Genetic and Environmental Determinants in Lung Cancer Progression and Survivorship

Genetic and Environmental Genetic and Environmental Determinants in in Lung Cancer Progression and Lung Cancer Progression and Survivorship

Ping Yang, M.D., Ph.D.Professor and Consultant

Department of Health Sciences ResearchDepartment of Medicine

Department of Medical GeneticsMayo Comprehensive Cancer Center

Mayo Clinic College of Medicine

OutlineOutline• Overview of lung cancer prognosis

• Known determinants of lung cancer survival: environmentenvironment and genes genes

• Identify and validate new predictors for lung cancer survival: ongoing effortsongoing efforts

• Current research using pharmacogenetic-epidemiologic tools: towards individualized medicineindividualized medicine

• Characteristics of long-term survivors:a multi-dimensional multi-dimensional approach

Acknowledgement: Survivorship Research Team

Medical Oncology Thoracic Surgery Chest PathologyAlex A. Adjei Mark S. Allen Marie-Christine AubryJames R. Jett Stephen D. Cassivi Aminah Jatoi Claude Deschamps BiostatisticsRandolph S. Marks Francis C. Nichols Sumithra J. Mandrekar Julian R. Molina Peter C. Pairolero V. Shane Pankratz

Victor F. Trastek Jeff A. Sloan (QoL expert)

Pulmonary MedicineEric S. Edell Molecular Biology PsychologyDavid E. Midthun Julie M. Cunningham Matthew M. Clark

Wilma L. LingleRadiation Oncology Wanguo Liu PharmocogenomicsYolanda I. Garces Stephen N. Thibodeau Richard M. Weinshilboum

Bioinformatics Nicotine Dependence ChaplainZhifu Sun Jon O. Ebbert Mary E. JohnsonGeorge Vasmatzis

Oncology Nursing EpidemiologyLinda Sarna (UCLA)Linda Sarna (UCLA) Ping Yang

Overview:Overview: An Old Story with Continued Challenge An Old Story with Continued ChallengeCarcinoma of the Lung and Bronchus

• High incidence rate:

12-13% cancer diagnosis in U.S.;

>60% diagnosed at a not-curable stage.

• High mortality rate:

5-year survival rate is ~15%.

• Kills more people than any other cancer:

~30% of all cancer deaths in U.S.

Known Predictors of Early-stage Lung Cancer Survival

Tumor-related factors:

Essential Lymph node involvement, hypercalcemia

Important Tumor size, pleural involvement, multi-focal, cell type, grade, vessel invasion

Promising Over 8 physio-pathological pathways and more than 30 cellular & molecular markers

Environment-related factors:

Essential Treatment modalities

Promising Smoking history, diet / supplement

Host-related factors:

Essential Weight Loss

Important Age, gender

Promising Marital status, race/ethnicity, mood, quality of life, metabolizing enzyme genes

Yang et al., 2004, Modified from Brundage et al. 2002

Background: A Lung Cancer Research Infrastructure

Tumor: e.g., histologic cell type and differentiation grade, biologic & mechanistic genes

Host Factors:e.g., genetic

predisposition and demo-

graphic factors

Health Related Behaviors: e.g., diet, smoking, & exercise

Physical & Psychosocial Status: e.g., symptoms, comorbidity, & supports

Staging, PS, & Treatment:

TNM, surgery, chemotherapy, & radiotherapy

Quantity and

Quality of Life

CHEST, 2006

A Prospectively Followed Patient Cohort: Newly Diagnosed Lung Cancer, 1997-Ongoing

Identification, Baseline data, Blood/Tissue~1000 patients

each year

6 months follow-up

1 year follow-up

Annually after

Progression and Death

Svobodnik A, et al, 2004;

Yang P, et al. 2005.

Identifying and Validating New Prognostic Factors1 of 4 groups







Promising Smoking history, diet / supplement






Example: treatment of recurrent lung cancerrecurrent lung cancer and post-recurrence survivalpost-recurrence survival

(continued)

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RS4

RS: 4-6RS: 6-8

RS>8

ATS, 2006

Treatment Modality by Risk Score

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SurgerySurgery + Chemo/RadiotherapyChemotherapyChemo + RadiotherapyRadiotherapy

ATS, 2006








Promising Smoking history, diet/supplement






Survival by Years Since Quit Smoking, WomenAdjusted for age, packs per day, years smoked,

histology, grade, stage, and treatment

0102030405060708090

100

0 1 2 3 4 5

0-10 yrs11-20 yrs21-30 yrs> 30 yrs

Lung Cancer, 2005

Dietary Supplement of Vitamins and Minerals

• In general population, ~40% take vitamin/ mineral supplements regularly.

• Approximately 80% of cancer patients do so.

• Both clinical and laboratory data have shown that certain micronutrients effect the growth of malignant cells:

i.e., vitamins and minerals appear to bemodulators of tumor growth.

• Are these supplements helping or hurting lung cancer patients?

