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Precision medicine:Shifting from one drug for all patients to personalized genomics-based therapeutic options
Victor Weigman, Ph.D., Associate Director, Translational Genomics, Q2 Solutions, a Quintiles Quest Joint Venture
Patrice Hugo, Ph.D., Chief Scientific Officer, Q2 Solutions, a Quintiles Quest Joint Venture
White Paper
With advancements in clinical informatics and genomics, there has been a paradigm shift from “one drug fits all” toward precision medicine – taking account of individual variability in genes, environment and lifestyle1 – across therapeutic areas. Precision medicine allows identification of patients who would likely benefit the most from a treatment and suffer the least side effects. For instance, such screening can reveal genetic make-up likely aligned with the molecular mechanisms of action of a drug, increasing the probability of a patient’s response to the therapy. This helps find better matches for existing and novel drugs, with the goal of ensuring that the right drug is used in the right patient at the right time.
Industry challenges
Precision medicine is now a reality for biomarker assessments that are predictive, prognostic and pharmacodynamic (PD) to be routinely used in development of most drugs across indications.2 Currently, some 56 percent of the 189 oncology therapies under development had an associated biomarker. Some 20 of the 34 oncology drugs expected to launch by 2017 will be targeted to biomarkers.3 In addition, of the more than 800 cancer drugs currently in clinical trials, almost all are targeted at particular gene products.
These trends reflect the increased need for earlier go/no-go decision-making based on biomarker-driven PD and safety/efficacy signals. There is a shift away from protein or single-analyte assessments to biomarker “signatures” with R&D and clinical applications. These signatures may include pathway analysis (such as PDs/target engagement, resistance mechanisms); immune gene signature and/or analysis of immune T-cell receptor and immunoglobulin repertoire, which is predictive for immuno-oncology therapies; and prognostic panels for oncology indications.
Greater adoption of testing for these signatures in the clinical space necessitates incorporation of genomics in drug and diagnostics development. A focus on translational research and biomarker-driven drug development has led to increased demand for assays incorporating higher sensitivity such as:
• Challenging/limiting input material, including “liquid biopsies,” minimal residual disease (MRD) and neoantigen identification
• Multiple gene-based biomarkers targeting cancer “hotspots” and disease risk portfolios
• Multiple genome identification, such as microbiome assessments.
These approaches are especially valuable in the clinical trial space, due to the push for biopharma companies to “do more with less,” and demanding less invasive and multi-parameter solutions tempered with efficiency, reduced sample input and faster turnaround times.
Biomarkers changing the way drugs are delivered
The use of biomarkers to provide information on whether a patient will respond appropriately to treatment, both in terms of efficacy and safety, is changing the way drugs are delivered (Figure 1).
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Rather than a “one-size-fits-all” paradigm, precision medicine takes a personalized approach, giving a particular therapy only to a pre-screened population with a biomarker that predicts a response along with a lack of adverse events.
Genomics powering the immuno-oncology landscape
Advances in oncology therapy are increasingly showing promise of turning a fatal disease into a chronic, treatable disorder. However, the complexity of cancer as group of diverse diseases remains a major challenge, despite improvements in understanding of cell signaling pathways and host immune responses. Genetic alterations play a pivotal role in the growth of all types of cancer, and research-ers have found that individual tumors have unique genetic profiles due to somatic mutations, which have been shown to evolve during the course of the disease.5
Analyzing these genetic profiles with next generation sequencing (NGS) aids in understanding the relationship between biomarkers and patient responses. Using genomic technologies provides a broader lens that can be leveraged to understand mechanisms of action and/or resistance pathways to better design clinical trials. Given the appropriate laboratory collaboration, this can be leveraged for custom Laboratory Developed Tests (LDTs) to power clinical trials where the tumor genetic profiles can be used to identify the best therapeutic options.
