Group 5 Biomarkers & Chemoprevention: Where are we? March 4 th - - PowerPoint PPT Presentation

March 4th/5th 2016

Biomarkers & Chemoprevention: Where are we? Group 5

Miriam Rosin PhD

Director, BC Oral Cancer Prevention Program, BC Cancer Agency GOCF Organizing Committee and Co-Chair, Group 5 Contact: mrosin.1987@gmail.com Risk prediction for dysplasia progression in developed countries

Sok Ching Cheong, PhD

Group Leader, Oral Cancer Research Programme, Cancer Research Malaysia Adjunct Professor, University of Malaya, Malaysia. Contact: sokching.cheong@cancerresearch.my What can be done now in less developed countries

Anil K Chaturvedi, PhD

Investigator, Division of Cancer Epidemiology and Genetics National Cancer Institute

Contact:chaturva@mail.nih.gov

Natural history of OPMDs & biomarkers: The establishment of national and regional programs

• J. Silvio Gutkind, PhD

Associate Director of Basic Science, UC San Diego Moores Cancer Center GOCF Organizing Committee and Co-Chair, Group 5 Contact: sgutkind@ucsd.edu Oral Cancer Prevention in the Era of Precision Medicine

Paul Brennan

Head of Genetic Section International Agency for Research on Cancer Contact: brennanp@iarc.fr Moderator

Scott Lippman, MD

Director, UC San Diego Moores Cancer Associate Vice Chancellor for Cancer Research and Care Contact: slippman@ucsd.edu Contributor to white paper

Eva Szabo, MD

Chief, Lung and Upper Aerodigestive Cancer Research Group Division of Cancer Prevention, National Cancer Institute

Contact: szaboe@mail.nih.gov

Discussant

Risk prediction for dysplasia progression in developed countries

Miriam P Rosin, PhD Director, BC Oral Cancer Prevention Program BC Cancer Agency

Evolution of Biological Framework

• Increased capacity to share knowledge & to craft

new technology

• What information do we collect?
• How do we collect this information?
• How do we share in process of creating knowledge?
• Focus of collection – depth & breadth

Evolution of a comprehensive progression biomarker set

Pure Nature’s Photos. Facebook

If yes, then what?

The need to look at the broader picture

Variation in natural history of disease in different settings Pictures from a biomarker/chemoprevention trial in Usbekistan in 1984. Nass users given multi-vitamin treatment Universal vs specific to setting

Superficial Intermediate Parabasal Basal Basement membran e Fibroblast Blood vessel

Inactivation of 9p21 (genetic / epigenetic mutations, viral oncogenes) Apoptosis Senescence Genomic instability Cell cycle deregulation

Progression OPMD

• Self-sufficiency in growth signals
• Insensitivity to antigrowth signals
• Evading apoptosis
• Limitless replicative potential
• Sustained angiogenesis
• Tissue invasion and metastasis

Invasive carcinoma

Evolution of progression models

• Detailed understanding of genetic change in clones, single cells
• Processes outside the clones / lesion, changing over time
• Developmental windows (sets of genes on / off, induced by exposure)
• Stressors, especially systemic, physiologic or tissue level with age
• Protectors (micro-environment, normal cells), e..g immune system
• Cross over studies, different cancers, chronic diseases: markers,

processes, drugs

Rosin et al., 2000; Kensler 2016

Impact of technology / new knowledge

Heterogeneity – how many ways to end-game

“Battle of the Clones”

• Guidelines
• Protocols
• Referral pathway
• Capacity building
• Switching norms
• Social marketing
• Register patients
• Monitor patient flow
• Outcome

Readiness of Dental Clinics Surveillance System

Oral Cancer Prediction Longitudinal Study (Primary dysplasia 10-15 yrs) Today focus on mild and moderate dysplasia

