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 | Leaders in Pharmaceutical Business Intelligence (LPBI) Group

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

January 27, 2015 by 2012pharmaceutical

Real Time Conference Coverage with Social Media

2:00PM – 3:00PM Panel: Personalizing Evidence Nigam Shah, Stanford (Chair)

Predictive Analytics in Healthcare

$1.7 Billion investment inAnalytics in HealthCare

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

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

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

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

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

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)