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Clinical Trials Using Pharmacogenetic Information: a Statistician's View

Rick Chappell, Ph.D.Professor,Department of Biostatistics and Medical InformaticsUniversity of Wisconsin Medical School

Stat 641 - 12/13/2010

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Outline

A. DefinitionsB. MotivationC. Statistical BackgroundD. Types of Clinical Trial Designs for Predictive

Biomarker ValidationE. ExamplesF. ConclusionsG. References

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A. Definitions

1. Genomics and Pharmacology (Sen, 2009):

• “Pharmacology is the science of drugs including materia medica, toxicology, and therapeutics;

• ... Pharmacodynamics deals with reactions between drugs and living structures;

• ... Pharmacokinetics relates to the study of the bodily absorption, distribution, metabolism, and excretion of drugs;

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A. Definitions

1. Genomics and Pharmacology (Sen, 2009, cont.):

• ... Pharmacogenetics deals with genetic variation underlying differential response to drugs as well as drug metabolism.

• ... The whole complex constitutes the discipline: Pharmacogenomics.”

1. Genomics and Pharmacology (Sen, 2009, cont.):

• Summary definition: "... genomics looks at the vast network of genes, over time, to determine how they interact, manipulate and influence biological pathways.”

• My comments:This is a multidimensional definition: a "vast number" of genes, plus time.Add more dimensions: drugs and how they interact with these genes and pathways.

• And, really, other factors too: radiation, devices, procedures ...

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A. Definitions

2. Pharmacogenetic mechanisms (Palmer, 2009):

• Variation in the metabolism of a drug among individuals, especially in enzymes involved in the catabolism or excretion of a drug;

• Variation among population members with respect to drug adverse effects; and

• Variation in the drug treatment target or target pathways, conceptually dividing a population into "Responders" and "Nonresponders”.

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A. Definitions

3. Biomarker (Chakraverty, 2005):

• A characteristic that is objectively measured and evaluated as an indicator of biologic processes or response to a therapeutic intervention.

• Here, biomarkers will be restricted to be classifiers, which means that they cannot change over the course of the study. There are two types (Chang, 2008).

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A. Definitions4. Types of constant biomarkers (classifiers)

Prognostic Biomarkers:– Inform clinical outcomes, independent of

treatment;– Provide information about course of disease in

all individuals, whether or not they have received the treatment under study;

– Can be used to separate good- and poor-prognosis patients at the time of diagnosis;

– If separation is good, can be used to aid the treatment decision, in particular its aggressiveness.

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A. Definitions4. Types of constant biomarkers (classifiers)

Prognostic Biomarkers:– Inform clinical outcomes, independent of

treatment;– Provide information about course of disease in

all individuals, whether or not they have received the treatment under study;

– Can be used to separate good- and poor-prognosis patients at the time of diagnosis;

– If separation is good, can be used to aid the treatment decision, in particular its aggressiveness.

Prognostic biomarkers create a staging system!

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A. Definitions4. Types of constant biomarkers (classifiers)

• Predictive Biomarkers:– Inform treatment effect on the clinical endpoint;– Can be used to determine treatment.

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B. Motivation - 1. The problem at hand

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B. Motivation - 2. A (personally) early example of medically early “biomarkers”

As a graduate student, I analyzed a retrospective dataset of colorectal tumors in order to refine Dukes’ staging (Michelassi, Block, Vanucci, Montag & Chappell, 1988).

I was surprised at the magnitude of association between 5-year survival and the predictors:

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B. Motivation - 3. RecentDevelopments with Iressa

BOSTON, May 29 (UPI, from the Wall Street Journal) -- “Thegene-targeting drug Iressa is proving beneficial for lung-cancerpatients who are Asian or non-smokers, a Harvard Medical School oncologist said.”

The success of Iressa is spurring pharmaceutical companies to develop genetically targeted drugs to improve the treatment of numerous forms of cancer, said Lecia Sequist, an oncologist at Harvard and Massachusetts General Hospital Cancer Center.”

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“… While Iressa made little impression as an overall lung cancer drug when introduced by AstraZeneca in 2002, it has proved effective among Asians and non-smokers who have lung cancer, said David Carbone, an oncologist at the Vanderbilt-Ingram Cancer Center in Nashville.

