Lokesh Agrawal, Ph.D., Program Director, BBRB, Cancer Diagnosis Program/DCTD, National Cancer Institute Rockville, MD

ISBER Meeting, Orlando, FL, May 23rd, 2014

Biomarkers for Biospecimen Integrity

FINANCIAL DISCLOSURE

No Financial disclosures to declare

(Work for the Government )

Improving Biospecimen Processes is Essential to Enable Better Molecular Medicine

Biospecimen Collection (Blood, Tissues, Urine, etc.)

Processing in Pathology Lab

Patient Care Clinical Trials Research

Storage Analysis

Clinical Data Collection Adapted from Peggy Devine

Why Quality of Biospecimens Matters?

Effects on Research Outcomes

• Irreproducible results

-Variations in gene expression

-Variations in post-translational

modification data

• Misinterpretation of artifacts

as biomarkers

Effects on Clinical Outcomes

• Potential for incorrect diagnosis

-Morphological/immunostaining artifacts

-Skewed clinical chemistry results

• Potential for incorrect treatment

Therapy linked to a diagnostic test on a

biospecimen (e.g., HER2 in breast cancer)

Patient Acquisition Handling/ Processing

Storage Distribution Scientific Analysis

Medical/ Surgical Procedures

Restocking Unused Sample

Multiple pre-analytical factors can affect the molecular integrity of the biospecimen

Time 0

Post-acquisition Pre-acquisition

Variables (examples):

Time at room temperature

Temperature of room

Type of fixative

Time in fixative

Rate of freezing

Size of aliquots

Variables (examples):

Antibiotics

Other drugs

Type of anesthesia

Duration of anesthesia

Arterial clamp time

Concern about how pre-analytical variation affects reproducibility of

biomarkers, in R&D and clinical testing & support best pathology practices

for patient care, clinical trials

BIOSPECIMEN RESEARCH NETWORK

Program aims:

1. Develop innovative approaches to the control, monitoring

and assessment of biospecimen quality.

2. Systematically define the impact of key pre-analytical

variables in human biospecimens of specific types on

downstream molecular data generated from specific

molecular analysis platforms.

PI Contracts: Studies of Pre-Analytical Factors in Cancer Tissues and Blood

• PI-driven research projects

– Awarded through competitive RFPs issued by Leidos Biomedical Research Inc.

• Yale: Breast Cancer (FFPE/IHC)

• Caprion : Plasma/Mass Spec Proteomics

• Roswell Park Cancer Institute: miRNA

• UCSF: Credentialing Plasma and serum

• MD Anderson: Breast Cancer (RNA)

• Indivumed: Colon and Liver intraoperative ischemia, protein/IHC/RNA

Biospecimen Integrity in Solid Tissues

Milestones for Tissue Quality project:

1. Construction of a time to fixation cohort

2. Validation and testing of a range of proteins to investigate possible

changes according to pre-analytical variables, especially delayed time to

formalin fixation, and to select markers for a Tissue Quality Index (TQI)

3. Validation of the model on other independent cohorts

4. Validation of the TQI as a predictor of time to fixation and performance

assessment via ER quantification

Intrinsic and Extrinsic controls for Formalin Fixed, Paraffin

Embedded (FFPE) tissue

Dr. David Rimm, Yale University

Intrinsic and Extrinsic controls for Formalin Fixed, Paraffin

Embedded (FFPE) tissue

Dr. David Rimm, Yale University

0

60

120

180

240

300

360

420

480

TN

85

TN

74

TN

28

TN

35

TN

65

TN

66

TN

84

TN

32

TN

11

TN

55

TN

15

TN

95

TN

98

TN

61

TN

41

TN

81

TN

93

TN

80

TN

73

TN

37

TN

106

TN

52

TN

101

TN

71

TN

88

TN

30

TN

16

TN

43

TN

86

TN

100

TN

50

TN

48

TN

29

TN

25

TN

102

TN

36

TN

103

TN

77

TN

17

TN

1

TN

9

TN

5

TN

21

TN

13

TN

10

TN

23

TN

2T

ime t

o F

ixati

on

(m

inu

tes)

