Enabling people with dementia to ‘live well’ is a policy and research priority in many countries. However, instruments for measuring outcomes of psychosocial interventions designed to promote well-being in dementia are often derived from a symptom-focused, loss/deficit approach, or from broad quality of life concepts. A pan-European dementia working group called for research on the development of an alternative asset/strengths-based conceptual framework of well-being in dementia. This paper takes forward this recommendation by developing such a framework and using this to map relevant self-report outcome measures.Three scoping reviews of published studies were conducted iteratively. First, we examined the literature on lived experiences of well-being and quality of life in people with dementia and then the wider dementia literature for application of well-being constructs. The synthesised findings generated conceptual domains of well-being in people with dementia. Corresponding self-report instruments used in dementia research were scoped, categorised within the conceptual framework and their potential value in measuring outcomes for people with dementia was examined.Six conceptual domains for the measurement of well-being and 35 self-report instruments that have been used with people with dementia were identified. Six instruments were developed specifically for people with dementia, five were derived from the gerontological literature and 24 from the well-being literature. Fifteen instruments and one sub-scale have been examined for psychometric properties amongst people with dementia. To date, 20 have been used as outcome measures, with seven measuring change over time. A number of identified instruments utilise traditional retrospective Likert-scaling response formats, limiting their potential for use with some groups of people with dementia.An assets/strengths-based framework is presented, outlining structural domains for selecting self-report measures of well-being in people with dementia. It provides a foundation for enhancing research into processes and outcomes of psychosocial interventions, including instrument development, more precise matching of intervention aims with outcome measurement, and newer technology-based ‘in-the-moment’ measurement. Internationally, supporting people to live well with dementia has become the focus of varied public health and research initiatives. Living well with a long-term health condition such as dementia implies the ongoing presence of well-being within a supportive social environment, in spite of health-related adversity. However, psychosocial intervention research in dementia has typically focused on the measurement of cognitive function and/or symptom-reduction (e.g. depression/neuropsychiatric symptoms), even in studies that have also incorporated measures of quality of life (QoL) / health-related quality of life (HRQoL).Whilst many studies demonstrate that people with dementia can give reliable accounts of their life using existing dementia-specific QoL/HRQoL self-report instruments , 2, suchMoreover, the constructs of QoL and well-being have often been used inter-changeably in dementia research, raising concern about the construct validity of QoL scales . For exaAs such, an important challenge lies in how best to conceptualise well-being and its associated measurement in people with dementia, beyond existing approaches based on QoL. Developing clear conceptual frameworks would in turn underpin valid measurement of well-being in dementia . The appThe application of asset-based perspectives in dementia care is relatively new. A recent trial aimed to improve everyday function through assisting people with dementia to achieve personally meaningful and relevant goals. Significant gains on the primary outcome measure   were notThe aim of the present study was to extend this work, using scoping reviews to develop an asset/strengths-based conceptual framework for the measurement of well-being in people with dementia, and to use this to map the full range of currently available corresponding self-report instruments that have been used with this population. Our approach is theory-based and empirically-informed, drawing on the successful/positive aging  and posiWhat key conceptual domains of well-being can be derived from existing literature involving people with dementia?How have positive psychology and successful / positive aging gerontological concepts been applied to understanding experiences of well-being in people with dementia?What corresponding self-report instruments have been used with people with dementia in published research?The specific questions underpinning this review were:We combined principles for scoping reviews , 28 withOverall Inclusion/exclusion criteria: these were based on previous expert consensus work involving several face-to face meetings with one person with dementia, two carers as well as representation from Alzheimer’s Europe (DG & AD) [DG & AD) . These cDG & AD) , inclusiDG & AD)  .Study procedures: study selection involved identifying eligible studies (CC), reviewing their titles/abstracts (CC and EM-C), scrutinising relevant full texts against inclusion criteria, removing duplicates (CC & EM-C), collating and tabulating records (CC) and obtaining independent advice from two authors (BW & GM) in order to resolve ambiguity or uncertainty with regard to study selection and data extraction. Such discussions ensured reliability and occurred at selection and synthesis stages. Independent advice from co-authors (BW & GM) occurred on five occasions; one related to study selection, three in relation to concept synthesis and one regarding instrument mapping.Three reviews were conducted using the following four steps:To obtain an overview of how people with dementia experience aspects of well-being, existing reviews, rather than primary studies, were sought. Web of Science and PsycINFO were searched using the following terms: ‘lived experience’, ‘well-being’, ‘positive experiences’, ‘quality of life’ and ‘dement*’. To be included, reviews had to focus explicitly on lived experiences of well-being and quality of life so that we would be able to develop conceptual domains rooted in the day-to-day experiences of people with dementia. Eligible reviews were therefore scrutinised to exclude associations with the loss/deficit paradigm (e.g. experiences of ‘suffering’ with dementia). Independent arbitration between reviewers was not required at this step. Using thematic synthesis , tabulatdement* combined with acceptance, autonomy, purpose, self-determination, positive affect, positive emotion, hope, optimism, humor(ur), spirituality, meaning, self-efficacy, self-esteem, self-identity, resilience, belonging, intimacy and social participation  but no other additional sources were used. Studies using a single-item question to measure well-being, rather than a formal instrument, as well as those unavailable in the English Language, , 38 wereInstruments were categorised according to conceptual domains of well-being in dementia and scrutinised for whether they were developed specifically for people with dementia, and designed as an outcome measure. Studies using each instrument were examined to ascertain if psychometric properties and sensitivity to change had been reported with a dementia sample and whether the instrument had been used in longitudinal or intervention studies. Acceptability and ease of use for people with dementia was also examined, covering aspects such as number of items, response formats, scaling and retrospective judgements.After excluding duplicates and applying limits, searches yielded 200 potentially relevant review articles. Most were excluded because they focused on caregivers or did not review well-being. Of the remaining full text articles, only four systematic reviews directly related to the lived experience of well-being or quality of life in people with dementia. These covered 86 separate studies, totalling 2545 people with dementia  across community and residential settings, from both developed and developing nations  and self-efficacy.Keeping Going and Being Active: sense of agency – adaptation/resilience; purpose/ autonomy. Demonstrated by active choices to function ‘normally’ and engage purposefully in meaningful activities which enhance positive emotions.Good Relationships: positive aspects of interpersonal and social relationships. Includes attachment and connection (a sense of belonging and safety) as well as feeling valued, loved and accepted by others.Feeling Well: a cross-cutting theme of feeling contented and satisfied with life ‘as it is’.Descriptive themes in lived experiences of well-being in dementia were synthesised into six preliminary themes:n = 9); community / out-patients/ memory clinics (n = 37) and mixed community / residential care (n = 2)  varied in methods and scope, mostly exploring specific constructs, including resilience [n = 4) studies covered topics ranging from goal-setting [Of the 20 quantitative studies, six involved the evaluation of psychosocial interventions, such as a pilot trial of self-management for people with dementia reporting positive impact on self-reported self-efficacy . Seven wsilience . Four stsilience  or specisilience . Mixed-m-setting  to exper-setting .Five constructs set within the overarching themes of emotional, psychological and social well-being emerged. Examination of studies of hope in people with dementia differentiated hope as a feeling (i.e. positive affect) from the notion of ‘going beyond’ personal goals to find broader meanings and connections, in spite of uncertainty. The sixth broader construct of ‘Valuing Life’ aligned to the construct of life satisfaction  and its Thirty-five self-report instruments  or intervention (n = 16) dementia research, but only three of these have been validated with people with dementia: DMPT [per se. These are: PANAS [Nineteen instruments were explicitly developed for outcome measurement. Twenty have been used to measure outcomes in longitudinal  oria: DMPT ; HHI [62ia: DMPT . Five hae: PANAS ; RSES-10e: PANAS ; SES-23 e: PANAS ; BSCS-8 e: PANAS  and the e: PANAS . The fulSeventeen instruments have more than ten items  and eudaimonic  well-beiOf the 16 measures that have been investigated for their psychometric properties with people with dementia, 11 fitted into the overarching domain of psychological well-being, involving agency and associated meaningful engagement. Positive relationships and personal growth across the life span  also emeSocial well-being aligned with the social health-dementia paradigm , 124, reEmotional well-being in dementia has not been well studied, possibly due to biases toward the traditional loss/deficit dominant paradigm of mood disorders. Within this conceptual framework the notion of ‘affect balance’ (i.e. the ratio of positive to negative affect over a specified time period) holds potential for the measurement of emotional well-being in dementia. Kolanowski et al.  found thThis fourth overarching conceptual domain of life satisfaction  emergedResilience was embedded across different well-being domains. The construct is represented as agency , social None of the instruments developed for people with dementia have yet demonstrated sensitivity to change and none have been formally evaluated for their responsiveness and interpretability i.e. the ability to detect clinically and personally meaningful change in aspects of well-being over time .Self-report outcome measures of well-being should be acceptable and easy to complete for the majority of people with dementia. The measures we identified vary in length (number of items) and complexity of Likert scaling, with some requiring retrospective judgements. This may be problematic for people with difficulties in episodic memory as well as lacking in face validity as people with dementia appear to prefer to report ‘in the moment’ benefits of interventions as they are experienced . This isper se; for example the General Self-Efficacy Scale may be seen as measuring a ‘trait-like’ variable. Examining instrument items is an important consideration when selecting outcome measures intended to be responsive to meaningful changes over time. Fourth, this review only included articles published in English. Fifth, our scope was to chart self-report instruments, so important proxy and observer-rated measures, e.g. MiDAS, [Our review has some limitations. First, despite making every attempt to include the relevant literature, some studies and instruments may not have been identified. This is an inherent problem with iterative scoping review methodology. Second, whilst key information was synthesised and reported in summary tables, statistical techniques were not used to assess methodological quality and we did not formally evaluate the quality of included instruments or examine their psychometric properties. This was in keeping with scoping review methods and the heterogeneity of included studies but also highlights the potential for a future systematic review to address these issues. Third, for some instruments used in dementia studies, it was not possible to categorically define if they had originally been developed as outcome measures . MiDAS,  were exc. MiDAS, , have goThe conceptual framework we present outlines structural domains for selecting self-report measures of well-being in dementia, going beyond QoL/HRQoL instruments used in research to date. It provides a foundation for further research into the process and outcomes of creative well-being enhancing interventions as well as supporting instrument development and the more precise matching of intervention aims with outcome measures. Thirty-five instruments used to date in dementia studies provide a bank of asset-based self-report instruments for future research on well-being in people with dementia. Valid asset/strength-based approaches to the measurement of psychosocial interventions in dementia require good underlying knowledge-frameworks of well-being, which should resonate with the lived experiences of people with dementia.The next steps in this research agenda include: co-producing  new and/Additional file 1. Summary Example of Search Strategy . Data: Summarised search results.Additional file 2. Review of Reviews - Lived Experiences of Well-Being and Quality of Life in Dementia: Key Findings and Themes. Data: Tabulated and synthesised findings.Additional file 3 Domains of well-being in dementia - conceptual synthesis. Data: Tabulated and synthesised findings.Additional file 4 . Domain-Based Measures of Wellbeing and Their Use in Dementia (n = 35). Data: Tabulated and synthesised findings.


