Artificial intelligence-based multi-omics analysis fuels cancer precision medicine

Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine

PURPOSE OF REVIEW

Isocitrate dehydrogenase wild-type glioblastoma is the most aggressive primary brain tumour in adults. Its infiltrative nature and heterogeneity confer a dismal prognosis, despite multimodal treatment. Precision medicine is increasingly advocated to improve survival rates in glioblastoma management; however, conventional neuroimaging techniques are insufficient in providing the detail required for accurate diagnosis of this complex condition.

RECENT FINDINGS

Advanced magnetic resonance imaging allows more comprehensive understanding of the tumour microenvironment. Combining diffusion and perfusion magnetic resonance imaging to create a multiparametric scan enhances diagnostic power and can overcome the unreliability of tumour characterisation by standard imaging. Recent progress in deep learning algorithms establishes their remarkable ability in image-recognition tasks. Integrating these with multiparametric scans could transform the diagnosis and monitoring of patients by ensuring that the entire tumour is captured. As a corollary, radiomics has emerged as a powerful approach to offer insights into diagnosis, prognosis, treatment, and tumour response through extraction of information from radiological scans, and transformation of these tumour characteristics into quantitative data. Radiogenomics, which links imaging features with genomic profiles, has exhibited its ability in characterising glioblastoma, and determining therapeutic response, with the potential to revolutionise management of glioblastoma. The integration of deep learning algorithms into radiogenomic models has established an automated, highly reproducible means to predict glioblastoma molecular signatures, further aiding prognosis and targeted therapy. However, challenges including lack of large cohorts, absence of standardised guidelines and the 'black-box' nature of deep learning algorithms, must first be overcome before this workflow can be applied in clinical practice.

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.

Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review

Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.

Establishment and evaluation of on-chip intestinal barrier biosystems based on microfluidic techniques

As a booming engineering technology, the microfluidic chip has been widely applied for replicating the complexity of human intestinal micro-physiological ecosystems in vitro. Biosensors, 3D imaging, and multi-omics have been applied to engineer more sophisticated intestinal barrier-on-chip platforms, allowing the improved monitoring of physiological processes and enhancing chip performance. In this review, we report cutting-edge advances in the microfluidic techniques applied for the establishment and evaluation of intestinal barrier platforms. We discuss different design principles and microfabrication strategies for the establishment of microfluidic gut barrier models in vitro. Further, we comprehensively cover the complex cell types (e.g., epithelium, intestinal organoids, endothelium, microbes, and immune cells) and controllable extracellular microenvironment parameters (e.g., oxygen gradient, peristalsis, bioflow, and gut-organ axis) used to recapitulate the main structural and functional complexity of gut barriers. We also present the current multidisciplinary technologies and indicators used for evaluating the morphological structure and barrier integrity of established gut barrier models in vitro. Finally, we highlight the challenges and future perspectives for accelerating the broader applications of these platforms in disease simulation, drug development, and personalized medicine. Hence, this review provides a comprehensive guide for the development and evaluation of microfluidic-based gut barrier platforms.

Omics-based molecular classifications empowering in precision oncology

BACKGROUND

In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine.

CONCLUSION

This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

Precision Oncology, Artificial Intelligence, and Novel Therapeutic Advancements in the Diagnosis, Prevention, and Treatment of Cancer: Highlights from the 59th Irish Association for Cancer Research (IACR) Annual Conference

Advancements in oncology, especially with the era of precision oncology, is resulting in a paradigm shift in cancer care. Indeed, innovative technologies, such as artificial intelligence, are paving the way towards enhanced diagnosis, prevention, and personalised treatments as well as novel drug discoveries. Despite excellent progress, the emergence of resistant cancers has curtailed both the pace and extent to which we can advance. By combining both their understanding of the fundamental biological mechanisms and technological advancements such as artificial intelligence and data science, cancer researchers are now beginning to address this. Together, this will revolutionise cancer care, by enhancing molecular interventions that may aid cancer prevention, inform clinical decision making, and accelerate the development of novel therapeutic drugs. Here, we will discuss the advances and approaches in both artificial intelligence and precision oncology, presented at the 59th Irish Association for Cancer Research annual conference.

