Clinical Trials for Brain Tumor Patients
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Audio and slides for this presentation are available on YouTube: http://youtu.be/6W_xoH4s-Yk Dr. Patrick Wen, of Dana-Farber Cancer Institute's Center for Neuro-Oncology, discusses current clinical trial options for brain tumor patients and some of the new therapies available in neuro-oncology. This presentation was originally given at Dana-Farber Cancer Institute on Dec. 4, 2013.
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Clinical Trials for Brain Tumor Patients
- 1. Clinical Trials In Neuro-Oncology Patrick Y. Wen, M.D. Center For Neuro-Oncology, Dana-Farber Cancer Institute Division of Cancer Neurology, Department of Neurology Brigham and Women’s Hospital Harvard Medical School
- 2. Clinical Trials • Phase I • Find the maximum safe dose • Phase II • Determine efficacy of drug at the maximum safe dose • Phase III • Compare effectiveness of drug to standard of care
- 3. Update on Novel Therapies • Targeted Molecular Therapies • Antiangiogenic Therapies • Stem Cell Therapies • Immunotherapies • Tumor Metabolism
- 5. Wen & Kesari. N Engl J Med 2008;359:492-507
- 6. Frequent genetic alterations in three critical signalling pathways. The Cancer Genome Atlas Research Network Nature 000, 1-8 (2008) doi:10.1038/nature07385
- 9. PATIENTS WITH SAME DIAGNOSIS ARE NOT ALL THE SAME Predicted good response to drug or combination of drugs Predicted poor or no response to drug or combination of drugs Increased likelihood of toxicity of drug or combination of drugs CHANGE DRUGS CHANGE DRUGS
- 10. PATIENTS WITH SAME DIAGNOSIS ARE NOT ALL THE SAME Predicted good response to drug or combination of drugs Predicted poor or no response to drug or combination of drugs Increased likelihood of toxicity of drug or combination of drugs CHANGE DRUGS CHANGE DRUGS
- 11. PATIENTS WITH SAME DIAGNOSIS ARE NOT ALL THE SAME Predicted good response to drug or combination of drugs Predicted poor or no response to drug or combination of drugs Increased likelihood of toxicity of drug or combination of drugs CHANGE DRUGS CHANGE DRUGS
- 12. PATIENTS WITH SAME DIAGNOSIS ARE NOT ALL THE SAME Predicted good response to drug or combination of drugs Predicted poor or no response to drug or combination of drugs Increased likelihood of toxicity of drug or combination of drugs CHANGE DRUGS CHANGE DRUGS
- 13. Personalized Medicine The Right Drug for the Right Person at the Right Time This is the overarching goal of Dana-Farber’s research
- 14. Dramatic clinical responses to drugs targeting BRAF only in patients with the BRAF mutation! Baseline Day 15 Flaherty et al., ASCO 2009 (abstract #9000)
- 15. Sequencing Epigenetic Analysis Set of activated kinases and pathways Combinations of appropriate drugs Ivy Foundation Early Phase Clinical Trials Consortium DF/HCC MSKCC UCLA UCSF MDACC U Utah
- 16. • Oncopanel 275 genes • Array CGH
