Genes from 3 Approaches
Identification ofPredictive Biomarkersand Applications inPatient Enrichment Strategies
Case Study
The PurposeThe partner had a pipeline molecule that was under clinical development. The interest area was to
identify biomarkers indicative of drug-response in patients and further utilize the biomarkers for patient
stratification in clinical trials.
About the Client
The Excelra Approach
Client RequirementThe focus was on analysing the proprietary gene-expression data of 118 cell-lines that were treated with
the drug. Furthermore, after prediction of drug-response biomarkers, gene expression profiles of 11
patients was shared by the partner to retrospectively classify them into responders and non-responders.
Machine learning models were built using three different methods to prioritize biomarkers associated
with drug-response. Pathway enrichment analysis was performed to understand the role of the
biomarkers in disease pathophysiology. Stratification of patients based on these biomarkers resulted in
correct prediction of drug response in 8 out of 11 patients.
For 118 cell Lines: Data collection & Normalization (expression, mutation, response class)
LOCATION
USA
THERAPEUTIC AREA
Non-Hodgkin's Lymphoma
INDUSTRY
Small Pharma
COSMIC Array Express
Supervised ML ApproacheandAlgorithms (3 methods)
Supervised ML analysis
Random Forest (RF)based regression analysisto assign weighted scoreto each gene
Heat map to visualizepattern between resistantand sensitive cell lines
Partial Least Squares (PLS)method to stratify patientsinto sub-types
CCLE
Client data:IC50 values will be usedto annotate sample toDrug-XXXX sensitive/resistant class
Sensitive Set Resistant Set
Cumulative Rank
Client data:
3 Approaches of
11 patients
*Retrospective validation
Functional enrichment& assessment:
PPI, Pathways & Biological Rationale
Prediction of eachpatient’s drug
response
Match with originaldata of each patient’s
drug response
Predicted (Excelra) vs. Observed (Client)
Prioritized genes forDrug-XXXX response
8 biomarkersidentified for
drug response.
9 patients’ data werepredicted correctly
out of 11.82% prediction
accuracy.
Gene signatures used toperform sub type-levelanalysis and patient
stratification.
Transition from NHLto other tumor types.
Establish theimmune-modulatory
role and defined MOA.Opened possibilities for
combinations with IO agents.
For more information, visit https://www.excelra.com/clinical/#precision_oncology
www.excelra.com
About Excelra
Excelra's data and analytics solutions empower innovation in life sciences across the value chain from discovery to market.The Excelra Edge comes from a seamless amalgamation of proprietary curated data assets, deep domain expertise anddata science. The company's multifaceted teams harmonize and analyse large volumes of disparate unstructured data using cutting-edge technologies. We galvanize data-driven decisions to unlock operational efficiencies to accelerate drug discoveryand development. Over the past 18 years, Excelra has been the preferred data and analytics partner to over 90 global clients,including 15 of the top 20 large Pharma.
Excelra’s Contribution
Excelra’s Service Portfolio
Chemistry Curation Services
Biology Curation Services
GOBIOMBiomarker intelligence database
Clinical Trial Outcomes Database
RWE & Big Data Realization
SLR & Meta-analysis
GOSTARStructure Activity Relationship database
Data
Clinical
Technology
Solutions
Discovery
Translational
ValueEvidence
Target Identification
Target Dossier Services
Data Science DrivenDrug Discovery
Biomarker Discovery
Drug Repositioning
Life Cycle Management
Systems Biology Informatics
Precision Oncology Informatics
Clinical Pharmacology
Outcomes Research
Epidemiology Modelling
Economic Modelling
Value Evidence Communication
Insights
Enterprise Data Strategy
Enterprise Cloud Ops
Enterprise Digital Transformation
PortfolioAugmentation for a Potential Biologic Drug
The Excelra Approach
The partner had a large molecule asset that was under development for blood cancer. They wereinterested in expanding the therapeutic potential of the molecule to solid tumours to augmentthe existing portfolio.
