Omics Profiling in Precision Oncology

Diabetes management in the era of artificial intelligence

Artificial intelligence is growing quickly, and its application in the global diabetes pandemic has the potential to completely change the way this chronic illness is identified and treated. Machine learning methods have been used to construct algorithms supporting predictive models for the risk of getting diabetes or its complications. Social media and Internet forums also increase patient participation in diabetes care. Diabetes resource usage optimisation has benefited from technological improvements. As a lifestyle therapy intervention, digital therapies have made a name for themselves in the treatment of diabetes. Artificial intelligence will cause a paradigm shift in diabetes care, moving away from current methods and toward the creation of focused, data-driven precision treatment.

Infant Bronchiolitis Endotypes and the Risk of Developing Childhood Asthma: Lessons From Cohort Studies

Severe bronchiolitis (i.e., bronchiolitis requiring hospitalization) during infancy is a heterogeneous condition associated with a high risk of developing childhood asthma. Yet, the exact mechanisms underlying the bronchiolitis-asthma link remain uncertain. Birth cohort studies have reported this association at the population level, including only small groups of patients with a history of bronchiolitis, and have attempted to identify the underlying biological mechanisms. Although this evidence has provided valuable insights, there are still unanswered questions regarding severe bronchiolitis-asthma pathogenesis. Recently, a few bronchiolitis cohort studies have attempted to answer these questions by applying unbiased analytical approaches to biological data. These cohort studies have identified novel bronchiolitis subtypes (i.e., endotypes) at high risk for asthma development, representing essential and enlightening evidence. For example, one distinct severe respiratory syncytial virus (RSV) bronchiolitis endotype is characterized by the presence of Moraxella catarrhalis and Streptococcus pneumoniae, higher levels of type I/II IFN expression, and changes in carbohydrate metabolism in nasal airway samples, and is associated with a high risk for childhood asthma development. Although these findings hold significance for the design of future studies that focus on childhood asthma prevention, they require validation. However, this scoping review puts the above findings into clinical context and emphasizes the significance of future research in this area aiming to offer new bronchiolitis treatments and contribute to asthma prevention.

Biology and Development of DNA-Targeted Drugs, Focusing on Synthetic Lethality, DNA Repair, and Epigenetic Modifications for Cancer: A Review

DNA-targeted drugs constitute a specialized category of pharmaceuticals developed for cancer treatment, directly influencing various cellular processes involving DNA. These drugs aim to enhance treatment efficacy and minimize side effects by specifically targeting molecules or pathways crucial to cancer growth. Unlike conventional chemotherapeutic drugs, recent discoveries have yielded DNA-targeted agents with improved effectiveness, and a new generation is anticipated to be even more specific and potent. The sequencing of the human genome in 2001 marked a transformative milestone, contributing significantly to the advancement of targeted therapy and precision medicine. Anticipated progress in precision medicine is closely tied to the continuous development in the exploration of synthetic lethality, DNA repair, and expression regulatory mechanisms, including epigenetic modifications. The integration of technologies like circulating tumor DNA (ctDNA) analysis further enhances our ability to elucidate crucial regulatory factors, promising a more effective era of precision medicine. The combination of genomic knowledge and technological progress has led to a surge in clinical trials focusing on precision medicine. These trials utilize biomarkers for identifying genetic alterations, molecular profiling for potential therapeutic targets, and tailored cancer treatments addressing multiple genetic changes. The evolving landscape of genomics has prompted a paradigm shift from tumor-centric to individualized, genome-directed treatments based on biomarker analysis for each patient. The current treatment strategy involves identifying target genes or pathways, exploring drugs affecting these targets, and predicting adverse events. This review highlights strategies incorporating DNA-targeted drugs, such as PARP inhibitors, SLFN11, methylguanine methyltransferase (MGMT), and ATR kinase.

Heterogeneity-Preserving Discriminative Feature Selection for Subtype Discovery

The discovery of subtypes is pivotal for disease diagnosis and targeted therapy, considering the diverse responses of different cells or patients to specific treatments. Exploring the heterogeneity within disease or cell states provides insights into disease progression mechanisms and cell differentiation. The advent of high-throughput technologies has enabled the generation and analysis of various molecular data types, such as single-cell RNA-seq, proteomic, and imaging datasets, at large scales. While presenting opportunities for subtype discovery, these datasets pose challenges in finding relevant signatures due to their high dimensionality. Feature selection, a crucial step in the analysis pipeline, involves choosing signatures that reduce the feature size for more efficient downstream computational analysis. Numerous existing methods focus on selecting signatures that differentiate known diseases or cell states, yet they often fall short in identifying features that preserve heterogeneity and reveal subtypes. To identify features that can capture the diversity within each class while also maintaining the discrimination of known disease states, we employed deep metric learning-based feature embedding to conduct a detailed exploration of the statistical properties of features essential in preserving heterogeneity. Our analysis revealed that features with a significant difference in interquartile range (IQR) between classes possess crucial subtype information. Guided by this insight, we developed a robust statistical method, termed PHet (Preserving Heterogeneity) that performs iterative subsampling differential analysis of IQR and Fisher's method between classes, identifying a minimal set of heterogeneity-preserving discriminative features to optimize subtype clustering quality. Validation using public single-cell RNA-seq and microarray datasets showcased PHet's effectiveness in preserving sample heterogeneity while maintaining discrimination of known disease/cell states, surpassing the performance of previous outlier-based methods. Furthermore, analysis of a single-cell RNA-seq dataset from mouse tracheal epithelial cells revealed, through PHet-based features, the presence of two distinct basal cell subtypes undergoing differentiation toward a luminal secretory phenotype. Notably, one of these subtypes exhibited high expression of BPIFA1. Interestingly, previous studies have linked BPIFA1 secretion to the emergence of secretory cells during mucociliary differentiation of airway epithelial cells. PHet successfully pinpointed the basal cell subtype associated with this phenomenon, a distinction that pre-annotated markers and dispersion-based features failed to make due to their admixed feature expression profiles. These findings underscore the potential of our method to deepen our understanding of the mechanisms underlying diseases and cell differentiation and contribute significantly to personalized medicine.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