- 17. DFCI/BWH “Living” Tissue Bank Program CNS Tumor Patient Primary tumor Gentle Dissociation Papain Tumorsphere culture - Hydrogel Comprehensive Analysis - EGF and FGF Laminin culture - Laminin coating - EGF and FGF IHC Stem/Lineage Assessment RNA Expression Affy U133 2.0 Plus Whole Genome Copy Number Agilent aCGH 1M Somatic Mutation Sequenom Xenograft - Orthotopic (striatum) - SCID mice - Serial passage Sphere culture for GBM – Howard A. Fine, Cancer Cell 06 Laminin - Peter Dirks, Cell Stem Cell 09 Slide courtesy of Keith Ligon MD
- 18. GBM Patient-derived Cell Lines Reproduce Key Features of GBM as in vivo preclinical models A Infiltrating borders BT112 Necrosis BT112 B Pushing borders BT189 Microvascular Proliferation BT187 C Gliomatosis BT179 Numa Intratumoral Hemorrhage BT189 Slide courtesy of Keith Ligon MD
- 19. Access Across Blood Brain Barrier is Important
- 20. BKM120 Crosses the BBB BKM120 557 BKM 4h BKM120 Heme
- 22. GRN1005
- 23. GRN1005 Low density lipoprotein receptor related protein (LRP1)
- 24. GRN1005
- 25. GRN1005
- 26. GRN1005
- 27. GRN1005
- 28. GRN1005
- 30. Wen & Kesari. N Engl J Med 2008;359:492-507
- 31. PI3 Kinase/Akt/MEK Inhibitors MEK inhibitor Growth Factors, etc AZD6244 GSK1120212 GDC0973 MSC1936369B MEK162 Ras Raf Mek Erk Proliferation PI3K inhibitor XL765 XL147 BKM120 PX866 GDC0084
- 36. Mechanisms of Resistance (Bergers and Hanahan Nat Rev Cancer 2008)
- 37. Adaptive Evasion to VEGF inhibitors FGFs Angiopoietins Src, integrins, FAK, etc Ephrins Increased invasiveness Anti-VEGF therapy leads to tumor stasis or shrinkage, and blood vessel regression Upregulation of additional proangiogenic growth factors PIGF SDF-1 PDGF Increased pericyte coverage to protect blood vessels Recruitment of vascular progenitors and proangiogenic monocytes
- 38. Strategies To Overcome Resistance FGFR – BIBF1120 (VEGFR+FGFR+PDGFR) – E7080 (VEGR+FGFR+PDGFR) Angiopoietins – AMG 386 (angiopoietin 1/2 neutralizing peptibody) CXCR4 – Plerixafor (AMD3100) CD105 – TRC105
- 40. Single cell from neural tube “neurosphere” disaggregate & subcultivate plate onto adherent surface in factor-fee medium Neuron Astrocyte Oligodendrocyte
- 41. Glioma Stem Cells Wen PY, Kesari S. N Engl J Med 2008;359:492-507
- 42. Grow as neurospheres in vitro. Neurospheres are multipotent Highly tumorigenic in SCID mice. Negative control: (hemispherectomy tissue) Oligodendrocyte Astrocyte Neuron
- 44. GDC-0449 D. D. Von Hoff et al., N. Eng. J. Med. 164, 1164(2009). Science 23 October 2009: Vol. 326. no. 5952, pp. 572 - 574 DOI: 10.1126/science.1179386
- 45. Glioma Stem Cells Wen PY, Kesari S. N Engl J Med 2008;359:492-507
- 46. PTEN null lines: BKM120 and LDE225 Targeting tumor cells and stem cells Courtesy of Rosalind Segal, DFCI
- 47. Immunotherapy
- 49. Immunotherapies Vaccines • Dendritic cell • DCVax • ICT107 • Peptides • Stemline • CDX110 (Rindopepimut) Ipilimumab PD1 and PDL1 antibodies
- 50. Targeting Tumor Metabolism The Warburg Effect Otto Heinrich Warburg, 1883–1970 Vander Heiden et al, 2009.
- 51. Vander Heiden et al, 2009. Teicher et al. Clin Cancer Res 2012;18:5537-5545
- 52. IDH1 and IDH2 Mutations in Human Gliomas Yan H et al. N Engl J Med 2009;360:765-773
- 53. Survival of Adult Patients with Malignant Gliomas with or without IDH Gene Mutations Median Survival 31 mo vs 15 mo Median Survival 65 mo vs 25 mo Yan H et al. N Engl J Med 2009;360:765-773
- 54. IDH 1 & 2 as a Therapeutic Targets Reitman et al, 2010.