The focus was on leveraging public gene expression data-sets from cancer patients treated with a drugcandidate with similar mechanism of action. Machine-learning based predictive models were built usinginformation on drug responsiveness and disease gene signatures.
Predictive models were built using an iterative approach wherein patient-level and disease-level geneexpression profiles were used as input data. Clustering of cancer indications was done to prioritizeindications which were potentially sensitive to the treatment. Biological rationale was built was each ofthe prioritized indications by converging the drug mechanism of action with disease pathophysiology.
Client Requirement
About the ClientLOCATIONEurope
THERAPEUTIC AREAOncology
INDUSTRYBiotech
The Purpose
2months
3 FTE
Multiple Corroborative ML Classifiers
Public domain patientdrug response data
Disease geneexpression datasets
Disease sub-typespecific datasets
Literature genes fromother indications
Classifier-2ML
Bladder CancerCholangiocarcinoma
Stomach CancerTNBC_BL1
Sensitivepatient
Resistantpatient
Classifier-1
ML
Perturbed genes in sensitive patient
Predicted gene signature
Prioritized Indications for given drug
Disease sub type clustering
Gene centric pathway elucidation
Cancer patient data from client(known drug response)
ALL Liver cancerOvarian cancer
AML Drug Responders Potential Responders
TNBC
Non-Responders
Case Study
Excelra’s Contribution
Created value for shareholders andthe Board to fund future programs. Potential revenue generation >$2Bil*.
Excelra facilitated portfolio optimization and expansion for the partner, by Prioritizing 10 oncology indications, a mix of solid & liquid tumors types.
Determined causal genesignatures and provided acomprehensive biological
rationale and pathway analysis.
ALL (Acute Lymphoblastic Leukemia) determined as a top-priority indication by
Excelra, was further confirmed by the client, that successfully
validated our approaches.
Portfolio enhanced fornext 2 years.
Prediction of drug-response at a
cancer subtype level.
Increased applicationof partner’s technology
platform & external validation.
Excelra’s Service Portfolio
Chemistry Curation Services
Biology Curation Services
GOBIOMBiomarker intelligence database
Clinical Trial Outcomes Database
RWE & Big Data Realization
SLR & Meta-analysis
GOSTARStructure Activity Relationship database
Data
Clinical
Technology
Solutions
Discovery
Translational
ValueEvidence
Target Identification
Target Dossier Services
Data Science DrivenDrug Discovery
Biomarker Discovery
Drug Repositioning
Life Cycle Management
Systems Biology Informatics
Precision Oncology Informatics
Clinical Pharmacology
Outcomes Research
Epidemiology Modelling
Economic Modelling
Value Evidence Communication
Insights
Enterprise Data Strategy
Enterprise Cloud Ops
Enterprise Digital Transformation
For more information, visit https://www.excelra.com/clinical/#precision_oncology
www.excelra.com
About Excelra
Excelra's data and analytics solutions empower innovation in life sciences across the value chain from discovery to market.The Excelra Edge comes from a seamless amalgamation of proprietary curated data assets, deep domain expertise anddata science. The company's multifaceted teams harmonize and analyse large volumes of disparate unstructured data using cutting-edge technologies. We galvanize data-driven decisions to unlock operational efficiencies to accelerate drug discoveryand development. Over the past 18 years, Excelra has been the preferred data and analytics partner to over 90 global clients,including 15 of the top 20 large Pharma.
0 Resistant, 1 Sensitive
CombinationFeasibility Predictionfor Checkpoint Inhibitors for a Biologic
Case Study
The PurposeThe partner had a large molecule in the development pipeline for cancer indications. They wereinterested in combining their proprietary molecule with already approved immune check-pointinhibitors to improve therapeutic efficacy.
Client RequirementTo prioritize cancer indications based on their sensitivity towards the combination of the biologicwith a check point inhibitor (anti-PD-1/PDL-1). Publicly available data on successful and failed drug combinations was used for building predictive models.