"It is a great example of how genetics can be used to guide therapy,” Carbone said. …”

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What do these two examples have incommon?

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What do these two examples have incommon?

The first looks at the influence of prognostic markers.

The second example identifies predictive markers.

But they are both based on clinical or demographiccharacteristics (tumor stage, histology, pathology,race, smoking status), not directly measured genomic information.

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Some investigators (Simon, 2004a) worry that lack of progress in cancer therapy is due to the failure to jump to genomic markers:

“The development of traditional prognostic and diagnostic biomarkers has been largely disappointing. The extensive literature on this topic is often contradictory and relatively few oncologic biomarkers have been adopted in clinical practice.”

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The title alone of Hilsenbeck (1992) reflects dissatisfaction with traditional prognostic information:

“Why do so many prognostic factors fail to pan out?”

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How do we move into the new age of pharmagenomics?

Move straight to the source of genetic variation by looking directly at genes.

In both examples, the observed markers are likely to be important only because they are confounders for genetic markers.

By “confounders” I mean that the observed markers are uninformative given knowledge about the genetic ones.

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The effect of Iressa on lung cancer certainly has been examined with respect to direct genomic information:

Tumors with mutations in the kinase domain of their epidermal growth factor receptor gene are highly sensitive to EGFR inhibitors like Iressa. This was no surprise.

But what about other drugs/treatments (e.g., radiation) with less obvious genomic predictors?

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Simon goes on to point out the difficulties in readying a gene expression biomarker for clinical use due to the very large number of candidate biomarkers, which may be orders of magnitude larger than the number of cases.

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B. Motivation - 4. Follicular Lymphoma Released Proteins

• Candidate List of yoUr Biomarkers contains 56 candidate lists of biomarkers

• Including one by Vaughn et al. (link in references) with 391 proteins for FL along with the relevant genes.

• “CLUB is a freely available online system which allows the biomarkers research community to share, compare and analyze their list of candidate genes, transcripts or proteins.”

:::::::

CandidatesIPI Protein Id

Protein NameEntrez Gene Id

Gene Name

Original DatabaseOriginal Identifier

View Annotations

IPI00011569

ACETYL-COA CARBOXYLASE 1. 31 ACACASwissprot/Trembl

AccessionQ13085

IPI00011938

ISOFORM 1 OF ADENYLATE CYCLASE TYPE 6.

112 ADCY6Swissprot/Trembl

AccessionO43306

IPI00220444

ISOFORM 2 OF ADENYLATE CYCLASE TYPE 6.

112 ADCY6Swissprot/Trembl

AccessionO43306

...

And 388 others ...

Name: Candidate List of Follicular Lymphoma Released ProteinsDescription: Using a two-peptide minimum per protein and standard criteria, 391 proteins (5.6% maximum predicted error rate) released from the FL cells were identified

Sample InformationSample Type: Cell LineSample Description: Follicular lymphoma-derived cell line SU-DHL-4 was used.

Experiment DetailsDetection Type: Protein ExpressionExperiment Design: Normal vs Diseased ComparisonDescription: Tandem mass spectrometry analysis was used for the identification of proteins.

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A Gross Simplification:

• This presentation (in accordance with the current state of research) will assume a single quantity to be tested in conjunction with a treatment to determine their effects upon patient outcomes.

• This quantity can either be a single marker or a multigene expression profile, also called a “prediction function.”

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• Simon (2004b) suggested that pharmacogenomic predictors be developed using phase II data and validated in a phase III trial.

• In a purely clinical sense, we don’t care about including unnecessary genes in the prediction profile as long as we have predictive accuracy.

• “The phase III trial is free from the problems of data dredging” because it relies on this single prespecified predictor.

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• In the following (and often in the design literature), we will assume a single binary predictor and call it a “marker”, regardless of whether it is based on a single gene or a multigene predictor profile.

• I focus on the design of Phase III clinical trials of a treatment in the presence of a single marker.

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C. Statistical Background

Suppose we have a binary outome (e.g., “response / no response”).

The outcome might depend on two binary factors, T (+/-) and M (+/-).

We denote the probability of response as P(resp.; levels of T and M).

(Similar results will also hold for time-to-event and continuous responses.)

