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CBA
HoliRD REPORT:
Familial Melanoma
Marina Esteban Medina
María Peña-Chilet
Carlos Loucera
Joaquín Dopazo
Clinical Bioinformatics Area - FPS
Sevilla, January 27, 2020
Collaborators:
Dra.Susana Puig’s group - U726 CIBERER
Research team “Melanoma: imaging, genetics and immunology" at the IDIBAPS -
Hospital Clínic, Barcelona
CBA
Objectives and methodology:
The Holistic Rare Disease project (HoliRD) aims to build Diseases Maps for as many
Rare Diseases as possible and to model them to systematize research in drug
repurposing. In order to achieve this purpose several databases such as ORPHANET,
OMIM, HPO, PubMed, KEGG, STRING, as well as the literature is used to collect all
the up-to-date knowledge of the diseases under study and defining a Disease Map
that contains the functional relationships among the known disease genes, as well as
the functional consequences of their activity. Then, a mechanistic model that
accounts for the activity of such map is used. The HiPathia algorithm, which has
successfully proven to predict cell activities related to cancer hallmarks (Hidalgo et
al., Oncotarget 2017; 8:5160-5178; Hidalgo et al., Biol Direct. 2018;13:16) as well as
the effect of protein inhibitions on cell survival (Cubuk et al., Cancer Res. 2018;
78:6059-6072) is used to simulate the activity of the disease map.
Finally, machine learning algorithms are used to find other proteins, already target of
drugs with another indication, which display a potential causal effect on the activity
of the previously defined disease map. The drugs that target these proteins are
potential candidates for repurposing.
Schematic representation of the method used.
Examples of the use of this approach can be found in Esteban-Medina et al., BMC
Bioinformatics. 2019, 20(1):370.CBA
Report
This report describes the results of the different steps of the HoliRD approach
applied to Familial Melanoma.
Identification of genes highly related to the rare disease (RD) under study in
Orphanet
A total of 12 genes annotated as Familial melanoma (FM) were found in the
Orphanet database.
FM highly related genes
Disease ID Entrez ID Gene Symbol
ORPHA:618 65057 ACD
ORPHA:618 54386 TERF2IP
ORPHA:618 1029 CDKN2A
ORPHA:618 1030 CDKN2B
ORPHA:618 7015 TERT
ORPHA:618 1032 CDKN2D
ORPHA:618 25913 POT1
ORPHA:618 8314 BAP1
ORPHA:618 1019 CDK4
ORPHA:618 4157 MC1R
ORPHA:618 4286 MITF
ORPHA:618 4255 MGMTCBA
Identification of highly related HPO to the RD under study:
A total of 4 HPO codes associated to Familial melanoma were found.
FM highly related HPOs
HPO term ID HPO term name CATEGORY
HP:0002894 Neoplasm of the pancreas Neoplasm
HP:0100013 Neoplasm of the breast Neoplasm
HP:0006753 Neoplasm of the stomach Neoplasm
HP:0002861 Melanoma Neoplasm
Identification of genes that shared at least RD-HPO codes
Genes with >= 1 FM-HPO code
Gene Symbol Entrez Gene Symbol Entrez
AKT1 207 RAF1 5894
BRAF 673 RELA 5970
BRCA2 675 SDHB 6390
CDKN2A 1029 SDHC 6391
CLCNKB 1188 SDHD 6392
DDB2 1643 SLC12A3 6559
DKC1 1736 STK11 6794
ERCC2 2068 TERC 7012
ERCC3 2071 TERT 7015
ERCC4 2072 TP53 7157
ERCC5 2073 WT1 7490
ERCC6 2074 XPA 7507
EWSR1 2130 SEC23B 10483
GNAS 2778 TINF2 26277
KRAS 3845 RTEL1 51750
SMAD4 4089 RNF43 54894
MC1R 4157 WRAP53 55135
OCA2 4948 NOP10 55505
PARN 5073 NHP2 55651
PIK3CA 5290 C11orf95 65998
POLH 5429 USB1 79650
PTEN 5728 CTC1 80169
PTPN11 5781 KLLN 100144748CBA
Genes with >= 2 FM-HPO codes
Gene Symbol entrez
PTEN 5728
In order to maintain the specificity and not over expand the Disease Map of action
only genes with >=2 FM-HPO codes were selected.
