Harmesh Naik, MD. Hope Cancer Clinic PERSONALIZED CANCER TREATMENT USING LATEST IN MOLECULAR BIOLOGY

Harmesh Naik, MD. Hope Cancer Clinic PERSONALIZED CANCER TREATMENT USING LATEST IN MOLECULAR BIOLOGY

A NEW GENE A DAY.WHILE YOU ARE ENJOYING MORNING COFFEE From cancer.gov

GOALS FOR THE CME TODAY A brief review of new developments in molecular biology and its implications for oncological diseases Discuss major concepts in personalized cancer care Discussion of selected cases to demonstrate personalized cancer care Answer questions

CANCER MANAGEMENT: BASIC PRINCIPLES Determine level of risk posed by cancer Determine appropriate treatment based on risk

TRADITIONAL TREATMENT MODEL

CASE 1 A 70 year old Asian Indian Female presented with dysphagia and weight loss Squamous cell carcinoma of esophagus, T4NXMX (at least stage III). S/P FU and Cisplatin x 2 cycles with local radiation Alive and cancer free after 7 years Part of small % of long time survivors Could we have predicted this at time of diagnosis Can we predict the same response for next patient with the same therapy

CASE 2 A young man with Stage 4 esophageal cancer with extensive liver mets, up to 9 liters of ascites and liver failure No response to combination chemotherapy (wasted valuable time in ineffective therapy) Could we have predicted this at time of diagnosis

LIMITATIONS OF CURRENT MODEL Treatment Response No response Only small number of patients may benefit from therapy Many patients do not benefit and get side effects No easy way to predict who is going to respond and what treatment is likely to work Why not?

GENOMIC HETEROGENEITY

CRYSTAL BALL FOR FUTURE PREDICTION

CASE 2 Next step Hospice? Another chemotherapy? Analyzed tumor for HER 2 analysis HER 2 positive Added Trastuzumab (anti-her 2 therapy) with chemotherapy Ascites gone, liver improved, felt better, surviving months later, fully functional

BEFORE AFTER CASE 2

NEW PARADIGM: BIOLOGY BASED THERAPY More dependant on biological and clinical behavior rather than physical and histological characteristics Biological and clinical behavior is controlled by genetic blueprint of cells So we must understand pathways driving cell growth in order to block it

TARGETED THERAPY: NEED TO KNOW THE TARGET

PERSONALIZED THERAPY Molecular tumor profiling to identify individual targets Effect of hitting the target Must be clinically validated Intervention Successful outcome

PROMISE OF PERSONALIZED MEDICINE Better prognosis assessment (Prediction of risk) Better selection of best treatment Best outcome

INDIVIDUAL GENETIC SIGNATURE Identifies high risk patients who would benefit from chemotherapy and who would not Helps selecting a group of patient who could avoid morbidity and cost of adjuvant chemotherapy

PERSONALIZED MEDICINE IS NOT NEW Tamoxifen was an example of early targeted therapy based on Estrogen receptor (ER) status

BREAST CANCER: PROTOTYPE DISEASE FOR PERSONALIZED CARE HER2 positive Chemotherapy + Anti HER 2 based therapy (e.g. Trastuzumab) ER/PR positive ER/PR neg HER 2 neg Hormonal therapy Chemotherapy

GENE EXPRESSION ANALYSIS Breast cancer Oncotype DX (21 gene assay) Mamma print (70 gene signature) Rotterdam Signature (76 gene assay) Many more are in development

21-GENE ASSAY 16 Cancer and 5 Reference Genes From 3 Studies PROLIFERATION Ki-67 STK15 Survivin Cyclin B1 MYBL2 INVASION Stromelysin 3 Cathepsin L2 ESTROGEN ER PR Bcl2 SCUBE2 GSTM1 CD68 BAG1 HER2 GRB7 HER2 REFERENCE Beta-actin GAPDH RPLPO GUS TFRC Paik et al. N Engl J Med. 2004;351: 2817-2826

21 GENE ASSAY RECURRENCE SCORE Calculation of the Recurrence Score Result RS = Coefficient x Expression Level + 0.47 x HER2 Group Score - 0.34 x ER Group Score + 1.04 x Proliferation Group Score + 0.10 x Invasion Group Score + 0.05 x CD68-0.08 x GSTM1 Category RS (0-100) - 0.07 x BAG1 Low risk RS <18 Int risk RS 18 and <31 High risk RS 31 Paik et al. N Engl J Med. 2004;351: 2817-2826

21GENE ASSAY IN NODE NEGATIVE PATIENTS: CLINICAL VALIDATION Soonmyung Paik, M.D et al: NEJM: 2004: Volume 351:2817-2826

CASE 3 55 year old Asian Indian female An infiltrating ductal carcinoma of left breast 1.5 cm, high grade ER/PR negative HER-2 positive Stage T1N1M0, stage IIA Surgery, chemotherapy, radiation and anti- HER2 therapy Alive and disease free at 6 years

PERSONALIZED MEDICINE: WHAT IS NEXT

DNA MICRO ARRAY TECHNOLOGY IN ONCOLOGY http://www.riken.go.jp/engn/r-world/info/release/news/2004/oct/image/frol_02l.jpg

WHAT IS A DNA MICRO ARRAY A DNA microarray is a collection of microscopic DNA spots attached to a solid surface A DNA microarray is also commonly known as gene chip, DNA chip, or biochip DNA microarrays are created by robotic machines that arrange minuscule amounts of hundreds or thousands of gene sequences on a single microscope slide DNA microarrays are used to simultaneously measure the expression of large numbers of genes.

