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ADVANCES IN PERSONALIZED AND PRECISION MEDICINE Jeffrey Meyerhardt, MD, MPH Dana-Farber Cancer Institute Boston, MA WHAT IS CANCER? CANCER CELLS DON’T FOLLOW NORMAL RULES Gene mutations, chromosome alterations, and changes in other molecules enable them to replicate endlessly Ignore signals ordering them to self-destruct Migrate to distant sites in the body, such as the bones, liver, or brain, where they set up satellite tumors Usually, several genomic abnormalities are driving the cancer These can be different in different cancers that otherwise seem to be the same. Two people with the same type of colon cancer, for example, may not respond to treatment in the same way if their cancers are driven by different combinations of mutations. CANCER CELLS DON’T FOLLOW NORMAL RULES Cancer-driving abnormalities may occur spontaneously (de novo) or be passed from parent to child (hereditary). Spontaneous changes happen all the time – thousands of times a day in single cell – often due to DNA damage caused by factors like sunlight, diet, or smoking Some abnormalities are harmless, some cause the cell with the mutation to die, and some are fixed by the body’s elaborate repair mechanisms or are eliminated before cell replication occurs A very small minority of these changes, however, can evade repair and alter the cell in a way that, rather than die, develops an improved ability to grow or survive. These alterations lead to cancer. GENE ALTERATIONS THAT CAN DRIVE CANCER Single nucleotide variation (SNV) or “point mutation” A single base nucleotide May cause the gene to direct the cell to make an abnormal protein or may prevent the gene from making a protein GENE ALTERATIONS THAT CAN DRIVE CANCER Insertions and Deletions GENE ALTERATIONS THAT CAN DRIVE CANCER Copy number variation (CNV) Each of us typically has two identical copies of each gene. A copy number alteration (CNV) is when one or both of those copies are lost (“deletion”) or when extra copies are acquired (“amplification”). CNVs can activate a cancerrelated gene or silence genes that suppress tumors GENE ALTERATIONS THAT CAN DRIVE CANCER Structural alteration (SV) A chromosome may have broken and segments become rearranged or swapped with pieces of other chromosomes (“translocation”) or the same chromosome (“inversion”). When the chromosome breaks and rejoins (“breakpoints”), genes around the break are altered which can change behavior and function TRADITIONAL CHEMOTHERAPY VERSUS TARGETED THERAPIES Standard chemotherapy Destroys rapidly dividing cells, normal and cancerous in a wide range of tissues, often causing side effects like nausea, mouth sores, and hair loss. TRADITIONAL CHEMOTHERAPY VERSUS TARGETED THERAPIES Targeted therapies Engineered to attack tumor cells with specific abnormalities. Block the growth and spread of cancer while leaving normal cells largely unharmed. May strike directly at cells with specific genetic changes that drive tumors’ development and survival, or to inhibit overactive signaling pathways that allow cancer cells to grow and divide uncontrollably Other treatments enlist the immune system to identify and fight the cancer cells. www.usatoday.com METHODS TO TEST FOR GENETIC VARIATIONS Single mutation tests DNA