Typically, this characterization involves the assessment of dose-dependent, on-target, on-tumor activity in severely immunocompromised mice. While suitable for the purpose of evaluating T cell-expressed transgene function in a living host, this approach falls short in translating cellular therapy efficacy, safety, and persistence from preclinical models to humans. To comprehensively characterize cell therapy products in mice, we have developed a framework called “DIAL”. This framework aims to enable an end-to-end understanding of genetically engineered cellular immunotherapies in vivo, from infusion to tumor clearance and long-term immunosurveillance. The acronym DIAL stands for Distribution, Infiltration, Accumulation, and Longevity, compartmentalizing the systemic attributes of gene-modified cellular therapy and providing a platform for optimization with the ultimate goal of improving therapeutic efficacy. This review will discuss both existent and emerging examples of DIAL characterization in mouse models, as well as opportunities for future development and optimization.The preclinical characterization of gene modified adoptive cellular immunotherapy candidates for clinical development often requires the use of mouse models. Gene-modified lymphocytes (GML) incorporating chimeric antigen receptors (CAR) and T-cell receptors (TCR) into immune effector cells require  Adoptive transfer studies in mice, initially in the context of allograft transfer, underpin our understanding of T cell function, trafficking, and therapeutic potential –3. The fin vitro, these initial designs lacked the co-stimulatory framework that enabled enhanced T-cell function and persistence in vivo.The development of gene-modified chimeric T-cell receptors that enhance T cell function toward tumor cells in an MHC-independent manner was first described by Eshhar and colleagues in 1989 . These cIn 2002, the “second generation” chimeric antigen receptor (CAR) was introduced, addressing the limitations of the previous design . This evA few years later, the second-generation CAR-T design was reduced to practice with the first evidence of CAR-T-mediated tumor responses in human clinical trials in advanced follicular lymphoma . SubsequIn parallel with second-generation CAR-T, transgenic T-cell receptor (TCR-T) therapy gained momentum as another genetically modified approach for redirecting potent T-cell responses in solid tumors. Building upon the initial success of TIL therapy, Rosenberg et al. engineered autologous gene-modified TCR-T against the melanosome antigen MART-1 . These mThe future development of therapies against HLA-restricted cancer-testis (CT) antigens including NY-ESO-1, PRAME, MAGE-A3 and MAGE-A4, among others, showcased additional opportunities for the efficacy of TCR-T against solid tumors. However, the development of a fully analogous mouse model posed challenges due to the lack of homology, expression and/or cross-reactivity of TCRs with mouse peptide-MHC (pMHC). In most cases, representative antigens with known high-affinity CD8 TCRs such as PMEL-1, tyrosinase-related protein 1 (TRP1) or ovalbumin (OVA/OT-I) were used as proxies for human TCR-T.The process of preclinical validation for GML is constantly evolving, and new guidance is emerging . HoweverWhile animal models, particularly severely immunocompromised mice, are commonly employed for CAR-T validation, the current approach often focuses on assessing fundamental attributes such as dose-dependence, on-target on-tumor activity, and the absence of uncontrolled proliferation and toxicity. While these attributes validate the functionality of the transgene and the manufacturing process, they overlook several critical aspects that should be considered when designing GML as therapeutics for oncology.These aspects include modeling on-target, off-tumor toxicity and exhaustion, understanding the impact of lymphodepleting chemotherapy (LDC) on toxicity or cell expansion, evaluating tumor heterogeneity and antigen escape, evaluating the tumor microenvironment (TME) with its soluble factors and immune suppressor cells, overcoming barriers to infiltration, exploring the consequences (both positive and negative) of long-term expansion and persistence, and evaluating toxicities such as cytokine-release syndrome (CRS) and immune effector-cell-associated neurotoxicity syndrome (ICANS).in vivo, encompassing their journey from infusion to tumor regression and continued immunosurveillance. The acronym DIAL represents Distribution, Infiltration, Accumulation, and Longevity, which serve to compartmentalize the systemic attributes of gene modified cellular therapy  apart from other therapeutic approaches. During this phase, the levels of the therapeutic cells (measured by the presence of CAR or TCR-positive cell by flow cytometry or gene copy number by PCR) increase relative to the infusion levels. The peak and duration of this elevation depends on the antigenic burden and the microenvironmental context of antigen encounter, both of which influence the extent of proliferation and expansion. Over time, the infused cells undergo apoptosis in various tissues. The initial phase of decay occurs rapidly after the initial expansion, while the second phase of decay proceeds gradually over weeks to months. This gradual decline may be attributed to factors such as loss of stimulation, activation-induced cell death, and exhaustion .The composition of cell therapy infusion products plays a pivotal role in determining their distribution. When accounting for effector, memory and naïve subsets, both rodents and human exhibit a similar distribution pattern for ACT , 23–25. In the case of commonly used laboratory mice (Mus musculus), variations in CAR-T and TCR-T distribution may be attributed to factors like strain differences, extent of immune competence, and discrepancies in housing and sourcing facilities. Nonetheless, working with laboratory mice generally offers the advantage of predictability and consistency in experimental outcomes.2.1The ex vivo manipulation and expansion of GML, often referred to as cellular manufacturing, represents the initial stage where subsequent distribution patterns can be influenced. The manufacturing process involves complex interplay of factors that dictate the cellular kinetics (CK) of the grafted cells, including post-infusion cell survival, tissue infiltration, antigen-induced expansion, recirculation through the lymphatics into the bloodstream, terminal differentiation, and elimination.Early T cell manufacturing processes focused on scaling up large numbers of cells for infusion into patients who would subsequently receive supportive doses of IL-2 , 27. In in vivo . This subset of memory T cells with self-renewal capacity was first identified in mice  whole-body irradiation, whereas lymphodepleting chemotherapy (LDC) is almost exclusively used in humans. These differing protocols can lead to variations in the dynamics of lymphopenic immune cell recovery and cytokine production, which are crucial for supporting the expansion of transferred cells and promoting memory phenotypes.Despite these differences, genetically engineered fully murine model systems can still be utilized to highlight many of the advantages of generating stem-like T cells for ACT –55. In t2.2The choice of antigen and context of antigen encounter will influence the initial distribution of the gene-modified cells. After intravenous infusion, cell therapy products follows a pattern of distribution that involves vascular circulation, extravasation, diffusion into tissue, and recirculation primarily through lymphatics and vascular circuits. GML can distribute in tissues and encounter their cognate antigens in the tumor, triggering survival and proliferation signals that allow for expansion and continued distribution  or transgenic approaches to introduce the human antigen.This expanded repertoire of CAR targets opens up new possibilities for successful immunotherapy beyond B cell malignancies, where targets expressed in dispensable tissues have shown promising outcomes. Preclinical mouse models provide certain examples where on-target, off-tumor toxicity can be monitored. However, it’s important to note that these models often involve immunocompromised mice using mouse cross-reactive single-chain variable fragments . These targets primarily fall into the categories of cancer-testis antigens, tumor-viral antigens, and mutant neoantigens. The presence of these unique tumor-specific targets presents opportunities for precise and personalized immunotherapy approaches.GML TCR therapy allows for the targeting of antigens that are specifically expressed by tumor cells, offering potential advantages in terms of enhanced tumor specificity and reduced toxicity compared to CAR therapy. This approach broadens the range of tumor-specific antigens that can be targeted, expanding the possibilities for developing effective treatments against various types of cancer. However, achieving broad targeting will necessitate the rapid development of personalized TCR-T therapies , which cFor the majority of the aforementioned targets, fully mouse surrogate CAR/TCR systems, transgenic mice, or cross-reactive mouse antibodies are not typically used in preclinical studies. While on-tumor activity can be measured, there remains a significant gap in the investigation of off-tumor inflammation and toxicity. Consequently, examples of off-tumor engagement by GML are predominantly observed in human clinical trials, and the overall implications for cell survival, distribution, and toxicity in these scenarios remain poorly understood.Furthermore, it’s worth noting that many GML TCR therapy approaches are often combined with interleukin-2 (IL-2) to enhance cellular expansion and persistence. However, the use of IL-2 in these therapies brings its own considerations and potential dose-limiting toxicities. These toxicities may include vascular leak syndrome, hypotension, and renal dysfunction. Modeling IL-2 toxicity in mice is challenging, primarily because mice can tolerate high levels of IL-2 without experiencing the dose-limiting toxicities observed in humans.A recent advancement in GML engineering is the application of combinatorial antigen targeting technology. This approach aims to more precisely target tumor