The artificial intelligence and machine learning in lung cancer immunotherapy

Predicting Regional Recurrence and Prognosis in Stereotactic Body Radiation Therapy-Treated Clinical Stage I Non-small Cell Lung Cancer Using a Radiomics Model Constructed With Surgical Data

PURPOSE

Risk stratification of regional recurrence (RR) is clinically important in the design of adjuvant treatment and surveillance strategies in patients with clinical stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). This study aimed to develop a radiomics model predicting occult lymph node metastasis (OLNM) using surgical data and apply it to the prediction of RR in SBRT-treated early-stage NSCLC patients.

METHODS AND MATERIALS

Patients with clinical stage I NSCLC who underwent curative surgery with systematic lymph node dissection from January 2013 to December 2018 (the training cohort) and from January 2019 to December 2020 (the validation cohort) were included. A preoperative computed tomography-based radiomics model, a clinical feature model, and a fusion model predicting OLNM were constructed. The performance of the 3 models was quantified and compared in the training and validation cohorts. Subsequently, the radiomics model was used to predict RR in a cohort of consecutive SBRT-treated early-stage NSCLC patients from 2 academic medical centers.

RESULTS

A total of 769 patients were included. Eight computed tomography features were identified in the radiomics model, achieving areas under the curves of 0.85 (95% CI, 0.81-0.89) and 0.83 (95% CI, 0.80-0.88) in the training and validation cohorts, respectively. Nevertheless, adding clinical features did not improve the performance of the radiomics model. With a median follow-up of 40.0 (95% CI, 35.2-44.8) months, 32 of the 213 patients in the SBRT cohort developed RR and those in the high-risk group based on the radiomics model had a higher cumulative incidence of RR (P < .001) and shorter regional recurrence-free survival (P = .02), progression-free survival (P = .004) and overall survival (P = .006) than those in the low-risk group.

CONCLUSIONS

The radiomics model based on pathologically confirmed data effectively identified patients with OLNM, which may be useful in the risk stratification among SBRT-treated patients with clinical stage I NSCLC.

Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms

AIMS

To study programmed death ligand 1 (PD-L1) expression and tumour infiltrating lymphocytes (TILs) in patients with early-stage non-small cell lung carcinoma (NSCLC) with artificial intelligence (AI) algorithms.

METHODS

The study included samples from 50 early-stage NSCLCs. PD-L1 immunohistochemistry (IHC) stained slides (clone SP263) were scored manually and with two different AI tools (PathAI and Navify Digital Pathology) by three pathologists. TILs were digitally assessed on H&E and CD8 IHC stained sections with two different algorithms (PathAI and Navify Digital Pathology, respectively). The agreement between observers and methods for each biomarker was analysed. For PD-L1, the turn-around time (TAT) for manual versus AI-assisted scoring was recorded.

RESULTS

Agreement was higher in tumours with low PD-L1 expression regardless of the approach. Both AI-powered tools identified a significantly higher number of cases equal or above 1% PD-L1 tumour proportion score as compared with manual scoring (p=0.00015), a finding with potential therapeutic implications. Regarding TAT, there were significant differences between manual scoring and AI use (p value <0.0001 for all comparisons). The total TILs density with the PathAI algorithm and the total density of CD8+ cells with the Navify Digital Pathology software were significantly correlated (τ=0.49 (95% CI 0.37, 0.61), p value<0.0001).

CONCLUSIONS

This preliminary study supports the use of AI algorithms for the scoring of PD-L1 and TILs in patients with NSCLC.

Nanomaterial-assisted delivery of CpG oligodeoxynucleotides for boosting cancer immunotherapy

Cancer immunotherapy aims to improve immunity to not only eliminate the primary tumor but also inhibit metastasis and recurrence. It is considered an extremely promising therapeutic approach that breaks free from the traditional paradigm of oncological treatment. As the medical community learns more about the immune system's mechanisms that "turn off the brake" and "step on the throttle", there is increasingly successful research on immunomodulators. However, there are still more restrictions than countermeasures with immunotherapy related to immunomodulators, such as low responsiveness and immune-related adverse events that cause multiple adverse reactions. Therefore, medical experts and materials scientists attempted to the efficacy of immunomodulatory treatments through various methods, especially nanomaterial-assisted strategies. CpG oligodeoxynucleotides (CpG) not only act as an adjuvant to promote immune responses, but also induce autophagy. In this review, the enhancement of immunotherapy using nanomaterial-based CpG formulations is systematically elaborated, with a focus on the delivery, protection, synergistic promotion of CpG efficacy by nanomaterials, and selection of the timing of treatment. In addition, we also discuss and prospect the existing problems and future directions of research on nanomaterials in auxiliary CpG therapy.

Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy

This review analyzes the application of machine learning (ML) in oncological pharmacogenomics, focusing on customizing chemotherapy treatments. It explores how ML can analyze extensive genomic, proteomic, and other omics datasets to identify genetic patterns associated with drug responses. This, in turn, facilitates personalized therapies that are more effective and have fewer side effects. Recent studies have emphasized ML's revolutionary role of ML in personalized oncology treatment by identifying genetic variability and understanding cancer pharmacodynamics. Integrating ML with electronic health records and clinical data shows promise in refining chemotherapy recommendations by considering the complex influencing factors. Although standard chemotherapy depends on population-based doses and treatment regimens, customized techniques use genetic information to tailor treatments for specific patients, potentially enhancing efficacy and reducing adverse effects.However, challenges, such as model interpretability, data quality, transparency, ethical issues related to data privacy, and health disparities, remain. Machine learning has been used to transform oncological pharmacogenomics by enabling personalized chemotherapy treatments. This review highlights ML's potential of ML to enhance treatment effectiveness and minimize side effects through detailed genetic analysis. It also addresses ongoing challenges including improved model interpretability, data quality, and ethical considerations. The review concludes by emphasizing the importance of rigorous clinical trials and interdisciplinary collaboration in the ethical implementation of ML-driven personalized medicine, paving the way for improved outcomes in cancer patients and marking a new frontier in cancer treatment.

The prognostic analysis and a machine-learning based disease-specific survival state model in pulmonary large-cell neuroendocrine carcinomas

Background

Pulmonary large-cell neuroendocrine carcinoma (PLCNEC) is a rare and highly malignant lung cancer. Due to the paucity of data from clinical studies, its clinical characteristics and treatment remain controversial. The present study explored factors influencing the prognosis and survival outcomes of patients with PLCNEC and developed a dependable prognostic model using machine learning.

Methods

The clinical data of PLCNEC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2020. A total of 2,897 PLCNEC patients were enrolled and univariate and multivariate Cox regression analyses were performed to explore independent prognostic factors for disease-specific survival (DSS). Ten machine learning algorithms were utilized to predict the 2-year survival. The clinicopathological data collected from The First Affiliated Hospital of Sun Yat-sen University between 2010 and 2022 were used to test the trained machine.

Results

Sex [hazard ratio (HR) 1.168, 95% confidence interval (CI): 1.063-1.284], age (HR 1.262, 95% CI: 1.144-1.391), surgery (HR 0.481, 95% CI: 0.413-0.559), chemotherapy (HR 0.450, 95% CI: 0.404-0.501), bone metastasis (HR 1.284, 95% CI: 1.124-1.466), brain metastasis (HR 1.167, 95% CI: 1.023-1.331), liver metastasis (HR 1.223, 95% CI: 1.069-1.399), American Joint Committee on Cancer-Node (AJCC-N), and tumor stage were independent prognostic factors. The gradient boosting decision tree (GBDT) performed better than other models, with an F1-score of 0.791 and an area under the curve of 0.831.

Conclusions

Male, age ≥65 years, distant metastasis to the bone, liver, and brain are associated with a worse prognosis in PLCNEC patients, while surgery and chemotherapy are associated with improved prognosis. GBDT showed promising performance in predicting 2-year survival, which can serve as a valuable reference for clinical diagnosis and treatment of PLCNEC.

