Personalizing non-small cell lung cancer treatment through patient-derived xenograft models: preclinical and clinical factors for consideration.

PURPOSE: In the pursuit of creating personalized and more effective treatment strategies for lung cancer patients, Patient-Derived Xenografts (PDXs) have been introduced as preclinical platforms that can recapitulate the specific patient's tumor in an in vivo model. We investigated how well PDX models can preserve the tumor's clinical and molecular characteristics across different generations. METHODS: A Non-Small Cell Lung Cancer (NSCLC) PDX model was established in NSG-SGM3 mice and clinical and preclinical factors were assessed throughout subsequent passages. Our cohort consisted of 40 NSCLC patients, which were used to create 20 patient-specific PDX models in NSG-SGM3 mice. Histopathological staining and Whole Exome Sequencing (WES) analysis were preformed to understand tumor heterogeneity throughout serial passages. RESULTS: The main factors that contributed to the growth of the engrafted PDX in mice were a higher grade or stage of disease, in contrast to the long duration of chemotherapy treatment, which was negatively correlated with PDX propagation. Successful PDX growth was also linked to poorer prognosis and overall survival, while growth pattern variability was affected by the tumor aggressiveness, primarily affecting the first passage. Pathology analysis showed preservation of the histological type and grade; however, WES analysis revealed genomic instability in advanced passages, leading to the inconsistencies in clinically relevant alterations between the PDXs and biopsies. CONCLUSIONS: Our study highlights the impact of multiple clinical and preclinical factors on the engraftment success, growth kinetics, and tumor stability of patient-specific NSCLC PDXs, and underscores the importance of considering these factors when guiding and evaluating prolonged personalized treatment studies for NSCLC patients in these models, as well as signaling the imperative for additional investigations to determine the full clinical potential of this technique.

The interaction of mast cells with membranes from lung cancer cells induces the release of extracellular vesicles with a unique miRNA signature.

Mast cells (MCs) are immune cells that play roles in both normal and abnormal processes. They have been linked to tumor progression in several types of cancer, including non-small cell lung cancer (NSCLC). However, the exact role of MCs in NSCLC is still unclear. Some studies have shown that the presence of a large number of MCs is associated with poor prognosis, while others have suggested that MCs have protective effects. To better understand the role of MCs in NSCLC, we aimed to identify the initial mechanisms underlying the communication between MCs and lung cancer cells. Here, we recapitulated cell-to-cell contact by exposing MCs to membranes derived from lung cancer cells and confirming their activation, as evidenced by increased phosphorylation of the ERK and AKT kinases. Profiling of the microRNAs that were selectively enriched in the extracellular vesicles (EVs) released by the lung cancer-activated MCs revealed that they contained significantly increased amounts of miR-100-5p and miR-125b, two protumorigenic miRNAs. We explored the pathways regulated by these miRNAs via enrichment analysis using the KEGG database, demonstrating that these two miRNAs regulate p53 signaling, cancer pathways, and pathways associated with apoptosis and the cell cycle.

Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.

Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.