Fundamental challenges in cancer treatment include the fact that cancers are rarely detected early, and rapidly develop resistance to targeted therapies and chemotherapies due to the aforementioned heterogeneity and clonal evolution of the disease. In addition, while immunotherapies are changing the paradigm by targeting the
immune system rather than the cancer, not all patients respond. Typical response rates, toxicities and survival rates for various classes of therapy are shown below (Table 1 and Figure 2).
Products are in development for a wide range of targets; a greater understanding of patient genomics and susceptibility would enable more to be matched to appropriate trials and therapies.
Adverse eventpatients
Non-responders
Lack of efficacy Lack of safety Population of interest
Responders
Treatedpopulation Prescreened
population
Predictive biomarkertesting
Classic “one-size-fits-all” Precision approach
Adverse eventpatients
Non-responders
Responders
Figure 1: Changing medication choices4
Changing how drugs are deliveredIdentify non-responders and safety issues before prescribing or treating
Table 1: Response rates, toxicity and long-term survival with various oncology therapies
Therapy Response rate ToxicityLong-term
survival
Chemotherapy/ radiation
Low High Poor
Targeted therapy High Low Moderate
Immunotherapy Mid Low Good
Time
Perc
ent s
urvi
val
Chemotherapy
Genomically targeted therapy
Immune checkpoint therapy
Combination with genomically targeted agent and immune checkpoint therapy
Figure 2: Survival rates for various cancer therapies6
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Pre-profiling to accelerate study recruitment
Genomic pre-profiling (Figure 3, 4) offers a way to identify and enroll biomarker-defined oncology patients, potentially enabling cost-ef-fective and timely development of drugs with anticipated high screen failure rates. With “just-in-time” (JIT) start-up, this approach offers:
• Reduction in set-up costs by avoiding sites that are not able to deliver on expected number of patient enrollments
• Faster study enrollment due to improved access to patients with the appropriate genomic signature, and quick start-up when potential patients are identified
• More rapid development decisions, based on faster enrollment and reduced time to key decision points, leading to a reduction in overall R&D spend
• Increased opportunities to develop drugs in niche settings, since protocols with high screen failure rates become feasible, opening up new commercial opportunities.
Genomic panels that are matched to the clinical trial design allow for increased biomarker information, enabling researchers to find the right patients and bring the trial to them (Table 2).
Figure 3: Genomic pre-profiling to identify and enroll biomarker-defined oncology patients
Genomic profiling with JIT start-up
• Improved access to patients with right genomic signature
• Quick start-up when potential patients are identified
• Faster enrollment
• Reduced time to key decision points
• Reduction in overall R&D spend
• Protocols with high screen failure rates are now feasible
• Opens up new commercial opportunities
How to cost-effectively develop a drug with an anticipated high screen failure rate in a timely fashion
Faster study enrollment
Quicker tokey development
decisions
More feasable to develop drugs in
niche settings
Genomic testingregistry
3-4WEEKS
Genomic testing atQ2 Solutions labs
Clinical report
Site staff activities oftreatment decisionsand study initiation
Patient presents- informed consent form (ICF), patient enrollment
sample collection
Genomic testing
Bioinformaticanalysis
Site request JIT start-up, ICF signed,
patient enrolled in treatment study
Physician-patientdiscuss options
Clinical annotationand reporting
Figure 4: Patient pre-profiling based on prospective genomic testing
Patient pre-profilingMatching patients to studies based on prospective genomic testing
Table 2: Matching the genomic panel to the clinical trial design
Matching panel to trial designSolutions for identifying and enrolling biomarker defined oncology patients
Model A – standard of care
Model B – big registry
Model C – limited scope
registry
Model D – one protocol – fast
turnaround time
Model E – one protocol – long screening
Model F – pre-profiling
Operational design
Identify sites where genomic sequenc-
ing is occurring for oncology pts in
indication
Longitudinal registry umbrella followed by treatment (Tx) trial
enrollment of variant patients
Pre-profiling limited scope
registry followed by Tx trial enrollment of
variant patients
Pre-screening/ genomic testing
within one protocol
Test patients – newly Dx, newly recurred
Pre-screening/ genomic testing
within one protocol
Test patients at stable disease
Pre-profiling limited scope registry and JIT for investigator
sites
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Potential for comprehensive cancer panels
Genomic tests – including comprehensive cancer panels, human leukocyte antigen (HLA) typing, Exome sequencing and pan-cancer immune-profiling – can supplement many endpoints for complex clinical trials. These assays can be used singly or in combination to:
• Detect the most common actionable somatic alterations currently reported in cancer, including point mutations, insertions, deletions and structural rearrangements;
• Build comprehensive molecular information on genes involved in the most well understood pathways, creating a data source that can be used to identify new patient populations likely to benefit from targeted therapies; and
• Do more with less material, for reduction in testing turnaround time and overall cost.