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1. Treatment effect = P(resp.; T+) - P(resp.; T-).

Three quantities of interest:

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1. Treatment effect = P(resp.; T+) - P(resp.; T-).

2. Marker’s prognostic effect = P(resp.; M+) - P(resp.; M-).

Three quantities of interest:

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3. Marker’s predictive effect (interaction of Marker, Treatment) =

{Treatment effect with M+} - {Treatment effect with M-} =

{P(resp.; T+, M+) - P(resp.; T-, M+)} -{P(resp.; T+, M-) - P(resp.; T-, M-)}.

Three quantities of interest:

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Because treatment effects are estimated with standard methods from randomized clinical trials, and

Prognostic effects are estimable from single-sample observational studies,

I will now concentrate on studies which assess or allow for a marker’s predictive effect. These all require treatment randomization.

Three quantities of interest:

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Who is interested in these quantities (for example, for T = Iressa, M = EGFR)?

1. Patients’ self-interest is served by knowing whether the treatment works for them. They may not care whether a marker is prognostic or even predictive as long as they significantly benefit from the treatment. They would care if a marker could show them to be in a group for which the treatment is ineffective.

2. AstraZenica, manufacturer of Iressa, is interested in finding indications for it, and are happy if a marker helps them do it. They are also primarily interested in the treatment effect, either in all patients or a subgroup.

3. Genzyme, manufacturer of the EGFR gene amplification test, would want to demonstrate a treatment effect and marker prognostic value.

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How many patients does it take to estimate these quantities?

1. Treatment effect = P(resp.; T+) - P(resp.; T-).

Estimating this with good (90%) power to detect a true treatment difference of 10% requires about500 + 500 = 1000 patients total.

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How many patients does it take to estimate these quantities?

2. Marker’s prognostic effect = P(resp.; M+) - P(resp.; M-).

Estimating this with good (90%) power to detect a true prognostic difference of 10% also requires about 500 + 500 = 1000 patients total.

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How many patients does it take to estimate these quantities?

3. Marker’s predictive effect = {Treatment effect with M+} - {Treatment effect with M-} ={P(resp.; T+, M+) - P(resp.; T-, M+)} -{P(resp.; T+, M-) - P(resp.; T-, M-)}.

Estimating this with good (90%) power to detect a true predictive difference of 10% requires about 1000 + 1000 + 1000 + 1000 = 4000 patients total if we use a factorial (four equal-sized samples) for maximum efficiency.

Estimating interactions (differences of differences) requires a lot of subjects.

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D. Types of Clinical Trial Designs for Predictive Biomarker Validation (Mandrekar & Sargent, 2009)

1. Retrospective - use of data from a previously conducted randomized clinical trial to validate a marker .

• Randomization is necessary in order to obtain an unbiased estimate of the treatment effect, without which any conclusions about the biomarker prediction are invalid.

• Practical - can use existing data.

• Not a “fishing license”: markers and hypotheses must be prespecified.

• Samples must be available from all/most patients in order to avoid selection bias.

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2. Enrichment (targeted) designs - all prospective subjects are screened for a marker or marker profile, and only those with (or without) certain molecular features are included.

• Prospective - inclusion criteria must be specified in advance.

• Appropriate when preliminary evidence hints that patients with (or without) a profile benefit from treatment.

• Do not yield information about the omitted patients. Results will only tell you if the treatment is effective for the targeted patients.

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3. Unselected (“all-comers”) designs - eligible patients of any biomarker profile are admitted into the trial.

• Also prospective.

• Ability to provide adequate tissue may be an eligibility criterion for these designs, but not the specific biomarker result.

• Can directly validate the biomarker profile.

• Subdivided based not on the design but on the prespecified analysis methods.

Unselected Designs

A. Sequential testing strategy designs - hypothesis of treatment effectiveness is tested in both the overall population and also the marker-defined prospectively planned subgroup.

• Similar in principle to a standard RCT design with a single primary hypothesis.

• Can either test the entire population first or test in the subgroup first and then the whole group if treatment is significant in the subgroup (closed testing procedure).

• Both analyses preserve type I error (not biased by multiple comparisons).

Unselected Designs

A. Sequential testing strategy designs (continued)

• “Entire population first” analysis is useful when marker is of secondary importance.