tissues while avoiding off-tumor inflammation and promoting optimal GML distribution. However, it is important to note that most logic-gated CAR-T systems have only been demonstrated at the preclinical level in mouse models.Logic gating approaches typically involve two distinct binding events and can fall into different categories:OR gating: Both antigen-binding fragments can simultaneously engage with the target and trigger signaling. This can occur through tandem engagement, where both antigen-binding molecules are joined and signal through a shared intracellular domain (ICD), or through the expression of separate CARs in a bicistronic format.AND gating: Both antigen-binding fragments are required to engage the target in order to initiate signaling.ON gating: The CAR is not expressed or fully functional until the addition of a second pharmacologic agent, which can be a small molecule, protein, or antibody-like drug. “Universal CARs” also fall in this category.NOT gating: The second antigen-binding fragment can disable or “turn off” the signaling initiated by the first antigen binder, typically expressed in normal tissues.IF … THEN gating: The first antigen binder drives signaling through a secondary transcription factor, leading to the expression of the second protein, which can be a fully competent CAR molecule.While “OR” gated CAR-T, used in multiple dual and tandem formats, is being evaluated in clinical trials for hematologic malignancies, most logic-gating approaches have only been described and validated in preclinical studies in mice –66. SincAn innovative approach aims to address some of the challenges faced by other technologies by combining distinct antigen binding domains with mutually dependent signaling domains downstream of the TCR complex. Researchers proposed coupling the intracellular domains (ICDs) of SLP76 and LAT to single-chain variable fragment (scFv) domains that recognize different antigens. By engineering mutations that prevent simultaneous binding of the Grb2 family member GADS, this approach creates a tightly regulated logic gate that has the potential to enable tumor-specific expansion of dual CAR-T cells .The SLP76/LAT logic gate was validated using a commonly used xenograft model of acute lymphoblastic leukemia (NALM6) in NSG mice, where the tumor cells expressed ROR1 and CD19 antigens as dual targets. Encouragingly, this approach demonstrated an improved balance between efficacy and off-tumor toxicity compared to traditional “split CAR” and “Syn-NOTCH” logic-gating strategies. The development of technologies that provide more precise control over dual antigen targeting in the future may not only help in managing off-tumor toxicity but also enhance the distribution properties of CAR targeting molecules.This advancement opens up possibilities for the exploration of dual-antigen-defined tumor types. It offers the potential for improved efficacy by combining the favorable attributes of TCR GML (tumor specificity) and CAR GML (targeting broader indications).2.3in vitro priming of CAR-T cells with oncolytic viruses through their native TCR has been combined with in vivo boosting, leading to impressive responses in preclinical models of melanoma and glioma  by targeting oxygen, nutrient, and co-stimulatory ligand-accessible regions of the body. Initially, the concept of enhancing adoptive T cell therapy involved using viral-specific T cells as a platform for tumor-directed CAR or TCR transgenes. These priming responses can be engaged passively, through endogenous viral antigen presentation, or actively, via vaccination against the viral antigen , 70. In d glioma .Despite the early enthusiasm and adoption of viral-specific T cells as a CAR platform, and the development of innovative approaches to prime peripheral expansion of CAR-T, only a limited number of human clinical trials have been initiated using these approaches. It’s important to note that most viral-specific T cells are being explored as an allogeneic platform due to their reduced potential for alloreactivity . In addition to viral-specific T cells, other lymphocyte lineages such as NK cells, iNKT cells, γδ T cells, and MR1 T cells that can recognize non-MHC antigens in tumors may also be considered as GML platforms (in vivo expansion and stimulation in lymphatic regions. The CARVac strategy demonstrated that LNP-mediated delivery of the CLDN6 antigen to antigen-presenting cells (APCs) in the lymphoid compartments of mice could promote robust expansion of CLDN6 CAR-T cells and improved anti-tumor efficacy  to lymph nodes  based on chemokine receptor expression, cytokines, and chemical gradients . While m3.1One important aspect of successful infiltration is the ability of T lymphocytes to undergo extravasation and chemotaxis towards sites of inflammation. Naturally circulating T cells acquire this capacity as a result of effector differentiation following antigen recognition and priming in secondary lymphoid organs (SLO). As described earlier, there are various methods to enhance GML priming using synthetic and natural mechanisms. Additionally, receptor overexpression in GML has been shown to augment their infiltration capacity. Some approaches focus on tumor-specific cues to modify chemotactic receptor expression, while others aim to increase lymphoid-homing capacity, recirculation, and survival in a more general manner, thereby enhancing anti-tumor function . Proof-oTumors are found to express sufficient levels of chemokine ligands, creating chemotactic gradients.Peripheral T cells or endogenous tumor-infiltrating lymphocytes (TIL) show low levels or downregulation of the cognate or paired chemokine receptor(s).Re-expression of the lost or downregulated receptor is shown to synthetically augment and/or restore infiltration in preclinical mouse models.Examples of receptors engineered into GML include CCR2, CCR4, CXCR2, CXCR3, CXCR4, and CX3CR1 –88. ReceOther strategies have been explored to improve infiltration and survival of transferred ACT without directly targeting the tumor. Some of these strategies involve factors such as IL-7/CCL19 or L-selectin overexpression to increase tumor penetration of ACT , 94. NotRM), which would enhance accumulation and promote stemness of the transferred CAR-T cells targeting MSLN. In CAR-TRM treated, regressing xenograft tumors, there was a marked increase in CAR-T cell infiltration, and depletion of CD103+ cells diminished the benefits of the so-called CAR-TRM cells. Interestingly, overexpression of the TRM transcription factor RUNX3, which had been used previously to enhance TRM expansion in murine tumor models, did not phenocopy the CAR-TRM cells . Studies using mouse models demonstrated that intra-tumoral injection of adenovirus engineered to produce IL-15 and RANTES could promote GD2 CAR-T cell infiltration into solid tumors in immunocompromised mice , and thi3.2In addition to challenges posed by suboptimal chemotactic gradients, solid tumors often create physical barriers that promote immune exclusion. Immune exclusion can be caused by immune or non-immune stroma in the TME, which may impede immune cell infiltration. The next section of this review will delve into the discussion of soluble and metabolic factors that contribute to immune exclusion. This section will primarily focus on tumors characterized by fibrogenic stromal cells, also known as cancer-associated fibroblasts (CAFs), which produce collagen-containing matrices that physically restrict lymphocytic infiltration. However, it is important to note that certain human tumors, such as pancreatic ductal adenocarcinoma, possess extensive stroma that cannot be accurately modeled in preclinical species. Despite this limitation, preclinical strategies have been developed to counteract non-immune stromal cells using CAR-T cells, either by directly eliminating CAFs or indirectly targeting stromal-derived barriers. In addition, use of combination agents that directly or indirectly target tumor fibrosis may represent an alternative strategy to improve GML infiltration into extracellular matrix-(ECM) rich tumor types (Developing strategies to target the extracellular matrix (ECM) presents multiple challenges. One of the key challenges is the development and validation of preclinical models that accurately replicate CAF-containing fibrogenic stroma observed in pancreatic, colon, hepatocellular, gastric, esophageal, head and neck, cervical, and breast cancers. While mouse models can partially capture certain aspects of fibrotic tumors, such as modeling the increased interstitial fluid pressure induced by fibrosis , they arStrategies encoded in GML to target fibrosis include directly eliminating fibrogenic cells in the tumor. One of the prominent targets on CAFs is the fibroblast-associated protein (FAP) –109. MurMyeloid cells can also impede GML infiltration into tumors. Targeting tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs) broadly for depletion can be challenging due to potential toxicities. However, recent studies suggest that certain receptors may serve as tumor-specific targets, allowing for safer targeting of these suppressive cell types. For TAMs, targeting the folate receptor β (FRβ) with a CAR in syngeneic mouse models was shown to be safe and promoted increased infiltration of MSLN CAR-T cells in multiple tumor models . MDSC ta3.3An indirect approach to disrupt factors that suppress lymphocytic infiltration involves tumor debulking through surgery, chemotherapy, or radiation. However, these therapies can have a dual effect as radiation and chemotherapy-resistant tumors often develop fibrosis as a consequence of treatment . This fiIn terms of targeting the ECM, small molecule inhibitors of lysyl oxidase (LOX) and LOX-blocking antibodies have shown impressive activity in a GEMM of breast cancer. However, these approaches have not yet demonstrated success in human clinical trials , 120. OtAdministration of GML directly following chemotherapy may improve infiltration by exerting anti-fibrotic effects and shifting suppressive myeloid cells to a pro-inflammatory phenotype. Murad et al. demonstrated improved infiltration following chemotherapy in murine preclinical studies using cyclophosphamide prior to PSCA CAR-T administration in metastatic prostate and pancreatic cancer models . InteresAnother