The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources

Molecular Profiling of Circulating Tumour Cells and Circulating Tumour DNA: Complementary Insights from a Single Blood Sample Utilising the Parsortix® System

The study of molecular drivers of cancer is an area of rapid growth and has led to the development of targeted treatments, significantly improving patient outcomes in many cancer types. The identification of actionable mutations informing targeted treatment strategies are now considered essential to the management of cancer. Traditionally, this information has been obtained through biomarker assessment of a tissue biopsy which is costly and can be associated with clinical complications and adverse events. In the last decade, blood-based liquid biopsy has emerged as a minimally invasive, fast, and cost-effective alternative, which is better suited to the requirement for longitudinal monitoring. Liquid biopsies allow for the concurrent study of multiple analytes, such as circulating tumour cells (CTCs) and circulating tumour DNA (ctDNA), from a single blood sample. Although ctDNA assays are commercially more advanced, there is an increasing awareness of the clinical significance of the transcriptome and proteome which can be analysed using CTCs. Herein, we review the literature in which the microfluidic, label-free Parsortix® system is utilised for CTC capture, harvest and analysis, alongside the analysis of ctDNA from a single blood sample. This detailed summary of the literature demonstrates how these two analytes can provide complementary disease information.

Domain loss enabled evolution of novel functions in the snake three-finger toxin gene superfamily

Three-finger toxins (3FTXs) are a functionally diverse family of toxins, apparently unique to venoms of caenophidian snakes. Although the ancestral function of 3FTXs is antagonism of nicotinic acetylcholine receptors, redundancy conferred by the accumulation of duplicate genes has facilitated extensive neofunctionalization, such that derived members of the family interact with a range of targets. 3FTXs are members of the LY6/UPAR family, but their non-toxin ancestor remains unknown. Combining traditional phylogenetic approaches, manual synteny analysis, and machine learning techniques (including AlphaFold2 and ProtT5), we have reconstructed a detailed evolutionary history of 3FTXs. We identify their immediate ancestor as a non-secretory LY6, unique to squamate reptiles, and propose that changes in molecular ecology resulting from loss of a membrane-anchoring domain and changes in gene expression, paved the way for the evolution of one of the most important families of snake toxins.

Challenges and considerations for single-cell and spatially resolved transcriptomics sample collection during spaceflight

Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have experienced rapid development in recent years. The findings of spaceflight-based scRNA-seq and SRT investigations are likely to improve our understanding of life in space and our comprehension of gene expression in various cell systems and tissue dynamics. However, compared to their Earth-based counterparts, gene expression experiments conducted in spaceflight have not experienced the same pace of development. Out of the hundreds of spaceflight gene expression datasets available, only a few used scRNA-seq and SRT. In this perspective piece, we explore the growing importance of scRNA-seq and SRT in space biology and discuss the challenges and considerations relevant to robust experimental design to enable growth of these methods in the field.

Downregulation of MACC1 facilitates the reversal effect of verapamil on the chemoresistance to active metabolite of irinotecan in human colon cancer cells

The aim of this study is to investigate the reversal effect of verapamil (VER) on chemoresistance to irinotecan (CPT-11) in human colon cancer cells and relevant mechanisms. Cell counting kit-8 (CCK-8) test and colony-forming unit (CFU) experiment results show that VER strengthens the sensitivity of human colon cancer cell line HT29 to CPT-11 but has a small effect on SW480 cells. High-throughput transcriptome sequencing, RT-PCR, and Western blot results show that the inhibition of metastasis-associated in colon cancer-1 (MACC1) expression by VER is the key factor for reversal effect on chemoresistance to CPT-11. Transfection experiments further show that VER can reverse the resistance of human colon cancer cells to SN-38, the active metabolite of CPT-11, when MACC1 is overexpressed. The nude mouse transplantation tumor experiment provides an in vivo proof that VER can strengthen sensitivity to CPT-11 in drug-resistant human colon cancer cells, and the effect might be related to the inhibited expression of MACC1. In summary, VER might strengthen the reversal effect of VER on chemoresistance to CPT-11 in human colon cancer cells and facilitate the apoptosis of human colon cancer cells by downregulating MACC1 expression.

