Sesion de Controversias nº 5 Conclusiones Carlos Camps Tratamiento de la Enfermedad Avanzada en NSCLC Perfil Genómico vs Perfil Clínico Sesion de Controversias nº 5 Conclusiones Carlos Camps

Individualized Medicine “In the very near future, primary care physicians will routinely perform genetic tests before writing a prescription because they will want to identify the poor responders” Dr. Francis Collins, HGRI Director AAFP Annual Meeting, 1998

Everybody is Different The Right Drug To The Right Patient For The Right Disease At The Right Time And The Right Physician The basis of pharmacogenomics and of molecular oncology is the realization that each body is different. The behavior of a specific tumor in a specific individual is unique; the response to specific drugs or even the need to be treated is unique. The challenge is to be able to administer The Right Drug   To The Right Patient For The Right Disease At The Right Time

Targeting the correct patients We already define patients on the basis of histological cell type and tumour location, age, gender, PS, etc. How do we identify the patients who may benefit most from molecular targeted therapy? Individualized manegement patient Who needs therapy and how intense? What agents are the most effective? When to stop treatment?

Who Can Be Spared Therapy? Which Therapy Will Work Best? Therapy Decision-Making for Lung Cancer Who Can Be Spared Therapy? Which Therapy Will Work Best? The therapy decision making for early breast cancer involves two major decision points: 1.     Who does not need systemic treatment? 2.     Which treatment is the most effective and least toxic for those who need treatment? Prognostic factors needed Predictive factors needed

Current scientific interest in biomarkers Definition of a biomarker ‘A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to therapeutic intervention’1 1Biomarker Definitions Working Group. Clin Pharm Ther 2001

Prognostic versus predictive markers Indicate likelihood of outcome (tumour recurrence, patient survival, adverse event), regardless of which treatment is used Predictive Indicate likelihood of an outcome to a specific therapy Biomarkers could be both predictive and prognostic value Source: www.cancerdiagnosis.nci.nih.gov

Individualized management patients Prognostic Factors Clinical and molecular ( indicators of tumor virulence) Predictive factors Clinical and molecular ( indicators of efficacy of specific therapies)

What defines a useful biomarker? 1. Reproducible: measured reproducibly by means of a reliable and simple assay 2. Applicable: information about the disease that is clinically relevant 3. Validated: confirmation of the above two from independent data set Source: www.cancerdiagnosis.nci.nih.gov

Clinical selection parameters Biologic markers Treatment Factors in Advanced NSCLC Clinical selection parameters Biologic markers  ERCC-1  RRM-1  Beta-tubulin  EGFR  k Ras mutation BRCA1  Sex Age Comorbidities Stage Histology PS Smoking story

Useful prognostic and predictive clinical characteristics Age No Gender no ¿yes? No /yes? (TKI’s) Histology PS Yes Yes(Chemo) Stage Smoking history Yes (Tarceva) Ethnicity

State of Art Therapeutic modality determined by stage Decision not to treat based on PS / Comorbidities Minimal treatment selection based on predictive factors ( clinical) First accurate assessment of efficacy performed not earlier than 6 weeks after starting therapy (CT scan)

Useful of clinical characteristics to select therapy Not useful to select the most effective chemoregimens Useful to exclude agents to avoid certain toxicities Potentially useful to select erlotinib for front line therapy

Useful Clinical characteristics predictive of Toxicity Age No Gender No Histology Yes ( Avastin) PS Yes ( Chemo) Stage No Smoking story No/Yes Ethnicity No/Yes

Molecular prognostic and predictive markers Unifactorial Single molecular determinants Multifactorial Genomic signatures Proteomic profiles

Molecular studies in Lung Cancer . Low RRM1 Longer survival No differences in response Rosell et al Oncogene Better than ERCC1 CC: 0.410 P < 0.001 CCR Low ERCC1 NSCLC Better response ASCO Low ERCC1 NSCLC Longer survival No differences in response Lord et al CCR 2001 2002 2003 2004 2005 2007 Potti Chen Zheng Olaussen Rosell Soria Simon Low ERCC1 Colorectal cancer Longer survival No differences in response Shirota JCO BRCA1 Differential modulator Quinn et al Cancer Res Low BRCA1 Better than RRM1 CC:0.815 P < 0.001 Taron et al Hum Mol Genet

Progression-Free Survival Ras Gene Mutations in TRIBUTE Progression-Free Survival Overall Survival 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 5 10 15 20 Months 5 10 15 20 Months Chemotherapy, wild type (n=103) Erlotinib + chemotherapy, wild type (n=104) Chemotherapy, mutant (n=30) Erlotinib + chemotherapy, mutant (n=25) Eberhard D, et al. J Clin Oncol 2005;25:5900–9.

