Cancer stem cells: advances in knowledge and implications for cancer therapy.

Cancer stem cells (CSCs), a small subset of cells in tumors that are characterized by self-renewal and continuous proliferation, lead to tumorigenesis, metastasis, and maintain tumor heterogeneity. Cancer continues to be a significant global disease burden. In the past, surgery, radiotherapy, and chemotherapy were the main cancer treatments. The technology of cancer treatments continues to develop and advance, and the emergence of targeted therapy, and immunotherapy provides more options for patients to a certain extent. However, the limitations of efficacy and treatment resistance are still inevitable. Our review begins with a brief introduction of the historical discoveries, original hypotheses, and pathways that regulate CSCs, such as WNT/ß-Catenin, hedgehog, Notch, NF-κB, JAK/STAT, TGF-ß, PI3K/AKT, PPAR pathway, and their crosstalk. We focus on the role of CSCs in various therapeutic outcomes and resistance, including how the treatments affect the content of CSCs and the alteration of related molecules, CSCs-mediated therapeutic resistance, and the clinical value of targeting CSCs in patients with refractory, progressed or advanced tumors. In summary, CSCs affect therapeutic efficacy, and the treatment method of targeting CSCs is still difficult to determine. Clarifying regulatory mechanisms and targeting biomarkers of CSCs is currently the mainstream idea.

Efficacy and safety of personalized optimal PD-(L)1 combinations in advanced NSCLC: a network Meta-Analysis.

INTRODUCTION: Programmed death 1 (PD-1)/programmed death 1 ligand 1 (PD-L1)-directed immunotherapy has revolutionized the treatments for advanced non-small cell lung cancer (NSCLC), whereas the optimal therapeutic combinations remain uncertain. METHODS: Our study encompassed phase Ⅱ/III randomized controlled trials (RCTs) that involved anti-PD-(L)1-based therapies for stage-IV NSCLC. The primary outcomes included overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and incidences of adverse events (AEs). Subgroup analyses were conducted by treatment lines, PD-L1 expression levels, histological types, and metastatic sites. RESULTS: Our analysis incorporated 38 publications, covering 14 therapeutic combinations and involving 18,048 participants. PD-(L)1+chemotherapy (CT), PD-(L)1+ cytotoxic T lymphocyte-associated antigen-4 (CTLA4) +CT, and PD-(L)1+ T-cell immunoglobulin and ITIM domain (TIGIT) were notably effective in prolonging OS. Overall, PD-(L)1+CT and PD-(L)1+CT+ vascular endothelial growth factor (VEGF) were significantly beneficial for PFS and ORR. As for the subsequent-line treatments, incorporating radiotherapy can enhance PFS and ORR (ranked fourth among enrolled treatments). For patients with PD-L1 < 1%, PD-(L)1+CT+VEGF and PD-(L)1+CTLA4+CT were favorable approaches. Conversely, in patients with PD-L1 ≥ 50%, PD-(L)1+CT represented an effective treatment. Patients with non-squamous cell carcinoma or liver metastases might benefit from the addition of VEGF. In cases of squamous cell carcinoma or brain metastases, the combination of PD-(L)1+CTLA4+CT yielded superior benefits. CONCLUSIONS: This study underscores the enhanced efficacy of combination immunotherapies over monotherapy. It highlights the necessity for personalized treatment, considering individual factors. These insights are vital for clinical decision-making in the management of advanced NSCLC.

Radiomics and deep learning models to differentiate lung adenosquamous carcinoma: A multicenter trial.

Adenosquamous carcinoma (ASC) is frequently misdiagnosed or overlooked in clinical practice due to its dual histological components and potential transformation from either adenocarcinoma (ADC) or squamous cell carcinoma (SCC). Our study aimed to differentiate ASC from ADC and SCC by incorporating features of enhanced CTs and clinical characteristics to build radiomics and deep learning models. The classification models were trained in Xiangya Hospital and validated in two other independent hospitals. The areas under the receiver operating characteristic curves (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to estimate the performance. The optimal three-class classification model achieved a maximum AUC of 0.89 and accuracy of 0.81 in external validation sets, AUC of 0.99 and accuracy of 0.99 in the internal test set. These findings highlight the efficacy of our models in differentiating ASC, providing a non-invasive, timely, and accurate diagnostic approach before and during the treatment.

MRI radiomics for brain metastasis sub-pathology classification from non-small cell lung cancer: a machine learning, multicenter study.

The objective of this study is to develop a machine-learning model that can accurately distinguish between different histologic types of brain lesions in patients with non-small cell lung cancer (NSCLC) when it is not safe or feasible to perform a biopsy. To achieve this goal, the study utilized data from two patient cohorts: 116 patients from Xiangya Hospital and 35 patients from Yueyang Central Hospital. A total of eight machine learning algorithms, including Xgboost, were compared. Additionally, a 3-dimensional convolutional neural network was trained using transfer learning to further evaluate the performance of these models. The SHapley Additive exPlanations (SHAP) method was developed to determine the most important features in the best-performing model after hyperparameter optimization. The results showed that the area under the curve (AUC) for the classification of brain lesions as either lung adenocarcinoma or squamous carcinoma ranged from 0.60 to 0.87. The model based on single radiomics features extracted from contrast-enhanced T1 MRI and utilizing the Xgboost algorithm demonstrated the highest performance (AUC: 0.85) in the internal validation set and adequate performance (AUC: 0.80) in the independent external validation set. The SHAP values also revealed the impact of individual features on the classification results. In conclusion, the use of a radiomics model incorporating contrast-enhanced T1 MRI, Xgboost, and SHAP algorithms shows promise in accurately and interpretably identifying brain lesions in patients with NSCLC.