Metabolomics of head and neck cancer in biofluids: an integrative systematic review

INTRODUCTION

Head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC.

OBJECTIVES

To summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine.

METHODS

A systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine).

RESULTS

Metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented.

CONCLUSION

The present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health.

Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients

Histopathologic assessment is indispensable for diagnosing colorectal cancer (CRC). However, manual evaluation of the diseased tissues under the microscope cannot reliably inform patient prognosis or genomic variations crucial for treatment selections. To address these challenges, we develop the Multi-omics Multi-cohort Assessment (MOMA) platform, an explainable machine learning approach, to systematically identify and interpret the relationship between patients' histologic patterns, multi-omics, and clinical profiles in three large patient cohorts (n = 1888). MOMA successfully predicts the overall survival, disease-free survival (log-rank test P-value<0.05), and copy number alterations of CRC patients. In addition, our approaches identify interpretable pathology patterns predictive of gene expression profiles, microsatellite instability status, and clinically actionable genetic alterations. We show that MOMA models are generalizable to multiple patient populations with different demographic compositions and pathology images collected from distinctive digitization methods. Our machine learning approaches provide clinically actionable predictions that could inform treatments for colorectal cancer patients.

Proteomic Characterization of Acute Myeloid Leukemia for Precision Medicine

Acute myeloid leukemia (AML) is a highly heterogeneous cancer of the hematopoietic system with no cure for most patients. In addition to chemotherapy, treatment options for AML include recently approved therapies that target proteins with roles in AML pathobiology, such as FLT3, BLC2, and IDH1/2. However, due to disease complexity, these therapies produce very diverse responses, and survival rates are still low. Thus, despite considerable advances, there remains a need for therapies that target different aspects of leukemic biology and for associated biomarkers that define patient populations likely to respond to each available therapy. To meet this need, drugs that target different AML vulnerabilities are currently in advanced stages of clinical development. Here, we review proteomics and phosphoproteomics studies that aimed to provide insights into AML biology and clinical disease heterogeneity not attainable with genomic approaches. To place the discussion in context, we first provide an overview of genetic and clinical aspects of the disease, followed by a summary of proteins targeted by compounds that have been approved or are under clinical trials for AML treatment and, if available, the biomarkers that predict responses. We then discuss proteomics and phosphoproteomics studies that provided insights into AML pathogenesis, from which potential biomarkers and drug targets were identified, and studies that aimed to rationalize the use of synergistic drug combinations. When considered as a whole, the evidence summarized here suggests that proteomics and phosphoproteomics approaches can play a crucial role in the development and implementation of precision medicine for AML patients.

Multimodal data fusion for cancer biomarker discovery with deep learning

Technological advances now make it possible to study a patient from multiple angles with high-dimensional, high-throughput multi-scale biomedical data. In oncology, massive amounts of data are being generated ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has significantly advanced the analysis of biomedical data. However, most approaches focus on single data modalities leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalised medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability, and standardisation of datasets.

New progress in the role of microRNAs in the diagnosis and prognosis of triple negative breast cancer

Triple negative breast cancer is distinguished by its high malignancy, aggressive invasion, rapid progression, easy recurrence, and distant metastases. Additionally, it has a poor prognosis, a high mortality, and is unresponsive to conventional endocrine and targeted therapy, making it a challenging problem for breast cancer treatment and a hotspot for scientific research. Recent research has revealed that certain miRNA can directly or indirectly affect the occurrence, progress and recurrence of TNBC. Their expression levels have a significant impact on TNBC diagnosis, treatment and prognosis. Some miRNAs can serve as biomarkers for TNBC diagnosis and prognosis. This article summarizes the progress of miRNA research in TNBC, discusses their roles in the occurrence, invasion, metastasis, prognosis, and chemotherapy of TNBC, and proposes a treatment strategy for TNBC by interfering with miRNA expression levels.

Mathematical modeling of cancer immunotherapy for personalized clinical translation

Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.

