The document describes a study that integrated pathway and gene expression data to identify novel pathway-specific cancer drugs. The researchers identified major cancer pathways including Sonic Hedgehog, PI3K/AKT, PTEN and Wnt/beta-catenin. They applied a modified Connectivity Map algorithm to identify drugs that specifically perturb these pathways. They successfully identified many novel drug indications for the PI3K, PTEN and Sonic Hedgehog pathways that could lead to new cancer treatments. Future work includes integrating additional pathway databases and the LINCS database to identify more pathway-specific drugs.
DOC2 - No significant risk levels 4 MI - CaliforniaToxiColaOrg
Esta es la propuesta que está presentando la Oficina de Riesgos Ambientales en la Salud de la Agencia de Protección Ambiental de California para establecer un nivel máximo de ingesta diaria de 4-Metilidimadazol (compuesto cancerígeno que se encuentra en el Caramelo IV utilizado en las bebidas de Cola) que no signifique riesgo a la salud. La propuesta de la autoridad en California es que no se consuma más de 16 microgramos de este compuesto en todo un día. Como se ve en el DOC3, la cantidad de 4-Metilidimadazol que se encuentra en una lata de bebida de cola puede ser 8 veces más de este límite.
MicroRNAs are short non- coding RNA molecules (~21 bases) that have been identified as important regulators of gene expression at the translational and transcriptional level. They are known to play a crucial role in cell development, differentiation, and disease. Dysregulation of miRNAs has been linked to cancer development as well as progression. In addition, miRNAs have been identified as cancer classifiers and disease biomarkers. Recent studies have shown that miRNAs are present in body fluids (serum, saliva, semen, urine) thus providing a non-invasive tool to study and monitor disease states. Earlier research studies identified specific miRNAs as characteristic for germ cell cancers, i.e., seminomas and nonseminomas (miR-372, miR-373).
Material and Methods
miRNA Profiling (~760 miRNAs) was performed to verify this observation and to identify additional miRNAs as candidate biomarkers in serum samples for testicular cancer (seminoma and non-seminoma types and from normal and cancer tissue in parallel with their matched serum samples collected from the same donors). For this research study we used a miRNA specific bead capture system to isolate miRNAs from serum and qPCR (TaqMan®Array Card platform) for profiling.
Results
Using this high throughput approach, consistent differences were identified in miRNA expression for the previously identified hsa-miR-371, hsa-miR- 372, and hsa-miR-302b between tumor and normal samples. In addition other interesting miRNAs showed relevant and significant differences as well.
Conclusions
These data suggest that there may be intrinsic differences in the overall miRNA expression profiles between seminoma and non-seminoma cancer types. An update on the actual status will be presented.
DOC2 - No significant risk levels 4 MI - CaliforniaToxiColaOrg
Esta es la propuesta que está presentando la Oficina de Riesgos Ambientales en la Salud de la Agencia de Protección Ambiental de California para establecer un nivel máximo de ingesta diaria de 4-Metilidimadazol (compuesto cancerígeno que se encuentra en el Caramelo IV utilizado en las bebidas de Cola) que no signifique riesgo a la salud. La propuesta de la autoridad en California es que no se consuma más de 16 microgramos de este compuesto en todo un día. Como se ve en el DOC3, la cantidad de 4-Metilidimadazol que se encuentra en una lata de bebida de cola puede ser 8 veces más de este límite.
MicroRNAs are short non- coding RNA molecules (~21 bases) that have been identified as important regulators of gene expression at the translational and transcriptional level. They are known to play a crucial role in cell development, differentiation, and disease. Dysregulation of miRNAs has been linked to cancer development as well as progression. In addition, miRNAs have been identified as cancer classifiers and disease biomarkers. Recent studies have shown that miRNAs are present in body fluids (serum, saliva, semen, urine) thus providing a non-invasive tool to study and monitor disease states. Earlier research studies identified specific miRNAs as characteristic for germ cell cancers, i.e., seminomas and nonseminomas (miR-372, miR-373).
Material and Methods
miRNA Profiling (~760 miRNAs) was performed to verify this observation and to identify additional miRNAs as candidate biomarkers in serum samples for testicular cancer (seminoma and non-seminoma types and from normal and cancer tissue in parallel with their matched serum samples collected from the same donors). For this research study we used a miRNA specific bead capture system to isolate miRNAs from serum and qPCR (TaqMan®Array Card platform) for profiling.
