HER2 as a potential therapeutic target on quiescent prostate cancer cells

Bone niches in the regulation of tumour cell dormancy

Secondary metastases, accounting for 90 % of cancer-related deaths, pose a formidable challenge in cancer treatment, with bone being a prevalent site. Importantly, tumours may relapse, often in the skeleton even after successful eradication of the primary tumour, indicating that tumour cells may lay dormant within bone for extended periods of time. This review summarises recent findings in the mechanisms underlying tumour cell dormancy and the role of bone cells in this process. Hematopoietic stem cell (HSC) niches in bone provide a model for understanding regulatory microenvironments. Dormant tumour cells have been shown to exploit similar niches, with evidence suggesting interactions with osteoblast-lineage cells and other stromal cells via CXCL12-CXCR4, integrins, and TAM receptor signalling, especially through GAS6-AXL, led to dormancy, with exit of dormancy potentially regulated by osteoclastic bone resorption and neuronal signalling. A comprehensive understanding of dormant tumour cell niches and their regulatory mechanisms is essential for developing targeted therapies, a critical step towards eradicating metastatic tumours and stopping disease relapse.

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

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 used 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 AND IMPLEMENTATION

The code is available at https://github.com/hzauzqy/TransGEM.

Prostate Cancer and the Mevalonate Pathway

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.

Efficient Convolution Network to Assist Breast Cancer Diagnosis and Target Therapy

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.