A Novel Pipeline for Drug Repurposing for Bladder Cancer Based on Patients’ Omics Signatures

Mokou, Marika; Lygirou, Vasiliki; Angelioudaki, Ioanna; Paschalidis, Nikolaos; Stroggilos, Rafael; Frantzi, Maria; Latosinska, Agnieszka; Bamias, Aristotelis; Hoffmann, Michèle J.; Mischak, Harald; Vlahou, Antonia

doi:10.3390/cancers12123519

Cited by 14 publications

(4 citation statements)

References 47 publications

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“…LINCS datasets, along with others, have been used to identify repurposing candidates from drugs that can reverse the expression profiles of cancer-specific gene signatures (obtained by comparing expression of cancer cells with normal cells; refs. [83][84][85]. DNNs trained on drug-perturbed transcriptional profiles from LINCS have also been used to predict the therapeutic use category for drugs (e.g., vasodilator, antineoplastic) and to prioritize repurposing candidates by their chemical structural similarity with approved cancer drugs (86).…”

Section: Drug Repurposingmentioning

confidence: 99%

Artificial Intelligence in Cancer Research and Precision Medicine

Bhinder

Gilvary²,

Madhukar³

et al. 2021

Cancer Discovery

315

153

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Convolutional neural networks (CNN) have been the most popular deep learning architectures used for image classification in cancer ( Fig. 1 ). CNNs apply a series of nonlinear transformations to structured data (such as raw pixels of an image) to learn relevant features automatically, unlike conventional machine learning models that frequently require manual feature curation. On the fl ip side, it is diffi cult to tell what features are learned by the CNNs, making them what many have referred to as a "black box." One consequence is that images used for CNNs should be carefully prepro cessed to reduce the risk that the model learns from image artifacts. There are two major approaches for CNN models; one is transfer learning that uses images from a large collection of natural objects (such as in ImageNet) to train the initial layers of a model (where the model learns to identify general features such as shapes and edges) and then uses the diseasespecifi c data to fi ne-tune the training parameters in the last layers. The second variation of CNN is based on an autoencoder where the model learns background features from a subset of representative images and encodes a compressed representation of the basic features later used to initialize the CNN. In the CAMELYON16 Challenge-a crowdsourced competition to identify and classify lymph node metastasis in patients with breast cancer from whole slide images (WSI) of hematoxylin and eosin (H&E)-stained tumors-25 of the 32 submitted algorithms were CNNs, and the top 5 classifi cation models were exclusively based on transfer learning, which were GoogLeNet, ResNet, VGG-16 ( 2 ). Khosravi and colleagues trained and tested several state-of-the-art deep learning models to classify WSI from H&E-stained tumor tissues of The Cancer Genome Atlas (TCGA) cohort and reported on the

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Section: Drug Repurposingmentioning

confidence: 99%

Artificial Intelligence in Cancer Research and Precision Medicine

Bhinder

Gilvary²,

Madhukar³

et al. 2021

Cancer Discovery

315

153

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“…This fact has inspired the development of the following approach: instead of targeting a specific mutation, we aimed at “normalizing” multiple disease-associated changes. The approach was also inspired by the application of the Connectivity Map (CMap) [ 31 , 32 , 33 ], where potentially beneficial drugs are defined based on the “normalization” of a disease-specific transcriptome or proteome signature.…”

Section: Discussionmentioning

confidence: 99%

Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study

Jaimes Campos,

Andújar,

Keller

et al. 2023

Pharmaceuticals

Self Cite

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(1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and progression of these diseases and should hold information about the optimal means of treatment and prevention. (2) Methods: We investigated the prediction of renal or cardiovascular events using previously defined urinary peptidomic classifiers CKD273, HF2, and CAD160 in a cohort of 5585 subjects, in a retrospective study. (3) Results: We have demonstrated a highly significant prediction of events, with an HR of 2.59, 1.71, and 4.12 for HF, CAD, and CKD, respectively. We applied in silico treatment, implementing on each patient’s urinary profile changes to the classifiers corresponding to exactly defined peptide abundance changes, following commonly used interventions (MRA, SGLT2i, DPP4i, ARB, GLP1RA, olive oil, and exercise), as defined in previous studies. Applying the proteomic classifiers after the in silico treatment indicated the individual benefits of specific interventions on a personalized level. (4) Conclusions: The in silico evaluation may provide information on the future impact of specific drugs and interventions on endpoints, opening the door to a precision-based medicine approach. An investigation into the extent of the benefit of this approach in a prospective clinical trial is warranted.

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“…The Connectivity Map (Map) developed by the Broad Institute has been used to identify drugs with the potential to interfere with diverse pathological situations such as epilepsy [ 18 ], pulmonary arterial hypertension [ 19 ], asthma [ 20 ]. Based on cancer GEPs, several molecules have been identified in silico and showed promising properties in vitro / in vivo in a variety of cancers such as renal cancer [ 21 ], colorectal cancer [ 22 ], bladder cancer [ 23 ], leukemia [ 24 ], neuroblastoma [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of potentially anti-COVID-19 active drugs using the connectivity MAP

2022

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Drug repurposing can be an interesting strategy for an emergency response to the severe acute respiratory syndrome-coronavirus-2, (SARS-COV-2), the causing agent of the coronavirus disease-19 (COVID-19) pandemic. For this, we applied the Connectivity Map (CMap) bioinformatic resource to identify drugs that generate, in the CMap database, gene expression profiles (GEP) that negatively correlate with a SARS-COV-2 GEP, anticipating that these drugs could antagonize the deleterious effects of the virus at cell, tissue or organism levels. We identified several anti-cancer compounds that target MDM2 in the p53 pathway or signaling proteins: Ras, PKBβ, Nitric Oxide synthase, Rho kinase, all involved in the transmission of proliferative and growth signals. We hypothesized that these drugs could interfere with the high rate of biomass synthesis in infected cells, a feature shared with cancer cells. Other compounds including etomoxir, triacsin-c, PTB1-IN-3, are known to modulate lipid metabolism or to favor catabolic reactions by activating AMPK. Four different anti-inflammatory molecules, including dexamethasone, fluorometholone and cytosporone-b, targeting the glucocorticoid receptor, cyclooxygenase, or NUR77 also came out of the analysis. These results represent a first step in the characterization of potential repositioning strategies to treat SARS-COV-2.

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A Novel Pipeline for Drug Repurposing for Bladder Cancer Based on Patients’ Omics Signatures

Cited by 14 publications

References 47 publications

Artificial Intelligence in Cancer Research and Precision Medicine

Artificial Intelligence in Cancer Research and Precision Medicine

Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study

Identification of potentially anti-COVID-19 active drugs using the connectivity MAP

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