Identificación de vulnerabilidades en tumores sólidos: ¿aportes hacia una medicina personalizada?
Alberto Ocana Albacete University Hospital Salamanca May 19th, 2016 WHAT IS PERSONALIZED MEDICINE?
Molecular alteration------targeted agents------companion diagnostic Drugs with companion diagnostic
Ocana A et al, Oncotarget 2016 Meta-analyses companion vs non-companion diagnostics
PFS OS Meta-analyses of toxicities • Methods to identify oncogenic and non-oncogenic vulnerabilities
• How to evaluate novel agents against them
Our personal experience Synthetic Lethality Interactions
Ocana A and Pandiella A, Personalized therapies in the cancr omics era. Mol Cancer 2010 Summary
Mutation and copy number analyses
Phospho-kinase profiling
Transcriptomic analyses
Synthetic Lethality Interactions- Evaluation of new drugs
“Academic iniciatives: Drug developer” software as a tool Mutation and copy number analyses
No very useful if not linked with a functional evaluation in a specific genetic context A somatic cancer driver event may depend on the constellation of polymorphisms already present in an individual’s germline . (Germline variants single nucleotide polymorphisms, SNPs). But it can be useful for:
Clonal evolution of tumors
Heterogeneity of tumors
Monitor response to treatments and identify mechanism of resistance
Single cell sequencing Circulating tumor DNA
As a part of a global study; pathway level alterations
Example:
Evaluation of mutations in ER using circulating DNA in patients treated with palbociclib + hormonotherapy Phospho-kinase profiling Human samples------protein extraction------phosphokinase evaluation-----drug selection
Software tools: Gene-drugs
Phase Ib/II Trial of BEZ235 With Paclitaxel in Patients With HER2 Negative, Locally Advanced or Metastatic Breast Cancer. NCT01495247. https://www.clinicaltrials.gov/ct2/show/NCT01495247?term=BEZ235&rank=15 Transcriptomic analyses ……to identify biological functions with druggable genes like kinases-- linked with outcome
………to identify gene signatures asociated with worse outcome in relevant clinical situations Normal Breast Basal Like Breast Cancer
Protein kinases with increased expression Deregulated genes Stem Cell 10 Druggable targets
Angiogenesi 9 Probe Set Gene Name Fold Change s Response to drug 8 204641_at NEK2. NIMA (never in mitosis gene a)-related kinase 2 18,24 219148_at PBK. PDZ binding kinase 12,07
Cell migration 7 204822_at TTK protein kinase 11,91
Regulation of 208079_s_at AURKA. Aurora kinase A 10,01 6 • TTK protein kinase. immune system 209464_at AURKB. Aurora kinase B 4,06 process • AURKA. Aurora kinase A Regulation of cell 5 209642_at BUB1. Budding uninhibited by benzimidazoles 1 homolog 11,78 • PLK1. Polo-like kinase 1. death 203755_at BUB1B. Budding uninhibited by benzimidazoles 1 homolog beta 6,42 Transcription factor 4 203213_at CDK1. CDC2. Cell division cycle 2, G1 to S and G2 to M 17,95 Cell cycle 3 Cell cycle genes 243831_at MAPK6. Mitogen-activated protein kinase 6 6,27
202240_at PLK1. Polo-like kinase 1 6,13 Response to stress 2
Cell differentiation 1
0 2 44 66 88 101 121 14 % of genes included 0 2
7007000
65006500 0 HCC3153 6006000 BT549 05500 Drug Target IC50 nM MDA 2500 HS578T 250 HS 5000 MDA-MB-231 HS578T BT549 HCC3153 0 MDABT -MB- HCC3153 Volasertib PLK1. Polo-like kinase 1 7,5 7,5 20 22 20045002000 231