Fused Raman spectroscopic analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics

As a rapid, label-free, non-destructive analytical measurement requiring little to no sample preparation, Raman spectroscopy shows great promise for liquid biopsy cancer detection and diagnosis. We carried out Raman analysis and mass spectrometry of plasma and saliva from more than 50 subjects in a cohort of head and neck cancer patients and benign controls (e.g., patients with benign oral masses). Unsupervised data models were built to assess diagnostic performance. Raman spectra collected from either biofluid provided moderate performance to discriminate cancer samples. However, by fusing together the Raman spectra of plasma and saliva for each patient, subsequent analytical models delivered an impressive sensitivity, specificity, and accuracy of 96.3%, 85.7%, and 91.7%, respectively. We further confirmed that the metabolites driving the differences in Raman spectra for our models are among the same ones that drive mass spectrometry models, unifying the two techniques and validating the underlying ability of Raman to assess metabolite composition. This study bolsters the relevance of Raman to provide additive value by probing the unique chemical compositions across biofluid sources. Ultimately, we show that a simple data augmentation routine of fusing plasma and saliva spectra provided significantly higher clinical value than either biofluid alone, pushing forward the potential of clinical translation of Raman spectroscopy for liquid biopsy cancer diagnostics.

Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions

Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with -omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).

Liquid Biopsy as a Tool for the Diagnosis, Treatment, and Monitoring of Breast Cancer

Breast cancer (BC) is a highly heterogeneous disease. The treatment of BC is complicated owing to intratumoral complexity. Tissue biopsy and immunohistochemistry are the current gold standard techniques to guide breast cancer therapy; however, these techniques do not assess tumoral molecular heterogeneity. Personalized medicine aims to overcome these biological and clinical complexities. Advances in techniques and computational analyses have enabled increasingly sensitive, specific, and accurate application of liquid biopsy. Such progress has ushered in a new era in precision medicine, where the objective is personalized treatment of breast cancer, early screening, accurate diagnosis and prognosis, relapse detection, longitudinal monitoring, and drug selection. Liquid biopsy can be defined as the sampling of components of tumor cells that are released from a tumor and/or metastatic deposits into the blood, urine, feces, saliva, and other biological substances. Such components include circulating tumor cells (CTCs), circulating tumor DNA (ctDNA) or circulating tumor RNA (ctRNA), platelets, and exosomes. This review aims to highlight the role of liquid biopsy in breast cancer and precision medicine.

Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification

Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient's tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients.

Advances in mass cytometry and its applicability to digital pathology in clinical-translational cancer research

The development and subsequent adaptation of mass cytometry for the histological analysis of tissue sections has allowed the simultaneous spatial characterization of multiple components. This is useful to find the correlation between the genotypic and phenotypic profile of tumor cells and their environment in clinical-translational studies. In this revision, we provide an overview of the most relevant hallmarks in the development, implementation and application of multiplexed imaging in the study of cancer and other conditions. A special focus is placed on studies based on imaging mass cytometry (IMC) and multiplexed ion beam imaging (MIBI). The purpose of this review is to help our readers become familiar with the verification techniques employed on this tool and outline the multiple applications reported in the literature. This review will also provide guidance on the use of IMC or MIBI in any field of biomedical research.

A Web Screening on Training Initiatives in Cancer Genomics for Healthcare Professionals

The disruptive advances in genomics contributed to achieve higher levels of precision in the diagnosis and treatment of cancer. This scientific advance entails the need for greater literacy for all healthcare professionals. Our study summarizes the training initiatives conducted worldwide in cancer genomics field for healthcare professionals. We conducted a web search of the training initiatives aimed at improving healthcare professionals’ literacy in cancer genomics undertaken worldwide by using two search engines (Google and Bing) in English language and conducted from 2003 to 2021. A total of 85,649 initiatives were identified. After the screening process, 36 items were included. The majority of training programs were organized in the United States (47%) and in the United Kingdom (28%). Most of the initiatives were conducted in the last five years (83%) by universities (30%) and as web-based modalities (80%). In front of the technological advances in genomics, education in cancer genomics remains fundamental. Our results may contribute to provide an update on the development of educational programs to build a skilled and appropriately trained genomics health workforce in the future.

Analysis of cancer omics data: a selective review of statistical techniques

Cancer is an omics disease. The development in high-throughput profiling has fundamentally changed cancer research and clinical practice. Compared with clinical, demographic and environmental data, the analysis of omics data-which has higher dimensionality, weaker signals and more complex distributional properties-is much more challenging. Developments in the literature are often 'scattered', with individual studies focused on one or a few closely related methods. The goal of this review is to assist cancer researchers with limited statistical expertise in establishing the 'overall framework' of cancer omics data analysis. To facilitate understanding, we mainly focus on intuition, concepts and key steps, and refer readers to the original publications for mathematical details. This review broadly covers unsupervised and supervised analysis, as well as individual-gene-based, gene-set-based and gene-network-based analysis. We also briefly discuss 'special topics' including interaction analysis, multi-datasets analysis and multi-omics analysis.

A roadmap for multi-omics data integration using deep learning

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made. This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly. Many of the machine learning algorithms cannot manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use. Consequently, our confidence in AI systems can be hindered by the lack of explainability in these black-box models. The XAI becomes more and more crucial for deep learning powered applications, especially for medical and healthcare studies, although in general these deep neural networks can return an arresting dividend in performance. The insufficient explainability and transparency in most existing AI systems can be one of the major reasons that successful implementation and integration of AI tools into routine clinical practice are uncommon. In this study, we first surveyed the current progress of XAI and in particular its advances in healthcare applications. We then introduced our solutions for XAI leveraging multi-modal and multi-centre data fusion, and subsequently validated in two showcases following real clinical scenarios. Comprehensive quantitative and qualitative analyses can prove the efficacy of our proposed XAI solutions, from which we can envisage successful applications in a broader range of clinical questions.