Location of the selected disease related genes in KEGG pathways to define the
Disease Map of action.
After locating the RD associated genes within KEGG pathways, a total of 116 circuits
belonging to 10 KEGG pathways were found as part of the disease map.
KEGG pathway KEGG-pathway code
FoxO signaling pathway hsa04068
Sphingolipid signaling pathway hsa04071
Cell cycle hsa04110
p53 signaling pathway hsa04115
mTOR signaling pathway hsa04150
PI3K-Akt signaling pathway hsa04151
TGF-beta signaling pathway: hsa04350
Focal adhesion: hsa04510
Tight junction hsa04530
Melanogenesis hsa04916
HiPathia is a signal propagation algorithm that considers pathways as collections of circuits defined
as sub-pathways or sequences of proteins connecting signal receptor proteins to effector proteins.
HiPathia uses expression values genes as proxies of the level of activation of the corresponding
protein in the circuit. Taking into account the inferred protein activity and the interactions between
the proteins (activation or inhibition) defined in the pathway, the level of activity of a circuit is
estimated using a signal propagation algorithm. Ultimately, effector proteins are annotated with a
cellular function.CBA
In order to enable a better visualization of the RD Map the HiPathia viewer has been
used. The circuits that define the RD Map are marked in RED (please ignore the color
legend). The pathways that contain these circuits are highlighted in the right window
with a red arrow. The only purpose of this report is to represent the components
(genes and interaction) and functions of the circuits that compose the RD Map.
Click to access the RD Map Report
HiPathia uses KEGG pathway for the graphical representation of the circuits. The
original pathways can also be visualized in the KEGG repository
https://www.genome.jp/kegg/pathway.html
Select prefix: hsa (Organism) Enter keywords: e.g. FoxOsignalingpathway
(any HiPathia pathway)
Prediction of relevance of gene targets from approved drugs extracted from
DRUGBANK database (release 5.1.4)
The HoliRD approach takes the mechanistic model of the disease map as the proxy for the molecular
basis of the disease outcome. Then, a Multi-Output Random Forest (MORF) regressor, a machine
learning algorithm that predicts the circuit activities across the whole disease map, is trained on GTEx
gene expression data to find proteins (which are targets for drugs with indications for other diseases)
that correctly predict the behavior of the disease map. The drugs targeting the best predictor proteins
are candidate for drug repurposing. The relevance score accounts for the accuracy of the prediction
contributed by each individual protein. Relevance are absolute values and do not account for the
direction of the prediction, that is, if the interaction is an activation or an inhibition.CBA
From a total of 683 targets for approved drugs (AT) in the DRUGBANK database
(release 5.1.4) the machine learning algorithm selected the 44 most relevant ones
(top AT).
Relevance Relevance
Entrez ID Gene Symbol Entrez ID Gene Symbol
score score
10381 TUBB3 0,17939599 3566 IL4R 0,005357486
706 TSPO 0,099480519 3561 IL2RG 0,004669664
7132 TNFRSF1A 0,074298119 11255 HRH3 0,00461867
7040 TGFB1 0,050957856 3043 HBB 0,004487982
7035 TFPI 0,044539596 3039 HBA1 0,004433632
6510 SLC1A5 0,04226144 2335 FN1 0,004299058
57468 SLC12A5 0,038896597 2280 FKBP1A 0,004166661
6261 RYR1 0,023152031 2212 FCGR2A 0,004046426
9475 ROCK2 0,020820834 2207 FCER1G 0,004043206
5914 RARA 0,020263022 1956 EGFR 0,004022999
2185 PTK2B 0,017201092 1441 CSF3R 0,003668921
5696 PSMB8 0,013509705 1137 CHRNA4 0,003402125
5156 PDGFRA 0,012475012 1019 CDK4 0,003345413
4790 NFKB1 0,009126229 595 CCND1 0,003254775
5595 MAPK3 0,008776378 775 CACNA1C 0,003219639
3791 KDR 0,008633871 774 CACNA1B 0,003172455
3785 KCNQ2 0,008427748 716 C1S 0,003139547
3764 KCNJ8 0,008412134 657 BMPR1A 0,002958771
3746 KCNC1 0,008032614 558 AXL 0,002695054
3688 ITGB1 0,007646944 8639 AOC3 0,00262768
3685 ITGAV 0,007352583 302 ANXA2 0,002541003
3683 ITGAL 0,005611779 87 ACTN1 0,00248294CBA
Relevance plot depicting the 44 most relevant gene targets (top AT).