-Omics potential of in vitro skin models for radiation exposure

There is a growing need to uncover biomarkers of ionizing radiation exposure that leads to a better understanding of how exposures take place, including dose type, rate, and time since exposure. As one of the first organs to be exposed to external sources of ionizing radiation, skin is uniquely positioned in terms of model systems for radiation exposure study. The simultaneous evolution of both MS-based -omics studies, as well as in vitro 3D skin models, has created the ability to develop a far more holistic understanding of how ionizing radiation affects the many interconnected biomolecular processes that occur in human skin. However, there are a limited number of studies describing the biomolecular consequences of low-dose ionizing radiation to the skin. This review will seek to explore the current state-of-the-art technology in terms of in vitro 3D skin models, as well as track the trajectory of MS-based -omics techniques and their application to ionizing radiation research, specifically, the search for biomarkers within the low-dose range.

An equivalence test between features lists, based on the Sorensen-Dice index and the joint frequencies of GO term enrichment

Background

In integrative bioinformatic analyses, it is of great interest to stablish the equivalence between gene or (more in general) feature lists, up to a given level and in terms of their annotations in the Gene Ontology. The aim of this article is to present an equivalence test based on the proportion of GO terms which are declared as enriched in both lists simultaneously.

Results

On the basis of these data, the dissimilarity between gene lists is measured by means of the Sorensen–Dice index. We present two flavours of the same test: One of them based on the asymptotic normality of the test statistic and the other based on the bootstrap method.

Conclusions

The accuracy of these tests is studied by means of simulation and their possible interest is illustrated by using them over two real datasets: A collection of gene lists related to cancer and a collection of gene lists related to kidney rejection after transplantation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04739-2.

Fabrication approaches for high-throughput and biomimetic disease modeling

There is often a tradeoff between in vitro disease modeling platforms that capture pathophysiologic complexity and those that are amenable to high-throughput fabrication and analysis. However, this divide is closing through the application of a handful of fabrication approaches-parallel fabrication, automation, and flow-driven assembly-to design sophisticated cellular and biomaterial systems. The purpose of this review is to highlight methods for the fabrication of high-throughput biomaterial-based platforms and showcase examples that demonstrate their utility over a range of throughput and complexity. We conclude with a discussion of future considerations for the continued development of higher-throughput in vitro platforms that capture the appropriate level of biological complexity for the desired application. STATEMENT OF SIGNIFICANCE: There is a pressing need for new biomedical tools to study and understand disease. These platforms should mimic the complex properties of the body while also permitting investigation of many combinations of cells, extracellular cues, and/or therapeutics in high-throughput. This review summarizes emerging strategies to fabricate biomimetic disease models that bridge the gap between complex tissue-mimicking microenvironments and high-throughput screens for personalized medicine.

Systems epidemiology and cancer: A review of the National Institutes of Health extramural grant portfolio 2013-2018

OBJECTIVES

Systems epidemiology approaches may lead to a better understanding of the complex and dynamic multi-level constellation of contributors to cancer risk and outcomes and help target interventions. This grant portfolio analysis aimed to describe the National Institutes of Health (NIH) and the National Cancer Institute (NCI) investments in systems epidemiology and to identify gaps in the cancer systems epidemiology portfolio.

METHODS

The analysis examined grants funded (2013-2018) through seven NIH systems science Funding Opportunity Announcements (FOAs) as well as cancer-specific systems epidemiology grants funded by NCI during that same time. Study characteristics were extracted from the grant abstracts and specific aims and coded.

RESULTS

Of the 137 grants awarded under the NIH FOAs, 52 (38%) included systems epidemiology. Only five (4%) were focused on cancer systems epidemiology. The NCI-wide search (N = 453 grants) identified 35 grants (8%) that included cancer systems epidemiology in their specific aims. Most of these grants examined epidemiology and surveillance-based questions (60%); fewer addressed clinical care or clinical trials (37%). Fifty-four percent looked at multiple scales within the individual (e.g., cell, tissue, organ), 49% looked beyond the individual (e.g., individual, community, population), and few (9%) included both. Across all grants examined, the systems epidemiology grants primarily focused on discovery or prediction, rather than on impacts of intervention or policy.

CONCLUSIONS

The most notable finding was that grants focused on cancer versus other diseases reflected a small percentage of the portfolio, highlighting the need to encourage more cancer systems epidemiology research. Opportunities include encouraging more multiscale research and continuing the support for broad examination of domains in these studies. Finally, the nascent discipline of systems epidemiology could benefit from the creation of standard terminology and definitions to guide future progress.