A comprehensive cancer panel has been successfully used to leverage understanding of drug/gene interactions for FDA-approved drugs, and for mining clinical trial databases to highlight biomarkers that can be used to help increase clinical trial enrollment (Figure 5). This panel
also has been used to identify value for drugs across indications based on knowledge of biomarkers.
Due to the risk involved in searching for low-frequency genetic variants and complex changes, accuracy is critical. Shifting from single- to multi-analyte assays increases risk; each variant is associated with its own level of error, and this is compounded when as many as 1.34 million positions are interrogated. Validation has shown variants in up to 10 percent of cells, and variant reporting needs to account for risks with sequencer error. In addition, high sequencing depth is needed, which increases noise.
One panel can power many protocols
A single panel can power multiple protocols across the world (Figure 6), opening the door to multiple protocol delivery options. Using a central laboratory avoids the need to use multiple local laboratories, with their risk of inconsistencies between testing sites. The rapidly growing range of biomarkers also can be stored and mined for indications data across global network.
Figure 5: A comprehensive cancer panel linking mutations and drug responses
Q2 Solutions Comprehensive Cancer PanelFocusing on pairing patient to treatment
• Leveraged relationships between mutation and drug response/resistance• Pan-cancer biomarker approach identifying novel associations to indications• Can help segment price-value comparisons for drug pricing
Important markers for chemotherapy
and novel inhibitors e.g. PARPi
Oncogenic signaling pathways targeted in cancer
High frequency variants in genes involved in important cancer pathways
CSF1RINSRFLT1FLT4ESR1PGRIL-7R
NTRK3
ATRCHEK1CHEK2ERCC2FANCA
MRE11ANBNP63
CARD11BCL2
CASP8
SMARCA4EP300KDM6ASETD2H3F3A
BCORMITFPHF6
PRDM1NFE2L2
TERTCCND2CCND3CDC73
AURKAAURKB
TOP1TOP2A
PPP2R1A GRIN2A U2AF1GATA3KLF4
SRSF2
DNA repair
Apoptosis Cell cycle Metabolism SplicingMitoticpathway
DNAreplication
PhosphataseTranscriptionalregulation
Chromatinmodification
Receptor/growth factor
PI3K-AKT RAS-MAPK JAK-STAT WNT-APC Hedgehogsignaling
TGFßsignaling
EGFRsignaling
P73PARP1RAD50RAD51ERCC3
XPCWRNBLM
AKT3PIK3R2PIK3R5RICTORRAPTORPIK3CGmTOR
ARAFCRAF
MAP2K1MAP2K2MAP2K4MAP3K1
SOCS1STAT3STAT1SRC
FAM123BAXIN1
PTCH2GLI1SUFU
ACVR1B ERRFI1
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Increasing the adoption of genomic pre-profiling has positive impacts for both patients and biopharma companies. These include:
• High ‘actionability,’ which increases patient and physician interest.– One test and/or one biopsy with quick turnaround times
(2-3 weeks)– General oncology assay can identify many pan-cancer biomarkers– Increases chance for rare positive biomarkers in the population to
be found for various trials– Patients can be matched with approved drug as first line therapy
• Enhanced trial enrollment, with a large sample study finding that 11 percent of 2,000 patients went onto genotype-matched trials. Many groups facilitate this matching, including Molecular Match,7 Caris,8 and IBM’s Watson platform.9
New protocols are leveraging this power, such as the American Society of Clinical Oncology’s Targeted Agent and Profiling Utilization Registry (TAPUR),10 the National Cancer Institute’s Molecular Profiling-Based Assignment of Cancer Therapy for Patients with Advanced Solid Tumors (NCI-MPACT),11 the Lung Cancer Master Protocol Trial (Lung-MAP),12 the Randomized Study Evaluating Molecular Profiling and Targeted Agents in Metastatic Cancer (IMPACT-2),13 and molecular selection of therapy in colorectal cancer: a molecularly stratified randomized controlled trial program (FOCUS-4, UK).14
There remain opportunities for molecular characterization, including approaches such as immunosuppression, development of cancer-specific vaccines or stimulation of a T-cell response. Immuno-oncology offers potential to gain a deeper understanding of drug safety and pharmacokinetics/pharmacodynamics (PK/PD), coupled with more precise tumor characterization (Figure 7).
