👤 SM Mumenthaler

Non-small cell lung cancer (NSCLC) is one of the most prevalent and lethal types of cancers worldwide and its high incidence and mortality rates pose a significant public health challenge. Despite significant advances in targeted therapy and immunotherapy, the overall prognosis of patients with NSCLC remains poor. Hypoxia is a critical driving factor in tumor progression, influencing the biological behavior of tumor cells through complex molecular mechanisms. The present review systematically examined the role of the hypoxic microenvironment in NSCLC, demonstrating its crucial role in promoting tumor cell growth, invasion and metastasis. Additionally, it has been previously reported that the hypoxic microenvironment enhances tumor cell resistance by activating hypoxia-inducible factor and regulating exosome secretion. The hypoxic microenvironment also enables tumor cells to adapt to low oxygen and nutrient-deficient conditions by enhancing metabolic reprogramming, such as through upregulating glycolysis. Further studies have shown that the hypoxic microenvironment facilitates immune escape by modulating tumor-associated immune cells and suppressing the antitumor response of the immune system. Moreover, the hypoxic microenvironment increases tumor resistance to radiotherapy, chemotherapy and other types of targeted therapy through various pathways, significantly reducing the therapeutic efficacy of these treatments. Therefore, it could be suggested that early detection of cellular hypoxia and targeted therapy based on hypoxia may offer new therapeutic approaches for patients with NSCLC. The present review not only deepened the current understanding of the mechanisms of action and role of the hypoxic microenvironment in NSCLC but also provided a solid theoretical basis for the future development of precision treatments for patients with NSCLC. Show less

Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism. This approach reveals the network-wide effects of metabolic perturbations. Our results highlighted hexokinase (HK) as a crucial target, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF-conditioned media exhibited increased sensitivity to HK inhibition, confirming the model predictions. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC-CAF crosstalk. Show less