04000 Alisertib AURKA. Aurora kinase A 125 70 250 150 MDA 1500 1503500 HS Docetaxel Inhibitor of depolymerisation of microtubules 180 312 600 1000 0 BT 3000 HCC3153 1000 1000 0 20 40 60 80 100 120 AZ3146 TTK protein kinase 800 1000 2500 6500
500500
0 0 0 20 40 60 80 100 120
Luminal tumors
OS OVARIAN SURFACE OVARIAN CANCER EPITHELIA (OSE) EPITHELIAL CELLS (CEPISs)
≥ 4 fold change 2925 deregulated genes
98 kinases
32 kinases with decreased expression 66 kinases with increased expression
14 kinases with increased expression involved in cell cycle Gene functions • TTK protein kinase. Angiogenesisangiogenesis • CDC28 protein kinase regulatory subunit 2. Growthgrowth factor factor • CHK1 checkpoint homolog (S. pombe). • NIMA (never in mitosis gene a)-related kinase 2. Protoproto-oncogene-oncogene • AURKA. Aurora kinase A. Extracellularextracellular matrix matrix • AURKB. Aurora kinase B. • BUB1 Budding uninhibited by benzimidazoles 1 Cell celladhesion adhesion homolog (yeast). Cellcell cycle cycle • BUB1ß Budding uninhibited by benzimidazoles 1 homolog beta (yeast). cytoskeleton Cytoskeleton • CDKN2A. Cyclin-dependent kinase inhibitor 2A. 0 1 2 3 4 5 0 1 2 3 4 5 • NIMA (never in mitosis gene a)-related kinase 4. % of genes included • CDC2. Cell division cycle 2, G1 to S and G2 to M. • GAK. Cyclin G associated kinase. • GSG2. Germ cell associated 2 (haspin). Ocana A, et al. Oncotarget in press • MAPK12. Mitogen-activated protein kinase 12. Combination of five genes (A and B, respectively in stage I/II and stage I (in early stage) ovarian cancer for PFS …….to evaluate the mechanism of action of new compounds to find synergistic interactions EC70124 (IC50 dose)
RNA (12, 24, 48 hours)
Gene expression analyses
Functional genomics Gene Set Enrichment Analyses-Cytoscape
Synthetic Lethality Interactions- Evaluation of new drugs Functional genomics Normal Breast Basal Like Breast Cancer
Protein kinases with increased expression Deregulated genes Stem Cell 10 Druggable targets
Angiogenesi 9 Probe Set Gene Name Fold Change s Response to drug 8 204641_at NEK2. NIMA (never in mitosis gene a)-related kinase 2 18,24 BET 219148_at PBK. PDZ binding kinase 12,07 Cell migration 7 204822_at TTK protein kinase 11,91
Regulation of Inhibitors 208079_s_at AURKA. Aurora kinase A 10,01 immune system 6 • TTK protein kinase. JQ1 209464_at AURKB. Aurora kinase B 4,06 • AURKA. Aurora kinase A Regulation cell death 5 209642_at BUB1. Budding uninhibited by benzimidazoles 1 homolog 11,78 • PLK1. Polo-like kinase 1. 203755_at BUB1B. Budding uninhibited by benzimidazoles 1 homolog beta 6,42 Transcription factor 4 203213_at CDK1. CDC2. Cell division cycle 2, G1 to S and G2 to M 17,95 Cell cycle 3 Cell cycle 243831_at MAPK6. Mitogen-activated protein kinase 6 6,27 202240_at PLK1. Polo-like kinase 1 6,13 Response to stress 2 genes Cell differentiation 1
0 2 44 66 88 101 121 14 % of genes included 0 2
Lentiviral shRNA library + stress condition Academic iniciatives: “Drugdeveloper” software as a tool
MD Anderson Experience
50 genes---35 actionable
Summary
• Integrating omics
• Confirm with functional genomics
• Confirm with biochemical evaluation: genomic context dependent
• Personalized medicine: Softwares: far away to be accurate
• Personalized clinical trials or studies, are under “evaluation” Thank you for your atention
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