Cytochrome c: Using Biological Insight toward Engineering an Optimized Anticancer Biodrug

The heme protein cytochrome c (Cyt c) plays pivotal roles in cellular life and death processes. In the respiratory chain of mitochondria, it serves as an electron transfer protein, contributing to the proliferation of healthy cells. In the cell cytoplasm, it activates intrinsic apoptosis to terminate damaged cells. Insight into these mechanisms and the associated physicochemical properties and biomolecular interactions of Cyt c informs on the anticancer therapeutic potential of the protein, especially in its ability to subvert the current limitations of small molecule-based chemotherapy. In this review, we explore the development of Cyt c as an anticancer drug by identifying cancer types that would be receptive to the cytotoxicity of the protein and factors that can be finetuned to enhance its apoptotic potency. To this end, some information is obtained by characterizing known drugs that operate, in part, by triggering Cyt c induced apoptosis. The application of different smart drug delivery systems is surveyed to highlight important features for maintaining Cyt c stability and activity and improving its specificity for cancer cells and high drug payload release while recognizing the continuing limitations. This work serves to elucidate on the optimization of the strategies to translate Cyt c to the clinical market.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Real-world data analyses unveiled the immune-related adverse effects of immune checkpoint inhibitors across cancer types

Immune checkpoint inhibitors have demonstrated significant survival benefits in treating many types of cancers. However, their immune-related adverse events (irAEs) have not been systematically evaluated across cancer types in large-scale real-world populations. To address this gap, we conducted real-world data analyses using nationwide insurance claims data with 85.97 million enrollees across 8 years. We identified a significantly increased risk of developing irAEs among patients receiving immunotherapy agents in all seven cancer types commonly treated with immune checkpoint inhibitors. By six months after treatment initialization, those receiving immunotherapy were 1.50-4.00 times (95% CI, lower bound from 1.15 to 2.16, upper bound from 1.69 to 20.36) more likely to develop irAEs in the first 6 months of treatment, compared to matched chemotherapy or targeted therapy groups, with a total of 92,858 patients. The risk of developing irAEs among patients using nivolumab is higher compared to those using pembrolizumab. These results confirmed the need for clinicians to assess irAEs among cancer patients undergoing immunotherapy as part of management. Our methods are extensible to characterizing the effectiveness and adverse effects of novel treatments in large populations in an efficient and economical fashion.

Precision therapy with anaplastic lymphoma kinase inhibitor ceritinib in ALK-rearranged anaplastic large cell lymphoma

BACKGROUND

More than 80% of anaplastic lymphoma kinase (ALK)-positive anaplastic large cell lymphoma (ALCL) patients harbor the (nucleophosmin) NPM1-ALK fusion gene t(2;5) chromosomal translocation. We evaluated the preclinical and clinical efficacy of ceritinib treatment of this aggressive lymphoma.

MATERIALS AND METHODS

We studied the effects of ceritinib treatment in NPM1-ALK+ T-cell lymphoma cell lines in vitro and on tumor size and survival advantage in vivo utilizing tumor xenografts. We treated an NPM1-ALK+ ALCL patient with ceritinib. We reviewed all hematologic malignancies profiled by a large hybrid-capture next-generation sequencing (NGS)-based comprehensive genomic profiling assay for ALK alterations.

RESULTS

In our in vitro experiments, ceritinib inhibited constitutive activation of the fusion kinase NPM1-ALK and downstream effector molecules STAT3, AKT, and ERK1/2, and induced apoptosis of these lymphoma cell lines. Cell cycle analysis following ceritinib treatment showed G0/G1 arrest with a concomitant decrease in the percentage of cells in S and G2/M phases. Further, treatment with ceritinib in the NPM1-ALK+ ALCL xenograft model resulted in tumor regression and improved survival. Of 19 272 patients with hematopoietic diseases sequenced, 58 patients (0.30%) harbored ALK fusions that include histiocytic disorders, multiple myeloma, B-cell neoplasms, Castleman's disease, and juvenile xanthogranuloma. A multiple relapsed NPM1-ALK+ ALCL patient treated with ceritinib achieved complete remission with ongoing clinical benefit to date, 5 years after initiation of therapy.

CONCLUSIONS

This ceritinib translational study in NPM1-ALK+ ALCL provides a strong rationale for a prospective study of ceritinib in ALK+ T-cell lymphomas and other ALK+ hematologic malignancies.

Emerging role of artificial intelligence in nuclear medicine

The role of artificial intelligence is increasing in all branches of medicine. The emerging role of artificial intelligence applications in nuclear medicine is going to improve the nuclear medicine clinical workflow in the coming years. Initial research outcomes are suggestive of increasing role of artificial intelligence in nuclear medicine workflow, particularly where selective automation tasks are of concern. Artificial intelligence-assisted planning, dosimetry and procedure execution appear to be areas for rapid and significant development. The role of artificial intelligence in more directly imaging-related tasks, such as dose optimization, image corrections and image reconstruction, have been particularly strong points of artificial intelligence research in nuclear medicine. Natural Language Processing (NLP)-based text processing task is another area of interest of artificial intelligence implementation in nuclear medicine.