Microbiota-induced inflammatory responses in bladder tumors promote epithelial-mesenchymal transition and enhanced immune infiltration

The intratumoral microbiota can modulate the tumor immune microenvironment (TIME); however, the underlying mechanism by which intratumoral microbiota influences the TIME in urothelial carcinoma of the bladder (UCB) remains unclear. To address this, we collected samples from 402 patients with UCB, including paired host transcriptome and tumor microbiome data, from The Cancer Genome Atlas (TCGA). We found that the intratumoral microbiome profiles were significantly correlated with the expression pattern of epithelial-mesenchymal transition (EMT)-related genes. Furthermore, we detected that the genera Lachnoclostridium and Sutterella in tumors could indirectly promote the EMT program by inducing an inflammatory response. Moreover, the inflammatory response induced by these two intratumoral bacteria further enhanced intratumoral immune infiltration, affecting patient survival and response to immunotherapy. In addition, an independent immunotherapy cohort of 348 patients with bladder cancer was used to validate our results. Collectively, our study elucidates the potential mechanism by which the intratumoral microbiota influences the TIME of UCB and provides a new guiding strategy for the targeted therapy of UCB.NEW & NOTEWORTHY The intratumoral microbiota may mediate the bladder tumor inflammatory response, thereby promoting the epithelial-mesenchymal transition program and influencing tumor immune infiltration.

A narrative review on perioperative systemic therapy in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) that is operable still carries a high risk of recurrence, approaching 50% of all operable cases despite adding adjuvant chemotherapy. However, the utilization of immunotherapy and targeted therapy moving beyond the metastatic NSCLC setting and into early-stage perioperative management has generated tremendous enthusiasm and has been practice-changing. Adjuvant atezolizumab in NSCLC first demonstrated a clinical benefit with an immune checkpoint inhibitor. Then, with studies studying a significant benefit in major pathologic response in surgical patients treated preoperatively with immunotherapy compared to only chemotherapy, neoadjuvant nivolumab and chemotherapy were evaluated and showed significant event-free survival benefit leading to subsequent studies evaluating perioperative immunotherapy and chemotherapy. Meanwhile, with regards to targeted therapies, adjuvant osimertinib in EGFR-mutated NSCLC and adjuvant alectinib in ALK-rearranged NSCLC have both received regulatory approvals following demonstrated clinical benefit in clinical trials. With rapidly evolving changes in the field, new combinations such as multiple immunotherapy agents and antibody-drug conjugates in development, perioperative NSCLC management has quickly become complicated with different pathways to perioperative treatment. Furthermore, circulating tumor DNA and studies looking at better tools to prognosticate immunotherapy response will help with decision-making regarding which patients should receive immunotherapy and if so, either only pre-operatively or both pre- and post-operatively. In this review, we look at the evolution of systemic therapy in the perioperative setting from adjuvant chemotherapy to adjuvant immunotherapy to perioperative immunotherapy and look at perioperative targeted therapy while looking ahead to future considerations.

Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma

Objective

Lung adenocarcinoma poses a major global health challenge and is a leading cause of cancer-related deaths worldwide. This study is a review of three molecular biomarkers screened by machine learning that are not only important in the occurrence and progression of lung adenocarcinoma but also have the potential to serve as biomarkers for clinical diagnosis, prognosis evaluation and treatment guidance.

Methods

Differentially expressed genes (DEGs) were identified using comprehensive GSE1987 and GSE18842 gene expression databases. A comprehensive bioinformatics analysis of these DEGs was conducted to explore enriched functions and pathways, relative expression levels, and interaction networks. Random Forest and LASSO regression analysis techniques were used to identify the three most significant target genes. The TCGA database and quantitative polymerase chain reaction (qPCR) experiments were used to verify the expression levels and receiver operating characteristic (ROC) curves of these three target genes. Furthermore, immune invasiveness, pan-cancer, and mRNA-miRNA interaction network analyses were performed.

Results

Eighty-nine genes showed increased expression and 190 genes showed decreased expression. Notably, the upregulated DEGs were predominantly associated with organelle fission and nuclear division, whereas the downregulated DEGs were mainly associated with genitourinary system development and cell-substrate adhesion. The construction of the DEG protein-protein interaction network revealed 32 and 19 hub genes with the highest moderate values among the upregulated and downregulated genes, respectively. Using random forest and LASSO regression analyses, the hub genes were employed to identify three most significant target genes.TCGA database and qPCR experiments were used to verify the expression levels and ROC curves of these three target genes, and immunoinvasive analysis, pan-cancer analysis and mRNA-miRNA interaction network analysis were performed.

