memorial descritivo e projeto de atuação...
TRANSCRIPT
Technologies
2001
Explosion of the “Omics”
• Proteomics• Allergenomics• Bibliomics• Biomics• Cardiogenomics• Cellomics• Chemogenomics• Chemoproteomics• Chromatinomics• Chromonomics• Chromosomics• Combinatorial Peptidomics• Computational RNomics• Cryobionomics
Crystallomics
Cytochromics
Cytomics
Degradomics
Ecotoxicogenomics
Eicosanomics
Embryogenomics
Enviromics
Epigenomics
Epitomics
Expressomics
Fluxomics
Fragmentomics
Fragonomics
Etc…
http://www.genomicglossaries.com
Ries LAG, Eisner MP, Kosary CL, Hankey BF, Miller BA, Clegg L, Mariotto A, Feuer EJ, Edwards BK (eds). SEER Cancer Statistics Review, 1975-2002, National Cancer Institute. Bethesda, MD, http://seer.cancer.gov/csr/1975_2002/, based on Nov 2004 SEER data submission, posted to the SEER web site 2005.
Leukemias and Lymphomas
80
Years Ago
Leukemia or Lymphoma60
Years Ago
Chronic Leukemia
Acute Leukemia
Preleukemia
Indolent Lymphoma
Aggressive Lymphoma
100
Years Ago
“Disease of the Blood”
Today
38 Leukemia types identified:
Acute myeloid leukemia (12 types)
Acute lymphoblastic leukemia (2 types)
Acute promyelocytic leukemia (2 types)
Acute monocytic leukemia (2 types)
Acute erythroid leukemia (2 types)
Acute megakaryoblastic leukemia
Acute myelomonocytic leukemia (2 types)
Chronic myeloid leukemia
Chronic myeloproliferative disorders (5 types)
Myelodysplastic syndromes (6 types)
Mixed myeloproliferative/myelodysplastic syndromes (3 types)
51 Lymphomas identified:
Mature B-cell lymphomas (14 types)
Mature T-cell lymphomas (15 types)
Plasma cell neoplasm (3 types)
Immature (precursor) lymphomas (2 types)
Hodgkin’s lymphoma (5 types)
Immunodeficiency associated lymphomas (5 types)
Other hematolymphoid neoplasms (7 types)
5-YearSurvival
~ 0%
70%
Personalized medicine - concept
The right treatment, for the right
patient, at the right time
From European Union
Personalized Medicine Reduces Ineffective Treatment in Colon Cancer
Langreth, R. (2008), ‘Imclone’s Gene Test Battle’, Forbes.com, 16May
kras Testing
Do Not Treat
Treat with Erbitux
Treat with Erbitux
TreatmentSuccess
Burrell et al., 2013 - Nature
Tumor Heterogeneity
Red – chromosome 2Green – chromosome 18
Heterogeneity of MAP-Tau expression in a whole tissue section of breast carcinoma. (a) H&E stain. (b) Immunofluorescence. Nuclei are labeled with DAPI. Cytokeratin is labeled with Cy3. MAP-Tau is labeled with Cy5.
Tolles et al. Breast Cancer Research 2011 13:R51
Therapy?
