Skip to main content
Download PDF

Blood-based biomarkers of inflammation in amyotrophic lateral sclerosis

Molecular Neurodegeneration volume 17, Article number: 11 (2022) Cite this article

Abstract

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease in which many processes are detected including (neuro)inflammation. Many drugs have been tested for ALS in clinical trials but most have failed to reach their primary endpoints. The development and inclusion of different types of biomarkers in diagnosis and clinical trials can assist in determining target engagement of a drug, in distinguishing between ALS and other diseases, and in predicting disease progression rate, drug responsiveness, or an adverse event. Ideally, among other characteristics, a biomarker in ALS correlates highly with a disease process in the central nervous system or with disease progression and is conveniently obtained in a peripheral tissue. Here, we describe the state of biomarkers of inflammation in ALS by focusing on peripherally detectable and cellular responses from blood cells, and provide new (combinatorial) directions for exploration that are now feasible due to technological advancements.

Background

The efforts to develop an effective treatment in Amyotrophic Lateral Sclerosis (ALS), have resulted in over 40 failed drugs in clinical trials for ALS, testing a broad range of targets [1]. In most of these clinical trials, it is difficult to ascertain the reason for the failure. The drug may have failed to engage its target, had efficacy in a subgroup of patients, or the therapeutic target was not relevant to disease progression at all, or within the clinical trial duration. To promote more informative clinical trials, the Food and Drug Administration (FDA) has included the co-development of biomarkers in their ALS drug development guidance for industry (finalized September 2019), and defines a biomarker as ‘a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions’ [2]. In this manuscript, we describe the state of biomarkers of inflammation in ALS by focusing on peripherally detectable and cellular responses from blood cells, per FDA defined subtype of biomarker. In addition, we provide new (combinatorial) directions for exploration that are now feasible due to technological advancements.

Definitions of biomarkers

To harmonize used terminology, the Food and Drugs Administration (FDA) has defined a biomarker and seven subtypes of biomarkers through the Biomarker EndpointS and other Tools (BEST) resource [2], as the following.

Biomarker

A defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions. Molecular, histologic, radiographic, or physiologic characteristics are types of biomarkers. A biomarker is not an assessment of how a patient feels, functions, or survives.

Diagnostic biomarker

A biomarker used to detect or confirm presence of a disease or condition of interest or to identify individuals with a subtype of the disease.

Monitoring biomarker

A biomarker measured serially for assessing status of a disease or medical condition or for evidence of exposure to (or effect of) a medical product or an environmental agent.

Pharmacodynamic/Response biomarker: A biomarker used to show that a biological response has occurred in an individual who has been exposed to a medical product or an environmental agent.

Predictive biomarker

A biomarker used to identify individuals who are more likely than similar individuals without the biomarker to experience a favorable or unfavorable effect from exposure to a medical product or an environmental agent.

Prognostic biomarker

A biomarker used to identify likelihood of a clinical event, disease recurrence or progression in patients who have the disease or medical condition of interest.

Safety biomarker

A biomarker measured before or after an exposure to a medical product or an environmental agent to indicate the likelihood, presence, or extent of toxicity as an adverse effect.

Susceptibility/risk biomarker

A biomarker that indicates the potential for developing a disease or medical condition in an individual who does not currently have clinically apparent disease or the medical condition.

Search strategy and selection criteria

References for this review were identified by searches of PubMed up to February 2021, and references from relevant articles. The search terms “ALS + blood + inflammation” and “Amyotrophic + blood + inflammation” were used, with recommended additions by reviewers. Phase 3 studies in Table 3 were identified by assessing clinicaltrials.gov for entries up to February 2021 that matched the search terms “ALS” (condition or disease) and “phase 3” (other terms). During filtering, studies with a recruitment status of “suspended”, “terminated”, or “withdrawn” were removed from assessment. The remaining studies were subsequently analyzed for the medicinal product’s main target and suitability for this review. There were no language restrictions. The final reference list was generated on the basis of relevance to the topics covered in this review

Introduction

ALS is a devastating neurodegenerative disease, characterized by a progressive loss of motor neurons, paralysis, and inflammation with an average survival of 3–5 years after diagnosis. Although ~ 5–10% of cases are familial, and ~ 70% of the familial cases can be explained by identified gene mutations, e.g. the C9orf72 repeat expansion [7, 8], most cases are of a sporadic nature whereby no other family member is known with the disease [9]. Only two drugs have received market approval in the USA, Riluzole and Edaravone, and one in the EU, Riluzole, providing only a moderate extension of patient life [10]. With the number of cases expected to rise to nearly 400,000 world-wide by 2040 [11], ALS represents a large unmet medical need for which many drugs are in development [12]. The use of validated and qualified biomarkers in ALS clinical trials could greatly increase the informative value of any conducted trial regarding whether the target was properly engaged (pharmacodynamic biomarker), the therapeutic potential of the drug or target (pharmacodynamic, prognostic biomarker), as well as assist in population stratification for likely responders to a specific treatment in this heterogeneous disease (predictive biomarker). Better biomarkers would assist in differentiating ALS from other diseases with similar presentation and the earlier diagnosis of ALS will allow patients to receive treatment sooner, as well as be eligible for clinical trials sooner (diagnostic biomarker). Furthermore, a biomarker that tracks with disease would aid in disease classification, prognostication, and assist in the perhaps personalized need for clinical intervention, as ALS phenotypes are largely heterogeneous [13].

Characteristics of an ideal biomarker for ALS include a high correlation with a disease process in the central nervous system (CNS) or with disease state / progression, stable throughout the day, high sensitivity and specificity depending on its use, and ease of obtaining samples, (e.g. blood) [14]. Biomarker research in ALS has led to the identification of p75ECD from urine [15], phosphorylated neurofilament heavy (pNfH) in cerebrospinal fluid (CSF) [16], and neurofilament light (NfL) in serum [17] as diagnostic, prognostic, and pharmacodynamic biomarkers. However, these three are not unique to ALS, while potentially having a role in prognostication and disease monitoring, these biomarkers have low specificity for ALS. These biomarkers are also dependent on axonal loss and may reflect later stages of degeneration and therefore miss the spectrum of disease activity. Finally, CSF extraction for pNfH, while useful for diagnosis, does involve what can be a difficult procedure for some patients and serial lumbar puncture is unrealistic outside of clinical research. To develop an ideal biomarker for ALS a more rounded test or panel of tests is needed that is easy to reproduce, addresses the spectrum of disease, and is simple to obtain multiple specimens. To address the spectrum of disease activity this biomarker or group of biomarkers should focus on neuronal loss as well as other disease mechanisms.

Inflammation occurs in the CNS during ALS, and is also detected peripherally in blood as altered immune cell population abundance and released factors [18,19,20]. The potential causative or disease driving role of this inflammation in ALS is not fully clear, as there are studies that support both a primary or secondary role during ALS disease progression. Inflammatory biomarkers could reflect that bridge to other disease activity and, in combination with assays of neuronal degeneration, provide a better sense of disease subtypes and disease activity. In this review, we discuss the state of biomarkers of inflammation in ALS, focusing on peripherally detectable and cellular responses from blood cells, and provide new (combinatorial) directions for exploration that are now feasible due to technological advancements. We start with potential diagnostic biomarkers of inflammation in ALS, proceeding to prognostic biomarkers and conclude with how other biomarkers, pharmacodynamic and predictive, can assist in the design and interpretation of clinical trials in ALS.

Diagnostic biomarkers

The diagnosis of ALS depends on the exclusion of other disease mimics first, and often takes over 12 months [21]. Hereditary neuropathy, acquired neuropathies, Myasthenia Gravis (MG), and central demyelinating disease can present similar to ALS and be difficult to fully exclude. This is compounded even more by the varied presentation of ALS (bulbar vs. limb onset, primary lateral sclerosis vs. progressive muscle atrophy, young onset vs. old onset), as well as by the addition of non-diagnostic characteristics such as flail-arm syndrome and frontotemporal involvement [13]. A robust and specific ALS diagnostic biomarker would aid to decrease the uncertainty and the time to diagnosis for all patients, allowing for earlier interventions and increasing the time window of eligibility to participate in clinical trials. With the exception of a few of these biomarkers (e.g. acetylcholine receptor antibodies in MG and MRI in multiple sclerosis) a specific and sensitive biomarker has been difficult to identify for these conditions, and even more so with (neuro) inflammation not solely occurring in ALS. As a result, therapeutic trials are based primarily on clinical scales and require longer observation to assess efficacy, thereby increasing cost and slowing drug development [22,23,24,25]. The initial hurdle for a diagnostic biomarker is to distinguish between ALS and controls (as the studies in Table 1 were designed to detect), but in practical use the biomarker(s) need to distinguish between ALS and other ALS-mimicking diseases, e.g. those that are excluded at diagnosis.