Introduction: The optimal treatment for small, asymptomatic, nonfunctioning pancreatic neuroendocrine neoplasms (NF-PanNEN) is still controversial. European Neuroendocrine Tumor Society (ENETS) guidelines recommend a watchful strategy for asymptomatic NF-PanNEN <2 cm of diameter. Several retrospective series demonstrated that a non-operative management is safe and feasible, but no prospective studies are available. Aim of the ASPEN study is to evaluate the optimal management of asymptomatic NF-PanNEN ≤2 cm comparing active surveillance and surgery.Methods: ASPEN is a prospective international observational multicentric cohort study supported by ENETS. The study is registered in ClinicalTrials.gov with the identification code NCT03084770. Based on the incidence of NF-PanNEN the number of expected patients to be enrolled in the ASPEN study is 1,000 during the study period (2017–2022). Primary endpoint is disease/progression-free survival, defined as the time from study enrolment to the first evidence of progression (active surveillance group) or recurrence of disease (surgery group) or death from disease. Inclusion criteria are: age >18 years, the presence of asymptomatic sporadic NF-PanNEN ≤2 cm proven by a positive fine-needle aspiration (FNA) or by the presence of a measurable nodule on high-quality imaging techniques that is positive at 68Gallium DOTATOC-PET scan.Conclusion: The ASPEN study is designed to investigate if an active surveillance of asymptomatic NF-PanNEN ≤2 cm is safe as compared to surgical approach. The current World Health Organization (WHO) classification identified three categories of NF-PanNEN  based on Ki-67 value . In the dvocated  The safedvocated –21. All dvocated . Neverthut-off s –19. Moreut-off s . Aim of The ASPEN study aims to determine the best management for small, nonfunctioning, asymptomatic NF-PanNEN ≤2 cm comparing active surveillance (AS) and surgical resection (SR). The hypothesis is that AS is a safe approach that prevents unnecessary surgery in a considerable number of cases thus avoiding surgical-related morbidity and mortality.ClinicalTrials.gov with the identification code: NCT03084770. Participating study centers identify, recruit patients and send pseudonymized data to the lead center, which is responsible for statistical analysis, storing and controlling data. The research database will be managed and analyzed by the Lead Study center research team.The study is designed as a prospective international observational multicentric cohort study, coordinated by the Pancreatic Surgery Unit and Pancreas Translational & Clinical Research Center at San Raffaele Scientific Institute, Milan, Italy (Lead Study Centre) under the auspices of the European Neuroendocrine Tumor Society (ENETS). In total, 41 centers from 16 countries  are actively participating in the trial. The study duration is 6 years, ethical committee of the Lead Study Center approved the study in June 2017 and patients are being recruited for 5 years from August 2017 to August 2022, with a follow-up of 1 year at least (end of the study: July 2023). The ASPEN study is registered in The primary endpoint is disease/progression-free survival, defined as the time from study enrolment to the first evidence of progression (AS group) or recurrence of disease (SR group) or death from disease.Secondary endpoints are: (i) to evaluate the frequency of asymptomatic sporadic NF-PanNEN ≤2 cm among overall sporadic NF-PanNEN. For this purpose, participating centers are required to give yearly the number of patients with NF-PanNEN referred to their institution, (ii) to analyze the outcome of patients with an indication for surgical resection, in terms of number of operated patients, surgical procedures, morbidity, mortality, and NF-PanNEN recurrence after surgery, (iii) to evaluate NF-PanNEN evolution, in terms of development of symptoms, tumor growth, development of distant metastases and secondary pancreatic duct dilatation, (iv) to measure the perceived burden of surveillance or follow-up after surgery for participants, as assessed by questionnaires regarding attitude toward surveillance and general anxiety and depression [Hospital Anxiety and Depression scale, HADS , EORTC QThe reported incidence rate of PanNEN is 0.4/100.000 inhabitants , 7 consiInclusion criteria include:- Age > 18 years- Individuals with asymptomatic sporadic NF-PanNEN ≤2 cm68Gallium DOTATOC-PET- Diagnosis has to be proven by a positive fine-needle aspiration (FNA) or by the presence of a measurable nodule on high-quality imaging techniques that is positive at - Patients who undergo surgery for NF-PanNEN ≤2 cm within 12 months. In these cases, diagnosis has to be proven by histological confirmation of NF-PanNEN- Informed consent.Exclusion Criteria include:- NF-PanNEN > 2 cm of diameter- Presence of genetic syndrome - Specific symptoms suspicious of a clinical syndrome related to hypersecretion of bioactive compounds or unspecific symptoms (functioning PanNEN).68Gallium DOTATOC-PET scan. Patients who undergo surgery for NF-PanNEN ≤2 cm within 12 months can also be enrolled, in these cases, diagnosis has to be proven by histological confirmation of NF-PanNEN.Diagnostic work-up chart is provided in The treatment will be decided at the hospital where patients are enrolled and all therapeutics decision will be decided and coordinated by the treating physicians. Recommended surveillance strategy consists of imaging studies (CT or MR), every 6 months for the first 2 years and yearly thereafter for 2 years in the absence of significant changes on imaging or symptoms appearance. During surveillance, a high-quality imaging technique (CT or MRI) is mandatory at least every 12 months or every 6 months if Ki67 is > 2%. Determination of Chromogranin A (CgA) during follow-up is at physician's discretion. During active surveillance, the treating physicians are responsible for patient management and decision-making. If follow-up parameters change during observation, the decision for further investigations, surgery, or an intensified follow-up schedule is at the discretion of the treating physicians . If surgt-test for normally distributed continuous data; either the chi-square test for categorical data (with Yates' correction when appropriate) or Fisher exact test for categorical data; and the median test for non-normally distributed continuous data. All reported p-values will be two-sided and a value < 0.05 will be considered significant. For the primary endpoints, univariate comparisons will be conducted, to identify individual patient and NF-PanNEN risk factors for progression/recurrence. Outcomes will be evaluated in the intention-to-treat population based on treating physician-assessed tumor progression/recurrence. Survival analysis techniques and Cox regression with time-dependent recurrent covariates measures will be applied. Progression/recurrence is defined according to Response Evaluation Criteria in Solid Tumors (RECIST) version 1.0 criteria , or medians with interquartile ranges (IQR) will be reported. Statistical significance will be assessed with use of the Student's criteria . In the wait and see” approach can be considered for asymptomatic PanNEN with a diameter of 2 cm or smaller. Similarly, recent recommendations by the North America Neuroendocrine Tumor Society (NANETS) support initial observation for asymptomatic NF-PanNEN smaller than 1 cm  of asymptomatic NF-PanNEN ≤2 cm. This study aims to provide evidence on the safety of an observational management of these tumors evaluating also the impact on patients' anxiety and quality of life. If this hypothesis is confirmed, a watchful attitude toward these small lesions will be more accepted worldwide reducing the surgery-related risks and improving patients' outcomes.The first patient was enrolled on 31th August 2017. At the time of protocol submission (August 2019), 41 centers were actively recruiting patients for the study and 480 out of 1,000 patients (48%) had been enrolled. Inclusion is according to schedule.The studies involving human participants were reviewed and approved by IRRCS San Raffale Scientific Institute Ethics Committee. The patients/participants provided their written informed consent to participate in this study.All the authors contributed to the conception and design of the study. Analysis of the literature and drafting of the manuscript was performed by SP, FM, and MF.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer GL declared a shared affiliation, though no other collaboration, with one of the authors MC to the handling Editor.