- 55. Science 2013
- 56. Andronesi et al Sci Transl Med. 2012
- 59. GBM Trials Newly-diagnosed GBM With RT • • • • • • BKM120 (PI3K inhibitor) MK1775 (WeeI Inhibitor) ABT414 DCVax Rindopepimut (CDX110) PLX3397 (CSF-1R) Adjuvant • BKM120 (PI3K) • MK1775 (Wee1 Inhibitor) • ABT 414
- 60. Recurrent GBM Trials Anti-VEGF/VEGFR •Avastin + Plerixafor + Avastin •Avastin + AMG386 •Avastin + TRC105 •Avastin + MLN0128 •VB-111 (viral vector targeting angiogenesis) Targeted Therapies • BKM120 (PI3K) • BKM120 + LDE225 (Smo) • BKM120 +INC280 • GDC0084 (PI3K) • PF-00299804 (pan EGFR) • ABT414 +TMZ (EGFR antibody) • LY835219 (CDK4/6) • BGJ398 (FGFR) • Fliclatuzumab (HGF) • AG120 (IDH-1 inhibitor) Immunotherapy •Avastin + CDX110 vaccine • Ipilimumab/Nivolumab/Avastin • Avastin + HSPPC96
- 61. Other Brain Tumor Trials Therapeutic Trials Low-Grade Glioma • RT vs TMZ (ECOG/RTOG/NCCTG) •AG120 (IDH1 inhibitor) Meningioma • Avastin Brain Metastases • GRN1005 Ependymona • TMZ+lapatinib NF2 Schwannomas • PTC299 Plexiform neurofibroma • Rapamycin Fatigue • Armodafinil (Nuvigil) + RT Primary CNS lymphoma • Premetrexed • Allo-BMT Non-Therapeutic Trials • Vimpat seizure prophylaxis • FLT-PET, FET-PET • FMiso-PET
- 62. Thank You!
Editor's Notes
- The pharmacogenetics has many clinical potentials. Patients with the same diagnosis are typically treated with the same manner, although their responses to drug therapy will not be the same. Phamacogenetics has the potential to provide a tool for predicting those patients who are likely to have the desired response to the drug, those who are likely to have little or no benefit and those at risk for toxicity. This will allow tailored therapy that should reduce adverse reactions to drugs and increase efficacy rates.
- The pharmacogenetics has many clinical potentials. Patients with the same diagnosis are typically treated with the same manner, although their responses to drug therapy will not be the same. Phamacogenetics has the potential to provide a tool for predicting those patients who are likely to have the desired response to the drug, those who are likely to have little or no benefit and those at risk for toxicity. This will allow tailored therapy that should reduce adverse reactions to drugs and increase efficacy rates.
- The pharmacogenetics has many clinical potentials. Patients with the same diagnosis are typically treated with the same manner, although their responses to drug therapy will not be the same. Phamacogenetics has the potential to provide a tool for predicting those patients who are likely to have the desired response to the drug, those who are likely to have little or no benefit and those at risk for toxicity. This will allow tailored therapy that should reduce adverse reactions to drugs and increase efficacy rates.
- The pharmacogenetics has many clinical potentials. Patients with the same diagnosis are typically treated with the same manner, although their responses to drug therapy will not be the same. Phamacogenetics has the potential to provide a tool for predicting those patients who are likely to have the desired response to the drug, those who are likely to have little or no benefit and those at risk for toxicity. This will allow tailored therapy that should reduce adverse reactions to drugs and increase efficacy rates.
- Please use your best judgment… Because the AAN logo must appear on screen, please reserve this template exclusively for: Large Charts and Large Scale Images
- Inhibitor of smoothened, also showed benefit in basal cell carcinoma The team found that a point mutation in Smoothened, a G-to-C substitution at position 1697 along the protein's length, prevented GDC-0449 from binding but did not alter the ability of Smoothened to switch on the Hedgehog pathway.