Dental Health Professions

• Fluorescence Visualization
• Demographics
• Risk Factors
• Clinical

Specialists

• Biopsyg

Oral Biopsy Service

• Histology
• Cytology
• QP

Next Generation Clinic

• Assessment

BC Cancer Agency

Back to community

BC model of integration of dental & medical systems

• Treatment

p21

Cell Cycle Progression

E2F Cyclin D CDK4 RB E2F RB

P

MDM2

Apoptosis Senescence

p16INK4a p15INK4b ARF

1 2 1β 1α 2 3

p53

21,950K 22,000K 24,500K 24,550K D9S1748 D9S171 21,550K D9S1751 p14/ARF p15/CDKN2B p16/CDKN2A Human 9p21

• Test: 9p21 a “gatekeeper” – 1st separation
• Combined with change to markers on 2
• ther critical sites on 4q and 17p =>

Separate out high-risk (HR) cases - Genome

marker based technology (gMART)

How to help patients now? Concept of Triage

10

INFA-Arf region on 9p 21

Mild Dysplasia RR 0.86 (0.33- 2.22)* Moderate dysplasia RR 0.96 (0.39-2.46)**

9p Retention RR 1

Overall Progression

9p & either 4q/17p LOH RR 11.6 (2.7-49.9)* 9p, 4q & 17p LOH RR 52.1 (11.8-230.6)**

*p=0.001 **p<0.0001

By Risk Pattern (gMART)

5-year lesion progression risks for gMART categories

• Low risk (LR) = 3% (47% of cases)
• Medium risk (MR) = 16% (44% of cases)
• High risk (HR) = 63% (10% of cases)

Genome marker based technology (gMART)

14% progressed

(Median = 44 months)

*p=0.75 **p=0.98

By Histology

Histology did not predict

• utcome

Hyperplasia

N = 296 TRIAGE

Mild dysplasia Moderate dysplasia

Progression Rate (%) 100 80 60 40 20 100 80 60 40 20 LR MR HR LR MR HR

LR 47% IR 43% HR 10% LR 59% IR 38% HR 3%

2-year 3-year 5-year

Prospective Cohort Retrospective Cohort

• Similar results for independent retrospective cohort
• gMART alone – identifies 54% of cases as low or high risk
• What about medium risk (MR) group? Further sorting

Development-Validation Process

Progression

Nuclear Phenotype Score (NPS)

• High throughput quantifiable indication of risk of progression
• Automated -objectively measures subtle changes to tissue

phenotype (amount and distribution of DNA in nuclei)

• Likely to be globally relevant marker

20 40 60 80 100 120 140 160 180 200 220 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Cumulative Proportion Sur

Quantitative pathology alone What happens when done together with gMART?? =>

Low Risk, RR = 1 High Risk, RR = 10

Integration of technologies: QP - LOH

gMART+ QP gMART alone

Performance of gMART+

• Addition of QP places up to 80% of patients with oral dysplasia into the

HR and LR categories

• Independent validation study – 12/15 progressors detected as high-

risk (80% sensitivity)

• Other fusions: QP-LOH-TB, LOH-FISH (EPOC study) etc

• Goal: Move to community

settings to triage OPLs

• Treatment of HR cases (metformin)
• Watchful waiting LR
• Further genomic profiling for MR
• Look at specific settings

– South Asian community

• High-risk communities

Clinical application of tool: “Next Gen” Clinic

Importance of depth & breadth in focus – Data collection for future research

??? What is stopping clones with MR or HR genetic patterns from progressing ???

March 4th/5th 2016

What can be done now in less developed countries?

Sok Ching Cheong

Cancer Research Malaysia University of Malaya

Group 5

Oral cancer

A significant burden in less developed countries and Asia

(all regions of Africa, Asia (excluding Japan), Latin America and the Caribbean, Melanesia, Micronesia and Polynesia)

Source: Globocan 2012 (Total cases: 300,373) Africa 6% Latin America & Caribbean 7% Asia 56% Europe 20% Oceania 1% Northern America 10% DISTRIBUTION OF LIP/ORAL CANCERS IN DIFFERENT GEOGRAPHICAL REGIONS More develope d 34% Less develope d 66% DISTRIBUTION OF LIP/ORAL CANCERS IN MORE DEVELOPED AND LESS DEVELOPED GLOBAL REGIONS

