rdk biomarker science talk_v2

Best Practice in Biomarker Development

Richard Kennedy

VP and Diagnostic Lab Director, Almac Diagnostics

Professor of Medical Oncology , Queen’s University BelfastConsultant Medical Oncologist, Northern Ireland Cancer Centre

Clinical Biomarkers

Kennedy, Harkin, Salto-Tellez and Johnston et al, Oxford Textbook of Oncology 2013, In print

Predictive Biomarkers

• Predict benefit from a specific therapy

• Over 15,000 manuscripts reporting predictive biomarkers in cancer

• Few have made an impact on clinical practice

What to Consider

Discovery and Development

• What does it measure? Quantitative/Qualitative

• Preclinical/retrospective/prospective discovery

• Technology/reagents/lab effects

• Centre effects

• Population effects

• Convoluting factors

Validation

• Analytical validation

• Clinical validation

Material• Disease biopsy• Blood/plasma• Skin/hair bulb• Mucosa• Radiological

Technology• Immunohistochemistry• Mutation specific or Q-PCR• DNA-Microarray• Next generation sequencing• Mass spectrometry• MRI/USS/CT scan/PET

Biomarker• Protein expression/modification• mRNA expression• miRNA expression• DNA mutation/methylation• Metabolite • Radiological measurement

Compare samples to

identify distinguishing

features

Biomarker QuantitativeOr Qualitative

Biomarker Discovery

Two Major Types of Predictive Biomarker

Qualitative

• Mutation / no mutation (KRAS / BRAF / p53)

• Expression / no expression (c-KIT)

Quantitative

• Score based

• Positive or negative result depends on a score

• IHC for estrogen receptor

• Q-PCR / DNA microarray multigene signatures (OncotypeDx, Mammoprint)

What to Consider





• Centre effects



Validation



Biomarker Discovery Strategies

1. Preclinical Model Systems

2. Retrospective Archived Tissue

3. Prospective Discovery

Pre-clinical Biomarker Discovery

• Human cell lines and animal models

• Advantages: • Early in drug trial process• Allows behavior of drug to be modeled in specific

molecular contexts

• Disadvantages:• Different physiology• Can be little genetic variation between animals• No immune system in xenografts• No tumour stroma in cell culture• No reliable cut-off for quantitative assays

Almac DiagnosticsAACR 2012

Biomarker for SRC Inhibitor

Isogenic Cell Line and Xenograft Data

7 gene classifier for SRC activity

Test on independent cell lines

Retrospective Biomarker Discovery

• Use archived tissue from tumour banks

• Advantages:• Relevant human material• Full clinical annotation including outcome is often available • Large numbers may be available- clustering analysis• Can set population distribution based cut-off for

quantitative assays

• Disadvantages:• Unlikely to be possible for novel therapies entering trials• Tissue may not have been collected appropriately• Archived tissue can degrade over time

Example: Biomarker for Angiogenic Agents

63 gene microarray assay for non-angiogenesis

Retrospectively validate in ICON7 Bevacizumab In Ovarian cancer study

300 High grade serous ovarian

samplesAngiogenesis

Gourley C., Michie C, Keating K, Gavigan A, DeHaroS, Hill L, Harkin DP, Kennedy RD ASCO 2011

Predictive Biomarker Discovery

• Analysis of tissue from responding and non-responding patients on a clinical study

• Advantages:• Material is relevant to the drug in question

• Disadvantages:• New drug may be given in combination with other

therapies, difficult to develop specific biomarker • Can require large numbers of patients- needs adequate

numbers of responding and non responding patients

Simple Biomarker Discovery Trial Design

• If predicted response rate is 10% in unselected population will need 500 people to get 50 responder samples!

• Adaptive trial designs may help reduce numbers.

Patient Enrolment

Sample Biomarker Studies

Biomarker Generation

New Treatment

Responders Non-Responders

What to Consider





• Centre effects



Validation



Technology

Material Type

• Storage and shipping (fresh/archived)

• Patient safety/comfort (biopsy/resection/blood)

Technology

• Quality (CE marked, GMP)

• Maintenance (calibration and scheduled servicing)

• Practicality (cost, turnaround time, ease of use)

• Longevity (will it be obsolete soon?)