Tumor Heterogeneity
Heterogeinity: Primary tumor X metastasis
Primary renal-cell carcinoma
AdaptationHeterogeinity
ResistancePlasticity
Microenvironment
Therapy failure
Major challenge to precision medicine and biomarker development
Case: Breast Cancer
Breast Cancer
PubMed and clinicaltrials.gov (September, 2016)
Problems about Breast Cancer
1. New cases: 1.7 million wordwide
2. Second most common cause of cancer mortality in developed countries
drugs + timeline + side effects (70%)
3. Therapy costs: $1 billion a year (US)
AdaptationHeterogeinity
ResistancePlasticity
Microenvironment
Therapy failure
Major challenge to precision medicine and biomarker development
1. Delineate a strategy to identify targets to breastcancer treatment
2. Avoid side effetcs
Goals
(intact-micluster)
~10,000 genes
~600 genes
Carels et al., 2015_PlosOnePatent: BR1020150308191
Study design
Up-regulated genes
Down-regulated genes
P < 0.001
MDA-MB-231 vs. MCF-10A
HSP90AB1
VIM
CSNK2B
TK1
YWHAB
Carels et al., 2015_PlosOnePatente: BR1020150308191
Are they actionable? Combination -> 5 genes
Bench validation: Top-5 inhibition
Downregulation using siRNAs
MCF-10AMDA-MB-231 MCF-7
HSP90AB1GRB2EEF1GMCM7KPNA2
HSP90AB1TK1
CSNK2BVIM
YWHAB
HSP90AB1VIMCSNK2BYWHABTK1
Functional assays
Tilli et al., 2016 Oncotarget
100nM – 48h
(A)
(B)
MDA-MB-231 MCF-10A MCF-7
Bench validation: Top-5 inhibition
Cell Proliferation and Survival
Tilli et al., 2016 Oncotarget
Scrambled All siRNA
All siRNA
All siRNA
Scrambled
Scrambled
Control
Control
Control
MD
A-M
B-2
31
MC
F-1
0A
MC
F-7
(A)
(B)MDA-MB-231
MCF-10A
MCF-7
Bench validation: Top-5 inhibition
Cell proliferation
Tilli et al., 2016 Oncotarget
MCF-10A Scrambled
MCF-10A All siRNA
MDA-MB-231 Scrambled
MDA-MB-231 All siRNA
MCF-10A MDA-MB-231
(A) (B)
(C) (D)
Tilli et al., 2016 Oncotarget
(A)
Control Scrambled All siRNA
MD
A-M
B-2
31
MC
F-1
0A
MC
F-7
Bench validation: Top-5 inhibition
Migration
Tilli et al., 2016 Oncotarget
Control Scrambled All siRNAM
DA
-MB
-231
MC
F-1
0A
MC
F-7
Bench validation: Top-5 inhibition
Invasion
Tilli et al., 2016 Oncotarget
(A)
(B)
Control Scrambled All siRNA
MD
A-M
B-2
31
MC
F-1
0A
MC
F-7
Bench validation: Top-5 inhibition
Metastatic Potential – Colony formation soft agarMDA-MB-231
MCF-7
MCF-10A
Tilli et al., 2016 Oncotarget
(A) (B)
Bench validation: Individual transfections
Cell Proliferation and Survival
Tilli et al., 2016 Oncotarget
(C)
(E)
(G)
(D)
(F)
(H)
Bench validation: Individual transfections
Cell death
Tilli et al., 2016 Oncotarget
Scrambled
HSP90AB1
YWHAB
VIM
CSNK2B
TK1
(A)
(B)
(C) (D) (E)
Bench validation: Individual transfections
Tilli et al., 2016 Oncotarget
HSP90AB1TK1
CSNK2BVIM
YWHAB
MDA-MB-231 MCF-7
HSP90AB1GRB2EEF1GMCM7KPNA2
MCF-10A
Strategy
Drugs combination
Summary
Interactome + Transcriptome
Selection of targets
Drug development
Network pharmacology
Summary
HSP90AB1CSNK2B
MDA-MB-468
BT-20
MAGOHEEF1G
VIMYWHAB
TK1
CHD3HDGF
MDA-MB-231
EGFR
Individualized treatment
Personalized medicine - concept
The right treatment, for the right
patient, at the right time
TCGA database: unveil protein target for therapy.
• 85 patients -> breast tumor versus normal, including molecular subtypes (~75 genes). (Alessandra Conforte Thesis)
• ~50% of the targets are FDA-approved.
• In silico pharmacology targets -> pharma industry. (Dr Carlyle Ribeiro, Pos Doc)
Targeted Therapy: A Giant Step Forward
Startup : Development of a molecular diagnostic approach to assist breast cancer treatment
Project contemplated -> FAPERJ
Amplify for other tumors and diseases.
