autonomy
We use algorithms to find products
We use algorithms to fly airplanes
This is where we put Tanya’s argument for the miracles that happen all the time, but are dismissed as outliers to fulfill an “average criteria”.
We intend to pool the miracles!
Can we use algorithms to treat patients ?
RNA sequencing saved Dr. Wartman’s Life
But it took a whole team of researchers month and cost $10,000 of dollars
OneRNA™ Products, Patents and WorkflowWe have reduced the cost 10X and data by 1000X by organizing the RNA’s prior to sequencing
• Issued IP on the sample prep methods for sequencing RNA• Provisional patents on specific applications and Next Generation platforms• Proprietary sequence algorithms and databases with all actionable RNA targets in oncology• Trademark and URLs on OneRNA and RNADx• IBM is our strategic parter delivering OneRNA™ in a HIPAA cloud solution
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RNA sequencing in Breast Cancer - an exampleTriple negative breast cancer patient have very few options and poor prognosis. 15% of all breast cancers are triple negative translating into a +$100 mill opportunity for this indication alone
Triple-negative breast cancer challenges • Standard breast cancer drugs (Herceptin, hormone
therapies) are ineffective• Very poor prognosis• Care guidelines encourage participation in clinical
trials, but there are >2000 to choose from
OneRNA™ matched the tumor profile for a single patient to • 1 drug approved in breast cancer• 5 drugs approved in other cancers, including one
novel immune therapy being tested in an ongoing breast cancer clinical trial
• 30 active clinical trials: immune therapies (11) check point inhibitors (6) targeted therapies including Parp inhibitors (13)
Variable Tumor Response to an Anti-PDL1 Checkpoint Inhibitor
Chart part of Figure from Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients, Roy Herbst et al., Nature 515, 563–567 (27 November 2014) doi:10.1038/nature14011
To deliver miracles to more cancer patients we need two things:
widespread collection datasets that capture the molecular diversity of each patient's disease
algorithm that match the tumor's molecular profile to the most effective therapy available
Access: Data is AvailablePatient Tumor Samples
Outcome Data
Assays
Speed of Big DataUsing RNA we reduce Input Sequence to 1-2% compared to DNA
Using our patented approach in sample prep we reduce data 10x while we improve quality!
≈ 1000x Reduction
An Algorithm connects the dots
Trusting An Algorithm
Any sufficiently advanced technology is indistinguishable from magic.
Arthur C. Clarke
Genomic Expression’s Goal:Cancer, if not cured, should be no more than a chronic disease
Patients’ tumors must be analyzed before treatment
Let’s start generating and collecting data
Academia & Pharma Partners Welcomed