DNA CHIPS The thumbnail-sized devices are microscopic grids that have pieces of DNA representing every gene in the human genome stuck on them. Scientists use them to measure the activity of our 20,000-plus genes at the same time.

DNA MICRO ARRAY HEAT MAP Gene expression values from microarray experiments are represented as heat maps to visualize the result of data analysis. Accessed in 2010-http://en.wikipedia.org/wiki

MORE ON GENE EXPRESSION PATTERNS IN BREAST CANCER Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications Sørlie T et al. Proc National Acad Sciences 2001;98:10869-10874 doi: 10.1073/pnas.191367098 PNAS September 11, 2001 vol. 98 no. 19 10869-10874

Sørlie T et al. PNAS 2001;98:10869-10874 Gene expression patterns of 85 experimental samples representing 78 carcinomas, three benign tumors, and four normal tissues, analyzed by hierarchical clustering using the 476 cdna intrinsic clone set. Tumor subtypes Full cluster ERB B2 cluster Basal cell cluster Normal like cluster Luminal cluster

Sørlie T et al. PNAS 2001;98:10869-10874 Overall and relapse-free survival analysis of the 49 breast cancer patients, uniformly treated in a prospective study, based on different gene expression classification. Luminal A cluster Basal cell cluster

ANALYSIS CALGB 9344 : PROGNOSIS Tumor subtypes identified by tissue microarray predicted prognosis Luminal A subtype had favorable outcome Basal or HER-2 enriched subtypes had poorer outcomes Clinical breast cancer: Vol 9 (4) Nov 2009: 213-214.

BREAST CANCER SUBTYPES AND BENEFIT FROM PACLITAXEL CALGB 9344 Subtyp e No. pts Med RFS yrs RFS (AC- T vs AC) P value Luminal A 790 NR 1.04 0.73 Luminal B 340 11.13 0.69 0.018 HER2 enriched 221 9.08 0.57 0.0032 Core basal 444 8.93 0.75 0.0033 ER -ve, HER-2 -ve 557 0.8 0.07 Clinical breast cancer: Vol 9 (4) Nov 2009: 213-214.

ANALYSIS CALGB 9344 : RESPONSE PREDICTION Tumor subtypes identified by tissue microarray predicted benefit from Paclitaxel Core Basal subtype, HER-2 enriched subtype and Luminal B : improvement in RFS with Paclitaxel Luminal A: no benefit from Paclitaxel Clinical breast cancer: Vol 9 (4) Nov 2009: 213-214.

98 patients SUPERVISED CLASSIFICATION ON ER AND BRCA1 SIGNATURES 550 ER reporter genes ER +ve signature ER -ve signature 38 ER neg 100 BRCA 1 rep genes BRCA1 signature BRCA1 negative Gene expression profiling predicts clinical outcome of breast cancer : Laura J. van 't Veer, et al: Nature 415, 530-536(31 January 2002)

78 patients 70 genes Disease free Distant mets Disease free Distant mets Gene expression profiling predicts clinical outcome of breast cancer : Laura J. van 't Veer, et al: Nature 415, 530-536(31 January 2002)

PERSONALIZED MEDICINE: WHAT IS NEXT

NEW MOLECULAR MARKERS: LUNG CANCER EGFR ALK Therapy selection KRAS RRM1 ERCC1 39

AND THEN SOME MORE: TARGETABLE PATHWAYS IN SQUAMOUS CELL LUNG CANCER PS Hammerman et al. Nature 489, 519 525 (27 September 2012) The Cancer Genome Atlas (TCGA) Research Network study 40

Genomic Landscape of Non-Small Cell Lung Cancer in Smokers and Never-Smokers : Ramaswamy Govindan et al: Cell - 14 September 2012 (Vol. 150, Issue 6, pp. 1121-1134) MORE INSIGHT IN TO BIOLOGY:? THERAPY IMPLICATIONS 41

TRADITIONAL: HISTOLOGY BASED THERAPY ONE REGIMEN FITS ALL NSCLC Squamous Non squamous 42

EGFR IN LUNG CANCER: EGFR mutations are seen more frequently in non smokers, adenocarcinoma, females, Asian origin Predicts response to EGFR inhibitors (Exon 19 deletions and the L858R point mutation) K-RAS mutation predicts resistance to EGFR inhibitors

