Application of biomimetic nanovaccines in cancer immunotherapy: A useful strategy to help combat immunotherapy resistance.

Breakthroughs in actual clinical applications have begun through vaccine-based cancer immunotherapy, which uses the body's immune system, both humoral and cellular, to attack malignant cells and fight diseases. However, conventional vaccine approaches still face multiple challenges eliciting effective antigen-specific immune responses, resulting in immunotherapy resistance. In recent years, biomimetic nanovaccines have emerged as a promising alternative to conventional vaccine approaches by incorporating the natural structure of various biological entities, such as cells, viruses, and bacteria. Biomimetic nanovaccines offer the benefit of targeted antigen-presenting cell (APC) delivery, improved antigen/adjuvant loading, and biocompatibility, thereby improving the sensitivity of immunotherapy. This review presents a comprehensive overview of several kinds of biomimetic nanovaccines in anticancer immune response, including cell membrane-coated nanovaccines, self-assembling protein-based nanovaccines, extracellular vesicle-based nanovaccines, natural ligand-modified nanovaccines, artificial antigen-presenting cells-based nanovaccines and liposome-based nanovaccines. We also discuss the perspectives and challenges associated with the clinical translation of emerging biomimetic nanovaccine platforms for sensitizing cancer cells to immunotherapy.

Clinical efficacy and immune response of neoadjuvant camrelizumab plus chemotherapy in resectable locally advanced oesophageal squamous cell carcinoma: a phase 2 trial.

BACKGROUND: Neoadjuvant immunotherapy is under intensive investigation for esophageal squamous cell carcinoma (ESCC). This study assesses the efficacy and immune response of neoadjuvant immunochemotherapy (nICT) in ESCC. METHODS: In this phase II trial (ChiCTR2100045722), locally advanced ESCC patients receiving nICT were enrolled. The primary endpoint was the pathological complete response (pCR) rate. Multiplexed immunofluorescence, RNA-seq and TCR-seq were conducted to explore the immune response underlying nICT. RESULTS: Totally 42 patients were enrolled, achieving a 27.0% pCR rate. The 1-year, 2-year DFS and OS rates were 89.2%, 64.4% and 97.3%, 89.2%, respectively. RNA-seq analysis highlighted T-cell activation as the most significantly enriched pathway. The tumour immune microenvironment (TIME) was characterised by high CD4, CD8, Foxp3, and PD-L1 levels, associating with better pathological regression (TRS0/1). TIME was categorised into immune-infiltrating, immune-tolerant, and immune-desert types. Notably, the immune-infiltrating type and tertiary lymphoid structures correlated with improved outcomes. In the context of nICT, TIM-3 negatively influenced treatment efficacy, while elevated TIGIT/PD-1 expression post-nICT correlated positively with CD8+ T cell levels. TCR-seq identified three TCR rearrangements, underscoring the specificity of T-cell responses. CONCLUSIONS: Neoadjuvant camrelizumab plus chemotherapy is effective for locally advanced, resectable ESCC, eliciting profound immune response that closely associated with clinical outcomes.

Ferroptosis is a protective factor for the prognosis of cancer patients: a systematic review and meta-analysis.

BACKGROUND: Cancer is a leading global cause of death. Conventional cancer treatments like surgery, radiation, and chemotherapy have associated side effects. Ferroptosis, a nonapoptotic and iron-dependent cell death, has been identified and differs from other cell death types. Research has shown that ferroptosis can promote and inhibit tumor growth, which may have prognostic value. Given the unclear role of ferroptosis in cancer biology, this meta-analysis aims to investigate its impact on cancer prognosis. METHODS: This systematic review and meta-analysis conducted searches on PubMed, Embase, and the Cochrane Library databases. Eight retrospective studies were included to compare the impact of ferroptosis inhibition and promotion on cancer patient prognosis. The primary endpoints were overall survival (OS) and progression-free survival (PFS). Studies lacking clear descriptions of hazard ratios (HR) and 95% confidence intervals for OS and PFS were excluded. Random-effects meta-analysis and meta-regression were performed on the included study data to assess prognosis differences between the experimental and control groups. Meta-analysis results included HR and 95% confidence intervals. This study has been registered with PROSPERO, CRD 42023463720 on September 27, 2023. RESULTS: A total of 2,446 articles were screened, resulting in the inclusion of 5 articles with 938 eligible subjects. Eight studies were included in the meta-analysis after bias exclusion. The meta-analysis, after bias exclusion, demonstrated that promoting ferroptosis could increase cancer patients' overall survival (HR 0.31, 95% CI 0.21-0.44) and progression-free survival (HR 0.26, 95% CI 0.16-0.44) compared to ferroptosis inhibition. The results showed moderate heterogeneity, suggesting that biological activities promoting cancer cell ferroptosis are beneficial for cancer patient's prognosis. CONCLUSIONS: This systematic review and meta-analysis demonstrated that the promotion of ferroptosis yields substantial benefits for cancer prognosis. These findings underscore the untapped potential of ferroptosis as an innovative anti-tumor therapeutic strategy, capable of addressing challenges related to drug resistance, limited therapeutic efficacy, and unfavorable prognosis in cancer treatment. REGISTRATION: CRD42023463720.