Immunotherapies have changed the paradigm of oncologic treatment for several cancers where the field had fallen short in providing competent therapies. Despite the improvement, broadly acting and highly effective therapies capable of eliminating or preventing human cancers with insufficient mutated antigens are still missing. Adenoviral vector-based vaccines are a successful tool in the treatment of various diseases including cancer; however, their success has been limited. In this review we discuss the potential of adenovirus as therapeutic tools and the current developments to use them against cancer. More specifically, we examine how to use them to target endogenous retroviruses (ERVs). ERVs, comprising 8% of the human genome, have been detected in several cancers, while they remain silent in healthy tissues. Their low immunogenicity together with their immunosuppressive capacity aid cancer to escape immunosurveillance. In that regard, virus-like-vaccine (VLV) technology, combining adenoviral vectors and virus-like-particles (VLPs), can be ideal to target ERVs and elicit B-cell responses, as well as CD8 ENV gene encodes sufficient information in the cytoplasmic tail for appropriate antigen display. The result is that adenovirus transduced cells become in situ producers of VLPs. Immunologically this is a perfect scenario: direct transduction provided by adenoviral vectors yields optimal CD8+ T-cell (cytotoxic T-cells) responses, and secreted VLPs present structurally accurate antigen providing optimal CD4+ T-cell (helper T-cells) and antibody producing B-cell responses) [Adenoviral vectors have been used for decades in experimental vaccines against retroviruses, such as the human immunodeficiency virus (HIV), and the most powerful immunisation schedules tried in humans are adenovirus based prime-boost regimens . The extsponses) .+ and CD8+ T-cells in the tumour microenvironment with effective tumour rejection [While cancer remains a major challenging disease in our societies , it has ejection . It has ejection . Hence, Stimulating the right immunity, however, only matters if the relevant antigens are expressed by the cancer cells and are accessible to the immune system. In that regard, using endogenous retroviruses (ERVs) as targets seems to be the immediate solution but, accordingly, it requires that immunogenic forms of retroviruses are also expressed in cancers. Fortunately, finding retroviruses in cancers is not problematic as ERVs constitute 8% of the human genome . NeverthHere, we review the evidence of ERVs as ideal targets for adenovirus-based immunotherapy starting with an overview of the mouse models, where a definitive ERV tumour-promoting role is strongly suggested. This is followed by the human data where, likewise, a strong ERV involvement can be found in cancer, at least in cell-based assays. Additionally, we describe some genetic studies indicating ERV families as targets that could be used for therapies against specific cancer groups. Succeeding the discussion on the ERV involvement in cancer, we discuss the previous attempts at targeting ERVs in cancer. Lastly, we highlight the experiences derived from using adenovirus vectors in anti-HIV vaccines and in immunotherapeutic studies to conclude with the emerging experience of using them to target ERVs—a novel strategy in cancer immunotherapy.Env gene, as its overexpression was found to allow engraftment of cells normally incapable of growth in immunocompetent mice. Moreover, part of the effect was shown to be local in the growing tumours [Env gene was later targeted by RNA interference in Mangeney et al., where the authors demonstrated the importance of Env since knocking down the murine ERV led to tumour rejection in wild type (WT) mice. The knock down allowed murine melanoma tumour cells to be recognised by the immune system and tumour growth to be arrested, which led to an increase in survival. These beneficial effects depended on a functional immune system, suggesting that the mouse ERV acted as an immune modulator. To identify the molecular target, the expression of ENV protein was restored by overexpression, leading to reversion of tumour rejection. Interestingly, and highlighting an immune mediated effect, injecting T regulatory (Treg) cells from a control melanoma-engrafted mouse allowed tumour cells to grow, despite the absence of murine ERV gene expression [Unlike humans, rodents have ERVs of which infectious counterparts still exist. Therefore, the necessary reagents for studying rodent ERVs and detecting an association with cancer became available rather early, suggesting a near uniform presence of C-type retroviruses in carcinogenesis . An earl tumours . The Envpression .Env overexpression and inhibition clearly pointed to a causal involvement in at least some murine cancers. Quite staggeringly, the cancer types that had trouble developing when ERVs were targeted include carcinogen-induced cancers, which suggests a rather broad involvement. Indeed, Scrimieri et al. demonstrated a broad association of high levels of retroviral ENV expression with a panel of different tumour cell lines, and absence of ENV expression in normal tissue. Interestingly, a few tumour cell lines only express low levels of endogenous retrovirus and a closer look at these types revealed that most of them express human papilloma virus (HPV) oncogenes [The molecular studies with ncogenes .The search for human ERVs (HERVs) started as genetic tools became available. Reports in the last few decades suggest a wide association of HERVs with many cancer types and few of these viruses are broadly or consistently associated with specific cancer types . UndoubtAmong the different HERV-K subfamilies, the most studied is called HML-2 . This seENV transcripts are present in most breast cancer tissues and that the expression level is negatively associated with survival [ENV SU RNA to successfully detect HERV-K specific T-cell responses in the patients’ PBMCs and tumour infiltrating lymphocytes. Lastly, the team was able to distinguish basal carcinoma as a subgroup of cancers with the most pronounced increased expression of HERV-K ENV from a cohort of 512 breast cancer patients with invasive ductal carcinoma [Wang-Johanning et al. have been able to show in several publications that both full length proviral and spliced survival ,16. Her arcinoma ,18,19,20ENV expression by shRNA reduced proliferation, migration and invasion, resulting in smaller tumours when they were engrafted in immunodeficient nude mice. Importantly, the shRNA targeting could be seen to reduce expression of Ras, p-RSK and p-ERK by single cell RNA seq, which are all part of oncogenic pathways [ENV resulted in activation of the ERK pathway and epithelial to mesenchymal transition (EMT), leading to an invasive and migration profile. At the molecular level, the signalling events were linked to the cytoplasmic domain of ENV [In addition to studies associating the presence of HERV-K and cancer prognosis in patient samples, in vitro and in vivo studies have backed up this correlation. In breast cancer cells, interference of pathways . Conversn of ENV .In addition to breast cancer, pancreatic, ovarian and prostate cancers have also demonstrated a functional role of HERV-K. Feng Wang-Johanning’s team has used shRNA interference to study pancreatic cancer progression achieving similar and significant results to what was observed in breast cancers. This includes diminished cell growth and impairment of RAS, p-ERK, p-RSK and p-AKT expression following HERV-K knock-down . In addiWe can find the same pattern in ovarian cancers. As mentioned for breast cancer, pulsing DCs with HERV-K ENV antigens led to T-cell responses (measured by T-cell proliferation and INFγ production) much stronger in ovarian tumour patients than in normal or benign patient samples. In a cohort of 89 patients with both ovarian cancer and benign ovarian diseases, cancer patients showed increased mRNA levels of HERV-K, HERV-R and HERV-E when compared to healthy controls. Additionally, HERV-K ENV protein expression on the surface of the patients’ tumours was also significantly higher than that in immortalised ovarian non-tumorigenic cells when assessed with both flow cytometry and immunohistochemistry. Furthermore, the HERV-K ENV protein and the reverse transcriptase were also detected in the plasma of the patients by Western blot ,24,25. ARegarding prostate cancer, both full-length and spliced forms of HERV-K were detected in the prostate cancer cell lines LNCaP, DU145, PC3 and VCaP . SeveralOther cancers have also been related to HERV-K, like renal carcinoma ,33, lympAs mentioned above, several ERVs have been reported to be expressed in different tumours, and even if HERV-K is the most extensively researched, other ERVs may also have a role in oncogenesis, especially HERV-H and HERV-W.In the case of HERV-H, it has been shown to be expressed in colon cancer. Even if there are several different loci of HERV-H, all of them are generally more actively transcribed in cancer tissues than in the adjacent normal tissue . The mecApart from colon cancer, HERV-H has been associated with a potential role in head and neck squamous cell carcinoma (HNSCC) development . HNSCCs ENV mRNA levels and protein expression in UCC patient tissue was higher (75.6%) when compared to tumour-adjacent tissue (6.1%). Similar levels were found when staining the tissue by immunohistochemistry (78% to 7.3%). In vivo experiments have additionally demonstrated the oncogenic coupling of HERV-W, as it was able to induce proliferation and tumour formation [Transitional cell carcinoma (TCC), also known as urothelial cell carcinoma (UCC) of the bladder, was also linked to ERV expression. HERV-W ormation . HERV-E ormation . Finallyormation .In addition to the aforementioned relevant examples, there is a broad registry of ERVs in other cancers. HERV-R has been detected in renal carcinoma , leukaemENV region of the latter was found in pregnant women’s blood [Nonetheless, some of the HERVs are also expressed in normal tissues, usually to a lower extent that in the cancer associated tissue. For example, HERV-K transcripts have been found by RT-qPCR in few uninvolved breast tissues, stomach and small intestine, but with