Cancer drug response (CDR) prediction is an important area of research that aims to personalize cancer therapy, optimizing treatment plans for maximum effectiveness while minimizing potential negative effects. Despite the advancements in Deep learning techniques, the effective integration of multi-omics data for drug response prediction remains challenging. In this paper, a regression method using Deep ResNet for CDR (DRN-CDR) prediction is proposed. We aim to explore the potential of considering sole cancer genes in drug response prediction. Here the multi-omics data such as gene expressions, mutation data, and methylation data along with the molecular structural information of drugs were integrated to predict the IC50 values of drugs. Drug features are extracted by employing a Uniform Graph Convolution Network, while Cell line features are extracted using a combination of Convolutional Neural Network and Fully Connected Networks. These features are then concatenated and fed into a deep ResNet for the prediction of IC50 values between Drug - Cell line pairs. The proposed method yielded higher Pearson's correlation coefficient (r_p) of 0.7938 with lowest Root Mean Squared Error (RMSE) value of 0.92 when compared with similar methods of tCNNS, MOLI, DeepCDR, TGSA, NIHGCN, DeepTTA, GraTransDRP and TSGCNN. Further, when the model is extended to a classification problem to categorize drugs as sensitive or resistant, we achieved AUC and AUPR measures of 0.7623 and 0.7691, respectively. The drugs such as Tivozanib, SNX-2112, CGP-60474, PHA-665752, Foretinib etc., exhibited low median IC50 values and were found to be effective anti-cancer drugs. The case studies with different TCGA cancer types also revealed the effectiveness of SNX-2112, CGP-60474, Foretinib, Cisplatin, Vinblastine etc. This consistent pattern strongly suggests the effectiveness of the model in predicting CDR.

01 Oct 2024·Current Gene Therapy

ZNF695, A Potential Prognostic Biomarker, Correlates with Im mune Infiltrates in Cervical Squamous Cell Carcinoma and Endoce rvical Adenocarcinoma: Bioinformatic Analysis and Experimental Verification

Article

Author: Qi, Xin ; Liu, Zhao ; Jiang, Ke'er ; Wan, Ailing ; Chen, Buze ; Ding, Xiaojuan

Background::The role of Zinc Finger Protein 695 (ZNF695) is unclear in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC).Objective::The objective of this study was to conduct a comprehensive analysis and experimental validation of ZNF695 in CESC.Methods::The study investigated the expression of ZNF695 in both pan-cancer and CESC, utilizing data from The Cancer Genome Atlas (TCGA) database to assess its diagnostic value. The present study investigated the association between ZNF695 expression levels and clinical characteristics, as well as prognosis, in patients with CESC. The study explored potential regulatory networks involving ZNF695, including its association with immune infiltration, immune score, stemness index based on mRNA expression (mRNAsi), and drug sensitivity in CESC. We explored the expression of ZNF695 in CESC single cells. ZNF695 expression was validated using GSE29570.Results::ZNF695 was found to be aberrantly expressed in pan-cancer and CESC. There was a significant correlation observed between an elevated level of ZNF695 expression in patients with CESC and histological grade (p = 0.017). Furthermore, a strong association was found between high ZNF695 expression in CESC patients and poorer overall survival (OS) (HR: 1.87; 95% CI: 1.17-3.00; p = 0.009), Progression-free Survival (PFS) (HR: 1.86; 95% CI: 1.16-2.98; p = 0.010), and Disease-specific Survival (DSS) (HR: 1.98; 95% CI: 1.15-3.42; p = 0.014). The expression of ZNF695 in CESC patients (p = 0.006) was identified as an independent prognostic determinant. ZNF695 was associated with steroid hormone biosynthesis, oxidative phosphorylation, and so on. ZNF695 expression correlated with immune infiltration, immune score, and mRNAsi in CESC. ZNF695 expression significantly and negatively correlated with AICA ribonucleotide, BIX02189, QL-XI-92, STF-62247, and SNX-2112 in CESC. ZNF695 gene was upregulated in CESC tissues and cell lines. ZNF695 was significantly upregulated in the CESC cell lines.Conclusion::ZNF695 may be a potential prognostic biomarker and immunotherapeutic target for CESC patients.

01 Feb 2024·Combinatorial Chemistry & High Throughput Screening

Identification of circRNA-miRNA-mRNA Network Regulated by Hsp90 in Human Melanoma A375 Cells

Article

Author: Su, Juan ; Zhang, Jianglin ; Fu, Qiang ; Gao, Hengyuan ; Guo, Xiaojing ; Liu, Kaisheng ; Yang, Fang

Background:Melanoma is the deadliest form of skin cancer. Heat shock protein 90 (Hsp90) is highly expressed in human melanoma. Hsp90 inhibitors can suppress the growth of human melanoma A375 cells; however, the underlying mechanism remains unclear.Methods:A375 cells were treated with SNX-2112, an Hsp90 inhibitor, for 48 h, and wholetranscriptome sequencing was performedResults:A total of 2,528 differentially expressed genes were identified, including 895 upregulated and 1,633 downregulated genes. Pathway enrichment analyses of differentially expressed mRNAs identified the extracellular matrix (ECM)-receptor interaction pathway as the most significantly enriched pathway. The ECM receptor family mainly comprises integrins (ITGs) and collagens (COLs), wherein ITGs function as the major cell receptors for COLs. 19 upregulated miRNAs were found to interact with 6 downregulated ITG genes and 8 upregulated miRNAs were found to interact with 3 downregulated COL genes. 9 differentially expressed circRNAs in SNX-2112- treated A375 cells were identified as targets of the ITG- and COL-related miRNAs. Based on the differentially expressed circRNAs, miRNAs, and mRNAs, ITGs- and COL-based circRNAmiRNA- mRNA regulatory networks were mapped, revealing a novel regulatory mechanism of Hsp90-regulated melanoma.Conclusion:Targeting the ITG-COL network is a promising approach to the treatment of melanoma.

100 Deals associated with PF-04928473

R&D Status

10 top R&D records.

to view more data

Indication	Highest Phase	Country/Location	Organization	Date
Neoplasms	Phase 2	US	Esanex, Inc.	01 Apr 2013
Leukemia	Phase 2	US	Esanex, Inc.	01 Mar 2008
Lymphoma	Phase 2	US	Esanex, Inc.	01 Mar 2008
Solid tumor	Phase 2	US	Esanex, Inc.	01 Mar 2008
Breast Cancer	Phase 2	-	Pfizer Inc.	-

Clinical Result

Indication

Phase

Evaluation

View All Results

Translational Medicine

Boost your research with our translational medicine data.

view full example data

Deal

Boost your decision using our deal data.

view full example data

Core Patent

Boost your research with our Core Patent data.

view full example data

Clinical Trial

Identify the latest clinical trials across global registries.

view full example data

Approval

Accelerate your research with the latest regulatory approval information.

view full example data

Regulation

Understand key drug designations in just a few clicks with Synapse.

view full example data

Chat with Hiro

Get started for free today!

Accelerate Strategic R&D decision making with Synapse, PatSnap’s AI-powered Connected Innovation Intelligence Platform Built for Life Sciences Professionals.

Start your data trial now!

Synapse data is also accessible to external entities via APIs or data packages. Empower better decisions with the latest in pharmaceutical intelligence.

Bio

Bio Sequences Search & Analysis

Chemical

Chemical Structures Search & Analysis


No Data

PF-04928473

Overview

Basic Info

Structure

Related

R&D Status

Clinical Result

Translational Medicine

Deal

Core Patent

Clinical Trial

Approval

Regulation