microarray testing Multiplex of key potential prognostic and predictive markers Actionable genetic mutations Next generation sequencing test can test for base pair substitutions, insertions/deletions, copy number variations and rearrangements (depending on which test used) Whole exome sequencing Eg. RAS or BRAF for colorectal cancers Sequencing of the expressed genes in genome Whole genome sequencing SOMATIC V GERMLINE MUTATIONS Somatic – within the tumor Germline – born with Adenomatous polyposis coli gene familial adenomatous polyposis Mismatch repair genes hereditary non-polyposis colorectal cancer (Lynch) MYH – MYH-associated polyposis LKB1 – Peutz-Jeghers syndrome SMAD4, BMPR1A -Juvenile polyposis PTEN _Cowden disease DNA MICROARRAY From National Human Genome Research Inst website DNA MICROARRAY DFCI PROFILE DFCI PROFILE DFCI PROFILE DFCI WHOLE EXOME SEQUENCING – CANSEQ CHANGE IN COST OF DNA SEQUENCING CHANGE IN COST OF WHOLE GENOME SEQUENCING CHROMOSOMAL VERSUS MICROSATELLITE INSTABILITY LEADING TO COLORECTAL CANCER Markowitz and Bertagnolli. NEJM 361: 2449-60 MISMATCH REPAIR MECHANISM Banno K, Yanokura M, Kobayashi Y, Kawaguchi M, Nomura H, Hirasawa A, Susumu N, Aoki D - Curr. Genomics (2009) MOLECULAR DIVERSITY OF COLORECTAL CANCER (16%) (84%) TCGA Network. Nature. 2012; 487: 330-337. MOLECULAR DIVERSITY OF COLORECTAL CANCER TCGA Network. Nature. 2012; 487: 330-337. TCGA BOTTOM LINE Colorectal No cancers are molecularly very diverse single driver that leads to cancer Backup One pathways when one pathway not working right strategy will not work for everyone’s cancer even with a particular known mutation EXAMPLES OF PRECISION MEDICINE FOR COLORECTAL CANCER PREDICTORS OF EPIDERMAL GROWTH FACTOR RECEPTOR INHIBITORS EPIDERMAL GROWTH FACTOR RECEPTOR IN CRC Ligand Extracellular EGFR PTEN PI3K Akt Ras Raf MEK MAPK Cell survival Proliferation DNA Angiogenesis Intracellular Cell motility Metastasis PHASE 4 STUDY: CETUXIMAB VS BEST SUPPORTIVE CARE • Patients previously treated with fluoropyrimidine, oxaliplatin, irinotecan • EGFR expression required R A N D O M I Z E Cetuximab 400 mg/m2 loading then 250 mg/m2 weekly N = 287 Stratification based on ECOG score (0/1 v 2) and center Best supportive care N = 285 Jonkers et al. N Engl J Med 2007; 357:2040-2048 Kaplan–Meier Curves for Overall Survival (Panel A) and Progression-free Survival (Panel B). PHASE 4 STUDY: CETUXIMAB VS BEST SUPPORTIVE CARE Jonkers et al. N Engl J Med 2007; 357:2040-2048 FINDING A PREDICTIVE MARKER Khambata-Ford S et al. J Clin Oncol. 2007;25:3230-3237. \ for Progression-free Survival According to Treatment. PHASE Kaplan–Meier 4 STUDY:Curves CETUXIMAB VS BEST SUPPORTIVE CARE KARAPETIS CS ET AL. N ENGL J MED 2008;359:1757-1765. Curves for Progression-free Survival According to Treatment. FKaplan–Meier IRST LINE PANITUMUMAB: PRIME STUDY Douillard J Y et al. Ann Oncol 2014;25:1346-1355 BEYOND KRAS CODON 12 AND 13 Data from 773 patients treated with cetuximab at 11 European centres De Roock et al. Lancet Oncology. 