Current updates on precision therapy for breast cancer associated brain metastasis: Emphasis on combination therapy

Cancer therapies have undergone a tremendous progress over the past decade. Precision medicine provides a more tailored approach, making the combination of existing therapies more precise. Different types of cancers are characterized by unique biomarkers that are targeted using various genomic approaches by clinicians and companies worldwide to achieve efficient treatment with minimal side effects. Precision medicine has two broad approaches namely stratified and personalized medicine. The driver mutations could vary within a subtype while the same driver mutations could be found across different subtypes. Precision medicine has recently gained a lot of importance for breast cancer therapy. Various kinds of mutations like hotspot mutations, gene alterations, gene amplification mutations are targeted to design a more specific therapy. Apart from these known gene mutations there are various unknown mutations. Thus, tumor heterogeneity can pose a challenge to precision medicine. For breast cancer, one of the most successful models developed in case of precision medicine is the anti-HER2 therapies as HER2 was considered to have the worst prognosis being highly malignant. But now due to the advent of HER2 receptor targeted therapies, it has a good prognosis. Moreover, precision medicine helps in identifying if the drug molecules being used for the treatment of one kind of cancer can be beneficial in the treatment of another kind of cancer as well, considering the signaling pathways and machinery is similar in most of the cancers. This reduces the time for new drug development and is economically more feasible. Precision medicine will prove to be very advantageous in case of brain metastasis.

Prediction of Target-Drug Therapy by Identifying Gene Mutations in Lung Cancer With Histopathological Stained Image and Deep Learning Techniques

Lung cancer is a kind of cancer with high morbidity and mortality which is associated with various gene mutations. Individualized targeted-drug therapy has become the optimized treatment of lung cancer, especially benefit for patients who are not qualified for lung lobectomy. It is crucial to accurately identify mutant genes within tumor region from stained pathological slice. Therefore, we mainly focus on identifying mutant gene of lung cancer by analyzing the pathological images. In this study, we have proposed a method by identifying gene mutations in lung cancer with histopathological stained image and deep learning to predict target-drug therapy, referred to as DeepIMLH. The DeepIMLH algorithm first downloaded 180 hematoxylin-eosin staining (H&E) images of lung cancer from the Cancer Gene Atlas (TCGA). Then deep convolution Gaussian mixture model (DCGMM) was used to perform color normalization. Convolutional neural network (CNN) and residual network (Res-Net) were used to identifying mutated gene from H&E stained imaging and achieved good accuracy. It demonstrated that our method can be used to choose targeted-drug therapy which might be applied to clinical practice. More studies should be conducted though.

Genetics of Food Allergy

The risk factors for food allergy (FA) include both genetic variants and environmental factors. Advances using both candidate-gene association studies and genome-wide approaches have led to the identification of FA-associated genes involved in immune responses and skin barrier functions. Epigenetic changes have also been associated with the risk of FA. In this chapter, we outline current understanding of the genetics, epigenetics and the interplay with environmental risk factors associated with FA. Future studies of gene-environment interactions, gene-gene interactions, and multi-omics integration may help shed light on the mechanisms of FA, and lead to improved diagnostic and treatment strategies.

Optimised data-independent acquisition strategy recaptures the classification of early-stage hepatocellular carcinoma based on data-dependent acquisition

Proteomics is increasingly used for exploring disease biomarkers and therapeutic targets. The data-independent acquisition (DIA) method collects all peptide signals in a sample, and provides a convenient way to archive disease-related molecular features for further exploration. In this study, we first established a high-coverage human hepatocellular carcinoma (HCC) spectral library containing 9393 protein groups, 119,903 peptides. Furthermore, we optimised the DIA method with respect to four key parameters: settings for mass spectrometry acquisition, gradient length, amount of sample loading, and length of analytical column. More than 6000 proteins from HepG2 cells could be stably quantified using the optimised one-shot DIA approach with a 2 h gradient time. One-shot DIA identified a similar number of proteins as did multi-fraction data-dependent acquisition (DDA) from the same group of HCC samples, but at a quarter of the total acquisition time. DIA data could recapture the classification results obtained from DDA data, thus paving the way for large-scale, multi-centre proteomics analysis of clinical samples. SIGNIFICANCE: The organ-specific spectral library for HCC and the optimised 2 h DIA approach met the urgent demands for large-scale quantitative proteomics analysis of HCC clinical samples. Compared with multi-fraction-DDA, the optimised one-shot DIA could reach a similar identification while consuming shorter acquisition time, thus making it possible to analyse thousands of clinical samples.

Multi-Omic Biomarkers as Potential Tools for the Characterisation of Pancreatic Cystic Lesions and Cancer: Innovative Patient Data Integration

Pancreatic cancer (PC) is regarded as one of the most lethal malignant diseases in the world, with GLOBOCAN 2020 estimates indicating that PC was responsible for almost half a million deaths worldwide in 2020. Pancreatic cystic lesions (PCLs) are fluid-filled structures found within or on the surface of the pancreas, which can either be pre-malignant or have no malignant potential. While some PCLs are found in symptomatic patients, nowadays many PCLs are found incidentally in patients undergoing cross-sectional imaging for other reasons-so called 'incidentalomas'. Current methods of characterising PCLs are imperfect and vary hugely between institutions and countries. As such, there is a profound need for improved diagnostic algorithms. This could facilitate more accurate risk stratification of those PCLs that have malignant potential and reduce unnecessary surveillance. As PC continues to have such a poor prognosis, earlier recognition and risk stratification of PCLs may lead to better treatment protocols. This review will focus on the importance of biomarkers in the context of PCLs and PCand outline how current 'omics'-related work could contribute to the identification of a novel integrated biomarker profile for the risk stratification of patients with PCLs and PC.