Multiomics approach towards characterization of tumor cell plasticity and its significance in precision and personalized medicine

Cellular plasticity refers to the ability of cells to change their identity or behavior, which can be advantageous in some cases (e.g., tissue regeneration) but detrimental in others (e.g., cancer metastasis). With a better understanding of cellular plasticity, the complexity of cancer cells, their heterogeneity, and their role in metastasis is being unraveled. The plasticity of the cells could also prove as a nemesis to their characterization. In this review, we have attempted to highlight the possibilities and benefits of using multiomics approach in characterizing the plastic nature of cancer cells. There is a need to integrate fragmented evidence at different levels of cellular organization (DNA, RNA, protein, metabolite, epigenetics, etc.) to facilitate the characterization of different forms of plasticity and cell types. We have discussed the role of cellular plasticity in generating intra-tumor heterogeneity. Different omics level evidence is being provided to highlight the variety of molecular determinants discovered using different techniques. Attempts have been made to integrate some of this information to provide a quantitative assessment and scoring of the plastic nature of the cells. However, there is a huge gap in our understanding of mechanisms that lead to the observed heterogeneity. Understanding of these mechanism(s) is necessary for finding targets for early detection and effective therapeutic interventions in metastasis. Targeting cellular plasticity is akin to neutralizing a moving target. Along with the advancements in precision and personalized medicine, these efforts may translate into better clinical outcomes for cancer patients, especially in metastatic stages.

Discovery of the potential biomarkers for early diagnosis of endometrial cancer via integrating metabolomics and transcriptomics

Endometrial cancer (EC) is one of the most common malignant tumors in women, and the increasing incidence and mortality pose a serious threat to the public health. Early diagnosis of EC could prolong the survival period and optimize the survivorship, greatly alleviating patients' suffering and social medical pressure. In this study, we collected urine and serum samples from the recruited patients, analyzed the samples using LC-MS approach, and identified the differential metabolites through metabolomic analysis. Then, the differentially expressed genes were identified through the systematic transcriptomic analysis of EC-related dataset from Gene Expression Omnibus (GEO), followed by network profiling of metabolic-reaction-enzyme-gene. In this experiment, a total of 83 differential metabolites and 19 hub genes were discovered, of which 10 different metabolites and 3 hub genes were further evaluated as more potential biomarkers based on network analysis. According to the KEGG enrichment analysis, the potential biomarkers and gene-encoded proteins were found to be involved in the arginine and proline metabolism, histidine metabolism, and pyrimidine metabolism, which was of significance for the early diagnosis of EC. In particular, the combination of metabolites (histamine, 1-methylhistamine, and methylimidazole acetaldehyde) as well as the combination of RRM2, TYMS and TK1 exerted more accurate discrimination abilities between EC and healthy groups, providing more criteria for the early diagnosis of EC.

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

Stem Cell-Derived Exosomes: An Advanced Horizon to Cancer Regenerative Medicine

Cancer research has made significant progress in recent years, and extracellular vesicles (EVs) based cancer investigation reveals several facts about cancer. Exosomes are a subpopulation of EVs. In the present decade, exosomes is mostly highlighted for cancer theranostic research. Tumor cell derived exosomes (TEXs) promote cancer but there are multiple sources of exosomes that can be used as cancer therapeutic agents (plant exosomes, stem cell-derived exosomes, modified or synthetic exosomes). Stem cells based regenerative medicine faces numerous challenges, such as promote tumor development, cellular reprogramming etc., and therefore addressing these complications becomes essential. Stem cell-derived exosomes serves as an answer to these problems and offers a better solution. Global research indicates that stem cell-derived exosomes also play a dual role in the cellular system by either inhibiting or promoting cancer. Modified exosomes which are genetically engineered exosomes or surface modified exosomes to increase the efficacy of the therapeutic properties can also be considered to target the above concerns. However, the difficulties associated with the exosomes include variations in exosomes heterogenity, isolation protocols, large scale production, etc., and these have to be managed effectively. In this review, we explore exosomes biogenesis, multiple stem cell-derived exosome sources, drug delivery, modified stem cells exosomes, clinical trial of stem cells exosomes, and the related challenges in this domain and future orientation. This article may encourage researchers to explore stem cell-derived exosomes and develop an effective and affordable cancer therapeutic solution.