• “Marker + (or -) subgroup first” analysis is best when there is strong prior evidence that marker is predictive and also subgroup has sufficient power.

• The choice of procedures is important; you may be sorry (too late) if you pick the “wrong one”.

• It is possible to have an adjustment for multiple comparisons which lets you pick the more significant result, but this requires an adjustment (downwards) of the p-value as its price.

Unselected Designs

B. Marker by treatment interaction designs - a marker by treatment factorial design in which patients are stratified by marker status and then randomized to treatment/control within each marker group.

• Hypothesis of marker predictive validity is formally tested by examining the Marker/Treatment interaction.

• Mandrekar and Sargent call it “Clearly, a prospective and definitive marker validation trial.”

• As above, requires a large sample size.

Unselected Designs

C. Marker-based strategy designs - randomizes patients to have their treatment either based on or independent of the marker status.

• For example, all M+ patients receive the treatment and M- patients are randomized to the treatment vs. control.

• Essentially a clinical trial in which the intervention is the marker assay; it directly tests the assay’s use.

• Because some patients in both the M+ and M- arms each receive the treatment, this design is inefficient.

• Requires an even larger sample size than the interaction designs.

• I recommend interaction designs for testing the utility of a marker assay.

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4. Hybrid randomized/nonrandomized designs - only marker + (or -) patients are randomized to treatment/control; all others receive only the control.

• Also prospective.

• Useful when unethical to randomize some patients.

• Powered to detect differences only in the randomized group, like in enriched designs.

• Cannot directly validate marker’s predictive ability because treatment effect not estimable in one group.

• Can provide data on other markers for future studies.

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5. Adaptive designs (Chang, 2008) • A general term for designs with two or more stages in which

the structure of later stages can depend upon the results of the earlier ones. The following design parameters can be modified:

• Optimal marker cut points can be chosen based on performance in the first stage.

• Marker subgroups in which patients are randomized. Trial begins with patients accrued in all subgroups; futility analyses determine whether some subgroups are discontinued

• Overall sample size can be modified.

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Advantages and disadvantages of adaptive designs

• Flexibility in the face of uncertainty about treatment effect, overall event rates, marker cut points, and marker effects.

• Requirement for quick outcomes, eliminating time to event endpoints in most cases.

• Requirement for statistical adjustment in order to use the same data for definition and validation of cut points, or for the design and analysis of trial populations.

• Logistical complexity.

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E. Examples

1. Retrospective analysis of a marker in existing RCTs:

• M = KRAS (wild type vs. mutant)• T = panitumumab / cetuximab (vs. best

supportive care)• Disease = advanced colorectal cancer; • Primary outcome = PFS

WT, wild type; MT, mutant; cmab, cetuximab; CT, chemotherapy; pmab, panitumumab

Objective Response

N (%)

ReferenceTreatment

(panitumumab or cetuximab)No of patients

(WT:MT)MT WT

A. Liévre, et al. (AACR Proceedings, 2007) cmab ± CT 76 (49:27) 0 (0%) 24 (49%)

S. Benvenuti, et al. (Cancer Res, 2007) pmab or cmab or cmab + CT 48 (32:16) 1 (6%) 10 (31%)

W. De Roock, et al.(ASCO Proceedings, 2007) cmab or cmab + irinotecan 113 (67:46) 0 (0%) 27 (40%)

D. Finocchiaro, et al. (ASCO Proceedings, 2007) cmab ± CT 81 (49:32) 2 (6%) 13 (26%)

F. Di Fiore, et al. (Br J Cancer, 2007) cmab + CT 59 (43:16) 0 (0%) 12 (28%)

S. Khambata-Ford, et al. (J Clin Oncol, 2007)

cmab 80 (50:30) 0 (0%) 5 (10%)

Single-Arm Studies Support the Hypothesis for KRAS as a Biomarker for EGFr Inhibitors

Randomization stratification• ECOG score: 0-1 vs. 2• Geographic region: Western EU vs. Central & Eastern EU vs. Rest of World

1:1

Panitumumab PD Follow-up6.0 mg/kg Q2W+ BSC

BSC PD Follow-up

RANDOMIZE

Optional Panitumumab

Crossover Study

Hypothesis: The treatment effect of panitumumab monotherapy is larger in patients with wild-type KRAS compared to patients with mutant KRAS

KRAS Retrospective Analysis of a Phase 3, Randomized, Controlled Trial Comparing Panitumumab vs Best Supportive Care (BSC) in Colorectal Cancer

Van Cutsem, Peeters et al. JCO. 2007;25:1658-1664.