2010. Volume 11, Issue 8, Pages 753-762 OTHER RAS MUTATIONS: CRYSTAL TRIAL 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 HR (95% CI) 0.70 (0.56–0.87) FOLFIRI + cetuximab FOLFIRI 0 4 8 12 16 RAS wild-type Probability of PFS Probability of PFS KRAS codon 12/13 wild-type 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 HR (95% CI) 0.56 (0.41–0.76) FOLFIRI + cetuximab FOLFIRI 20 0 1 0 Number of patients at risk 178 153 114 75 189 154 92 44 3 6 9 Months Number of patients at risk 316 227 350 237 128 111 40 22 8 4 12 Months 31 11 15 18 21 24 8 5 4 3 0 0 0 0 Ciardiello et al. ASCO 2014 BRAF MUTATIONS IN LATER LINE THERAPY Author Lambrechts1† Ruzzo2‡ Di Nicolantonio3‡ Treatment BRAF wt : mt BRAF mt frequency [in KRAS wt] Response Rate BRAF mt vs wt Cetuximab + irinotecan in chemorefractory pts 540 : 26 4.6% [not reported] 8 vs 26% Cetuximab + irinotecan in irinotecan-refractory pts 57 : 9 8% [14%] 0 vs 33%‡ Panitumumab or cetuximab monotherapy or cetuximab + CT 68 : 11 9.7% [13.9%] 0 vs 32%‡ *Significant; †All patients; ‡KRAS wt patients only (Ruzzo, n = 66 of 117; Di Nicolantonio, n = 79 of 113); §Data not available. 1Lambrechts D et al. J Clin Oncol. 2009;27:15s: Abstract 4020. A et al. J Clin Oncol. 2009;27:15s: Abstract 4058. 3Di Nicolantonio F et al. J Clin Oncol. 2008;26:5705-5712. 2Ruzzo BRAF MUTATIONS IN 1ST LINE THERAPY Probability of OS (%) KRAS WT/BRAF WT No. of patients CT + cetuximab CT CT + cetuximab CT 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 HR = 0.840; 95% CI, 0.710-0.993 P = 0.041 FOLFIRI/FOLFOX4 + cetuximab: (n = 349) FOLFIRI/FOLFOX4: (n = 381) median KRAS WT/BRAF MT HR = 0.633; 95% CI, 0.378-1.060 P = 0.079 FOLFIRI/FOLFOX4 + cetuximab: (n = 32) FOLFIRI/FOLFOX4: (n = 38) 0 6 12 18 24 349 381 32 38 317 350 25 24 268 283 16 14 225 212 12 6 163 149 8 6 30 36 42 48 54 60 120 107 5 3 80 63 2 3 63 46 2 1 19 17 2 0 4 2 0 0 0 0 0 0 Time (months) Bokemeyer et al. European Journal of Cancer, 2012-07-01, Volume 48, Issue 10, Pages 1466-1475 POTENTIAL UPFRONT RESISTANCE TO ANTI-EGFR MABS IN KRAS WT CRC Sensitive population Dienstmann, Salazar & Tabernero, ASCO Educational Book 2014 MORE ON BRAF MUTATED TUMORS SINGLE AGENT ACTIVITY OF VEMURAFENIB IN BRAF-MUTANT MCRC Modest activity in BRAF-mutant mCRC compared with BRAF mutant melanoma (Response rate of ~ 5% vs ~ 60%-80%[2]) %Change From Baseline (Sum of Lesion Size) 100 mCRC (N = 19)[1] 75 50 25 0 -25 -50 -75 (RECIST cutoff for PR, 30%) -100 1. Kopetz S, et al. ASCO 2010. Abstract 3534. 2. Flaherty KT, et al. N Engl J Med. 2010;363:809-19. LABORATORY DISCOVERIES ON WHY BRAF INHIBITORS DON’T WORK BY THEMSELVES IN COLORECTAL CANCER Need to target more than one pathway BRAF inhibitors increase level of EGFR receptors Strategy - combine BRAF and EGFR Inhibitor Lack of sustained suppression of another protein (ERK) with BRAF inhibitors alone Combined inhibition of BRAF and MEK may lead to superior suppression of this signal Prahallad A, et al. Nature. 2012;483:100-103. Corcoran RB, et al. Cancer Discov. 2012;2:227-235. Signaling in BRAF mutated CRC Reactivation of EGFR signaling upon BRAF inhibition Robust inhibition of MAPK pathway signaling with inhibition of BRAF, MEK, EGFR Bendell. et al. ASCO 2014. COBRIM UPDATED PFS: ASCO ‘15 Survival Distribution Function (%) Kaplan-Meier Plot for PFS Intent-to-Treat Population 100 + + + 80 + ++ + 60 20 + Vemurafenib + cobimetinib Vemurafenib + placebo + 0.58b (0.460-0.719) ++++ + ++ Median follow-up was 14.2 months + +++++++ +++ + +++ Data cutoff of January 16, 2015 was 1 year ++ ++ +++ + + + from enrollment of last patient +++ + ++++ ++ ++++ ++ +++ +++ + ++ + +++ + +++++ Cobimetinib + vemurafenib (n=247) + + Placebo + vemurafenib (n=248) Censored 1 Months No. of patients at risk HRa (95% CI) + + 40 0 + 5 Months 9 Months 13 Months 17 Months 21 Months 25 Months Time 238 240 215 205 190 150 168 115 142 87 116 67 79 45 46 30 21 17 8 3 1 Larkin et al ASCO ‘15 Abs 9006 D+P+T (N = 35) CR+PR: 9 (26%) Stable disease: 18 (51%) Color: confirmed response Height of bar: best unconfirmed response Maximum % Change from Baseline D+P (N=20) CR+PR: 2 (10%) Stable disease: 16 (80%) 100 80 60 40 20 0 -20 -40 -60 -80 -100 Maximum % Change from Baseline DABRAFENIB (BRAF-I) + PANITUMUMAB (EGFR-I) +/TRAMETINIB (MEK-I) IN BRAFV600-MUTANT MCRC 100 80 60 40 20 0 -20 -40 -60 -80 -100 Progressive disease Stable disease Partial response Complete response + + *Maximum reduction from baseline is 0% + + + + + + * + + + + + + + + + + + + + + + + + + *Maximum reduction from baseline is 0% +RP2R cohort Atreya et al ASCO ‘15 Abs 103 EFFICACY OF DABRAFENIB (BRAF-I) + PANITUMUMAB (EGFR-I) +/TRAMETINIB (MEK-I) IN BRAFV600-MUTANT MCRC – TIME ON STUDY Bendell. et al. ASCO 2014. CHECKPOINT INHIBITORS AND MISMATCH REPAIR DEFICIENT CRC PD1/PDL1 AND INHIBITORS DURABLE RESPONSE WITH ANTI-PD1 ANTIBODY Lipson E J et al. Clin Cancer Res 2013;19:462-468 Pembrolizumab Single Agent in MSI and MSS CRC LE DT ET AL. N ENGL J MED 2015;372:2509-2520. LE DT ET AL. N ENGL J MED 2015;372:2509-2520. HER2 TUMORS – ROLE OF TRASTUZUMAB/LAPATINIB HER2 PATHWAY HERCALES TRIAL FOR HER2 + METASTATIC COLORECTAL CANCER 849 patients with mCRC KRAS exon 2 WT Trastuzumab iv 4mg/kg load and then 2mg/kg/qw Lapatinib po 1000 mg/qd 803 HER2-negative 46 HER2+ (5.4%) 22 not eligible because PS ≥2 or tumor-related comorbidities 24 enrolled PD 1 too early for safety & efficacy assessment 23 evaluable for response HERCALES TRIAL FOR HER2+ METASTATIC CRC Best Response RECIST 1.1 by centralized revision N % Responses (PR+CR) 8 34.7 Complete Response 1 4.3 Partial Response 7 30.4 Stable Disease >4 mos 7 30.4 Stable Disease <4 mos 3 13.0 Progressive Disease 5 21.7 23 100 Total Primary endpoint met in advance with 8/23 objective responses (as per protocol, 6/27 needed to declare the study positive) 78% disease control HERCALES TRIAL FOR HER2+ METASTATIC CRC RESPONSE BY HER2 SCORING Spaghetti plot Waterfall plot NEW LESION Change in target lesion from baseline (%) Patients on treatment PD HER2 2+ *3 patients are not shown: 122026 (IHC 2+), not assessed yet; 121011 (IHC 3+) and 121013 (IHC 3+) early clinical PD. 56 Change in target lesion from baseline (%) HER2 3+ CONCLUSIONS FOR MOLECULAR DEFINED COLORECTAL CANCER THERAPY Colorectal cancer is a molecularly diverse disease Predictive markers growing RAS testing should be performed for all metastatic CRC patients BRAF may not predict anti-EGFR therapy but should be tested for potential trials MSI testing for genetic screening and for checkpoint inhibitor trials HER2 testing may be important as well While mutations are found, finding agents to target most of what is found has been a challenge I would recommend next generation sequencing for most patients – role of whole exome and whole genome testing is more uncertain – lots of data, need to understand how to use it