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact:  anirban@klyuniv.ac.in.

The importance of protein domain mutations in cancer therapy

Cancer is a complex disease that is caused by multiple genetic factors. Researchers have been studying protein domain mutations to understand how they affect the progression and treatment of cancer. These mutations can significantly impact the development and spread of cancer by changing the protein structure, function, and signalling pathways. As a result, there is a growing interest in how these mutations can be used as prognostic indicators for cancer prognosis. Recent studies have shown that protein domain mutations can provide valuable information about the severity of the disease and the patient's response to treatment. They may also be used to predict the response and resistance to targeted therapy in cancer treatment. The clinical implications of protein domain mutations in cancer are significant, and they are regarded as essential biomarkers in oncology. However, additional techniques and approaches are required to characterize changes in protein domains and predict their functional effects. Machine learning and other computational tools offer promising solutions to this challenge, enabling the prediction of the impact of mutations on protein structure and function. Such predictions can aid in the clinical interpretation of genetic information. Furthermore, the development of genome editing tools like CRISPR/Cas9 has made it possible to validate the functional significance of mutants more efficiently and accurately. In conclusion, protein domain mutations hold great promise as prognostic and predictive biomarkers in cancer. Overall, considerable research is still needed to better define genetic and molecular heterogeneity and to resolve the challenges that remain, so that their full potential can be realized.

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

Application of Artificial Intelligence to Advance Individualized Diagnosis and Treatment in Emergency and Critical Care Medicine

Emergency and critical illnesses refer to severe diseases or conditions characterized by rapid changes in health that may endanger life within a short period [...].

The application of artificial intelligence in EUS

Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

Novel Pathogenic Mutation of P209L in TRPC6 Gene Causes Adult Focal Segmental Glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS) is a leading kidney disease, clinically associated with proteinuria and progressive renal failure. The occurrence of this disease is partly related to gene mutations. We describe a single affected family member who presented with FSGS. We used high-throughput sequencing, sanger sequencing to identify the pathogenic mutations, and a systems genetics analysis in the BXD mice was conducted to explore the genetic regulatory mechanisms of pathogenic genes in the development of FSGS. We identified high urinary protein (++++) and creatinine levels (149 μmol/L) in a 29-year-old male diagnosed with a 5-year history of grade 2 hypertension. Histopathology of the kidney biopsy showed stromal hyperplasia at the glomerular segmental sclerosis and endothelial cell vacuolation degeneration. Whole-exome sequencing followed by Sanger sequencing revealed a heterozygous missense mutation (c.643C > T) in exon 2 of TRPC6, leading to the substitution of arginine with tryptophan at position 215 (p.Arg215Trp). Systems genetics analysis of the 53 BXD mice kidney transcriptomes identified Pygm as the upstream regulator of Trpc6. Those two genes are jointly involved in the regulation of FSGS mainly via Wnt and Hippo signaling pathways. We present a novel variant in the TRPC6 gene that causes FSGS. Moreover, our data suggested TRPC6 works with PYGM, as well as Wnt and Hippo signaling pathways to regulate renal function, which could guide future clinical prevention and targeted treatment for FSGS outcomes.

TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data

BACKGROUND

Pan-cancer analysis examines both the commonalities and heterogeneity among genomic and cellular alterations across numerous types of tumors. Pan-cancer analysis of gene expression, tumor mutational burden (TMB), microsatellite instability (MSI), and tumor immune microenvironment (TIME), and methylation becomes available based on the multi-omics data from The Cancer Genome Atlas Program (TCGA). Some online tools provide analysis of gene and protein expression, mutation, methylation, and survival for TCGA data. However, these online tools were either Uni-functional or were not able to perform analysis of user-defined functions. Therefore, we created the TCGAplot R package to facilitate perform pan-cancer analysis and visualization of the built-in multi-omic TCGA data.