a significantly lower expression than the tumour tissue, and HERV-H and HERV-E have also been found in normal pancreatic tissue ,55,56. T’s blood ,26,57. T’s blood ,58,59,60Taken as a whole, the studies of human ERVs have made a comeback and now point to equally strong cancer involvement as seen in the pivotal and conclusive rodent studies from the last decades. Likewise, evidence suggests a degree of cancer selectivity which might make ERVs good cancer biomarkers and desirable targets for immunotherapies.ENV protein as p15E depleted serum lost the immunosuppressive activity [Based on the resemblance between ERVs and HIV, researchers have tried to elucidate if there is a structural resemblance that allows the immune evasion. HIV research established the presence of an immunosuppressive domain in the transmembrane subunit of the ENV protein which regulates gene expression and cytokine release . The earactivity .The importance of the immunosuppressive function of the TM domain has been further confirmed for several other ERVs, such as HERV-W and HERV-FDR, in relation with foetal development, where immunosuppression was defined by the ability of these proteins to inhibit the stimulated expansion of PBMCs and T-cells . The ISDExosomes (VLPs) containing other ERVs have been studied too. For the first-time, zebrafish embryos were injected with HERV-K positive VLPs generated from two colon cancer cell lines. Expression of the pro-inflammatory cytokine IL1-β was lower in cancer derived extracellular vesicle injected samples, compared to control samples. Additionally, there was a strong association between cytokine release and HERV-K presence, indicating a role in immunomodulation of the immune response and an effect in tumour progression .Overall, it is quite clear that cancer cells manage to avoid recognition by the immune system with the help of the ISD present in the ENV protein encoded by ERV sequences. The extensive association of tumorigenesis and the presence of ERVs, in combination with their role in immune evasion, make ERVs an interesting target candidate for immunotherapy against cancer.ENV exhibited a high efficacy against tumours eliciting antigen specific CD8+ T-cells. Therefore, vaccination efficacy has been improved boosting the CD8+ T-cell activity by using adjuvants such as CD40 or co-expressing other elements such as with β-galactosidase or part of a tetanus toxin sequence [As mentioned before, already in the late 1900s some cancers responded to treatment targeting murine ERVs, with perhaps the most relevant one being Thiel et al. Their team slowed the development of progressed leukaemia by a combination therapy with specific antibodies targeting both ERVs’ TM and SU ENV proteins (with limited effect of monotherapy) . Moving sequence ,73,74.ENV and inhibited pancreatic cell proliferation and transformation in vitro. In mouse xenograft models challenged with knocked down HERV-K pancreatic tumour cells, the tumour growth was reduced with absence of lung metastasis [ENV and GAG prevented lung tumour outgrowth and metastasis in mice. The same vaccine actually proved to have prophylactic potential when vaccinated mice did not develop lung metastasis [Wang-Johanning et al. suggested in 2006 already that HERV-K proteins could act as tumour-associated antigens (TAAs) and used as targets in immunotherapy . Besidestastasis . As menttastasis . Similartastasis ,77GAG sequence were shown to be able to expand HERV-K specific T-cells ex-vivo, and such T-cells could subsequently eradicate HERV-K expressing tumours (PCT/EP2019/073883).Recently, a patent filing for a peptide-based therapy was published. Three peptides from the HERV-K The last revolution in the cancer immunotherapy field has been the introduction of chimeric antigen receptor (CAR) T-cell therapy, where the immune cells from the patient are re-educated to fight cancer by introducing the sequence of interest into the T-cell receptor (TCR). Zhou et al. have managed to introduce HERV-K specific CAR T-cells by designing a TCR with an anti-HERV-K mAb sequence. The HERV-K CAR T-cells were able to inhibit breast tumour growth in xenograft mouse models and exhibit specific cytotoxicity  against cancer cells while normal breast cells were not targeted. Adding to previous prophylactic observations, these HERV-K specific CAR T-cells were also able to prevent metastasis to other organs . Using t+ T-cell epitope or tested in immunologically simplified model systems .Targeting of ERVs in cancer is still in its inception with the majority of reports focused on murine ERVs or HERV-K. Notably, previous attempts have been rather simple vaccine designs inducing a single CD8Adenoviruses (Ads) are non-enveloped viruses containing double-strained DNA genomes ranging from 34 to 43 kb. The selection of adenoviruses as vectors for vaccination strategies is based on their inherent adjuvant characteristics, which trigger innate immunity . AdenoviHIV is a valuable example of a challenge in vaccine research and development. Despite many attempts, a viable HIV vaccine is still missing in the market. The difficulty to achieve protection, among other reasons, is the need of a combined antibody and T-cell inducing vaccine for full protection. In order to prevent HIV infection and restrict viremia after infection, both antibody and cellular responses are necessary to block virus transmission and eliminate HIV-infected cells. Additionally, robust and effective memory responses are also required to mount a quick, specific and greater secondary response upon re-encounter with the primary antigen .+ T-cells. Additionally, their particulate structure and size eases an efficient cross-presentation of VLP-derived peptides on major histocompatibility complex class I (MHC-I) molecules and therefore the stimulation of CD8+ T-cells. This feature constitutes an additional advantage and therefore it has been broadly used when designing VLP-based vaccines for cancer treatments. Actually, several studies have indicated that VLPs are able to overcome the immunosuppressive state of the tumour microenvironment [Even the existing powerful techniques, such as live-attenuated viral vaccines, that mimic many aspects of resolved acute infection , have limitations in terms of safety and efficacy against chronic infections such as HIV. The discovery of the VLP technology has allowed great advances in the field due to its versatility and favourable immunological characteristics. VLPs do not only offer many of the same desirable properties as live-attenuated vaccines, but they also constitute an advantageous platform due to their size, repetitive surface pattern, capacity to generate both innate and adaptive immune response, as well as being a safe and economically profitable system . Upon suironment ,91 and bironment ,93,94.+ T-cell responses, their principal outcome is mainly translated into effective triggering of B- and CD4+ T-cell responses. So, when compared to other vaccination strategies, such as viral vector-encoded antigens, they do not seem to have an added effect, but to rather show an inferior VLP incorporation [Despite the potential of VLPs to generate CD8poration . This leporation .+ T-cells via the expression of the antigen intracellularly for MHC-I presentation on the surface of transduced antigen presenting cells (APCs) such as DCs  with homologous GAG and ENV had higher and broader GAG specific CD8+ T-cell response. This strategy showed more GAG than ENV specific CD8+ T-cell responses and higher ENV binding antibody, compared to expressing different GAG and homologous ENV [+ T-cell ratio and diversified T-cell responses were obtained when encoding the invariant chain fused to accessory SIV antigens as a genetic adjuvant in the adenovirus [The first-generation adenoviral vectors used as VLVs had a cassette encoding HIV/ SIV ,101. To gous ENV . Further+ T-cells among other immune responses [The exact mechanisms by which ERVs are activated have not been determined yet, but several studies point to epigenetic modifications. More specifically, ERV activation has been associated with DNA hypomethylation, which had already been widely associated with tumour progression ,103. In esponses . Similar+, CD8+ T-cells and B-cells in the tumour microenvironment [The development of vaccines against cancer has remained very challenging. So far only prophylactic vaccines based on VLPs have shown to successfully prevent the establishment of certain cancers caused by viruses (Hepatitis B and HPV) and are now licenced. However, the efficacy of such vaccines only work before the disease is established and only against specific cancers . Therapeironment ,5. While+ T-cell responses, but also CD4+ T-cell responses, which has been associated with enhance protection against several diseases such as malaria, Ebola and hepatitis C virus [One strategy has been to perform a second immunisation with a different adenoviral vector serotype, or another viral vector, to boost the transgene specific memory T-cell responses elicited from the first immunisation. So far, the most promising combination has been to prime with an adenoviral vector and boost with an MVA vector. This strategy not only increased transgene specific CD8 C virus . + T cells and a significant reduction of tumour growth in mice challenged with murine B16.F10 melanomas expressing the TAA: GP33-41 from lymphocytic choriomeningitis virus (LCMV), even in LCMV tolerant mice [Another strategy has been to increase the transgene antigen presentation relative to vector antigens. This was surprisingly achieved by the genetic fusion of the TAA to the C-terminal domain of the MHC-II associated invariant chain (Ii) in the viral vectors . Althougant mice .The T-cell adjuvant effect of the Ii technology in adenovirus vectored vaccines has been observed against several antigens from different pathogens/diseases. For instance, in cynomolgus macaques naturally infected with papillomaviruses, the Ii technology fused to ancestral PV antigens in a prime-boost regimen (Ad-MVA) could raise sufficient T-cell responses against a certain type of PV (MfPV3) and eliminate the infection caused by it . This PVENV packaged into GAG based VLPs. These vaccines were highly effective at managing to target growing colorectal