potential approach to enhance GML infiltration involves intra-tumoral injection of surgically resected or debulked tumors to facilitate T cell-mediated clearance of minimal disease. Mouse models utilizing gel-embedded CAR-T have been described, demonstrating clearance of partially resected MSLN+ xenograft tumors . The fib4In comparison to hematological malignancies, solid tumors present numerous challenges within the TME that hinder the activation and functionality of T cells, thereby limiting the efficacy of cell therapies , 127. GeWhile there are diverse strategies to enhance the performance of CAR-T cells, a primary objective is to improve their accumulation within the TME. This can be achieved by enhancing their survival or promoting increased cell division rates. In this review, we classify accumulation strategies as either “defensive” or “offensive.” Defensive strategies aim to protect GML from factors that suppress their proliferation or survival, while offensive strategies involve the elimination of suppressive cells or factors to facilitate GML accumulation  in the biology of tumor-infiltrating lymphocytes (TILs). The scarcity of these amino acids in the TME can drive dysfunction of CAR-T cells and TILs. The essential role of arginine in T-cell activation, proliferation, and cytokine production has been demonstrated in TCR transgenic mouse models of CD4+ T cells specific for the influenza HA110–119 peptide . Sustaino assays . This waferation , 137.4.1.2In addition to its role in forcing metabolic reprogramming, hypoxia induces the accumulation of extracellular adenosine. The ectonucleotidases CD39 and CD73 lead to the sequential dephosphorylation of extracellular ATP and their expression on tumour cells, immune cells, fibroblasts, endothelial cells, and stromal cells is upregulated by hypoxia and TGFβ in the TME . Activatin vivo function of CAR-T cells  is produced by multiple cells in the TME, and its expression is further enhanced by hypoxia . Due to 4.1.3Engineering inhibitory molecules to bypass negative signaling is a promising approach to enhance CAR-T cell accumulation. Inhibition of the programmed death-1 (PD-1) checkpoint pathway through the expression of a dominant negative PD-1 receptor (PD-1 DNR) has shown efficacy in enhancing the antitumor function of mesothelin-targeted CAR-T cells in xenograft models of pleural mesothelioma with high PD-1 expression . PD-1 DNin vivo persistence and tumor eradication , also known as FAS. In syngeneic tumor models, expression of FAS DNR in CAR- or TCR-engineered T cells protected the cells from FAS ligand (FASL)-induced apoptosis, leading to improved dication . Lymphocdication . Howeverdication .4.24.2.1Improving the effectiveness of CAR T-cell therapy hinges on strategies that can shape the immune regulatory milieu within tumor tissue. In this context, offensive armoring strategies play a significant role. One approach is to engineer CAR-T cells with a transgenic cytokine that can overcome the insufficient production of pro-inflammatory factors in the TME. The transgenic cytokine can provide autocrine stimulation to sustain the survival and amplification of CAR-T cells, as well as paracrine modulation of the endogenous immune cell environment, thereby reducing systemic toxicity and remodeling the TME.IL-12 has been studied as a cytokine for armoring CAR T cells, and its antitumor efficacy has been demonstrated in xenograft and immunocompetent syngeneic mouse models of ovarian cancer or hepatocellular carcinoma , 165. ToIL-18 is another proinflammatory cytokine being explored in preclinical models and early clinical trials for cell therapy. Similar to IL-12, the efficacy of CAR T cells engineered to secrete IL-18 has been studied in xenograft and syngeneic mouse models , 169. Muin vivo anti-tumor activity in xenograft mouse models of Burkitt lymphoma, glioma, and metastatic neuroblastoma, respectively  are another important target in cancer treatment due to their abundance in tumors and association with poor prognosis . One exaAnother approach involves equipping CAR-T cells with an additional CAR targeting FRβ, which is specifically expressed on immunosuppressive TAMs . Specifiin vitro proof-of-concept showing that NK-92 cells expressing an anti-CD25 CAR can target Tregs, in vivo data are lacking, and concerns about on-target, off-tumor toxicity remain  can also be targeted by cellular immunotherapies. However, systemic blockade of Tregs has led to life-threatening autoimmune side effects in patients due to depletion of CD25+ effector T cells. While there is early y remain . In vivoimmunity . Althoug5One of the unique characteristics of GML which sets it apart from traditional therapeutics, is its ability to persist in the host for months or even years. This prolonged persistence enables GML to potentially drive long-term disease remission and provide ongoing immunosurveillance against residual tumor cells . While sLong-term engraftment of human T cells in NOD-scid IL2Rγnull (NSG) mice is complicated by xenograft-versus-host (xGvHD) immune-reactivity. This immune reactivity arises from both the MHC mismatch between human and mouse and the compromised immune system of the NSG mice, which is unable to reject the human T cell graft. Consequently, long-term engraftment studies in NSG mice may be confounded by toxicity that is specific to the model and may not accurately represent autologous GML therapy in humans. While NSG mice with Class I and Class II MHC knock-out can attenuate this toxicity , 184, thModifications aimed at enhancing GML longevity encompass various approaches, including alterations to intracellular domains, introduction of cytokines through genetic or pharmacological means, and deletion of intracellular inhibitory proteins that limit lymphocyte function, cytotoxicity, and persistence. These strategies not only promote the longevity of GML but can also be utilized to recruit endogenous lymphocytes, thereby extending the durability of the therapeutic response  of second-generation CARs, referred to as BBζ, resulted in enhanced persistence compared to CARs containing the CD28 co-stimulatory domain (CD28ζ). The enhanced persistence of BBζ CAR-T cells was attributed to 4-1BB’s ability to activate TRAF/NF-κB signaling, promoting fatty acid oxidation and mitochondrial biogenesis, which contrasted with the predominantly glycolytic signaling downstream of CD28 . ClinicaSubsequent investigations consistently revealed distinct metabolic responses between CD28ζ and BBζ CAR-T cells, with CD28ζ CAR-T cells exhibiting a “fast burn” metabolic response driven by PI3K-induced aerobic glycolysis, and BBζ CAR-T cells displaying a “slow burn” oxidative metabolism primarily fueled by fatty acid breakdown, aligning with 4-1BB’s role in T memory development and longevity . These fAlthough preclinical mouse models cannot fully replicate the persistence of engrafted T cells observed in humans, studies comparing BBζ and CD28ζ CAR-T cell performance in standard xenograft models have shown similar tumor-clearing effects between the two at high doses. However, at lower doses, BBζ CAR-T cells demonstrated functional superiority, indicating better memory conversion and resistance to the stress of low cell dosing . While m5.2In clinical trials for TIL and TCR-T therapy, IL-2 is commonly used to support the function and persistence of the adoptively transferred cells. However, in the case of CAR-T therapy, IL-2 is rarely used to enhance CAR-T persistence. This may be attributed, in part, to the CAR-T cells’ ability to produce paracrine IL-2 through the engagement of co-stimulatory CAR intracellular domains (ICD). Additionally, IL-2 is avoided in GML therapy due to the toxicities associated with higher doses needed to maintain the anti-tumor function of transferred T cells .Recent advancements have led to the development of orthogonal versions of IL-2 that can be genetically engineered into CAR-T cells, promoting expansion and longevity with limited systemic toxicities , 200. Byin vivo enhancement  and upregulating antiapoptotic mediators like Mcl1 and Bcl-2 . IL-15-sTo mitigate IL-15 toxicity, researchers generated CD19 CAR-T cells that overexpressed both IL-15 and IL-15 receptor alpha. This strategy successfully blocked toxicity while maintaining the persistence and anti-tumor activity of CAR-T cells induced by IL-15 . Howeverin vitro expansion of CAR-T cells with IL-7 leads to a higher frequency of cells exhibiting a memory stem cell phenotype (Interleukin-7 (IL-7) plays a crucial role in enhancing the homeostasis and survival of memory and naïve T cells by upregulating Bcl-2 and repressing proapoptotic markers . Severalhenotype . These cin vivo efficacy of CD19 CAR-T cells in a xenograft model of leukemia. However, it did not improve long-term persistence compared to CAR-T cells expressing IL-2 or IL-15  is a crucial cytokine involved in regulating the function of CD8+ T cells and has emerged as a potential target for cancer immunotherapy. IL-21 enhances CD8+ T cell activity by promoting their proliferation, differentiation, and increasing the expression of cytotoxic molecules like perforin and granzyme B . Additioin vivo efficacy to GPC3 CAR-T cells against a xenograft model expressing varying levels of GPC3, compared to unarmored CAR-T cells or those with the IL-4/IL-7 inverted receptor. The study also observed a higher number of CD3+ T cells in the peripheral blood of mice receiving cells expressing the IL-4/IL-21 inverted receptor, indicating improved in vivo persistence  antibodies, antibody-like fragments, or soluble PD1. Several studies have reported the use of CAR-T cells engineered to secrete constitutive or inducible CPI in mouse models –228. Whiin vitro preclinical models but was not tested in mice  studies have also been conducted. Studies in non-murine species facilitate the study of unique GML attributes against tumors; canines present with spontaneous and heterogeneous tumors and are pathogen-exposed, as are several NHP species that provide the most genetically similar model to human, and present the most relevant model to recapitulate the consequences of LDC as well as allowing longitudinal sampling . Until nGM: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. MT: Writing – original draft, Writing – review & editing.