RESULTS

TCGAplot provides several functions to perform pan-cancer paired/unpaired differential gene expression analysis, pan-cancer correlation analysis between gene expression and TMB, MSI, TIME, and promoter methylation. Functions for visualization include paired/unpaired boxplot, survival plot, ROC curve, heatmap, scatter, radar chart, and forest plot. Moreover, gene set based pan-cancer and tumor specific analyses were also available. Finally, all these built-in multi-omic data could be extracted for implementation for user-defined functions, making the pan-cancer analysis much more convenient.\ CONCLUSIONS: We developed an R-package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. The source code and pre-built package are available at GitHub ( https://github.com/tjhwangxiong/TCGAplot ).

Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases

BACKGROUND

As the available information about breast cancer is growing every day, the decision-making process for the therapy is getting more complex. ChatGPT as a transformer-based language model possesses the ability to write scientific articles and pass medical exams. But is it able to support the multidisciplinary tumor board (MDT) in the planning of the therapy of patients with breast cancer?

MATERIAL AND METHODS

We performed a pilot study on 10 consecutive cases of breast cancer patients discussed in MDT at our department in January 2023. Included were patients with a primary diagnosis of early breast cancer. The recommendation of MDT was compared with the recommendation of the ChatGPT for particular patients and the clinical score of the agreement was calculated.

RESULTS

Results showed that ChatGPT provided mostly general answers regarding chemotherapy, breast surgery, radiation therapy, chemotherapy, and antibody therapy. It was able to identify risk factors for hereditary breast cancer and point out the elderly patient indicated for chemotherapy to evaluate the cost/benefit effect. ChatGPT wrongly identified the patient with Her2 1 + and 2 + (FISH negative) as in need of therapy with an antibody and called endocrine therapy "hormonal treatment".

CONCLUSIONS

Support of artificial intelligence by finding individualized and personalized therapy for our patients in the time of rapidly expanding amount of information is looking for the ways in the clinical routine. ChatGPT has the potential to find its spot in clinical medicine, but the current version is not able to provide specific recommendations for the therapy of patients with primary breast cancer.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.

Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making

Cancer is the second major cause of disease-related death worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient's life. Cancer, as a complex and heterogeneous disorder, results from the disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, the histopathology of a biopsy, and imaging (MRI, CT, PET, and US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient's response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient's big data, and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical-clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research.

The Investigation of the Association Between the Bcl-2 3'-UTR rs1564483 Polymorphism and miR-296-3p in the Development of Breast and Gastric Cancers

Background

B-cell leukemia/lymphoma 2 (Bcl-2) gene regulates carcinogenesis by inhibiting apoptosis. This study evaluated the association of Bcl-2 3'-untranslated regions (3' UTR) rs1564483 polymorphism and miR-296-3p with the development of breast and gastric cancers.

Methods

A microarray analysis was performed on the Genomic Spatial Event (GSE)29431 and GSE161533 datasets for breast and gastric cancers. Blood samples were taken from 222 (111 patients and 111 controls) and 210 (84 patients and 126 controls) individuals for breast and gastric cancers, respectively. Genomic DNA was extracted from the blood samples and genotyping was performed using real-time polymerase chain reaction (RT-PCR), followed by examining the high-temperature melting curve. Statistical analysis was conducted to examine the potential correlation between the rs1564483 polymorphism and the risk of breast and gastric cancers concerning pathological characteristics.