carcinomas and eliminating small tumours in mice. Unlike targeting with invariant chains, using neoantigens where T-cells were the only adaptive mediators of tumour resistance, it was observed that the effect of therapy was dependent on both CD4+ and CD8+ T-cells combined [Indeed, VLV technology was applied against cancers using an adenoviral vector encoding ERV combined ,117. FolWith the high number of people with cancer worldwide and the expectation of this number to increase drastically in the coming years , there i+ T-cell responses by virtue of direct infection of APCs. Despite these processes, adenoviruses have not been successful in human cancer yet. We speculate that a major drawback of adenoviral vectors is the competition between vector antigens and transgene encoded target antigen for immune dominance, where vector proteins block responses to minor responses in the transgene [+ T-cells in addition to CD8+ T-cells. We believe that the mentioned strategies will fail to exploit the full immunogen capacity of the ERVs [Adenoviruses have been used in thousands of reports as vaccines to target cancer. In that role, adenoviruses have a potent cell transduction and stimulate robust CD8ransgene . Such liransgene . It is tthe ERVs .As mentioned before, ERVs can be reactivated and overexpressed in many cancer cell types. Like VLPs, they can trigger both humoral and T-cell responses through the expression of ENV protein on the cell surface, and the secretion of ERV positive exosomes and particles. Although they can certainly trigger immune responses, they are also immunosuppressive. Therefore, a vaccine capable of breaking this immune suppression through the generation of robust T- and B-cell responses is required and adenovirus-vectored VLVs appear to fulfil this requirement .


N = 381) and breast invasive carcinoma subtypes , while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival . In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER  and BRCA-subtypes , improving KIRC-OS performance . INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizesRecent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER,  The challenge of integrating multi-omics data is as old as bioinformatics itself , 2, but,This study introduces Integrative Network Fusion (INF), a reproducible network-based framework for high-throughput omics data integration that leverages machine learning models to extract multi-omics predictive biomarkers. Originally conceptualized and tested on multi-omics metagenomics data in an early preliminary version , 4, INF We test INF on three datasets retrieved from the TCGA repository, to predict either the estrogen receptor status (ER) or the cancer subtype on the breast invasive carcinoma (BRCA) dataset, and to predict the overall survival (OS) on the kidney renal clear cell carcinoma (KIRC) and acute myeloid leukemia (AML) datasets. Overall, INF improves over the performance of single layers and naive juxtaposition on all four oncogenomics tasks, extracting a biologically meaningful compact set of predictive biomarkers. Notably, the transcriptomics layer is prevalent inside the inferred INF signatures, consistently with published findings .The INF framework is currently designed to integrate an arbitrary number of one-dimensional omics layers. We plan to further extend the framework by enabling the integration of histopathological features extracted from whole slide images  or deep https://www.cancer.gov/tcga) and four classification tasks are considered in this study. Protein expression (prot), gene expression (gene), and copy number variants (cnv) are used to predict breast invasive carcinoma (BRCA) estrogen receptor status  and subtypes . Methylation (meth), gene expression (gene), and microRNA expression (mirna) are used to predict acute myeloid leukemia (AML) and kidney renal clear cell carcinoma (KIRC) overall survival . The number of samples and features for each omic layer and classification task are detailed in Three multi-modal cancer datasets generated by The Cancer Genome Atlas (TCGA) Research Network  and the Broad GDAC Firehose (https://gdac.broadinstitute.org/), where further details on data generation can be found. The data was retrieved in December, 2019 and January, 2020 using the RTCGA R library , 3 pseudo-omics layers, and 400 features . The dataset is generated in-house using F-value. Secondly, the multi-omics features are integrated by Similarity Network Fusion (SNF)  , a methoIn a comparative review of scientific literature, SNF  emerged rSNF (ranked SNF), a feature-ranking scheme based on SNF-fused network clustering. In detail, a patient network Wi is built for each feature fi, based on fi alone, and spectral clustering is performed on it. Then, NMI score is computed comparing the samples clusters found inside Wi with those in the fused network; the higher the score, the more similar the clustering between the fused network and Wi. Thus, each feature fi is associated to a consistency score, ranking all multi-omics features with respect to their relative contribution to the whole network structure.This work proposes multi-omics integration as an approach to identify robust biomarkers of samples phenotypes or cancer subtypes ; consequently, it is necessary to extract measurements information from the SNF-fused network of samples. To this aim, we extended SNF by implementing grid = {0.3, 0.35, 0.4, 0.45, …, 0.8} and Kgrid = {i∈ℕ, 10 ≤ i ≤ 30} in a 10 × 5-fold cross-validation schema.The entire procedure of similarity networks inference and fusion relies on two hyperparameters: α, the scaling variance in the scaled exponential similarity kernel used for similarity networks construction, and K, the number of nearest neighbors in sparse kernel and scaled exponential similarity kernel construction. While the original method  assignedTo ensure the reproducibility of results and limit overfitting, the development of classification models is performed inside a Data Analysis Plan (DAP) , followiF-value  or by the rSNF ranking (see section 2.3) and different classification models are trained for increasing numbers of ranked features, namely 5, 10, 25, 50, 75, and 100% of the total features. A unified list of top-ranked features is then obtained by Borda aggregation of all the ranked CV lists , described above, the rSNF ranking is performed at each CV round on the training portion of the data. Since this procedure is quite demanding in terms of computational time, even if parallelized (≈ 9 feature/min), we devised an “accelerated” version of the DAP (aDAP), where the rSNF ranking is precomputed on the whole TR data and used as is at each CV round. We assessed the fDAP vs. aDAP performance on the synthetic dataset as well as BRCA-ER and BRCA-subtypes by comparing the overall metrics and measuring the dissimilarity of the rSNF BoB of the two DAPs by the Canberra distance : in this setup, the performance of a classifier unaffected by systematic bias should be close to that of a random predictor, with MCC close to zero.The complete INF pipeline is implemented through the workflow management tool Snakemake , 28, whiforeach R library.The predictive profiling module, including the DAP, is written in Python 3.6 on top of NumPy  and scikThe INF computations were run on the FBK Linux high-performance computing facility KORE, on a 8-core i7 3.4 GHz Linux workstation, and on a 72-vCPU 2.7 GHz Platinum Intel Xeon 8168 Microsoft Azure cloud machine (F72s v2 series).https://gitlab.fbk.eu/MPBA/INF. Additional information is included in the http://dx.doi.org/10.6084/m9.figshare.12052995.v1.To further foster reproducibility and support users and future developers, the full code of this benchmark is publicly shared on the GitLab repository BRCA-ER_LSVM, KIRC-OS_LSVM). The classifier performance for 3-layer integration is summarized in The INF workflow was run on all tasks considering 3-layer integration and all 2-layer combinations; the DAP was also run separately on all single-layer datasets in order to obtain a baseline. All results presented here refer to experiments performed with RF classifier. Experiments using LSVM were performed on BRCA-ER and KIRC-OS obtaining similar classification performances, top features and layer contributions (aDAP) and the full DAP (fDAP) was run on synthetic data, BRCA-ER and BRCA-subtypes data, with aDAP yielding similar performance metrics and top-ranked biomarker lists as fDAP , while being ≈30 × faster . All the results presented here were thus obtained using aDAP. Moreover, the INF workflow running in “random labels” mode achieved an average cross-validation MCC ≈0, as expected by a procedure unaffected by systematic bias.A comparison between the “accelerated” flavor of the DAP (gene-cnv and gene-prot is comparable to the best-performing single-layer data (gene) and superior to cnv and prot single layers, while INF on cnv-prot only improves over the cnv single layer. On the BRCA-ER task, the performance with INF integration of 2 or 3 layers is still better than using single layers, nevertheless to a smaller extent, except for cnv-prot integration which performs better than cnv alone but slightly worse than gene and prot single layers. The good performances achieved at the gene and prot single layers do not come unexpected, since the biological nature of the target ER-status is defined at transcriptomics level. On the more difficult AML-OS task, INF has better performance over both rSNF and juXT on gene-mirna and meth-mirna integration, still improving over single-layer performance both in terms of MCC and reduced signature sizes.Overall, integrating multiple omics layers with INF yields better or comparable classification performance than using only features from a single layer or naïve omics juxtaposition, at the same time with much more compact signature sizes. On 3-layer BRCA-subtypes and 2- or 3-layer KIRC-OS, INF outperforms the single layers, as well as juXT and rSNF , Table 4cnv-prot integration .For BRCA-ER, three-layer INF (rSNFi) integration performs better than either rSNF or juXT . All two-layer INF integrations perform similarly to, or better than, the corresponding rSNF and juXT integrations, in particular for gene single-layer performance (MCC test 0.821). However, the INF