Reagents

• Many laboratory regents are “research use only” (RUO)

• Can be considerable variation in performance batch to batch

• Biomarker may become “batch dependent”

• Ideally use GMP reagents, batch tested

• Can consider pooled batches if RUO only available

Batch Effects

Almac Diagnostics 2010

Lab Operator Effects

• Biomarkers discovered by a single lab operator may only work for that individual

• Modified lab protocols

• Very experienced in a particular assay

• Adhere to strict standard operating procedures

• Randomize samples between several operators during discovery phase

Lab Operator Effects


Operator 1

Operator 2

Operator 1

Operator 2

What to Consider





• Centre effects



Validation



Clinical Centre Effects

• Biomarkers discovered from a single centre may not be applicable elsewhere

• Specific surgical approaches

• Specific specimen fixation protocols

• User biases on assessment of response to drug

• Ideally use material and clinical data representing response/non-response from multiple centres

Centre Effects

Kennedy et al J Clin Oncol. 2011 Dec 10;29(35):4620-

6

What to Consider





• Centre effects



Validation



Population Effects

• Important to ensure that the population used for biomarker discovery is relevant to the population in which it will be applied

• E.g.

• Afro Caribbean variations in prostate or breast cancer biology

• Asian variations in lung cancer biology

What to Consider





• Centre effects



Validation



Balancing for Convoluting Factors

Positive and negative discovery samples must be balanced for potential confounding factors such as:

• Gender

• Age

• Ethnicity

• Other medication/smoking

• Other medical conditions

• Known prognostic/predictive factors• Tumour Stage

• Tumour Grade

• Lymphocyte infiltrate

Failure to do this may result in a biomarker for the wrong thing

What to Consider





• Centre effects



Validation



Biomarker Validation

1. Regulatory Landscape

2. Analytical Validation

3. Clinical Validation

Regulatory Landscape

• Different levels of regulatory approval exist

• This choice is influenced by the type of biomarker and

intended use and risk to patients:

• Companion diagnostics most stringent

• Relevant bodies are• CLIA (Clinical Laboratory Improvement Amendment act)

• FDA (Food and Drink Administration- PMA route for companion diagnostics

• EMA (European Medicines Agency)

• Needs to be considered at the start of research

Analytical Validation

• Precision

• Accuracy

• Limits of Detection

Precision

• Measure of biomarker repeatability

• Loss of precision can occur due to:

• Inherent variability in technology (IHC for phospho-

proteins, plasma protein measurement)

• Variability in reagents, equipment or technique

• Normal/Stromal/malignant cellular content

Effects

Full Biopsy Material Macrodissected Biopsy Material


Effects of Macrodissection on Precision of a q-PCR-based Biomarker


• Precision

• Accuracy


Accuracy

• A measure of how close the result is to the known truth

• Truth may be a:• Result from a reference lab• Gold standard technology

• Can be affected by:• Site / type of biopsy - tumour heterogeneity• Incorrect sample fixation or lab technique • further treatment since diagnostic biopsy taken

Gene Expression Differences

Gene Expression Differences Between Original Diagnostic Tissue and Recurrent Disease

• Series of ovarian cancers analysed pre-chemotherapy and on recurrence

• 486 genes >2 fold differentially expressed p<0.005


• Precision

• Accuracy


Limits of Detection

• Measures how much material required to give an accurate

result

• In cancer this will also include percentage tumour content

(usually over 20% for mutation detection)

Tumour

Clinical Validation

• Absolute requirement for a companion diagnostic to be used

for drug selection in regular clinical practice

• Needs strategy agreed with regulatory authority prior to

study• Simple biomarker validation

• Complex biomarker validation

• Must show that the biomarker can adequately stratify

patients (sensitivity, specificity, hazard ratio)

Patients Enrol to Study

Biomarker

Experimental Drug

Predicted Responder Predicted Non-Responder

Randomise Not on Trial

Standard Therapy

Compare outcome

Simple Biomarker Validation Study

Patients Enrol to Study

Biomarker

Receive Experimental

Drug

Predicted Responder Predicted Non-Responder

Randomise Randomize

Receive Standard Therapy

Receive Standard

Treatment

Receive Experimental

Drug

Calculate Sensitivity / Specificity etc.

Mandrekar and Sargent J Clin Oncol 2009 27(24):4027-34

Complex Biomarker Validation Study

What to consider for a predictive biomarker to be used in the clinic:

• Discovery and Development • Correct discovery dataset• Correct technology and reagents• Not convoluted by other known factors

• Validation• Regulatory requirements depending on use• Analytical: precision and accuracy• Clinical: sensitivity, specificity etc

Conclusions

rdk biomarker science talk_v2

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