The Multi-faceted Role of the PI3K-AKT Pathway in Melanoma
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1 The Multi-faceted Role of the PI3K-AKT Pathway in Melanoma Melanoma Bridges Naples, Italy December 4, 2014 Michael A. Davies, M.D., Ph.D. Associate Professor Melanoma Medical Oncology and Systems Biology University of Texas, MD Anderson Cancer Center
2 Disclosure Information I have the following financial relationships to disclose: Consultant (with honorarium) for: GlaxoSmithKline, Roche/Genentech, Novartis, Sanofi- Aventis Grant/Research support from: GlaxoSmithKline, Roche/Genentech, AstraZeneca, Merck, Oncothyreon, Myriad, Sanofi-Aventis
3 Regulator of Proliferation Apoptosis/Survival Motility Angiogenesis Metabolism PI3K-AKT Pathway Activated by RTKs RAS Cell-Cell Contacts Vivanco, Sawyers 2002 Genetic Alterations in Cancer Activating Mutations: RTKs, RAS, PI3K, AKT Copy Number: RTKs, PI3K, PTEN, TSC
4 Overview: PI3K-AKT Pathway in Advanced Melanoma Molecular and clinical correlates pathway activation GF Receptors Resistance to BRAF/MEK inhibitors NRAS PTEN PI3K AKT Targeting of Melanomas with High Oxidative Phosphorylation mtor
5 Oncogenic Mutations in Cutaneous Melanoma Activating Signals NRAS ~20% *RAS NF1 15% Pl3K PTEN* 1-2% 10-30% BRAF ~45% *RAF P AKT/PKB 1-2% MEK P IKK P FKHR P BAD P P mtor P GSKβ P MAPK IKB NFKB IKB NFKB P
6 Phospho-AKT Thr308 PTEN Loss: AKT Activation vs NRAS RPPA of 96 OCT-embedded melanoma metastases & 58 cell lines Y-axis, P-AKT expression; Color ~ PTEN expression Compared PI3K-AKT pathway activation to mutation status BRAF Mutation + PTEN Loss Tumors (n=96) PTEN PTEN Loss BRAF NRAS WT n=39 n=18 n=37 NRAS PTEN PI3K AKT Loss of PTEN associated with greater activation of PI3K pathway (P-AKT) than NRAS mutations Similar results in cell lines Supports that PTEN is key regulator of constitutive pathway activation in melanoma Davies, CCR, 2009
7 PTEN IHC on FFPE Melanoma Intensity relative to internal (+)ive controls Clonal Absent Markedly Reduced Tumor Mildly Reduced Tumor Normal/Increased Alexander Lazar, MD, PhD Pathology, MDACC
8 PTEN: IHC versus RPPA PTEN IHC on FFPE blocks from accessions used for RPPA (frozen tissue) 3 PTEN IHC Score N (%) Staining Pattern (vs Internal Controls) 0 20 (21.3%) Absent 1 22 (23.4%) Markedly Reduced 2 33 (18.1%) Mildly Reduced 3 17 (18.1%) Normal/Increased Clonal 2 (2.1%) Distinct Areas +/- Absent RPPA (Ave Linear) PTEN 0 IHC: Absent Markedly Reduced Mildly Reduced Normal/ Increased T-Test, Absent vs: <0.001 <0.001 <0.001 T-Test, Markedly Reduced vs: Good correlation between IHC- and RPPA-based measurement of PTEN Bucheit, CCR, 2014
