Multi-omics analysis of intra-tumoural and inter-tumoural heterogeneity in pancreatic ductal adenocarcinoma.

The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is associated with the tumour heterogeneity. To explore intra- and inter-tumoural heterogeneity in PDAC, we analysed the multi-omics profiles of 61 PDAC lesion samples, along with the matched pancreatic normal tissue samples, from 19 PDAC patients. Haematoxylin and Eosin (H&E) staining revealed that diversely differentiated lesions coexisted both within and across individual tumours. Whole exome sequencing (WES) of samples from multi-region revealed diverse types of mutations in diverse genes between cancer cells within a tumour and between tumours from different individuals. The copy number variation (CNV) analysis also showed that PDAC exhibited intra- and inter-tumoural heterogeneity in CNV and that high average CNV burden was associated poor prognosis of the patients. Phylogenetic tree analysis and clonality/timing analysis of mutations displayed diverse evolutionary pathways and spatiotemporal characteristics of genomic alterations between different lesions from the same or different tumours. Hierarchical clustering analysis illustrated higher inter-tumoural heterogeneity than intra-tumoural heterogeneity of PDAC at the transcriptional levels as lesions from the same patients are grouped into a single cluster. Immune marker genes are differentially expressed in different regions and tumour samples as shown by tumour microenvironment (TME) analysis. TME appeared to be more heterogeneous than tumour cells in the same patient. Lesion-specific differentially methylated regions (DMRs) were identified by methylated DNA immunoprecipitation sequencing (MeDIP-seq). Furthermore, the integration analysis of multi-omics data showed that the mRNA levels of some genes, such as PLCB4, were significantly correlated with the gene copy numbers. The mRNA expressions of potential PDAC biomarkers ZNF521 and KDM6A were correlated with copy number alteration and methylation, respectively. Taken together, our results provide a comprehensive view of molecular heterogeneity and evolutionary trajectories of PDAC and may guide personalised treatment strategies in PDAC therapy.

Current progress in clinical, molecular, and genetic aspects of adult fibromuscular dysplasia.

Fibromuscular dysplasia (FMD) is a non-atherosclerotic vascular disease that may involve medium-sized muscular arteries throughout the body. The majority of FMD patients are women. Although a variety of genetic, mechanical, and hormonal factors play a role in the pathogenesis of FMD, overall, its cause remains poorly understood. It is probable that the pathogenesis of FMD is linked to a combination of genetic and environmental factors. Extensive studies have correlated the arterial lesions of FMD to histopathological findings of arterial fibrosis, cellular hyperplasia, and distortion of the abnormal architecture of the arterial wall. More recently, the vascular phenotype of lesions associated with FMD has been expanded to include arterial aneurysms, dissections, and tortuosity. However, in the absence of a string-of-beads or focal stenosis, these lesions do not suffice to establish the diagnosis. While FMD most commonly involves renal and cerebrovascular arteries, involvement of most arteries throughout the body has been reported. Increasing evidence highlights that FMD is a systemic arterial disease and that subclinical alterations can be found in non-affected arterial segments. Recent significant progress in FMD-related research has led to improve our understanding of the disease's clinical manifestations, natural history, epidemiology, and genetics. Ongoing work continues to focus on FMD genetics and proteomics, physiological effects of FMD on cardiovascular structure and function, and novel imaging modalities and blood-based biomarkers that can be used to identify subclinical FMD. It is also hoped that the next decade will bring the development of multi-centred and potentially international clinical trials to provide comparative effectiveness data to inform the optimal management of patients with FMD.

Recent Advances in Developmental Hematopoiesis: Diving Deeper With New Technologies.

The journey of a hematopoietic stem cell (HSC) involves the passage through successive anatomical sites where HSCs are in direct contact with their surrounding microenvironment, also known as niche. These spatial and temporal cellular interactions throughout development are required for the acquisition of stem cell properties, and for maintaining the HSC pool through balancing self-renewal, quiescence and lineage commitment. Understanding the context and consequences of these interactions will be imperative for our understanding of HSC biology and will lead to the improvement of in vitro production of HSCs for clinical purposes. The aorta-gonad-mesonephros (AGM) region is in this light of particular interest since this is the cradle of HSC emergence during the embryonic development of all vertebrate species. In this review, we will focus on the developmental origin of HSCs and will discuss the novel technological approaches and recent progress made to identify the cellular composition of the HSC supportive niche and the underlying molecular events occurring in the AGM region.

Top Trends in Multiomics Research: Evaluation of 52 Published Studies and New Ways of Thinking Terminology and Visual Displays.

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.

Ferroptosis-related gene signature predicts prognosis and immunotherapy in glioma.

