Multi-omics Biomarker Research Articles

Abstract PO-008: Diagnostic accuracy of blood-based multi-omic biomarkers for pancreatic adenocarcinoma: A systematic review and meta-analysis

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is the most lethal form of pancreatic cancer, being responsible for ~90% of all pancreatic cancers and having a 5-year survival rate of ~8.5%. The current clinical gold-standard for diagnosis of PDAC is the blood-based biomarker CA19-9. However, many studies have highlighted the limitations of CA19-9, specifically its relatively low sensitivity and specificity, and its inaccuracy in patients with certain underlying conditions. As such, there is an unmet need for robust diagnostic biomarkers for PDAC. Here, the diagnostic accuracy of all blood-based biomarkers examined in PDAC, reporting specifically on CA19-9, multi-marker panels containing CA19-9, novel single markers, and novel multi-marker panels for the diagnosis of PDAC. Methods: A systematic review of blood-based biomarkers for the diagnosis of PDAC was conducted in accordance with PRISMA standards. Individual search strategies using medical subject headings (MeSH) and ‘text words’ were developed for three academic databases: Medline, EMBASE and Web of Science. The 5,885 studies identified were subjected to two rounds of screening by two independent reviewers, with 250 studies being included in the meta-analysis. Data were extracted and assessed for bias using the QUADAS-2 Risk of Bias tool. Data were separated into two subgroups: those including CA19-9, and those without CA19-9 (novel). Patient cohorts examined were classified as “PDAC vs healthy”, “PDAC vs benign” and “PDAC vs mixed”. A multivariate three-level meta-analysis with subgroup moderators was run in R (v1.3.959) on all CA19-9 containing biomarker studies and subsequently on all novel biomarker studies, using reported AUC values as effect size. Results: Based on the three-level meta-analytic model, the pooled AUC value for CA19-9 alone (AUC=0.8473, 95% CI: 0.82-0.87) was significantly lower compared to the multi-marker panels containing CA19-9 (AUC=0.91, 95% CI:0.90-0.93) (p<0.0001). The estimated between-study variance in the model was I2Level 3= 63.55%, and the within-study variance was I2Level 2=36.45%. For the novel markers, the pooled AUC for single markers (AUC=0.79, 95% CI:0.75-0.83) was also significantly lower compared to novel multi-marker panels (AUC=0.87, 95% CI:0.85-0.89) (p<0.0001). Marker robustness was also influenced by the patient cohort examined, with CA19-9 markers performing best in all cohorts compared to novel markers; PDAC vs healthy (AUC=0.91, 95% CI:0.88-0.94), PDAC vs benign (AUC=0.85, 95% CI:0.84-0.87), and PDAC vs mixed (AUC=0.87, 95% CI:0.82-0.91) (p<0.0001). Conclusion: Overall, multi-marker panels show higher pooled AUC values than single markers, for both CA19-9 and novel datasets. Multi-marker panels containing CA19-9 demonstrate the most promising pooled AUC value, with CA19-9 alone performing inferiorly to novel multi-marker panels. These results indicate that CA19-9 may be best used as an addition to a panel of markers rather than alone, and that multi-marker panels ultimately generate the most robust results in a diagnostic capacity. Citation Format: Laura E. Kane, Gregory S. Mellotte, Eimear Mylod, Rebecca O'Brien, Fiona O'Connell, Khanh Nguyen, Croí E. Buckley, Jennifer Arlow, David Mockler, Aidan D. Meade, Barbara M. Ryan, Stephen G. Maher. Diagnostic accuracy of blood-based multi-omic biomarkers for pancreatic adenocarcinoma: A systematic review and meta-analysis [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-008.

MoNET: an R package for multi-omic network analysis.

The increasing availability of multi-omic data has enabled the discovery of disease biomarkers in different scales. Understanding the functional interaction between multi-omic biomarkers is becoming increasingly important due to its great potential for providing insights of the underlying molecular mechanism. Leveraging multiple biological network databases, we integrated the relationship between single nucleotide polymorphisms (SNPs), genes/proteins and metabolites, and developed an R package Multi-omic Network Explorer Tool (MoNET) for multi-omic network analysis. This new tool enables users to not only track down the interaction of SNPs/genes with metabolome level, but also trace back for the potential risk variants/regulators given altered genes/metabolites. MoNET is expected to advance our understanding of the multi-omic findings by unveiling their transomic interactions and is likely to generate new hypotheses for further validation. The MoNET package is freely available on https://github.com/JW-Yan/MONET. Supplementary data are available at Bioinformatics online.

