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Simple Summary: KRAS mutations have critical roles in the etiology of cancers such as NSCLC, CRC, and PDAC. However, therapeutic targeting of KRAS has proven difficult due to the lack of typical drug-binding sites. Recent improvements have seen the emergence of inhibitors specially tailored for the p.G12C mutation, with Sotorasib showing promising results in NSCLC. Nonetheless, the CodeBreaK 200 study found no significant difference in overall survival between Docetaxel and Sotorasib. This study compares the structural configurations of KRAS isoforms KRAS4A and KRAS4B and analyzes the effects of mutations on drug binding. The present study reveals unique aggregation propensities in wild-type and mutant isoforms, highlighting the complexities of KRAS as a therapeutic target. Sotorasib's stable structure may allow for more effective binding to KRAS4B, despite the steric constraints imposed by KRAS4A and its mutations. This emphasizes the necessity for additional research into the complicated dynamics of KRAS targeting in cancer therapy. Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) gene variations are linked to the development of numerous cancers, including non-small cell lung cancer (NSCLC), colorectal cancer (CRC), and pancreatic ductal adenocarcinoma (PDAC). The lack of typical drug-binding sites has long hampered the discovery of therapeutic drugs targeting KRAS. Since "CodeBreaK 100" demonstrated Sotorasib's early safety and efficacy and led to its approval, especially in the treatment of non-small cell lung cancer (NSCLC), the subsequent identification of specific inhibitors for the p.G12C mutation has offered hope. However, the CodeBreaK 200 study found no significant difference in overall survival (OS) between patients treated with Docetaxel and Sotorasib (AMG 510), adding another degree of complexity to this ongoing challenge. The current study compares the three-dimensional structures of the two major KRAS isoforms, KRAS4A and KRAS4B. It also investigates the probable structural changes caused by the three major mutations (p.G12C, p.G12D, and p.G12V) within Sotorasib's pocket domain. The computational analysis demonstrates that the wild-type and mutant isoforms have distinct aggregation propensities, resulting in the creation of alternate oligomeric configurations. This study highlights the increased complexity of the biological issue of using KRAS as a therapeutic target. The present study stresses the need for a better understanding of the structural dynamics of KRAS and its mutations to design more effective therapeutic approaches. It also emphasizes the potential of computational approaches to shed light on the complicated molecular pathways that drive KRAS-mediated oncogenesis. This study adds to the ongoing efforts to address the therapeutic hurdles presented by KRAS in cancer treatment. [ABSTRACT FROM AUTHOR]

The incidence and death rate of pancreatic ductal adenocarcinoma (PDAC) have increased in recent years, therefore the identification of novel targets for treatment is extremely important. Interactions between cancer and stromal cells are critically involved in tumour formation and development of metastasis. Here we report that PDAC cells secrete BAG3, which binds and activates macrophages, inducing their activation and the secretion of PDAC supporting factors. We also identify IFITM-2 as a BAG3 receptor and show that it signals through PI3K and the p38 MAPK pathways. Finally, we show that the use of an anti-BAG3 antibody results in reduced tumour growth and prevents metastasis formation in three different mouse models. In conclusion, we identify a paracrine loop involved in PDAC growth and metastatic spreading, and show that an anti-BAG3 antibody has therapeutic potential.

Ductal adenocarcinoma of the pancreas is a cancer with a high mortality rate. Among the main reasons for this baleful prognosis is that, in most patients, this neoplasm is diagnosed at a too advanced stage. Clinical oncology research is now particularly focused on decoding the cancer molecular onset by understanding the complex biological architecture of tumor cell proliferation. In this direction, machine learning has proved to be a valid solution in many sectors of the biomedical field, thanks to its ability to mine useful knowledge by biological and genetic data. Since the major risk factor is represented by genetic predisposition, the aim of this study is to find a mathematical model describing the complex relationship existing between genetic mutations of the involved genes and the onset of the disease. To this end, an approach based on evolutionary algorithms is proposed. In particular, genetic programming is used, which allows solving a symbolic regression problem through the use of genetic algorithms. The identification of these correlations is a typical objective of the diagnostic approach and is one of the most critical and complex activities in the presence of large amounts of data that are difficult to correlate through traditional statistical techniques. The mathematical model obtained highlights the importance of the complex relationship existing between the different gene's mutations present in the tumor tissue of the group of patients considered. [ABSTRACT FROM AUTHOR]