Results

The results of the microarray showed that the Bcl-2 gene was up-regulated in gastric cancer (logFC [log fold change]: 0.65, adjusted P < .05). Clinical outcome showed no notable relationship between the rs1564483 polymorphism and breast cancer risk; however, for gastric cancer, it identified a large difference between healthy controls and patients for an allelic frequency of rs1564483 (P ⩽ .001). Moreover, an assay of different models (dominant, recessive, and co-dominant) showed a significant association between the AG genotype between control and gastric cases (Pearson chi-square test, P = .046). In addition, the prevalence of the AG genotype was greater in persons under the age of 45 and in patients with H. pylori infection (P ⩽ .001). The AG genotype was not related to smoking, although the AA genotype was associated with increased cancer incidence in smokers (P ⩽ .001).

Conclusions

In silico studies and calculations of the ΔG binding of micro ribonucleic acid (miRNA) hsa-miR-296-3p to the mutant and wild alleles of the rs15644833 single nucleotide polymorphism (SNP) have revealed that Bcl-2 mRNA expression in gastric cancer decreases, thus confirming the tumor suppressor role of the Bcl-2 gene.

Integration of incomplete multi-omics data using Knowledge Distillation and Supervised Variational Autoencoders for disease progression prediction

OBJECTIVE

The rapid advancement of high-throughput technologies in the biomedical field has resulted in the accumulation of diverse omics data types, such as mRNA expression, DNA methylation, and microRNA expression, for studying various diseases. Integrating these multi-omics datasets enables a comprehensive understanding of the molecular basis of cancer and facilitates accurate prediction of disease progression.

METHODS

However, conventional approaches face challenges due to the dimensionality curse problem. This paper introduces a novel framework called Knowledge Distillation and Supervised Variational AutoEncoders utilizing View Correlation Discovery Network (KD-SVAE-VCDN) to address the integration of high-dimensional multi-omics data with limited common samples. Through our experimental evaluation, we demonstrate that the proposed KD-SVAE-VCDN architecture accurately predicts the progression of breast and kidney carcinoma by effectively classifying patients as long- or short-term survivors. Furthermore, our approach outperforms other state-of-the-art multi-omics integration models.

RESULTS

Our findings highlight the efficacy of the KD-SVAE-VCDN architecture in predicting the disease progression of breast and kidney carcinoma. By enabling the classification of patients based on survival outcomes, our model contributes to personalized and targeted treatments. The favorable performance of our approach in comparison to several existing models suggests its potential to contribute to the advancement of cancer understanding and management.

CONCLUSION

The development of a robust predictive model capable of accurately forecasting disease progression at the time of diagnosis holds immense promise for advancing personalized medicine. By leveraging multi-omics data integration, our proposed KD-SVAE-VCDN framework offers an effective solution to this challenge, paving the way for more precise and tailored treatment strategies for patients with different types of cancer.

Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption

Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better interpretation of morphology, improved quantitation of biomarkers, introduction of novel concepts to discovery and diagnostics (such as spatial distribution of cellular elements), and the promise of a new paradigm of cancer biomarkers. The application of AI to tissue analysis can take several conceptual approaches, within the domains of language modelling and image analysis, such as Deep Learning Convolutional Neural Networks, Multiple Instance Learning approaches, or the modelling of risk scores and their application to ML. The use of different approaches solves different problems within pathology workflows, including assistive applications for the detection and grading of tumours, quantification of biomarkers, and the delivery of established and new image-based biomarkers for treatment prediction and prognostic purposes. All these AI formats, applied to digital tissue images, are also beginning to transform our approach to clinical trials. In parallel, the novelty of DP/AI devices and the related computational science pipeline introduces new requirements for manufacturers to build into their design, development, regulatory and post-market processes, which may need to be taken into account when using AI applied to tissues in cancer discovery. Finally, DP/AI represents challenge to the way we accredit new diagnostic tools with clinical applicability, the understanding of which will allow cancer patients to have access to a new generation of complex biomarkers.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

Combination of multiple omics techniques for a personalized therapy or treatment selection

Despite targeted therapies and immunotherapies have revolutionized the treatment of cancer patients, only a limited number of patients have long-term responses. Moreover, due to differences within cancer patients in the tumor mutational burden, composition of the tumor microenvironment as well as of the peripheral immune system and microbiome, and in the development of immune escape mechanisms, there is no "one fit all" therapy. Thus, the treatment of patients must be personalized based on the specific molecular, immunologic and/or metabolic landscape of their tumor. In order to identify for each patient the best possible therapy, different approaches should be employed and combined. These include (i) the use of predictive biomarkers identified on large cohorts of patients with the same tumor type and (ii) the evaluation of the individual tumor with "omics"-based analyses as well as its ex vivo characterization for susceptibility to different therapies.