Nutritional intervention studies to influence the gut microbiome and improve Parkinson's disease

Non-invasive microbiome-derived multi-omic biomarkers for the early-stage detection and stratification of Parkinson's disease (PD) revealed a bacterial dysbiosis in early and prodromal PD with an increased abundance of e.g. Akkermansia spp., that increase the permeability of the gut by degrading the mucus barrier through regulation of tight junctions. The mucus erosion of the gut epithelial layer leads to increased gut permeability which favors inflammatory responses by pathogenic and/or toxin intrusions. Additionally microbiome-derived metabolites are involved in neuroinflammatory and/or neurotoxic processes in PD, that can foster the spreading of α-synuclein aggregates and neuronal damage. In multiple animal models, restructuring of the gut microbiome by dietary interventions (resistant starch or fasting regimens) restores the intestinal mucus barrier and prolongs survival. In human studies, high fiber diet/resistant starch impacts the human gut microbiome, metaproteome and metabolome, restores the mucus barrier and can thereby improve the glucose and lipid metabolism. In our PD cohort we have observed correlations of inflammation markers, i.e. IL-17A or CCL20 in plasma with operational taxonomic units. We also identified correlations when comparing the microbiome data to clinical parameters, such as UPDRS scores. Under high-fiber diet regimen we identified changes in microbiome within 24 h by increasing the Shannon diversity, short chain fatty acids and various metabolites. Increasing evidence in humans also suggest, that intermittent fasting regiments may directly influence the gut microbiota and its metabolic function and secondarily impact peripheral and central inflammation. We will present data of our short- and longterm dietary interventional trials in PD.

Antitumor immune effects of preoperative sitravatinib and nivolumab in oral cavity cancer: SNOW window-of-opportunity study

BackgroundSitravatinib, a tyrosine kinase inhibitor that targets TYRO3, AXL, MERTK and the VEGF receptor family, is predicted to increase the M1 to M2-polarized tumor-associated macrophages ratio in the tumor microenvironment...

Abstract 568: Nanonets for multiomics blood analysis and cancer biomarker discovery

Abstract Despite the tremendous potential of liquid biopsies to revolutionise cancer care, there has been limited success translating blood-circulating proteomic and genomic biomarkers into the clinic. This is fundamentally due to the extremely low concentration of tumour-derived biomolecules in blood circulation, particularly at an early disease stage, which makes the discovery phase of the biomarker pipeline extremely challenging. Nanotechnology offers a promising solution, with a nanoparticle-biomolecule enrichment tool recently developed to enrich low-abundant, low molecular weight proteins in the blood of ovarian cancer patients.[1] Proteomic analysis followed by immunoassay-based validation of selected proteins demonstrated the potential of the nanoparticle-platform proposed to discover novel biomarkers with greater specificity and sensitivity than the clinically used biomarkers. In addition, we recently confirmed the presence of cell-free DNA (cfDNA) captured onto the surface lipid nanoparticles incubated ex vivo with human plasma.[2] A significantly higher abundance of cfDNA was detected in the nanoparticle-enriched plasma samples of late-stage ovarian cancer patients compared to age-matched female controls. Proteomic analysis of the same samples also revealed tumour-specific elevations in histone proteins, which are commonly found in circulation complexed with cfDNA. These findings have highlighted the opportunity for the development of a nano-proteogenomics platform able to simultaneously purify both proteins and cell-free nucleic acids from human plasma, an important step in the discovery of novel multi-omic biomarker panels. Utilising the above patented nanotechnology, we have compared proteomic and genomic profiles derived from nanoparticle-biomolecule samples of cancer patients with age- and sex-matched controls to uncover new potential blood-based biomarkers in a proof-of-principle study. In brief, ex-vivo plasma samples were incubated with lipid-based nanoparticles and purified using a two-step size-based purification protocol. The purified samples were then analysed by label-free proteomics (LC-MS/MS) and next-generation sequencing to uncover both proteomic and genomic tumour-specific signatures, including differentially abundant proteins, genomic copy number alterations and tumour-specific mutations. This work highlights the potential of our nanotechnology-based enrichment platform to enhance the discovery of cancer-specific proteogenomic biomarker panels, a vital step in developing sensitive and specific liquid biopsies for the early detection of cancer.