Photodynamic Therapy (PDT) is an effective tumor treatment strategy that not only induces photocytotoxicity to kill tumor cells directly but also activates the immune system in the body to generate tumor-specific immunity, preventing cancer metastasis and recurrence. However, some limitations of PDT limit the therapeutic efficacy in deep tumors. Previous studies have used different types of nanoparticles (NPs) as drug carriers of photosensitizers (PSs) to overcome the shortcomings of PDT and improve therapeutic efficacy. Among them, bacterial outer membrane vesicles (OMVs) have natural advantages as carriers for PS delivery. In addition to the targeted delivery of PSs into tumor cells, their unique immunogenicity helps them to serve as immune adjuvants to enhance the PDT-induced immune effect, providing new ideas for photodynamic anticancer therapy. Therefore, in this review, we will introduce the biogenesis and anticancer functions of OMVs and the research on them as drug delivery carriers in PDT. Finally, we also discuss the challenges and prospects of OMVs as a versatile drug delivery carrier for photodynamic anticancer therapy. Photodynamic Therapy (PDT) is a photochemical treatment triggered by light irradiation, and it has played a good role in the clinical treatment of tumors, precancerous lesions, infectious diseases, and other diseases as an alternative therapy in recent years. The principle of PDT is based on the excited photosensitizers (PSs) to produce reactive oxygen species (ROS) upon light irradiation at a specific wavelength, resulting in photocytotoxicity to damage the target tissue or cells . Low-tox2O2, and H+) or externally applied physical stimuli  to realize controlled release of PSs at specific sites  are nanosized lipid-bound vesicles with a size range of 30–150 nm in diameter released from cells. EVs have a lipid bilayer membrane that contributes to carrying exogenous anticancer drugs or therapeutic agents. In contrast to conventional synthetic nanomaterials, EVs have some advantages, such as low toxicity, low immunogenicity, high biocompatibility, and good stability in blood circulation . EVs alsTherefore, in this review, we will introduce the biogenesis and anticancer functions of OMVs and the research on them as drug delivery carriers in PDT. Finally, we also discuss the challenges and prospects of OMVs as a drug delivery carrier for photodynamic anticancer therapy.OMVs are non-living spherical double-layer vesicle structures released by Gram-negative bacteria and some Gram-positive bacteria, mainly composed of the peptidoglycan layer and periplasm, containing bacterial bioactive proteins, lipids, nucleic acids, and metabolites . Gram-neOMVs are derived from the OM and have the outer membrane proteins (OMP), LPS, lipoproteins (LPP), and peptidoglycan, which belong to pathogen-related molecular patterns (PAMPs). PAMPs can bind to pathogen recognition receptors on immune cells, activating the innate immune response. OMVs can passively target the tumor via the enhanced permeability and retention (EPR) effect . With thE. coli DH5α competent cells to induce Trx-BFGF fusion protein expression and localized in the periplasmic space under the guidance of the Trx protein and loaded onto OMVs to form BFGF-modified OMVs (BFGF-OMVs). They found that BFGF-OMVs is a novel therapeutic tumor vaccine that can induce the body to produce antiangiogenesis autoantibodies to inhibit tumor angiogenesis through active immunization. The persistent anti-BFGF autoantibodies can be observed in tumor-bearing mice after only three times BFGF-OMVs intervention, which exerts tumor suppression effects  had been inactivated to avoid possible adverse effects by endotoxin lipopolysaccharide. After being injected intravenously via the tail vein, OMVs can target tumors and accumulate in tumor tissue without any targeted modification. OMVs induced long-term antitumor immune responses in tumor-bearing mice models and eradicated tumors by producing IFN-γ within the tumor microenvironment  mutant ironment .Klebsiella pneumonia as the delivery carriers to load DOX. In their study, DOX and OMVs were gently mixed in PBS and then incubated at 37°C to form the DOX-loaded OMVs (DOX-OMV). DOX was efficiently transported into non-small-cell lung cancer cells by OMVs, leading to intensive cytotoxic effects and cell apoptosis. While OMVs elicit appropriate immune responses to enhance the antitumor effect of DOX with no evident toxic side effects and adverse reactions in tumor-bearing nude mice -specific affibody in the membrane as a targeting ligand, named AffiHER2OMVs. The TAMRA-labeled siRNA targeting kinesin spindle protein (KSP) as a model therapeutic agent was loaded into AffiHER2OMVs using electroporation. The siRNA-loaded OMVs can induce targeted gene silencing and significant inhibition of cell proliferation in HER2-overexpressing cell lines and tumor growth regression in tumor animal models rather than elicit their antitumor effects by overstimulating inflammatory or immunological pathways -loaded MSNs by high-pressure co-extrusion. OMVs as delivery carriers can improve the stability of the 5-FU-loaded MSNs, reduce the leakage of 5-FU, and enhance the accumulation of 5-FU-loaded MSNs in colon cancer cells that contribute to MSNs accurately releasing 5-FU in the lesion  from hydrophilic catalase (CAT) and hydrophobic PS  via self-assembly. CAT-Ce6, as an amphiphilic complex, can avoid the aggregation of Ce6 in the aqueous solution, improving biocompatibility and distribution of Ce6 in tumor tissues. CAT-Ce6 has the catalytic activity of CAT, which can decompose H2O2 in tumor tissues to generate oxygen to improve the hypoxic state, thereby enhancing the photodynamic killing effect on tumors. Then they modified the programmed death-ligand 1 antibody (aPDL1) on the surface of OMVs by extrusion, forming OMV-aPDL1 as the delivery carriers for CAT-Ce6. After transferring CAT-Ce6 to tumor tissues, OMV-aPDL1 can activate anti-tumor immune responses and dendritic cells (DCs) to induce CD8 T cells to move to tumor tissues. While aPDL1 can blockade PD-1 to relieve the immunosuppressive effect, leading to eliminating tumor immune escape. Their experimental results prove that CAT-Ce6@OMV-aPDL1 can achieve the synergy of oxygenerated PDT and immunity for improving the efficiency of cancer treatment and even induced Immunological memory effect in mice receiving treatment twice (E. coli and then modified αvβ3 integrin targeting peptide (RGP) on the surface of OMVs and load the indocyanine green (ICG) by simply co-incubation to form I-P-OMVs. Based on their design features, I-P-OMVs can penetrate SC via follicle routes and target skin melanoma through a specific binding with αvβ3 integrin at the surface of melanoma cells. The ICG loaded by I-P-OMVs can induce photodynamic/photothermal effects in melanoma spheroids under NIR irritation, leading to a high level of ROS and hyperthermia to kill melanoma cells. The photothermal effects can destroy OMVs to release TRAIL, which binds to death receptors in melanoma cells’ surface, activating the apoptosis in residual melanoma cells. These synergistic therapeutic effects can prevent cell proliferation and invasion of melanoma cells by interfering with the relevant genes and proteins and delaying the progression, relapse, and metastasis of melanoma with good biosafety in the mice model, indicating that I-P-OMVs represent a feasible OMVs-based versatile nano-platform with great potential in photo-treatment of melanoma  for laser-triggered photodynamic effect and synergic antitumor therapy  instead of conventional PSs because these special membranes contain various enzymes and photosystems (photosensitive chlorophyll) that can induce efficient photodynamic effects. OMVs are fused with thylakoid membranes by co-extrusion to form bacteria-plant hybrid vesicles (BPNs). BPNs can target tumor tissues, stimulate the immune response, and even generate ROS under 660 nm light irradiation, indicating that BPNs integrated the immune-modulatory functions of OMVs as cancer vaccines with the photodynamic effects of Tk as PS. After only one light irradiation, BPNs can eliminate tumor growth and prevent tumor metastasis without any evident side effects in colon cancer CT26 tumor xenografted mice, suggesting that phytochemical-engineered OMVs represent a novel versatile membrane-based hybrid system for highly efficient tumor treatment  shells were employed to cover the surface of OMVs to attenuate toxicity . OMVs fu


Introduction: Systemic inflammatory response syndrome (SIRS) is frequently observed following decannulation from extracorporeal membrane oxygenation (ECMO). Differentiating cytokine release due to infection from endothelial injury from cannula removal and/or discontinuation from the ECMO circuit has been shown to impact treatment and outcomes. This response, however, may be complicated in COVID-19 patients due to prevalent glucocorticoid and immune modulator use. It remains unclear whether COVID-19 infection and/or associated immune modulator use impact the incidence of SIRS following decannulation.Objectives: The aim of this study is to investigate the incidence of the SIRS phenomenon and associated outcomes in patients with COVID-19 after ECMO decannulation.Methods: An IRB-approved retrospective chart review of all patients who survived ECMO between June 31, 2010 and July 7, 2021 was done to identify patients who experienced SIRS within 48 hours of decannulation from ECMO support. Patients with COVID-19 were confirmed by a positive reverse transcription polymerase chain reaction (RT-PCR) assay for SARS-CoV2. SIRS was confirmed when two out of three of the following criteria were met: fever, leukocytosis, and/or initiation/escalation of vasopressors. Patients who developed post-ECMO SIRS were then distinguished based on the presence of infection. Infection was defined by the presence of either a new or positive culture following decannulation. We compared the incidence of SIRS and infection within 48 hours of decannulation in patients with and without COVID-19.Results: We identified 227 eligible patients who survived ECMO. Twenty-eight patients (12%) had COVID-19. Of these patients, ten patients with COVID-19 (36%) experienced post-ECMO SIRS, including those with true SIRS (n=3) and associated infections (n=7). Five of the ten patients with COVID-19 who experienced post-ECMO SIRS were exposed to immune modulators within two weeks of decannulation. Ninety-five (42%) patients without COVID-19 developed post-ECMO SIRS. Thirty-day survival in COVID patients who experienced post-ECMO SIRS compared to COVID patients who did not experience post-ECMO SIRS was 73% vs. 94%. (p=0.11).Conclusion: Post-ECMO SIRS is common. The incidence of SIRS following decannulation was similar when historically compared to non-COVID patients who survived ECMO in a previously reported cohort from our institution. Immune-modulation exposure within two weeks of decannulation did not affect the incidence of SIRS in patients with COVID-19. Extracorporeal membrane oxygenation (ECMO) is a technology most often used to support patients with acute severe cardiac and/or respiratory failure and high mortality risk despite the use of maximal conventional therapy -3. In thWe conducted an IRB-approved retrospective chart review of all patients who survived ECMO at Thomas Jefferson University Hospital between June 31, 2010 and July 7, 2021. Patients who died within 24 hours of ECMO cannulation were excluded. Patients who died within 48 hours of decannulation were also excluded since these deaths were presumably attributed to preceding organ failure or complications rather than the SIRS response. Patients with COVID-19 were confirmed by a positive