Results
Using this high throughput approach, consistent differences were identified in miRNA expression for the previously identified hsa-miR-371, hsa-miR- 372, and hsa-miR-302b between tumor and normal samples. In addition other interesting miRNAs showed relevant and significant differences as well.
Conclusions
These data suggest that there may be intrinsic differences in the overall miRNA expression profiles between seminoma and non-seminoma cancer types. An update on the actual status will be presented.
This presentation is intended for all urology and oncology physicians and staff. Learn more about the utilization of Enzalutamide in metastatic prostate cancer and the prostate cancer patient journey with a specialty pharmacy.
• Enzalutamide outcomes prior to chemotherapy
• Adverse events associated with Enzalutamide
• Utilization of a specialty pharmacy
More information is available to you for urology medications, treatment options and referral forms here: http://www.avella.com/specialties/urology
Thecomparison methods of amphetamines detection in urine samples in Thammasat...kridsada31
Thecomparison methods of amphetamines detection in urine samples in ThammasatUniversity hospital, Pathumtaniprovince.
KridsadaSirisabhabhornSupapornPumpaNarumonSereekhajornjaruand PalakornPuttarak
Department of Medical Technology Laboratory, ThammasatUniversity Hospital Pathumtani, Thailand
This presentation is intended for all urology and oncology physicians and staff. Learn more about the utilization of Enzalutamide in metastatic prostate cancer and the prostate cancer patient journey with a specialty pharmacy.
• Enzalutamide outcomes prior to chemotherapy
• Adverse events associated with Enzalutamide
• Utilization of a specialty pharmacy
More information is available to you for urology medications, treatment options and referral forms here: http://www.avella.com/specialties/urology
Thecomparison methods of amphetamines detection in urine samples in Thammasat...kridsada31
Thecomparison methods of amphetamines detection in urine samples in ThammasatUniversity hospital, Pathumtaniprovince.
KridsadaSirisabhabhornSupapornPumpaNarumonSereekhajornjaruand PalakornPuttarak
Department of Medical Technology Laboratory, ThammasatUniversity Hospital Pathumtani, Thailand
g protein coupled receptors, ion channels, types of receptors, wnt signalling, cell signalling, tranduction pathway, disorders regarding the signalling
Decades of cancer research including comprehensive molecular profiling combined with the
development of a broad array of targeted therapies have created the opportunity to transform
cancer care in the near future by implementing precision oncology based approaches. An
important element of this system is the widespread availability of robust and cost-effective
multivariate profiling methods in order to characterize relevant cancer associated molecular
alterations.
Current commercially available multivariate profiling methods vary dramatically with regard to
the number of cancer genes interrogated. Given that many large scale and detailed molecular
profiling studies have been completed, the landscape of somatic alterations in solid tumors is
reasonably well-known. Furthermore, the specific gene variants that are relevant to application
of targeted therapies are also a matter of record. Therefore, we set out to define the number of
relevant cancer genes for precision oncology research based on the currently available
empirical evidence.
Dr. José Baselga - Simposio Internacional 'Terapias oncológicas avanzadas'Fundación Ramón Areces
Los días 15 y 16 de octubre de 2014, la Fundación Ramón Areces y la Real Academia Nacional de Farmacia, en colaboración con la Fundación de la Innovación Bankinter, reunieron en Madrid a algunos de los mayores expertos mundiales en nuevas terapias contra el cáncer. El Simposio Internacional, coordinado por la profesora y académica María José Alonso, analizó el momento actual de la lucha contra esta enfermedad. También fue un punto de encuentro para científicos de los más innovadores institutos de investigación en oncología, quienes debatieron sobre tres grandes temas: la Medicina Personalizada contra el cáncer, los nanomedicamentos en la terapia del cáncer y las terapias basadas en la inmunomodulación.
