Exploration of targeted anti-tumor therapy最新文献

Medulloblastoma (MB) is the commonest primary malignant brain cancer. The current treatment of MB is usually surgical resection combined with radiotherapy or chemotherapy. Although great progress has been made in the clinical management of MB, tumor metastasis and recurrence are still the main cause of death. Therefore, definitive and timely diagnosis is of great importance for improving therapeutic effects on MB. In 2016, the World Health Organization (WHO) divided MB into four subtypes: wingless-type mouse mammary tumor virus integration site (WNT), sonic hedgehog (SHH), non-WNT/non-SHH group 3, and group 4. Each subtype of MB has a unique profile in copy number variation, DNA alteration, gene transcription, or post-transcriptional/translational modification, all of which are associated with different biological manifestations, clinical features, and prognosis. This article reviewed the research progress of different molecular pathology markers in MB and summarized some targeted drugs against these molecular markers, hoping to stimulate the clinical application of these molecular markers in the classification, diagnosis, and treatment of MB.

Transforming growth factor-β (TGF-β) signaling is an important pathway for promoting the pathogenesis of inflammatory diseases, including cancer. The roles of TGF-β signaling are heterogeneous and versatile in cancer development and progression, both anticancer and protumoral actions are reported. Interestingly, increasing evidence suggests that TGF-β enhances disease progression and drug resistance via immune-modulatory actions in the tumor microenvironment (TME) of solid tumors. A better understanding of its regulatory mechanisms in the TME at the molecular level can facilitate the development of precision medicine to block the protumoral actions of TGF-β in the TME. Here, the latest information about the regulatory mechanisms and translational research of TGF-β signaling in the TME for therapeutic development had been summarized.

Oncologic emergencies are a wide spectrum of oncologic conditions caused directly by malignancies or their treatment. Oncologic emergencies may be classified according to the underlying physiopathology in metabolic, hematologic, and structural conditions. In the latter, radiologists have a pivotal role, through an accurate diagnosis useful to provide optimal patient care. Structural conditions may involve the central nervous system, thorax, or abdomen, and emergency radiologists have to know the characteristics imaging findings of each one of them. The number of oncologic emergencies is growing due to the increased incidence of malignancies in the general population and also to the improved survival of these patients thanks to the advances in cancer treatment. Artificial intelligence (AI) could be a solution to assist emergency radiologists with this rapidly increasing workload. To our knowledge, AI applications in the setting of the oncologic emergency are mostly underexplored, probably due to the relatively low number of oncologic emergencies and the difficulty in training algorithms. However, cancer emergencies are defined by the cause and not by a specific pattern of radiological symptoms and signs. Therefore, it can be expected that AI algorithms developed for the detection of these emergencies in the non-oncological field can be transferred to the clinical setting of oncologic emergency. In this review, a craniocaudal approach was followed and central nervous system, thoracic, and abdominal oncologic emergencies have been addressed regarding the AI applications reported in literature. Among the central nervous system emergencies, AI applications have been reported for brain herniation and spinal cord compression. In the thoracic district the addressed emergencies were pulmonary embolism, cardiac tamponade and pneumothorax. Pneumothorax was the most frequently described application for AI, to improve sensibility and to reduce the time-to-diagnosis. Finally, regarding abdominal emergencies, AI applications for abdominal hemorrhage, intestinal obstruction, intestinal perforation, and intestinal intussusception have been described.

Hepatocellular carcinoma (HCC) is a complex process that plays an important role in its progression. Abnormal glucose metabolism in HCC cells can meet the nutrients required for the occurrence and development of liver cancer, better adapt to changes in the surrounding microenvironment, and escape the attack of the immune system on the tumor. There is a close relationship between reprogramming of glucose metabolism and immune escape. This article reviews the current status and progress of glucose metabolism reprogramming in promoting immune escape in liver cancer, aiming to provide new strategies for clinical immunotherapy of liver cancer.

Aim: ¹⁷⁷Lu-Dotatate (Lu-177), a form of peptide receptor radionuclide therapy (PRRT), was approved by Food and Drug Administration (FDA) for the treatment of somatostatin-receptor-positive neuroendocrine tumors (NETs) in 2018. Clinical trials prior to the FDA approval of Lu-177 showed favorable outcomes but there is limited published real world outcomes data. This study aims to describe and analyze real world outcomes of patients with NET who received Lu-177.

Methods: After obtaining institutional review board approval, retrospective evaluation was performed to analyze the efficacy of Lu-177 for somatostatin receptor-positive gastro-entero-pancreatic NETs (GEP-NETs) patients at the University of Kansas Cancer Center between June 2018 and September 2021. This study aims to determine the response rate to the treatment of the entire cohort and subgroups.