Abstract 478: Extracellular vesicle based multi-omics prediction model for the early detection of pancreatic cancer

Abstract Introduction: Pancreas cancer (PC) is projected to become the third leading cause of cancer deaths in the US, however there is no screening test in routine clinical use. A minimally invasive test for early detection of PC is a critical unmet clinical need. Extracellular vesicles (EVs) shed into the circulation include a diverse array of surface and cargo biomolecules that can be leveraged as biomarkers for the early detection of PC. We hypothesize that systemic molecular characterization of plasma EVs, secreted by latent yet progressing precursor lesions (PL) of the pancreas, may lead to the development of high accuracy classification algorithms for stratification of patients for the early detection of PC. Herein, we developed a liquid biopsy-based PACE assay (Pancreatic Cancer Exosomics) comprised of a multi-omics (proteomics, metabolomics, and lipidomics) biomarker panel. Methods: To develop a functional EV-based multi-omics biomarker panel, we performed metabolomics, lipidomics and proteomics analysis of EVs from high quality pre-surgery, fasting plasma samples obtained from patients with early stage pancreatic cancer (IA, IB and IIA) (N= 60), normal control subjects without cancer (N = 50), patients diagnosed with pancreatitis (benign) (N= 39), and early stage colorectal cancer (N=30) as related disease cohorts. For selection of candidate biomarkers, we randomly divided 3/5th of the sample set as discovery and the remainder 2/5th as the test cohort. To consider the effect of potential correlation for predictors, we applied the elastic net penalty to generalized linear model (ELNET). A 100-fold cross-validation approach was used to determine tuning parameters and calibrate the prediction model in the discovery set. Results: These analyses allowed us to develop a 12 analyte multi-omics panel that could discriminate early stage PC from normal controls (NC) with 98.2% accuracy (Specificity (SP) = 85.2% and Sensitivity (SN) = 71.5%) and 95.4% accuracy (SP = 92.8% and SN = 59.3%) for discovery and validation sets, respectively. A unique aspect of the study design was the inclusion of control cohorts to eliminate non-specific markers. Furthermore, extensive clinical outcome data allowed us to develop multi-analyte predictors of overall survival and progression free survival with high accuracy. Validation of these findings using orthogonal methods is ongoing. Conclusions: In summary, these preliminary data provide strong support for the development of a liquid biopsy-based EV biomarker panel for the early detection of PC. We believe the positive predictive value (PPV) of this panel can be further improved in conjunction with serum CA 19-9 that is in routine clinical use. Citation Format: Yaoxiang Li, Keith Unger, Charles Hinzman, Meth Jayatilake, Anton Iliuk, Shivani Bansal, Michael Girgis, Partha Banerjee, John B. Tyburski, Todd Bauer, Amrita K. Cheema. Extracellular vesicle based multi-omics prediction model for the early detection of pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 478.

Biomarker Identification through Multiomics Data Analysis of Prostate Cancer Prognostication Using a Deep Learning Model and Similarity Network Fusion.

