The Therapeutic Significance of HER3 in Non-small Cell Lung Cancer (NSCLC): A Review Study

Transforming breast cancer diagnosis and treatment with large language Models: A comprehensive survey

Breast cancer (BrCa), being one of the most prevalent forms of cancer in women, poses many challenges in the field of treatment and diagnosis due to its complex biological mechanisms. Early and accurate diagnosis plays a fundamental role in improving survival rates, but the limitations of existing imaging methods and clinical data interpretation often prevent optimal results. Large Language Models (LLMs), which are developed based on advanced architectures such as transformers, have brought about a significant revolution in data processing and medical decision-making. By analyzing a large volume of medical and clinical data, these models enable early diagnosis by identifying patterns in images and medical records and provide personalized treatment strategies by integrating genetic markers and clinical guidelines. Despite the transformative potential of these models, their use in BrCa management faces challenges such as data sensitivity, algorithm transparency, ethical considerations, and model compatibility with the details of medical applications that need to be addressed to achieve reliable results. This review systematically reviews the impact of LLMs on BrCa treatment and diagnosis. This study's objectives include analyzing the role of LLM technology in diagnosing and treating this disease. The findings indicate that the application of LLMs has resulted in significant improvements in various aspects of BrCa management, such as a 35% increase in the Efficiency of Diagnosis and BrCa Treatment (EDBC), a 30% enhancement in the System's Clinical Trust and Reliability (SCTR), and a 20% improvement in the quality of patient education and information (IPEI). Ultimately, this study demonstrates the importance of LLMs in advancing precision medicine for BrCa and paves the way for effective patient-centered care solutions.

Comprehensive review of reinforcement learning in lung cancer diagnosis and treatment: Taxonomy, challenges and recommendations

Lung cancer (LuC) is one of the leading causes of death in the world, and due to the complex mechanisms and widespread metastasis, diagnosis and treatment are challenging. In recent years, the application of reinforcement learning (RL) techniques as a new tool to improve LuC diagnosis and treatment has been dramatically expanded. These techniques can potentially increase the accuracy of diagnosis and optimize treatment processes by learning from limited data and improving clinical decisions. However, RL in LuC diagnosis and treatment faces challenges such as limited access to clinical data, the complexity of algorithms, and the need for technical expertise for proper implementation. Our systematic review article aims to evaluate the latest developments in applications and challenges of using RL techniques in LuC diagnosis and treatment. The findings showed that RL has increased the accuracy of identifying disease trends by 37 % and enhancing treatment decisions by 23 %. Also, using this approach reduces data processing time by 17 % and streamlining treatment processes by 12 %. Ultimately, analyzing the current challenges and offering recommendations to researchers could help develop new strategies for improving the diagnosis and treatment of LuC.

Mechanisms of acquired resistance to HER2-Positive breast cancer therapies induced by HER3: A comprehensive review

Receptor tyrosine kinases (RTKs) are cell surface receptors with kinase activity that play a crucial role in diverse cellular processes. Among the RTK family members, Human epidermal growth factor receptor 2 (HER2) and HER3 are particularly relevant to breast cancer. The review delves into the complexities of receptor tyrosine kinase interactions, resistance mechanisms, and the potential of anti-HER3 drugs, offering valuable insights into the clinical implications and future directions in this field of study. It assesses the potential of anti-HER3 drugs, such as pertuzumab, in overcoming resistance observed in HER2-positive breast cancer therapies. The review also explores the resistance mechanisms associated with various drugs, including trastuzumab, lapatinib, and PI3K inhibitors, providing insights into the intricate molecular processes underlying resistance development. The review concludes by emphasizing the necessity for further clinical trials to assess the efficacy of HER3 inhibitors and the potential of developing safe and effective anti-HER3 treatments to improve treatment outcomes for patients with HER2-positive breast cancer.

HER3 (ERBB3) amplification in liposarcoma - a putative new therapeutic target?

BACKGROUND

Liposarcomas are among the most common mesenchymal malignancies. However, the therapeutic options are still very limited and so far, targeted therapies had not yet been established. Immunotherapy, which has been a breakthrough in other oncological entities, seems to have no efficacy in liposarcoma. Complicating matters further, classification remains difficult due to the diversity of morphologies and nonspecific or absent markers in immunohistochemistry, leaving molecular pathology using FISH or sequencing as best options. Many liposarcomas harbor MDM2 gene amplifications. In close relation to the gene locus of MDM2, HER3 (ERBB3) gene is present and co-amplification could occur. Since the group of HER/EGFR receptor tyrosine kinases and its inhibitors/antibodies play a role in a broad spectrum of oncological diseases and treatments, and some HER3 inhibitors/antibodies are already under clinical investigation, we hypothesized that in case of HER3 co-amplifications a tumor might bear a further potential therapeutic target.

METHODS

We performed FISH analysis (MDM2, DDIT3, HER3) in 56 archived cases and subsequently performed reclassification to confirm the diagnosis of liposarcoma.

RESULTS

Next to 16 out of 56 cases needed to be re-classified, in 20 out of 54 cases, a cluster-amplification of HER3 could be detected, significantly correlating with MDM2 amplification. Our study shows that the entity of liposarcomas show specific molecular characteristics leading to reclassify archived cases by modern, established methodologies. Additionally, in 57.1% of these cases, HER3 was cluster-amplified profusely, presenting a putative therapeutic target for targeted therapy.

CONCLUSION

Our study serves as the initial basis for further investigation of the HER3 gene as a putative therapeutic target in liposarcoma.