Table 1 Blood-based diagnostic biomarkers of inflammation in ALS
Full size table

Inflammation as a biomarker in ALS, historically, was studied in context of the spinal cord disease. Inflammation-based biomarkers started with candidate-based analyses of cerebrospinal fluid (CSF), assessing cell populations such as lymphocytic infiltrates and secreted factors of the CNS [19]. The detection of peripheral inflammation in blood samples of ALS patients [20] led to the assessment of these blood-based markers of inflammation as potentially diagnostic biomarkers. Initially, studies were conducted on candidate-based cell populations, such as the increased neutrophil-to-lymphocyte ratio in ALS [28], which has potential as a diagnostic biomarker in multiple sclerosis [62], and soluble factors (Table 1). In-depth immunophenotyping by flow cytometry identified 116 distinct blood-derived cell populations of which 32 populations were altered, including elevated cell counts of granulocytes, NK cells and T cells in ALS samples compared to controls [55], and was confirmed by others [27]. Technological advancement led to the multiplexing of many soluble factors on the same sample simultaneously, but this also did not identify a robust ALS diagnostic biomarker (as of yet). Although candidate-based analyses suggest inflammatory pathways and factors (e.g. cytokines) may be differentially expressed in ALS, the variability among the reports of these measurements is high, with the most commonly measured cytokines are reported, in different studies, to be upregulated and/or downregulated and/or unaltered in ALS, including C-reactive protein (CRP) [26, 28, 32], interleukin 6 (IL-6) [27, 29, 33,34,35, 38,39,40, 56], IL-8 [32, 34, 38, 39, 44, 56], tumor necrosis factor (TNF) [27, 29, 31, 32, 34, 38, 39, 56], IL-1β [27, 31, 32, 38, 40], IL-17 [27, 32, 40], IL-33 [40, 41], IL-10 [27, 32, 34, 39], monocyte chemoattractant protein 1 (MCP-1) [34, 38, 44, 47, 48], and interferon gamma (IFNγ) [27, 31, 34, 45, 56] (Table 1). Please note that many of these studies are underpowered ‘pilot’ studies that require replication. To minimize inter-sample variability simultaneous combinations of single factors were assessed to strengthen the diagnostic value of the combined biomarkers, e.g. IL-2 and IL-6 [34], or microRNA (miRNA) pairs (ratios) [36]. (Please note, that these specific examples of combined markers results have not (yet) been replicated.)

With many of the subpopulations of blood cells producing different expression levels of genes or soluble factors, these analyses in blood provide an averaged data point, losing the cell-type specific resolution of the obtained data. This may partly explain why there is no established diagnostic biomarker for ALS (yet) by using single factor analyses, in addition to the other possible explanations including technology used [63], sample sizes, co-morbidities, (anti-inflammatory) medications, and disease state at biosample donation [64].

Assessing the gene or protein expression of specific cell populations (i.e. not using plasma, serum, or peripheral blood mononuclear cells (PBMCs) in a bulk analysis) provides higher resolution data, and has identified more detailed potential biomarkers. For instance, transcriptomic analyses of monocytes identified an unique inflammation-related gene expression profile, including altered IL-1B, IL-8, FOSB, C-X-C Motif Chemokine Ligand 1 (CXCL1), and CXCL2 gene expression, which distinguished ALS from control samples [56]. By differentiating blood monocytes into macrophages and conducting transcriptomic analyses on these cells McCauley et al., identified an impaired type I interferon response in the gene expression pathway analysis of ALS cells harboring the C9orf72 repeat expansion [52]. Additionally, in-depth immunophenotyping of 80 ALS and 50 control samples by Gustafson et al., detected an ALS immune cell profile of 32 leukocyte populations, including T cell phenotypes, and the levels of the activation marker Human Leukocyte Antigen DR isotype’s (HLA-DR) expression on monocytes [55], which has been confirmed by others [61]. To further assess potential biomarkers in a cell-type specific manner, in vitro assays designed to detect the inflammatory-response post stimulation by lipopolysaccharide (LPS) identified elevated levels of IL-8 and CCL-2, but not of TNF, IL-1β, IL-6, IL-12p40, IL-8, C-C Motif Chemokine Ligand 2 (CCL2) and IL-10, in the conditioned media from ALS dendritic cells [54], a cell type closely related to monocytes. Interestingly, miRNA analysis of neural-enriched extracellular vesicles from plasma identified that miR-146a-5p, also associated to monocyte inflammatory response [49], is increased in ALS [50]. Alternative assays are used by stimulating cells with SOD1 proteins [57, 60] or staphylococcal enterotoxin B (SEB) [58] to increase the detectable levels of cytokines, often with a low number of samples included per study, and have not (yet) identified a biomarker.

Although beyond the scope of this review, the reoccurring indication of (activated) monocyte biology as a diagnostic biomarker in ALS begs the question how this cell type may contribute to ALS disease pathophysiology. One such direction may be the increased production of chitinases by monocyte-derived macrophages [65], and/or by the secretion of cytotoxic cytokines (reviewed in [18]). More research is needed to further our understanding of the contribution of monocyte biology to ALS.

Although some of these biomarkers, or biomarker profiles, distinguish ALS from controls, more research is needed to assess their usefulness to ALS, including their sensitivity, specificity, and robustness to assist in the diagnosis of ALS as many cytokines are also elevated or decreased in other indications. In view of the promising results from cell-type specific analyses, future work could include cell-type specific analyses of circulating or in vitro-invoked inflammatory responses.

Prognostic biomarkers

In clinical trials, an FDA accepted endpoint of an effective drug for ALS is the slowing of disease-related decline, stabilization, improvement of function in daily activities as measured, for example, by the ALS Functional Rating Scale-Revised (ALSFRS-R) or a similar scale [66]. With the progressively increased inflammation and the progressive decline in ALSFRS-R during disease progression, blood-based markers of inflammation may be exceptionally suitable as prognostic biomarkers. These would predict the rate of decline in a patient, and potentially distinguish between disease sub groups (slow and rapid progressors) and adjust clinical trial eligibility criteria to minimize inter-subject variability. Disease duration is often defined as the number of months post disease onset or diagnosis and survival [30, 67]. As there is significant heterogeneity in the rate of ALS disease progression [68], ‘disease duration’ may not reflect changes in motor function or independence in daily living as well as the ALSFRS-R, or other quantifiable scales or measurements.

During disease progression, electromyographical measures are altered, including a reduction in the compound motor action potential (CMAP) of the phrenic nerve, as well as a reduction in respiratory measures such as Forced Vital Capacity (FVC). These measurements were both not correlated to CC-16 expression [37], but had a weak inverse correlation with plasma IL-6 expression [33]. The broad and responsive inflammation marker CRP is reported to have a weak correlation with ALS disease duration [26, 28, 67, 69], as well as CD5L, TNF, IFNγ, and nitric oxide (NO) [30, 45]. Strikingly, studies assessing pro- and/or anti-inflammatory cytokines (IP-10, MCP-1, MIG, RANTES, CC-16 IL-2, IL-4, IL-6, IL-8, IL-10, IL-17a, TNF, IFNγ, sTNFR1, sTNFR2, IL-1β, IL-18, IL-33, IL-37, IL-1Ra, sIL-1R2, IL-18BP, sIL-1R4) in the blood of ALS patients (Table 2) were unable to detect a correlation with ALSFRS-R scores of patients that they assessed at the time of biosample donation [27, 33, 34, 37, 40, 70]. In addition, Devos et al. assessed the levels of IL-6 in the plasma of 109 ALS patients at the beginning of a clinical trial (NCT00868166) with ALSFRS-R measurements up to 18 months. Although the authors identified a predictive correlation with non-inflammation blood-based markers with ALSFRS-R rate of decline (NfL, ferritin, 4-HNE, 8-OHdG), IL-6 levels at baseline were not predictive [70]. Interestingly, a recent study with 79 ALS patient samples, detected a correlation between IL-6 and the ALSFRS-R [29]. These studies indicate that a bulk measurement of a cytokine in plasma or serum may not provide sufficient resolution as a prognostic biomarker, similar to discussed for diagnostic biomarkers. In support of this idea, increasing the resolution of measurement by assessing the expression levels of cytokines, perhaps in combination, on specific blood-based immune cell populations provides potential. As an example of this approach, during the clinical trial assessing the safety of NP001 (regulator of macrophage and monocyte activation) in ALS (NCT01091142), the expression levels of the monocyte activators CD16 and HLA-DR on the surface of monocytes were measured throughout the clinical trial. Although these measurements were not correlated with the score of the ALSFRS-R on the day of biosample donation, they were correlated with ALSFRS-R rate of decline. Moreover, the combination of both markers simultaneously correlated with the ALSFRS-R rate of decline more strongly [72]; a higher the level of activation correlated with a more rapid ALS disease progression [61, 72].

