2:00PM–5:00PM, January 27, 2015 – Personalizing Evidence in the Learning Healthcare System & Biomarker Discovery Technologies, LIVE @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA
Reporter: Aviva Lev-Ari, PhD, RN
Real Time Conference Coverage with Social Media
@Computer History Museum by Dr. Aviva Lev-Ari, PhD, RN
Personalizing Evidence in the Learning Healthcare System
2:00PM – 3:00PM Panel: Personalizing Evidence Nigam Shah, Stanford (Chair)
- Training data aggregation
- relationship search
- case data collection
- individual case characterization
- Recommendation contextualization
- performance capture
Predictive Analytics in Healthcare
$1.7 Billion investment inAnalytics in HealthCare
- Providers is the focus of the Analytics not the Patients
- Clinical 71% type of data included in Predictive Analytics
- Can Big Data tell
- Practice based Medicine vs Evidence based Medicine – Learn from patients not unde one’s care.
- Achieving a NationwideLearning Health System
- every child with Cancer was placed on a trial — only 50% of adult, though
Lawrence “Rusty” Hofmann, Grand Rounds
- Two-Tier Health System for Patients – Medical insiders
- Patient Portal – Delightful experience for MDs and Patients
- Dig data in use of Patients
- 65% of the time – Diagnosis change after consultation with GroundRounds DB of Experts
Applying Complementary Technologies Towards Biomarker Discovery
3:30PM – 3:45PM Bonnie Anderson, Veracyte (Chair)
LIVE FROM THE PODIUM
Using Multiple Sources Of High-Dimensional Genomic Data to Build Diagnostic Algorithms
- Thyroid Nodule Before Afirma – 60%-75% are benign: Surgery, aspiration for cytology, Molecular testing
- Afirma Test: 50% of surgeries were unnecessary surgery – cost is minimized
- BRAF testing misses 60% of the Cancers
- Add NGS Panel: Coin toss — 50% specificity and 50% sensitivity
- Biology and data need be matched
3:45PM – 4:00PM
Murali Prahalad, Epic Sciences – Series C Funding
CTCs Come of Age as Biomarkers
- Profile rare cells in cancer
- Cancer metastasized via tumor cells in the blood
- separation of normal from cancer cells
- CTC identification: Small CTC and Apoototic cells, CK -CTC, speckled, macro nucleoli CTC
- Comperative of: cfDNA/ctDNA;
- Genomics enabled clonal drift traceablility
- Range of analyses
- CTC give information on cancer’s current state: Biopsy; bone metastatic; blood sample
- Cell types; ctc numbers
- CTC Heterogeniety
- epic confirms MOA & PD, predicts drug response
- epic technology identified
- Sample insights we’re generating to guide clinical decisions
- Heterogeneity of android
- Predicting de vono resistence to AR Tx utilizing phenotypical
- Biomarker predicts AR Tx failures but not taxane failure
- CTC Phynotype
- Adding Genomics datafor Drug Targeting via pathways
- Epic’s cision od combo therapy from diagnostics to extended life
4:00Pm – 4:15PM John Sninsky, CareDx
New Era In Post-Transplant Surveillance: Insights From Gene Expression & Cell Free DNA
- Clinical diagnostics for immuno-suppresive life long therapy
- Patient care Post-Transplantation is challenging
- Transplantation Outcomes Vary Over Time – no improvement over decades
- Unmet clinical needs – Transplantation of Lung, Heart, Liver, Kidney
- AlloMap Surveillance Solutions
- indicated for heart transplant reciepients: 55 days after transplant age 12 and higher
- Validation of studies in place – minimally acceptable performance criteria to be met
- AlloMAp Regulatory Pathways: CLIA and FDA
- Predictors of Outcomevevents: age transplant
- Utility of cf-DNA in Transplantation – Prospective study about rejection
- Statistical metrics for Risk prediction
- NGS of cfDNA represents a Universal Assay
4:15PM – 4:30PM Shawn M. Marcell, Metamark Genetics
A New Age of Proteomic Biomarker Discovery
- Agenda
- Clinical Proteomics
- Clinical Applications
- Beyond Prognosis
- Actionable Disease outocmes: BRCA1/2
- Accelerated drug approval: Zelboraf ~50% melanoma
- Tissue architecture and cellular resolution is utilizing by analyzing protein biomarkers in situ
- Platform for research and clinical applications
- Prostate Cancer is over treated
- ProMark integrates molecular and morphological information and classifiers – Five Clinical Trials
- De Novo Performance-based Biomarker Selection Approach
- Biomarker Selection too overcome Biopsy
- Biomarket predictors both aggressiveness and bioinformatics analysis
- Optimal Clinical Nomogram
- ProMark is actionable on 80% of the population
- 7-10 days from specimen reciept to result to patient
- Course of action; Patient responder selection (Co
- Disrupt Disease-causing pathway by Companion Diagnostics)
4:30PM –5:00PM Panel Q&A
Last comment by Dr. Kennedy: Cost effectiveness to improve the test used in disease detection
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