Figure 6: Single panel can power multiple protocols worldwide
Cancer patients in pre-profiling network with alterations identified
Pre-profiled cancer patients matched to right protocol JIT
Protocols with specific genomic alteration entry criteria
Figure 7: Innovation in immuno-oncology
Immuno-oncology innovationNew opportunities for molecular characterization
SamplesRoutinely collected clinical sample
Molecular ServicesData can be mined for molecular
immuno-oncology characterization
Self-recognitionHLA alleles
BenefitsThese characterizations can be
used in drug studies
Block immuno-suppression
• CTLA4 • ID01• PD1/PDL1 • OX40
Develop cancer-specific vaccines (neoantigens)
• NY-ESO-1 • gp100• MAGE-3
Stimulate T-cell response
• Cell therapiesThrough immuno-oncology:• Gain a deeper understanding of drug safety and PK/PD• More precise tumor characterization• More effective, personalized therapies
Immune activationB and T-cell
Tumor characterizationDNA and RNA
Slides
Biopsy
Blood
Saliva
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Data value from genomic panels
These types of genomic panels have great value to power precision medicine for a given patient, helping find targets to help boost treatment options and trial enrollment. However, the data generated also have immense longer-term value. This is due to the ability to compare biomarker information across panels, enabling future matching of profiled patients with new trials or discovery efforts. Collection of hundreds to thousands of biomarker results per patient can be leveraged to:
• Find patients for new trials
• Mine biomarker and indication interactions to identify new associ-ations in disease development and drug responses15
• Provide information on valuable variant and wild type responses for different genes, reducing the need for re-testing
Developing an assay
For biopharma companies, engaging laboratory resources and
expertise to complement therapeutic efforts can provide access to a diverse network of clinicians and scientists from the earliest point in product development. This network may include bioinformaticists, geneticists, flow cytometrists, mass spectrometrists, microbiologists, virologists, immunologists, anatomic pathologists, cytopathologists, hematopathologists, regulatory experts, bioanalytical/medical writers and physicians. This expertise can help determine appropriate approaches to gaining informed consent from patients and develop data organization analysis strategies for future studies. Taken together, these approaches can offer insights to optimize protocol design and execution. The development pathway for biomarkers and companion diagnostics (CDx) is illustrated below (Figure 8).
Many of the materials required to start developing a genomic assay are already available from typical specimen collection (Figure 9).
The outputs that can be obtained from integrated bioinformatics capabilities – including assay validation, custom panel design, computer system validation, data mining and biomarker development – are shown below (Figure 10).