Table 2 Blood-based prognostic biomarkers of inflammation in ALS
Full size table

In addition, in-depth immunophenotyping of 116 leukocyte populations from blood-derived immune cells identified a monocyte immune profile, as well as an T-reg immune profile, in a subpopulation of ALS patients correlated to the ALSFRS-R and survival [55]. This subpopulation accounts for ~ 39% of the tested cohort, were generally younger (54.8 vs. 60.5 years old), were more likely familial ALS (26.1% vs 2.8%), and survived longer than patients without this specific immune profile (344 vs. 184 weeks) [55], indicating that this specific inflammatory profile may be protective. Moreover, specifically assessing the transcriptomic profile of blood-derived monocytes additionally also identified alternative gene expression in monocytes between patients with a rapid and slow disease progression [56].

The value of cell population specific studies is further illustrated by the study from Henkel et al., in which they define a slow and rapid disease progression on basis on the Appel ALS score (AALS) [74]. The abundance of T-regs and the their FoxP3 protein expression was lower in rapidly progressing ALS patients, and was inversely correlated with disease progression rates determined by the AALS [73]. In addition, the gene expression levels of FoxP3, TGF-b, IL-4 and Gata3 from leukocytes were also decreased in rapidly progressing patients, and also inversely correlated to progression rate [73], whereas when measured in plasma the levels of TGF-b [71] and IL-4 [34] were not correlated to disease progression.

The subpopulations described by Jin et al., Beers et al., Henkel et al., Zhao et al., and Gustafson et al., imply that there are discernable subtypes of progression in ALS that are associated with inflammatory responses detectable in blood. Monitoring these responses could be used in population stratification for clinical trials, or as biomarkers that predict responders. The population-specific characteristics between these studies overlap in the identification of monocytes, T-regs and their responses as possible distinguishers of these subpopulations [26, 27, 55, 56, 73].

The future of prognostic biomarkers in ALS may include their use to predict disease progression rate, and thus clinical trial eligibility. Strides have been made to use a disease progression predicting algorithm to minimize variation and expand potential clinical trial eligibility [75, 76], as well as to perform post-hoc analyses to assess drug efficacy per participant, although these have not yet incorporated biological biomarkers. Future studies may determine whether inclusion of blood-based biomarkers (of inflammation) may increase the accuracy of the algorithm, e.g. for each of the subpopulations identified by immune profiles by Gustafson et al. [55]. In addition, with novel research, prognostic biomarkers may be applied to predict not only the disease progression after disease onset, but perhaps also predict phenoconversion from a pre-symptomatic to a symptomatic disease stage in high-risk individuals (e.g. those harboring a known ALS mutation) [77, 78]. A longitudinal study with healthy aging, mild cognitive impairment, and Alzheimer’s Disease participants over 5 years identified approximate time windows prior to phenoconversion and to the next disease symptom on the basis of biomarker changes detected many years prior [79]. In individuals at risk for ALS, NfL is increased in plasma and CSF ~ 12 months prior to symptom onset [80]. The future identification of additional prognostic biomarkers for phenoconversion in ALS may allow for an understanding of possible prevention intervention windows [77].

Pharmacodynamic biomarkers

Despite the prediction that neuroinflammation contributes significantly to ALS pathophysiology [19], anti-inflammatory therapeutic strategies have failed in clinical trials in ALS [1]. The inclusion of readouts to confirm target engagement (pharmacodynamic biomarkers) in clinical trial design assist in confirming whether the therapeutic exerted the expected effect per participant throughout the treatment paradigm informing also target validity, and is highly recommended [81, 82]. In fact, the hypothetic inclusion of pharmacodynamic biomarkers in previous clinical trials may have aided in the understanding of the drugs or targets in ALS, and perhaps would have minimized clinical trials on the same target due to this increased knowledge. Although many therapeutics aim to target inflammation of the CNS, peripheral readouts may be established as a proxy readout. An example is monocyte activation as measurement of NP001 target engagement [72], or cytokine profiling in CSF and blood [83, 84]. Interestingly, a Phase 2 trial of tocilizumab (a humanized monoclonal antibody against the IL-6 receptor) in ALS (NCT02469896) assessed both mRNA profiling of PBMCs and CRP expression in blood and CSF, and although the transcriptomic readout was inadequate as a pharmacodynamic (or predictive) biomarker [84], the CRP levels were significantly altered by the treatment and may function as a pharmacodynamic biomarker.

Table 3 includes the biomarkers used in Phase 3 clinical trials targeting inflammation in ALS, as published or listed on clinicaltrials.gov supplemented with any peer-reviewed published information. Even in the case of targeting (neuro) inflammation with a medical product in ALS, few clinical trial designs include pharmacodynamic biomarkers (Table 3), although these may have been included at an earlier clinical stage. Perhaps due to the labor-intensive nature of in vitro assays, and the needed standardization or coordination between clinical sites, these paradigms have not (yet) been utilized as pharmacodynamic biomarkers in ALS during treatment (Table 3).

Table 3 Phase 3 clinical trials in ALS with a medical product targeting (neuro)inflammation
Full size table

Predictive biomarkers

The strong heterogeneity of the ALS clinical phenotypes [85] and the identification of specific immune phenotypes and inflammatory responses that correlate with rate of disease progression [26, 55, 73], imply that subsets of ALS patients exist and that some may be particularly responsive to a therapeutic target and drug. A priori predictions of the potential responsiveness per patient to a therapeutic strategy could lead to clinical trial inclusion criteria on basis of such a predictive biomarker, and thus an enrichment of potentially responsive participants per clinical trial, as well as support personalized medicine endeavors [82]. For instance in ALS, the Phase 2 clinical trial for NP001, a monocyte and macrophage regulator, identified CRP levels as a predictor of responsiveness [69].

Safety biomarkers

With the testing of novel medical products there is a need to identify potential safety biomarkers that will predict adverse events prior to the event occurring. An example is the monitoring of liver enzymes during the treatment with a drug that may cause liver damage, or particularly in those participants with a specific genetic polymorphism that affects drug metabolism to predict whether e.g. irreversible liver damage will occur. With the advancement of novel technologies in medical products, and with the genetic component of ALS, new technologies are developed with new (unknown) risk profiles, in particular biologics [86], including monoclonal antibodies, antisense oligonucleotides (ASOs), AAV-delivered gene therapies, and cell therapies. The safety risks can include excessive inflammation, immunogenicity, cytokine storms, and ultimately sepsis. Monoclonal antibody treatment, e.g. to address (neuro) inflammation in ALS, can give rise to cytokine release syndrome, which is an exaggerated systemic immune response involving the potential release of more than 150 inflammatory mediators [87]. This syndrome can be identified by monitoring cytokine levels in the blood post treatment, and can be treated promptly when detected [87]. ASO treatment has found increased application in clinical development in ALS; initially by targeting of the SOD1 gene for ALS patients harboring an ALS-causing mutation in this gene in clinical trials [88], and with the additional development of ASO treatment for C9orf72 repeat expansion carriers (NCT03626012, NCT04288856) as well as the targeting of the CAG repeat expansion of the genetic disease modifier ATXN2 (NCT04494256). Please note that these potential ASO treatments are still in clinical development and have not (yet) received marketing approval. These potential treatments involve the intrathecal injection of targeting ASOs, and provide the risk of infection [89], including meningitis, and of the above-described cytokine release syndrome, which has also been observed with ASO treatment [90]. Both of these safety risks can be detected early in blood for subsequent treatment. An alternative delivery of gene-based therapies is the use of viral vectors, e.g. adeno-associated virus (AAV), in preclinical development and as marketed medical products (e.g. Zolgensma for spinal muscular atrophy). Immunogenicity to the viral vector provides a risk to the efficacy of the potential therapy as well as a risk to adverse effects and complications from the treatment. For CNS bioavailable gene therapies this can include the neuroinflammatory response [91]. Treatment of ALS with cells that are intended to replace non-neuronal cells and/or support motor neuron health by controlling the milieu with trophic or anti-inflammatory factors have not (yet) demonstrated efficacy for ALS patients, but are in clinical development [92, 93]. These cell therapies may also harbor severe safety risks including immunogenicity and cytokine storms, which must be monitored and may be predicted with biomarkers. To this end, the use of blood-based inflammation biomarkers may be particularly useful to predict, and thereby potentially prevent, (serious) adverse events in clinical trials and/or in the initial or prolonged use of a marketed product.