Conclusion

Three target genes identified by machine learning: BUB1B, CENPF, and PLK1 play key roles in LUAD development of lung adenocarcinoma.

Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam

Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitations such as subjectivity in interpretation and constraints in handling complex image features. This research paper proposes an integrated deep learning approach utilizing pre-trained models-VGG16, ResNet50, and InceptionV3-combined within a unified framework to improve diagnostic accuracy in medical imaging. The method focuses on lung cancer detection using images resized and converted to a uniform format to optimize performance and ensure consistency across datasets. Our proposed model leverages the strengths of each pre-trained network, achieving a high degree of feature extraction and robustness by freezing the early convolutional layers and fine-tuning the deeper layers. Additionally, techniques like SMOTE and Gaussian Blur are applied to address class imbalance, enhancing model training on underrepresented classes. The model's performance was validated on the IQ-OTH/NCCD lung cancer dataset, which was collected from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases over a period of three months in fall 2019. The proposed model achieved an accuracy of 98.18%, with precision and recall rates notably high across all classes. This improvement highlights the potential of integrated deep learning systems in medical diagnostics, providing a more accurate, reliable, and efficient means of disease detection.

Mechanistic in silico explorations of the immunogenic and synergistic effects of radiotherapy and immunotherapy: a critical review

Immunotherapy is a rapidly evolving field, with many models attempting to describe its impact on the immune system, especially when paired with radiotherapy. Tumor response to this combination involves a complex spatiotemporal dynamic which makes either clinical or pre-clinical in vivo investigation across the resulting extensive solution space extremely difficult. In this review, several in silico models of the interaction between radiotherapy, immunotherapy, and the patient's immune system are examined. The study included only mathematical models published in English that investigated the effects of radiotherapy on the immune system, or the effect of immuno-radiotherapy with immune checkpoint inhibitors. The findings indicate that treatment efficacy was predicted to improve when both radiotherapy and immunotherapy were administered, compared to radiotherapy or immunotherapy alone. However, the models do not agree on the optimal schedule and fractionation of radiotherapy and immunotherapy. This corresponds to relevant clinical trials, which report an improved treatment efficacy with combination therapy, however, the optimal scheduling varies between clinical trials. This discrepancy between the models can be attributed to the variation in model approach and the specific cancer types modeled, making the determination of the optimum general treatment schedule and model challenging. Further research needs to be conducted with similar data sets to evaluate the best model and treatment schedule for a specific cancer type and stage.

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine learning algorithm, XGBoost. The algorithm's performance was assessed using confusion matrix metrics and the area under the Receiver Operating Characteristics (ROC) curve. The study included 140 patients (mean age 67.7 ± 8.9 years), and the algorithm was found to be predictive of all eight treatment recommendations made by the board. The model's predictions were more accurate than those based on published therapeutic guidelines by ESMO and NCCN. The study concludes that a machine learning model incorporating clinical and imaging variables can predict treatment recommendations made by an expert multidisciplinary tumor board, potentially aiding clinical decision-making in settings lacking subspecialty expertise.

Artificial Intelligence and Machine Learning in Predicting the Response to Immunotherapy in Non-small Cell Lung Carcinoma: A Systematic Review

Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.

Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review

This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.

Application of Artificial Intelligence in Neuroendocrine Lung Cancer Diagnosis and Treatment: A Systematic Review

Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these tumors is crucial for appropriate management and improved patient outcomes. In recent years, exciting advancements in artificial intelligence (AI) technologies have been revolutionizing medical diagnostics, particularly in the realm of detecting and characterizing pulmonary NETs, offering promising avenues for improved patient care. This article aims to provide a comprehensive overview of the role of AI in diagnosing lung NETs. We discuss the current challenges associated with conventional diagnostic approaches, including histopathological examination and imaging modalities. Despite advancements in these techniques, accurate diagnosis remains challenging due to the overlapping features with other pulmonary lesions and the subjective interpretation of imaging findings. AI-based approaches, including machine learning and deep learning algorithms, have demonstrated remarkable potential in addressing these challenges. By leveraging large datasets of radiological images, histopathological samples, and clinical data, AI models can extract complex patterns and features that may not be readily discernible to human observers. Moreover, AI algorithms can continuously learn and improve from new data, leading to enhanced diagnostic accuracy and efficiency over time. Specific AI applications in the diagnosis of lung NETs include computer-aided detection and classification of pulmonary nodules on CT scans, quantitative analysis of PET imaging for tumor characterization, and integration of multi-modal data for comprehensive diagnostic assessments. These AI-driven tools hold promise for facilitating early detection, risk stratification, and personalized treatment planning in patients with lung NETs.