9 PTEN IHC versus P-AKT PTEN IHC vs RPPA data for frozen tumor from same accession PTEN PI3K AKT RPPA: P-AKT Thr308 (Ave Linear) Absent P-AKT Thr308 Markedly Reduced Mildly Reduced Normal/ Increased T-Test, Absent vs: T-Test, Markedly Reduced vs: RPPA: P-AKT Ser473 (Ave Linear) Absent P-AKT Ser473 Markedly Reduced Mildly Reduced Normal/ Increased T-Test, Absent vs: T-Test, Markedly Reduced vs: Complete loss of PTEN correlates with P-AKT activation Assess samples as PTEN Absent versus Not Absent (Additional benefit: Reproducibility) Bucheit, CCR, 2014
10 MDACC Stage IIIB/C Tissue Microarray Demographics and Molecular Features Measure All Patients (N=137) Stage IIIB (N=38) Stage IIIC (N=99) p-value Gender, n (%) Male 94 (68.6) 25 (65.8) 69 (69.7) a Female 43 (31.4) 13 (34.2) 30 (30.3) Age at Stage III diagnosis (years), median (range) 55.1 (24.9, 85.4) 54.4 (26.1, 85.4) 55.7 (24.9, 84.0) b Mutation, n (%) BRAF 55 (40.1) 15 (39.5) 40 (40.4) a NRAS 13 (9.5) 4 (10.5) 9 (9.1) Wild-type 69 (50.4) 19 (50.0) 50 (50.5) PTEN, n (%) Absent 31 (24.8) 8 (24.2) 23 (25.0) a Not Absent 94 (75.2) 25 (75.8) 69 (75.0) Missing Bucheit, CCR, 2014
11 Stage IIIB/C TMA: Outcomes by PTEN Absent (N=31) Not Absent (N=94) Measure p-value Gender, n (%) Male 22 (25.9) 63 (74.1) a Female 9 (22.5) 31 (77.5) Age at Stage III diagnosis (years), median (range) 48.0 (26.3, 82.4) 57.7 (24.9, 85.4) b Clinical/Pathological Type, n (%) Acral 4 (23.5) 13 (76.5) a Cutaneous 20 (23.5) 65 (76.5) Mucosal 1 (25.0) 3 (75.0) Unknown Primary 6 (31.6) 13 (68.4) Breslow thickness (mm), median (range) 3.20 (0.40, 25.00) 3.25 (0.25, 33.00) b Ulceration, n (%) No 6 (37.5) 10 (62.5) a Yes 14 (20.0) 56 (80.0) Missing Time to any recurrence (years), median (95% CI) 0.67 (0.52, 1.09) 0.67 (0.46, 0.87) c Time to any distant recurrence (years), median 0.90 (0.57, 1.41) 1.20 (0.73, 1.68) c (95% CI) Time to overall survival (years), median (95% CI) 1.88 (1.49, 2.12) 3.05 (1.98, 4.15) c Time to CNS metastasis (years), median (95% CI) 1.84 (1.15, 2.48) 4.87 (2.41, NA) c Time to lung metastasis (years), median (95% CI) 2.12 (1.09, NA) 3.27 (1.87, 6.09) c Time to liver metastasis (years), median (95% CI) NA (1.43, NA) 6.26 (2.97, NA) c Time to bone metastasis (years), median (95% CI) NA (1.44, NA) NA (6.26, NA) c Bucheit, CCR, 2014
12 Stage IIIB/C- PTEN Multivariate Analysis of OS Bucheit, CCR, 2014 Overall Survival PTEN Not Absent (n=93) PTEN Absent (n=31) Distant Metastasis-Free Survival CNS Metastasis-Free Survival Lung Metastasis-Free Survival Bucheit, CCR, 2014
13 PTEN: BRAF V600 Mutant & BRAF/NRAS Wild-Type BRAF Mutant 16/52 (31%) Absent PTEN BRAF/NRAS WT 14/59 (24%) Absent PTEN Overall Survival CNS Metastasis Free Survival PTEN loss prognostic & predictive of brain metastasis specifically in BRAF-mutant melanomas Bucheit, CCR, 2014