Co-inhibition of TIGIT and PD-1/PD-L1 in Cancer Immunotherapy: Mechanisms and Clinical Trials.

Over the past decade, immune checkpoint inhibitors (ICIs) have emerged as a revolutionary cancer treatment modality, offering long-lasting responses and survival benefits for a substantial number of cancer patients. However, the response rates to ICIs vary significantly among individuals and cancer types, with a notable proportion of patients exhibiting resistance or showing no response. Therefore, dual ICI combination therapy has been proposed as a potential strategy to address these challenges. One of the targets is TIGIT, an inhibitory receptor associated with T-cell exhaustion. TIGIT has diverse immunosuppressive effects on the cancer immunity cycle, including the inhibition of natural killer cell effector function, suppression of dendritic cell maturation, promotion of macrophage polarization to the M2 phenotype, and differentiation of T cells to regulatory T cells. Furthermore, TIGIT is linked with PD-1 expression, and it can synergize with PD-1/PD-L1 blockade to enhance tumor rejection. Preclinical studies have demonstrated the potential benefits of co-inhibition of TIGIT and PD-1/PD-L1 in enhancing anti-tumor immunity and improving treatment outcomes in several cancer types. Several clinical trials are underway to evaluate the safety and efficacy of TIGIT and PD-1/PD-L1 co-inhibition in various cancer types, and the results are awaited. This review provides an overview of the mechanisms of TIGIT and PD-1/PD-L1 co-inhibition in anti-tumor treatment, summarizes the latest clinical trials investigating this combination therapy, and discusses its prospects. Overall, co-inhibition of TIGIT and PD-1/PD-L1 represents a promising therapeutic approach for cancer treatment that has the potential to improve the outcomes of cancer patients treated with ICIs.

A multiomics approach-based prediction of radiation pneumonia in lung cancer patients: impact on survival outcome.

PURPOSE: To predict the risk of radiation pneumonitis (RP), a multiomics model was built to stratify lung cancer patients. Our study also investigated the impact of RP on survival. METHODS: This study retrospectively collected 100 RP and 99 matched non-RP lung cancer patients treated with radiotherapy from two independent centres. They were divided into training (n = 175) and validation cohorts (n = 24). The radiomics, dosiomics and clinical features were extracted from planning CT and electronic medical records and were analysed by LASSO Cox regression. A multiomics prediction model was developed by the optimal algorithm. Overall survival (OS) between the RP, non-RP, mild RP, and severe RP groups was analysed by the Kaplan‒Meier method. RESULTS: Sixteen radiomics features, two dosiomics features, and one clinical feature were selected to build the best multiomics model. The optimal performance for predicting RP was the area under the receiver operating characteristic curve (AUC) of the testing set (0.94) and validation set (0.92). The RP patients were divided into mild (≤ 2 grade) and severe (> 2 grade) RP groups. The median OS was 31 months for the non-RP group compared with 49 months for the RP group (HR = 0.53, p = 0.0022). Among the RP subgroup, the median OS was 57 months for the mild RP group and 25 months for the severe RP group (HR = 3.72, p < 0.0001). CONCLUSIONS: The multiomics model contributed to improving the accuracy of RP prediction. Compared with the non-RP patients, the RP patients displayed longer OS, especially the mild RP patients.

Optimal timing and sequence of combining stereotactic radiosurgery with immune checkpoint inhibitors in treating brain metastases: clinical evidence and mechanistic basis.

Recent evidence has shown that immune checkpoint inhibitors (ICIs) are efficacious for treating brain metastases of various primary tumors. However, the immunosuppressive tumor microenvironment and the blood-brain barrier (BBB) or blood-tumor barrier (BTB) essentially restrict the efficacy of ICIs. Stereotactic radiosurgery (SRS) can be a powerful ally to ICIs due to its trait of disrupting the BBB/BTB and increasing the immunogenicity of brain metastases. The combination of SRS + ICI has shown synergy in brain metastases in several retrospective studies. Nevertheless, the optimal schedule for the combination of SRS and ICI in brain metastases is yet to be determined. In this review, we summarized the current clinical and preclinical evidence on the timing and sequence of SRS + ICI to provide insight into the current state of knowledge about this important area in patient care.

The value of postoperative radiotherapy in thymoma patients with myasthenia gravis.