median signature size is only 301.5, compared to 1801 for rSNF and juXT, and 891 for the gene layer alone. All two-layer INF integrations yield better performance than their corresponding juXT or rSNF integrations.On BRCA-subtypes, the 3-layer INF integration performs better than either rSNF or juXT , nevertheless without improving over the meth-mirna, gene-mirna, gene-meth), and 0.253, 0.261, 0.249 .Omics integration is particularly effective for KIRC-OS, as all 2- and 3-layer INF integrations outperform juXT, rSNF, and each of the single-layer classifiers. In fact, 3-layer rSNFi achieves MCC test 0.378 vs. 0.274, 0.305 , 0.296, 0.327, 0.333 . Moreover, cross-validation MCCs corresponding to INF integration are better than any single layer MCC as well as rSNF and juXT.For AML-OS, INF feature sets are always more compact than either juXT or rSNF, with three-layer integration giving better MCC than any of the INF two-layer integrations  median features (rSNFi vs. juXT), the largest reduction in size occurs for AML-OS 3-layer integration, while the least reduction is observed for BRCA-subtypes task, with 301.5 vs. 1801 (16.7%) median features (rSNFi vs. juXT).gene layer generally provides the largest number of features to the signatures identified by the INF workflow. In particular for the BRCA dataset, in both ER and subtypes tasks, the gene layer contributes over 95% of the top features for juXT and rSNFi, with rSNF signatures being slightly more balanced . This is expected as the class label is defined mainly at transcriptomics level. In AML-OS experiments, the layer contributing the most is still gene, accounting for ca. 78, 73, and 81% of the top feature sets for RF juXT, rSNF and rSNFi experiments, respectively. In KIRC-OS experiments, gene is the layer contributing the most to the top juXT and rSNF feature sets, while meth is the major contributor for rSNFi. The percentage of features from each omic layer contributing to the top signatures for juXT, rSNF and rSNFi 3-layer integrations are reported in layer_contribution. The RF rSNFi signatures for all tasks are available in BRCA-ER_RF_rSNFi, BRCA-subtypes_RF_rSNFi, AML-OS_RF_rSNFi and KIRC-OS_RF_rSNFi.In terms of contributions from the omics datasets being integrated, the gene and ER-alpha from the prot layer) and the transcription factor GATA3 (from both gene and prot layers)  . Both th layers) . Interes layers) .mirna features of the AML-OS signature, MIR-203 expression was recently found to be associated with AML patient survival  by concatenating the features before fitting a model (early-integration), (ii) by incorporating the integration step into the model training (intermediate-integration), or (iii) by combining the outputs of distinct models for the final prediction (late-integration) , 70.In the early-integration approach, also known as juxtaposition-based, the multi-omics datasets are first concatenated into one matrix. To deal with the high-dimensionality of the joint dataset, these methods generally adopt matrix factorization , 58, 71,In order to maximize the contribution of the single-omics layer, the late-integration methods first model each dataset individually, and then merge or average the results; they are also known as model-driven , 81. AltThe intermediate-integration strategies aim at developing a joint model that accounts for the correlation between the omics layers, to boost their combined predictive power . Among tMore recently, the success of deep learning algorithms in various bioinformatics fields  promptedAlthough it is clear that no single method is consistently preferable, and that most of the proposed approaches are task and/or data dependent , the comIn this context, it is clear that omics-integration is one of the most promising and demanding challenges of the modern bioinformatics, and that there is an urgent need to prove the reproducibility, interpretability, and generalization capability of the proposed methods , 101.We present the INF framework for the characterization of cancer patient phenotypes by integrated multi-omics signatures, combining an improved version of a state-of-the-art integration technique  with preThe simplest approach for multi-omics data integration consists in juxtaposition of normalized measurements into one joint matrix, followed by the development of a predictive model. Juxtaposition-based integration is considered as a baseline technique, since it is the most naïve approach to combine two datasets; moreover, it enables to identify multi-omics signatures by borrowing discriminatory strength from information derived by all datasets. Juxtaposition further dilutes the already possible low signal-to-noise ratio in each data type, affecting the understanding of the biological interactions at the different omics levels.Conversely, the INF method for omics data integration is an improvement of the popular Similarity Network Fusion (SNF) approach , which hTwo innovative solutions have been implemented in this study: (i) we devised a SNF-based procedure to rank variables according to their importance in clustering samples with similar phenotypes; and (ii) predictive models were developed exploiting the SNF-ranked variables, inside a rigorous Data Analysis Plan which ensures reproducibility , 19.The performance of INF was assessed both in terms of statistical properties as well as biological interest. Concerning the statistical aspect, INF was compared with predictive models developed on the juxtaposed datasets (juXT technique), as well as on the single-layer datasets. With INF, smaller signature sizes were systematically derived to achieve comparable or even better performance both in cross-validation and in test. This is an added value for INF, as biological validation of biomarkers can definitely benefit from signatures of small size in terms of both costs and required time. This main achievement is mainly due to the novel rSNF ranking, which increases the signal-to-noise ratio from the combined layers by prioritizing the most discriminant biomarkers in terms of network mutual information. rSNF exploits two main SNF advantages: integration of heterogeneous data and clustering of sample networks. The main peculiarity of the SNF integrative procedure is its robustness to noise , becauseIt should be noted that, especially in computational biology, multicollinearity between pairs of predictors and/or layers is intrinsic in the problem. Nevertheless, most machine learning models are indeed designed to identify the relevant predictors even in the presence of strong linear or non-linear correlations, provided that an appropriate DAP, feature ranking method, and diagnostic tools  are adopted against selection bias. To this aim, the application of a DAP derived from the MAQC-II initiative for model selection is a core attribute of the INF framework.A fair comparison of INF results with other integration methods is currently unfeasible due to the number and variety of computational pipelines with dissimilar datasets, preprocessing methods, data analysis plans, and performance metrics.snfpy for Python (snfpy or a similar Python-based implementation, in order to drop the dependency on R and to potentially improve the overall performance.This work is based on the original R implementation of the SNF algorithm . Howeverr Python . In a fuIn its current version, the INF framework supports the integration of two or more one-dimensional omics layers. As part of our future effort we will add support for the integration of medical imaging layers, for example leveraging the extraction of histopathological features from whole slide images by deep learning  or usingThe original contributions presented in the study are included in the article/supplementary files, further inquiries can be directed to the corresponding author/s.CA, LT, and GJ: conceptualization. MC, NB, AM, AZ, LT, CA, and GJ: methodology. MF: interpretation. GJ: coordination. MC, NB, AM, MF, GJ, and CF: writing. All authors contributed to the article and approved the submitted version.AZ was employed by the company NIDEK Technologies Srl. CF was employed by the company HK3 Lab. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


DNA methylation data can be used to estimate proportions of leukocyte subsets retrospectively, when directly measured cell counts are unavailable. The methylation-derived neutrophil-to-lymphocyte and lymphocyte-to-monocyte ratios (mdNLRs and mdLMRs) have proven to be particularly useful as indicators of systemic inflammation. As with directly measured NLRs and LMRs, these methylation-derived ratios have been used as prognostic markers for cancer, although little is known about them in relation to other disorders with inflammatory components, such as cardiovascular disease (CVD). Recently, methylation of five genomic cytosine-phosphate-guanine sites (CpGs) was suggested as proxies for mdNLRs, potentially providing a cost-effective alternative when whole-genome methylation data are not available. This study compares seven methylation-derived inflammatory markers  with five conventionally used protein-based inflammatory markers  and a protein-based inflammation score, in their associations with cardiovascular function (CVF) and risk. We found that markers of CVF were more strongly associated with methylation-derived than protein-based markers. In addition, the protein-based and methylation-derived inflammatory markers complemented rather than proxied one another in their contribution to the variance in CVF. There were no strong correlations between the methylation and protein markers either. Therefore, the methylation markers could offer unique information on the inflammatory process and are not just surrogate markers for inflammatory proteins. Although the five CpGs mirrored the mdNLR well in their capacity as proxies, they contributed to CVF above and beyond the mdNLR, suggesting possible added functional relevance. We conclude that methylation-derived indicators of inflammation enable individuals with increased CVD risk to be identified without measurement of protein-based inflammatory markers. In addition, the five CpGs investigated here could be useful surrogates for the NLR when the cost of array data cannot be met. Used in tandem, methylation-derived