0102030405060708090

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Years After Diagnosis

% S

UR

VIV

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Vitamin/Mineral Users

Non-Users

P < 0.01

Dietary Supplement of Vitamins and Minerals: NSCLC

Multivariable Model-Based Survival Curves

Lung Cancer, 2005












Promising Marital status, race/ethnicity, mood, quality of life, drug metabolizing enzyme genes


Chemotherapy & Treatment Outcome

• For stage III (and IV) NSCLC and limited stage SCLC, combined modality of concurrent chemo- and radiotherapy is considered as the standard of care.

• The goal of such treatment is to improve loco-regional tumor control and minimize metastases without increasing morbidity.

• Overall, there is a significant benefit in survival, but only in a subset of 25-30% among all treated. Who and why?

Chemotherapy Agents (in %) Used at Mayo Clinic During the Past Eight Years (1997-2004)

All Chemotherapy First-Line Subsequent Chemotherapy Chemotherapy

Drug Groups Stage III/IV Stage III&IV Stage III&IV NSCLC SCLC NSCLC SCLC NSCLC SCLC

Total Count (denominator) 1093 247 1093 247 463 107

Platinum-containing Agents (P) 90.1 94.7 85.7 91.5 51.8 61.7 Taxane-containing agents (T) 76.2 30.8 66.1 10.5 45.8 52.3 Gemcitabine (G) 32.0 4.9 13.0 0 47.5 11.2 EGFR inhibitor (E) 8.0 0 2.7 0 12.5 0 Either P or T 91.7 97.2 88.2 96.4 64.4 84.1 Both P and T 74.7 28.3 63.7 5.7 33.3 29.9 Either P or G 94.0 94.7 91.1 91.5 76.9 68.2 Both P and G 28.2 4.9 7.6 0 22.5 4.7 Either P or E 92.2 94.7 88.2 91.5 59.6 61.7 Both P and E 5.9 0 0.3 0 4.8 0 Either T or G 85.3 31.2 78.0 10.5 77.8 56.1 Both T and G 23.0 4.5 1.1 0 15.6 7.5 Either T or E 79.2 30.8 68.6 10.5 54.0 52.3 Both T and E 4.9 0 0.3 0 4.3 0 Either G or E 35.9 4.9 15.6 0 54.0 11.2 Both G and I 4.1 0 0.1 0 6.0 0 None of the above 3.1 2.8 4.6 3.6 9.7 14.0

A BRIEF BACKGROUND

• Platinum-based drugs are commonly used in lung cancer chemotherapy.

• The glutathione metabolic pathway is directly involved in the inactivation of platinum compounds.

The Glutathione Pathway and Its Role in Drug Detoxification – Yang et al., 2006; JCO

Glutathione

GCLC Gene, Platinum-based Drugs, & Lung Cancer Survival

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Est

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Plat GCLC-00 Plat GCLC-77 Stage III-IV GCLC-00 Stage III-IV GCLC-77

Yang et al., 2005

Clinical Implications

• Genotypes of glutathione-related enzymes may be used as host factors in predicting patients’ survival after treatment with platinum-based drugs.

• The distribution of GCLC repeats marker:

GCLC-77: 19% - not use platinum drugs?

GCLC-7_: 50% - balancing benefit vs. harm?

GCLC-other: 31% - suitable for platinum-drugs?

Yang et al., 2005

Many ShortcomingsMany Shortcomings

Much needed to be done…

Other pathways

Paradoxical “toxicities”

Accurate follow-up data

…

Identifying and Validating New Prognostic Factors- 4 -













JTCVS., 2006

Biological Markers: Promises and Challenges

• Treatment response is generally poor.

• Limited markers to predict prognosis and apply to individualized management.

• Gene expression profiling, “microarray”, has been widely used to search for answers at molecular level for differed lung cancer survival

• (Note: DNA microarray measures tens of thousands expressed genes via mRNA simultaneously in tissue or cells)

Emerging evidence shows that the accuracy of expression-based outcome prediction varies greatly among studies.

Converging questions have been raised from researchers and clinicians:

• Why does gene-based prediction vary? • Can DNA expression profiles provide more

accurate prediction than conventional predictors? • Are gene panels or molecular signatures

independent predictors or merely surrogates of conventional factors?

Three Pioneer Studies: Larger Samples in “Top-Tier” Journals

• Stanford group (PNAS 2001;98(24):13784-9):56 cases of lung cancer

- 41 AD, 16 SCC, 5 LCLC, 5 SCLC

• Harvard group (PNAS 2001;98(24):13790-5):186 cases of lung cancer

- 127 AD, 21 SCC, 20 carcinoid, 6 SCLC

• Michigan group (Nat Med 2002;8:816-24): - 86 cases of lung adenocarcinoma

Survival Prediction on Harvard Data From 50 Genes Selected From Michigan Data

Survival Curves Predicted by Different Gene Markers on an Independent Sample

Top 50 genes selected from univariate analysis and cross validation

Top 50 genes from multivariate adjustment (age, gender, stage, cell type), original data