Drugs from DRUGBANK db (release 5.1.4) that target top AT.
And the list of drugs that target the 44 most relevant genes follows:
You can click on the hyperlink of the Drug ID to see more detailed information about the drug in
DrugBank DB.
Relevance
Drug ID Drug Name Drug Effect Target Associated condition
score
incorporation
DB06773 Human calcitonin into and ACTN1 0,1794
destabilization
Benign Prostatic
DB01162 Terazosin inducer TGFB1 0,09948
Hyperplasia (BPH)
Foreskin
DB10772 keratinocyte agonist TGFB1 0,09948
(neonatal)
Chemotherapy Induced
DB00019 Pegfilgrastim agonist CSF3R 0,0743
Neutropenia
Hematopoietic
DB00099 Filgrastim stimulator CSF3R Subsyndrome of Acute 0,0743
Radiation SyndromeCBA
DB13144 Lenograstim agonist CSF3R 0,0743
DB13200 Lipegfilgrastim agonist CSF3R Neutropenia, Febrile 0,0743
DB00996 Gabapentin inhibitor CACNA1B Partial-Onset Seizures 0,05096
DB01202 Levetiracetam inhibitor CACNA1B Epilepsies 0,05096
High Risk
DB00041 Aldesleukin agonist IL2RG 0,04454
Neuroblastoma
DB00586 Diclofenac other KCNQ2 Actinic Keratosis (AK) 0,04226
Soft Tissue Sarcoma
DB11626 Tasonermin agonist TNFRSF1A 0,0389
(STS)
DB11639 Dibotermin alfa ligand BMPR1A 0,02315
Fluocinolone
DB00591 inducer ANXA2 Atopic Dermatitis (AD) 0,02082
acetonide
DB08814 Triflusal antagonist NFKB1 0,02026
Antithymocyte
DB00098 immunoglobulin ITGAV Acute cellular rejection 0,0172
(rabbit)
DB00451 Levothyroxine ITGAV Hypothyroidism 0,0172
DB01275 Hydralazine inhibitor AOC3 Heart Failure 0,01351
Antithymocyte
DB00098 immunoglobulin ITGB1 Acute cellular rejection 0,01248
(rabbit)
DB00893 Iron Dextran activator HBB 0,00913
Pentaerythritol
DB06154 agonist HBB 0,00913
tetranitrate
DB09112 Nitrous acid oxidizer HBB 0,00913
Sodium ferric
DB09517 binding HBB Anemias 0,00913
gluconate complex
Ferric
DB13995 pyrophosphate binder HBB 0,00913
citrate
DB00184 Nicotine agonist CHRNA4 Dental Cavity 0,00878CBA
DB01273 Varenicline partial agonist CHRNA4 0,00878
DB00358 Mefloquine antagonist HBA1 Plasmodium Infections 0,00863
DB00893 Iron Dextran activator HBA1 0,00863
Pentaerythritol
DB06154 agonist HBA1 0,00863
tetranitrate
DB09112 Nitrous acid oxidizer HBA1 0,00863
DB09146 Iron saccharate component of HBA1 Hyperphosphataemia 0,00863
Ferric
DB13995 pyrophosphate binder HBA1 0,00863
citrate
DB00962 Zaleplon other TSPO 0,00843
DB01544 Flunitrazepam agonist TSPO 0,00843
DB01587 Ketazolam agonist TSPO 0,00843
Human C1-esterase
DB06404 inhibitor C1S 0,00841
inhibitor
DB09228 Conestat alfa inhibitor C1S 0,00841
Moderate, active
DB11817 Baricitinib inhibitor PTK2B 0,00803
Rheumatoid arthritis
Advanced Renal Cell
DB00398 Sorafenib antagonist KDR 0,00765
Carcinoma
Advanced Renal Cell
DB01268 Sunitinib inhibitor KDR 0,00765
Carcinoma
DB05578 Ramucirumab antagonist KDR Advanced Gastric Cancer 0,00765
Advanced Renal Cell
DB06589 Pazopanib inhibitor KDR 0,00765
Carcinoma
antagonist,inhi Leukemia Acute Myeloid
DB06595 Midostaurin KDR 0,00765
bitor Leukemia (AML)
Severe Aplastic Anemia
DB06626 Axitinib inhibitor KDR 0,00765
(SAA)
Advanced Renal Cell