and protein-based inflammatory markers explain more variance than protein-based inflammatory markers alone. R2 estimate of at least 0.95 when methylation-derived estimates of leukocyte sub-types and NLRs are regressed on those measured directly , are increasingly being used as robust alternatives to flow cytometry-based cell count ratios as indicators of systemic inflammation –3. One kdirectly , 5.Similar to cytometry-based ratios, mdNLRs and mdLMRs are considered prognostic markers of overt inflammatory diseases such as rheumatoid arthritis  and cancBlood-based protein inflammatory markers, such as C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α), are useful epidemiological tools for quantifying inflammatory state and disease risk . HoweverTo this end, we investigated methylation-derived and protein-based biomarkers of inflammation in relation to cardiovascular function (CVF) in a cohort of black South African men. We included seven methylation-derived  and five high-sensitivity protein-based -γ] markers of inflammation. In addition, we used a protein-based inflammation score  to proviThis is a cross-sectional investigation of 120 self-identified Batswana men who were enrolled in the North West province, South African arm of the international Prospective Urban and Rural Epidemiology study (PURE-SA-NW) in 2015 . IndividFlowSorted.Blood.EPIC R software package . Details regarding DNA extraction, quality control, methylation quantification, data processing, and data normalization have been reported previously . Sample  package . Neutrop package , 2.© Integra 400  was used to quantify high-sensitivity CRP concentrations. High-sensitivity Q-Plex™ planar-based multiplexed enzyme-linked immunosorbent assays  were performed to measure IL-6, IL-10, TNF-α, and IFN-γ. An inflammation summary score was calculated to amalgamate related inflammatory proteins as suggested previously  and HR were measured using the OMRON M6 device . Participants were seated in an upright position with legs uncrossed. After participants had rested for 10 min, the correct cuff size was fitted on their right arms, whereafter two measurements were recorded with a 5-min interval. Data from the second measurement were used for analysis. Pulse pressure (PP) was then calculated as the difference between SBP and DBP. Large artery stiffness was investigated using the current gold standard measurement, carotid-femoral pulse wave velocity [cfPWV ], using current, former, or never, but has been dichotomized here to never and ever (where ever denotes formerly and currently). Participants also reported the frequency and quantity of usage, age at the start of use, and previous attempts at abstinence. Participants were asked by interviewers to provide information on any prescribed or over-the-counter medication they regularly make use of. Body mass index (BMI) was calculated as weight  per unit height (measured using a stadiometer) squared (kg/m2). Waist circumference was measured using a steel tape , according to standard anthropometric procedures. Physical activity is reported as a continuous physical activity index measure determined using data from a modified Baecke's questionnaire validated for use in South African adults . The Cobas© Integra 400 plus  was used to determine serum gamma-glutamyl transferase concentrations. Glycated hemoglobin was quantified using the D-10 Hemoglobin testing system from Bio-Rad© (#220-0101).Blood samples for the subsequent analyses were collected and processed in the same manner as described above. Fasting glucose, total cholesterol, triglycerides, and low- and high-density lipoprotein cholesterol (LDL-C and HDL-C) were quantified with the Cobase-transformed. A Bonferroni adjustment, based on the number of independent comparisons, was used to account for multiple testing. Variables were considered dependent when the coefficient of determination (R2) between them was >0.2. Based on these criteria, we regarded the number of independent inflammatory markers tested here as four  and the CVF markers as three [[1] BP markers, [2] HR, and [3] cfPWV].Analyses were performed using version 3.5.0 of R statistical software . Data diHmisc R package for quantification and corrplot for visualization, p < 0.003 .Relationships among the biomarkers of inflammation were assessed with partial Spearman correlations, controlling for age and smoking status .Next, the associations between individual inflammatory biomarkers (protein-based and methylation-derived) and CVF, and the variance in CVF explained by these inflammatory markers, were investigated using linear multivariate regression models adjusted for known cardiovascular risk markers (car and relaimpo packages), this time adjusting for known cardiovascular risk markers including inflammation, represented by the protein-based inflammatory score .Thereafter, a combination of methylation-derived inflammatory markers (selected using backwards-stepwise regression models) were investigated in similar linear multivariate regression models , body composition (BMI and waist circumference), level of education, physical activity, smoking status, self-reported alcohol consumption status, and medicine use. In addition, blood lipid levels , markers of glucose metabolism (fasting glucose and glycated hemoglobin), and gamma-glutamyl transferase were tested. With the exception of dwelling place, smoking and alcohol consumption status, and medicine use, all variables were investigated as continuous variables. Only risk markers retained by the stepwise regression models were adjusted for in subsequent models to avoid over fitting, given our limited sample size. Based on these results, we made use of two main covariate clusters in all regression analyses. First (hereafter referred to as Model 1), we adjusted for age only. Second (hereafter referred to as Model 2), we adjusted for age, smoking status, dwelling place , BMI, LDL-C, HDL-C, and medicine use . When cfPWV was the outcome, mean arterial pressure was additionally adjusted for in both models. In ever smokers.The clinical characteristics of our cohort are provided in The methylation-derived cell ratios were in agreement with the CVD risk portrayed by CRP and the CVF and CVD risk markers . The mdNr = 0.44, p = 9.9 × 10−7) and between TNF-α and IFN-γ . All inflammatory proteins also associated strongly with the inflammatory score . Comparatively stronger correlations were observed among the methylation-derived biomarkers. The negative CpG-mdNLR and positive CpG-mdLMR correlation coefficients reflect the positive associations of these CpGs with monocytes  and lymphocytes , and the negative association with neutrophils . No convincing evidence for protein-methylation correlations was observed.p ≤ 0.004) were between cg25938803 and HR , cg25938803 and cfPWV , and cg10456459 and HR .Summary statistics of the linear regression models quantifying the relative contribution of the investigated inflammatory biomarkers to the variance in CVF markers are shown in Markers of CVF appeared to be more strongly associated with methylation-derived inflammatory markers than with protein-based biomarkers . RegardiNext, we investigated whether the methylation-derived inflammatory markers can increase the variance explained in CVF when added to a model containing known CVD risk markers, including inflammation. To this end, we included the protein-based inflammatory score, as an amalgamated biomarker of inflammation in the covariate list of Model 2 (referred to below as Model 3). To identify which methylation-derived biomarkers to investigate, we performed a backward stepwise regression analysis for all CVF phenotypes. Age  and the seven methylation-derived inflammatory biomarkers were added to these models as independent variables. Only the markers retained by the backward stepwise regression were included in further analyses . For allMyeloid CpGs, cg03621504, and cg25938803 were the only methylation-derived markers that had strong evidence of individual contribution to the variance in SBP and cfPWV, respectively. The retained methylation-derived inflammatory markers contributed an additional ~7% to the variance explained in SBP and cfPWV, after age, smoking status, body composition, blood lipids, socio-economic status , medication use and inflammation (protein-based) had been accounted for. For every ~2% methylation increase in cg03621504 a 10 mmHg change in the geometric mean of SBP was observed. For cfPWV, an increase of 1 m/s in the geometric mean resulted from a ~3% methylation increase in cg25938803. Again, findings remained robust upon replacing total medication use with CVD medication use.In this study we report the value of methylation-derived indicators of inflammation in relation to CVF, including BP and large artery stiffness. Although the methylation-derived and protein-based inflammatory markers did not demonstrate strong associations with each other, both reflected a similar degree of increased CVD risk. Methylation-derived markers appeared to be more strongly associated with CVF than the protein-based inflammatory markers tested. Furthermore, when exploring models explaining variance in CVF, we found that methylation biomarkers, particularly the myeloid CpGs, explained variance in addition to variance already explained by known CVD risk markers, including inflammation reflected by a protein-based inflammatory score. This suggests that methylation-derived inflammatory markers may complement protein-based inflammatory markers in explaining CVF marker variance, rather than simply being a proxy thereof.Although the evidence for appropriate cut-off values for increased CVD rather than overt disease risk remains unclear, a meta-analysis of 38 studies, investigating NLRs in relation to stroke, acute coronary syndrome, coronary artery disease, and a composite of these events , provideIn agreement with the adjusted mdNLR, the mdLMR also classified half of the PURE-SA-NW population as being at increased risk of CVD. Contrary to the mdNLR, no adjustment is required when comparing the mdLMR with directly measured ratios . On averThe central finding of this study is that, although current research relies heavily on protein-based inflammatory markers for CVD risk estimation , 25, metPrior