Using X-ray Diffraction Techniques for Biomimetic Drug Development, Formulation, and Polymorphic Characterization

Drug development is a decades-long, multibillion dollar investment that often limits itself. To decrease the time to drug approval, efforts are focused on drug targets and drug formulation for optimal biocompatibility and efficacy. X-ray structural characterization approaches have catalyzed the drug discovery and design process. Single crystal X-ray diffraction (SCXRD) reveals important structural details and molecular interactions for the manifestation of a disease or for therapeutic effect. Powder X-ray diffraction (PXRD) has provided a method to determine the different phases, purity, and stability of biological drug compounds that possess crystallinity. Recently, synchrotron sources have enabled wider access to the study of noncrystalline or amorphous solids. One valuable technique employed to determine atomic arrangements and local atom ordering of amorphous materials is the pair distribution function (PDF). PDF has been used in the study of amorphous solid dispersions (ASDs). ASDs are made up of an active pharmaceutical ingredient (API) within a drug dispersed at the molecular level in an amorphous polymeric carrier. This information is vital for appropriate formulation of a drug for stability, administration, and efficacy purposes. Natural or biomimetic products are often used as the API or the formulation agent. This review profiles the deep insights that X-ray structural techniques and associated analytical methods can offer in the development of a drug.

Resolving Clinical Phenotypes into Endotypes in Allergy: Molecular and Omics Approaches

Allergic diseases are highly complex with respect to pathogenesis, inflammation, and response to treatment. Current efforts for allergic disease diagnosis have focused on clinical evidence as a binary outcome. Although outcome status based on clinical phenotypes (observable characteristics) is convenient and inexpensive to measure in large studies, it does not adequately provide insight into the complex molecular determinants of allergic disease. Individuals with similar clinical diagnoses do not necessarily have similar disease etiologies, natural histories, or responses to treatment. This heterogeneity contributes to the ineffective response to treatment leading to an annual estimated cost of $350 billion in the USA alone. There has been a recent focus to deconvolute the clinical heterogeneity of allergic diseases into specific endotypes using molecular and omics approaches. Endotypes are a means to classify patients based on the underlying pathophysiological mechanisms involving distinct functions or treatment response. The advent of high-throughput molecular omics, immunophenotyping, and bioinformatics methods including machine learning algorithms is facilitating the development of endotype-based diagnosis. As we move to the next decade, we should truly start treating clinical endotypes not clinical phenotype. This review highlights current efforts taking place to improve allergic disease endotyping via molecular omics profiling, immunophenotyping, and machine learning approaches in the context of precision diagnostics in allergic diseases. Graphical Abstract.

Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks

OBJECTIVE

Non-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its diagnosis. However, the morphological patterns associated with the molecular subtypes have not been systematically studied. To bridge this gap, we developed a quantitative histopathology analytic framework to identify the types and gene expression subtypes of non-small cell lung cancer objectively.

MATERIALS AND METHODS

We processed whole-slide histopathology images of lung adenocarcinoma (n = 427) and lung squamous cell carcinoma patients (n = 457) in the Cancer Genome Atlas. We built convolutional neural networks to classify histopathology images, evaluated their performance by the areas under the receiver-operating characteristic curves (AUCs), and validated the results in an independent cohort (n = 125).

RESULTS

To establish neural networks for quantitative image analyses, we first built convolutional neural network models to identify tumor regions from adjacent dense benign tissues (AUCs > 0.935) and recapitulated expert pathologists' diagnosis (AUCs > 0.877), with the results validated in an independent cohort (AUCs = 0.726-0.864). We further demonstrated that quantitative histopathology morphology features identified the major transcriptomic subtypes of both adenocarcinoma and squamous cell carcinoma (P < .01).

DISCUSSION

Our study is the first to classify the transcriptomic subtypes of non-small cell lung cancer using fully automated machine learning methods. Our approach does not rely on prior pathology knowledge and can discover novel clinically relevant histopathology patterns objectively. The developed procedure is generalizable to other tumor types or diseases.

