# Presentation: 488940

## Bringing Discoveries to Clinical Application: Clinical 	Epidemiology and Validation Centers

***Ian Thompson MD***
***Department of Urology***
***The University of Texas Health Science Center at San Antonio***

## From the outside looking in, how are new cancermarkers discovered & validated?

**Most commonly, patients with and without cancer are selected.**
**Biologic samples queried.**
**Differences identified.**
**Biomarkers identified.**
**Publication of “new cancer test”**

## What are the problems with the approach?

**First, individuals with expertise in molecular discovery rarely have an expertise in the clinical presentation of cancer or in clinical diagnostic needs.**
**Clinicians generally know the questions but are not experts in biochemistry/technology; rarely have epidemiology/biostatistics expertise.**
**Epidemiologists & biostatisticians are needed to fully understand analyses, mitigate bias, **_**and**_** to select appropriate populations for discovery and validation.**

## How do you achieve such an environment?

**You put the ‘discoverers’ together with the ‘users’ and supervise them with the ‘methodologists’.**
**	****Discoverers – scientists**
**	****Users – Clinician scientists**
**	****Methodologists – Epidemiologists/statisticians**
**Together, they function as a *****single team***** with a single goal: to develop a valid test that will **_**change the way medicine is practiced, preventing suffering and death from cancer**_**.**

## GU Group as a microcosm of the EDRN

**How do we prioritize/select biomarkers?**
**Regular meetings and conf calls, invited speakers, intra-EDRN and extra-EDRN discovery.**
**Methodologic scrutiny.**
**Biologic rationale.**
**Concurrent development of appropriate reference sets/identification of appropriate specimens in biorepositories.**

## Three vignettes

**A highly-promising technology we investigated, learned about methodology, and found was not valuable.**
**An example of the *****process***** of prioritization.**
**An example of a clinical success.**

## Vignette One.SELDI for prostate cancer

**The challenge of proteomics.**
**Extremely promising data from multiple institutions.**
**Multiple series suggested sensitivity and specificities exceeding 90%.**
**EDRN GU group: “High Priority: **_**Design the trial, now**_**”**

## Trial Design

**Three phases:**
**I: Portability and reproducibility.  *****Can SELDI as a clinical test provide comparable serum protein profiles in multiple laboratories? (3 sub-aims)***
***	***

## Slide 9

## Phase Two

**Refinement of predictive algorithm in multi-institutional case-control population.**
**Original plan for Phase II study**

## Revised study design (from phase 1)

**125 samples from high grade, 125 low grade, 125 biopsy-negative controls, 50 with inflammatory disease, 50 with other cancer.**
**Analysis at 2 EDRN laboratories. Obsessive-compulsive QC. Age/race-matched.**

**Revised study design (from phase 1)**

## Performance of the SELDI classifier system: 

**Cancer versus biopsy-negative controls – error rate 52% at EVMS and 50% at UAB.**
**High grade versus ‘controls’ without high grade cancer – error rate 52% at EVMC and 48% at UAB**
**Phase III study not pursued (validation in large prospective study, i.e., PCPT)**

## Lessons learned

**Previous studies use of suboptimal samples for discovery source of significant bias.**
**Controls must be carefully selected – **_**fully ascertained**_**, include other cancers and/or inflammation (non-specific markers of disease).**
**Sample size must be sufficient to reach **_**clinically meaningful**_** decisions.  (We had an 86% power to confirm test benefit 965% specificity at 95% sensitivity) against a clinically unacceptable differentiation (50% specificity at 85% sensitivity).**
**Also appropriate to include *****biologic***** issues related to tumor diagnosed (Gleason 7-10 versus Gleason **_**<**_** 6).**
**This publication is probably the current standard for validation of a disease biomarker****.**

## “The most important experiments are those that are not only worthwhile if the result is positive – but rather those that give major insights irrespective of whether or not they are positive or negative” Barnett S. Kramer

## Vignette Two.Biomarker ‘cook-off’

**Multiple promising biomarkers related to prostate cancer risk.**
**Question: Which to pursue?**
**Answer: Develop standardized reference set. **
- A reference set in which the question of cancer/no cancer is clinically-relevant.
- Offer the reference set to _multiple_ competing opportunities.
- Develop standards that, if met or exceeded, might justify moving to the next stage of validation.
- Rigorous sample set but expeditiously respond to opportunities.