Simple SummaryAround 30% of men treated with adjuvant therapy experience recurrences of prostate cancer (PC). Current monitoring of the relapse of PC requires regular postoperative prostate-specific antigen (PSA) value follow-up. Our study aims to identify potential multiomics biomarkers using modern computational analytic methods, deep learning (DL), similarity network fusion (SNF), and the Cancer Genome Atlas (TCGA) prostate adenocarcinoma (PRAD) dataset. Six significantly intersected omics biomarkers from the two models, TELO2, ZMYND19, miR-143, miR-378a, cg00687383 (MED4), and cg02318866 (JMJD6; METTL23) were collected for multiomics panel construction. The difference between the Kaplan–Meier curves of high and low recurrence-risk groups generated from the multiomics panels and clinical information achieve p-value = 2.97 × 10−15 and C-index = 0.713, and the prediction performance of five-year recurrence achieves AUC = 0.789. The results show that the multiomics panel provided valuable biomarkers for the early detection of high-risk recurrent patients, and integrating multiomics data gave us the power to detect the complex mechanisms of cancer among the interactions of different genetic and epigenetic factors.This study is to identify potential multiomics biomarkers for the early detection of the prognostic recurrence of PC patients. A total of 494 prostate adenocarcinoma (PRAD) patients (60-recurrent included) from the Cancer Genome Atlas (TCGA) portal were analyzed using the autoencoder model and similarity network fusion. Then, multiomics panels were constructed according to the intersected omics biomarkers identified from the two models. Six intersected omics biomarkers, TELO2, ZMYND19, miR-143, miR-378a, cg00687383 (MED4), and cg02318866 (JMJD6; METTL23), were collected for multiomics panel construction. The difference between the Kaplan–Meier curves of high and low recurrence-risk groups generated from the multiomics panel achieved p-value = 5.33 × 10−9, which is better than the former study (p-value = 5 × 10−7). Additionally, when evaluating the selected multiomics biomarkers with clinical information (Gleason score, age, and cancer stage), a high-performance prediction model was generated with C-index = 0.713, p-value = 2.97 × 10−15, and AUC = 0.789. The risk score generated from the selected multiomics biomarkers worked as an effective indicator for the prediction of PRAD recurrence. This study helps us to understand the etiology and pathways of PRAD and further benefits both patients and physicians with potential prognostic biomarkers when making clinical decisions after surgical treatment.

An exploratory study of nivolumab (nivo) with or without ipilimumab (ipi) according to the percentage of tumoral CD8 cells in advanced metastatic cancer.

2573 Background: Immune checkpoint inhibitors (ICIs) have demonstrated durable clinical responses and improved survival in patients (pts) across numerous indications. Despite this progress, the benefit of ICIs is limited to a minority of overall metastatic cancer patients. There is a critical need for biomarkers agnostic of tumor type to inform which pts will benefit from nivo alone versus ipi/nivo combination treatment. Both pre-treatment tumoral CD8 + cells and recruitment of CD8+ T cells in response to ICIs are associated with improved clinical outcomes in patients treated with anti-PD-1 therapy. 1,2,3,4 Here we report the final results of a prospective clinical study in which pts with varying advanced solid tumors were assigned to nivo, with or without ipi, based on the percentage of tumoral CD8 cells at the time of treatment. Methods: We performed a prospective, non-randomized, open-label, multicenter study in which pts with tumoral CD8+ cells ≥ 15% (CD8+ high) received nivo 360mg IV Q3W, followed by nivo maintenance 480mg Q4W. Pts with tumoral CD8+ cells < 15% (CD8+ low) received nivo 360 mg IV Q3W, and ipi at 1 mg/kg IV Q3W for 2 doses and then Q6W for 2 doses, followed by nivo maintenance 480 mg IV Q4W until PD or intolerable toxicity. Primary endpoints were Disease Control Rate (DCR: CR, PR, or SD ≥ 6 months) and CD8 low to high conversion (< 15% to ≥ 15%). Baseline and on-treatment tumor, blood and stool samples were collected for multiomic biomarker analyses. This study was not powered for formal statistical analysis. Up to 200 pts could be enrolled to allow for adaptive exploration of response and CD8 changes. Results: N = 79 pts were enrolled:7 in CD8+ high arm (nivo) and 72 in CD8+ low arm (ipi/nivo). The study enrolled a wide variety of primary solid tumors; the most common were gynecological (n = 15), prostate (12), and head and neck (7). DCR was 14% (1/7; 95% CI 1 - 44) and 24% (17/72; 95% CI 15 - 34) in the CD8 high and CD8 low arms, respectively. Of 39 pts in CD8 low arm with an on-treatment biopsy, 14 (36%; 95% CI 22 - 51) had CD8 conversion; 7/14 pts (50%) who converted had DCR. Immune-related AEs (irAEs) were consistent with known safety profile of both drugs. Conclusions: Ipi/nivo demonstrated clinical responses and increased CD8% in a range of “cold” tumors with low tumoral CD8 cells. There may be an association between increasing CD8% and response. Baseline high CD8% alone does not appear to be sufficient as a pan-cancer predictive biomarker of response to nivo monotherapy. CD8 conversion, response, and irAEs associated with circulating and stool-based biomarkers are under evaluation as composite biomarkers may improve their predictive value. Clinical trial information: 03651271.