DB08875 Cabozantinib antagonist KDR 0,00765
CarcinomaCBA
Metastatic
DB08896 Regorafenib inhibitor KDR Gastrointestinal Stromal 0,00765
Tumor
Advanced Renal Cell
DB09078 Lenvatinib inhibitor KDR 0,00765
Carcinoma
Decreased Pulmonary
DB09079 Nintedanib inhibitor KDR 0,00765
Function
DB06637 Dalfampridine antagonist KCNC1 0,00735
DB00270 Isradipine inhibitor CACNA1C 0,00561
DB00308 Ibutilide activator CACNA1C Atrial Fibrillation (AF) 0,00561
DB00343 Diltiazem blocker CACNA1C Anal Fissures 0,00561
DB00381 Amlodipine inhibitor CACNA1C Anginal Pain 0,00561
DB00393 Nimodipine inhibitor CACNA1C 0,00561
DB00401 Nisoldipine inhibitor CACNA1C 0,00561
DB00568 Cinnarizine inhibitor CACNA1C 0,00561
Chronic Stable Angina
DB00622 Nicardipine inhibitor CACNA1C 0,00561
Pectoris
DB00661 Verapamil inhibitor CACNA1C Atrial Fibrillation (AF) 0,00561
DB00825 Levomenthol antagonist CACNA1C Coughing 0,00561
DB01023 Felodipine inhibitor CACNA1C 0,00561
DB01054 Nitrendipine inhibitor CACNA1C 0,00561
Chronic Stable Angina
DB01115 Nifedipine inhibitor CACNA1C 0,00561
Pectoris
DB01373 Calcium ligand CACNA1C 0,00561
DB06712 Nilvadipine inhibitor CACNA1C 0,00561
Irritable Bowel
DB09089 Trimebutine inhibitor CACNA1C 0,00561
Syndrome (IBS)
DB09236 Lacidipine antagonist CACNA1C 0,00561
DB09238 Manidipine blocker CACNA1C 0,00561
DB12278 Propiverine antagonist CACNA1C Micturition urgency 0,00561CBA
DB00922 Levosimendan inducer KCNJ8 0,00536
DB01154 Thiamylal inhibitor KCNJ8 0,00536
DB01251 Gliquidone inhibitor KCNJ8 0,00536
Graft Versus Host
DB00864 Tacrolimus inhibitor FKBP1A 0,00467
Disease (GVHD)
DB00877 Sirolimus other FKBP1A Chordomas 0,00467
Metastatic Colorectal
DB00002 Cetuximab antagonist EGFR 0,00462
Cancers
DB00317 Gefitinib antagonist EGFR 0,00462
Locally Advanced
DB00530 Erlotinib antagonist EGFR Non-Small Cell Lung 0,00462
Cancer
Metastatic Breast
DB01259 Lapatinib antagonist EGFR 0,00462
Cancer (MBC)
DB01269 Panitumumab suppressor EGFR 0,00462
Metastatic Non-Small
DB08916 Afatinib inhibitor EGFR 0,00462
Cell Lung Cancer
DB09330 Osimertinib inhibitor EGFR 0,00462
DB09559 Necitumumab antagonist EGFR 0,00462
Foreskin
DB10772 keratinocyte agonist EGFR 0,00462
(neonatal)
DB11828 Neratinib inhibitor EGFR 0,00462
DB11963 Dacomitinib inhibitor EGFR 0,00462
DB12267 Brigatinib inhibitor EGFR 0,00462
DB08888 Ocriplasmin cleavage FN1 0,00449
Locally Advanced Breast
DB04845 Ixabepilone inhibitor TUBB3 0,00443
Cancer (LABC)
DB09073 Palbociclib inhibitor CDK4 Advanced Breast Cancer 0,0043
antagonist,inhi
DB11730 Ribociclib CDK4 Advanced Breast Cancer 0,0043
bitorCBA
DB12001 Abemaciclib inhibitor CDK4 Advanced Breast Cancer 0,0043
DB13146 Fluciclovine (18F) binder SLC1A5 0,00417
DB00459 Acitretin agonist RARA Keratinization disorders 0,00405
Chronic Eczema of the
DB00523 Alitretinoin agonist RARA 0,00405
hand
Psoriasis Vulgaris
DB00799 Tazarotene agonist RARA 0,00405
(Plaque Psoriasis)
DB12141 Gilteritinib inhibitor AXL 0,00404
DB05381 Histamine agonist HRH3 0,00402
DB06698 Betahistine antagonist HRH3 0,00402
antagonist,inve Excessive Daytime