Chemoprevention and biomarker development

Opportunities in the less developed world

Randomized clinical trials

High statistical power

Biomarker validation

Large number of specimens

Understanding genetic progression

Robust identification of genetic drivers

Validation of genetic drivers

Genetic heterogeneity represented in models

Large number

• f patients

Genetic alterations in the different stages of oral cancer development

Dionne et al. IJC 2014

Weaknesses and potential limitations

• Endpoint of dysplasia weak: oral submucous fibrosis (OSF)
• Endpoint: status of malignant transformation
• Large number of association studies – causative role of biomarkers

unclear

• The tumor as an organ – understanding progression includes an

understanding of the tumor microenvironment and systemic changes (important for OSF in particular)

• Wide implementation of biomarkers in low resource setting

challenging – expertise, infrastructure, who pays?

Biomarker Discovery and Validation Process

Clinical Examination Biopsy

• Clinical features
• Optics and paints (FV &

TB)

QP* QC

• Universal

Clinical visualization Changes to tissue structure

• LOH • FISH •

IHC

Copy number change; Gene specific changes and pathway alterations; Gene expression

• Conventional / Already

validated

• New technology

platforms

• NGS • Saliva markers
• Blood markers •

????? Animal Models Development of Clinical Prototype for Application

Identify and choose markers

Markers

Validation in a New Cohort

• Histological

risk

Translation

Cell Culture

Develop markers

Dysplasia OSF

New Markers Models

• Cellular biomarkers tested

in several laboratories in different geographical locations - quite universal

• Molecular markers –

universality remains unclear, tested in limited laboratories

Biomarkers rarely tested in less developed countries

Use of QC in the low resource setting

Multi-country collaborative project

(Prof Rosnah Binti Zain)

COE

Fluorescence visualization Cytopathology DNA Cytometry Histopathology

Indonesia

(Dr Rahmi Amtha)

India

(Dr VG Mahima)

Taiwan

(Dr Yang Yi-Hsin Dr Ho Pei San)

Malaysia

(Prof Rosnah Zain)

Objectives:

• 1. To determine the

accuracy of these approaches in predicting dysplasia

• 2. To determine the utility
• f these approaches as

screening/adjunctive tools in community and hospital-based settings

• Lack of biomarkers remains,

particularly those associated with malignant transformation

Clinical Examination Biopsy

• Clinical features
• Optics and paints (FV & TB)

QP* QC

• Universal

Clinical visualization Changes to tissue structure

• LOH • FISH • IHC

Copy number change; Gene specific changes and pathway alterations; Gene expression

• Conventional / Already validated
• New technology

platforms

• NGS • Saliva markers
• Blood markers • ?????

Animal Models Development of Clinical Prototype for Application

Identify and choose markers

Markers

Validation in a New Cohort

• Histological risk

Translation

Cell Culture

Develop markers

Dysplasia OSF

New Markers Models

Sequencing of cancer genomes

International Cancer Genome Consortium, ICGC: http://www.icgc.org/home Zhang et al. Database, 2011 Chin et al. Genes and Dev, 2011

ICGC:

• 50 tumor types
• >20,000 genomes

TCGA:

• 33 tumor types
• >11,000 genomes

India: Oral cavity: Gingivobuccal carcinoma USA (TCGA): Head and Neck: Head and neck squamous cell carcinoma & thyroid papillary carcinomas

A call for PCGA

Large number of patients affords an opportunity to understand the genetic progression of OPMD Gaps and challenges:

• Bio-banking of specimens

for genetic work

• Expertise, research tools

and funding

Early events Therapeutic targets/Biomarkers for monitoring intervention

Campbell et al, Cancer Prev Res 2016

Building resources

Led primarily by Prof Rosnah Binti Zain

Understanding the genetic progression of OSCC in the less developed world

Identification of novel immunogenic targets

Cell line models – heterogeneity representation

(those associated with betel quid chewing)