Objectives and Analysis MethodologyObjectives and Analysis MethodologyPrimary Objective

• To assess if the effect of panitumumab on progression-free survival (PFS) was significantly greater in patients with wild-type KRAS compared to patients with mutant KRAS

Secondary Objectives

• To assess whether panitumumab improves PFS compared with BSC alone in patients with wild-type KRAS

• To assess whether panitumumab improves OS compared with BSC alone in patients with wild-type KRAS

Compare PFS in wild-type KRAS

subset

Test for a PFS effect among all

randomized patients at a 5% level

Test for quantitative PFS effect interaction,

i.e., wild-type effect > mutant

p ≤ 0.05 p > 0.05

p ≤ 0.05 p > 0.05

Compare OS in wild-type KRAS

subset STOP

STOP

KRASKRAS Evaluable Pts (92% of population): Evaluable Pts (92% of population):PFS by TreatmentPFS by Treatment

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34

Weeks

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

36 38 40 42 44 46 48 50 52

1 7188 106 7 71 55 49 37 25 19 1 15 1 12 9 6 620 168 75 34 23 1 16 1 14 10 10 8 6 4 4 4 3

208219

Pro

po

rtio

n w

ith

PF

S

Pmab + BSCBSC Alone

Patients at Risk

191/208 (92) 8.0 15.4209/219 (95) 7.3 9.6

Pmab + BSCBSC Alone

Events/N (%)Median

In WeeksMean

In Weeks

HR = 0.59 (95% CI: 0.48–0.72)

Mutant Mutant KRASKRAS Subgroup: Subgroup:PFS by TreatmentPFS by Treatment

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Pro

po

rtio

n w

ith

PF

S

Pmab + BSCBSC Alone

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34

Weeks

36 38 40 42 44 46 48 50 52

Patients at Risk

78 76 72 26 10 8 6 5 5 5 5 4 4 4 4 2 2 2 2 2 2 2 1 1 191 77 61 37 22 19 10 9 8 6 5 5 4 4 4 4 4 4 3 3 3 2 2 2 2

84100

76/84 (90) 7.4 9.995/100 (95) 7.3 10.2

Pmab + BSCBSC Alone

Events/N (%)Median

In WeeksMean

In Weeks

HR = 0.99 (95% CI: 0.73–1.36)

Wild-type Wild-type KRASKRAS Subgroup: PFS by Treatment Subgroup: PFS by Treatment

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34

Weeks

36 38 40 42 44 46 48 50 52

7 7 6 5 510 9 9 6 6 6 5 4 3 3 2 2 2 2 1

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115/124 (93) 12.3 19.0114/119 (96) 7.3 9.3

Pmab + BSCBSC Alone

Events/N (%)Median

In WeeksMean

In Weeks

HR = 0.45 (95% CI: 0.34–0.59)Stratified log-rank test, p < 0.0001

Pmab + BSCBSC Alone

119 112 106 80 69 63 58 50 45 44 44 33 25 21 20 17 13 13 13 10119 109 91 81 38 20 15 15 14 11 6

Pro

po

rtio

n w

ith

PF

Sp < 0.0001 for quantitative-interaction test comparing PFS log-HR

(pmab/BSC) between KRAS groups

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Conclusion for Example 1.

• Suggestive evidence from phase II trials that patients with wild-type KRAS receiving panitumumab and cetuximab do better than patients with mutated KRAS (prognostic).

• Convincing evidence from a phase III RCT that KRAS is predictive of superior performance by panitumumab.

• Similar evidence (not shown) for cetuximab from retrospective analysis of CRYSTAL RCT.

• Strong clinical evidence of efficacy only in KRAS wild type patients.

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E. Examples

2. Enrichment design of HER2 as a marker for Herceptin in Breast Cancer (BC).

Trastuzumab (Herceptin) is currently approved for treatment of HER2 positive BC patients in the adjuvant setting:

• Based on improvement in disease free survival from a combined analysis of 2 national intergroup adjuvant BC trials (NSABP B-31, NCCTG N9831).