DB11642 Pitolisant HRH3 0,00402
rse agonist Sleepiness
DB13931 Netarsudil inhibitor ROCK2 0,00367
Refractory Multiple
DB08889 Carfilzomib inhibitor PSMB8 0,0034
Myeloma
Moderate to Severe
DB12159 Dupilumab antagonist IL4R 0,00335
Asthma
DB00887 Bumetanide inhibitor SLC12A5 0,00325
Benzylpenicilloyl
DB00895 agonist FCER1G Penicillin Allergy 0,00322
Polylysine
DB00095 Efalizumab antibody ITGAL 0,00317
Antithymocyte
DB00098 immunoglobulin ITGAL Acute cellular rejection 0,00317
(rabbit)
DB11611 Lifitegrast antagonist ITGAL 0,00317
DB01219 Dantrolene antagonist RYR1 Malignant Hyperthermia 0,00314
DB09085 Tetracaine modulator RYR1 0,00314
Advanced Renal Cell
DB01268 Sunitinib inhibitor PDGFRA 0,00296
Carcinoma
DB06043 Olaratumab antagonist PDGFRA 0,00296CBA
Advanced Renal Cell
DB06589 Pazopanib inhibitor PDGFRA 0,00296
Carcinoma
antagonist,inhi Leukemia Acute Myeloid
DB06595 Midostaurin PDGFRA 0,00296
bitor Leukemia (AML)
Metastatic
DB08896 Regorafenib inhibitor PDGFRA Gastrointestinal Stromal 0,00296
Tumor
Foreskin
DB10772 keratinocyte agonist PDGFRA 0,00296
(neonatal)
DB01169 Arsenic trioxide inducer MAPK3 0,0027
DB01169 Arsenic trioxide antagonist CCND1 0,00263
DB11718 Encorafenib inhibitor CCND1 Metastatic Melanoma 0,00263
DB06779 Dalteparin inhibitor TFPI Cardiovascular Events 0,00254
DB14562 Andexanet alfa inhibitor TFPI 0,00254
Immune Globulin B-Cell Chronic
DB00028 antagonist FCGR2A 0,00248
Human Lymphocytic Leukemia
Location of top AT in HiPathia circuits and selection of those circuits shared
with the Disease Map. A total of 11 /116 circuits from RD Map containing top
AT are shown.
Disease Map circuits with top AT
HiPathia pathway: Effector gene
FoxO signaling pathway: CDKN2B
FoxO signaling pathway: CDKN2D
Cell cycle: TFDP1 E2F4
Cell cycle: CDC6 ORC3 ORC5 ORC4 ORC2 ORC1 ORC6
p53 signaling pathway: CDK4 CCND1
PI3K-Akt signaling pathway: CCND1 CDK2
TGF-beta signaling pathway: CDKN2B
Tight junction: TJP1 YBX3 CDK4
Melanogenesis: TYR*
Melanogenesis: TYRP1
Melanogenesis: DCTCBA
Matrix correlation of the expression of top AT with the activity of the circuits
that compose the RD Map.
In order to understand the nature of the interaction (activation or inhibition)
between the most relevant targets and the circuits of the disease map a correlation
plot was derived. The plot represents the correlation between the expression level
of the most relevant targets and the activity levels of the circuits in the disease map
inferred by HiPathia across the GTEx data set. Additionally, the KO of the disease
genes has been simulated and these circuits in the disease map affected have been
marked in blue on the left side of the plot. Also, the top of the figure represents the
effect (pharmacological action) of the drugs.
Hint: a drug with an inhibitory effect in a given target will potentially produce an inhibitory effect in
circuits positively correlated (and, it is likely that activation in circuits negatively correlated with the
target.)
Click to access and download the Correlation MatrixYou can also read