Fadlullah et al. Submitted

Identification of genetic drivers

Cell line models

Line Designation Risk Habits Mortality Status D17 Smoking Tobacco Senescent D25 Smoking Tobacco Senescent D30 Smoking Tobacco Senescent D41 Smoking Tobacco Senescent D47 Smoking Tobacco Senescent D48 Smoking Tobacco Senescent BICR E1

• Senescent

BICR E2

• Senescent

BICR E4

• Senescent

BICR E5

• Senescent

BICR D6 Smoking Tobacco Senescent BICR D8 Smoking Tobacco Senescent D19 Smoking Tobacco Immortal D20 Smoking Tobacco Immortal D34 Smoking Tobacco Immortal D35 Smoking Tobacco Immortal D38 Smoking Tobacco Immortal DOK Smoking Tobacco Immortal BICR E3

• info unavailable

BICR D4 Smoking Tobacco info unavailable BICR D9 Smoking Tobacco info unavailable HOK 16A

• info unavailable

HOK 16B

• info unavailable

Leuk2

• info unavailable

Increasing OSCC and OPMD cell lines representation

OPMD Normal OSCC

ORL 336 ORL 333 ORL 333 ORL 333 ORL 336 ORL 336 ORL 335 ORL 340 ORL 343

Magnification 40X

Chemoprevention and biomarker development

Addressing the challenges in the less developed world Randomized clinical trials

High statistical power

Biomarker validation

Large number of specimens

Understanding genetic progression

Robust identification of genetic drivers

Validation of genetic drivers

Genetic heterogeneity represented in models

Collaboration Resources

(Funding, infrastructure, expertise)

Need to familiarize with clinical structure Need bio- specimen and data repository

Immediate goals Long-term goals

Need systematic and large collection of bio-specimens for culture Need systematic and large collection of bio- specimens (Inclusion of OSMF) (Inclusion of OSMF) (Inclusion of OSMF)

Resource-matched biomarkers and interventions

The International Conference on Betel Quid and Areca Nut

April 27-28, 2016

Organizers: Center for Global Health, US NCI, in partnership with the MD Anderson Cancer Center, NIDCR, Oral Cancer Research and Coordinating Center (OCRCC) ,University of Malaya

See you in Kuala Lumpur, Malaysia in April 2016!

Biomarkers and natural history of OPMDs: The establishment of national and regional programs

Anil K. Chaturvedi, PhD Division of Cancer Epidemiology and Genetics, NCI

Why do we need biomarkers?

• Only 5% of OPMDs ever progress to cancer

– Current clinico-pathologic characteristics, while associated with risk of progression, do not have predictive accuracy

• Biomarkers

– Biopsy-based – Generalized sampling: Cytology, saliva, oral rinse

• Utility: Accurate identification of progressing

and non-progressing lesions

Statistical rules for biomarkers

• Transition from relative measures of risk (odds

ratios, risk/rate ratios, hazard ratios) to absolute measures of risk

• Sholom Wacholder rule: “Never make a

translational claim without absolute measures

• f risk”

Statistical characteristics of an ideal biomarker: risk stratification

• High positive predictive

value (disease+|test+)

• High negative predictive

value (disease-|test-)

• Predictive values (PPV)

– High specificity Low false-positives – Pre-test prevalence/risk – Need for pre-test risk stratification

Wentzensen and Wacholder; Cancer Discovery 2013 Castle P; JCO 2014

Ideal study designs for biomarkers

• Prospective designs: Nested case-control or

cohort studies

– Retrospective designs for discovery – For OPMDs prior to oral cancer

• Measurement in the etiologically relevant

window/s

• Independent replication in high-risk and

general population settings

Absolute measures in nested studies

• Need to account for a critical fact in the calculation of

progression rates and predictive values – Generally sample a 100% of progressors and a small proportion of non-progressors – Need to weight-up the sampled non-progressors to the universe of non-progressors