Research status and prospects of biomarkers for nasopharyngeal carcinoma in the era of high‑throughput omics (Review)

As a malignant tumor type, nasopharyngeal carcinoma (NPC) is characterized by distinct geographical, ethnic and genetic differences; presenting a major threat to human health in many countries, especially in Southern China. At present, no accurate and effective methods are available for the early diagnosis, efficacious evaluation or prognosis prediction for NPC. As such, a large number of patients have locoregionally advanced NPC at the time of initial diagnosis. Many patients show toxic reactions to overtreatment and have risks of cancer recurrence and distant metastasis owing to insufficient treatment. To solve these clinical problems, high‑throughput '‑omics' technologies are being used to screen and identify specific molecular biomarkers for NPC. Because of the lack of comprehensive descriptions regarding NPC biomarkers, the present study summarized the research progress that has been made in recent years to discover NPC biomarkers, highlighting the existing problems that require exploration. In view of the lack of authoritative reports at present, study design factors that affect the screening of biomarkers are also discussed here and prospects for future research are proposed to provide references for follow‑up studies of NPC biomarkers.

Multiomics profiling of paired primary and recurrent glioblastoma patient tissues

Background

Despite maximal therapy with surgery, chemotherapy, and radiotherapy, glioblastoma (GBM) patients have a median survival of only 15 months. Almost all patients inevitably experience symptomatic tumor recurrence. A hallmark of this tumor type is the large heterogeneity between patients and within tumors itself which relates to the failure of standardized tumor treatment. In this study, tissue samples of paired primary and recurrent GBM tumors were investigated to identify individual factors related to tumor progression.

Methods

Paired primary and recurrent GBM tumor tissues from 8 patients were investigated with a multiomics approach using transcriptomics, proteomics, and phosphoproteomics.

Results

In the studied patient cohort, large variations between and within patients are observed for all omics analyses. A few pathways affected at the different omics levels partly overlapped if patients are analyzed at the individual level, such as synaptogenesis (containing the SNARE complex) and cholesterol metabolism. Phosphoproteomics revealed increased STMN1(S38) phosphorylation as part of ERBB4 signaling. A pathway tool has been developed to visualize and compare different omics datasets per patient and showed potential therapeutic drugs, such as abobotulinumtoxinA (synaptogenesis) and afatinib (ERBB4 signaling). Afatinib is currently in clinical trials for GBM.

Conclusions

A large variation on all omics levels exists between and within GBM patients. Therefore, it will be rather unlikely to find a drug treatment that would fit all patients. Instead, a multiomics approach offers the potential to identify affected pathways on the individual patient level and select treatment options.

Comparison of commercially available whole-genome sequencing kits for variant detection in circulating cell-free DNA

Circulating cell-free DNA (ccfDNA) has great potential for non-invasive diagnosis, prognosis and monitoring treatment of disease. However, a sensitive and specific whole-genome sequencing (WGS) method is required to identify novel genetic variations (i.e., SNVs, CNVs and INDELS) on ccfDNA that can be used as clinical biomarkers. In this article, five WGS methods were compared: ThruPLEX Plasma-seq, QIAseq cfDNA All-in-One, NEXTFLEX Cell Free DNA-seq, Accel-NGS 2 S PCR FREE DNA and Accel-NGS 2 S PLUS DNA. The Accel PCR-free kit did not produce enough material for sequencing. The other kits had significant common number of SNVs, INDELs and CNVs and showed similar results for SNVs and CNVs. The detection of variants and genomic signatures depends more upon the type of plasma sample rather than the WGS method used. Accel detected several variants not observed by the other kits. ThruPLEX seemed to identify more low-abundant SNVs and SNV signatures were similar to signatures observed with the QIAseq kit. Accel and NEXTFLEX had similar CNV and SNV signatures. These results demonstrate the importance of establishing a standardized workflow for identifying non-invasive candidate biomarkers. Moreover, the combination of variants discovered in ccfDNA using WGS has the potential to identify enrichment pathways, while the analysis of signatures could identify new subgroups of patients.

A Network-Based "Phenomics" Approach for Discovering Patient Subtypes From High-Throughput Cardiac Imaging Data

OBJECTIVES

The authors present a method that focuses on cohort matching algorithms for performing patient-to-patient comparisons along multiple echocardiographic parameters for predicting meaningful patient subgroups.