Two fold redundancy N=125 , tumor=93, normal=2, cell lines=10 control breast tumor=10 ,control lung tumor = 10 Collected by Dr. David Hicks and colleague, University of Rochester Medical Center

Construction of a time to fixation cohort in collaboration with Dr. David Hicks, Rochester University

Validation and testing of several proteins to investigate possible changes due to delayed time to formalin fixation

Survey of change in levels of proteins in the 0.5 – 4 hour window

Conventional Series

Protein Tested Sample Size 95% C.I. 95% C.I. Inferred Slope Category of the Change on

Same Sample Size 10x Sample Size TMA Analysis Biopsy-Resection CohortER alpha 87 [ -0.324 0.171 ] [ -0.163 -0.013 ] -0.091 trend down none

PgR 51 [ -0.486 0.215 ] [ -0.219 -0.011 ] -0.112 trend down none

HER2 71 [ -0.283 0.244 ] [ -0.097 0.061 ] -0.018 no change none

Ki67 81 [ -0.29 0.196 ] [ -0.124 0.026 ] -0.052 no change none

Cytokeratin 82 [ -0.148 0.409 ] [ 0.036 0.212 ] 0.122 trend up none

beta-Actin 78 [ -0.446 0.113 ] [ -0.238 -0.071 ] -0.153 trend down not evaluated

Beta-Tubulin 81 [ -0.249 0.173 ] [ -0.098 0.031 ] -0.033 no change not evaluated

GAPDH 85 [ -0.16 0.204 ] [ -0.046 0.066 ] 0.009 no change not evaluated

Lamin A/C 83 [ -0.191 0.363 ] [ 0.029 0.194 ] 0.111 trend up not evaluated

Lactat Dehydrogenase A 84 [ -0.215 0.256 ] [ -0.032 0.107 ] 0.039 no change not evaluated

Cyclin D1 76 [ -0.289 0.183 ] [ -0.123 0.017 ] -0.052 no change not evaluated

Cyclin B1 64 [ -0.172 0.512 ] [ 0.046 0.261 ] 0.151 trend up not evaluated

Histone 3 78 [ -0.104 0.317 ] [ 0.029 0.158 ] 0.093 trend up not evaluated

Histone4 77 [ -0.742 -0.044 ] [ -0.504 -0.277 ] -0.391 decrease none

SUMO1 84 [ -0.24 0.245 ] [ -0.06 0.084 ] 0.014 no change not evaluated

CDC42 93 [ -0.113 0.192 ] [ -0.021 0.075 ] 0.026 no change not evaluated

Cleaved Caspase 3 78 [ -0.053 0.427 ] [ 0.112 0.259 ] 0.183 trend up not evaluated

HIF 2 alpha 43 [ -0.289 0.413 ] [ -0.046 0.168 ] 0.061 no change not evaluated

HIF 1 alpha 77 [ -0.088 0.38 ] [ 0.082 0.221 ] 0.151 trend up increase, p=0.046

AKAP 13 66 [ -0.016 0.576 ] [ 0.185 0.368 ] 0.274 trend up increase, p=0.009

Acetylated Lysine 78 [ 0.056 0.457 ] [ 0.195 0.315 ] 0.255 increase not evaluated

NEDD8 77 [ -0.082 0.397 ] [ 0.067 0.212 ] 0.139 trend up not evaluated

Antiphopspho Tyrosine 4G10 79 [ -0.578 -0.172 ] [ -0.441 -0.311 ] -0.376 decrease decrease, p=0.0048

TMA Series

Strategy: We screen for relative changes, in particular when the intensity of a marker becomes larger than the intensity of another marker

Screening for internally calibrated combination of markers

Time

inte

nsi

ty

Marker1

Marker2

TQI Model Construction

A TQI with pair wise combinations of markers

AUC is a measure of

performance.