Inverse Relationship between RRM-1 Expression & Outcomes after Cis/Gem Phase II trial in patients with locally advanced NSCLC treated with cisplatin/gemcitabine (n=35) 0,0 0,2 0,4 0,6 0,8 1,0 Influence of ß-Tubulin Levels on Survival In Advanced NSCLC P=0.039 100 80 60 40 20 Percent Disease Free 200 400 600 800 1000 1200 TubIII <50% n=22 TubIII >50% n=25 P = 0.0039 Paclitaxel Cumulative survival < 7.5 > 7.5 10 20 30 40 50 60 70 Time (months) Bepler et al. JCO 2006;24:4731-7. Seve et al. Mol Cancer Ther 2005;4:2001-7.

docetaxel/gemcitabine ERCC1 and BRCA1 Profiling: Pharmacogenomics Control arm docetaxel/cisplatin Experimental arm ERCC1 levels GENOTYPE B1 (low ERCC1 mRNA) GENOTYPE B2 docetaxel/gemcitabine (high ERCC1 mRNA) 1:2 Cobo et al. J Clin Oncol 2007;25:2747-54. BRCA1 in NSCLC GILT II Induces resistance to cisplatin-mediated apoptosis Increased levels associated with poorer OS in completely resected, chemotherapy-naïve NSCLC  BRCA1 > 5 vs ≤ 5: HR 1.98, p=0.02

Biomarkers for chemotherapy in NSCLC Biomarker for cytotoxic therapy RRM1 (low-level expression is associated with resistance to gemcitabine) ERCC1 (high-level expression is associated with resistance to cisplatin) others: BRCA1, XRCC,tubulin III, etc. Biomarker for EGFR inhibition protein expression by IHC gene copy expression by FISH EGFR mutation by sequencing Ueno H, et al. Br J Cancer 2007

Molecular subclasses of NSCLC

Tumor-Cell Behavior Is Determined By The Activity Of Many Genes The activity of one or a few genes cannot predict tumor-cell behavior in a reliable way. We need tools to measure the activity of many genes in a single experiment Thus, tumor-cell behavior is determined by the activity of many genes. The activity of one or a few genes cannot predict tumor-cell behavior in a reliable way. We need tools to measure the activity of many genes in a single experiment.  

Few genes, Little Information We can look at the molecular profile of a tumor as pieces of a big puzzle. If we look at only one piece, let’s say the presence of an estrogen receptor, these two tumors look identical. But if we look at a multitude of pieces, that is, the expression of many genes, we see that these two tumors are nothing alike.

Assessment of Multigenic basis for individualized patient management Multiple genes involved in hots cases Protein function more important than gene expression Methods must be accurate and reproducible

Lung Cancer – A Paradigm for Tailored Medicine Current practice Histological profile of the tumor Treatment decisions according to population prognostic factors Drug choice according to population predictive factors of efficacy and safety Emerging practice Molecular profile of the tumor Treatment decisions according to individual prognostic factors Drug choice according to individual predictive factors of efficacy and safety Since my main clinical and research interests are in the field of breast cancer, I will try and illustrate the application of molecular oncology and phrmacogenomics in the treatment decision process of this disease  Breast cancer can serve as a paradigm for tailored medicine. Our current practice involves the use of a histological profile of the tumor, treatment decisions, and drug choices that are based on population prognostic and predictive factors. The emerging practice involves the molecular profile of the tumor, with treatment and drug choice based on individual prognostic and predictive factors that are derived from the unique molecular profile of the tumor and the unique genetic make-up of the patient.

Information Based Medicine will require unprecedented access to diverse, integrated information Challenges Volume and complexity of data Integrating massive volumes of disparate data Need for sophisticated analytics Growing collaboration across ecosystem

an Important Step Toward Personalized Medicine – But Barriers remain Lack of technology standards (genomics, informatics, emerging technologies) Lack of common technology platforms to enable the sharing of information and transfer to clinical application Inability to manage and interpret large quantities of data Lack of a coordinated, integrated system Lack of common vocabularies Need for new funding mechanisms to facilitate data sharing and collaboration Need for new clinical trials design models Existence of cultural barriers

The Problem of Fresh Tissue The major problem with this assay is the need for fresh or frozen tumor tissue. Logistically, this is quite difficult.  

Conclusion and future prospects Clinical characteristics have proved so far its use as prognostic factor and treatment selection in the case of erlotinib Age and PS/Comorbidities should be considered separately when evaluating a patient for chemotherapy Uni / Multigenetic and Proteomic assessment of prognostic and predictive factors has shown promising results in the case of erlotinib and several cytotoxic agents Molecular markers undoubtedly will play a larger role in the selection of patients for chemotherapy as well as molecularly targeted agents in the futurerelated Clinical and Biological factors are RELATED The treatment selection tools developed need to be validated in prospective clinical studies Clinical Data Molecular Data

Convergence: Molecular Biology, Advanced Technologies, Bioinformatics/Clinic knowledges Unprecedented Potential for Exponential Progress Toward Personalized Medicine (Molecular Oncology) Image courtesy of Nature, Feb. 15, 2001