reverse transcription polymerase chain reaction (RT-PCR) assay for SARS-CoV2. Per institutional protocol, all patients were confirmed to be afebrile without evidence of uncontrolled or active infection at the time of ECMO decannulation. ECMO decannulation was initially exclusively performed in the operating room with direct vessel repair; however, during COVID, bedside decannulation was increasingly performed to optimize resource utilization. All patients received perioperative antibiotics post-decannulation for 24 hours unless a predetermined course for controlled infection was being completed. SIRS was confirmed when two out of three of the following criteria  were met within 48 hours of decannulation. Conventional SIRS criteria, including tachycardia and tachypnea, were not used due to potential confounding effects related to concurrent analgesia and sedation, vasopressors, inotropes, neuromuscular blocking agents, deconditioning, or specific ventilator settings. Patients who developed post-ECMO SIRS were then distinguished based on the presence of infection. Infection was defined by either (a) a previously recognized infection during ECMO that was appropriately controlled at the time of decannulation or (b) new positive culture data from blood, sputum, urine, or stool assays following decannulation. Patients who developed SIRS criteria without infection were further characterized as having "true SIRS." Administration of immune modulating therapies, including tocilizumab, baricitinib, hydroxychloroquine, and glucocorticoids, during the treatment course for COVID-19 infection was also noted. Veno-arterial ECMO (VA-ECMO) via femoral cannulation was primarily used for patients with cardiogenic shock, whereas the majority of patients with respiratory failure underwent veno-venous ECMO (VV-ECMO) using either a single-site, dual-lumen right internal jugular (IJ) venous cannulation or traditional dual-site cannulation via the femoral and right IJ venous cannulations. A minority of patients with predominant respiratory failure received VA-ECMO due to concurrent heart failure, hypotension, or anatomical or technical limitations. ECMO circuits included a Rotaflow centrifugal pump  and a Quadrox-D diffusion membrane hollow-fiber oxygenator .Statistical analysisData were expressed as a number paired with the percentage, mean, or median (quantile), as appropriate. The two groups were compared using chi-squared tests for categorical variables and standard t-tests for continuous variables. Statistical significance was accepted at a level of p = 0.05.We identified 227 eligible patients who survived ECMO. Twenty-eight patients (12%) had COVID-19. Of these, ten patients with COVID-19 experienced post-ECMO SIRS (36%), with a subset of five patients (50%) exposed to immune-modulating therapies within two weeks of decannulation. Among the ten with COVID-19 and SIRS, three had "true SIRS" (30%) and seven had SIRS associated with infection (70%).Patients with COVID-19 who experienced post-ECMO SIRS had lower 30-day survival compared to patients with COVID-19 who did not experience post-ECMO SIRS . Patients with COVID-19 who experienced post-ECMO SIRS were significantly older ; however, there were no other significant differences in other baseline demographics, pre-ECMO vital signs, pre-ECMO or pre-decannulation laboratory values, or listed complication rates  . Patients with COVID-19 who developed SIRS tended to be older  and on ECMO longer  compared to those with SIRS but without COVID-19. Despite this, there was no statistically significant difference in 30-day mortality between these groups . Additionally, there were no other significant differences in other baseline demographics, pre-ECMO vital signs, comorbidities, or listed complications (Table This is the first study to further describe the incidence and clinical implications of SIRS following decannulation from ECMO in patients with COVID-19. The clinical correlation between ECMO and systemic inflammation has been well described in the literature . The intThis study demonstrated that SIRS was commonly observed following decannulation from ECMO support, with an overall incidence of approximately 44% in this cohort. The incidence was similar when comparing those with and without concomitant COVID-19 infection. Despite COVID-19 patients being older and enduring longer durations of ECMO support, neither of these variables significantly impacted the incidence of SIRS response or relevant outcomes. It was initially hypothesized that prolonged cannulation would result in endothelial activation, which might sustain pro-inflammatory activation. It was additionally considered that the severe inflammatory state associated with COVID-19 might predispose to more frequent SIRS following decannulation . These dAmong those who developed SIRS, patients with COVID-19 tended to have lower creatinine, hepatic aminotransferase, and lactate levels prior to ECMO cannulation. However, there was no sustained difference in these markers prior to decannulation, which may have contributed to comparable rates of SIRS and subsequent 30-day mortality.When evaluating patients with COVID-19 more closely, we did not identify any pertinent pre-ECMO or pre-decannulation variable, other than age, which was associated with an increased risk of developing post-ECMO SIRS. Advanced age, in contrast to relevant studies in cardiac surgery, was associated with an increased risk of an inflammatory response and resultant SIRS. Despite an older cohort, SIRS was not associated with worse outcomes, which is consistent with a previous study of non-COVID patients at our institution [When evaluating the SIRS response further, the majority of patients with COVID-19 developed SIRS in the context of an associated infection, whereas only three patients developed "true SIRS." Unlike our previous experience with post-ECMO SIRS in patients without COVID-19, the development of post-ECMO SIRS with infection was not associated with worse 30-day survival.LimitationsThis study has several limitations owing to its retrospective methodology, small sample size, and single-institution experience. Furthermore, our definition of SIRS included the initiation/escalation of vasopressors, which varies from conventional definitions and excludes patients with associated tachycardia or tachypnea.Post-ECMO SIRS is common. The incidence of SIRS, whether infectious or not, is similar in COVID patients and non-COVID patients who survive ECMO. Immune-modulation exposure within two weeks of decannulation did not affect the incidence of SIRS in patients with COVID-19.


Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning, which is used to maximize the mutual information between different types of omics, is employed before latent feature concatenation. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicated that the proposed CLCLSA outperformed the state-of-the-art approaches for multi-omics data classification using incomplete multiomics data. These high-throughput techniques, including genomics, transcriptomics, proteomics and epigenomics, allow us to profile the genetic expression and interaction of molecules from different biological perspectives 2. However, each omics technique only provides a limited view of the underlying biological process. Integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes.The development of high-throughput omics technologies has revolutionized our ability to study biological systems at a molecular level 3, machine learning-based integration 4, and pathway-based integration 5. Neural network-based integration involves constructing networks for each omics data type and then integrating them to generate a more comprehensive network that captures the interactions between different omics layers, such as mRNA-miRNA interaction 6. Machine learning-based integration uses machine learning models to integrate different types of omics data 2. Pathway-based integration involves mapping omics data into known biological pathways to identify key pathways that are dysregulated in diseases 5. However, currently available integration methods for multi-omics data mostly rely on unreliable approaches, such as simply concatenating the latent feature representation 7 or raw input data 8 from different types of omics data, which limits the potential for computer aided diagnosis using multi-omics data 9.Numerous methods for integrating multi-omics data have been proposed, including neural network-based integration 11. A general solution to the multi-source data integration is to map the data from different omics layers into a common space and employing single-omics data integration method for downstream analysis 13. However, explicitly projecting the features from different omics types results in information loss due to the heterogeneity of multi-omics data 14 and further decreases the performance of downstream tasks, making it challenging yet important to effectively utilize incomplete omics data. Another general solution is to use complete case which considers only the set of subjects with complete observations across all omics data 15. This approach is convenient; however, it results in a decrease in sample size and the trained model only reflects the performance on the partial dataset. To fully use the entire dataset with incomplete multi-omics data, the multi-omics imputation based methods perform the data completion on raw input 16, which is challenging because of the high dimension of multi-omics data. Thus, an effective method, which could fully use partial multi-omics data, is necessary to integrate incomplete multi-omics data.Due to the high cost or limited sensitivity of instruments, it is possible that one or more omics data types of a biological sample may be missing. The presence of partially missing individual-level observations poses a major challenge in multi-omics data integration In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA), which contains three key components, as shown in 1) Utilizing complete multi-omics data as supervision, the model uses cross-omics autoencoders to learn the cross-omics feature representation. With cross-omics embedding, CLCLSA can reconstruct the incomplete multi-omics data by calculating the modality-specific representation.2) The multi-omics contrastive learning is used to maximize the mutual information between different omics layers as shown in in 3) Feature-level self-attention and omics-level self-attention are employed to dynamically select the most informative features for multi-omics data integration. The cross-omics autoencoders are used to restore missing omics data and the contrastive learning and self-attention are used to boost model performance.https://github.com/MIILab-MTU/CLCLSA.Extensive experiments were performed on four public datasets for multi-omics data classification. Experimental results indicated that the CLCLSA achieved the state-of-the-art performance on these tasks using both complete and incomplete multi-omics data. Our code is available in 2.2.1.17. Cotraining exchanges the separately trained models and provides the pseudo labels with discriminative information between views to accelerate model training, which focuses more on semi-supervised learning tasks 18. Multi-kernel learning aims at finding the mapping between different views with different kernels, and then combines the projected features for information fusion 19. Subspace learning aims at finding the common space between different views where the shared latent space contains the information of all views 20. For subspace learning, autoencoder is widely used for extracting the common representation of the multi-view data.Multi-view learning (MVL) is a strategy for fusing data from different modalities. MVL