It is 1 when the classification

is perfect. It is 0.5 for random

predictions, and it is less than

0.5 for bad predictions.

ER

K 1

/2

TQI values scores were

calculated for

-cytokeratin: pHSP27

-ERK 1/2: pHSP27

-100

-80

-60

-40

-20

0

20

40

60

80

100

CK-pHSP27

MAPK-pHSP27

TTF in min

TQI on time to fixation

Performance of the TQI on the time to fixation array

TQI values of combinations of cytokeratin: pHSP27:ERK 1/2 could be used as

measure of tissue quality

Association of a negative TQI value is an indicator of loss of tissue quality

ERK 1/2 -

Validation on an Independent Cohort

TQI assessment show excellent tissue quality, due to vacuum preservation (40C) used in an independent cohort specimens

Time to fixation

TQI score

ERK 1/2

Anova: P=0.0313 Anova: P=0.0311

Validation in two cohorts shows ER is lower when TQI<0

Cohort 1 Cohort 2

•The markers pHSP27, ERK 1/2 and CK were selected

as the best set for construction of the TQI

•The TQI was validated on different cohorts

•The TQI was significantly associated with lower ER

scores which might be indicative of protein degradation

due to loss of tissue quality

Intrinsic and Extrinsic controls for Formalin Fixed, Paraffin

Embedded (FFPE) tissue

Dr. David Rimm, Yale University

Biospecimen Integrity in Blood Plasma

Controlled Analysis of Preanalytical Variables in Clinical

Blood Collection, Processing and Storage

Dr. Daniel Chelsky, Caprion Proteomics

Time & temperature on bench before and after centrifugation

Blood on bench

Plasma or serum

Centrifugation

Plasma on bench

hour ⁰C

0.5 20

1

4

24

48

96

4 or 20

20 or 37

hour ⁰C

0.5 20

1

4

24

48

96

4 or 20

20 or 37

Control

Blood cells

Post-Spin vs. Control

20 °C 37 °C 48 h 96 h 48 h 96 h

Pre-Spin vs. Control

20 °C 48 h 96 h

Control: 20 °C, 0.5 h for Pre & Post-Spin

TIME AND TEMPERATURE EFFECTS

Total number of components: 13,111

Sample integrity study

Time on Bench

Verified Blood incubation Markers

(Threshold: fold change =1.25, p & q-value < 0.01)

EDTA P100 Serum Heparin Extracellular matrix protein 1 ECM1 o -1.5 6.10E-12 Plasma kallikrein KLKB1 o o o o -1.5 1.27E-03 Vitamin K-dependent protein S PROS1 o o o -1.4 1.68E-13 Coagulation factor IX F9 o -1.4 1.00E-18 Apolipoprotein C-IV APOC4 o o -1.3 2.72E-06 Prothrombin F2 o -1.3 2.38E-05 Extracellular matrix protein 1 ECM1 o 1.4 1.22E-11 Platelet glycoprotein Ib alpha chain GP1BA o o 1.6 6.70E-18 L-lactate dehydrogenase A chain LDHA o o o o 2.2 9.90E-11 Plastin-2 LCP1 o o 2.0 8.90E-15 Alpha-enolase ENO1 o o o o 2.5 5.85E-09 14-3-3 protein zeta/delta YWHAZ o o o o 2.5 6.78E-17 Talin-1 TLN1 o o o o 5.5 5.45E-15 Annexin A1 ANXA1 o o 2.7 1.11E-16 Protein S100-A4 S100A4 o o o o 3.0 2.41E-12 Protein S100-A8 S100A8 o o 16.5 1.65E-20 Protein S100-A9 S100A9 o o 25.0 1.23E-19 Fibronectin FN1 o 4.1 7.87E-17 Profilin-1 PFN1 o o o o 5.6 7.79E-19

Protein Description Tube Types

Fold change p-value Symbol

Verified Serum/Plasma Incubation Markers

(Threshold: fold change =1.25, p & q-value < 0.01)