Figure 8: The development pathway for CDx
Q2 SolutionsLaboratory partner of choice for CDx development from biomarker ID to CDx launch
Pre-clinical FDA filing/approval Launch
Clinical
Phase I Phase II Phase III
2 3 4 5 6
Developprototype
Analyticalvalidation
Clinicalvalidation
Drug-diagnosticregistration
CommercializationModification
1
Research/biomarker ID
Rx
Dx
CRO
Biomarker R&D services and CDx strategic consulting
Assay development expertise and Dx partnerships
Assay optimization/Bridging study/CLIA laboratory testing
Global study execution for pivotal CDx trials
Regulatory support, including BIMO/FDA inspection support
Commercial, diagnostic and late-phase expertise
Dx LabFlexible support across the CDx life cycle
1
2
3
4
5
6
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Figure 9: Input needs for genomic assays
HLA allele calling
Genotyping risk alleles
IGVH testing
Targeted (NGS) panel
Whole exome (NGS)
PCR-enriched (NGS)
qPCR
ctDNA allele calling
40 500PCR-enriched (NGS)
30qPCR
10ctDNA allele calling
375Whole exome (NGS)
375Targeted (NGS) panel
500Genotyping risk alleles
500HLA allele calling
500RNA-Seq
500IGVH testing
500
1000
1000
1000
1000
1250
1250
Bloo
d
FFPE
Solid
tiss
ue
Plas
ma
50 250
FFPE Input (ng)Standard Input (ng)
450 850 1050
Assay input needs
650 1250
-150 50 250 450 650 850 1050 1250
-150 50 250 450 650 850 1050 1250
Datageneration
Bioinformaticsteam
Applicationsdevelopment
Translationalgenomics
• GxP compliance• Part 11 validated systems• Automated execution of
workflows• Comprehensive Cancer
Panel case study
Computer systemvalidation
• NGS delivery to FDA• HL7 formats• Formal validation
procedures
Validation analysisplatforms
• Senior Ph.D. staff• Assigned to specific
pharma• Direct research efforts
across sites
Preclinical biomarkerdevelopment
• Comb results from genomic panels to find novel biomarkers
• Integrate genomics with Electronic Health Records (EHR)
Data mining/associations
• >45 custom assays• >9,000 samples• 3 sequencing platforms• 3 PCR platforms
Custom paneldesign
• CLIA LDTs method validations
• Fit for purpose• Directed across
central labs
Assay validation
• 10,000s of samples per year • Pipeline execution/QC• Customized delivery (HDs/Cloud)
• Software creation/maintenance• Pipeline efficiencies• High-performance computing
• Preclinical biomarker development • Pipeline creation/algorithm dev.• Assay CLIA/GxP compliance
Figure 10: Outputs from integrated bioinformatics capabilities
Integrated bioinformatics capabilitiesPerformance, scalability, flexibility
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Conclusion
Taken together, earlier adoption of genomic panels into testing of trial patients can provide a mechanism to identify targeted therapy, along with ability to predict the toxicity of therapies. Building broad genomic datasets across indications has high long-term potential for data mining payoff. Trends in using genomics to power trials include:
• The shift from single-analyte to multi-use panels• Genomic pre-profiling as a successful model• Bringing trials to the patient• Immuno-oncology applications of genomics• Custom assay design and specimen collection needs
Multi-analyte genomic assays provide a mechanism for enhancing trial design for precision medicine, with many successful examples of genomics-based laboratory-developed tests (LDTs) pairing patient to the best treatment option, and biomarker identification helping in trial enrollment.
Existing trials are already collecting specimens that can be used for genomic assays to power targeted therapy and immuno-oncology applications. For biopharma companies, working with laboratory service providers from the earliest stage can help with trial design and, in partnership with a diagnostics manufacturer, with companion diagnostic applications. The long-term payoff of genomic panels is to provide a wellspring of information for identifying novel biomark-ers via data mining.