approaches can be divided into three categories: 1) co-training, 2) multi-kernel learning, and 3) subspace learning 8. Wang et al. 2 developed a deep learning based method for multi-omics data integration using graph convolutional network (GCN) for within omics feature integration and view correlation discovery network for cross-omics feature integration. Han et al. developed a dynamic fusion method for trustworthy multi-omics data classification, which dynamically calculated the feature importance and modality importance for omics data integration 9. Although these methods achieved impressive performance for multi-omics data integration, they were designed for complete multi-omics data integration.Machine learning and artificial intelligence have also shown promising results in multi-omics integration tasks. Xie et al. performed early concatenation of raw features from different omics and employed a deep neural network for implicit feature embedding 2.2.21. Most of the incomplete multi-view integration methods adopt the two-stage training strategy, which constructs complete multi-view observations based on imputation methods 23 and performs multi-omics integration based on the existing multi-view data and the imputed data. Lee employed the variational information bottleneck approach to integrate incomplete multi-omics data for classification 24. Multi-omics factor analysis (MOFA) 25 and MOFA+ 26 are unsupervised integrative approaches that factorize each omics matrix into the products of two components and the shared component is used to generate latent representation for downstream tasks. Multimodal variational autoencoder was proposed as an unsupervised approach for integrative multi-view analysis 27. Though these unsupervised learning methods are capable of learning latent representation, the task-relevant information may be lost because the labels of the biological samples are not used during the training. Compared to existing incomplete multi-view integration methods, our proposed CLCLSA not only imputes the missing omics using unsupervised learning, but also uses the supervised learning to guide the imputation module to generate a precise latent representation for missing data. In addition, our CLCLSA imputes the missing omics data on latent space rather than the input space, which reduces the dimensionality of the imputation task and improves the quality of the imputed data.Missing data are common in generating multi-omics data. It can arise due to a variety of reasons, such as insufficient sample volume, instrument sensitivity, data collection cost and poor tissue quality 2.3.28. Existing methods maximize the mutual information between the augmented data pairs 29 and adopt InfoNCE as the contrastive loss. However, our method uses the multi-view omics data as the compared pairs to maximize the consistency across different omics data.Contrastive learning is one of the most effective unsupervised learning methods in the field of representation learning. Contrastive learning aims at seeking a latent space that separates the pairs from different classes and maximizes the similarity for samples with the same classes 3.We assume that there are 3.1.9, we employ the self-attention component for feature-level feature selection and omics-level feature selection. Though sparsity of the high-dimensional data enables a high-level feature embedding, the informativeness of different features among different subjects are more important for multi-omics integration 30. The calculated feature-level attention scores are denoted asInspired by Han et al.31. We further employ the omics-level self-attention encoder The importance of different modalities is not fixed during the multi-omics fusion A nested multi-omics fusion is employed in this study which concatenates the features performed by feature-level self-attention and omics-level self-attention, as shown in 3.2.The proposed CLCLSA is a deep generative model that aligns feature representation from different omics after building a partially paired multi-omics data. The key component of CLCLSA for missing omics completion is that it models the shared latent feature representation as a combination of modality specific sub-spaces of all existing modalities. Instead of completing missing modality in the raw feature space, the cross-omics representation learning is employed to complete the missing omics in the latent feature space. Suppose the latent feature representation for Under the bi-view setting, the cross-omics data reconstruction loss is defined as33. To overcome this issue, we train the CLCLSA using both subjects with complete omics data and subjects with missing omics data. For the subjects with complete omics data, we feed them into the network to generate the feature representation for all views and train the autoencoders Under the multi-view setting, the cross-omics data reconstruction loss is defined in a fully connected fashion in 3.3.32. Most of the contrastive learning algorithms maximize the lower bound of the mutual information between the augmented samples and the raw training samples 34 using single-view data. However, we maximize the lower bound using the mutual information between different omics layers. We adopt the contrastive learning loss function proposed in 32 to maximize the entropy of the feature representation for each omics layer as well as the mutual information between different omics layers. The cross-view contrastive learning loss is defined as:Contrastive learning is a widely used technique in deep learning for incomplete multi-modality fusion, which guarantees the consistency between different modalities For the multi-omics contrastive learning, we average the contrastive loss between each of two omics layers, as shown in 3.4.Given an incomplete multi-omics dataset During the model training, we set the cross-entropy loss as the loss for the classifier 9. We adopt the auxiliary classification loss for each omics as a regularity for both omics-specific attention encoder Since both of the omics attention score and omics auxiliary classifier probability are the maximal Softmax outputs, they promisingly reflect the classification confidence The overall loss function for CLCLSA is defined in 4.To validate the proposed CLCLSA model, we applied the proposed model on four empirical multi-omics datasets. Extensive experimental results indicated the superiority of the proposed method on multi-omics data classification tasks using both complete and incomplete multi-omics data. In addition, we also analyzed the hyperparameter settings and performed ablation studies to demonstrate the effectiveness of different components.4.1.2, and set the same feature dimension for each omics. To fairly compare the proposed CLCLSA with existing approaches, we follow the same protocol of 9 to build the training set and testing set for evaluation.ROSMAP dataset 36, which contains 351 subjects, is used for distinguishing Alzheimer’s Disease (AD) subjects from normal controls.LGG dataset 37 is for low-grade glioma classification, which contains 246 grade-2 subjects and 264 grade-3 subjects.BRCA dataset 38 is for breast invasive carcinoma PAM50 subtype classification, which contains 876 subjects among 5 classes.KIPAN dataset 38 is used for kidney cancer type classification, which contains 658 subjects from 3 classes.We conduct experiments on the following four widely used multi-omics datasets. For each subject in these four datasets, the mRNA expression, DNA methylation and miRNA expression data were used. We adopt the same data preprocessing pipeline as KNN) 39, Support Vector Machine (SVM) 40, Linear Regression with L1 regularization (LR), Random Forest (RF) 41 and fully connected neural networks (NN).Five machine learning algorithms. K-nearest neighbor classifier (GRridge) 42. GRridge is a method for adaptive group-regularized ridge regression with group-specific penalties for high-dimensional data classification.Adaptive group-regularized ridge regression (BPLSDA) and Block sparse partial least squares discriminant analysis (BSPLSDA) 43. BPLSDA extends sparse generalized canonical correlation analysis to a classification framework, and BSPLSDA adds sparse constraints to BPLSDA.Block partial least squares discriminant analysis (MOGONET) 2: MOGONET jointly explores omics-specific learning using graph convolution network and cross-omics correlation learning using view correlation discovery network for multi-omics integration and classification.Multi-Omics Graph cOnvolutional NETworks (TMC) 44. TMC dynamically computes the trustworthiness of each modality for different subjects with reliable integration for multi-view classification.Trusted multi-view classification (CF) 45. CF performs multi-view information fusion based on late fusion and compactness-based fusion.Concatenation of final multimodal representations (GMU) 46. GMU employs the gates for selecting the most important parts of the input of each modality to correctly generate the desired output.Gated multimodal units for information fusion (MMDynamic) 9. MMDynamic employs the feature-level and modality-specific gates for multi-modality data fusion.Multi-modality dynamic fusion  47. MCCA extends the canonical correlation analysis (CCA) into multi-view settings. CCA is a typical subspace learning algorithm, aiming at finding the pairs of projections from different views with the maximum correlations. For more than 2 views, MCCA optimizes the sum of pairwise correlations. For samples with missing modalities, the trained MCCA projects the existing modalities into the subspace as the feature representations.Multiview canonical correlation analysis (KCCA) 48. KCCA extends MCCA by adding kernel techniques.Kernel CCA (KGCCA) 49. KGCCA extends KCCA with a prior-defined graph between different modalities.Kernel generalized CCA (SCCA) 50. SCCA extends CCA with modality-specific sparse penalty.Sparse CCA (MVAE) 51. MVAE extends variational autoencoders for latent feature extraction and employs the product of the multivariate Gaussian distributions for information fusion. For samples with missing modalities, the trained MVAE generates latent representation by the product of the latent features from the existing modalities.Multi-view Variation AutoEncoder (CPM) 52. CPM performs feature embedding using incomplete multi-view data by focusing on the completeness and versatility of the feature embedding.Cross Partial Multi-View Networks (DCP) 32. DCP employs maximizes the conditional entropy through dual prediction to recover the missing views and employs dual contrastive loss to learn the consistent representation among different modalities.Dual Contrastive Prediction , F1-score (F1) and area under the receiver operating characteristic curve (AUC) to evaluate the performance. To evaluate the multi-class classification performance, we employed the ACC, weighted F1 score (WeightedF1) and macro-averaged F1 score (MacroF1).4.2.53 and ReLU activation functions. The The proposed CLCLSA contains 6 components, i.e., the feature-level self-attention encoder To investigate the effectiveness of different components of the proposed CLCLSA, we performed the hyperparameter finetuning by grid search. In detail, we trained the CLCLSA on four datasets using different 54, and the experiments were carried on a workstation with an NVIDIA RTX A6000 GPUs. Adam optimizer 55 with the initial learning rate of 0.0001 and the learning rate decay technique was employed to optimize the model weights. The maximum training epochs were set fixed as 2,500 for all experiments. The batch size was set as 245, 357, 612 and 460 for ROSMAP, LGG, BRCA and KIPAN datasets. The training times were We implemented the CLCLSA using PyTorch 1.10 4.3.We compared our CLCLSA