AIMS: Glioma is a highly invasive brain tumor, which makes prognosis challenging and renders patients resistant to various treatments. Induction of cell death is promising in cancer therapy. Ferroptosis, a recently discovered regulated cell death, can be induced for killing glioma cells. However, the prognostic prediction of ferroptosis-related genes (FRGs) in glioma remains elusive. METHODS: The mRNA expression profiles and gene variation and corresponding clinical data of glioma patients and NON-TUMOR control were downloaded from public databases. Risk score based on a FRGs signature was constructed in REMBRANDT cohort and validated in other datasets including CGGA-693, CGGA-325, and TCGA. RESULTS: Our results demonstrated that the majority of FRGs was differentially expressed among GBM, LGG, and NON-TUMOR groups (96.6%). Furthermore, the glioma patients with low-risk score exhibited a more satisfactory clinical outcome. The better prognosis was also validated in the glioma patients with low-risk score no matter to which grade they were affiliated. Functional analysis revealed that the high-risk score group was positively correlated with the enrichment scores for immune checkpoint blockade-related positive signatures, indicating the critical role of glioma immunotherapy via risk score. CONCLUSION: A novel FRGs-related risk score can predict prognosis and immunotherapy in glioma patients.

Transcriptomic Analyses Reveal Gene Expression Profiles and Networks in Nasopharyngeal Carcinoma.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a rare but highly aggressive tumor that is predominantly encountered in Southeast Asia and China in particular. Aside from radiotherapy, no effective therapy that specifically treats NPC is available, including targeted drugs. Finding more sensitive biomarkers is important for new drug discovery and for evaluating patient prognosis. METHODS: mRNA expression datasets from the Gene Expression Omnibus database (GSE53819, GSE64634, and GSE40290) were selected. After all samples in each dataset were subjected to quality control using principal component analyses, the qualified samples were used for additional analyses. The genes that were significantly expressed in each dataset were intersected to identify the most significant of these. Gene functional enrichment analyses were performed on these genes, using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses. The protein-protein interaction network of selected genes was analyzed using the Search Tool for the Retrieval of Interacting Genes database. Significantly, differentially expressed genes were further verified with two RNA-seq datasets (GSE68799 and GSE12452), as well as in clinical samples. RESULTS: In all, 34 (8 upregulated genes and 26 downregulated) genes were identified as significantly differentially expressed. The immune response and the regulation of cell proliferation were the most enriched biological GO terms. Using reverse transcription quantitative real-time PCR (RT-qPCR), the genes MMP1, AQP9, and TNFAIP6 were detected to be upregulated, and FAM3D, CR2, and LTF were downregulated in NPC tissue samples. CONCLUSION: This study provides information on the genes that may be involved in the development of NPC and suggests possible druggable targets and biomarkers for diagnosing and evaluating the prognosis of NPC.

Spatially resolved transcriptomics and its applications in cancer.

Spatially resolved transcriptomics (SRT) offers the promise of understanding cells and their modes of dysfunction in the context of intact tissues. Technologies for SRT have advanced rapidly with a large number being published in recent years. Diverse methods for SRT produce data at widely varying depth, throughput, accessibility and cost. Many published SRT methods have been demonstrated only in their labs of origin, while others have matured to the point of commercialization and widespread availability. Here we review technologies for SRT, and their application in studies of tumor heterogeneity.

Moving Molecular Profiling to Routine Clinical Practice: A Way Forward?

Molecular profiling of a patient's tumor to guide targeted treatment selection offers the potential to advance patient care by improving outcomes and minimizing toxicity (by avoiding ineffective treatments). However, current development of molecular profile (MP) panels is often based on applying institution-specific or subjective algorithms to nonrandomized patient cohorts. Consequently, obtaining reliable evidence that molecular profiling is offering clinical benefit and is ready for routine clinical practice is challenging. In particular, we discuss here the problems with interpreting for clinical utility nonrandomized studies that compare outcomes in patients treated based on their MP vs those treated with standard of care, studies that compare the progression-free survival (PFS) seen on a MP-directed treatment to the PFS seen for the same patient on a previous standard treatment (PFS ratio), and multibasket trials that evaluate the response rates of targeted therapies in specific molecularly defined subpopulations (regardless of histology). We also consider some limitations of randomized trial designs. A two-step strategy is proposed in which multiple mutation-agent pairs are tested for activity in one or more multibasket trials in the first step. The results of the first step are then used to identify promising mutation-agent pairs that are combined in a molecular panel that is then tested in the step-two strategy-design randomized clinical trial (the molecular panel-guided treatment for the selected mutations vs standard of care). This two-step strategy should allow rigorous evidence-driven identification of mutation-agent pairs that can be moved into routine clinical practice.