Quantitative SWATH-Based Proteomic Profiling for Identification of Mechanism-Driven Diagnostic Biomarkers Conferring in the Progression of Metastatic Prostate Cancer

Prostate cancer (PCa), the most frequently diagnosed malignancy in men is associated with significant mortality and morbidity. Therefore, demand exists for the identification of potential biomarkers for patient stratification according to prognostic risks and the mechanisms involved in cancer development and progression to avoid over/under treatment of patients and prevent relapse. Quantitative proteomic mass spectrometry profiling and gene enrichment analysis of TGF-β induced-EMT in human Prostate androgen-dependent (LNCaP) and androgen-independent (PC-3) adenocarcinoma cell lines was performed to investigate proteomics involved in Prostate carcinogenesis and their effect onto the survival of PCa patients. Amongst 1,795 proteins, which were analyzed, 474 proteins were significantly deregulated. These proteins contributed to apoptosis, gluconeogenesis, transcriptional regulation, RNA splicing, cell cycle, and MAPK cascade and hence indicating the crucial roles of these proteins in PCa initiation and progression. We have identified a panel of six proteins viz., GOT1, HNRNPA2B1, MAPK1, PAK2, UBE2N, and YWHAB, which contribute to cancer development, and the transition of PCa from androgen dependent to independent stages. The prognostic values of identified proteins were evaluated using UALCAN, GEPIA, and HPA datasets. The results demonstrate the utility of SWATH-LC-MS/MS for understanding the proteomics involved in EMT transition of PCa and identification of clinically relevant proteomic biomarkers.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

Autoimmune Effects of Lung Cancer Immunotherapy Revealed by Data-Driven Analysis on a Nationwide Cohort

The autoimmune adverse effects of lung cancer immunotherapy are not fully understood at the population level. Using observational data from commercial health insurance claims, we compared autoimmune diseases risk of immune checkpoint inhibitors (including pembrolizumab and nivolumab) and that of chemotherapy using the matching method. By 6 months after treatment initialization, the cumulative incidence of new autoimmune diseases among patients receiving immunotherapy was 13.13% (95% confidence interval (CI), 10.79-15.50%) and that of the matched chemotherapy patients was 6.65% (95% CI, 5.79-7.50%), constituting a hazard ratio (HR) of 1.97 (95% CI, 1.58-2.48). Both pembrolizumab (HR = 2.06 (95% CI, 1.20-3.65), P = 0.0032) and nivolumab (HR = 1.76 (95% CI, 1.39-2.24), P < 0.0001) were associated with higher risks of developing autoimmune diseases, especially for hypothyroidism (P < 0.0001). Our findings suggest the need to monitor autoimmune side effects of immunotherapy.

Omics-based insights into therapy failure of pediatric B-lineage acute lymphoblastic leukemia

B-lineage acute lymphoblastic leukemia (B-ALL) is the most common type of cancer seen in children and is characterized by a variable clinical course. Although there have been remarkable improvements in the therapy outcomes of pediatric B-ALL, treatment failure remains the leading-cause of death in 18% of the afflicted patients during the first 5 years after diagnosis. Molecular heterogeneities of pediatric B-ALL play important roles as determinants of the therapy response. Therefore, many of these molecular abnormalities have an established prognostic value in the disease. The present review discusses the omics-based revelations from epigenomics, genomics, transcriptomics and proteomics about treatment failure in pediatric B-ALL. Next it highlights the promise of the molecular aberration-targeted therapy to improve the treatment outcomes.

Classification of Lupus Nephritis; Time for a Change?

Renal biopsy plays a critical role in the diagnosis and management of kidney disease in patients with systemic lupus erythematosus. The current pathologic classification of lupus nephritis is widely accepted but remains a work in progress. We discuss the key challenges in lupus nephritis classification and review new approaches to improve clinical utility and prognostic value.

Machine learning empowers phosphoproteome prediction in cancers

MOTIVATION

Reversible protein phosphorylation is an essential post-translational modification regulating protein functions and signaling pathways in many cellular processes. Aberrant activation of signaling pathways often contributes to cancer development and progression. The mass spectrometry-based phosphoproteomics technique is a powerful tool to investigate the site-level phosphorylation of the proteome in a global fashion, paving the way for understanding the regulatory mechanisms underlying cancers. However, this approach is time-consuming and requires expensive instruments, specialized expertise and a large amount of starting material. An alternative in silico approach is predicting the phosphoproteomic profiles of cancer patients from the available proteomic, transcriptomic and genomic data.

RESULTS

Here, we present a winning algorithm in the 2017 NCI-CPTAC DREAM Proteogenomics Challenge for predicting phosphorylation levels of the proteome across cancer patients. We integrate four components into our algorithm, including (i) baseline correlations between protein and phosphoprotein abundances, (ii) universal protein-protein interactions, (iii) shareable regulatory information across cancer tissues and (iv) associations among multi-phosphorylation sites of the same protein. When tested on a large held-out testing dataset of 108 breast and 62 ovarian cancer samples, our method ranked first in both cancer tissues, demonstrating its robustness and generalization ability.

AVAILABILITY AND IMPLEMENTATION

Our code and reproducible results are freely available on GitHub: https://github.com/GuanLab/phosphoproteome_prediction.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

Iron and Copper Intracellular Chelation as an Anticancer Drug Strategy

A very promising direction in the development of anticancer drugs is inhibiting the molecular pathways that keep cancer cells alive and able to metastasize. Copper and iron are two essential metals that play significant roles in the rapid proliferation of cancer cells and several chelators have been studied to suppress the bioavailability of these metals in the cells. This review discusses the major contributions that Cu and Fe play in the progression and spreading of cancer and evaluates select Cu and Fe chelators that demonstrate great promise as anticancer drugs. Efforts to improve the cellular delivery, efficacy, and tumor responsiveness of these chelators are also presented including a transmetallation strategy for dual targeting of Cu and Fe. To elucidate the effectiveness and specificity of Cu and Fe chelators for treating cancer, analytical tools are described for measuring Cu and Fe levels and for tracking the metals in cells, tissue, and the body.