14 Melanoma Patients with Resected Brain and Extracranial Metastases: Proteomic Analysis 7 paired brain & non-cns mets + 2 un-paired brain and 13 un-paired non-cns RPPA (163 proteins) Orange=Brain met Significant (p<0.05) Matched Brain vs Extracranial Mets BM/EM (log2) Paired t- test, p Akt_pS Rb_pS807_S mtor_ps Bax eef2k JNK_pT183_pT _epsilon Smad VASP Src Chen, CCR, 2014 A375 Brain Met Model LGX818 (BRAFi) + BKM120 (PI3Ki) + PI3Ki (med) BRAFi + PI3Ki(low) Control
15 Role of the PI3K-AKT Pathway in Resistance to MAPK Pathway Inhibitors
16 % Inhibition (vs Mock) PI3K-AKT Activation: Functional Significance AZD6244 (MEKi): Growth Inhibition Sensitive Resistant Complete Resistance AZD6244 (um) Cancer Res, 2010 Cell lines with low PTEN/Hi P-AKT: Cytostasis Similar results with MEKi and BRAFi Paraiso, Can Res 2011; Xing, Oncogene 2011; Deng, PCMR 2012 Pts with PTEN loss: worse outcomes with BRAFi Nathanson, CCR, 2013; Trunzer, JCO, 2013 Cell Numbers (%) Cell Numbers (%) AZD6244 Dose (nm) Cell Death SKMEL5 BRAF mut PTEN nl Cell Death Sub-G1 G1 S G2/M WM35 BRAF mut PTEN nl AZD6244: FACS Sub-G1 G1 S G2/M AZD6244 Dose (nm)
17 Cell Death G1 Arrest Only None Time-Dependent Effects of AZD6244 (MEKi) Subset of BRAF-mutant/PTEN intact lines resistant to apoptosis Proteomic profiling: Sensitive Cells : upregulate PTEN, apoptotic markers Resistant Cells : increased IGF1R; compensatory IGF1 AKT activation Protein: Low High Sensitive Resistant
18 AZD6244 Activates AKT via IGF1R Axis IGF1-R: correlated with resistance to AZD6244 (r 2 = 0.55 vs IC50, p=0.07) BRAF-mutant/PTEN wild-type resistant cell lines: IGF1R mrna and ELISAs: AZD6244 treatment IGF1 SKMEL5 Sub-G1 G1 S G2/M Cell Numbers (%) Multiple RTKs now implicated in BRAFi acquired resistance (ie, PDGFRβ, IGF1R, c-met, EGFR, ERBB3) 10 sirna: AZD6244: sirisc sirisc 360nM siigf1r siigf1r 360nM
19 AZD PI3K Pathway Inhibitors MEL624: BRAF mutation, PTEN normal Drug 1: AZD6244 (MEKi) Drug 2: AZD8055 (TORC1/2i), PX-866 (PI3Ki), MK-2206 (AKTi) Sub-G1 G1 S DMSO AZD 6244 (MEKi) AZD PX MK AZD PX MK 8055 (TORC) 866 (PI3K) 2206 (AKT) 8055 (TORC) 866 (PI3K) 2206 (AKT) G2/M + AZD6244 (MEKi)
20 Whole Genome sirna Synthetic Lethality Screen MEL624 Whole genome sirna screen Alone + MEKi + BRAFi ~150 genes synthetic lethal effect Netwalk analysis Glucose Metabolism Mitochondrial Oxidation Kakajan Komurov, Prahlad Ram, MDACC
21 Sensitive vs De Novo Resistant: Changes in mrna mrna Changes after MEKi (24hr): Resistant vs Sensitive Lines -log (p-value) sirna Screen: Synthetic Lethal & Expression PGC1α sirna: Apoptosis Fold Change in Expression Apoptotic Cells (%) Gopal, Cancer Res, 2014
22 High OxPhos, MAPKi-Resistant: MEKi + TORC1/2i High OxPhos detected in 7/14 cell lines with MEKi de novo resistance Including 2 cell lines sensitive to MEKi + mtorc1/2i Treated full panel with MEKi (AZD6244) + TORC1/2i (AZD8055) Growth Inhibition Apoptosis OCR (pmoles/min) P< Combination Index >1.0 Combination Index <1.0 Apoptotic Cells (%) Low OxPhos High OxPhos No apparent difference in on-target on compensatory signaling effects by RPPA Correlation of OxPhos with synergy observed in BRAF- and NRAS-mutant human melanoma cell lines Gopal, Cancer Res, 2014