BACKGROUND

Recent efforts in collecting multiomics data open numerous opportunities for comprehensive integration of highly heterogenous data to classify a patient's cardiovascular state, eventually leading to tailored therapies.

METHODS

A total of 42 echocardiography features, including 2-dimensional and Doppler measurements, left ventricular (LV) and atrial speckle-tracking, and vector flow mapping data, were obtained in 297 patients. A similarity network was developed to delineate distinct patient phenotypes, and then neural network models were trained for discriminating the phenotypic presentations.

RESULTS

The patient similarity model identified 4 clusters (I to IV), with patients in each cluster showed distinctive clinical presentations based on American College of Cardiology/American Heart Association heart failure stage and the occurrence of short-term major adverse cardiac and cerebrovascular events. Compared with other clusters, cluster IV had a higher prevalence of stage C or D heart failure (78%; p < 0.001), New York Heart Association functional classes III or IV (61%; p < 0.001), and a higher incidence of major adverse cardiac and cerebrovascular events (p < 0.001). The neural network model showed robust prediction of patient clusters, with area under the receiver-operating characteristic curve ranging from 0.82 to 0.99 for the independent hold-out validation set.

CONCLUSIONS

Automated computational methods for phenotyping can be an effective strategy to fuse multidimensional parameters of LV structure and function. It can identify distinct cardiac phenogroups in terms of clinical characteristics, cardiac structure and function, hemodynamics, and outcomes.

Proteomic investigations into hypertension: what's new and how might it affect clinical practice?

Introduction: Hypertension is a multifactorial disease that has, thus far, proven to be a difficult target for pharmacological intervention. The application of proteomic strategies may help to identify new biomarkers for the early diagnosis and prompt treatment of hypertension, in order to control blood pressure and prevent organ damage. Areas covered: Advances in proteomics have led to the discovery of new biomarkers to help track the pathophysiological processes implicated in hypertension. These findings not only help to better understand the nature of the disease, but will also contribute to the clinical needs for a timely diagnosis and more precise treatment. In this review, we provide an overview of new biomarkers identified in hypertension through the application of proteomic techniques, and we also discuss the difficulties and challenges in identifying biomarkers in this clinical setting. We performed a literature search in PubMed with the key words 'hypertension' and 'proteomics', and focused specifically on the most recent literature on the utility of proteomics in hypertension research. Expert opinion: There have been several promising biomarkers of hypertension identified by proteomics, but too few have been introduced to the clinic. Thus, further investigations in larger cohorts are necessary to test the feasibility of this strategy for patients. Also, this emerging field would profit from more collaboration between clinicians and researchers.

Artificial Intelligence and Personalized Medicine

The development of high-throughput, data-intensive biomedical research assays and technologies has created a need for researchers to develop strategies for analyzing, integrating, and interpreting the massive amounts of data they generate. Although a wide variety of statistical methods have been designed to accommodate 'big data,'  experiences with the use of artificial intelligence (AI) techniques suggest that they might be particularly appropriate. In addition,  the results of the application of these assays reveal a great heterogeneity in the pathophysiologic factors and processes that contribute to disease, suggesting that there is a need to tailor, or 'personalize,' medicines to the nuanced and often unique features possessed by individual patients. Given how important data-intensive assays are to revealing appropriate intervention targets and strategies for  treating an individual with a disease, AI can play an important role in the development of personalized medicines. We describe many areas where AI can play such a role and argue that AI's ability to advance personalized medicine will depend critically on not only the refinement of relevant assays, but also on ways of storing, aggregating, accessing, and ultimately integrating, the data they produce. We also point out the limitations of many AI techniques in developing personalized medicines as well as consider areas for further research.

Cancer Informatics in 2017: A New Beginning and a Bright Future

Objective:  To summarize significant research contributions on cancer informatics published in 2017.

Methods:  An extensive search using PubMed/Medline, Google Scholar, and manual review was conducted to identify the scientific contributions published in 2017 that address topics in cancer informatics. The selection process comprised three steps: (i) 15 candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of three best papers was conducted by the editorial board of the Yearbook.

Results:  Results: The three selected best papers present studies addressing many facets of cancer informatics, with immediate applicability in the research and clinical domains.

Conclusion: Cancer informatics is a broad and vigorous subfield of biomedical informatics. Strides in knowledge management, crowdsourcing, and visualization are especially notable in 2017.