Cytokines and soluble mediators as architects of tumor microenvironment reprogramming in cancer therapy

Navigating the intricate landscape of the tumor microenvironment (TME) unveils a pivotal arena for cancer therapeutics, where cytokines and soluble mediators emerge as double-edged swords in the fight against cancer. This review ventures beyond traditional perspectives, illuminating the nuanced interplay of these elements as both allies and adversaries in cancer dynamics. It critically evaluates the evolving paradigms of TME reprogramming, spotlighting innovative strategies that target the sophisticated network of cytokines and mediators. Special focus is placed on unveiling the therapeutic potential of novel cytokines and mediators, particularly their synergistic interactions with extracellular vesicles, which represent underexplored conduits for therapeutic targeting. Addressing a significant gap in current research, we explore the untapped potential of these biochemical players in orchestrating immune responses, tumor proliferation, and metastasis. The review advocates for a paradigm shift towards exploiting these dynamic interactions within the TME, aiming to transcend conventional treatments and pave the way for a new era of precision oncology. Through a critical synthesis of recent advancements, we highlight the imperative for innovative approaches that harness the full spectrum of cytokine and mediator activities, setting the stage for breakthrough therapies that offer heightened specificity, reduced toxicity, and improved patient outcomes.

Just how transformative will AI/ML be for immuno-oncology?

Immuno-oncology involves the study of approaches which harness the patient's immune system to fight malignancies. Immuno-oncology, as with every other biomedical and clinical research field as well as clinical operations, is in the midst of technological revolutions, which vastly increase the amount of available data. Recent advances in artificial intelligence and machine learning (AI/ML) have received much attention in terms of their potential to harness available data to improve insights and outcomes in many areas including immuno-oncology. In this review, we discuss important aspects to consider when evaluating the potential impact of AI/ML applications in the clinic. We highlight four clinical/biomedical challenges relevant to immuno-oncology and how they may be able to be addressed by the latest advancements in AI/ML. These challenges include (1) efficiency in clinical workflows, (2) curation of high-quality image data, (3) finding, extracting and synthesizing text knowledge as well as addressing, and (4) small cohort size in immunotherapeutic evaluation cohorts. Finally, we outline how advancements in reinforcement and federated learning, as well as the development of best practices for ethical and unbiased data generation, are likely to drive future innovations.

Current diagnosis and treatment of salivary gland-type tumors of the lung

Salivary gland-type tumors of the lung are thought to originate from the submucosal exocrine glands of the large airways. Due to their rare occurrence, reports of their study are limited to small-scale or case reports. Therefore, daily clinical practices often require a search for previous reports. In the last 20 years, several genetic rearrangements have been identified, such as MYB::NF1B rearrangements in adenoid cystic carcinoma, CRTC1::MAML2 rearrangements in mucoepidermoid carcinoma, EWSR1::ATF1 rearrangements in hyalinizing clear cell carcinoma and rearrangements of the EWSR1 locus or FUS (TLS) locus in myoepithelioma and myoepithelial carcinoma. These molecular alterations have been useful in diagnosing these tumors, although they have not yet been linked to molecularly targeted therapies. The morphologic, immunophenotypic, and molecular characteristics of these tumors are similar to those of their counterparts of extrapulmonary origin, so clinical and radiologic differential diagnosis is required to distinguish between primary and metastatic disease of other primary sites. However, these molecular alterations can be useful in differentiating them from other primary lung cancer histologic types. The management of these tumors requires broad knowledge of the latest diagnostics, surgery, radiotherapy, bronchoscopic interventions, chemotherapy, immunotherapy as well as therapeutic agents in development, including molecularly targeted agents. This review provides a comprehensive overview of the current diagnosis and treatment of pulmonary salivary gland tumors, with a focus on adenoid cystic carcinoma and mucoepidermoid carcinoma, which are the two most common subtypes.