Prognostic value of matrix metalloproteinase-2 protein and matrix metalloproteinase-9 protein in colorectal cancer: a meta-analysis.

INTRODUCTION: Matrix metalloproteinase-2 (MMP-2) and matrix metalloproteinase-9 (MMP-9) are critical components of the extracellular matrix (ECM) in colorectal cancer (CRC). We aimed to evaluate the prognostic value of MMP-2 and MMP-9 in patients with CRC. METHODS: We performed a meta-analysis of cohort studies with available data on the effect of MMP-2 and MMP-9 expression on both disease-free survival (DFS) and overall survival (OS) by the risk ratios (RRs) with their 95% confidence intervals (CIs). Studies were subgrouped based on the different tissue types, including cancer tissue and normal tissue, and the subgroup effect of MMP expression in different tissues was analyzed through meta-regression. To ensure the quality and reduce the risk of bias, the Newcastle‒Ottawa Scale (NOS) was used to assess the included studies. A sensitivity analysis was randomly performed to assess the potential impact of each study on our results. RESULTS: Eighteen trials were selected (Table 1) and included a total of 3944 patients. According to our primary meta-analysis, the expression of MMP-2 was significantly associated with a decrease in OS (RR = 1.75, 95% CI = 1.34 to 2.29, P < 0.001) and DFS (RR = 2.62, 95% CI = 1.25 to 5.49, P < 0.001), and the expression of MMP-9 was not significantly associated with a decrease in OS (RR = 1.48, 95% CI = 0.97 to 2.24, P = 0.069) or DFS (RR = 1.60, 95% CI = 0.87 to 2.94, P = 0.133). According to the subgroup analysis of MMPs in different tissues, high MMP-2 expression in cancer tissue (RR = 1.90, 95% CI = 1.29 to 2.79) and normal tissue (RR = 1.59, 95% CI = 1.17 to 2.17) were significant indicators of poor OS. High MMP-2 expression in cancer tissue was significant indicator of poor DFS (RR = 2.12, 95% CI = 1.09 to 4.11). MMP-9 expression was also associated with poor OS (RR = 1.40, 95% CI = 0.85 to 2.29), but the difference in OS between the high and low expression groups was not statistically significant. CONCLUSIONS: High MMP-2 expression, especially in cancer tissue, is significantly associated with both poor DFS and poor OS in patients with CRC. High MMP-9 expression tended to indicate a poor prognosis of CRC but the correlation was not significant.

Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer.

OBJECTIVES: This study evaluates the accuracy of radiomics in predicting lymph node metastasis in non-small cell lung cancer, which is crucial for patient management and prognosis. METHODS: Adhering to PRISMA and AMSTAR guidelines, we systematically reviewed literature from March 2012 to December 2023 using databases including PubMed, Web of Science, and Embase. Radiomics studies utilizing computed tomography (CT) and positron emission tomography (PET)/CT imaging were included. The quality of studies was appraised with QUADAS-2 and RQS tools, and the TRIPOD checklist assessed model transparency. Sensitivity, specificity, and AUC values were synthesized to determine diagnostic performance, with subgroup and sensitivity analyses probing heterogeneity and a Fagan plot evaluating clinical applicability. RESULTS: Our analysis incorporated 42 cohorts from 22 studies. CT-based radiomics demonstrated a sensitivity of 0.84 (95% CI: 0.79-0.88, p < 0.01) and specificity of 0.82 (95% CI: 0.75-0.87, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.92), indicating no publication bias (p-value = 0.54 > 0.05). PET/CT radiomics showed a sensitivity of 0.82 (95% CI: 0.76-0.86, p < 0.01) and specificity of 0.86 (95% CI: 0.81-0.90, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.93), with a slight publication bias (p-value = 0.03 < 0.05). Despite high clinical utility, subgroup analysis did not clarify heterogeneity sources, suggesting influences from possible factors like lymph node location and small subgroup sizes. CONCLUSIONS: Radiomics models show accuracy in predicting lung cancer lymph node metastasis, yet further validation with larger, multi-center studies is necessary. CLINICAL RELEVANCE STATEMENT: Radiomics models using CT and PET/CT imaging may improve the prediction of lung cancer lymph node metastasis, aiding personalized treatment strategies. RESEARCH REGISTRATION UNIQUE IDENTIFYING NUMBER (UIN): International Prospective Register of Systematic Reviews (PROSPERO), CRD42023494701. This study has been registered on the PROSPERO platform with a registration date of 18 December 2023. https://www.crd.york.ac.uk/prospero/ KEY POINTS: The study explores radiomics for lung cancer lymph node metastasis detection, impacting surgery and prognosis. Radiomics improves the accuracy of lymph node metastasis prediction in lung cancer. Radiomics can aid in the prediction of lymph node metastasis in lung cancer and personalized treatment.