evidence has shown a multitude of possible mechanisms through which individual leukocyte sub-types, proxied for in this study by methylation-derived cell count markers, may directly contribute to CVD . Neutrophils, for example, secrete inflammatory mediators and proteolytic enzymes related to vascular wall degeneration . MacrophThe implication of these findings is that using a single protein marker  to adjust for “inflammation” might not capture all the variance of the true inflammatory effect. The superiority of cell counts lies in their ability to reflect information about both the innate and complementary inflammatory processes. Adding to this advantage is the ability to provide methylation-derived cell count estimates retrospectively, from any well-preserved whole-blood or leukocyte samples where DNA is available, thus enabling the reinvestigation of large sample sets already collected.p < 0.05  that are both methylation quantitative trait loci (mQTLs) for these CpGs and are expression QTLs for local genes  at the North-West University's Africa Unit for Transdisciplinary Health Research.The datasets generated for this study will not be made publicly available. The data presented in this study are part of the larger South African and international PURE study and are therefore not under the sole jurisdiction of the corresponding author. Data will be made available upon reasonable request and with the permission of the Health Research Ethics Committee of the North-West University and the principal investigator of the PURE-SA-NW study, Prof. I.M. Kruger (This study was reviewed and approved by the Health Research Ethics Committee of the Ethics Office for Research, Training and Support at the North-West University, South Africa. The participants provided their written informed consent to participate in this study.HC, MP, HE, and CN-R conceptualized the study. Funding was acquired by MP and FG. AS acquired the cardiovascular function data. HC performed the data analysis and wrote the manuscript. HE and MP supervised the data analysis and interpreted the results with HC. All authors contributed to the critical review and editing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In recent years, the microfluidic technique has been widely used in the field of tissue engineering. Possessing the advantages of large-scale integration and flexible manipulation, microfluidic devices may serve as the production line of building blocks and the microenvironment simulator in tissue engineering. Additionally, in microfluidic technique-assisted tissue engineering, various biomaterials are desired to fabricate the tissue mimicking or repairing structures . Among the materials, gelatin methacrylate (GelMA)-based hydrogels have shown great potential due to their biocompatibility and mechanical tenability. In this work, applications of GelMA hydrogels in microfluidic technique-assisted tissue engineering are reviewed mainly from two viewpoints: Serving as raw materials for microfluidic fabrication of building blocks in tissue engineering and the simulation units in microfluidic chip-based microenvironment-mimicking devices. In addition, challenges and outlooks of the exploration of GelMA hydrogels in tissue engineering applications are proposed. Tissue engineering has raised considerable attention as a potential alternative to tissue or organ transplantation in the area of biomedical engineering ,2. TissuDespite the progress achieved in microfluidic devic-assisted tissue engineering, the ability to mimic the final tissue orientation still remains a challenge that needs to be addressed . These lSince 2010, when Nichol et al.  demonstrMicrofibers have been widely used in biomedical applications, especially in the fields of tissue engineering, 3-D cell culture, and cell encapsulation ,56. Micr2 solution. The hybrid bioink composed of alginate and GelMA was delivered through the core of a concentric printhead with a smaller blunt needle inserting into the center of the outer one. A layered microfibrous scaffold formed by co-extrusion of the two flows. The microfiber scaffold was used as a vascular bed by encapsulating endothelial cells in the microfibers. Moreover, the GelMA component allowed for long-term stability of this vascularized tissue, which could be used for in vivo tissue regeneration or in vitro tissue modeling.The extrusion method refers to extruding pre-gel solution into a gelator solution by using a syringe needle . Liu et In the laminar flow-based method, pre-gel solution and gelator solution would form a coaxial or parallel laminar flow within a microfluidic channel. Shao et al. ,62 fabriw/v) exhibited uniform and well-arranged grooves on their surfaces. The micro-grooved surfaces could efficiently facilitate cell encapsulation and adhesion. These microfibers could be used as templates for the creation of fiber-shaped tissues or tissue microstructures as well. Chen et al. [Electrospinning is also one of the most widely used methods to generate GelMA hydrogel microfibers. Shi et al.  proposedn et al.  presenten et al.  fabricatn et al.  developeAs a potential and revolutionary technology, bioprinting has been applied in biomedical science. Recent studies have shown that bioprinting is applicable for converting biocompatible materials, cells, and supporting components into complex 3-D functional living tissues. Three-dimensional bioprinting is also applied in regenerative medicine . In addiBioink is a key element in the bioprinting platform for tissue engineering based on microfluidic technology . Chen etColosi et al.  used a mKnowlton et al.  demonstrVarious hydrogels have been developed for cell culture based on microfluidic devices ,77,78. GTakeuchi et al.  created w/v% GelMA hydrogel as a semi-permeable physical barrier to control the molecular diffusion in a microfluidic co-culture device. For example, the larger pore size of GelMA resulted in an increased oxygen diffusion rate that promoted cell differentiation. Michigan Cancer Foundation-7 (MCF-7) human breast carcinoma cells and metastatic U87MG human glioblastoma were co-cultured in this device to study photo-thermal therapy efficacy and cancer cell migration. Cui et al. [Moreover, GelMA hydrogels are used for 3-D cell co-culture microfluidic facility, and the most innovative studies are summarized in i et al.  designedOrgans-on-a-chip refers to a bionic system that simulates the in vivo microenvironment of living organs and provides more in vitro models related to the physiology of human organs ,93. GelMNie et al.  reportedZhang et al.  reportedLi et al.  developeWang et al.  reportedAung et al.  describeIn addition, other microfluidic approaches based on GelMA hydrogels were studied in biomedical fields recently ,83,99.For instance, Takeuchi et al.  developeGenerally, GelMA hydrogels provide the organs-on-a-chip system the ability to mimic the microenvironment of living organs and offer more physiologically relevant models of human organs in vitro. GelMA hydrogel-based organs-on-a-chip is also in favor to study the organic biological function in both physiological or pathological situations. Beyond this, GelMA hydrogels can serve as excellent ECM-mimicking materials for encapsulated cells.With the development of microfluidic technologies, tissue engineering assisted by microfluidic devices has emerged as a promising approach to solve the challenges like complex structures and organs mimicking and construction of cell-laden scaffolds and high-fidelity tissue microstructures. GelMA hydrogels have become an attractive biomaterial category applied in tissue engineering due to their tunable physicochemical properties and good biocompatibility. This work reviewed the recent (mainly after 2015) applications of GelMA hydrogels in tissue engineering assisted by microfluidic devices. The applications mainly include two aspects: (1) GelMA hydrogels serve as the raw materials in tissue engineering assisted by microfluidic devices, such as microfibers and scaffolds based on bioprinting; and (2) GelMA hydrogels serve as the simulation unit, such as organ-on-chip and cell culture systems.Although microfluidics offers numerous advantages , the microfluidic technique-assisted GelMA hydrogels still face a series of challenges when applied in the real biomedical field. Firstly, there are many complex structures in real tissues that cannot be simulated through the microfluidic technique at present, and what has been reported are just relatively simple structures like microspheres and microfibers. The inability to create large-scale tissue constructs containing micro-vascularized network channels and the lack of control over long-term cell survival remain unsolved. Secondly, current microfluidic devices also encounter some limitations, such as the feasibility, cost-effectiveness, and large-scale manufacturing. Microfluidics combined with GelMA have not been widely applied for disease modeling, pathophysiological studies, and pharmaceutical research due to the complicated protocols. Thirdly, the dimension of the GelMA hydrogel materials is limited to the micron level. It is difficult to prepare nanomaterials with fine structures via the microfluidic technique even if the 3-D bioprinting method is combined. In addition, the fact that cell death is induced by free radicals, which are produced by the photoinitiators during the cross-linking process, cannot be ignored .In the future work, micro-vascularized networks that can be used to create mesoscale tissue-like constructs may be one of the most essential tasks. Additionally, the mixing of hydrogels with different or complementary properties to form a composite material that may possess enhanced features over a single hydrogel should be encouraged. Furthermore, more innovative and functional microfluidic devices should be set up to meet the demands of various applications in tissue engineering. Beyond the microfluidic technique, the combination with other techniques, such as electro-spraying, nanofluidic devices, or femtosecond laser engraving, to prepare nanoscale GelMA hydrogel-based materials is also an innovative direction. Importantly, exploring mild cross-linking methods that are friendly to living organisms is also a future direction for GelMA preparation.