• Example: Cohort of 2000 OPMDs—100 progressors

– Nested study: 100 progressors and 200 non- progressors – Progression rates need to account for all 2000 OPMDs, not just the tested 300 samples

Current biomarkers in the literature

• >50 proposed
• ~3-5 replicated

– LOH, EGFR, DNA cytometry, Quantitative pathology

Practical considerations for an ideal biomarker

• Ease of use and cost
• Performance in real-world settings bench-

to-bedside translation

• Broad applicability regional vs. global

relevance

– Geographic regions/etiologies – Anatomic sites

Etiologic and anatomic heterogeneity

• f oral cancers
• Smoking

And alcohol

• Oral tongue
• Tobacco

/areca-nut/ Betel-quid

• Buccal

gingival

Etiologic/anatomic heterogeneity in natural history of oral cancer?

• 14 studies: Taiwan,

Netherlands, Japan, UK, US, Denmark, India…..

• 992 oral dysplasias
• Follow-up: 1.5-9.3 years
• Progression: 0% to 36%
• Pooled: 12.1%
• Rate: 1.73% per year

Mehanna et al., Head Neck, 2009

Etiologic/anatomic heterogeneity in natural history? Log-log age-incidence plots

• 0.5

0.5 1 1.5 2 27 42 47 52 57 62 67 72 77

India Malaysia France US

Log-Incidence rate per 100,000 Log Age

Etiologic/anatomic heterogeneity in next-gen sequencing studies?

US exome (HNSCC)

• TP53
• CDKN2A
• FAT1
• CASP8
• HRAS
• NOTCH1

India exome (Gingivo-buccal)

• US patters, plus
• USPX9
• MLL4
• ARID2
• UNC13C
• TRMP2

TCGA, Nature 2015 India Project Team, Nat Commun 2013

Prospective cohort study in Taiwan

Histologically-defined precursors N=3000 High-grade dysplasia/cancer

Follow-up every 6 months for 5 years

• Rates of progression/regression
• Epidemiologic/molecular predictors
• Screening adjuncts
• Efficacy of treatments

Biospecimens

• Biopsies (annual)
• Cytology
• Saliva
• Oral rinse
• Serum, plasma, buffy coat

Call for action

• Creation of a concerted global

effort/consortium

– Coordinate prospective natural history studies: Standardization of protocols, procedures, and sample collections to allow comparability – Coordinate biomarker discovery, replication, and evaluations of global relevance – Creation of a data warehouse/repository for retrospective analyses and biomarker studies

Oral Cancer Prevention in the Era

• f Precision Medicine
• J. Silvio Gutkind, Ph.D

UC San Diego Associate Director for Basic Science Moores Cancer Center Thanks to Scott Lippman (MCC-UCSD) and NIDCR

Risk Benefit

A molecular revolution:

• Molecular and cellular mechanisms
• Identify patients at risk
• Intersect

(Precision Cancer Prevention) (Mechanistic Biomarkers)

• Challenges and opportunities

Oral Cancer Chemoprevention

13cRA–OPL RCTs

* Hong et al., NEJM 1986 † Lippman et al., NEJM 1993 Lotan et al., NEJM 1995 ‡ Papadimitrakopoulou et al., JCO 2009

Dose, duration N Results

2 mg/kg/d, 3 mo.* 44 67% RR, relapse 55% 1.5 / 0.5 mg/kg/d, 12 mo.† 70 55% I-RR, 92% M-RR 0.5 / 0.25 mg/kg/d, 36 mo.‡ 162 NS

Signaling Networks in OSCC

Epidermal Growth Factor Receptor EGFR: 7p12 ~80-90% overexpression ~20-50% overactivity

EGFR gene amplification and overexpression in OSCC correlates with poor prognosis

P01 CA106451 (Lippman) New paradigm for risk stratification in cancer prevention: Real-time LOH profiling of genetic driver events