LINC01559 promotes lung adenocarcinoma metastasis by disrupting the ubiquitination of vimentin

BACKGROUND

Distant metastasis is the major cause of lung adenocarcinoma (LUAD)-associated mortality. However, molecular mechanisms involved in LUAD metastasis remain to be fully understood. While the role of long non-coding RNAs (lncRNAs) in cancer development, progression, and treatment resistance is being increasingly appreciated, the list of dysregulated lncRNAs that contribute to LUAD pathogenesis is also rapidly expanding.

METHODS

Bioinformatics analysis was conducted to interrogate publicly available LUAD datasets. In situ hybridization and qRT-PCR assays were used to test lncRNA expression in human LUAD tissues and cell lines, respectively. Wound healing as well as transwell migration and invasion assays were employed to examine LUAD cell migration and invasion in vitro. LUAD metastasis was examined using mouse models in vivo. RNA pulldown and RNA immunoprecipitation were carried out to test RNA-protein associations. Cycloheximide-chase assays were performed to monitor protein turnover rates and Western blotting was employed to test protein expression.

RESULTS

The expression of the lncRNA LINC01559 was commonly upregulated in LUADs, in particular, in those with distant metastasis. High LINC01559 expression was associated with poor outcome of LUAD patients and was potentially an independent prognostic factor. Knockdown of LINC01559 diminished the potential of LUAD cell migration and invasion in vitro and reduced the formation of LUAD metastatic lesions in vivo. Mechanistically, LINC01559 binds to vimentin and prevents its ubiquitination and proteasomal degradation, leading to promotion of LUAD cell migration, invasion, and metastasis.

CONCLUSION

LINC01559 plays an important role in LUAD metastasis through stabilizing vimentin. The expression of LINC01559 is potentially an independent prognostic factor of LUAD patients, and LINC01559 targeting may represent a novel avenue for the treatment of late-stage LUAD.

A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma

BACKGROUND

Identifying precise biomarkers of immunotherapy response for non-small cell lung carcinoma (NSCLC) before treatment is challenging. This study aimed to construct and investigate the potential performance of a sub-regional radiomics model (SRRM) as a novel tumor biomarker in predicting the response of patients with NSCLC treated with immune checkpoint inhibitors, and test whether its predictive performance is superior to that of conventional radiomics, tumor mutational burden (TMB) score and programmed death ligand-1 (PD-L1) expression.

METHODS

We categorized 264 patients from retrospective databases of two centers into training (n = 159) and validation (n = 105) cohorts. Radiomic features were extracted from three sub-regions of the tumor region of interest using the K-means method. We extracted 1,896 features from each sub-region, resulting in 5688 features per sample. The least absolute shrinkage and selection operator regression method was used to select sub-regional radiomic features. The SRRM was constructed and validated using the support vector machine algorithm. We used next-generation sequencing to classify patients from the two cohorts into high TMB (≥ 10 muts/Mb) and low TMB (< 10 muts/Mb) groups; immunohistochemistry was performed to assess PD-L1 expression in formalin-fixed, paraffin-embedded tumor sections, with high expression defined as ≥ 50% of tumor cells being positive. Associations between the SRRM and progression-free survival (PFS) and variant genes were assessed.

RESULTS

Eleven sub-regional radiomic features were employed to develop the SRRM. The areas under the receiver operating characteristic curve (AUCs) of the proposed SRRM were 0.90 (95% confidence interval [CI] 0.84-0.96) and 0.86 (95% CI 0.76-0.95) in the training and validation cohorts, respectively. The SRRM (low vs. high; cutoff value = 0.936) was significantly associated with PFS in the training (hazard ratio [HR] = 0.35 [0.24-0.50], P < 0.001) and validation (HR = 0.42 [0.26-0.67], P = 0.001) cohorts. A significant correlation between the SRRM and three variant genes (H3C4, PAX5, and EGFR) was observed. In the validation cohort, the SRRM demonstrated a higher AUC (0.86, P < 0.001) than that for PD-L1 expression (0.66, P = 0.034) and TMB score (0.54, P = 0.552).

CONCLUSIONS

The SRRM had better predictive performance and was superior to conventional radiomics, PD-L1 expression, and TMB score. The SRRM effectively stratified the progression-free survival (PFS) risk among patients with NSCLC receiving immunotherapy.

