The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence

Omics-based deep learning approaches for lung cancer decision-making and therapeutics development

Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized biological research and medicinal practice in lung cancer treatment. In response to the demands for minimally invasive procedures and technology development over the past decade, many types of multi-omics data at various genome levels have been generated. As omics data grow, artificial intelligence models, particularly deep learning, are prominent in developing more rapid and effective methods to potentially improve lung cancer patient diagnosis, prognosis and treatment strategy. This decade has seen genome-based deep learning models thriving in various lung cancer tasks, including cancer prediction, subtype classification, prognosis estimation, cancer molecular signatures identification, treatment response prediction and biomarker development. In this study, we summarized available data sources for deep-learning-based lung cancer mining and provided an update on recent deep learning models in lung cancer genomics. Subsequently, we reviewed the current issues and discussed future research directions of deep-learning-based lung cancer genomics research.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

BACKGROUND

The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false-positive reduction, nodule classification, and prognosis.

METHODOLOGY

This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false-positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening.

RESULTS

AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false-positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing.

CONCLUSIONS

AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large-scale validation of new deep learning-based algorithms and multi-center studies to improve the efficacy of AI in lung cancer screening.

Metabolic barriers in non-small cell lung cancer with LKB1 and/or KEAP1 mutations for immunotherapeutic strategies

Currently, immune checkpoint inhibitors (ICIs) are widely considered the standard initial treatment for advanced non-small cell lung cancer (NSCLC) when there are no targetable driver oncogenic alternations. NSCLC tumors that have two alterations in tumor suppressor genes, such as liver kinase B1 (LKB1) and/or Kelch-like ECH-associated protein 1 (KEAP1), have been found to exhibit reduced responsiveness to these therapeutic strategies, as revealed by multiomics analyses identifying immunosuppressed phenotypes. Recent advancements in various biological approaches have gradually unveiled the molecular mechanisms underlying intrinsic reprogrammed metabolism in tumor cells, which contribute to the evasion of immune responses by the tumor. Notably, metabolic alterations in glycolysis and glutaminolysis have a significant impact on tumor aggressiveness and the remodeling of the tumor microenvironment. Since glucose and glutamine are essential for the proliferation and activation of effector T cells, heightened consumption of these nutrients by tumor cells results in immunosuppression and resistance to ICI therapies. This review provides a comprehensive summary of the clinical efficacies of current therapeutic strategies against NSCLC harboring LKB1 and/or KEAP1 mutations, along with the metabolic alterations in glycolysis and glutaminolysis observed in these cancer cells. Furthermore, ongoing trials targeting these metabolic alterations are discussed as potential approaches to overcome the extremely poor prognosis associated with this type of cancer.

Multiomic Investigations into Lung Health and Disease

Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.

Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review

Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists' routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist's routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.

Understanding inequities in precision oncology diagnostics

Advances in molecular diagnostics have enabled the identification of targetable driver pathogenic variants, forming the basis of precision oncology care. However, the adoption of new technologies, such as next-generation sequencing (NGS) panels, can exacerbate healthcare disparities. Here, we summarize data on use patterns of advanced biomarker testing, highlight the disparities in both accessing NGS testing and using this data to match patients to appropriate personalized therapies and propose multidisciplinary strategies to address inequities looking forward.

Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials

Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body's interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed.

Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction

In recent years, lung cancer prediction is an essential topic for reducing the death rate of humans. In the literature section, some papers are reviewed that reduce the accuracy level during the prediction stage. Hence, in this paper, we develop a Multi-Process Remora Optimized Hyperparameters of Convolutional Neural Network (MPROH-CNN) aimed at lung cancer prediction. The proposed technique can be utilized to detect the CT images of the human lung. The proposed technique proceeds with four phases, including pre-processing, feature extraction and classification. Initially, the databases are collected from the open-source system. After that, the collected CT images contain unwanted noise, which affects classification efficiency. So, the pre-processing techniques can be considered to remove unwanted noise from the input images, such as filtering and contrast enhancement. Following that, the essential features are extracted with the assistance of feature extraction techniques such as histogram, texture and wavelet. The extracted features are utilized to classification stage. The proposed classifier is a combination of the Remora Optimization Algorithm (ROA) and Convolutional Neural Network (CNN). In the CNN, the ROA is utilized for multi process optimization such as structure optimization and hyperparameter optimization. The proposed methodology is implemented in MATLAB and performances are evaluated by utilized performance matrices such as accuracy, precision, recall, specificity, sensitivity and F_Measure. To validate the projected approach, it is compared with the traditional techniques CNN, CNN-Particle Swarm Optimization (PSO) and CNN-Firefly Algorithm (FA), respectively. From the analysis, the proposed method achieved a 0.98 accuracy level in the lung cancer prediction.

Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and meta-analysis

BACKGROUND

Lung cancer is the principal cause of cancer-related deaths worldwide. Early detection of lung cancer with screening is indispensable to reduce the high morbidity and mortality rates. Artificial intelligence (AI) is widely utilised in healthcare, including in the assessment of medical images. A growing number of reviews studied the application of AI in lung cancer screening, but no overarching meta-analysis has examined the diagnostic test accuracy (DTA) of AI-based imaging for lung cancer screening.

OBJECTIVE

To systematically review the DTA of AI-based imaging for lung cancer screening.

METHODS

PubMed, EMBASE, Cochrane Library, CINAHL, IEEE Xplore, Web of Science, ACM Digital Library, Scopus, PsycINFO, and ProQuest Dissertations and Theses were searched from inception to date. Studies that were published in English and that evaluated the performance of AI-based imaging for lung cancer screening were included. Two independent reviewers screened titles and abstracts and used the Quality Assessment of Diagnostic Accuracy Studies-2 tool to appraise the quality of selected studies. Grading of Recommendations Assessment, Development, and Evaluation to diagnostic tests was used to assess the certainty of evidence.

RESULTS

Twenty-six studies with 150,721 imaging data were included. Hierarchical summary receiver-operating characteristic model used for meta-analysis demonstrated that the pooled sensitivity for AI-based imaging for lung cancer screening was 94.6 % (95 % CI: 91.4 % to 96.7 %) and specificity was 93.6 % (95 % CI: 88.5 % to 96.6 %). Subgroup analyses revealed that similar results were found among different types of AI, region, data source, and year of publication, but the overall quality of evidence was very low.

CONCLUSION

AI-based imaging could effectively detect lung cancer and be incorporated into lung cancer screening programs. Further high-quality DTA studies on large lung cancer screening populations are required to validate AI's role in early lung cancer detection.

CT imaging features regarding ground-glass nodules and solid lesions reflect prognostication of synchronous multiple lung adenocarcinoma

The prognosis of synchronous multiple lung adenocarcinoma (SMLA) dramatically differs due to its nature of multiple primaries or intrapulmonary metastases. This study aimed to assess computed tomography (CT)-reflected SMLA features regarding ground-glass nodules (GGNs) and solid lesions and their correlation with prognostication. One seventy eight SMLA patients who underwent surgical resection were reviewed. According to preoperative CT features, patients were categorized as: multiple GGN (MG) group: MGs without solid lesions; solid plus GGN (SPG) group: one solid lesion and at least one GGN; multiple solid (MS) group: MS lesions, with or without GGNs. Clinical characteristics, disease-free survival (DFS), and overall survival (OS) were retrieved. Largest tumor size (P < .001) and lymph-node metastasis prevalence (P < .001) were different among three groups, which were highest in the MS group, followed by the SPG group, and lowest in the MG group. Besides, the dominant tumor subtype also varied among the three groups (P < .001), while no difference in other clinical characteristics was discovered. DFS was more deteriorative in the MS group compared to the SPG group (P = .017) and MG group (P < .001), while of no difference between the SPG group and MG group (P = .128). Meanwhile, OS exhibited similar treads among the three groups. Besides, after multivariate Cox analyses adjustment, MS versus MG independently correlated with DFS (P = .030) and OS (P = .027), but SPG versus MG did not. In conclusion, preoperative CT-imaging MS lesions reflect advanced disease features and poor prognosis compared to MG and solid lesion plus GGN in SMLA patients who underwent surgical resection.

Artificial Intelligence in Biological Sciences

Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.