CYFIP2 serves as a prognostic biomarker and correlates with tumor immune microenvironment in human cancers

BACKGROUND

The mechanisms whereby CYFIP2 acts in tumor development and drives immune infiltration have been poorly explored. Thus, this study aimed to identifying the role of CYFIP2 in tumors and immune response.

METHODS

In this study, we first explored expression patterns, diagnostic role and prognostic value of CYFIP2 in cancers, particularly in lung adenocarcinoma (LUAD). Then, we performed functional enrichment, genetic alterations, DNA methylation analysis, and immune cell infiltration analysis of CYFIP2 to uncover its potential mechanisms involved in immune microenvironment.

RESULTS

We found that CYFIP2 significantly differentially expressed in different tumors including LUAD compared with normal tissues. Furthermore, CYFIP2 was found to be significantly correlated with clinical parameters in LUAD. According to the diagnostic and survival analysis, CYFIP2 may be employed as a potential diagnostic and prognostic biomarker. Moreover, genetic alterations revealed that mutation of CYFIP2 was the main types of alterations in different cancers. DNA methylation analysis indicated that CYFIP2 mRNA expression correlated with hypomethylation. Afterwards, functional enrichment analysis uncovered that CYFIP2 was involved in tumor-associated and immune-related pathways. Immune infiltration analysis indicated that CYFIP2 was significantly correlated with immune cells infiltration. In particular, CYFIP2 was strongly linked with immune microenvironment scores. Additionally, CYFIP2 exhibited a significant relationship with immune regulators and immune-related genes including chemokines, chemokines receptors, and MHC genes.

CONCLUSION

Our results suggested that CYFIP2 may serve as a prognostic cancer biomarker for determining prognosis and might be a promising therapeutic strategy for tumor immunotherapy.

Multimodal analysis and the oncology patient: Creating a hospital system for integrated diagnostics and discovery

We propose that an information technology and computational framework that would unify access to hospital digital information silos, and enable integration of this information using machine learning methods, would bring a new paradigm to patient management and research. This is the core principle of Integrated Diagnostics (ID): the amalgamation of multiple analytical modalities, with evolved information technology, applied to a defined patient cohort, and resulting in a synergistic effect in the clinical value of the individual diagnostic tools. This has the potential to transform the practice of personalized oncology at a time at which it is very much needed. In this article we present different models from the literature that contribute to the vision of ID and we provide published exemplars of ID tools. We briefly describe ongoing efforts within a universal healthcare system to create national clinical datasets. Following this, we argue the case to create "hospital units" to leverage this multi-modal analysis, data integration and holistic clinical decision-making. Finally, we describe the joint model created in our institutions.

Metastasis-associated lung adenocarcinoma transcript 1 molecular mechanisms in gastric cancer progression

Gastric cancer (GC) remains among the most common cancers worldwide with a high mortality-to-incidence ratio. Accumulated evidence suggests that long noncoding RNAs (lncRNAs) are involved in gastric carcinogenesis. These transcripts are longer than 200 nucleotides and modulate gene expression at multiple molecular levels, inducing or inhibiting biological processes and diseases. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is one of the best-studied lncRNAs with comprehensive actions contributing to cancer progression. This lncRNA regulates gene expression at the transcriptional and posttranscriptional levels through interactions with microRNAs and proteins. In the present review, we discussed the molecular mechanism of MALAT1 and summarized the current knowledge of its expression in GC. Moreover, we highlighted the potential use of MALAT1 as a biomarker, including liquid biopsy.