INTRODUCTION: Surgery is the first-line treatment for patients with thymoma associated with myasthenia gravis (MG); however, the value of radiotherapy among these patients remains debatable. Herein, we examined the impact of postoperative radiotherapy (PORT) on the efficacy and prognosis of patients with thymoma and MG. METHODS: This retrospective cohort study included 126 patients with thymoma and MG who were enrolled in the Xiangya Hospital clinical database between 2011 and 2021. Demographic and clinical data were collected including sex, age, histologic subtype, Masaoka-Koga staging, primary tumor, lymph node, metastasis (TNM) staging, and therapeutic modalities. To evaluate short-term MG symptom improvement following PORT, we examined changes in the quantitative myasthenia gravis (QMG) scores within 3 months post-treatment. Minimal manifestation status (MMS) was the main endpoint for assessing long-term improvement in MG symptoms. Overall survival (OS) and disease-free survival (DFS) were primary endpoints to determine the impact of PORT on prognosis. RESULTS: Effects of PORT on MG symptoms: QMG scores significantly differed between the non-PORT and PORT groups (χ2 = 6.300, p = 0.012). The median time to achieve MMS was significantly shorter in the PORT group than that in the non-PORT group (2.0 years vs. 4.4 years; p = 0.031). Multivariate analysis revealed that radiotherapy was associated with a reduced time to achieve MMS (hazard ratio [HR] 1.971, 95% confidence interval [CI]:1.102-3.525, p = 0.022). Effects of PORT on DFS and OS: The 10-year OS rate of the entire cohort was 90.5%, whereas OS rates for the PORT and non-PORT groups were 94.4 and 85.1%, respectively. The 5-year DFS rates for the whole cohort, PORT group, and non-PORT group were 89.7, 95.8, and 81.5%, respectively. PORT was associated with improved DFS (HR 0.139, 95% CI: 0.037-0.533, p = 0.004). In the high-risk histologic subgroup (type B2, B3), patients who received PORT had better OS (p = 0.015) and DFS (p = 0.0053) than those who did not receive PORT. PORT was associated with improved DFS (HR 0.232, 95% CI: 0.069-0.782, p = 0.018) in Masaoka-Koga stages II, III, and IV disease. CONCLUSIONS: Overall, our findings indicate that PORT positively impacts thymoma patients with MG, particularly those with a higher histologic subtype and Masaoka-Koga staging.

The Long-Term and Short-Term Efficacy of Immunotherapy in Non-Small Cell Lung Cancer Patients With Brain Metastases: A Systematic Review and Meta-Analysis.

Background: Although immunotherapy has been widely used, there is currently no research comparing immunotherapy for non-small cell lung cancer (NSCLC) patients with brain metastases (BMs). This meta-analysis addresses a gap in the comparison of immunotherapy efficacy, including immune checkpoint inhibitors (ICIs), chemotherapy (CT), radiotherapy (RT), and ICI combined CT or RT. Methods: A search of Pubmed, Cochrane, EMBASE, and ClinicalTrial.gov was conducted to identify studies which enrolled NSCLC patients with BM treated with ICIs. The outcomes consisted of intracerebral overall response rate (iORR), intracerebral disease control rate (iDCR), extracranial overall response rate (EORR), distant brain failure (DBF), local control (LC), progression-free survival (PFS), and overall survival (OS). Results: A total of 3160 participants from 46 trials were included in the final analysis. Patients treated with immunotherapy were associated with a longer PFS (0.48, 95%CI: 0.41-0.56), and a longer OS (0.64, 95%CI: 0.60-0.69) compared with immunotherapy-naive patients. In prospective studies, dual ICI combined CT and ICI combined CT achieved a better OS. The hazard ratio (HR) of dual ICI combined CT versus dual ICI was 0.61, and the HR of ICI combined CT versus ICI monotherapy was 0.58. Moreover, no statistical difference in PFS, OS, EORR, iORR, iDCR, and EDCR was found between patients with ICI monotherapy and ICI combined cranial radiotherapy. Concurrent ICI combined RT was shown to decrease the rate of DBF (OR = 0.15, 95% CI: 0.03-0.73) compared with RT after ICI. Patients treated with WBRT might have an inferior efficacy than those with SRS because the iORR of SRS was 0.75 (0.70, 0.80) and WBRT was 0. Furthermore, no obvious difference in PFS and OS was observed among the three different types of ICI, which targets PD-1, PD-L1, and CTLA-4, respectively. Conclusions: Patients treated with ICI got superior efficacy to those without ICI. Furthermore, dual ICI combined CT and ICI combined CT seemed to be optimal for NSCLC patients with BM. In terms of response and survival, concurrent administration of SRS and ICI led to better outcomes for patients with BMs than non-concurrent or non-SRS. Importance of the Study: In the new era of immunotherapy, our meta-analysis validated the importance of immunotherapy for non-small cell lung cancer (NSCLC) patients with brain metastases (BMs). By comparing the long-term and short-term impacts of various regimens, all immunotherapy treatments had superior efficacy to immunotherapy-naive. At the same time, through pairwise comparison in immunotherapy, our findings can help clinicians to make treatment decisions for NSCLC patients with BMs. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=269621, identifier CRD42021269621.