Biomarkers predicting the efficacy of immune checkpoint inhibitors in hepatocellular carcinoma

In recent years, immune checkpoint inhibitors (ICIs) have emerged as a transformative approach in treating advanced hepatocellular carcinoma (HCC). Despite their success, challenges persist, including concerns about their effectiveness, treatment costs, frequent occurrence of treatment-related adverse events, and tumor hyperprogression. Therefore, it is imperative to identify indicators capable of predicting the efficacy of ICIs treatment, enabling optimal patient selection to maximize clinical benefits while minimizing unnecessary toxic side effects and economic losses. This review paper categorizes prognostic biomarkers of ICIs treatment into the following categories: biochemical and cytological indicators, tumor-related markers, imaging and personal features, etiology, gut microbiome, and immune-related adverse events (irAEs). By organizing these indicators systematically, we aim to guide biomarker exploration and inform clinical treatment decisions.

The expanding Pandora's toolbox of CD8+T cell: from transcriptional control to metabolic firing

CD8+ T cells are the executor in adaptive immune response, especially in anti-tumor immunity. They are the subset immune cells that are of high plasticity and multifunction. Their development, differentiation, activation and metabolism are delicately regulated by multiple factors. Stimuli from the internal and external environment could remodel CD8+ T cells, and correspondingly they will also make adjustments to the microenvironmental changes. Here we describe the most updated progresses in CD8+ T biology from transcriptional regulation to metabolism mechanisms, and also their interactions with the microenvironment, especially in cancer and immunotherapy. The expanding landscape of CD8+ T cell biology and discovery of potential targets to regulate CD8+ T cells will provide new viewpoints for clinical immunotherapy.

Potential of alisols as cancer therapeutic agents: Investigating molecular mechanisms, pharmacokinetics and metabolism

Albeit remarkable achievements in anti-cancer endeavors, the prevention and treatment of cancer remain unresolved challenges. Hence, there is an urgent need to explore new and efficacious natural compounds with potential anti-cancer therapeutic agents. One such group of compounds is alisols, tetracyclic triterpene alcohols extracted from alisma orientale. Alisols play a significant role in cancer therapy as they can suppress cancer cell proliferation and migration by regulating signaling pathways such as mTOR, Bax/Bcl-2, CHOP, caspase, NF-kB and IRE1. Pharmacokinetic studies showed that alisols can be absorbed entirely, rapidly, and evenly distributed in vivo. Moreover, alisols are low in toxicity and relatively safe to take. Remarkably, each alisol can be converted into many compounds with different pathways to their anti-cancer effects in the body. Thus, alisols are regarded as promising anti-cancer agents with minimal side effects and low drug resistance. This review will examine and discuss alisols' anti-cancer molecular mechanism, pharmacokinetics and metabolism. Based on a comprehensive analysis of nearly 20 years of research, we evaluate the therapeutic potential of alisols for various types of cancer and offer insights and strategies for developing new cancer treatments.

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.

Artificial intelligence applications in computed tomography in gastric cancer: a narrative review

Background and Objective

Artificial intelligence (AI) is a revolutionary technique which is deeply impacting and reshaping clinical practice in oncology. This review aims to summarize the current status of the clinical application of AI-based computed tomography (CT) for gastric cancer (GC), focusing on diagnosis, genetic status detection and risk prediction of metastasis, prognosis and treatment efficacy. The challenges and prospects for future research will also be discussed.

Methods

We searched the PubMed/MEDLINE database to identify clinical studies published between 1990 and November 2022 that investigated AI applications in CT in GC. The major findings of the verified studies were summarized.

Key Content and Findings

AI applications in CT images have attracted considerable attention in various fields such as diagnosis, prediction of metastasis risk, survival, and treatment response. These emerging techniques have shown a high potential to outperform clinicians in diagnostic accuracy and time-saving.

Conclusions

AI-powered tools showed great potential to increase diagnostic accuracy and reduce radiologists' workload. However, the goal of AI is not to replace human ability but to help oncologists make decisions in their practice. Therefore, radiologists should play a predominant role in AI applications and decide the best ways to integrate these complementary techniques within clinical practice.