Conclusions

The peripheral inflammatory signature in ALS includes changes in cell population abundance, gene expression changes per cell population, and soluble factors such as cytokines; which elements reach pathologic significance is not at all clear. Therefore, it is not surprising that a single inflammatory marker derived from serum or plasma may not provide the needed sensitivity to distinguish between an ALS disease state, predict ALS disease progression, or assess whether a tested therapy is efficacious. The first potential solution is the inclusion of multiple readouts per sample to determine a combinatorial biomarker’s validity, which can be gene expression from the same cell [72], as well as combinations of different assays, including from cellular-based assays. In particular, the monocytic and T-reg pathway may be enriched for useful biomarkers in ALS. Conversely, monocytic and macrophage-based inflammation has been targeted therapeutically by NP001, and was not efficacious on the needed primary endpoints for FDA market approval in ALS [69, 72, 94]. Despite that targeting these pathways was not efficacious, these pathways may be enriched for useful biomarkers for future cell-specific and combined assessments, and may indicate disease processes that are more upstream than what was targeted. In particular, an in-depth assessment of monocyte-populations and their cell-specific transcription profiles (as described below), either by single cell sequencing or nuclear sequencing after monocyte-enrichment, may be a fruitful strategy for high-resolution biomarker detection. In addition, incorporating this combination approach with patient level clinical phenotype data could identify inflammatory traits and subgroups more responsive to selected therapies [13]. For example, the pattern of inflammation in upper motor neuron predominant disease may differ significantly from patients with lower motor neuron specific disease. This identification of novel associations may help to better classify disease and treatment approaches, allowing, ultimately, for stratification by inflammatory, or some other, biomarker trait.

The advanced technologies over the last decade allow for more detailed, in-depth, and higher resolution analyses, promoting the potential of combinatorial readouts from niche cell populations with e.g. the advances in single cell transcriptomic analyses which provides a large number of data points for all cell types in a sample, also for cells in blood and CSF in disease [95]. Current limitations of single cell sequencing involve the detection of poly-A adenylated gene expression transcripts only, ensuring that for assessing miRNAs or circRNAs per cell population, prior cell-sorting will be needed. miRNA studies on ALS serum, without cell-type specific sorting, also report variable results [96,97,98], reminiscent of the variation described above regarding cytokine analyses on serum. Cell type specific miRNAs and/or circRNAs may provide novel avenues for biomarker development in (neuro) inflammation [99] and in ALS [100]. These readouts can additionally be assessed in cell type specific in vitro assays, and both unstimulated and stimulated responses should be assessed, as the level of T-reg and monocyte activation is important [56, 61, 72].

In view of the studies discussed here, a compelling direction is the combination of single cell transcriptomic analyses on whole blood samples, in addition to cellular-based assays of a specific cell population. In addition, the combined biomarkers may gain in sensitivity and specificity when combined with other biomarkers, such as the established p75ECD and/or neurofilament light. Other biomarkers under development include miRNAs, circRNAs, and the content of extracellular vesicles and exosomes from circulating blood cells or from in vitro assays, or other biomarkers such as neuroimaging (e.g. DTI or PET) and/or electrophysiological measurements (e.g. MUNIX) [64].

For the development of diagnostic and prognostic biomarkers in ALS of blood-based circulating factors (most cellular-based assays require fresh samples, as cell quality may be affected by long-term storage conditions [101]), many previously collected samples are available at biobanks and biorepositories. New sample collections e.g. during clinical trials of a potential therapeutic agent are recommended for monitoring, prognostic, pharmacodynamic, and predictive biomarkers, or those requiring fresh samples for cellular-based assays (as conducted in Table 2). During the collection of new samples, also across multiple clinical trial sites, it is essential that the samples are collected and prepared as similarly as possible, especially when intended to assess immune or inflammatory biomarkers. The variability during sample collection and preparation can result in variable levels of immune/inflammatory cell “activation”, which can introduce experimental artifacts into both the results and interpretations [102]. Systematic approaches, standard operating protocol adherence and (repeated) training across sites may be of assistance [102, 103]. In addition, large-scaled retrospective studies of electronic medical records (EMRs) (e.g. Sun et al., [67]), and correlating biomarkers or biomarker profiles with disease-progression predictions [76], is recommended.

The studies assessed in this review highlight the presence of peripheral inflammation in ALS, and more specifically, the differential monocytic gene expression profile both compared to controls and correlated to disease severity. Further interrogation of this cell population and its response to ALS-relevant stressors in vitro, by combining soluble factor analysis, proteomics, transcriptomic analysis (RNA, miRNA, circRNA), and machine-learning approaches may be particularly fruitful for the identification of novel biomarkers, or biomarker profiles, in ALS.

Availability of data and materials

Not applicable.

Abbreviations

AALS:

Appel ALS score

ALS:

Amyotrophic Lateral Sclerosis

ALSFRS-R:

ALS Functional Rating Scale-Revised

ASO:

Antisense oligonucleotides

AAV:

Adeno-associated virus

BEST:

Biomarker EndpointS and other Tools

CCL2:

C-C Motif Chemokine Ligand 2

CMAP:

Compound motor action potential

CNS:

Central nervous system

CSF:

Cerebrospinal fluid

CRP:

C-reactive protein

CXCL1:

C-X-C Motif Chemokine Ligand 1

DTI:

Diffusion tensor imaging

EMRs:

Electronic Medical Records

EU:

European Union

FDA:

Food and Drug Administration

FVC:

Forced Vital Capacity

IFNγ:

Interferon gamma

IL:

Interleukin

MCP-1 :

Monocyte chemoattractant protein 1

MG:

Myasthenia Gravis

miRNA:

microRNA

MRI:

Magnetic Resonance Imaging

MUNIX:

motor unit number index

NfL:

Neurofilament light

NK cells:

Natural Killer cells

NO:

Nitric oxide

PBMCs:

Peripheral blood mononuclear cells

PET:

Positron emission tomography

pNfH:

Phosphorylated neurofilament heavy

SEB:

Staphylococcal enterotoxin B

SOD1:

Superoxide Dismutase

TNF:

Tumor necrosis factor

References

  1. Mitsumoto H, Brooks BR, Silani V. Clinical trials in amyotrophic lateral sclerosis: why so many negative trials and how can trials be improved? Lancet Neurol. 2014;13(11):1127–38. https://doi.org/10.1016/S1474-4422(14)70129-2.

    Article  PubMed  Google Scholar 

  2. FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource [Internet]. Silver Spring (MD): Food and Drug Administration (US); 2016 [cited 2020]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK326791/

  3. Gordon PH, Moore DH, Miller RG, Florence JM, Verheijde JL, Doorish C, et al. Efficacy of minocycline in patients with amyotrophic lateral sclerosis: a phase III randomised trial. Lancet Neurol. 2007;6(12):1045–53. https://doi.org/10.1016/S1474-4422(07)70270-3.

    Article  CAS  PubMed  Google Scholar 

  4. Amirzagar N, Nafissi S, Tafakhori A, Modabbernia A, Amirzargar A, Ghaffarpour M, et al. Granulocyte Colony-stimulating factor for amyotrophic lateral sclerosis: a randomized, double-blind. Placebo-Controlled Study of Iranian Patients J Clin Neurol. 2015;11(2):164. https://doi.org/10.3988/jcn.2015.11.2.164.