EDTA P100 Serum Heparin

Talin-1 TLN1 o o o o -1.7 5.60E-07

Apolipoprotein C-IV APOC4 o o -1.4 1.47E-09

Prothrombin F2 o -1.3 6.19E-06

Sex hormone-binding globulin SHBG o o -1.3 7.20E-06

Apolipoprotein C-III APOC3 o o -1.3 4.23E-03

Adipocyte plasma membrane-associated APMAP o o 1.3 1.44E-04

Extracellular matrix protein 1 ECM1 o 1.4 1.68E-11

Clusterin CLU o o 1.4 5.20E-14

Serglycin SRGN o o 1.4 1.39E-03

Serum albumin ALB o o o o 1.5 8.13E-03

Complement factor D CFD o o o o 1.4 3.08E-11

Complement C3 C3 o o o o 2.5 1.54E-04

Fibronectin FN1 o 6.1 7.89E-18

p-value Protein Description Tube Types

Fold change Symbol

Controlled Analysis of Preanalytical Variables in Clinical Blood Collection, Processing and Storage Dr. Daniel Chelsky, Caprion Proteomics

•Mechanical separator in P100 tube help protect against change when left on bench

•Relative protein amounts differ, depending on the tube type

•Short times on bench OK for plasma <24h; 37˚C a problem

•87 biomarker proteins were identified for time on bench.

• A total of ~168 Biomarker proteins have been identified as sentinel markers of

plasma quality. This also includes 19 biomarker proteins identified for freeze/thaw

cycles, 62 biomarker proteins were identified for time in freezer (not shown as a

part of this study)

A successful MRM assay has been developed for sample integrity to define the impact of key pre-analytical variables and utility for use in clinical trials

miRNA Integrity in Blood Plasma

Background

•Small RNA molecules(~21nt), evolutionary conserved, regulate gene expression

•1527 hu-miRNA’s regulating 60% of protein encoding genes.

•Role in tumor suppressor genes and/or oncogenes, biomarkers of cancer.

miRNA are detected in

Effects of Pre-analytic Variables on Circulating microRNAs Hua Zhao, Ph.D., MD Anderson Cancer Center/ Roswell Park Cancer Institute)

Milestones

1. Discover a panel of “housekeeping” circulating

microRNAs which are ubiquitous expressed in

circulation

2. Development of the circulating microRNA QC

tools by studying the effects of the pre-analytic

variables on the “housekeeping” microRNAs

identified in Milestone 1

Objectives in Milestone 1

Testing: miRNA profiling performed in 20 cancer patients and 20 healthy controls.

Validation: miRNA profiling in an additional 200 plasma/whole blood samples from 100 cancer cases and 100 healthy controls.

miRNA were identified using quantitative real-time PCR based analysis based on the following criteria:

occur in all 40 tested samples

expression levels are not significantly different between cases and controls

show little inter-individual variations

Data Analysis Procedure

Effects of Pre-analytic Variables on Circulating microRNAs Hua Zhao, Ph.D., MD Anderson Cancer Center/ Roswell Park Cancer Institute)

microRNA Internal Controls in Whole Bloods

microRNAs Overall fold changes/CV

Breast cancer fold changes/CV

Prostate Cancer fold change/CV

miR-346 1.05/0.07 1.08/0.09 1.06/0.11

miR-134 1.09/0.11 1.08/0.09 1.12/0.12

miR-934 1.07/0.06 1.07/0.09 1.08/0.08

miR-16 1.05/0.06 1.08/0.10 1.06/0.09

miR-421 1.13/0.11 1.12/0.14 1.14/0.15

miR-222 1.14/0.16 1.13/0.12 1.18/0.17

miR-1207 1.21/0.21 1.24/0.30 1.20/0.19

miR-339 1.25/0.26 1.24/0.34 1.23/0.18

miR-505 1.32/0.30 1.29/0.28 1.24/0.36

miR-183 1.29/0.26 1.31/0.40 1.30/0.26

miR-374b 1.31/0.17 1.29/0.21 1.29/0.31

Summary of the Results

miR-346, miR-134 and miR-934 are potential candidates for microRNA internal controls in whole bloods.

miR-346 is a potential candidate for microRNA internal control in plasma.