Urine Biomarkers for Prostate Cancer Diagnosis and Progression

Prostate cancer (PCa) can be highly heterogeneous and multifocal, and accurate assessment of the volume, grade, and stage of PCa in situ is not a simple task. Urine has been investigated as a source of PCa biomarkers for over 70 years, and there is now strong evidence that analysis of urine could provide more accurate diagnosis and a better risk stratification that could aid clinical decisions regarding disease surveillance and treatment. Urine diagnostics is a developing area, moving towards multiomic biomarker integration for improved diagnostic performance. Urine tests developed by strong collaborations between scientists and clinicians have the potential to provide targeted and meaningful data that can guide treatment and improve men’s lives.

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates

We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6–10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819–0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.

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.

Estimation of the effect of surrogate multi-omic biomarkers.

Multiple technologies which measure the same omics data set but are based on different aspects of the molecules exist. In practice, studies use different technologies and have therefore different biomarkers. An example is the glycan age index, which is constructed by three different ultra-performance liquid chromatography (UPLC) IgG glycans, and is a biomarker for biological age. A second technology is liquid chromatography- mass spectrometry (LCMS). To estimate the effect of a biomarker on an outcome variable, two issues need to be addressed. Firstly, a measurement error is needed to map one technology to the other one using a calibration study. Here, we consider two approaches, namely one based on the chemical properties of the two technologies and one based on the estimation of this relationship using O2PLS. Secondly, the use of an approximation of the biomarker in the main study needs to be taken into account by use of a regression calibration method. The performance of the two approaches is studied via simulations. The methods are used to estimate the relationship between glycan age and menopause. We have data from two cohorts, namely Korcula and Vis. In conclusion, (1) both measurement error models give similar results and suggest that there is an association between the glycan age index and the menopause status, (2) the chemical mapping approach outperforms O2PLS in the low measurement error variance, while on the larger measurement error variance, O2PLS works better, (3) statistical efficiency is lost due to increased noise level by adding irrelevant information.

Biomarkers associated with cheese quality uncovered by integrative multi-omic analysis

Cheese quality is determined by many dynamic complex interactions that occur between cheese microbiota, metabolites and milk substrates. Here we report that application of multi-omics and data integration (by Data Integration Analysis for Biomarker discovery using Latent components, DIABLO) is able to identify and rank biomarkers (taxa and metabolites) which could discriminate, at the molecular level, cheddar cheeses of different quality made by the same manufacturer and to the same specifications. Samples of high-quality mature cheddar had higher amounts of proline, histidine, isoleucine and aspartic acid but lower amounts of stearic acid and octadecanol relative to the lower-quality cheddar. Furthermore, Streptococcus species (presumably S. thermophilus) were present in higher abundance relative to Lactococcus lactis in the higher-quality cheddar. Integrative analysis also revealed significant relationships between these multi-omics biomarkers. Together these results highlight the potential for this approach for identifying biomarkers that could be used to discriminate cheeses of varying quality and as a complementary approach to the current manual sensory evaluation of cheese.

Virtual Reality-Based Treatment for Military Members and Veterans With Combat-Related Posttraumatic Stress Disorder: Protocol for a Multimodular Motion-Assisted Memory Desensitization and Reconsolidation Randomized Controlled Trial.