    Article  PubMed  Google Scholar 

  5. Urbi B, Broadley S, Bedlack R, Russo E, Sabet A. Study protocol for a randomised, double-blind, placebo-controlled study evaluating the efficacy of cannabis-based medicine extract in slowing the disease pRogression of amyotrophic lateral sclerosis or motor neurone disease: the EMERALD trial. BMJ Open. 2019;9(11):e029449. https://doi.org/10.1136/bmjopen-2019-029449.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Mora JS, Genge A, Chio A, Estol CJ, Chaverri D, Hernández M, et al. Masitinib as an add-on therapy to riluzole in patients with amyotrophic lateral sclerosis: a randomized clinical trial. Amyotroph Lateral Scler Front Degener. 2020;21(1-2):5–14. https://doi.org/10.1080/21678421.2019.1632346.

    Article  CAS  Google Scholar 

  7. Renton AE, Majounie E, Waite A, Simón-Sánchez J, Rollinson S, Gibbs JR, et al. A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD. Neuron. 2011;72(2):257–68. https://doi.org/10.1016/j.neuron.2011.09.010.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. DeJesus-Hernandez M, Mackenzie IR, Boeve BF, Boxer AL, Baker M, Rutherford NJ, et al. Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron. 2011;72(2):245–56. https://doi.org/10.1016/j.neuron.2011.09.011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Renton AE, Chiò A, Traynor BJ. State of play in amyotrophic lateral sclerosis genetics. Nat Neurosci. 2014;17(1):17–23. https://doi.org/10.1038/nn.3584.

    Article  CAS  PubMed  Google Scholar 

  10. Bensimon G, Lacomblez L, Meininger V. A controlled trial of Riluzole in amyotrophic lateral sclerosis. N Engl J Med. 1994;330(9):585–91. https://doi.org/10.1056/NEJM199403033300901.

    Article  CAS  PubMed  Google Scholar 

  11. Arthur KC, Calvo A, Price TR, Geiger JT, Chiò A, Traynor BJ. Projected increase in amyotrophic lateral sclerosis from 2015 to 2040. Nat Commun. 2016;7(1):12408. https://doi.org/10.1038/ncomms12408.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Barp A, Gerardi F, Lizio A, Sansone VA, Lunetta C. Emerging drugs for the treatment of amyotrophic lateral sclerosis: a focus on recent phase 2 trials. Expert Opin Emerg Drugs. 2020;25(2):145–64. https://doi.org/10.1080/14728214.2020.1769067.

    Article  CAS  PubMed  Google Scholar 

  13. Al-Chalabi A, Hardiman O, Kiernan MC, Chiò A, Rix-Brooks B, van den Berg LH. Amyotrophic lateral sclerosis: moving towards a new classification system. Lancet Neurol. 2016;15(11):1182–94. https://doi.org/10.1016/S1474-4422(16)30199-5.

    Article  PubMed  Google Scholar 

  14. Turner MR, Kiernan MC, Leigh PN, Talbot K. Biomarkers in amyotrophic lateral sclerosis. Lancet Neurol. 2009;8(1):94–109. https://doi.org/10.1016/S1474-4422(08)70293-X.

    Article  CAS  PubMed  Google Scholar 

  15. Shepheard SR, Wuu J, Cardoso M, Wiklendt L, Dinning PG, Chataway T, et al. Urinary p75ECD: a prognostic, disease progression, and pharmacodynamic biomarker in ALS. Neurology. 2017;88(12):1137–43. https://doi.org/10.1212/WNL.0000000000003741.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Rossi D, Volanti P, Brambilla L, Colletti T, Spataro R, La Bella V. CSF neurofilament proteins as diagnostic and prognostic biomarkers for amyotrophic lateral sclerosis. J Neurol. 2018;265(3):510–21. https://doi.org/10.1007/s00415-017-8730-6.

    Article  CAS  PubMed  Google Scholar 

  17. Verde F, Steinacker P, Weishaupt JH, Kassubek J, Oeckl P, Halbgebauer S, et al. Neurofilament light chain in serum for the diagnosis of amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. 2019;90(2):157–64. https://doi.org/10.1136/jnnp-2018-318704.

    Article  PubMed  Google Scholar 

  18. McCombe PA, Lee JD, Woodruff TM, Henderson RD. The peripheral immune system and amyotrophic lateral sclerosis. Front Neurol. 2020;11:279. https://doi.org/10.3389/fneur.2020.00279.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Philips T, Robberecht W. Neuroinflammation in amyotrophic lateral sclerosis: role of glial activation in motor neuron disease. Lancet Neurol. 2011;10(3):253–63. https://doi.org/10.1016/S1474-4422(11)70015-1.

    Article  CAS  PubMed  Google Scholar 

  20. Beers DR, Appel SH. Immune dysregulation in amyotrophic lateral sclerosis: mechanisms and emerging therapies. Lancet Neurol. 2019;18(2):211–20. https://doi.org/10.1016/S1474-4422(18)30394-6.

    Article  CAS  PubMed  Google Scholar 

  21. Paganoni S, Macklin EA, Lee A, Murphy A, Chang J, Zipf A, et al. Diagnostic timelines and delays in diagnosing amyotrophic lateral sclerosis (ALS). Amyotroph Lateral Scler Front Degener. 2014;15(5-6):453–6. https://doi.org/10.3109/21678421.2014.903974.

    Article  Google Scholar 

  22. Khoo A, Frasca J, Schultz D. Measuring disease activity and predicting response to intravenous immunoglobulin in chronic inflammatory demyelinating polyneuropathy. Biomark Res. 2019;7(1):3. https://doi.org/10.1186/s40364-019-0154-2.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Stino AM, Naddaf E, Dyck PJ, Dyck PJB. Chronic inflammatory demyelinating polyradiculoneuropathy—diagnostic pitfalls and treatment approach. Muscle Nerve. 2021;63(2):157–69. https://doi.org/10.1002/mus.27046.

    Article  PubMed  Google Scholar 

  24. Porter L, Shoushtarizadeh A, Jelinek GA, Brown CR, Lim CK, de Livera AM, et al. Metabolomic biomarkers of multiple sclerosis: a systematic review. Front Mol Biosci. 2020;7:574133. https://doi.org/10.3389/fmolb.2020.574133.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rossor AM, Shy ME, Reilly MM. Are we prepared for clinical trials in Charcot-Marie-tooth disease? Brain Res. 1729;2020:146625. https://doi.org/10.1016/j.brainres.2019.146625.

    Article  CAS  Google Scholar 

  26. Beers DR, Zhao W, Neal DW, Thonhoff JR, Thome AD, Faridar A, et al. Elevated acute phase proteins reflect peripheral inflammation and disease severity in patients with amyotrophic lateral sclerosis. Sci Rep. 2020;10(1):15295. https://doi.org/10.1038/s41598-020-72247-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Jin M, Günther R, Akgün K, Hermann A, Ziemssen T. Peripheral proinflammatory Th1/Th17 immune cell shift is linked to disease severity in amyotrophic lateral sclerosis. Sci Rep. 2020;10(1):5941. https://doi.org/10.1038/s41598-020-62756-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Keizman D, Rogowski O, Berliner S, Ish-Shalom M, Maimon N, Nefussy B, et al. Low-grade systemic inflammation in patients with amyotrophic lateral sclerosis. Acta Neurol Scand. 2009;119(6):383–9. https://doi.org/10.1111/j.1600-0404.2008.01112.x.

    Article  CAS  PubMed  Google Scholar 

  29. Tortelli R, Zecca C, Piccininni M, Benmahamed S, Dell’Abate MT, Barulli MR, et al. Plasma inflammatory cytokines are elevated in ALS. Front Neurol. 2020;11:552295. https://doi.org/10.3389/fneur.2020.552295.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Mohanty L, Henderson RD, McCombe PA, Lee A. Levels of clusterin, CD5L, ficolin-3, and gelsolin in ALS patients and controls. Amyotroph Lateral Scler Front Degener. 2020;21(7-8):1–4. https://doi.org/10.1080/21678421.2020.1779303.