Gene expression profiling in melanoma: past results and future potential.

The field of melanoma oncology is rapidly evolving with advances in detection, staging and treatment. There is heterogeneity in all stages of melanoma where some patients fare better than others for reasons currently unknown and it is sometimes unclear which patients warrant closer surveillance, multidisciplinary care, increased imaging, more aggressive surgery or adjuvant therapy. Early studies have shown the predictive ability of gene expression profiling (GEP) and institutions that have adopted GEP for melanoma treatment have demonstrated changes in practice patterns and patient management. The goal of this paper is to review the clinical evidence for a new diagnostic test, DecisionDx-Melanoma, the only GEP test for cutaneous melanoma with prospective studies analyzing its utility.

Tissue-based multigene expression tests for pretreatment prostate cancer risk assessment: current status and future perspectives.

Prostate cancer is a highly prevalent disease with ample spectrum of aggressiveness and treatment options. Low-risk disease can be safely managed by nonintervention strategies, such as active surveillance; however, accurate risk assessment is warranted. Molecular tests have been developed and validated to complement standard clinicopathological parameters and help to improve risk stratification in prostate cancer. Herein, we review selected tissue-based assays, including genomic prostate score, cell cycle progression score and genomic classifier, with particular emphasis on their role in patient risk assessment in a pretreatment setting, in view of their current or potential utilization in active surveillance.

Identification of key genes of papillary thyroid cancer using integrated bioinformatics analysis.

OBJECTIVE: To identify novel clinically relevant genes in papillary thyroid carcinoma from public databases. METHODS: Four original microarray datasets, GSE3678, GSE3467, GSE33630 and GSE58545, were downloaded. Differentially expressed genes (DEGs) were filtered from integrated data. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by protein-protein interaction (PPI) network construction. The CentiScape pug-in was performed to scale degree. The genes at the top of the degree distribution (≥ 95% percentile) in the significantly perturbed networks were defined as central genes. UALCAN and The Cancer Genome Atlas Clinical Explorer were used to verify clinically relevant genes and perform survival analysis. RESULT: 225 commonly changed DEGs (111 up-regulated and 114 down-regulated) were identified. The DEGs were classified into three groups by GO terms. KEGG pathway enrichment analysis showed DEGs mainly enriched in the PI3K-Akt signaling pathway, pathways in cancer, focal adhesion and proteoglycans in cancer. DEGs' protein-protein interaction (PPI) network complex was developed; six central genes (BCL2, CCND1, FN1, IRS1, COL1A1, CXCL12) were identified. Among them, BCL2, CCND1 and COL1A1 were identified as clinically relevant genes. CONCLUSION: BCL2, CCND1 and COL1A1 may be key genes for papillary thyroid carcinoma. Further molecular biological experiments are required to confirm the function of the identified genes.

Impact of Gene Expression Profiling on Decision-Making in Clinically Node Negative Melanoma Patients after Surgical Staging.

INTRODUCTION: The surgeon's role in the follow-up of pathologic stage I and II melanoma patients has traditionally been minimal. Melanoma genetic expression profile (GEP) testing provides binary risk assessment (Class 1-low risk, Class 2-high risk), which can assist in predicting metastasis and formulating appropriate follow up. We sought to determine the impact of GEP results on the management of clinically node negative cutaneous melanoma patients staged with sentinel lymph node biopsy (SLNB). METHODS: A retrospective review of prospectively gathered data consisting of patients seen from September 2015 - August 2016 was performed to determine whether GEP class influenced follow-up recommendations. Patients were stratified into four groups based on recommended follow-up plan: Dermatology alone, Surgical Oncology, Surgical Oncology with recommendation for adjuvant clinical trial, or Medical and Surgical Oncology. RESULTS: Of ninety-one patients, 38 were pathologically stage I, 42 stage II, 10 stage III, and 1 stage IV. Combining all stages, GEP Class 1 patients were more likely to be followed by Dermatology alone and less like to be followed by Surgical Oncology with recommendation for adjuvant trial compared to Class 2 patients (P less than 0.001). Among stage 1 patients, Class 1 were more likely to follow up with Dermatology alone compared to Class 2 patients (82 vs. 0%; P less than 0.001). Among stage II patients, GEP Class 1 were more likely to follow up with Dermatology alone (21 vs 0%) and more Class 2 patients followed up with surgery and recommendations for adjuvant trial (36 vs 64%; P less than 0.05). There was no difference in follow up for stage III patients based on the GEP results (P=0.76). CONCLUSION: GEP results were significantly associated with the management of stage I-II melanoma patients after staging with SLNB. For node negative patients, Class 2 results led to more aggressive follow up and management. J Drugs Dermatol. 2018;17(2):196-199.