Participating Sites: MD Anderson Emory Univ Chicago Memorial Sloan Kettering

Results: Oral Cancer-Free Survival

William WN…Lippman SM. JAMA Oncol 2015

Molecular Markers of Cancer Risk

Subgroup Analysis

William WN…Lippman SM. JAMA Oncol 2015

Oral Cancer-Free Survival

LOH Status EGFR Gene Copy Number Status LOH and EGFR Gene Copy Number Status William WN…Lippman SM. JAMA Oncol 2015

Signaling Networks in OSCC

Epidermal Growth Factor Receptor EGFR: 7p12 ~80-90% overexpression ~20-50% overactivity

Activation of the PI3K/mTOR Pathway in OSCC

AKT Pathway is Activated in OPMD and OSCC

~90% human HNSCC activation

Amornphimoltham et al., Clin Cancer Res, 2004

Activation of the PI3K/mTOR Pathway in OSCC

mTOR Pathway is Activated in OPMD and OSCC

Amornphimoltham et al., Cancer Res, 2005

Activation of the PI3K/mTOR Pathway in OSCC

mTOR Pathway is Activated in OPMD and OSCC mTOR Inhibition with Rapamycin in Multiple OSCC Xenografts

1999 FDA approved Immunosuppressant, often combined with cyclosporine and corticoids (kidney transplant) Coating coronary stents (angioplasty)

Amornphimoltham et al., Cancer Res, 2005

Activation of the PI3K/mTOR Pathway in OSCC

pEGFR p53 pAKTT308 COX-2 EGFR pS6 pAKTS473

• 3

3

n=327

pS6 pAKT473 pAKT308 p53 EGFR pEGFR

Activation of mTOR (independent of EGFR) in > 90% of OSCC: Pathway Analysis

Molinolo et al., Clin Cancer Res, 2007

International HNSCC Tissue Array Initiative HNSCC tissue Microarray (n=327)

~90% human HNSCC activation

The Oral Cancer Oncogenome

TCGA OSCC genomic alterations

Cancer Genome Atlas Network. Nature. 2015 Jan 29; 517 (7536): 572-82.

=Amplification =Homozygous deletion =Mutation

PI3K/mTOR Pathway: Key OSCC Driver

RPS6KB2 (S6K) 7% amplified 14% upregulated p110α p85 PIP3 PDK1 RHEB 4E-BP1 elF-4E S6K S6 PTEN mTOR RAPTOR p110γ p101 AKT P T308 P S473 P GPCRs TSC2 TSC1 P P P P P mTOR RICTOR P mTORC2 mTORC1 EGFR EGFR 10% amplified 23% upregulated HRAS 4% mutated PIK3R1 (p85α) 2% mutated PIK3CA (p110α) 21% mutated 20% amplified 52% upregulated PTEN 2% mutated 26% copy loss reduced protein expression AKT1 1% mutated 20% upregulated Multiple Other Targets MTOR 2% mutated RHEB 10% upregulated PIK3CG (p110γ) 3% mutated 4% amplified phospho-S6 80-90% positive HNSCC cases RAS Upregulated

Iglesias-Bartolome, Gutkind et al. Cancer Disc 2013

mTOR: A Druggable Target in OSCC

Multiple genetically-defined oral cancer animal models and oral chemical carcinogenesis

Cancer Prev Res, 2009; Cancer Res, 2009; Oncogene, 2011; Clin Cancer Res, 2012 Rakefet Czerninski et al., Cancer Prevention Research, 2009 Clinical Cancer Research, 2012

in HPV+ HNSCC

screen 1 2 3 Week

• 14 00

Labs CT, PET Biopsy 4

Rapamycin (daily/oral)

Surgical Resection Week 4 1 21 Labs CT, PET Biopsy Clinical endpoints Molecular endpoints

Phase I/II Neoadjuvant Trial in Newly Diagnosed OSCC Patients (Rapamycin)

OPCB/NIDCR in collaboration with: NCI (Eva Szabo), NIDCD (Carter Van Waes) Medical University of South Carolina (MUSC) (Terry Day)

Patient 1: Before After

Patient 1: Complete resolution of tumor mass on CT scan, reduction to normal values of SUV uptake, no pathological evidence of remaining HNSCC

Results are under evaluation Early evidence for primary tumor activity and acceptable toxicity profile NCT01195922

16 patients received rapamycin treatment (14 MUSC, 2 NIH) 4 patients had >25% tumor shrinkage, 3 met RECIST criteria for response (1 CR, 2 PR, 13 SD).