    Article  CAS  Google Scholar 

  31. Polverino A, Rucco R, Stillitano I, Bonavita S, Grimaldi M, Minino R, et al. In amyotrophic lateral sclerosis blood cytokines are altered, but do not correlate with changes in brain topology. Brain Connect. 2020;10(8):411–21. https://doi.org/10.1089/brain.2020.0741.

    Article  PubMed  Google Scholar 

  32. Huang F, Zhu Y, Hsiao-Nakamoto J, Tang X, Dugas JC, Moscovitch-Lopatin M, et al. Longitudinal biomarkers in amyotrophic lateral sclerosis. Ann Clin Transl Neurol. 2020;7(7):1103–16. https://doi.org/10.1002/acn3.51078.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Pronto-Laborinho A, Pinto S, Gromicho M, Pereira M, Swash M, de Carvalho M. Interleukin-6 and amyotrophic lateral sclerosis. J Neurol Sci. 2019;398:50–3. https://doi.org/10.1016/j.jns.2019.01.026.

    Article  CAS  PubMed  Google Scholar 

  34. de Prado L. GR, Rocha NP, de Souza LC, Bicalho ICS, Gomez RS, Vidigal-Lopes M, et al. Longitudinal assessment of clinical and inflammatory markers in patients with amyotrophic lateral sclerosis J Neurol Sci. 2018;394:69–74. https://doi.org/10.1016/j.jns.2018.08.033.

    Article  CAS  PubMed  Google Scholar 

  35. Andrés-Benito P, Moreno J, Domínguez R, Aso E, Povedano M, Ferrer I. Inflammatory gene expression in whole peripheral blood at early stages of sporadic amyotrophic lateral sclerosis. Front Neurol. 2017;8:546. https://doi.org/10.3389/fneur.2017.00546.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Sheinerman KS, Toledo JB, Tsivinsky VG, Irwin D, Grossman M, Weintraub D, et al. Circulating brain-enriched microRNAs as novel biomarkers for detection and differentiation of neurodegenerative diseases. Alzheimers Res Ther. 2017;9(1):89. https://doi.org/10.1186/s13195-017-0316-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Pronto-Laborinho AC. Gromicho M, Pereira M, Pinto S, Barros M do a, Swash M, et al. plasma level of club-cell (CC-16) predicts outcome in amyotrophic lateral sclerosis. Acta Neurol Scand. 2018;137(2):233–7. https://doi.org/10.1111/ane.12851.

    Article  CAS  PubMed  Google Scholar 

  38. Hu Y, Cao C, Qin X-Y, Yu Y, Yuan J, Zhao Y, et al. Increased peripheral blood inflammatory cytokine levels in amyotrophic lateral sclerosis: a meta-analysis study. Sci Rep. 2017;7(1):9094. https://doi.org/10.1038/s41598-017-09097-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Blasco H, Garcon G, Patin F, Veyrat-Durebex C, Boyer J, Devos D, et al. Panel of oxidative stress and inflammatory biomarkers in ALS: a pilot study. Can J Neurol Sci J Can Sci Neurol. 2017;44(1):90–5. https://doi.org/10.1017/cjn.2016.284.

    Article  Google Scholar 

  40. Italiani P, Carlesi C, Giungato P, Puxeddu I, Borroni B, Bossù P, et al. Evaluating the levels of interleukin-1 family cytokines in sporadic amyotrophic lateral sclerosis. J Neuroinflammation. 2014;11(1):94. https://doi.org/10.1186/1742-2094-11-94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lin CY, Pfluger CM, Henderson RD, McCombe PA. Reduced levels of interleukin 33 and increased levels of soluble ST2 in subjects with amyotrophic lateral sclerosis. J Neuroimmunol. 2012;249(1-2):93–5. https://doi.org/10.1016/j.jneuroim.2012.05.001.

    Article  CAS  PubMed  Google Scholar 

  42. Fiala M, Chattopadhay M, La Cava A, Tse E, Liu G, Lourenco E, et al. IL-17A is increased in the serum and in spinal cord CD8 and mast cells of ALS patients. J Neuroinflammation. 2010;7(1):76. https://doi.org/10.1186/1742-2094-7-76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Chen Y, Stankovic R, Cullen KM, Meininger V, Garner B, Coggan S, et al. The kynurenine pathway and inflammation in amyotrophic lateral sclerosis. Neurotox Res. 2010;18(2):132–42. https://doi.org/10.1007/s12640-009-9129-7.

    Article  CAS  PubMed  Google Scholar 

  44. Kuhle J, Lindberg RLP, Regeniter A, Mehling M, Steck AJ, Kappos L, et al. Increased levels of inflammatory chemokines in amyotrophic lateral sclerosis. Eur J Neurol. 2009;16(6):771–4. https://doi.org/10.1111/j.1468-1331.2009.02560.x.

    Article  CAS  PubMed  Google Scholar 

  45. Babu GN, Kumar A, Chandra R, Puri SK, Kalita J, Misra UK. Elevated inflammatory markers in a Group of Amyotrophic Lateral Sclerosis Patients from northern India. Neurochem Res. 2008;33(6):1145–9. https://doi.org/10.1007/s11064-007-9564-x.

    Article  CAS  PubMed  Google Scholar 

  46. Rentzos M, Nikolaou C, Rombos A, Boufidou F, Zoga M, Dimitrakopoulos A, et al. RANTES levels are elevated in serum and cerebrospinal fluid in patients with amyotrophic lateral sclerosis. Amyotroph Lateral Scler. 2007;8(5):283–7. https://doi.org/10.1080/17482960701419232.

    Article  CAS  PubMed  Google Scholar 

  47. Baron P, Bussini S, Cardin V, Corbo M, Conti G, Galimberti D, et al. Production of monocyte chemoattractant protein-1 in amyotrophic lateral sclerosis. Muscle Nerve. 2005;32(4):541–4. https://doi.org/10.1002/mus.20376.

    Article  CAS  PubMed  Google Scholar 

  48. Wilms H. Intrathecal synthesis of monocyte chemoattractant protein-1 (MCP-1) in amyotrophic lateral sclerosis: further evidence for microglial activation in neurodegeneration. J Neuroimmunol. 2003;144(1-2):139–42. https://doi.org/10.1016/j.jneuroim.2003.08.042.

    Article  CAS  PubMed  Google Scholar 

  49. Etzrodt M, Cortez-Retamozo V, Newton A, Zhao J, Ng A, Wildgruber M, et al. Regulation of monocyte functional heterogeneity by miR-146a and Relb. Cell Rep. 2012;1(4):317–24. https://doi.org/10.1016/j.celrep.2012.02.009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Banack SA, Dunlop RA, Cox PA. An miRNA fingerprint using neural-enriched extracellular vesicles from blood plasma: towards a biomarker for amyotrophic lateral sclerosis/motor neuron disease. Open Biol. 2020;10(6):200116. https://doi.org/10.1098/rsob.200116.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Xu Z, Lee A, Nouwens A, Henderson RD, McCombe PA. Mass spectrometry analysis of plasma from amyotrophic lateral sclerosis and control subjects. Amyotroph Lateral Scler Front Degener. 2018;19(5-6):362–76. https://doi.org/10.1080/21678421.2018.1433689.

    Article  CAS  Google Scholar 

  52. McCauley ME, O’Rourke JG, Yáñez A, Markman JL, Ho R, Wang X, et al. C9orf72 in myeloid cells suppresses STING-induced inflammation. Nature. 2020;585(7823):96–101. https://doi.org/10.1038/s41586-020-2625-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Perner C, Perner F, Stubendorff B, Förster M, Witte OW, Heidel FH, et al. Dysregulation of chemokine receptor expression and function in leukocytes from ALS patients. J Neuroinflammation. 2018;15(1):99. https://doi.org/10.1186/s12974-018-1135-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Rusconi M, Gerardi F, Santus W, Lizio A, Sansone VA, Lunetta C, et al. Inflammatory role of dendritic cells in amyotrophic lateral sclerosis revealed by an analysis of patients’ peripheral blood. Sci Rep. 2017;7(1):7853. https://doi.org/10.1038/s41598-017-08233-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Gustafson MP, Staff NP, Bornschlegl S, Butler GW, Maas ML, Kazamel M, et al. Comprehensive immune profiling reveals substantial immune system alterations in a subset of patients with amyotrophic lateral sclerosis. Raoul C, editor. PLOS ONE. 2017;12:e0182002.