CASE 4 56 year old Asian Indian male non smoker Non small cell lung cancer : Adenocarcinoma - biopsy proven left lung primary. Stage at least T4N3M1. Weight loss, respiratory symptoms, hospitalized What is the prognosis What is the right treatment

CASE 4 EGFR mutation detected, E746-A750 in exon 19. Started on oral Erlotinib Had local RT to hip for pain control No chemotherapy Significant clinical improvement Went back to full time work

BEFORE JUST FEW MONTHS LATER CASE 4

EGFR BASED THERAPY DECISION IN LUNG CANCER: NON SQUAMOUS HISTOLOGY EGFR on all non sq + Erlotinib - Alk testing + Crizotinib - Chemo 47

BIOLOGICAL TREATMENTS FOR STAGE 4 NSCLC: A STEP FORWARD More precise Less toxic Better responses 48

EGFR IN LUNG CANCER: ACQUIRED RESISTANCE Most responding patients eventually relapse T790M gatekeeper mutation responsible for 50% of cases with resistance MET gene amplification 20% cases of resistance MET drives activation of down stream effectors of cell survival Combination of EGFR and MET inhibitors are in clinical trial

Acquired resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) therapy in non small-cell lung cancer. McDermott U, and Settleman J JCO 2009;27:5650-5659

ACQUIRED RESISTANCE Similar to lung cancer, resistance to primary biological therapy has been reported in other disease sites CML: T315I gatekeeper mutation leading to reactivation of BCR ABL kinase leading to Imatinib resistance GIST: c-kit mutation leading to Imatinib resistance

PERSONALIZED CARE IS NOW AVAILABLE IN WIDE RANGE OF CANCERS Breast cancer: ER/PR/HER 2/Genetic signatures for risk prediction and treatment selection Non small lung cancer: EGFR, ALK, K RAS for treatment selection Colon-rectal cancer: Kras, Microsatellite instability for risk prediction and treatment selection

PERSONALIZED CARE IS NOW AVAILABLE IN WIDE RANGE OF CANCERS GIST: C KIT for treatment selection CML: BCR ABL for treatment Non Hodgkin's lymphoma: for diagnosis, risk prediction and treatment Leukemia's: for diagnosis, risk prediction and treatment Gastric cancer: Treatment selection Melanoma: Treatment selection

OLD: SITE SPECIFIC TREATMENT BASED ON SITE Different treatment for different cancer sites Lung Breast Colon Brain etc NEW (IN FUTURE): SINGLE GENOTYPE SPECIFIC THERAPY REGARDLESS OF SITE CANCER TREATMENT APPROACH: A PARADIGM SHIFT Single kinase targeted therapy for ALK mutation positive cancer in various sites ALK positive lung cancer ALK positive anaplastic large cell lymphoma ALK positive glioblastoma Single kinase targeted therapy for EGFR mutation postive cancer in various sites Lung cancer Gastric cancer Esophageal cancer Ovarian cancer Pancreatic cancer

CANCER TREATMENT APPROACH: CURRENT VERSUS FUTURE McDermott U, and Settleman J JCO 2009;27:5650-5659

MOLECULAR MARKERS OF CANCER RISK: IMPLICATIONS FOR PREVENTION Breast cancer: BRCA 1 and 2 gene mutations if postive require intensive screening and preventive interventions Colorectal cancer: Specific gene mutations if postive require intensive screening and preventive interventions

MOLECULAR MARKERS OF DRUG METABOLISM: CHOOSING SPECIFIC DRUGS CYP2D6 polymorphisms and drug metabolism Applicable for selection of non cancer drugs Anti psychotics and anti depressant Long list of various affected drug Testing for CYP2D6 is available

CYP2D6 POLYMORPHISMS AND DRUG METABOLISM: CASE OF TAMOXIFEN Poor metabolizer or mutant CYP2D6 low or completely deficient levels of CYP2D6 fail to activate Tamoxifen and thus are unable to benefit from its antitumor effects Variants of CYP enzymes leads to poor metabolism of Tamoxifen and lower level of Endoxifen May be seen in up to 10% of Caucasians May predict lack of response to Tamoxifen Clinical breast cancer: Vol 9 (4) Nov 2009: 214-215.

CHALLENGES Most of the new methods have yet to be validated in large prospective clinical trials Resistance: Oncology gene addiction phenomenon (driver pathways) leads to new progress, however, tumor cells activate alternate escape pathways when driver pathways are blocked

CHALLENGES Complicated molecular biology infra structure is necessary for processing Many drug sensitizing genetic mutations are low frequency and seen in multiple disease sites There may be too many markers and methods already creating confusion Testing is expensive at present time

CHALLENGES IN RESPONSE MONITORING Traditional radiological measurements may not be accurate to determine response to a biological Need new markers of response Tumor may need a repeat biopsy for detection new mutations need blood markers of resistance

HAPPY ENDING WITH A PERFECT FIT MOLECULAR BIOLOGY FINDS A PERFECT (FIT) THERAPY FOR EVERY CANCER PATIENT! ONLY QUESTION IS WHEN?