BackgroundMilitary members are at elevated risk of operational stress injuries, including posttraumatic stress disorder (PTSD) and moral injury. Although psychotherapy can reduce symptoms, some military members may experience treatment-resistant PTSD. Multimodular motion-assisted memory desensitization and reconsolidation (3MDR) has been introduced as a virtual reality (VR) intervention for military members with PTSD related to military service. The 3MDR intervention incorporates exposure therapy, psychotherapy, eye movement desensitization and reconsolidation, VR, supportive counselling, and treadmill walking.ObjectiveThe objective of this study is to investigate whether 3MDR reduces PTSD symptoms among military members with combat-related treatment-resistant PTSD (TR-PTSD); examine the technology acceptance and usability of the Computer Assisted Rehabilitation ENvironment (CAREN) and 3MDR interventions by Canadian Armed Forces service members (CAF-SMs), veterans, 3MDR clinicians, and operators; and evaluate the impact on clinicians and operators of delivering 3MDR.MethodsThis is a mixed-methods waitlist controlled crossover design randomized controlled trial. Participants include both CAF-SMs and veterans (N=40) aged 18-60 years with combat-related TR-PTSD (unsuccessful experience of at least 2 evidence-based trauma treatments). Participants will also include clinicians and operators (N=12) who have been trained in 3MDR and subsequently utilized this intervention with patients. CAF-SMs and veterans will receive 6 weekly 90-minute 3MDR sessions. Quantitative and qualitative data will be collected at baseline and at 1, 3, and 6 months postintervention. Quantitative data collection will include multiomic biomarkers (ie, blood and salivary proteomic and genomic profiles of neuroendocrine, immune-inflammatory mediators, and microRNA), eye tracking, electroencephalography, and physiological data. Data from outcome measures will capture self-reported symptoms of PTSD, moral injury, resilience, and technology acceptance and usability. Qualitative data will be collected from audiovisual recordings of 3MDR sessions and semistructured interviews. Data analysis will include univariate and multivariate approaches, and thematic analysis of treatment sessions and interviews. Machine learning analysis will be included to develop models for the prediction of diagnosis, symptom severity, and treatment outcomes.ResultsThis study commenced in April 2019 and is planned to conclude in April 2021. Study results will guide the further evolution and utilization of 3MDR for military members with TR-PTSD and will have utility in treating other trauma-affected populations.ConclusionsThe goal of this study is to utilize qualitative and quantitative primary and secondary outcomes to provide evidence for the effectiveness and feasibility of 3MDR for treating CAF-SMs and veterans with combat-related TR-PTSD. The results will inform a full-scale clinical trial and stimulate development and adaptation of the protocol to mobile VR apps in supervised clinical settings. This study will add to knowledge of the clinical effectiveness of 3MDR, and provide the first comprehensive analysis of biomarkers, technology acceptance and usability, moral injury, resilience, and the experience of clinicians and operators delivering 3MDR.Trial RegistrationISRCTN Registry 11264368; http://www.isrctn.com/ISRCTN11264368.International Registered Report Identifier (IRRID)DERR1-10.2196/20620

Identification of functionally connected multi-omic biomarkers for Alzheimer's disease using modularity-constrained Lasso.

Large-scale genome wide association studies (GWASs) have led to discovery of many genetic risk factors in Alzheimer’s disease (AD), such as APOE, TOMM40 and CLU. Despite the significant progress, it remains a major challenge to functionally validate these genetic findings and translate them into targetable mechanisms. Integration of multiple types of molecular data is increasingly used to address this problem. In this paper, we proposed a modularity-constrained Lasso model to jointly analyze the genotype, gene expression and protein expression data for discovery of functionally connected multi-omic biomarkers in AD. With a prior network capturing the functional relationship between SNPs, genes and proteins, the newly introduced penalty term maximizes the global modularity of the subnetwork involving selected markers and encourages the selection of multi-omic markers with dense functional connectivity, instead of individual markers. We applied this new model to the real data collected in the ROS/MAP cohort where the cognitive performance was used as disease quantitative trait. A functionally connected subnetwork involving 276 multi-omic biomarkers, including SNPs, genes and proteins, were identified to bear predictive power. Within this subnetwork, multiple trans-omic paths from SNPs to genes and then proteins were observed. This suggests that cognitive performance deterioration in AD patients can be potentially a result of genetic variations due to their cascade effect on the downstream transcriptome and proteome level.

Noninvasive Diagnosis of NAFLD and NASH.