William, Lippman, Gutkind, Molinolo, et al., unpublished

3.0±0.6 (n=95) 86.5±6.3 (n=175) 100 (n=500) 12.3±1.1 (n=130)

Activation of the PI3K/mTOR pathway in oral premalignant lesions

Human Oral Mucosa – Sequential Increase of mTOR Activation (pS6 IHC) During Progression (large sample collection)

Targeting mTOR with Meformin for Oral Cancer Prevention

Vitale-Cross et al., Cancer Prevention Research, 2012

Metformin Inhibits mTOR and Growth in Experimental (HPV- and HPV+) OSCC Lesions

Madera et al., Cancer Prevention Research, 2015 SCC47 (HPV+)

Madera et al., Cancer Prevention Research, 2015 SCC47 (HPV+)

metformin ↓gluconeogenesis ↓circulating insulin Oct1 Oct3 Metformin can target cancer cells directly reverted by OCT3 knock down!)

Metformin Inhibits mTOR and Growth in Experimental (HPV- and HPV+) OSCC Lesions: Mechanisms

Madera et al., Cancer Prevention Research, 2015 SCC47 (HPV+)

Mechanistic Biomarkers!

Metformin Inhibits mTOR and Growth in Experimental (HPV- and HPV+) OSCC Lesions: Mechanisms

Effect of Metformin on HN Cancer in Diabetes

Yen YC, et al. Head & Neck 2014 Tseng C-H, Oncotarget 2015 (Taiwan, ~300,000 patients)

M4OC-Prevent: NCI Phase IIa Metformin Trial for Oral Cancer Prevention

NCI N01 National (M4OC Trial) PI (UCSD): Scott Lippman Project Co-Lead: J. Silvio Gutkind

Participating Sites: UCSD (Cohen, Coffey, Brumund) Univ Minnesota (Frank Ondrey) British Columbia Cancer Agency (Miriam Rosin) NCI Contract # HHSN2612012000311 Eva Szabo, NCI

Oral leukoplakia or erythroplakia ≥ 18 y.o. (M/F) Pre-study / Baseline Evaluation Intervention

Daily metformin ER for 12-14 weeks

1st week – 500 mg 2nd week – 1,000 mg 3rd week – 2,000 mg for remaining

Interim Visit

(Week 6)

Post-Intervention Evaluation

(Week 12-14)

Follow-up

(2-4 weeks post-intervention) 2° Endpoints: Histologic Response (lesion) Tissue-based Biomarkers cell proliferation (Ki67)

• mTOR downstream targets

(pS6, pAKTS473, p4EBP, pACC)

• genomic analysis in lesion and

blood DNA Serum / Saliva based Biomarkers 1° Endpoint: Clinical Response (n= 26)

• Molecular and cellular mechanisms
• Premalignant Cancer Genome Atlas
• Identify patients at risk
• Early detection (molecular diagnostics?)
• Reduce risk
• Intersect
• Precision Cancer Prevention
• Evaluation of Metformin (others?)
• Prevention of recurrence? HPV+?
• Our global challenge:
• Match intervention with resources
• Match intervention to disease process
• Well designed (worldwide) clinical trials
• Patient accrual, we need you!
• Training in translational research

Chemoprevention: Premalignant Cancer Genome and Precision Cancer Prevention

The Fight Against ORAL CANCER is a Team Effort! A molecular revolution:

OPLs, EGFR Gene Amplification, and Lymphocyte Infiltration

Potential for Immunoprevention

EGFR Gene Amplification Lymphocyte Infiltration

Dysplastic Oral Premalignant Lesion