Clinical NLP, PubGene Clinical trials in Coremine Oncology Text - - PowerPoint PPT Presentation

Clinical NLP, PubGene

Clinical trials in Coremine Oncology Text processing and information extraction for surgery planning form

November 2017 Dag Are Steenhoff Hov, PubGene AS

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PubGene, founded 2001

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ArrayIt H25K microarray Scientific Literature Coremine Networks

COREMINE Oncology COREMINE Medical COREMINE Platform

Integration of structured and unstructured information

• Interpretation of biomedical analysis data
• General information
• Specialized information analysis

Clinical NLP in PubGene - examples

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• Clinical trials in Coremine Oncology
• PubGene in Ahus Optique

Courtesy of DNV-GL (Tore Hartvigsen)

Coremine Oncology

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AIM: To enable oncologists to make better treatment decisions HOW: Combine data from relevant sources to aid interpretation of oncogenomics data from NGS and

• ther platforms
• Input: Somatic mutations, copy number changes, gene expression, or similar

quantity

• Output: Gene/biomarker annotations, related drugs and drug sensitivity,

pathways, clinical trials, etc.

Coremine Oncology – Our Scope

We focus on:

• Analysis of “called events”; assumed that normalization and data quality

considerations have been taken care of

• Collecting and integrating information for interpretation
• Linking to potentially relevant treatments
• Linking to clinical trials related to the input data

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Coremine Oncology

• Currently three types of input data:

– (Somatic) mutations – Copy number changes – gene expression

• Analysis/Interpretation module to display information (annotations) about

– Mutation – Gene/Protein – Protein Domains

• Summary module to show patient level information with respect to:

– Statistics on mutations – Related drugs for targets with change (in progress: also biomarker and sensitivity info) – Pathways for targets with change – Relevant clinical trials for aberrations

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Example Somatic mutations input data

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• Input for Coremine Oncology, case from lung cancer

– Chromosome number – Position – Reference nucleotide – Alternate nucleotide

View of imported data file

Mutation annotation – 1 patient - 1 missense mutation

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Clinical Trials for Cetuximab

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Clinical Trials for biomarkers

AIM:

• To map biomarkers from patient data

to relevant clinical trials

METHOD:

• Identify how biomarkers are

mentioned (referred to) in clinical trials

• Download and index data from

clinicaltrials.gov

• Develop dictionaries of biomarkers

and methods for detecting these in trial descriptions

• Focus on eligibility

CHALLENGES

• Text mining is difficult!
• Biomarkers are described, or referred

to in many ways

• Ultimately, we want to identify

biomarkers related to eligibility, but this is not straightforward

• Complicated logic in

inclusion/exclusion criteria, e.g., negation

• Also need to check title, description,

and condition for biomarkers

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Clinical Trials text data mining

• Compiled several lists of biomarkers
• f different types:

– Single-Nucleotide mutations (Cosmic) – Polymorphisms – Fusion genes – Gene regulation (Exp-up/down) – Copy number changes

• Several strategies for finding these in

text:

– Detect explicit mentions – Detect patterns based on gene name and ‘marker’ type, e.g., “GENE amplification” “GENE activating mutation”

• Curated list of cancer types

matched with conditions

Statistics for patterns

• Expression: 135
• CNV: 32
• Other (positive/negative): 20/10
• Mutation: 37
• Fusion/rearrangement/translocation:

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Indexing statistics

• 5350 trials with at least one

biomarker

• 855 different biomarkers with hits
• Top markers: BCR/ABL1 (907), ERBB2

positive (725), ERBB2 negative (603), ESR1 positive (467), ERBB2 exp-up (403)

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Clinical Trials for example case – NSCLC and Erlotinib

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Clinical Trials for copy number data (CNV)

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Trials matching patient biomarkers and disease

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Cancer type, e.g., NSCLC CNA EXP SNA INDEL FUSION SNP

Domain knowledge Manual curation Filter

GUI or command line

Clinical Trials

Clinical Trials for combined data – NSCLC

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BRAF G469A BRAF D594G BRAF V600E EGFR T790M KIF5B/RET CD74/ROS1 KIF5B/ALK BCR/ABL1

Details from Clinical Trial information – NCT01922583

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Clinical Trials matching to patient data