Iron and Copper Intracellular Chelation as an Anticancer Drug Strategy

A very promising direction in the development of anticancer drugs is inhibiting the molecular pathways that keep cancer cells alive and able to metastasize. Copper and iron are two essential metals that play significant roles in the rapid proliferation of cancer cells and several chelators have been studied to suppress the bioavailability of these metals in the cells. This review discusses the major contributions that Cu and Fe play in the progression and spreading of cancer and evaluates select Cu and Fe chelators that demonstrate great promise as anticancer drugs. Efforts to improve the cellular delivery, efficacy, and tumor responsiveness of these chelators are also presented including a transmetallation strategy for dual targeting of Cu and Fe. To elucidate the effectiveness and specificity of Cu and Fe chelators for treating cancer, analytical tools are described for measuring Cu and Fe levels and for tracking the metals in cells, tissue, and the body.

Modified SureSelectQXT Target Enrichment Protocol for Illumina Multiplexed Sequencing of FFPE Samples

Background

Personalised medicine is nowadays a major objective in oncology. Molecular characterization of tumours through NGS offers the possibility to find possible therapeutic targets in a time- and cost-effective way. However, the low quality and complexity of FFPE DNA samples bring a series of disadvantages for massive parallel sequencing techniques compared to high-quality DNA samples (from blood cells, cell cultures, etc.).

Results

We performed several experiments to understand the behaviour of FFPE DNA samples during the construction of SureSelect^QXT libraries. First, we designed a quality checkpoint for FFPE DNA samples based on the quantification of their amplification capability (qcPCR). We observed that FFPE DNA samples can be classified according to DIN value and qcPCR concentration into unusable, or low-quality (LQ) and good-quality (GQ) DNA. For GQ samples, we increased the amount of input DNA to 150 ng and the digestion time to 30 min, whereas for LQ samples, we used 50 ng of DNA as input but we decreased the digestion time to 1 min. In all cases, we increased the cycles of the pre-hyb PCR to 10 but decreased the cycles of the post-hyb PCR to 8. In addition, we confirmed that using half of the volume of reagents can be beneficial. Finally, in order to obtain better results, we designed a decision flow-chart to achieve a seeding concentration of 12–14 pM for MiSeq Reagent Kit v2.

Conclusions

Our experiments allowed us to unveil the behaviour of low-quality FFPE DNA samples during the construction of SureSelect^QXT libraries. Sequencing results showed that, using our modified SureSelect^QXT protocol, the final percentage of usable reads for low-quality samples was increased more than three times allowing to reach median depth/million reads values of 76.35. This value is equivalent to ~ 0.9 and ~ 0.7 of the values obtained for good-quality FFPE and high-quality DNA respectively.

Electronic supplementary material

The online version of this article (10.1186/s12575-018-0084-7) contains supplementary material, which is available to authorized users.

Artificial intelligence in healthcare

Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

A Cloud-Based Metabolite and Chemical Prioritization System for the Biology/Disease-Driven Human Proteome Project

Targeted metabolomics and biochemical studies complement the ongoing investigations led by the Human Proteome Organization (HUPO) Biology/Disease-Driven Human Proteome Project (B/D-HPP). However, it is challenging to identify and prioritize metabolite and chemical targets. Literature-mining-based approaches have been proposed for target proteomics studies, but text mining methods for metabolite and chemical prioritization are hindered by a large number of synonyms and nonstandardized names of each entity. In this study, we developed a cloud-based literature mining and summarization platform that maps metabolites and chemicals in the literature to unique identifiers and summarizes the copublication trends of metabolites/chemicals and B/D-HPP topics using Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores. We successfully prioritized metabolites and chemicals associated with the B/D-HPP targeted fields and validated the results by checking against expert-curated associations and enrichment analyses. Compared with existing algorithms, our system achieved better precision and recall in retrieving chemicals related to B/D-HPP focused areas. Our cloud-based platform enables queries on all biological terms in multiple species, which will contribute to B/D-HPP and targeted metabolomics/chemical studies.

A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework

A major rise in the prevalence and impact of Alzheimer's disease (AD) is projected in the coming decades, resulting from increasing life expectancy, thus leading to substantially increased healthcare costs. While brain disfunctions at the time of diagnosis are irreversible, it is widely accepted that AD pathology develops decades before clinical symptoms onset. If incipient processes can be detected early in the disease progression, prospective intervention for preventing or slowing the disease can be designed. Currently, there is no noninvasive biomarker available to detect and monitor early stages of disease progression. The complex etiology of AD warrants a systems-based approach supporting the integration of multimodal and multilevel data, while network-based modeling provides the scaffolding for methods revealing complex systems-level disruptions initiated by the disease. In this work, we review current state-of-the-art, focusing on network-based biomarkers at molecular and brain functional connectivity levels. Particular emphasis is placed on outlining recent trends, which highlight the functional importance of modular substructures in molecular and connectivity networks and their potential biomarker value. Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for a comprehensively noninvasive approach in the early diagnosis of AD. Finally, we propose a framework for integrating knowledge from molecular and brain connectivity levels, which has the potential to enable noninvasive diagnosis, provide support for monitoring therapies, and help understand heretofore unexamined deep level relations between genotype and brain phenotype.