- Fig. 2. Schematic representation of the differences between oxidative phosphorylation, anaerobic glycolysis, and aerobic glycolysis (Warburg effect). In the presence of oxygen, nonproliferating (differentiated) tissues first metabolize glucose to pyruvate via glycolysis and then completely oxidize most of that pyruvate in the mitochondria to CO2 during the process of oxidative phosphorylation. Because oxygen is required as the final electron acceptor to completely oxidize the glucose, oxygen is essential for this process. When oxygen is limiting, cells can redirect the pyruvate generated by glycolysis away from mitochondrial oxidative phosphorylation by generating lactate (anaerobic glycolysis). This generation of lactate during anaerobic glycolysis allows glycolysis to continue (by cycling NADH back to NAD+), but results in minimal ATP production when compared with oxidative phosphorylation. Warburg observed that cancer cells tend to convert most glucose to lactate regardless of whether oxygen is present (aerobic glycolysis). This property is shared by normal proliferative tissues. Mitochondria remain functional and some oxidative phosphorylation continues in both cancer cells and normal proliferating cells. Nevertheless, aerobic glycolysis is less efficient than oxidative phosphorylation for generating ATP. In proliferating cells, ~10% of the glucose is diverted into biosynthetic pathways upstream of pyruvate production. (Vander Heiden et al, 2009)
- Fig. 3. Metabolic pathways active in proliferating cells are directly controlled by signaling pathways involving known oncogenes and tumor suppressor genes. This schematic shows our current understanding of how glycolysis, oxidative phosphorylation, the pentose phosphate pathway, and glutamine metabolism are interconnected in proliferating cells. This metabolic wiring allows for both NADPH production and acetyl-CoA flux to the cytosol for lipid synthesis. Key steps in these metabolic pathways can be influenced by signaling pathways known to be important for cell proliferation. Activation of growth factor receptors leads to both tyrosine kinase signaling and PI3K activation. Via AKT, PI3K activation stimulates glucose uptake and flux through the early part of glycolysis. Tyrosine kinase signaling negatively regulates flux through the late steps of glycolysis, making glycolytic intermediates available for macromolecular synthesis as well as supporting NADPH production. Myc drives glutamine metabolism, which also supports NADPH production. LKB1/AMPK signaling and p53 decrease metabolic flux through glycolysis in response to cell stress. Decreased glycolytic flux in response to LKB/AMPK or p53 may be an adaptive response to shut off proliferative metabolism during periods of low energy availability or oxidative stress. Tumor suppressors are shown in red, and oncogenes are in green. Key metabolic pathways are labeled in purple with white boxes, and the enzymes controlling critical steps in these pathways are shown in blue. Some of these enzymes are candidates as novel therapeutic targets in cancer. Malic enzyme refers to NADP+-specific malate dehydrogenase [systematic name (S)-malate:NADP+ oxidoreductase (oxaloacetate-decarboxylating)]. (Vander Heiden et al, 2009)
- Figure 1. IDH1 and IDH2 Mutations in Human Gliomas. Panel A shows mutations at codon R132 in IDH1 and R172 in IDH2 that were identified in human gliomas, along with the number of patients who carried each mutation. Codons 130 to 134 of IDH1 and 170 to 174 of IDH2 are shown. Panel B shows the number and frequency of IDH1 and IDH2 mutations in gliomas and other types of tumors. The roman numerals in parentheses are the tumor grades, according to histopathological and clinical criteria established by the World Health Organization. CNS denotes central nervous system.
- Figure 3. Survival of Adult Patients with Malignant Gliomas with or without IDH Gene Mutations. For patients with glioblastomas, the median survival was 31 months for the 14 patients with mutated IDH1 or IDH2, as compared with 15 months for the 115 patients with wild-type IDH1 or IDH2 (Panel A). For patients with anaplastic astrocytomas, the median survival was 65 months for the 38 patients with mutated IDH1 or IDH2, as compared with 20 months for the 14 patients with wild-type IDH1 or IDH2 (Panel B). Patients with both primary and secondary tumors were included in the analysis. For patients with secondary glioblastomas, survival was calculated from the date of the secondary diagnosis. Survival distributions were compared with the use of the log-rank test.
- Figure 1. Mutations in the Active Site of IDH1 and IDH2 Lead to a Neomorphic Enzyme Activity Wild-type IDH1 and IDH2 normally catalyze the conversion of isocitrate to a-KG (left reaction) and at the same time reduce NADP+ to NADPH and produce CO2. R132 in wild-type IDH1, as well as R140 and R172 in wild- type IDH2, form hydrogen bonds with the b-carboxyl (green) of isocitrate. Cancer-derived mutations affecting these residues cause the enzymes to instead convert a-KG to 2HG while at the same time oxidizing NADPH to NADP+ (right reaction). 2HG and isocitrate share an identical chemical back- bone but differ solely in the presence of the b-carboxyl on isocitrate, but not 2HG. IDH1 R132, IDH2 R140, and IDH2 R172 mutation apparently favors conversion to 2HG rather than isocitrate given that 2HG lacks this group. (Reitman et al, 2010).