Machine learning models were built using to assess the sensitivity of cancer indications as well aspatients to the drug combination. Based on the analysis, some cancer indications were prioritized forfurther assessment. A biological hypothesis was built to establish the synergistic role of the combinationpartners for cancer treatment.
About the ClientTHERAPEUTIC AREA
Oncology
LOCATION
Europe
INDUSTRY
Biotech
Each patient level insightOverall cancer level
ALL
Anti-PD1Sensitivity(RPART)
0 2.08 750 131 619 17.47
49.60
31.84
89.07
14.21
48.77
36.77
39.52
80.00
188
122
61
465
209
23
251
4
18557
497
77
199
13
16
164
373
179
558
542
408
415
36
20
0.55 Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
0.31
0.29
0.93
0.72
-0.40
0.61
0.32
0.01
0.74
0.34
2.26
2.44
-0.83 -0.30
-1.05
0.41
0.88
-0.20
0
0
-1
-1
-1
-1
-1
-1
-1
Anti-PD1PLS
score
DrugX PLS score
Both (AntiPD1+
DrugX)Sensitive
TotalSample
Sensitivesample
(both DrugX+Anti PD1)
ResistantSample
ResponseRate
(Anti-PD1blocker)
LIHC
PAAD
OV
AML
BLCA
CHOL
STAD
TNBC BL1
BRCA
Col
or s
chem
e is
bas
ed o
n D
rug
X re
spon
se
RPART PLS scorePositive SensitiveNegative Resistant
Yes Must be Drug X positiveand either of Anti PD1 predictor
(RPART or PLS) as sensitive -1 Partial Sensitive
The Excelra Approach
Scoring System
BioGRID
GDSC
CCLE
STITCH
TCGA
Network Based Analysis
Algorithmic-guided Screening of Drug Combinations
Drug Combinations Based on Clinical Side-effects
Based on Molecular & Pharmacological Data
Semi-supervised Learning
Mathematical Modeling of Drug-targeted Signaling Pathway
Algorithms
Excelra’s Contribution
Feasibility/synergy predictionof the two-drug combination.
Widen the list of indicationwhere the query drug may
be developed.
Indications resistant or werepartially sensitive to the
monotherapy were predicted tobe sensitive towards combination
with the checkpoint inhibitor.
Prioritize the indication where indication therapy with PD-1 willwork the best.
Custom pathways were generatedto understand crosstalk between the
drug-induced signaling and checkpointinhibitor signaling pathways.
For more information, visit https://www.excelra.com/clinical/#precision_oncology
www.excelra.com
About Excelra
Excelra's data and analytics solutions empower innovation in life sciences across the value chain from discovery to market.The Excelra Edge comes from a seamless amalgamation of proprietary curated data assets, deep domain expertise anddata science. The company's multifaceted teams harmonize and analyse large volumes of disparate unstructured data using cutting-edge technologies. We galvanize data-driven decisions to unlock operational efficiencies to accelerate drug discoveryand development. Over the past 18 years, Excelra has been the preferred data and analytics partner to over 90 global clients,including 15 of the top 20 large Pharma.
Excelra’s Service Portfolio
Chemistry Curation Services
Biology Curation Services
GOBIOMBiomarker intelligence database
Clinical Trial Outcomes Database
RWE & Big Data Realization
SLR & Meta-analysis
GOSTARStructure Activity Relationship database
Data
Clinical
Technology
Solutions
Discovery
Translational
ValueEvidence
Target Identification
Target Dossier Services
Data Science DrivenDrug Discovery
Biomarker Discovery
Drug Repositioning
Life Cycle Management
Systems Biology Informatics
Precision Oncology Informatics
Clinical Pharmacology
Outcomes Research
Epidemiology Modelling
Economic Modelling
Value Evidence Communication
Insights
Enterprise Data Strategy
Enterprise Cloud Ops
Enterprise Digital Transformation