model with existing multi-view classification algorithms, as shown in For the binary classification tasks, we compared the CLCLSA with the existing methods on ROSMAP and LGG datasets. Experimental results demonstrated that the proposed CLCLSA outperformed other methods on binary classification significantly. For the ROSMAP dataset, the CLCLSA improved the ACC from 9 and MOGONET 2. Classifying kidney cancer type using KIPAN dataset was a relatively simpler task that all methods achieved quite high performance.For the multi-classification tasks, we compared the CLCLSA with the existing methods on BRCA and KIPAN datasets. For the BRCA dataset, the proposed method slightly improved the ACC from 4.4.We further compared the proposed CLCLSA with other existing multi-view incomplete data classification methods on these four public multi-omics datasets. We manually specified the missing rates ranging from 0.1 to 0.8 with an increment of 0.1, and the performance of multi-omics data classification using incomplete multi-omics data is shown in For ROSMAP dataset, the CLCLSA achieved the AUCs of 0.8 if the missing rate was smaller than 0.6. For the performance using the multi-omics data with the missing rate of 0.3, the MVAE achieved a higher ACC and F1Score than our CLCLSA.For LGG dataset, the CLCLSA achieved the highest performance using incomplete multi-omics data with different missing rates. Compared to CCA based methods, i.e., MCCA, KCCA, KGCCA and SCCA, the ACC, F1-score and AUC obtained by CLCLSA dropped slightly as the increase of the missing rates. CCA based methods cannot impute missing views and the results indicate that CLCLSA benefits from the missing omics completion using cross-omics autoencoders.56, the features for missing omics layers were not recovered which lowered the performance for the downstream classification tasks. For KIPAN dataset, the achieved ACCs of CLCLSA were higher than For multi-class classification task, the proposed CLCLSA achieved the highest ACCs and WeightedF1s among all compared methods with different missing rates on the BRCA dataset. The CCA based methods cannot recover the feature representations of missing omics layers. Since the CCA based methods were trained using partial training subjects with complete omics data, the performance dropped significantly when using incomplete multi-omics data with large missing rates. With limited training subjects, CCA methods cannot achieve a high performance with large missing rates, such as the missing rates greater than 0.5. For MVAE method, the multi-omics fusion is achieved by the product of latent distributions from the existing omics layers. Though MVAE employed ‘view dropout’ during the model trainingWe further performed the ablation study on these 4 multi-omics datasets using partial omics layers. Under the two-view setting, the cross-omics loss degraded from Integrating three types of omics proved to be advantageous for classification, particularly when dealing with complete data or when the missing rate is small. For example, in the case of multiomics data classification for LGG and BRCA datasets, integrating three types of omics was found to be beneficial when the missing rates were smaller than 0.1. Similarly, when the missing rates were smaller than 0.3, integrating three-omics layers were beneficial for multi-omics data classification on KIPAN dataset.Integrating mRNA expression data can be advantageous even for multi-omics data classification when the missing rate was high. For the ROSMAP dataset, the CLCLSA models trained with mRNA expression and DNA methylation (in green dashed line) and CLCLSA models trained with mRNA expression and miRNA expression (in orange dashed line) outperformed the CLCLSA models trained using three omics layers when the missing rates were set as From 4.5.We further investigated the model performance under different hyperparameter settings, i.e. From From From 4.6.We carried out the ablation studies to investigate the role of the contrastive learning, i.e., By comparing the performance of the proposed model with baseline models, i.e. ctst, aux and plain models, we observed that the all components of CLCLSA promoted the model performance in most of the settings. Without employing contrastive learning, self-attention and the auxiliary classifier, the plain models achieved the lowest ACCs among different missing rates. When using either contrastive loss or auxiliary classification loss alone, the model showed a slight improvement in terms of F1-scores. However, employing both contrastive learning and auxiliary classifiers with confidence loss, the performance was significantly boosted to a higher level among different missing rates. This ablation study indicated that all loss terms play indispensable roles in these incomplete multi-omics classification tasks.5.In this paper, we proposed a novel algorithm for multi-omics integration and classification, which can jointly exploit all training samples and is flexible for training samples with arbitrary missing omics data. Our CLCLSA model employs cross-omics autoencoders to predict the representation of missing omics data and uses contrastive learning and self-attention modules to boost model performance. Extensive experiments were conducted on four public multi-omics datasets and the experimental results indicated that the proposed CLCLSA outperformed the state-of-the-art approaches for multi-omics data classification using incomplete multi-omics data.


The risks and impact of COVID19 disease and vaccination in patients with Immune Mediated Inflammatory Diseases (IMID) remain incompletely understood. IMID patients and particularly patients receiving immunosuppressive treatment were excluded from the original, registrational phase-3 COVID19 vaccination efficacy and safety trials. Real-world observational data can help to fill this gap in knowledge. The BELCOMID study aims to explore the interaction between IMIDs, immune-modulating treatment modalities and SARS-CoV-2 infection and vaccination in a real-life patient cohort.A multidisciplinary, prospective, observational cohort study was set up. Consecutive patients with IMIDs of the gut, joints and skin followed at two high-volume referral centers were invited. Both patients under conventional treatment or targeted immune modulating therapies were included. Patient data and serological samples were collected at 3 predefined periods . Primary endpoints were positive PCR-test and SARS-CoV-2 serology reflecting previous SARS-CoV-2 infection or vaccination. Associations with IMID treatment modality and IMID disease activity were assessed. Results of the first two inclusion periods (before booster vaccination) are reported.At the first inclusion period data was assessed of 2165 IMID-patients before COVID19 vaccination. At the second inclusion period, data of 2065 patients was collected of whom 1547 had received complete baseline COVID19 vaccination and 222 were partially vaccinated. SARS-CoV-2 infection rate remained low in both groups. No significant increase in IMID flare-up rate was noted in patients with prior SARS-CoV-2 infection. Multiple logistic regression analyses did not show a significant influence of IMID-treatment modality or IMID activity on SARS-CoV-2 infection risk (based on PCR positivity or N-serology). Patients treated with conventional immunomodulators, systemic steroids, and patients on advanced therapies such as biologics or small molecules, had reduced S-antibody seroconversion. S-antibody response was also lower in patients without prior SARS-CoV-2 infection and in active smokers. A subset of patients (4.1%) had no S- nor N-antibody seroconversion following complete baseline vaccination.The BELCOMID study results confirm the benign course of COVID19 infection and vaccination in a large real-life IMID-population. However, our results underscore the need for repeated vaccination and smoking cessation in patients with IMIDs treated with immune-modulating therapies or systemic steroids during the pandemic. However, COVID19 can progress to acute respiratory distress due to a cytokine storm characterized by massive production of inflammatory cytokines . FurtherIMIDs reflect a spectrum of disorders of the gut , joints  and skin . IMIDs are believed to originate from an inappropriate immune response to environmental triggers in genetically susceptible hosts. Their management has been revolutionized in the past two decades by anti-cytokine therapies, T- and B-cell targeting therapies and most recently, small molecules such as Janus Kinase inhibitors (JAKi) and PDE4 inhibitor apremilast. As the risk of infections is generally considered higher in patients under Targeted Immune-Modulating Therapies (TIMT), this could result in patients mistakenly stopping their medication, putting them at risk for flare, requiring steroid treatment and even hospitalization. Now that vaccines have been developed, it is unclear whether TIMT, and broader immunosuppression, interfere with SARS-CoV-2 serologic responses as IMID-patients were excluded from the original, registrational phase 3 COVID19 vaccine efficacy and safety studies –12.As a consequence, many questions remain regarding the impact of COVID19 infection and vaccination in patients with IMIDs. We therefore explored the interaction between IMIDs, their immune modulating treatment modalities and SARS-CoV-2 in a large and real-life patient cohort.22.1.th 2020 and February 28th 2021. Both patients under conventional treatment or TIMT were included. Conventional treatment comprised therapies without immunomodulatory effect (N-IM) and immunomodulators (IMM). N-IM included mesalazine, sulfasalazine, acitretin, metformin, zinc, antibiotics, topical treatment options and light therapy. IMM included methotrexate, ciclosporin, dimethyl fumarate, mycophenolate mofetil, leflunomide, hydroxychloroquine and thiopurines. TIMT options included biologics, JAKi and apremilast.Consecutive patients with IMIDs of the gut, joints and skin seen at the two hospitals were invited to participate between December 17The study goal was twofold. The initial aim was to explore the association between COVID19 and IMIDs in a large, real-life population. This included prospective analysis of exposure to and infection with SARS-CoV-2 and relating this information to the underlying IMID disease course and treatment modalities within the study population. Secondly, as national vaccination campaigns started, the response to COVID19 vaccination in these patients depending on their different treatment modalities was studied in the same cohort.Both clinical patient data and serial blood samples were collected at predefined registration periods between December 2020 until February 2022 with a time interval of at least 4 months between sequential sampling (see Data on vaccination date and type were verified by check of the Flemish database Vaccinet.2.2®SARS-CoV-2 TrimericS IgG assay (≥33.8BAU/mL=seroconversion)  assay (>1.4=positive) was used to detect anti-nucleocapsid antibodies (N-antibodies) and the Abbott Architect™ SARS-CoV-2 IgGII Quant assay (≥50AU/mL=seroconversion) was used to detect anti-spike protein antibodies (S-antibodies) , 14. Bloversion) .2.3First we explored prevalence of SARS-CoV-2 infection and serologic outcome of vaccination in our IMID cohort. Primary endpoints were therefore: positive PCR test and SARS-CoV-2 serology reflecting previous SARS-CoV-2 infection or vaccination. As a second step we explored potential associations between infection and vaccination with IMID treatment modality, IMID disease activity  and increased SARS-CoV-2 exposure risk. Last but not least, we performed subgroup analysis on the cohort of patients who seemed to mount a lower antibody response.2.4® software. For