    Article  Google Scholar 

  56. Zhao W, Beers DR, Hooten KG, Sieglaff DH, Zhang A, Kalyana-Sundaram S, et al. Characterization of gene expression phenotype in amyotrophic lateral sclerosis monocytes. JAMA Neurol. 2017;74(6):677–85. https://doi.org/10.1001/jamaneurol.2017.0357.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Lam L, Chin L, Halder RC, Sagong B, Famenini S, Sayre J, et al. Epigenetic changes in T-cell and monocyte signatures and production of neurotoxic cytokines in ALS patients. FASEB J Off Publ Fed Am Soc Exp Biol. 2016;30(10):3461–73. https://doi.org/10.1096/fj.201600259RR.

    Article  CAS  Google Scholar 

  58. Saresella M, Piancone F, Tortorella P, Marventano I, Gatti A, Caputo D, et al. T helper-17 activation dominates the immunologic milieu of both amyotrophic lateral sclerosis and progressive multiple sclerosis. Clin Immunol. 2013;148(1):79–88. https://doi.org/10.1016/j.clim.2013.04.010.

    Article  CAS  PubMed  Google Scholar 

  59. Fiala M, Mizwicki MT, Weitzman R, Magpantay L, Nishimoto N. Tocilizumab infusion therapy normalizes inflammation in sporadic ALS patients. Am J Neurodegener Dis. 2013;2(2):129–39.

    PubMed  PubMed Central  Google Scholar 

  60. Mizwicki MT, Fiala M, Magpantay L, Aziz N, Sayre J, Liu G, et al. Tocilizumab attenuates inflammation in ALS patients through inhibition of IL6 receptor signaling. Am J Neurodegener Dis. 2012;1(3):305–15.

    PubMed  PubMed Central  Google Scholar 

  61. Zhang R, Gascon R, Miller RG, Gelinas DF, Mass J, Hadlock K, et al. Evidence for systemic immune system alterations in sporadic amyotrophic lateral sclerosis (sALS). J Neuroimmunol. 2005;159(1-2):215–24. https://doi.org/10.1016/j.jneuroim.2004.10.009.

    Article  CAS  PubMed  Google Scholar 

  62. Olsson A, Gustavsen S, Gisselø Lauridsen K, Chenoufi Hasselbalch I, Sellebjerg F, Bach Søndergaard H, et al. Neutrophil-to-lymphocyte ratio and CRP as biomarkers in multiple sclerosis: a systematic review. Acta Neurol Scand. 2021;143(6):577–86. https://doi.org/10.1111/ane.13401.

    Article  PubMed  Google Scholar 

  63. Gururaj S, Sampognaro PJ, Argouarch AR, Kao AW. Progranulin adsorbs to polypropylene tubes and disrupts functional assays: implications for research, biomarker studies, and therapeutics. Front Neurosci. 2020;14:602235. https://doi.org/10.3389/fnins.2020.602235.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Benatar M, Boylan K, Jeromin A, Rutkove SB, Berry J, Atassi N, et al. ALS biomarkers for therapy development: state of the field and future directions. Muscle Nerve. 2016;53(2):169–82. https://doi.org/10.1002/mus.24979.

    Article  PubMed  Google Scholar 

  65. Gaur N, Huss E, Prell T, Steinbach R, Guerra J, Srivastava A, et al. Monocyte-derived macrophages contribute to Chitinase dysregulation in amyotrophic lateral sclerosis: a pilot study. Front Neurol. 2021;12:629332. https://doi.org/10.3389/fneur.2021.629332.

    Article  PubMed  PubMed Central  Google Scholar 

  66. . F. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/amyotrophic-lateral-sclerosis-developing-drugs-treatment-guidance-industry.

  67. Sun J, Carrero JJ, Zagai U, Evans M, Ingre C, Pawitan Y, et al. Blood biomarkers and prognosis of amyotrophic lateral sclerosis. Eur J Neurol. 2020;27(11):2125–33. https://doi.org/10.1111/ene.14409.

    Article  CAS  PubMed  Google Scholar 

  68. Bendotti C, Bonetto V, Pupillo E, Logroscino G, Al-Chalabi A, Lunetta C, et al. Focus on the heterogeneity of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Front Degener. 2020:1–11.

  69. Lunetta C, Lizio A, Maestri E, Sansone VA, Mora G, Miller RG, et al. Serum C-reactive protein as a prognostic biomarker in amyotrophic lateral sclerosis. JAMA Neurol. 2017;74(6):660–7. https://doi.org/10.1001/jamaneurol.2016.6179.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Devos D, Moreau C, Kyheng M, Garçon G, Rolland AS, Blasco H, et al. A ferroptosis–based panel of prognostic biomarkers for amyotrophic lateral sclerosis. Sci Rep. 2019;9(1):2918. https://doi.org/10.1038/s41598-019-39739-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Liu G, Fiala M, Mizwicki MT, Sayre J, Magpantay L, Siani A, et al. Neuronal phagocytosis by inflammatory macrophages in ALS spinal cord: inhibition of inflammation by resolvin D1. Am J Neurodegener Dis. 2012;1(1):60–74.

    PubMed  PubMed Central  Google Scholar 

  72. Miller RG, Zhang R, Block G, Katz J, Barohn R, Kasarskis E, et al. NP001 regulation of macrophage activation markers in ALS: a phase I clinical and biomarker study. Amyotroph Lateral Scler Front Degener. 2014;15(7-8):601–9. https://doi.org/10.3109/21678421.2014.951940.

    Article  CAS  Google Scholar 

  73. Henkel JS, Beers DR, Wen S, Rivera AL, Toennis KM, Appel JE, et al. Regulatory T-lymphocytes mediate amyotrophic lateral sclerosis progression and survival. EMBO Mol Med. 2013;5(1):64–79. https://doi.org/10.1002/emmm.201201544.

    Article  CAS  PubMed  Google Scholar 

  74. Haverkamp LJ, Appel V, Appel SH. Natural history of amyotrophic lateral sclerosis in a database population. Validation of a scoring system and a model for survival prediction. Brain J Neurol. 1995;118(Pt 3):707–19. https://doi.org/10.1093/brain/118.3.707.

    Article  Google Scholar 

  75. van Eijk RPA, Westeneng H-J, Nikolakopoulos S, Verhagen IE, van Es MA, Eijkemans MJC, et al. Refining eligibility criteria for amyotrophic lateral sclerosis clinical trials. Neurology. 2019;92(5):e451–60. https://doi.org/10.1212/WNL.0000000000006855.

    Article  PubMed Central  Google Scholar 

  76. Westeneng H-J, Debray TPA, Visser AE, van Eijk RPA, Rooney JPK, Calvo A, et al. Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction model. Lancet Neurol. 2018;17(5):423–33. https://doi.org/10.1016/S1474-4422(18)30089-9.

    Article  PubMed  Google Scholar 

  77. Benatar M, Turner MR, Wuu J. Defining pre-symptomatic amyotrophic lateral sclerosis. Amyotroph Lateral Scler Front Degener. 2019;20(5-6):303–9. https://doi.org/10.1080/21678421.2019.1587634.

    Article  Google Scholar 

  78. Wainberg M, Magis AT, Earls JC, Lovejoy JC, Sinnott-Armstrong N, Omenn GS, et al. Multiomic blood correlates of genetic risk identify presymptomatic disease alterations. Proc Natl Acad Sci. 2020;117(35):21813–20. https://doi.org/10.1073/pnas.2001429117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Wang H, Shen X, Li J, Suckling J, Tan C, Wang Y, et al. Clinical and biomarker trajectories in sporadic Alzheimer’s disease: A longitudinal study. Alzheimers Dement Diagn Assess Dis Monit. 2020;12 Available from: https://onlinelibrary.wiley.com/doi/10.1002/dad2.12095. Cited 2021 Feb 20.

  80. Benatar M, Wuu J, Andersen PM, Lombardi V, Malaspina A. Neurofilament light: a candidate biomarker of presymptomatic amyotrophic lateral sclerosis and phenoconversion. Ann Neurol. 2018;84(1):130–9. https://doi.org/10.1002/ana.25276.