Results: A total of 65 patients received Lu-177 of which 58 completed treatment. The 58 patients had a median age of 61.5 years, 24 females and 34 males, 86% Caucasian and 12% black. The origins of NETs were primarily small bowel (n = 24) and pancreatic (n = 14). Pathology showed grades 1 (n = 21), 2 (n = 25), and 3 (n = 4) and were primarily well-differentiated tumors (n = 4). Among the cohort, 52 patients had imaging to assess response with 14 (26.9%) patients with partial response (PR), 31 (59.6%) with stable disease (SD), and 7 (13.5%) with progressive disease (PD). In a subset analysis, patients with non-functional disease (n = 29) had higher rates of PR 42.3% (compared to 11.5%, P = 0.0147) and higher disease control rate of 96% (compared to 78%, P = 0.042) than patients with functional disease (n = 29). Patients with non-functional disease had a lower PD of 3.85% (compared to 23%, P = 0.0147) than those with functional disease.

Conclusions: This real world outcomes analysis of NETs treated with Lu-177 shows improved PR when compared to the initial clinical trials and is promising for patients. In addition, patients with non-functional tumors were found to have a statistically significant improved response rate which has not been described in the literature before. If these study findings are validated in a larger cohort they may guide patient selection for Lu-177 therapy in the future.

Aim: Sarcopenia and skeletal muscle density (SMD) have been shown to be both predictive and prognostic marker in oncology. Advanced lung cancer inflammation index (ALI) has been shown to predict overall survival (OS) in small cell lung cancer (SCLC). Computed tomography (CT) enables skeletal muscle to be quantified, whereas body mass index (BMI) cannot accurately reflect body composition. The purpose was to evaluate the prognostic value of modified ALI (mALI) using CT-determined third lumbar vertebra (L3) muscle index beyond original ALI and see the interaction between sarcopenia, SMD, neutrophil-lymphocyte ratio (NLR), ALI and mALI at baseline and post 4 cycles of chemotherapy and their effects on OS and progress free survival (PFS) in patients with advanced non-SCLC (NSCLC).

Methods: This retrospective study consisted of a total of 285 advanced NSCLC patients. The morphometric parameters such as SMD, skeletal muscle index (SMI) and fat-free mass (FFM) were measured by CT at the L3 vertebra. ALI was defined as BMI × serum albumin/NLR and mALI was defined as SMI × serum albumin/NLR.

Results: Sarcopenia was observed in over 70% of patients across all BMI categories. Patients having sarcopenia suffered from a higher incidence of chemotherapeutic drug toxicities but this was not found to be statistically significant. Concordance was seen between ALI and mALI in the pre-treatment setting and this was statistically significant. A significant proportion of patients with poor ALI (90.9%), poor pre-chemotherapy mALI (91.3%) and poor post-chemotherapy mALI (89%) had poor NLR and each of them was statistically significant.

Conclusions: In both univariate and multivariate analyses, this study demonstrated the statistical significance of sarcopenia, SMD, and mALI as predictive factors for OS. Additionally, sarcopenia and SMD were also found to be statistically significant factors in predicting PFS. These biomarkers could potentially help triage patients for active nutritional intervention for better outcomes.

Aspirin is a well-known nonsteroidal anti-inflammatory drug (NSAID) that has a recognized role in cancer prevention as well as evidence to support its use as an adjuvant for cancer treatment. Importantly there has been an increasing number of studies contributing to the mechanistic understanding of aspirins' anti-tumour effects and these studies continue to inform the potential clinical use of aspirin for both the prevention and treatment of cancer. This review focuses on the emerging role of aspirin as a regulator of metabolic reprogramming, an essential "hallmark of cancer" required to support the increased demand for biosynthetic intermediates needed for sustained proliferation. Cancer cells frequently undergo metabolic rewiring driven by oncogenic pathways such as hypoxia-inducible factor (HIF), wingless-related integration site (Wnt), mammalian target of rapamycin (mTOR), and nuclear factor kappa light chain enhancer of activated B cells (NF-κB), which supports the increased proliferative rate as tumours develop and progress. Reviewed here, cellular metabolic reprogramming has been identified as a key mechanism of action of aspirin and include the regulation of key metabolic drivers, the regulation of enzymes involved in glycolysis and glutaminolysis, and altered nutrient utilisation upon aspirin exposure. Importantly, as aspirin treatment exposes metabolic vulnerabilities in tumour cells, there is an opportunity for the use of aspirin in combination with specific metabolic inhibitors in particular, glutaminase (GLS) inhibitors currently in clinical trials such as telaglenastat (CB-839) and IACS-6274 for the treatment of colorectal and potentially other cancers. The increasing evidence that aspirin impacts metabolism in cancer cells suggests that aspirin could provide a simple, relatively safe, and cost-effective way to target this important hallmark of cancer. Excitingly, this review highlights a potential new role for aspirin in improving the efficacy of a new generation of metabolic inhibitors currently undergoing clinical investigation.