They are better internal control candidates than miR-16, which is normally used in literature.

Effects of Preanalytical Variables on “housekeeping” microRNAs- Whole Blood

Processing delay time

2 hours vs 24 hours delay

Storage conditions

-20C vs -80C

Storage duration

0 vs 6 months

Freeze/Thaw cycles

0 vs 1 and 2 cycles

miR-346

miR-134

miR-934

miR-16

Whole Blood

• miR-346, miR-134 and miR-934 were proposed as potential candidates for

internal controls in whole bloods

• Number of freeze-thaws: Number of freeze-thaw cycles positively associated with

the Ct-value of the “housekeeping” microRNAs.

• Storage condition, Delayed time and Storage Duration: No association was found

in whole blood samples. Some miRNA degradation was observed in plasma after

24h on bench.

Impact on assessing miRNA as cancer biomarkers

in diagnostics and therapeutics

Effects of Pre-analytic Variables on Circulating microRNAs Hua Zhao, Ph.D., MD Anderson Cancer Center/ Roswell Park Cancer Institute)

Conclusions

1. A potential model for tissue quality index for Breast cancer specimens

which may help to accurately diagnose and guide therapy in patients.

2. A step toward development of global plasma quality biomarkers

-Handling artifacts

-Time in freezer and freeze thaw

3. A potential model for miRNA stability in plasma to asses

diagnostic/prognostic miRNA biomarkers

Questions?

1. Will TQI model apply to other solid tissues/cancer types?

2. Can we confidently assess quality of plasma using these biomarker

panels?

3. Do we need more user-friendly technology platforms (for

implementation at Biobanks) for assessment of these quality

biomarkers in plasma?

4. How will assessment of biospecimen quality help validate clinical

biomarkers?

Next Steps

Poster ID: RM_13, Thursday, May 22 12:30 PM-13:30 PM

“CDR – NCI’s Comprehensive IT Platform Managing Biorepository Operations”

Poster ID: BRS_12, Friday May 23rd from 11:30 AM-12:30 PM

“The Biospecimen Methodological Study (BMS): Evidence to Guide Best Practices

for Postmortem Tissue Preservation”

Other BBRB Poster Presentations

Poster ID: HSR 28, Friday, May 23, 11:30 AM-1PM

“The Genotype-Tissue Expression Project”

Poster ID: RM_30, Friday, May 23 11:30 AM – 12:30 PM

“Developing a Cost-recovery Modeling Tool for Long-Term Sustainability of

Biobanks”

Biospecimen Research Database

• Curated literature database for Biospecimen Science

• Over 2000 articles represented; incorporates ISBER references

• One published meta-analysis, three in preparation

Soon to be launched

BRD 3.0

1. Intrinsic controls for FFPE tissues –

Yale University, Dr. David Rimm

2. R&D on Human Biospecimen Integrity-

Caprion Proteomics, Dr. Daniel Chelsky and

Dr. Mimi Roy

3. Effects of Pre-analytical Factors on

Circulating miRNAs-

Roswell Park Cancer Institute,

Dr. Hua Zhao and Christine Ambrosone

Acknowledgements

Principal Investigators National Cancer Institute

1. Dr. Helen Moore,

Chief,

BBRB/CDP, NCI

2. Dr. Jim Vaught

Consultant,

BBRB/CDP,

NCI

3. Leidos Biomedical

Research Inc.

Lokesh Agrawal, Ph.D., Program Director, BBRB, Cancer Diagnosis Program/DCTD, National Cancer Institute Rockville, MD Lokesh.Agrawal@nih.gov

ISBER meeting, May 23rd, 2014

Biomarkers for Biospecimen Integrity

http://biospecimens.cancer.gov

http://www.cancerdiagnosis.nci.nih.gov/about/bbrb.htm