Rational approach to select drugs1. Efficacy
2. Avoid side effects
Tumor and normal tissue
Transcritome and Interactome
6 days
Solution: Molecular approach
Proposed value
Breast cancer panelP
ate
nt
pe
nd
ing
Breast cancer panelRecurrence
Patent pending
mR
NA
exp
ress
ion
(RN
Ase
qV
2) Gene A Gene B Gene C
Breast cancer panelPatient Vital Status
Patent pending
mR
NA
exp
ress
ion
(RN
Ase
qV
2) Gene A Gene B Gene C
Breast cancer panelOverall Survival
Patent pending
Breast cancer panelNetwork
Patent pending
Breast cancer panelNext steps.......
Patent pending
Clinical trial
Nanomedicine – New Era of Personalized Medicine
Drugdelivery Therapy
ImagingDiagnosis
Prognosis
Gene delivery
Labelling
Labelling Monitoring
TCGA: unveil membrane proteins specifically expressed in breastcancer tissues.
95 patients -> tumor versus normal, including molecular subtypes
siRNA
Specific protein
Specific receptor
Manuscript in preparationPatent pending
Membrane proteins: breast cancer
Não-tumoral Tumor
Nanoparticle -> therapy and imaging
Monoclonal antibodies -> therapy
Biomarker -> Diagnosis
free margin surgery -> prognosis, therapy
Pharma industry
Alunas: Júlia Badaró, Luiza Gomes, Alice Gomes
Clinicalapplications
BioinformaticsCell Biology
Molecular BiologySystem Biology
Modelling
Dia
gno
sis
The
rap
y
Summary
Acknowledgements
• UofA, Alberta, Canada– Jack Tuszynski
• Fiocruz/CDTS– Nicolas Carels
– Alessandra Conforte
– Julia Badaró
– Milena Magalhães
– Carlyle Lima
– Luiza Gomes
• PROCC– Fabrício Alves
Hallmarks: estudo da medicina personalizada
1. Desenvolvimento de novos fármacos2. Sistema de delivery específicos
Projeto 3: Validação in vivo
Objetivo 1. Avaliar a formação de tumor -> ortotópico e subcutâneoMetástase -> veia da cauda – pulmão
intra-cardíaca - óssea
RNAi
Objetivo 2. Avaliar a formação de tumor -> ortotópico e subcutâneo
CRISPR-Cas9
Objetivo 3. Avaliação terapêutica -> tumor subcutâneo + injeção intra-tumoralRNAi
Projeto 10: Células tronco tumorais, glioma
1. Melhor compreensão desse tipo celular2. Identificar alvos para aumentar a eficiência terapêutica
Dr Kiran Velpula
Sem soro+EGF / +bFGF
Nanog / Oct3-4 / Sox2
ZR-741XMCF-10A
Actin
GRB2
PDIA3
NPM1
GAPDH
Up-regulated genes
Down-regulated genes
HSP90AB1
BT-474XMCF10A
HSP90AB1
Actin
ERBB3
YWHAB
ERBB2
IKBA
GRP78
GAPDH
PA2G4
A strategy to unveil housekeeping genes suitable for analyses in breast cancer diseases
Manuscript in preparation
Oncotype DX Test – 21 genesNational Comprehensive Cancer Center Network (NCCN) and the American Society of Clinical
Oncology (ASCO) treatment guidelines
Aim: identify HKGs for breast cancer
Tumor Heterogeinity
Breast cell lines
(A)
(B)
novel HKGs X traditional HKGs
ER
STAT5
STAT3
HIF
SP1
TP53
MYC
AP1
NFKB
NOTCH
FOXA1
GATA3
ELK1
RPL13AGAPDH
PGK1
ACTB
TUBA1A
DIMT1
PUM1
GUSB
B2M
DHX9
LARP1
MZT2B
TAF2
STX5
UBXN4
CCSER2
ANKRD17
SYMPK
TMEM11
Transcription factorsnHKGstHKGs
(A) (B)
Bench validation: Real Time PCR
Luminal A -> MCF-7 and T47D Triple Negative -> MDA-MB-231 and MDA-MB-468 Non-tumoral -> MCF-10A
International Cancer Genome Consortium (ICGC): 394 patients
CCSER2, SYMPK and ANKRD17