[Cancer: three eras of personalized medicine]. / Cancer : les trois époques de la médecine personnalisée - Chroniques génomiques.

Since the completion of the first human DNA sequence, genomic approaches have penetrated into cancer research and therapy: first through expression profiling for diagnostic, prognostic and predictive purposes, then by sequencing of tumour DNA in order to define and apply targeted therapies. These overlapping changes occurred quite rapidly and are now overshadowed by immuno-oncology approaches that show much promise. There is however still much left to understand to make this more widely applicable, and the extreme cost of these therapies is a serious concern.

Molecular profiling of sarcomas: new vistas for precision medicine.

Sarcoma is a large and heterogeneous group of malignant mesenchymal neoplasms with significant histological overlap. Accurate diagnosis can be challenging yet important for selecting the appropriate treatment approach and prognosis. The currently torrid pace of new genomic discoveries aids our classification and diagnosis of sarcomas, understanding of pathogenesis, development of new medications, and identification of alterations that predict prognosis and response to therapy. Unfortunately, demonstrating effective targets for precision oncology has been elusive in most sarcoma types. The list of potential targets greatly outnumbers the list of available inhibitors at the present time. This review will discuss the role of molecular profiling in sarcomas in general with emphasis on selected entities with particular clinical relevance.

Managing the genomic revolution in cancer diagnostics.

Molecular tumor profiling is now a routine part of patient care, revealing targetable genomic alterations and molecularly distinct tumor subtypes with therapeutic and prognostic implications. The widespread adoption of next-generation sequencing technologies has greatly facilitated clinical implementation of genomic data and opened the door for high-throughput multigene-targeted sequencing. Herein, we discuss the variability of cancer genetic profiling currently offered by clinical laboratories, the challenges of applying rapidly evolving medical knowledge to individual patients, and the need for more standardized population-based molecular profiling.

Colorectal cancer molecular profiling: from IHC to NGS in search of optimal algorithm.

Advances in defining the mutational landscape of colorectal cancer (CRC) over the past decades have revolutionized the molecular understanding and clinical testing algorithms for this disease. Mutation testing is standard of care for the work-up of CRCs. This review focuses on the current indications and strategies for molecular testing in CRC and discusses the potential changes in CRC testing approach associated with the emerging clinical application of genomic-based technologies.

Circulating tumor DNA-From bench to bedside.

In the era of personalized medicine, tumor sampling is paramount to enable the assessment of actionable molecular aberrations to help rationalize and guide treatment decisions. Longitudinal tracking of such aberrations may also be helpful to detect emerging drug resistance and to allow for timely modifications to ongoing therapies to improve patient outcomes. Nevertheless, tumor tissue sampling involves an invasive procedure with potential risks to patients and involves logistical challenges. As such, other less invasive and safer methods such as blood sampling for molecular profiling has been gaining traction. In this article, we discuss the concept of circulating tumor DNA, the technology platforms available for its interrogation, and its current applications in the clinic. We also envision how circulating tumor DNA may be applied at multiple time points along a patient's cancer journey to guide diagnosis, prognostication, and therapeutic decisions.

Transcriptomics of human multipotent mesenchymal stromal cells: Retrospective analysis and future prospects.

The plastic-adherent, fibroblast-like, clonogenic cells found in the human body now defined as multipotent "Mesenchymal Stromal Cells" (MSCs) hold immense potential for cell-based therapies. Recently, research and basic knowledge of these cells has fast-tracked, both from fundamental and translational perspectives. There have been important discoveries with respect to the available variety of tissue sources, the development of protocols for their easy isolation and in vitro expansion and for directed differentiation into various cell types. In addition, there has been discovery of novel abilities such as immune-modulation and further development of the use of biomaterials to aid isolation, expansion and differentiation together with improved delivery to the selected optimal tissue site. However, the molecular fingerprint of MSCs in these contexts remains imprecise and inadequate. Consequently, without this crucial knowledge it is difficult to achieve progress to determine with precision their practical developmental potentials. Detailed investigations on the global gene expression, or transcriptome, of MSCs could offer essential clues in this regard. In this article, we address the challenges associated with MSC transcriptome studies, the paradoxes observed in published experimental results and the need for careful transcriptomic analysis. We describe the exemplary applications with various transcriptome platforms that are used to address the variation in biomarkers and the identification of differentiation processes. The evolution and the potentials for adapting next-generation sequencing (NGS) technology in transcriptome analysis are discussed. Lastly, based on review of the existing understanding and published studies, we propose how NGS may be applied to promote further understanding of the biology of MSCs and their use in allied fields such as regenerative medicine.

Next generation of network medicine: interdisciplinary signaling approaches.

In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.