• Both trials utilized an enrichment design strategy of allowing only HER2 positive BC patients, based on preliminary evidence.

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Enrichment strategy was advantageous here:• Only approximately 20% of women are HER2

positive• If truly no benefit of Herceptin in 80% of women

deemed HER2 negative, a much larger sample size would have been required to establish statistically significant results in an unselected study

Using markers to restrict trial eligibility: success – Her 2+ Breast Cancer

Romond, NEJM 2005

Using markers to restrict trial eligibility: beware

• What about Herceptin in Her2- breast cancer?

• New Data: No difference in benefit based on strength of HER2+ using two assays

• After 10 years, may need new study of Herceptin in Her2-

patients

Paik, ASCO 2007

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• Enrichment strategy MAYBE not so successful? • High degree of discordance between central and

local testing for FISH and IHC.– Post-hoc central testing for HER2 expression

suggests patients with tumors negative for FISH and less than IHC 3+ staining also derived benefit from Herceptin.

• Patients deemed HER2 negative not enrolled onto the trials, so cannot fully establish the predictive utility of HER2.

Conclusion for Example 2.

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• While the enrichment strategy did - Clearly and quickly define an effective treatment for a subset of patients,

• It did not answer - Questions regarding the predictive utility of HER2 due to the issues of assay reproducibility and inclusion of only biomarker defined subgroups .

• An unselected design, allowing for both HER2 positive and negative patients, may have helped provide these answers in a definitive and ultimately more timely manner.

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F. Conclusions (Simon, 2010)

“Most cancer drugs being developed today are targeted at the protein products of specific deregulated genes. … The ideal approach is prospective drug development with a companion diagnostic [involving]:

1. Development of a promising completely specified genomic classifier using pre-clinical and early phase clinical studies;

2. … Development of an analytically validated test for measuring that classifier; and

3. … Use of that completely specified classifier and analytically validated test to design and analyze a new clinical trial to evaluate the effectiveness of that drug and how the effectiveness relates to the classifier.”

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Acknowledgements

Daniel Sargent, Ph.D., Mayo Clinicfor permission to modify/use his slides of clinical trial design schematics.

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G. Selected References

Chakraverty, A. Chapter in Burzykowski, Molenberghs, and Buyese (eds.). The Evaluation of Surrogate Endpoints. Springer. 2005.

Chang, M. Adaptive Design Theory and Implementation Using SAS and R. Chapman & Hall/CRC. 2008.

Hilsenbeck, S.G. et al. Why do so many prognostic factors fail to pan out? Breast Cancer Research Treatments 22, pp. 197-206, 1992.

PAREXEL. PAREXEL's Pharmaceutical R&D Statistical Sourcebook 2002/2003. PAREXEL. 2003.

Mandrekar, S.J. and D.J. Sargent. Clinical Trial Designs for Predictive Biomarker Validation: Theoretical Considerations and Practical Challenges. In press, Journal of Clinical Oncology.

Michelassi, F., G. Block, L. Vanucci, A. Montag, and R. Chappell. Five to twenty-one year follow-up and analysis of 250 patients with rectal adenocarcinoma. Annals of Surgery 208, pp. 379-389. 1988.

Palmer, L.J. “Pharmacogenetics.” In P. Armitage and T. Colton, The Encyclopedia of Biostatistics, online ed., Wiley. 2009.

Sen, P.K. Biological Assay, Overview. In P. Armitage and T. Colton, The Encyclopedia of Biostatistics, online ed., Wiley. 2009.

Simon, R. When is a genomic classifier ready for prime time? Nature Clinical Practice: Oncology 1, pp. 4-5. 2004a.

Simon, R. An agenda for Clinical Trials: clinical trials in the genomic era. Clinical Trials 1, pp. 468-470. 2004b.

Simon, R. Clinical trials for predictive medicine: new challenges and paradigms. Clinical Trials 7, pp. 516-524. 2010.

Vaughn, C.P., Crockett, D.K., Lin, Z., Lim, M.S., and Elenitoba-Johnson K.S.Candidate List of Follicular Lymphoma Released Proteins. http://club.bii.a-star.edu.sg/browse/view.do