Oncotarget最新文献

Restoration of the p53 pathway has been a long-term goal in the field of cancer research to treat tumors with mutated p53 and aggressive clinical behavior. p53 pathway restoration in p53-deficient cancers can be achieved by small molecules via p53-dependent or p53-independent processes. Hereafter p53-independent restoration of p53-pathway-signaling in p53-deficient/mutated tumors is referred to as 'restoration of the p53 pathway'. We compare activation of p53 target genes by novel compounds PG3 and PG3-Oc, that activate p53-target genes in a p53-independent manner, and four mutant p53-activating compounds while Nutlin-3a is used as negative control. PG3 and PG3-Oc upregulate p21, PUMA, and DR5 in five cancer cell lines with various p53 mutational statuses through ATF4 (Activating Transcriptional Factor 4) and integrated stress response (ISR) independent of p53. Mutant p53-targeting compounds induce expression of the 3 major downstream p53 target genes and ATF4 in a highly variable and cell-type-dependent manner. PG3 treatment activates ATF4 through ISR via kinase HRI (Heme-Regulated Inhibitor). ATF4 mediates upregulation of PUMA, p21, and NAG-1/GDF15 (Nonsteroidal anti-inflammatory drug-activated gene 1). We note that PUMA mediates apoptosis through activation of caspase-8 in HT29 cells and potentially caspase-10 in SW480 cells. We provide a novel mechanism engaged by PG3 to induce cell death via the HRI/ATF4/PUMA axis. Our results provide unique insights into the mechanism of action of PG3 as a novel cancer therapeutic targeting p53 pathway-like tumor suppression.

The emergence of immunotherapy (IO), and more recently intratumoral IO presents a novel approach to cancer treatment which can enhance immune responses while allowing combination therapy and reducing systemic adverse events. These techniques are intended to change the therapeutic paradigm of oncology care, and means that traditional assessment methods are inadequate, underlining the importance of adopting innovative approaches. Artificial intelligence (AI) with machine learning algorithms and radiomics are promising approaches, offering new insights into patient care by analyzing complex imaging data to identify biomarkers to refine diagnosis, guide interventions, predict treatment responses, and adapt therapeutic strategies. In this editorial, we explore how integrating these technologies could revolutionize personalized oncology. We discuss their potential to enhance the survival and quality of life of patients treated with intratumoral IO by improving treatment effectiveness and minimizing side effects, potentially reshaping practice guidelines. We also identify areas for future research and review clinical trials to confirm the efficacy of these promising approaches.

Lifelong hematopoiesis is sustained by crosstalk between hematopoietic stem and progenitor cells (HSPCs) and specialized bone marrow niches. Acute myeloid leukemia (AML) upends that balance, as leukemic blasts secrete factors that remodel the bone marrow into a self-reinforcing leukemic niche. The inflammatory secretome behind this compartmental adaptation accounts for a progressive decline in hematopoietic function that leads to diagnosis and persists through early treatment. Not surprisingly, the mediators of an acute inflammatory injury and HSPC suppression have attracted much attention in an effort to alleviate morbidity and improve outcomes. HSPCs typically recover during disease remission and re-expand in the bone marrow (BM), but little is known about potentially lasting consequences for stem cells and progenitors. We recently showed that AML-experienced HSPCs actively participate in the inflammatory process during leukemic progression. HSPCs are constituent components of the innate immune system, and elegant studies of infection and experimental inflammation over the past decade have described the generation of an adoptively transferable, innate immune memory. Building on this paradigm, we discuss the potential translational relevance of a durable legacy in AML-experienced HSPC.

Generative AI is revolutionizing oncological imaging, enhancing cancer detection and diagnosis. This editorial explores its impact on expanding datasets, improving image quality, and enabling predictive oncology. We discuss ethical considerations and introduce a unique perspective on personalized cancer screening using AI-generated digital twins. This approach could optimize screening protocols, improve early detection, and tailor treatment plans. While challenges remain, generative AI in oncological imaging offers unprecedented opportunities to advance cancer care and improve patient outcomes.

Artificial intelligence (AI) is revolutionizing society and healthcare, offering new possibilities for precision medicine. Immunotherapy in oncology (IO) has similarly transformed cancer treatment through novel mechanisms of therapeutic action, but has also led to atypical response patterns that challenge traditional methods for response evaluation. This editorial explores the role of AI in addressing these challenges through the development of new biomarkers for precise disease characterization, and in particular those built on imaging for the early response assessment of patients diagnosed with cancer and treated with IO. Properly leveraged AI-based techniques could herald a new era of precision medicine guided by non-invasive, imaging-based disease evaluation.

Despite advances in breast cancer screening and treatment, prognosis for metastatic disease remains dismal at 30% five-year survival. This is due, in large, to the failure of current therapeutics to target properties unique to metastatic cells. One of the drivers of metastasis is miR-10b, a small noncoding RNA implicated in cancer cell invasion, migration, viability, and proliferation. We have developed a nanodrug, termed MN-anti-miR10b, that delivers anti-miR-10b antisense oligomers to cancer cells. In mouse models of metastatic triple-negative breast cancer, MN-anti-miR10b has been shown to prevent onset of metastasis and eliminate existing metastases in combination with chemotherapy, even after treatment has been stopped. Recent studies have implicated miR-10b in conferring stem cell-like properties onto cancer cells, such as chemoresistance. In this study, we show transcriptional evidence that inhibition of miR-10b with MN-anti-miR10b activates developmental processes in cancer cells and that stem-like cancer cells have increased miR-10b expression. We then demonstrate that treatment of breast cancer cells with MN-anti-miR10b reduces their stemness, confirming that these properties make metastatic cells susceptible to the nanodrug actions. Collectively, these findings indicate that inhibition of miR-10b functions to impair breast cancer cell stemness, positioning MN-anti-miR10b as an effective treatment option for stem-like breast cancer subtypes.

The vacuolar H⁺-ATPase (V-ATPase) is an ATP-dependent proton pump that functions to control the pH of intracellular compartments as well as to transport protons across the plasma membrane of various cell types, including cancer cells. We have previously shown that selective inhibition of plasma membrane V-ATPases in breast tumor cells inhibits the invasion of these cells in vitro. We have now developed a nanobody directed against an extracellular epitope of the mouse V-ATPase c subunit. We show that treatment of 4T1-12B mouse breast cancer cells with this nanobody inhibits V-ATPase-dependent acidification of the media and invasion of these cells in vitro. We further find that injection of this nanobody into mice implanted with 4T1-12B cells orthotopically in the mammary fat pad inhibits metastasis of tumor cells to lung. These results suggest that plasma membrane V-ATPases represent a novel therapeutic target to limit breast cancer metastasis.