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Biomarker and Pharmacogenomic Modeling in Upper GI Cancer: Fantasy or Becoming Reality Heinz-Josef Lenz Professor of Medicine and Preventive Medicine Associate.

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Presentation on theme: "Biomarker and Pharmacogenomic Modeling in Upper GI Cancer: Fantasy or Becoming Reality Heinz-Josef Lenz Professor of Medicine and Preventive Medicine Associate."— Presentation transcript:

1 Biomarker and Pharmacogenomic Modeling in Upper GI Cancer: Fantasy or Becoming Reality Heinz-Josef Lenz Professor of Medicine and Preventive Medicine Associate Director, Clinical Research Kathryn Balakrishnan Chair for Cancer Research Co-Director, USC Center for Molecular Pathways and Drug Discovery Co-Leader GI Oncology Program USC/Norris Comprehensive Cancer Center

2 Discussion Pancreas Cancer (4016, 4017,4022) –SPARC for real and where do we look? (Sinn et al) –PG modeling: The Future is Here (Yu et al) –Early diagnosis using Vit D levels? Let the Sun Shine (Van Loon et al) Gastric Cancer (4019, 4020,4021) –Predict the site of recurrence TOP2, CGH, PECAM1: How Important is this ? (Terashima et al) –MAGIC: Will Gene Profiling give us the answer? We need your help (Smyth et al) –Expand: HER2ve better outcome? Is this true? (Lordick et al) Biliary Cancers (4018) –Cetuximab in mutant kras biliary cancers? Need more patients! (Chen et al)

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5 What is SPARC

6 SPARC in pancreas cancer Infante et al JCO 2007, vol 25, 319.

7 Sparc in the Stroma was associated with increased Median overall survival Von Hoff D D et al. JCO 2011;29: ©2011 by American Society of Clinical Oncology

8 Stromal and cytoplasmatic SPARC only in gemcitabine group not Obs

9 CONKO SPARC Sparc is prognostic……predictive? Gemcitabine effect? Sparc in the tumor and/or stroma? IHC (tissue handling/AB specificity and sensitivity/subjective reading)

10 Pharmacogenomic Modeling in Pancreatic Cancer, Yu KH, et al. Pharmacogenomics Modeling 3. Gene expression profiling 4. PGX Analysi s Drug(s)Gene expression 1. PGx Model Sensitive to A, not B Sensitive to B, not A Resistant to A and B 2. Patients with pancreatic cancer

11 Circulating tumor/invasive cells Surprisingly, PGx profiling of circulating invasive cell population mirrors tumor tissue Wilms Tumor Pharmacogenomic Modeling in Pancreatic Cancer, Yu KH, et al.

12 Liquid biopsies Circulating Tumor Cells (CTC) Tumor specific change (e.g. Mutation) Tumor Tumor cell release DNA Circulating tumor DNA Normal DNA CTC

13 Different therapy Studies show emergence of KRAS mutations during treatment with EGFR inhibitors Misale S, et al. Nature 2012;486:532 ‒ 536 Diaz LA, et al. Nature 2012;486;537 ‒ 540 Vilar E, Tabernero J. Nature 2012;486:482 ‒ 483 Anti-EGFR therapy KRAS-mutant ctDNA Other mutant ctDNA Weeks of treatment ctDNA levels Stable disease (by imaging) Progressive disease (by imaging) Blood biopsy Tumor Metastatic tumor

14 Results  Patients receiving treatment predicted by our model to be effective had longer PFS. Pharmacogenomic Modeling in Pancreatic Cancer, Yu KH, et al. n = 24 p-value = PPV = 0.87 NPV = 0.67 Sensitivity = 0.81 Specificity = 0.75 > 6 months < 6 months Performance of PGx Test PFS

15 Increased sonic hedgehog pathway disruption associated with shorter TTP Multiple pathways became more disrupted with progression: –PI3K pathway –E2F pathway –CREB pathway –PLC E pathway Pharmacogenomic Modeling in Pancreatic Cancer, Yu KH, et al. Pathway Analysis

16 Discussion Liquid Biopsies and Genomic Characterization will impact future trials and drug development Complete TCGA data need to be analyzed Dynamic Changes critical for novel Drug Development Explant Models but not possible in real time but CTC are Prospective Studies needed

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19 Vit D and Pancreas Cancer Vitamin D deficiency (<20 ng/mL) was highly prevalent among patients with a new diagnosis of APC (44.5%). Black patients had significantly lower 25(OH)D levels than white patients (median 10.7 vs ng/mL). 82.6% of blacks were deficient vs. 40.9% of whites.

20 Discussion 1.Vit D associated with cancer incidence 2.Vit D key regulators in many pathways (wnt etc) 3.Levels may be important prognostic markers (population based cohorts) 4.Larger Studies needed (ethnicity differences)

21 Gastric Cancer DISEASE HETEROGENEITY Gastric Cancer is not one disease –Histology(Intestinal vs Diffuse) –Location(Cardia/GEJ vs Antrum) –Biology(MET, CDH1, FGFR others?) –Etiology(H. pylori related, others?)