The aim of this review is to outline emerging biomarkers that can serve as early diagnostic tools to identify patients with nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) and, among them, the subgroup of best candidates for clinical trials on emerging compounds. Regarding possible predictors of NAFLD, a number of studies evaluated a combination of serum biomarkers either available in routine practice (or investigational) or proprietary and expensive. So far, magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF) appears to be the most accurate for fatty liver diagnosis. In clinical practice, the main question is how to diagnose NASH early. There are new promising biomarkers that can help in diagnosing early stages of NASH, yet they include variables not routinely tested. In the setting of NASH, most studies confirm that, in spite of several well-known limitations, transient elastography or point shear wave elastography can help in enriching the pool of patients that should be screened for investigational treatments. Newer multiomics biomarkers including those focusing on microbiota can be useful but require methods to be standardized and implemented. To date, one biomarker alone is not able to non- or minimally invasively identify patients with NASH and mild to moderate fibrosis.

Multi-omics Data Integration for Identifying Osteoporosis Biomarkers and Their Biological Interaction and Causal Mechanisms.

SummaryOsteoporosis is characterized by low bone mineral density (BMD). The advancement of high-throughput technologies and integrative approaches provided an opportunity for deciphering the mechanisms underlying osteoporosis. Here, we generated genomic, transcriptomic, methylomic, and metabolomic datasets from 119 subjects with high (n = 61) and low (n = 58) BMDs. By adopting sparse multiple discriminative canonical correlation analysis, we identified an optimal multi-omics biomarker panel with 74 differentially expressed genes (DEGs), 75 differentially methylated CpG sites (DMCs), and 23 differential metabolic products (DMPs). By linking genetic data, we identified 199 targeted BMD-associated expression/methylation/metabolite quantitative trait loci (eQTLs/meQTLs/metaQTLs). The reconstructed networks/pathways showed extensive biomarker interactions, and a substantial proportion of these biomarkers were enriched in RANK/RANKL, MAPK/TGF-β, and WNT/β-catenin pathways and G-protein-coupled receptor, GTP-binding/GTPase, telomere/mitochondrial activities that are essential for bone metabolism. Five biomarkers (FADS2, ADRA2A, FMN1, RABL2A, SPRY1) revealed causal effects on BMD variation. Our study provided an innovative framework and insights into the pathogenesis of osteoporosis.

Circulating tumor DNA as an early cancer detection tool.

Circulating tumor DNA holds substantial promise as an early detection biomarker, particularly for cancers that do not have currently accepted screening methodologies, such as ovarian, pancreatic, and gastric cancers. Many features intrinsic to ctDNA analysis may be leveraged to enhance its use as an early cancer detection biomarker: including ctDNA fragment lengths, DNA copy number variations, and associated patient phenotypic information. Furthermore, ctDNA testing may be synergistically used with other multi-omic biomarkers to enhance early detection. For instance, assays may incorporate early detection proteins (i.e., CA-125), epigenetic markers, circulating tumor RNA, nucleosomes, exosomes, and associated immune markers. Many companies are currently competing to develop a marketable early cancer detection test that leverages ctDNA. Although some hurdles (like early stage disease assay accuracy, high implementation costs, confounding from clonal hematopoiesis, and lack of clinical utility studies) need to be addressed before integration into healthcare, ctDNA assays hold substantial potential as an early cancer screening test.

In Silico Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis.

Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling. Background: Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tumor development and phenotype. Methods: We describe a computational approach that correlates phenotype from magnetic resonance imaging (MRI) of breast cancer (BC) lesions with microRNAs (miRNAs), mRNAs, and regulatory networks, developing a radiomiRNomic map. We validated our approach to the relationships between MRI and miRNA expression data derived from BC patients. We obtained 16 radiomic features quantifying the tumor phenotype. We integrated the features with miRNAs regulating a network of pathways specific for a distinct BC subtype. Results: We found six miRNAs correlated with imaging features in Luminal A (miR-1537, -205, -335, -337, -452, and -99a), seven miRNAs (miR-142, -155, -190, -190b, -1910, -3617, and -429) in HER2+, and two miRNAs (miR-135b and -365-2) in Basal subtype. We demonstrate that the combination of correlated miRNAs and imaging features have better classification power of Luminal A versus the different BC subtypes than using miRNAs or imaging alone. Conclusion: Our computational approach could be used to identify new radiomiRNomic profiles of multi-omics biomarkers for BC differential diagnosis and prognosis.