23 PGC1α: Acquired Resistance to MAPKi BRAF-mutant cells selected in vitro for resistance to MEKi (AZD6244) Cell Lines: OCR and ECAR MEKi + TORCi: Apoptosis Apoptotic Cells (%) Aca Acquired Resistance Clinical Samples: Ratio of PGC1α Expression Progression:Pre-Treatment Tumor n=10 n=9 10/19 at Progression vs Baseline Gopal, Cancer Res, 2014
24 MEL624: MEKi + TORC1/2i in Vivo * * Tumor Size (mm 3 ) ** *** *** Days Control SEL AZD2014 SEL+AZ D2014 PGC1α MITF P-ERK ERK2 P-AKT AKT P-S6 Gopal, Cancer Res, 2014 S6
25 MEKi + TORC1/2i: Regulation of MITF Localization mtorc1/2i: PGC1α but no change in MITF (mrna) Western blotting consistent with mrna WM3854 Control SEL AZD8055 SEL+AZD8055 Control SEL AZD8055 SEL+AZD8055 MEL624 Mock SEL AZD8055 SEL+8055 DAPI PGC1α MITF LaminA/C MITF Cytoplasm Nucleus Cav1 Merge Gopal, Cancer Res, 2014
26 Summary Treatment 1 Treatment 2 Treatment 3 Treatment 4
27 PI3K-AKT Pathway In Melanoma Constitutive activation in subset PTEN loss Correlation with CNS metastasis Apoptosis after MAPKi? Clinical outcomes with BRAFi Compensatory activation resistance/survival Likely thru RTKs PTEN PI3K AKT/PKB PI3K Inhibitors PX-866 XL-147 GDC0941 BKM120 FOXO BAD GSK3 TSC2 Rheb PI3K/mTOR Inhibitors BEZ235 BGT226 SF1126 XL-265 GSK AKT Inhibitors MK2206 Perifosine TSC1 Activation of mtor signaling mtor High OxPhos state Rationale to target clinically Matching drug to driver Pharmacodynamic measures PI3K/mTOR Inhibitors BEZ235 BGT226 SF1126 XL-265 GSK mtorc2 TOR Catalytic Domain Inhibitors AZD8055 OSI027 mtorc1 P70S6K S6 mtorc1 Inhibitors Rapamycin Temsirolimus Everolimus Deforolimus Feedback loops Effects on immune system, TME
28 Michael Davies, M.D., Ph.D. MD Anderson Cancer Center Davies Lab Guo Chen Wanleng Deng Vashisht Nanda Minguang Liu Sherise Ferguson Amanda Bucheit Jeff Yorio Jennifer McQuade Systems Biology Gordon Mills Yiling Lu Ju-Seog Lee Biostatistics Roland Bassett Patricia Fox Denai Milton Melanoma Patrick Hwu Scott Woodman Isabella Glitza Jeff Gershenwald Alexander Lazar Victor Prieto Jeff Weinberg Erik Sulman Eugene Koay Melcore Lauren Haydu Jared Malke Brenna Matejka Silva Frankian Cancer Biology Isaiah Fidler Sun-Jin Kim Ho-Jeong Lee Novartis Darrin Stuart Research Supported By Melanoma Research Alliance NCI Adelson Medical Research Foundation CPRIT American Society for Clinical Oncology AstraZeneca, GlaxoSmithKline Merck, Roche/Genentech Myriad, Oncothyreon, Sanofi-Aventis MDACC SPORE in Melanoma MDACC Moon Shot Program
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