22 Deep Sequencing KRAS, ERBB2, EGFR, MET, PIK3CA, FGFR2 and AURKA genes in gastric cancer and suggests some of the targeted therapies approved or in clinical development would be of benefit to 11 of the 50 patients studied. The data, also suggests that agents targeting the wnt and hedgehog pathways would be of benefit to a majority of patients. The previously undocumented DNA mutations discovered are likely to affect clinical response to marked therapeutics and may be good drug targets. Holbroook et al Journal of Translational Medicine 2011

23 (A) Focal regions exhibiting mutually exclusive patterns of genome amplification. (B) Focal regions exhibiting patterns of genomic co-amplification Deng et al 2012 BMJ

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25 Presented by: Identifying Biomarkers for local recurrence: Overlap of first recurrence site of 829 patients (from 1059 pts in the ACTS-GC trial) Lymph-node recurrence Peritoneal recurrence Hematogenous recurrence *) Local (L) & Peritoneal(P); n=3, L & Lymph(Ly); n=3, L & H; n=3, L & Ly & H; n=1, L alone; n= 15

26 Presented by: Results (RT-PCR candidates and low density array, DISH (her2), IHC and Kras status) 1) TOP2A significantly correlated with hematogenous recurrence. Hematogenous RFS was significantly worse in TOP2A-high patients than in TOP2A-low patients (HR, 2.35; 95% CI, ). 2) GGH significantly correlated with lymph-node recurrence. Lymph-node RFS was significantly worse in GGH-high patients than in GGH-low patients (HR, 1.87; 95% CI, ). 3) PECAM1 significantly correlated with peritoneal recurrence. Peritoneal RFS was significantly worse in PECAM1-high patients than in PECAM1-low patients (HR, 2.37: 95% CI, ).

27 GGH expression in breast cancer associated with OS

28 TransMAGIC NanoString panel E.g. Platinum treatment efficacy: ERCC1/2, BRCA1/2, OPRT Chemosensitivity markers: MYC, COX2, STAT3, HIF1a E.g. Amplified in GC: FGFR2, CCNE1,KRAS Deleted in GC: FHIT, CDKN2A, CDKN2B, RB1 E.g. GINT: TOX3, MYB, CEACAM1 GDIFF: ABL2, SIX4, RASSF8 Genes (n = controls) Presented by: Smyth EC, Tan IB, Cunningham D et al

29 Overall survival from time of surgery in years ChemotherapySurgery aloneOverall ERBB2 normalERBB2 highERBB2 normalERBB2 highERBB2 normalERBB2 high Patients Events Median survival 1.45Not reached Logrank p-value Hazard ratio 1 (REF)0.221 (REF)1.191 (REF)0.72 HR p-value TransMAGIC NanoString RTK survival analysis: ERBB2 There is some evidence of an interaction between treatment arm and ERBB2 (p=0.027); reflecting very high survival rates amongst the small group of patients on the chemotherapy arm with ERBB2 overexpression. Presented by: Smyth EC, Tan IB, Cunningham D et al Survival by ERBB2 (chemo pts)Survival by ERBB2 (surgery pts)Survival by ERBB2 (all pts) ERBB2 normal = ERBB2 high =

30 EXPAND Study Her2ve- has significant shorter OS (HR 1.55) Her2ve-response was significantly lower (OR 0.48) Cet, cetuximab; CT, chemotherapy

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32 Survival by EGFR (chemo pts)Survival by EGFR (surgery pts)Survival by EGFR (all pts) Overall survival from time of surgery in years ChemotherapySurgery aloneOverall EGFR normalEGFR highEGFR normalEGFR highEGFR normalEGFR high Patients Events Median survival Logrank p-value Hazard ratio 1 (REF)2.331 (REF)1.571 (REF)1.89 HR p-value TransMAGIC NanoString RTK survival analysis: EGFR EGFR normal = EGFR high = EGFR was overexpressed in 11 patients; their prognosis was poorer in both treatment arms, there is no evidence of an interaction between treatment arm and EGFR (p=0.601). Presented by: Smyth EC, Tan IB, Cunningham D et al

33 Discussion Her family needs to be evaluated her1-4 (IHC+/- FISH) TOP2A co amplified with her2 Unknown if prognostic (treatment effect)

34 Clinical Trials in Biliary Cancer using EGFR/VEGF/MEK inhibitors

35 Presented by: Chen et al. Unresectable, locally advanced or metastatic BTC Stratification: ECOG PS: 0 versus 1 KRAS: wt versus mutant Intra- versus extra-hepatic Gemcitabine 800 mg/m 2 Oxaliplatin 85 mg/m 2 Q 2 weeks Cetuximab 500 mg/m 2 Gemcitabine 800 mg/m 2 Oxaliplatin 85 mg/m 2 Q 2 weeks N=60N=62 Primary EP: ORR,C-GEMOX 30% vs GEMOX 20%, (  =0.2/  =0.5) Secondary EP: DCR ≥16 weeks, PFS, OS, Safety & Biomarker R Randomized, Phase II GEMOX ± Cetuximab in Advanced BTC: TCOG T Schema 35

36 Presented by: Chen et al. Randomized, Phase II GEMOX ± Cetuximab in Advanced BTC: comparing therapeutic outcome of treatment arms in KRAS mutation status-stratified subpopulations 36

37 Discussion Too small to draw any conclusions (RR, PFS and OS consistent with previous studies) Kras spectrum may be critical Braf mutations are important for biliary cancer No detremental effect of Cetuximab in these patients Previous trial negative for Cetuximab combinations

38 We have a Future There is Light on the end of the Tunnel Completion of TCGA for Gastric, Pancreas and Hepatobiliary Cancers Liquid Biopsies CTC/tumor DNA reflect pathway changes under therapy Biomarker/PG Modeling Driven Trials (based on mutation and gene expression data e.g. SPARC, FGFR….) International Collaborations to move science forward


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