## Description of the reference set

**123 specimens (63 PC, 60 non-malignant)**
**1 ml serum from each patient.**
**Contributed from three EDRN CEVCs (Harvard, Johns Hopkins, UTHSC San Antonio).**
**PSA > 2.5 ng/mL, rising PSA, %fPSA<15%, abnormal DRE. **_**>**_** 10 cores. Rigorous specimen processing.  Blinded labs.  Data analyzed by EDRN DMCC.**
**Specimen shipped to JHU reference lab for aliquoting, re-labeling, and shipping to four labs. Blinding by EDRN staff.**

## Slide 17

## Outcome of this process

**Formal reference set with larger sample size being collected.**
**proPSA being targeted for primary analysis in the same fashion as the ‘cook-off’ evaluation set.**

## Vignette Three.Risk assessment in Prostate Cancer

**Impact on mortality isn’t known; nonetheless, 75% of men have had a PSA and 50% have on regularly.**
**PSA cutoff of 4.0 ng/mL widely used for 20+ years.**
**Fundamental basis for PSA cutoff was never validated.**

## PCPT Schema

**Follow-up ****every 3 months**
**for 7 years**

**End of Study Biopsy**

**End of Study Biopsy**

**Enrollment**

**Placebo**

**Finasteride**

**Randomization**

## Thompson IM et al.  N Engl J Med 2004;350:2239-46

## Slide 22

## Development of an individualized risk calculator.

- 5519 men in placebo group of PCPT
- All had prostate biopsy _and_
-  - PSA and DRE at time of biopsy
-  - At least 2 prior PSA values

## Tested the impact on cancer detection of:

**Age***
**Family history of prostate cancer***
**PSA***
**Change in PSA (PSA velocity – 20 different methods of calculation)**
**Prostate examination***
**Prior negative prostate biopsy***
**Tested impact on both cancer and aggressive (high-grade cancer) detection**

## Slide 25

## Slide 26

## Validated calculator in external, multi-ethnic population

**Validated calculator in external, multi-ethnic population**

## How do we make the calculator more accurate?

**Add new measures of risk**
**Promising biomarker – PCA3.  Gene upregulated in prostate cancer cells – detectable in urine.**

## 65-year Caucasian with no prior biopsy, no family history of disease 

- 65-year Caucasian with no prior biopsy, no family history of disease
- and a normal DRE the PCPT prior risk according to PSA value and
- updated posterior risks for PCA3 values of 9.5 (25th percentile) and
- 93 (90th percentile). Gray shades indicate 95% confidence intervals.

## VALIDATION STUDIES IN PROGRESS: EDRN-PLCO-SPORE Ovarian Markers

**VALIDATION STUDIES IN PROGRESS: AFP versus DCP for Hepatocellular Carcinoma**

**    **

**Determine the sensitivity and specificity of des-gamma carboxyprothrombin (DCP) for the diagnosis of early hepatocellular carcinoma (HCC).**
**	**

**VALIDATION STUDIES IN PROGRESS: EDRN-PLCO-SPORE Ovarian Markers**

**Identify a consensus panel comprised of biomarkers that are most informative in detecting early ovarian cancers (CA 72-4, CA 15-3, CEA, CA 19-9, SMRP-1, OV-1.10, HE-4, Osteopontin, HK-11, HK -10, Spondin-2, Prolactin and CA-125).**

## VALIDATION STUDIES IN PIPELINE

**VALIDATION STUDIES IN PIPELINE**

**    **

_**Samir Hanash**_**:  Validation of Protein Markers of **_**Lung Cancer**_**. **
_**Harvey Pass**_**:  Serum Protein Biomarkers for Early Detection of **_**Mesothelioma**_**.**
_**David Sidransky**_** : Circulating DNA Methylation Markers of **_**Lung Cancer.**_
_**Alan **_**Partin: GSTP1 Methylation Markers in Screen-Detected **_**Prostate**_** Biopsy as reflex markers**
_**Stephen Meltzer **_**: A panel of methylation markers to determine the risk of progression from Barrett’s esophagus to **_**esophageal adenocarcinoma**_
_**Robert Getzenberg and Robert Schoen**_**: Novel serum based markers for detection of **_**colorectal cancer.**_
_**Brian B. Haab**_** : Discrimination of benign from malignant **_**prostatic**_** disease in men with elevated PSA using serum TSP-1.**
_**Eleftherios Diamandis**_**: Human Kallikreins, biomarkers for early detection and progression of **_**prostate cancer.**_
_**Robert Getzenberg**_**: EPCA (Early Prostate Cancer Antigen) as a markers for earlier detection of **_**prostate cancer **_**(sensitivity 92%,  specificity is 94%).**
**	****	****	****	****	****	****	**

## The bottom line

**Cancer biomarker discovery and validation requires the talents of multiple disciplines.**
**Requires a culture of:**
**Collaboration (the organizational objective and benefits and rewards to the organization are more important that those of the individual; a *****radical departure from historical perspective*****)**
**Seeking opportunities wherever they may be (partnering with industry, outside EDRN)**
*Focus on the primary objective:  Discovery and validation of biomarkers/biomeasures that ultimately reduce morbidity and mortality from cancer.*_**  **_