descriptive statistics, data was exported to SPSS Statistics version 27. Analyses were performed in R version 4.0.2 with support of the Ghent University Biostatistics Unit.All data was collected in a pseudonymized electronic case report form using REDCapBoth marginal and conditional associations were examined. Marginal associations were tested using two-sided Pearson’s chi-squared tests, not taking into account potential confounders. Conditional effects were tested using adjusted binary logistic regression models and multiple logistic regression models. Due to the limited number of expected events, the number of explanatory variables that could be included in the binary logistic regression models of positive PCR test and high SARS-CoV-2 serology was restricted. Hence, to test the conditional effect of IMID treatment modality in the total population, the binary logistic regression models were adjusted for the propensity score of the respective treatment, increased exposure risk and BMI category. The propensity score was estimated by fitting a logistic regression model where treatment was the response and potential confounders were the predictors. Potential confounders of the association between treatment modality and positive PCR test or SARS-CoV-2 serology status were considered to be age, gender, smoking status, exposure risk, BMI category, IMID type, comorbidities and vaccination status.For the continuous endpoint of S-antibody titer, linear regression analyses were performed.All hypothesis testing was performed at the two-sided 5% significance level. No adjustment for multiple testing was made as the analyses are considered to be exploratory and hypothesis-generating. Results should be interpreted carefully and should be confirmed by other research.3Results of data collection before the start of the national vaccination campaign (Inclusion period 1) and after onset of the vaccination campaign but before booster vaccination (Inclusion period 2) are presented.3.1At baseline, 2165 IMID-patients participated. At the second inclusion period, data was collected from 1895 (87.5%) of 2165 initial patients and of 170 additional patients. Demographics were comparable for both inclusion periods Table 1.3.2Before onset of the vaccination campaign, almost half of participants (47.9%) were considered to have increased exposure risk to SARS-CoV-2, based on their job-related COVID19-exposure, social contacts and/or shielding behavior.Symptoms suggestive of COVID19 (irrespective of confirmed infection) were reported by 395 (18.2%) participants. This followed the Belgian epidemiological curve . Hospita/A total of 104 (5.1%) patients reported a confirmed SARS-CoV-2 infection based on positive nasopharyngeal PCR test. PCR-positivity rate was 9.9% (104/1045 tested patients). PCR-positivity rate was not influenced by flare-up rate of the underlying IMID nor by IMID treatment modality in multiple logistic regression analysis  and in patients with prior positive PCR test ./Multiple logistic regression analysis did not show significant associations between N-seroconversion rate and IMID flare-up or IMID-treatment modality .3.3At the second study sampling timepoint, 1547 of 2065 participants had received complete baseline vaccination  and 222 were partially vaccinated. The majority (66.6%) received BNT162b2.Symptoms suggestive for COVID19 were experienced by 7.6% of patients, positive PCR test was reported by 4.6% and test positivity rate (9.3%) was comparable to before the vaccination campaign. Only seven patients were hospitalized for COVID19 and none required ICU admission. Multiple logistic regression analyses again showed no significant influence of IMID treatment modality on PCR-positivity rate Figure 1/An extensive overview of antibody seroconversion rates per vaccine can be found in /Seroconversion rate for S-antibodies was 91.2%. Active IMID disease significantly decreased the risk of S-seroconversion . In contrast to what was found for N-antibodies, IMID treatment modality interfered with the S-antibody response. The odds for S-seronegativity were significantly higher in patients using TIMT, IMM, combination treatment (IMM+TIMT) and systemic steroids . Patients with mRNA-1273 vaccination had lower risk compared to BNT162b2 .Longer interval between vaccination and blood draw led to a significant higher risk of being in the lowest quartile of S-antibody titers .Patients with complete baseline vaccination treated with TIMT , combination of TIMT/IMM  or systemic steroids  had significantly higher odds of being in the lowest S-antibody quartile. Among TIMT, treatment with anti-TNF had a higher risk of being in the lowest quartile of S-antibody titer . Rituximab also increased the risk of being in the lowest quartile .Lastly, patients with active IMID disease  but also active smokers  had a significantly higher risk of being in the lowest quartile of S-antibody titer.On the other hand, patients with a positive PCR test in the timeframe between first inclusion period and second inclusion had significantly lower risk of ending up in the lowest quartile .®SARS-CoV-2 TrimericS IgG assay, 3 additional patients with S-seroconversion were picked up, leaving 63 patients (4.07%) with negative serology despite complete vaccination. Interestingly, 90.5% of these combined seronegative patients were treated with a biologic, of which 23 (36.5%) were on anti-TNF and 12 (19%) on rituximab. Furthermore, over half of these patients experienced IMID flare-up since previous inquiry .At the second BELCOMID inclusion period, after onset of the vaccination campaign, IMID flare-up rate since previous inquiry was 29.1% and 5.7% of patients needed systemic steroids. Patients with positive SARS-CoV-2 PCR or N-antibody seroconversion prior to vaccination, did not experience increased IMID flare-up rates compared to patients without prior COVID19 .4IMID-patients may be more affected by the pandemic compared to healthy peers. Particularly patients taking immunosuppressants expressed increased concern about potential SARS-CoV-2 infection and interactions with their IMID therapy, leading to increased psychological distress and reduced quality of life , 18. PreIn BELCOMID, SARS-CoV-2 infection prevalence was assessed by combination of reported PCR-positivity and measured N-seroconversion. Prior to the vaccination campaign, prevalence of COVID19 in our patients remained low. Remarkably, N-antibody seroconversion rates were lower than PCR-positivity rates. This observation might have a multifactorial basis. On the one hand it might indicate a potential attenuation of serologic responses to SARS-CoV-2 infection in IMID populations. Indeed, Simon et al. found that IMID-patients receiving cytokine inhibitors, had lower N-seroconversion rates compared to healthy individuals . HoweverAnother potential explanation for the discrepancy between N-seroconversion and PCR-positivity rate lies in the durability of the humoral response and the relative timing of PCR-testing and blood withdrawal for serologic analysis. Indeed several prospective and observational studies have reported progressive waning of the humoral immune response to SARS-CoV-2 infection and COVID19 vaccination over time –29. FollFew studies assessed serologic responses to COVID19 vaccination in IMID-patients compared to healthy controls. IMID-patients and particularly patients receiving immunosuppressive treatment were excluded from the registrational COVID19 vaccination trials. Real-world observational data therefore helps to fill this gap in knowledge. A prospective study in patients with acquired or inherited immune disorders showed variable immune responses to vaccination with BNT162b2 . A meta-First, we found that TIMT, with or without combined IMM and systemic steroid treatment were associated with significantly reduced S-seroconversion rates and increased odds of being in the lowest S-antibody titer quartile. Subgroup analyses within the TIMT group, revealed anti-TNF and rituximab as risk factors for lower S-titers. This is in line with Garcillàn et al, who found that corticosteroids, B-cell depleting therapies and JAKi substantially affect vaccine immunogenicity  and withNext, vaccination characteristics seem to impact S-serology. Pairwise comparisons in our broader IMID cohort again confirmed the CLARITY-IBD study findings  by demonThirdly, just as for the N-antibodies, our results suggest a decline of S-antibody titer over time as an interval of more than 120 days between last vaccine dose and serology analysis led to a significantly increased risk of being in the lowest quartile of S-antibody titers. However, the same time interval was not associated with a significant difference in S-antibody seroconversion rate which might be related to a longer durability of S-antibody response. Indeed other observational and prospective studies have shown a faster and higher rate of decline in N-antibodies compared to S-antibodies , 31, 42.Remarkably, our study identified 63 patients with combined seronegativity after complete vaccination. A potential explanation for this observation could be that this subgroup consist of patients with more refractory IMID disease. This is suggested by the high rate of TIMT and combination of TIMT/IMM use. Furthermore, there is a numerically higher rate of IMID flare-up leading to higher corticosteroid use in this subgroup compared to the total BELCOMID population. Therefore, based on our previously described findings, this refractory subgroup may have been prone to increased attenuation of antibody responses. A third registration timepoint is foreseen within the BELCOMID study protocol. This registration timepoint will enable to assess the interaction between IMIDs and COVID19 after booster vaccination. If absence of seroconversion persists despite booster vaccination, additional analyses including assessment of cellular immunity and genetic predisposition may provide answers to whether these patients actually do mount sufficient protection against SARS-CoV-2.Another particular finding was the interaction between active smoking and antibody titers. Smoking might facilitate SARS-CoV-2 viral entry through epigenetic mechanisms that alter transcription of several key proteins implicated in the development of COVID19 . HoweverOur study has some limitations. First, patients were recruited during routine follow-up at their respective treatment facilities. This might have caused recruitment bias by overlooking patients that were, for example, admitted at ICU with severe COVID19 bypassing contact moments with their treating physician. Numbers of patients with severe COVID19 requiring ICU admission in our cohort were however very low. Therefore, we were unable to draw any conclusions towards the association between IMIDs and severe COVID19. Other observational studies however, confirm low rates of severe COVID19 even in unvaccinated IMID cohorts , 45. PooSecondly, evaluation of potential differences in effect on S-antibody response between different IMID types was not performed. This was not within the study goals and not reliably achievable with our statistical models.® antibody assays targeting the nucleocapsid protein has been reported  and University Hospitals of Leuven (S64422). The patients/participants provided their written informed consent to participate in this study.Within the BELCOMID study group all members played a substantial role in set-up, design, data collection and data processing. Statistical analyses were done by JG, TL, MT, and SV with support of the Ghent University Biostatistics Unit. Manuscript was drafted by JG and then reviewed and adapted by all members of the BELCOMID study group. All authors contributed to the article and approved the submitted version.