Nanomedicine embraces cancer radio-immunotherapy: mechanism, design, recent advances, and clinical translation.

Cancer radio-immunotherapy, integrating external/internal radiation therapy with immuno-oncology treatments, emerges in the current management of cancer. A growing number of pre-clinical studies and clinical trials have recently validated the synergistic antitumor effect of radio-immunotherapy, far beyond the "abscopal effect", but it suffers from a low response rate and toxicity issues. To this end, nanomedicines with an optimized design have been introduced to improve cancer radio-immunotherapy. Specifically, these nanomedicines are elegantly prepared by incorporating tumor antigens, immuno- or radio-regulators, or biomarker-specific imaging agents into the corresponding optimized nanoformulations. Moreover, they contribute to inducing various biological effects, such as generating in situ vaccination, promoting immunogenic cell death, overcoming radiation resistance, reversing immunosuppression, as well as pre-stratifying patients and assessing therapeutic response or therapy-induced toxicity. Overall, this review aims to provide a comprehensive landscape of nanomedicine-assisted radio-immunotherapy. The underlying working principles and the corresponding design strategies for these nanomedicines are elaborated by following the concept of "from bench to clinic". Their state-of-the-art applications, concerns over their clinical translation, along with perspectives are covered.

Artificial intelligence in radiotherapy.

Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.

Non-coding genome in small cell lung cancer between theoretical view and clinical applications.

Small cell lung cancer (SCLC) is a highly aggressive cancer of the neuroendocrine system, characterized by poor differentiation, rapid growth, and poor overall survival (OS) of patients. Despite the recent advances in the treatment of SCLC recently, the 2-year survival rate of patients with the cancer is only 14-15%, occasioned by the acquired resistance to drugs and serious off-target effects. In humans, the coding region is only 2% of the total genome, and 20% of that is associated with human diseases. Beyond the coding genome are RNAs, promoters, enhancers, and other intricate elements. The non-coding regulatory regions, mainly the non-coding RNAs (ncRNAs), regulate numerous biological activities including cell proliferation, metastasis, and drug resistance. As such, they are potential diagnostic or prognostic biomarkers, and also potential therapeutic targets for SCLC. Therefore, understanding how non-coding elements regulate SCLC development and progression holds significant clinical implications. Herein, we summarized the recent discoveries on the relationship between the non-coding elements including long non-coding RNAs (lncRNA), microRNAs (miRNAs), circular RNA (circRNA), enhancers as well as promotors, and the pathogenesis of SCLC and their potential clinical applications.

Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer.

Lung cancer accounts for the main proportion of malignancy-related deaths and most patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have great advances in application in clinics to treat lung cancer patients, yet the efficacy is unstable. The response rate of these therapies varies among patients. Some biomarkers have been proposed to predict the outcomes of immunotherapy and targeted therapy, including programmed cell death-ligand 1 (PD-L1) expression and oncogene mutations. Nevertheless, the detection tests are invasive, time-consuming, and have high demands on tumor tissue. The predictive performance of conventional biomarkers is also unsatisfactory. Therefore, novel biomarkers are needed to effectively predict the outcomes of immunotherapy and targeted therapy. The application of artificial intelligence (AI) can be a possible solution, as it has several advantages. AI can help identify features that are unable to be used by humans and perform repetitive tasks. By combining AI methods with radiomics, pathology, genomics, transcriptomics, proteomics, and clinical data, the integrated model has shown predictive value in immunotherapy and targeted therapy, which significantly improves the precision treatment of lung cancer patients. Herein, we reviewed the application of AI in predicting the outcomes of immunotherapy and targeted therapy in lung cancer patients, and discussed the challenges and future directions in this field.

Small cell lung cancer transformation: From pathogenesis to treatment.

Small cell lung cancer (SCLC) is a type of neuroendocrine tumor with high malignancy and poor prognosis. Besides the de novo SCLC, there is transformed SCLC, which has similar characteristics of pathological morphology, molecular characteristics, clinical manifestations and drug sensitivity. However, de novo SCLC and transformed SCLC have different pathogenesis and tumor microenvironment. SCLC transformation is one of the mechanisms of resistance to chemotherapy, immunotherapy, and targeted therapy in NSCLC. Two hypotheses have been used to explain the pathogenesis of SCLC transformation. Although SCLC transformation is not common in clinical practice, it has been repeatedly identified in many small patient series and case reports. It usually occurs in epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma after treatment with tyrosine kinase inhibitors (TKIs). SCLC transformation can also occur in anaplastic lymphoma kinase (ALK)-positive lung cancer after treatment with ALK inhibitors and in wild-type EGFR or ALK NSCLC treated with immunotherapy. Chemotherapy was previously used to treat transformed SCLC, yet it is associated with an unsatisfactory prognosis. We comprehensively review the advancements in transformed SCLC, including clinical and pathological characteristics, and the potential effective treatment after SCLC transformation, aiming to give a better understanding of transformed SCLC and provide support for clinical uses.