International Journal of Pharmaceutical Science Invention (IJPSI)inventionjournals
International Journal of Pharmaceutical Science Invention (IJPSI) is an international journal intended for professionals and researchers in all fields of Pahrmaceutical Science. IJPSI publishes research articles and reviews within the whole field Pharmacy and Pharmaceutical Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Changing landscape in the treatment of advanced prostate cancer Alok Gupta
This presentation describes how the treatment of stage 4 prostate cancer has improved over last 100 years. This was presented at URO ONCOLOGY UPDATE meeting of Delhi Urological Society on 18th March 2017
Statistical multivariate analysis to infer the presence breast cancerFahad B. Mostafa
The primary aim of this multivariate analysis is to show statistical significance of many statistical technique to analysis multivariate data. To do this we start with exploratory study to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis of 116 women. To conduct this process, we will plot the sample data and show the type of distribution it follows. Main aim of this research is to reduce dimensionality using eigen decomposition of data matrix. To perform it we use the most useful PCA method. Finally, we want to find some hypothesis tests for finding the normality assumption, equal mean and covariance test, as well as simultaneous confidence interval for our data sets. Moreover, to predict breast cancer we used logistic regression model as well as confusion matrix to show how confuse our model.
1. Integrating pathway and gene expression data to identify novel
pathway-specific cancer drugs
Charles Pei, Marina Sirota PhD, Bin Chen PhD and Atul Butte MD/PhD
Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine,
Stanford, CA 94305
Abstract Results
Conclusions and Future Work
The average cost to bring a single drug to
market has surpassed $5 billion, a statistic
driven by the fact that over 95% of
experimental medicines fail due to toxicity or
lack of efficacy1. However, drugs invented per
dollar spent have actually decreased by half
every nine years for the past 50 years1. As a
result, an unconventional method known as
drug repositioning, in which established drug
compounds are applied to new therapeutic
indications, has gained prominence due to its
lower development costs and shorter paths to
approval when compared to traditional drug
development.
Connectivity Map (CMap), an extensive
database of drug-treatment gene expression
data comprised of over 6000 experiments
across 1300 compounds, has proven to be a
valuable asset in drug repositioning2,3. It has
been used to recognize drugs with common
mechanisms of action (MOAs), discover new
MOAs and identify new treatments.
The goal of this project is to integrate
publicly available pathway information with
gene expression data from CMap in order to
discover novel pathway-specific cancer drugs.
We identified several major cancer related
pathways, the Sonic Hedgehog signaling, PI3K/
AKT signaling, PTEN signaling and Wnt/β-
catenin signaling pathways. We applied a
modified CMap algorithm to carry out pathway
specific queries across the two databases and
identified drugs that specifically perturb
pathways of interest.
Acknowledgements
The authors would like to thank the other members
of the Butte lab and the Stanford Institutes of
Medicine Summer Program (SIMR) and Tianyi
Wang for facilitating this research.
1. Retrieve pathway information from
Ingenuity Pathway Analysis (IPA)
2. Retrieve, process and re-rank CMap data
with MySQL
3. Enrichment analysis of non-directional
pathways using a rank-based pattern-
matching strategy based on the
Kolmogorov-Smirnov statistic for
nonparametric data in R
4. Permutation method for statistical
significance followed by False Discovery
Rate (FDR) calculation for multiple
hypotheses correction in R to yield drug
indications for each pathway
References
1. Herper, Matthew. (2013). “The cost of creating a
new drug now $5 billion, pushing big pharma to
change.” Forbes Magazine.
2. Lamb, J et al. (2006). “The Connectivity Map:
Using Gene-Expression Signatures to Connect
Small Molecules, Genes and Disease.” Science.
313(5795):1929-1935.
3. Qu, X. and Rajpal, D. (2012). “Applications of
Connectivity Map in drug discovery and
development.” Drug Discovery Today. 00, 1-10.
Methods & Materials Results
• We successfully developed a novel method for
identifying pathway specific drugs
• We identified many novel drug indications for
the PI3K, PTEN and Sonic Hedgehog
pathways, which could lead to cancer
treatments
• We noted that analysis of the WNT pathway
did not yield any significant drug indications
• Future work would be to integrate the method
with the databases KEGG and Reactome,
which contain thousands of biological
pathways, and LINCS, CMap’s successor
Figure 4. Overlap of drug predictions across pathways.
Pathway Information Gene Expression
Data
Enrichment Analysis
Pathways + Drug Indications
Figure 3. Heat map of top up- and down-regulated
experiments of Hedgehog Pathway. Yellow= up-
regulation. Red= down-regulation
Introduction
Our lab has successfully applied CMap to
repurpose drugs in various disease areas.