    Article  CAS  PubMed  Google Scholar 

  81. van den Berg LH, Sorenson E, Gronseth G, Macklin EA, Andrews J, Baloh RH, et al. Revised Airlie house consensus guidelines for design and implementation of ALS clinical trials. Neurology. 2019;92(14):e1610–23. https://doi.org/10.1212/WNL.0000000000007242.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Kiernan MC, Vucic S, Talbot K, McDermott CJ, Hardiman O, Shefner JM, et al. Improving clinical trial outcomes in amyotrophic lateral sclerosis. Nat Rev Neurol. 2021;17(2):104–18. https://doi.org/10.1038/s41582-020-00434-z.

    Article  PubMed  Google Scholar 

  83. Baumert B, Sobuś A, Gołąb-Janowska M, Paczkowska E, Łuczkowska K, Rogińska D, et al. Repeated application of autologous bone marrow-derived lineage-negative stem/progenitor cells—focus on immunological pathways in patients with ALS. Cells. 2020;9(8):1822. https://doi.org/10.3390/cells9081822.

    Article  CAS  PubMed Central  Google Scholar 

  84. Milligan C, Atassi N, Babu S, Barohn RJ, Caress JB, Cudkowicz ME, et al. Tocilizumab is safe and tolerable and reduces C -reactive protein concentrations in the plasma and cerebrospinal fluid of ALS patients. Muscle Nerve. 2021;64(3):309–20. https://doi.org/10.1002/mus.27339.

    Article  CAS  PubMed  Google Scholar 

  85. Rosenfeld J. Rethinking amyotrophic lateral sclerosis. Mayo Clin Proc. 2018;93(11):1543–5. https://doi.org/10.1016/j.mayocp.2018.09.009.

    Article  PubMed  Google Scholar 

  86. Pichler WJ. Adverse side-effects to biological agents. Allergy. 2006;61(8):912–20. https://doi.org/10.1111/j.1398-9995.2006.01058.x.

    Article  CAS  PubMed  Google Scholar 

  87. Bugelski PJ, Achuthanandam R, Capocasale RJ, Treacy G, Bouman-Thio E. Monoclonal antibody-induced cytokine-release syndrome. Expert Rev Clin Immunol. 2009;5(5):499–521. https://doi.org/10.1586/eci.09.31.

    Article  CAS  PubMed  Google Scholar 

  88. Miller T, Cudkowicz M, Shaw PJ, Andersen PM, Atassi N, Bucelli RC, et al. Phase 1–2 trial of antisense oligonucleotide Tofersen for SOD1 ALS. N Engl J Med. 2020;383(2):109–19. https://doi.org/10.1056/NEJMoa2003715.

    Article  CAS  PubMed  Google Scholar 

  89. Delhaas EM, Huygen FJPM. Complications associated with intrathecal drug delivery systems. BJA Educ. 2020;20(2):51–7. https://doi.org/10.1016/j.bjae.2019.11.002.

    Article  CAS  PubMed  Google Scholar 

  90. O’Brien SM, Cunningham CC, Golenkov AK, Turkina AG, Novick SC, Rai KR. Phase I to II multicenter study of Oblimersen sodium, a Bcl-2 antisense oligonucleotide, in patients with advanced chronic lymphocytic leukemia. J Clin Oncol. 2005;23(30):7697–702. https://doi.org/10.1200/JCO.2005.02.4364.

    Article  CAS  PubMed  Google Scholar 

  91. Perez BA, Shutterly A, Chan YK, Byrne BJ, Corti M. Management of Neuroinflammatory Responses to AAV-mediated gene therapies for neurodegenerative diseases. Brain Sci. 2020;10(2):119. https://doi.org/10.3390/brainsci10020119.

    Article  CAS  PubMed Central  Google Scholar 

  92. Je G, Keyhanian K, Ghasemi M. Overview of stem cells therapy in amyotrophic lateral sclerosis. Neurol Res. 2021;43(8):1–17. https://doi.org/10.1080/01616412.2021.1893564.

    Article  CAS  Google Scholar 

  93. Research C for BE and. Update on Amyotrophic Lateral Sclerosis (ALS) Product Development. FDA. FDA; 2021. Available from: https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products/update-amyotrophic-lateral-sclerosis-als-product-development. Cited 2021 Apr 7.

  94. Miller RG, Block G, Katz JS, Barohn RJ, Gopalakrishnan V, Cudkowicz M, et al. Randomized phase 2 trial of NP001-a novel immune regulator: safety and early efficacy in ALS. Neurol Neuroimmunol Neuroinflammation. 2015;2(3):e100. https://doi.org/10.1212/NXI.0000000000000100.

    Article  Google Scholar 

  95. Schafflick D, Xu CA, Hartlehnert M, Cole M, Schulte-Mecklenbeck A, Lautwein T, et al. Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis. Nat Commun. 2020;11(1):247. https://doi.org/10.1038/s41467-019-14118-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Raheja R, Regev K, Healy BC, Mazzola MA, Beynon V, Von Glehn F, et al. Correlating serum micrornas and clinical parameters in amyotrophic lateral sclerosis. Muscle Nerve. 2018;58(2):261–9. https://doi.org/10.1002/mus.26106.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Liguori M, Nuzziello N, Introna A, Consiglio A, Licciulli F, D’Errico E, et al. Dysregulation of MicroRNAs and target genes networks in peripheral blood of patients with sporadic amyotrophic lateral sclerosis. Front Mol Neurosci. 2018;11:288. https://doi.org/10.3389/fnmol.2018.00288.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Waller R, Goodall EF, Milo M, Cooper-Knock J, Da Costa M, Hobson E, et al. Serum miRNAs miR-206, 143-3p and 374b-5p as potential biomarkers for amyotrophic lateral sclerosis (ALS). Neurobiol Aging. 2017;55:123–31. https://doi.org/10.1016/j.neurobiolaging.2017.03.027.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Henshall DC, Hamer HM, Pasterkamp RJ, Goldstein DB, Kjems J, Prehn JHM, et al. MicroRNAs in epilepsy: pathophysiology and clinical utility. Lancet Neurol. 2016;15(13):1368–76. https://doi.org/10.1016/S1474-4422(16)30246-0.

    Article  CAS  PubMed  Google Scholar 

  100. Dolinar A, Koritnik B, Glavač D, Ravnik-Glavač M. Circular RNAs as potential blood biomarkers in amyotrophic lateral sclerosis. Mol Neurobiol. 2019;56(12):8052–62. https://doi.org/10.1007/s12035-019-1627-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Yang J, Diaz N, Adelsberger J, Zhou X, Stevens R, Rupert A, et al. The effects of storage temperature on PBMC gene expression. BMC Immunol. 2016;17(1):6. https://doi.org/10.1186/s12865-016-0144-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Hu WT, Trojanowski JQ, Shaw LM. Biomarkers in frontotemporal lobar degenerations—Progress and challenges. Prog Neurobiol. 2011;95(4):636–48. https://doi.org/10.1016/j.pneurobio.2011.04.012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Poste G. Bring on the biomarkers. Nature. 2011;469(7329):156–7. https://doi.org/10.1038/469156a.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Jon and Nancy Wilkins Fund for ALS research.

Author information

Authors and Affiliations

  1. Staats Life Sciences Consulting, LLC, Los Angeles, CA, USA

    Kim A. Staats

  2. Department of Neuroscience, University of Florida College of Medicine, McKnight Brain Institute, Center for Translational Research in Neurodegenerative Disease, University of Florida, Gainesville, Florida, USA

    David R. Borchelt

  3. Department of Neuroscience and Center for Translational Research in Neurodegenerative Disease at The University of Florida College of Medicine, Gainesville, Florida, USA

    Malú Gámez Tansey

  4. Department of Neurology, University of Florida College of Medicine, Gainesville, Florida, USA

    James Wymer

Authors
  1. Kim A. Staats
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David R. Borchelt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Malú Gámez Tansey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James Wymer
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the writing of this manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to James Wymer.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Staats, K.A., Borchelt, D.R., Tansey, M.G. et al. Blood-based biomarkers of inflammation in amyotrophic lateral sclerosis. Mol Neurodegeneration 17, 11 (2022). https://doi.org/10.1186/s13024-022-00515-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13024-022-00515-1

Keywords

  • Amyotrophic lateral sclerosis
  • Biomarker
  • Inflammation
  • Cytokines

Molecular Neurodegeneration

ISSN: 1750-1326