HER2 as a potential therapeutic target on quiescent prostate cancer cells

Yumoto, Kenji; Rashid, Jibraan; Ibrahim, Kristina G; Zielske, Steven P.; Wang, Yu; Omi, Maiko; Decker, Ann M.; Jung, Younghun; Sun, Dan; Remmer, Henriette A.; Mishina, Yuji; Buttitta, Laura; Taichman, Russell S.; Cackowski, Frank C.

doi:10.1016/j.tranon.2023.101642

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…Consistently, both APMAP mRNA and protein levels are elevated in clinical PCa samples [51]. HER2, also called EGFR 2, has been detected on the cell surface of quiescent (G0) PCa cells, which often exhibit resistance to chemotherapy and are associated with recurrence following dormancy [52].…”

Section: Growth Factor Receptors Reside Inside Lipid Raftsmentioning

confidence: 82%

Prostate Cancer and the Mevalonate Pathway

Guerrero-Ochoa,

Rodríguez-Zapater,

Anel

et al. 2024

IJMS

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Antineoplastic therapies for prostate cancer (PCa) have traditionally centered around the androgen receptor (AR) pathway, which has demonstrated a significant role in oncogenesis. Nevertheless, it is becoming progressively apparent that therapeutic strategies must diversify their focus due to the emergence of resistance mechanisms that the tumor employs when subjected to monomolecular treatments. This review illustrates how the dysregulation of the lipid metabolic pathway constitutes a survival strategy adopted by tumors to evade eradication efforts. Integrating this aspect into oncological management could prove valuable in combating PCa.

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Section: Growth Factor Receptors Reside Inside Lipid Raftsmentioning

confidence: 82%

Prostate Cancer and the Mevalonate Pathway

Guerrero-Ochoa,

Rodríguez-Zapater,

Anel

et al. 2024

IJMS

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“…Anti-HER2 therapies are now part of the care standard for HER2-amplified gastric cancer [46,47]. HER2 may also be a potential therapeutic target for quiescent prostate cancer [48]. Despite the fact that HER2 status in cancers of the female reproductive system has been explored for more than 20 years, the determination of HER2 gene status has not been widely recognized as a prognostic biomarker for response to anti-HER2 treatment in gynecologic cancers, unlike in the breast and the digestive system [49].…”

Section: Discussionmentioning

confidence: 99%

Efficient Convolution Network to Assist Breast Cancer Diagnosis and Target Therapy

Wang

Chu

Muzakky

et al. 2023

Cancers

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Breast cancer is the leading cause of cancer-related deaths among women worldwide, and early detection and treatment has been shown to significantly reduce fatality rates from severe illness. Moreover, determination of the human epidermal growth factor receptor-2 (HER2) gene amplification by Fluorescence in situ hybridization (FISH) and Dual in situ hybridization (DISH) is critical for the selection of appropriate breast cancer patients for HER2-targeted therapy. However, visual examination of microscopy is time-consuming, subjective and poorly reproducible due to high inter-observer variability among pathologists and cytopathologists. The lack of consistency in identifying carcinoma-like nuclei has led to divergences in the calculation of sensitivity and specificity. This manuscript introduces a highly efficient deep learning method with low computing cost. The experimental results demonstrate that the proposed framework achieves high precision and recall on three essential clinical applications, including breast cancer diagnosis and human epidermal receptor factor 2 (HER2) amplification detection on FISH and DISH slides for HER2 target therapy. Furthermore, the proposed method outperforms the majority of the benchmark methods in terms of IoU by a significant margin (p<0.001) on three essential clinical applications. Importantly, run time analysis shows that the proposed method obtains excellent segmentation results with notably reduced time for Artificial intelligence (AI) training (16.93%), AI inference (17.25%) and memory usage (18.52%), making the proposed framework feasible for practical clinical usage.

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“…AS for PC, the three known drug targets, poly ADP-ribose polymerase 1 (PARP1) ( Deshmukh and Qiu 2015 ), poly ADP-ribose polymerase 2 (PARP2) ( Gui et al 2019 ), and erb-b2 receptor tyrosine kinase 2 (ERBB2) ( Yumoto et al 2023 ), are selected for inclusion in this study. The crystal structure of the PARP1, PARP2, and ERBB2 are downloaded from the PDB database ( Burley et al 2021 ), with a PDB ID of 7kk4 ( Ryan et al 2021 ), 4bjc ( Haikarainen et al 2014 ), and 5my6 ( D’Huyvetter et al 2017 ), respectively.…”

Section: Methodsmentioning

confidence: 99%

TransGEM: a molecule generation model based on Transformer with gene expression data

Liu,

Yu,

Duan

et al. 2024

Bioinformatics

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Motivation It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information. Results We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is employed to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo drug design, and it will promote phenotype-based drug discovery. Availability The code is available at https://github.com/hzauzqy/TransGEM Supplementary information Supplementary data are available at Bioinformatics online.

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HER2 as a potential therapeutic target on quiescent prostate cancer cells

Cited by 4 publications

References 40 publications

Prostate Cancer and the Mevalonate Pathway

Prostate Cancer and the Mevalonate Pathway

Efficient Convolution Network to Assist Breast Cancer Diagnosis and Target Therapy

TransGEM: a molecule generation model based on Transformer with gene expression data

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