Functional Materials for Subcellular Targeting Strategies in Cancer Therapy: Progress and Prospects

Neoadjuvant and adjuvant therapies have made significant progress in cancer treatment. However, tumor adjuvant therapy still faces challenges due to the intrinsic heterogeneity of cancer, genomic instability, and the formation of an immunosuppressive tumor microenvironment. Functional materials possess unique biological properties such as long circulation times, tumor-specific targeting, and immunomodulation. The combination of functional materials with natural substances and nanotechnology has led to the development of smart biomaterials with multiple functions, high biocompatibilities, and negligible immunogenicities, which can be used for precise cancer treatment. Recently, subcellular structure-targeting functional materials have received particular attention in various biomedical applications including the diagnosis, sensing, and imaging of tumors and drug delivery. Subcellular organelle-targeting materials can precisely accumulate therapeutic agents in organelles, considerably reduce the threshold dosages of therapeutic agents, and minimize drug-related side effects. This review provides a systematic and comprehensive overview of the research progress in subcellular organelle-targeted cancer therapy based on functional nanomaterials. Moreover, it explains the challenges and prospects of subcellular organelle-targeting functional materials in precision oncology. The review will serve as an excellent cutting-edge guide for researchers in the field of subcellular organelle-targeted cancer therapy.

Omics Technologies Improving Breast Cancer Research and Diagnostics

Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

BACKGROUND

There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings.

METHODS

This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. It focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the biomedical domain. For this, we retrieved and analyzed 42 studies focusing on emerging architectural and methodological advances, the encoding of biological domain knowledge and the integration of explainability methods.

RESULTS

We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction networks) and interpretability. This represents a fundamental functional shift towards models which can integrate mechanistic and statistical inference aspects. We introduce a concept of bio-centric interpretability and according to its taxonomy, we discuss representational methodologies for the integration of domain prior knowledge in such models.

CONCLUSIONS

The paper provides a critical outlook into contemporary methods for explainability and interpretability used in DL for cancer. The analysis points in the direction of a convergence between encoding prior knowledge and improved interpretability. We introduce bio-centric interpretability which is an important step towards formalisation of biological interpretability of DL models and developing methods that are less problem- or application-specific.

Special Issue "Recent Advances in Neonatal Sepsis"

Perinatal medicine and neonatology have seen significant advancements in recent decades [...].

Immune-related pulmonary toxicities of checkpoint inhibitors in non-small cell lung cancer: Diagnosis, mechanism, and treatment strategies

Immune checkpoint inhibitors (ICI) therapy based on programmed cell death-1 (PD-1) and programmed cell death ligand 1 (PD-L1) has changed the treatment paradigm of advanced non-small cell lung cancer (NSCLC) and improved the survival expectancy of patients. However, it also leads to immune-related adverse events (iRAEs), which result in multiple organ damage. Among them, the most common one with the highest mortality in NSCLC patients treated with ICI is checkpoint inhibitor pneumonitis (CIP). The respiratory signs of CIP are highly coincident and overlap with those in primary lung cancer, which causes difficulties in detecting, diagnosing, managing, and treating. In clinical management, patients with serious CIP should receive immunosuppressive treatment and even discontinue immunotherapy, which impairs the clinical benefits of ICIs and potentially results in tumor recrudesce. Therefore, accurate diagnosis, detailedly dissecting the pathogenesis, and developing reasonable treatment strategies for CIP are essential to prolong patient survival and expand the application of ICI. Herein, we first summarized the diagnosis strategies of CIP in NSCLC, including the classical radiology examination and the rising serological test, pathology test, and artificial intelligence aids. Then, we dissected the potential pathogenic mechanisms of CIP, including disordered T cell subsets, the increase of autoantibodies, cross-antigens reactivity, and the potential role of other immune cells. Moreover, we explored therapeutic approaches beyond first-line steroid therapy and future direction based on targeted signaling pathways. Finally, we discussed the current impediments, future trends, and challenges in fighting ICI-related pneumonitis.