However, the current CMap method assesses the
effects on whole systems rather than individual
pathways. Since some drugs may have off-
pathway effects, we are interested in finding
pathway-specific drugs. We hypothesized that
analyzing diseases on a pathway, rather than
genome-wide, level could yield novel drug
indications. We focus on four cancer related
pathways, the Sonic Hedgehog signaling, PI3K/
AKT signaling, PTEN signaling and Wnt/β-
catenin signaling pathways and identify drugs
affecting each one.
1. 2.
3.
4.
calycanthine
cyclopenthiazide
altretamine
acebutolol
metformin
thiamphenicol
vincamine
brinzolamide
prazosin
conessine
chlortetracycline
hexestrol
alpha−ergocryptine
methylprednisolone
luteolin
flupentixol
dioxybenzone
guanethidine
N−acetylmuramicacid
fluocinonide
PIAS1
MED1
PRKDC
TP53
ATM
PLAGL1
CDKN2A
CDKN1A
CDK4
MAPK8
GSK3B
CDK2
RB1
ATR
CCNK
LRDD
CCND2
THBS1
ST13
TP63
HDAC9
PMAIP1
C12orf5
unknown symbol
DRAM
MDM4
BAI1
PCAF
CSNK1D
GNL3
BCL2L1
CASP6
SERPINB5
TP53INP1
CHEK2
EP300
CCND1
PTEN
SFN
CHEK1
SNAI2
JUN
PCNA
CABC1
RPRM
MDM2
HDAC1
RRM2B
CCNG1
BBC3
BRCA1
TRIM29
BAX
MAPK14
CTNNB1
APAF1
BCL2
TP53I3
JMY
TOPBP1
SIRT1
E2F1
PERP
SERPINE2
WT1
FAS
HIF1A
TP73
PML
GML
STAG1
5000
10000
15000
20000
PI3K
Ac(vators
rank
FDR
score
name
dose
(M)
cell
line
1
0.109
0.379
chlorprothixene
1.14E-‐05
MCF7
2
0.109
0.377
mephenytoin
1.84E-‐05
HL60
3
0.109
0.368
me(xene
1.16E-‐05
PC3
4
0.109
0.367
noscapine
9.60E-‐06
MCF7
5
0.109
0.367
acenocoumarol
1.14E-‐05
MCF7
6
0.109
0.365
clemas(ne
8.60E-‐06
MCF7
7
0.109
0.364
chlorpromazine
1.12E-‐05
HL60
8
0.109
0.362
R-‐atenolol
1.50E-‐05
MCF7
9
0.109
0.358
conessine
1.12E-‐05
MCF7
10
0.109
0.357
dihydroergocris(ne
5.60E-‐06
MCF7
Hedgehog
Ac(vators
rank
FDR
score
name
dose
(M)
cell
line
1
0.0321
0.287
acebutolol
1.08E-‐05
MCF7
2
0.0321
0.283
conessine
1.12E-‐05
MCF7
3
0.0321
0.282
thiamphenicol
1.12E-‐05
PC3
4
0.0321
0.281
calycanthine
1.16E-‐05
PC3
5
0.0321
0.272
meOormin
2.42E-‐05
HL60
6
0.0321
0.266
altretamine
1.90E-‐05
HL60
7
0.0321
0.265
cyclopenthiazide
1.06E-‐05
HL60
8
0.0321
0.260
prazosin
9.60E-‐06
PC3
9
0.0321
0.258
brinzolamide
1.04E-‐05
MCF7
10
0.0321
0.256
vincamine
1.12E-‐05
MCF7
PTEN
Ac(vators
rank
FDR
score
name
dose
(M)
cell
line
1
0.106
0.407
trimethobenzamide
9.40E-‐06
MCF7
2
0.106
0.395
LY-‐294002
1.00E-‐05
HL60
3
0.106
0.384
baclofen
1.88E-‐05
MCF7
4
0.106
0.381
cephaeline
6.00E-‐06
HL60
5
0.106
0.380
podophyllotoxin
9.60E-‐06
HL60
6
0.106
0.380
pheniramine
1.12E-‐05
MCF7
7
0.106
0.380
gentamicin
2.60E-‐06
MCF7
8
0.106
0.378
loperamide
7.80E-‐06
PC3
9
0.106
0.376
quinpirole
1.56E-‐05
HL60
10
0.106
0.374
dihydroergotamine
3.00E-‐06
HL60
PI3K
Inhibitors
rank
FDR
score
name
dose
(M)
cell
line
1
0.122
-‐0.385
sirolimus
1.00E-‐07
ssMCF7
2
0.122
-‐0.358