Top 50 genes from multivariate adjustment (age, gender, stage, cell type), Dchip data

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Sun &Yang, 2006;15:2063-8

Comparison of survival predictions by a 50-gene signature and combination of clinical and pathologic variables

Top 50 genes selected from univariate analysis and cross validation

Top 50 genes from multivariate adjustment (age, gender, stage, cell type), original data

Top 50 genes from multivariate adjustment (age, gender, stage, cell type), Dchip data

Common Genes

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OutlineOutline• Overview of lung cancer prognosis

• Known determinants of lung cancer survival: genes and environmentgenes and environment

• Identify and validate new predictors for lung cancer survival: ongoing effortsongoing efforts

• Current research using pharmacogenetic-epidemiologic tools: towards individualized medicineindividualized medicine

• Characteristics of long-term survivors:a multi-dimensional multi-dimensional approach

A Brief Background

• Individuals who are alive over 5 years after a lung cancer diagnosis are referred to as long-term lung cancer (LTLC) survivors.

• In the U.S., approximately 26,000 individuals become LTLC survivors annually.

• A paucity of information regarding the quality of life (QoL) among LTLC survivors.

Longitudinal Evaluation of Quality of Life in Long-Term Lung Cancer Survivors

A Short story

Overall QoL change between two time periods: under 3 years and over 5 years post diagnosis

Multi-dimension Follow-up MeasuresMulti-dimension Follow-up Measures

Besides medical records, multiple tools:

• SF-8 Health Survey• ECOG* Performance Status Score

(*Eastern Cooperative Oncology Group)

• Lung Cancer Symptom Scale (LCSS)• Linear Analogue Self-assessment (LASA)

(modified for lung cancer patients)

• Baecke Questionnaire for Habitual Activities • FACT-SP Spiritual Well Being Assessment• Other tools (diet, sleep, cognitive function, etc)

QoL Assessment

• Overall QoL was assessed using LCSS-9: - scores 0 (worst) to 100 points (best) - as continuous variable: distance in cm on a

VAS a raw score of the total 100 points - as a binary variablea poor QoL defined as <50 points (Sloan, 2004)

• Declining QoL was defined as:a 10-point or more decrease between the two time periods

A Prospective Lung Cancer Cohort:Long-term Survivors, 2002-2004

Patients diagnosed 1997-1999

5-yearfollow-up

Annually after

N = 448, 15.8%N = 2837

Declining Overall QoL Over Time: Higher Proportion with Poor

Overall QoL

48%

34%

18%No Change

QoL Declined

QoL Improved

Yang et al., 2005

Factors Influencing Overall QoL in Long-term Lung Cancer Survivors

Poor QoL at Characteristics <3 year >5

year

Age > 75 years Education < 16 years TNM staging- Stage I

Histology- Poorly/un-differentiated Lung cancer treatment

Chemotherapy – Yes Radiation therapy – Yes

Comorbid conditions COPD Heart failure

Recurrent/subsequent lung cancer

Implications• Our preliminary results show: among the LTLC survivors, the mean overall QoL declined significantly between the two time periods.

This is in a sharp contrast to long-term survivors of other cancers, e.g., breast cancer, whose overall QoL are compatibleto their age-matched controls.

• We found substantial differences in factors contributing to their poor QoL at each time period.

Future DirectionsFuture Directions

• Long-term lung cancer survivors may need additional help to improve their QoL.

• Further research efforts are needed. The next step is to identify factors that are associated with a declined vs. an improved QoL over time: environmental, genetic, biological, behavioral, psychosocial.

• Ultimately, we aim to define modifiable factors and improve QoL of “at risk” survivors.

Acknowledgement: Survivorship Research Team

Alex A. Adjei Mark S. Allen Marie-Christine AubryWilliam R. Bamlet Aaron O. Bungum Stephen D. CassiviJean M. Chovan Matthew M. Clark Claude DeschampsJulie M Cunningham Jon O. Ebbert Eric S. EdellChiaki Endo Susan M. Ernst Erin E. FinkeYolanda I. Garces Debra L. Hare Shauna L. HillmanAminah Jatoi James R. Jett Ruoxiang JiangMary E. Johnson Thomas D. Knowlton Farhad KosariWilma L. Lingle Wanguo Liu Sumithra J. MandrekarRandolph S. Marks Sheila R. McNallan Rebecca L. MeyerDavid E. Midthun Julian R. Molina Francis C. NicholsPaul J. Novotny Janice R. Offord Scott H. OkunoPeter C. Pairolero V Shane Pankratz Jeff A. SloanShawn M. Stoddard Hiroshi Sugimura Zhifu SunWilliam R. Taylor Stephen N. Thibodeau Victor F. TrastekJason A. Wampfler Richard M. WeihshilboumDiane K. Wilke Brent A. Williams Joel B. Worra George VasmatzisAnthony L. Visbal Xinghua Zhao

ALL STUDY PARTICIPANTS AND SUPPORTERS THANK YOU!

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Genetic and Environmental Determinants in Lung Cancer Progression and Survivorship