Role of functional genomics in identifying cancer drug resistance and overcoming cancer relapse

The genetic architecture of bone metastases: unveiling the role of epigenetic and genetic modifications in drug resistance

Bone metastases represent frequent and severe complications in various cancers, notably impacting prognosis and quality of life. This review article delves into the genetic and epigenetic mechanisms underpinning drug resistance in bone metastases, a key challenge in effective cancer treatment. The development of drug resistance in cancer can manifest as either intrinsic or acquired, with genetic heterogeneity playing a pivotal role. Intrinsic resistance is often due to pre-existing mutations, while acquired resistance evolves through genetic and epigenetic alterations during treatment. These alterations include mutations in driver genes like TP53 and RB1, epigenetic modifications such as DNA methylation and histone changes, and pathway alterations, notably involving RANK-RANKL signaling and the PI3K/AKT/mTOR cascade. Recent studies underline the significance of the tumor microenvironment in fostering drug resistance, with components such as cancer-associated fibroblasts and hypoxia playing crucial roles. The interactions between metastatic cancer cells and the bone microenvironment facilitate survival and the proliferation of drug-resistant clones. This review highlights the necessity of understanding these complex interactions to develop targeted therapies that can overcome resistance and improve treatment outcomes. Current therapeutic strategies and future directions are discussed, emphasizing the integration of genomic profiling and targeted interventions in managing bone metastases. The evolving landscape of genetic research, including the application of next-generation sequencing and CRISPR technology, offers promising avenues for novel and more effective therapeutic strategies. This comprehensive exploration aims to provide insights into the molecular intricacies of drug resistance in bone metastases, paving the way for improved clinical management and patient care.

Deciphering the Role of Cancer Stem Cells: Drivers of Tumor Evolution, Therapeutic Resistance, and Precision Medicine Strategies

Cancer stem cells (CSCs) play a central role in tumor progression, recurrence, and resistance to conventional therapies, making them a critical focus in oncology research. This review provides a comprehensive analysis of CSC biology, emphasizing their self-renewal, differentiation, and dynamic interactions with the tumor microenvironment (TME). Key signaling pathways, including Wnt, Notch, and Hedgehog, are discussed in detail to highlight their potential as therapeutic targets. Current methodologies for isolating CSCs are critically examined, addressing their advantages and limitations in advancing precision medicine. Emerging technologies, such as CRISPR/Cas9 and single-cell sequencing, are explored for their transformative potential in unraveling CSC heterogeneity and informing therapeutic strategies. The review also underscores the pivotal role of the TME in supporting CSC survival, promoting metastasis, and contributing to therapeutic resistance. Challenges arising from CSC-driven tumor heterogeneity and dormancy are analyzed, along with strategies to mitigate these barriers, including novel therapeutics and targeted approaches. Ethical considerations and the integration of artificial intelligence in designing CSC-specific therapies are discussed as essential elements of future research. The manuscript advocates for a multi-disciplinary approach that combines innovative technologies, advanced therapeutics, and collaborative research to address the complexities of CSCs. By bridging existing gaps in knowledge and fostering advancements in personalized medicine, this review aims to guide the development of more effective cancer treatment strategies, ultimately improving patient outcomes.

Recent advances in mRNA-based cancer vaccines encoding immunostimulants and their delivery strategies

The high prevalence of drug resistance, relapse, and unfavorable response rate of conventional cancer therapies necessitate the development of more efficient treatment modalities. Immunotherapy represents a novel therapeutic approach to cancer treatment in which the immune system's potential is harnessed to recognize and eliminate tumor cells. mRNA cancer vaccines, as a burgeoning field of immunotherapy, have recently drawn particular attention, and among mRNAs encoding tumor-associated antigens, tumor-specific antigens, and immune stimulatory factors, the latter has been relatively less explored. These immunostimulatory mRNAs encode a range of proteins, including stimulatory ligands, receptors, enzymes, pro-inflammatory cytokines, and inhibitory binding proteins, which collectively augment the host immune system's ability against cancerous cells. In this review, we aimed to provide a comprehensive account of mRNA-based cancer vaccines encoding immune stimulants, encompassing their current status, mechanisms of action, delivery strategies employed, as well as recent advances in preclinical and clinical studies. The potential challenges, strategies and future perspectives have also been discussed.

Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma

The poor prognosis of glioma patients brought attention to the need for effective therapeutic approaches for precision therapy. Here, we deployed algorithms relying on network medicine and artificial intelligence to design the framework for subtype-specific target identification and drug response prediction in glioma. We identified the driver mutations that were differentially expressed in each subtype of lower-grade glioma and glioblastoma multiforme and were linked to cancer-specific processes. Driver mutations that were differentially expressed were also subjected to subtype-specific disease module identification. The drugs from the drug bank database were retrieved to target these disease modules. However, the efficacy of anticancer drugs depends on the molecular profile of the cancer and varies among cancer patients due to intratumor heterogeneity. Hence, we developed a deep-learning-based drug response prediction framework using the experimental drug screening data. Models for 30 drugs that can target the disease module were developed, where drug response measured by IC50 was considered a response and gene expression and mutation data were considered predictor variables. The model construction consists of three steps: feature selection, data integration, and classification. We observed the consistent performance of the models in training, test, and validation datasets. Drug responses were predicted for particular cell lines derived from distinct subtypes of gliomas. We found that subtypes of gliomas respond differently to the drug, highlighting the importance of subtype-specific drug response prediction. Therefore, the development of personalized therapy by integrating network medicine and a deep learning-based approach can lead to cancer-specific treatment and improved patient care.