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About the authors
Victor Weigman, Ph.D.Associate Director, Translational Genomics, Q2 Solutions, a Quintiles Quest Joint Venture
Dr. Victor Weigman has been doing biomarker discovery research with genomics for more than 10 years, with seven of those being with Expression Analysis (EA), a Q2 Solutions Company. He obtained his Ph.D. at the University of North Carolina in Biology and Bioinformatics within the Lineberger Comprehensive Cancer Center. He has published 14 papers on biomarker identification and assay development and has contributed to the development and launch of several genomic CLIA Assays. Dr. Weigman currently directs the Translational Genomics Unit of Q2 Solutions, whose goal is the continued facilitation of preclinical drug development through biomarker identification. Ongoing research revolves around the genomic profiling of solid tumors from both DNA and RNA approaches including the development of robust assays that can be leveraged as laboratory developed tests.
Patrice Hugo, Ph.D. Chief Scientific Officer, Q2 Solutions, a Quintiles Quest Joint Venture
Dr. Patrice Hugo is Chief Scientific Officer at Q2 Solutions, a Quintiles Quest joint venture. He currently leads global scientific strategy and is responsible for the medical affairs and scientific activities in the central laboratories, genomic and BioAnalytical/ADME facilities worldwide. Dr. Hugo has more than 25 years of senior scientific leadership experience with extensive management expertise laboratory operations; biomarker discovery and validation applied to diagnostics; therapeutic targets and clinical trials. He obtained his Ph.D. at McGill University and completed five years of post-doctoral fellowship at the Walter Elisa Hall Institute in Australia, and Howard Hughes Medical Institute in Denver, Colorado. Dr. Hugo was Associated Vice President Chief Scientist, Scientific Affairs, at Laboratory Corporation of America (LabCorp)/Covance, and has held several
other senior leadership positions at Clearstone Central Laboratories, Caprion and PROCREA BioSciences. A noted industry expert, Dr. Hugo has more than 75 scientific publications in internationally renowned journals. He also played an active role in a number of industry organizations, including being a member of the Board of Directors for the non-for-profit Personalized Medicine Partnership for Cancer in Quebec and the Steering Committee for the Biomarker Factory.
Contact us
Toll free: 1 855.277.9929Direct: +1 919.998.7000International: +44 (0) 1506 814000Website: www.Q2LabSolutions.com
Copyright ©2016 Q2 Solutions. All rights reserved. Q2-05.08.01-Jan2016
References
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2 http://www.nature.com/nrd/journal/v11/n3/abs/nrd3651.html
3 According to recent data from McKesson Specialty Health/US Oncology Network (MSH) http://www.nxtbook.com/nxtbooks/pharmcomm/20130910/?utm_source=Campaigner&utm_campaign=SeptOct_Digital_Edition_eB-last_8-28-13&campaigner=1&utm_medium=HTMLEmail#/0
4 Image source: Wikimedia commons
5 http://www.nature.com/nrg/journal/v13/n11/full/nrg3317.html
6 Sharma P, Allison JP. Immune Checkpoint Targeting in Cancer Therapy: Toward Combination Strategies with Curative Potential: Cell, Volume 161, Issue 2, p205–214, 9 April 2015; http://www.cell.com/abstract/S0092-8674(15)00317-7
7 https://www.molecularmatch.com
8 http://www.carismolecularintelligence.com
9 http://www.ibm.com/smarterplanet/us/en/ibmwatson/?lnk=buwa
10 http://www.asco.org/practice-research/targeted-agent-and-profiling-utilization-registry-tapur-study-frequently-asked
11 https://clinicaltrials.gov/ct2/show/NCT01827384
12 http://www.cancer.gov/news-events/press-releases/2014/LungMAPlaunch
13 https://www.clinicaltrials.gov/ct2/show/NCT02152254
14 http://www.focus4trial.org
15 Moran D, Hamm CA, Rao K, Bacon-Trusk P, Pry K, Velculescu V, Massimo Cristofanilli M, Bacus SS. Abstract 3887: Genomic analysis identifies drug targetable pathways and predicts immune infiltration in inflammatory breast cancer tumors. Cancer Res August 1, 2015 75:3887; doi:10.1158/1538-7445.AM2015-3887 http://cancerres.aacrjournals.org/content/75/15_Supplement/3887.short