• Various levels of stringency for

matching trial to patient

• Perfect match
• Other alteration (incl. same effect)
• Same gene (other biomarker)
• Related gene
• S = weighted sum of scores
• Biomarker specific scoring models

due to different prioritization of relevance of other alterations

• AIM: To better map/identify other

alterations with same/similar effect, e.g., amplification/up-regulation with activating mutation Example: Patient ERBB2 Exp up Trial:

• 1. Perfect match: ERBB2 Exp up
• 2. Same effect: ERBB2 CNV gain
• 3. Similar effect: ERBB2 Positive
• 4. Other alteration: ERBB2 mutation
• 5. Likely opposite effect: ERBB2 Neg.
• 6. Opposite effect: ERBB2 Exp down
• r, ERBB2 CNV loss
• 7. Gene Only: ERBB2
• 8. Related Gene: EGFR

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Clinical NLP in PubGene - examples

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• PubGene in Ahus Optique

Courtesy of DNV-GL (Tore Hartvigsen)

Increase patient security by providing easier access to existing information

Human touch and empathy – with professional skill

Akershus University Hospital (Ahus) Optique project.

Courtesy of DNV-GL (Tore Hartvigsen)

The Surgery Planning Form is completed in 3 Stages

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Surgery Planning Form (“The Green Form”)

Stage 1: Examination Stage 3: Check/ QA Stage 2: Preparations Structured data Text

DIPS Ahus

Metavision O Metavision I Metavision DKS System System

Metavision Ahus Additional systems To complete the form, data must be collected from a number of systems! This is today done manually.

Courtesy of DNV-GL (Tore Hartvigsen)

Leave the data in the source systems!

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Metavision O DIPS Metav Ahus production databases Ahus research Databases. Metavision I Metavision DKS

DIPS (EPJ) (EPJ)

DIPS

(EPJ)

(EPJ)

Researchers/ Analysts

Data warehousing is an option

A semantic IT solution and

• ntology for clinical use in

Health Care Expert users «Ordinary» users

Courtesy of DNV-GL (Tore Hartvigsen)

We want to «lift» the data out of the silos!

24 Text mining Solutions provided by the Optique project A semantic IT solution and

• ntology for clinical use in

Health Care

Structured data Unstructured data (text) Expert users

«Ordinary» users

Courtesy of DNV-GL (Tore Hartvigsen)

PubGene in Ahus Optique, information extraction

Unstructured information

• Height 1,83 m

Structured information

• name=height, type=int,

unit=cm, value=183

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Fields

• ASA
• BMI
• Height
• Weight
• Puls
• Blood pressure
• Temperature
• Diagnose codes
• Treatment codes

PubGene i Ahus Optique, allergy information

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PubGene i Ahus Optique, status on smoking

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Sentence Status Røyker. Yes Røyker 15-20 om dagen. Yes Ifølge datter er han også storrøyker, 40/ dag siste 50 år. Yes Røykeplaster? Uncertain Tidligere storrøyker. Stopped Ikke røyker og drikker ikke alkohol, tidligere, måteholdent alkoholbruk. No Eks-røyker, lite alkohol. Stopped Text analysis

• Separate text in sentences, detection of sentences containing “røyke…”, “røyki…”, “røykt…”
• Classification of sentences based on recognition of keywords and word or sentence patterns
• NB: Based on a small database

Ahus Optique

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• Screenshots

Courtesy of DNV-GL (Tore Hartvigsen)

Courtesy of DNV-GL (Tore Hartvigsen)

Courtesy of DNV-GL (Tore Hartvigsen)

Page for surgery planning form

Courtesy of DNV-GL (Tore Hartvigsen)

BMI

Courtesy of DNV-GL (Tore Hartvigsen)

Courtesy of DNV-GL (Tore Hartvigsen)

Courtesy of DNV-GL (Tore Hartvigsen)

Courtesy of DNV-GL (Tore Hartvigsen)

Courtesy of DNV-GL (Tore Hartvigsen)

Courtesy of DNV-GL (Tore Hartvigsen)

Allergy

Courtesy of DNV-GL (Tore Hartvigsen)

Smoking

Courtesy of DNV-GL (Tore Hartvigsen)

Courtesy of DNV-GL (Tore Hartvigsen)

Surgery planning form

Further development, text processing/analysis

• A large set of options and potential

– Far more effective collection of more relevant information, e.g., by filling surgery forms (“The green form”) – Improved quality through automatic detection of errors in documents and control of consistency with structured data

• Further steps for Ahus Optique

– Simple: Extraction of more “static” fields, like lab results – Information about medication – Information on heart function, lung function – Exploit document structure and information on document types

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