sirolimus
1.00E-‐07
MCF7
3
0.122
-‐0.353
cy(sine
2.10E-‐05
MCF7
4
0.122
-‐0.350
natamycin
6.00E-‐06
MCF7
5
0.122
-‐0.349
sulfamethoxypyridazine
1.42E-‐05
MCF7
6
0.124
-‐0.347
trioxysalen
1.76E-‐05
MCF7
7
0.124
-‐0.344
hexamethonium
bromide
1.00E-‐05
MCF7
8
0.124
-‐0.334
ciprofloxacin
1.08E-‐05
MCF7
9
0.124
-‐0.332
metamizole
sodium
1.20E-‐05
MCF7
10
0.124
-‐0.329
monorden
1.00E-‐07
MCF7
Hedgehog
Inhibitors
rank
FDR
score
name
dose
(M)
cell
line
1
0.0203
-‐0.288
luteolin
1.40E-‐05
PC3
2
0.0203
-‐0.284
N-‐acetylmuramic
acid
1.36E-‐05
MCF7
3
0.0203
-‐0.277
flupen(xol
7.80E-‐06
HL60
4
0.0203
-‐0.277
hexestrol
1.48E-‐05
MCF7
5
0.0203
-‐0.276
fluocinonide
8.00E-‐06
MCF7
6
0.0203
-‐0.276
alpha-‐ergocryp(ne
7.00E-‐06
PC3
7
0.0203
-‐0.271
guanethidine
1.34E-‐05
MCF7
8
0.0203
-‐0.269
methylprednisolone
1.06E-‐05
MCF7
9
0.0203
-‐0.268
dioxybenzone
1.64E-‐05
HL60
10
0.0203
-‐0.268
chlortetracycline
7.80E-‐06
PC3
PTEN
Inhibitors
rank
FDR
score
name
dose
(M)
cell
line
1
0.0762
-‐0.424
ketanserin
7.00E-‐06
MCF7
2
0.0762
-‐0.422
indometacin
1.12E-‐05
MCF7
3
0.0762
-‐0.414
corynanthine
1.02E-‐05
MCF7
4
0.0762
-‐0.405
allantoin
2.52E-‐05
HL60
5
0.0762
-‐0.395
ciclacillin
1.18E-‐05
MCF7
6
0.0762
-‐0.386
flavoxate
9.40E-‐06
HL60
7
0.0762
-‐0.383
alprenolol
1.40E-‐05
MCF7
8
0.0762
-‐0.380
calcium
pantothenate
8.00E-‐06
MCF7
9
0.0762
-‐0.379
dihydrostreptomycin
2.80E-‐06
MCF7
10
0.0762
-‐0.377
fenbufen
1.58E-‐05
MCF7
Figure 2. A. Gene overlap between cancer pathways.
B. Overlap of known drugs affecting cancer pathways.
Results
Predicted
Known
1003
0
0
Hedgehog
Predicted
Known
234
150
72
PI3K
Predicted
Known
273
42
24
PTEN
Predicted
Known
0
32
0
Wnt
Figure 4. Predicted vs. known drug indications. 24
PTEN drugs and 72 PI3K predicted drugs were
verified by our analysis. Hedgehog pathway did not
have known drugs also found in CMap; Wnt pathway
had no statistically significant drugs.
Results
Figure 1. Workflow.
Table 1. Top 10 inhibitors and activators for PI3K,
PTEN and Hedgehog pathways
A.
B.
Figure 3. Pathway Enrichment Score distributions.
Inhibitors and activators are defined as (FDR < 0.2).
All random (n=10000), Hedgehog inhibitors (n=1272),
Hedgehog activators (n=663), no Wnt activators or
inhibitors, PI3K inhibitors (n=241), PI3K activators
(n=95), PTEN activators (n= 238), PTEN inhibitors
(n=101)
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−0.3−0.2−0.10.00.10.20.3
random inhibitors activators
Hedgehog
EnrichmentScore
−0.4−0.20.00.20.4
random inhibitors activators
Wnt
EnrichmentScore
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−0.4−0.20.00.20.4
random inhibitors activators
PTEN
EnrichmentScore
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−0.4−0.20.00.20.4
random inhibitors activators
PI3K
EnrichmentScore