Cross Analysis of Genomic-Pathologic Features on Multiple Primary Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignancy cancer worldwide with a poor prognosis. Hepatic resection is indicated as a potentially curative option for HCC patients in the early stage. However, due to multiple nodules, it leads to clinical challenges for surgical management. Approximately 41%–75% of HCC cases are multifocal at initial diagnosis, which may arise from multicentric occurrence (MO-HCC) or intrahepatic metastasis (IM-HCC) pattern with significantly different clinical outcomes. Effectively differentiating the two mechanisms is crucial to prioritize the allocation of surgery for multifocal HCC. In this study, we collected a multifocal hepatocellular carcinoma cohort of 17 patients with a total of 34 samples. We performed whole-exome sequencing and staining of pathological HE sections for each lesion. Reconstruction of the clonal evolutionary pattern using genome mutations showed that the intrahepatic metastogenesis pattern had a poorer survival performance than independent origins, with variants in the TP53, ARID1A, and higher CNV variants occurring more significantly in the metastatic pattern. Cross-modality analysis with pathology showed that molecular classification results were consistent with pathology results in 70.6% of patients, and we found that pathology results could further complement the classification for undefined patterns of occurrence. Based on these results, we propose a model to differentiate the pattern of multifocal hepatocellular carcinoma based on the pathological results and genome mutations information, which can provide guidelines for diagnosing and treating multifocal hepatocellular carcinoma.

FAM117A Is a New Prognostic Marker of Lung Adenocarcinoma and Predicts Sensitivity to PD0332991

Lung cancer is the second most common cancer and the leading cause for cancer mortality worldwide. Accelerated cell cycle progression is a well-characterized hallmark for cancer. The present study aims to identify biomarkers for clinical outcomes of lung cancer patients and their sensitivity to CDK inhibitors. To this end, bioinformatics analysis of transcriptome datasets from the Cancer Genome Atlas (TCGA) was first performed to identify survival-related genes; cell proliferation assay, colony formation assay, flow cell cytometry, western blot, EDU labelling, and xenograft models were then used to confirm the potential roles of the identified factors. Our results identified the decreased FAM117A expression as the most significant survival related factor for poor outcome. The cell cycle transition from G1 to S phase was suppressed upon FAM117A overexpression and was promoted upon FAM117A knockdown. Accordingly, the tumor cell growth induced by FAM117A depletion was completely blocked by treatment with PD0332991, which has been approved for cancer therapy. In summary, our work identified FAM117A as a new prognostic marker for poor outcomes of lung cancer patients, predicting sensitivity to PD0332991 treatment.

The Fight against Cancer by Microgravity: The Multicellular Spheroid as a Metastasis Model

Cancer is a disease exhibiting uncontrollable cell growth and spreading to other parts of the organism. It is a heavy, worldwide burden for mankind with high morbidity and mortality. Therefore, groundbreaking research and innovations are necessary. Research in space under microgravity (µg) conditions is a novel approach with the potential to fight cancer and develop future cancer therapies. Space travel is accompanied by adverse effects on our health, and there is a need to counteract these health problems. On the cellular level, studies have shown that real (r-) and simulated (s-) µg impact survival, apoptosis, proliferation, migration, and adhesion as well as the cytoskeleton, the extracellular matrix, focal adhesion, and growth factors in cancer cells. Moreover, the µg-environment induces in vitro 3D tumor models (multicellular spheroids and organoids) with a high potential for preclinical drug targeting, cancer drug development, and studying the processes of cancer progression and metastasis on a molecular level. This review focuses on the effects of r- and s-µg on different types of cells deriving from thyroid, breast, lung, skin, and prostate cancer, as well as tumors of the gastrointestinal tract. In addition, we summarize the current knowledge of the impact of µg on cancerous stem cells. The information demonstrates that µg has become an important new technology for increasing current knowledge of cancer biology.

Current Immunotherapeutic Strategies Targeting the PD-1/PD-L1 Axis in Non-Small Cell Lung Cancer with Oncogenic Driver Mutations

Treatment strategies targeting programed cell death 1 (PD-1) or its ligand, PD-L1, have been developed as immunotherapy against tumor progression for various cancer types including non-small cell lung cancer (NSCLC). The recent pivotal clinical trials of immune-checkpoint inhibiters (ICIs) combined with cytotoxic chemotherapy have reshaped therapeutic strategies and established various first-line standard treatments. The therapeutic effects of ICIs in these clinical trials were analyzed according to PD-L1 tumor proportion scores or tumor mutational burden; however, these indicators are insufficient to predict the clinical outcome. Consequently, molecular biological approaches, including multi-omics analyses, have addressed other mechanisms of cancer immune escape and have revealed an association of NSCLC containing specific driver mutations with distinct immune phenotypes. NSCLC has been characterized by driver mutation-defined molecular subsets and the effect of driver mutations on the regulatory mechanism of PD-L1 expression on the tumor itself. In this review, we summarize the results of recent clinical trials of ICIs in advanced NSCLC and the association between driver alterations and distinct immune phenotypes. We further discuss the current clinical issues with a future perspective for the role of precision medicine in NSCLC.