— APPLICATION OF MACHINE LEARNING TECHNIQUES TO PATIENT STRATIFICATIONALEXANDER IVLIEV, PHDOCTOBER 2016

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Why is it important?Technical definition of the problemExample from our researchExample from published research

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AGENDA

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Why is it important?Technical definition of the problemExample from our researchExample from published research

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AGENDA

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—CASE STUDY - GEFITINIB

Early stratification would have prevented the withdrawal, but retrospective analysis did allow a new Marketing approval

1st line therapy for metastatic NSCLC with exon 19 deletions or exon 21 (L858R) substitutions

Launched with accelerated

approval

Withdrawn from market after phase III

study

NDA resubmission

with companion diagnostic

• Accelerate approval but post-marketing trial requested for full approval

• Phase III studies showed no improvement in overall survival

• Drug withdrawn from the market• Retrospective data analysis: 10% partial response in genotype unselected patients

>70% responders have activating mutations in EGFR

Best responses seen in East Asians, females and non-smokers

• NDA resubmitted with narrowed labelling and mutation-based companion diagnostic

• Unfortunately, lost market share to Tarceva during elucidation of patient segment

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—TECHNICAL DEFINITION OF THE PROBLEM

Patients

Gene

s

Class 1,Disease type 1

Class 2,Disease type 2

Class ?

Patient

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—AIM: TO DISCOVER PATIENT SELECTION BIOMARKERS FOR ERLOTINIB USING PRECLINICAL DATA SUCH AS CELL LINE

VIABILITY SCREENS

Clinical trial design for erlotinib

Gene expression240 cell lines

Sensitive Resistant

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—INTEGRATED BIOMARKER DISCOVERY APPROACH

Statistical approaches

Pathway knowledgePathway-driven

approaches

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—MACHINE LEARNING WORKFLOW

54,675 probesets

3,787 probesets

485 genes

191 genes

51 genes

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ClinicalTrials.gov numbers: NCT00409968, NCT00411671, NCT00411632, NCT00410059, NCT00410189

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—VALIDATION: INDEPENDENT CLINICAL TRIAL

Thanks to Bin Li at Takeda for slide

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—VALIDATION: INDEPENDENT CLINICAL TRIAL

Thanks to Bin Li at Takeda for slide

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—BIOLOGICAL INTERPRETATION

Network Building Algorithms

Analyze networkAnalyze network (transcription factors)Analyze network (receptors)Transcription regulationShortest pathsTrace pathwaysSelf regulationDirect interactionsAuto expandExpand by one interactionManual expand

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Why is it important?Technical definition of the problemExample from our researchPublished research: community effort

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AGENDA

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—MAQC CONSORTIUM

Leming Shi et al. MAQC-II study of common practices for the development and validationof microarray-based predictive models. Nat. Biotech., 2010

• 6 datasets• 13 endpoints• 36 data analysis teams• 30,000 models

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—LESSONS LEARNED

The number one factor which predicts how accurate predictions will be is endpoint complexity

Leming Shi et al. MAQC-II study of common practices for the development and validationof microarray-based predictive models. Nat. Biotech., 2010

It’s easy to tell boys from girls

It’s hard to predict outcome of cancer treatment

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—LESSONS LEARNED

Leming Shi et al. MAQC-II study of common practices for the development and validationof microarray-based predictive models. Nat. Biotech., 2010

“The top-performing teams were mainly industrial participants with many years of experience in microarray data analysis, whereas bottom-performing teams were mainly less-experienced graduate students or researchers.”

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—LESSONS LEARNED

Leming Shi et al. MAQC-II study of common practices for the development and validationof microarray-based predictive models. Nat. Biotech., 2010

“Applying good modeling practices appeared to be more important than the actual choice of a particular algorithm.”

“Many models with similar performance can be developed from a given data set.”

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Thank you for your attention