Hallmarks of response to immune checkpoint blockade


Abstract

Unprecedented advances have been made in the treatment of cancer through the use of immune checkpoint blockade, with approval of several checkpoint blockade regimens spanning multiple cancer types. However, responses to this form of therapy are not universal, and insights are clearly needed to identify optimal biomarkers of response and to combat mechanisms of therapeutic resistance. A working knowledge of the hallmarks of cancer yields insight into responses to immune checkpoint blockade, although the focus of this is rather tumour-centric and additional factors are pertinent, including host immunity and environmental influences. Herein, we describe the foundation for pillars and hallmarks of response to immune checkpoint blockade, with a discussion of their relevance to immune monitoring and mechanisms of resistance. Evolution of this understanding will ultimately help guide treatment strategies to enhance therapeutic responses.

Introduction

The treatment of cancer has been arduous, marked by just as much heterogeneity in cancer treatment modality and outcome as is now known to exist at a cellular and molecular level within tumours themselves. Cancer burden, morbidity, and mortality have a wide reaching impact globally, but recent advancements in precision medicine have given the field of oncology an opportunity to greatly improve therapeutic strategies. As the relative ‘new kid on the block’, cancer immunotherapy differs from conventional chemotherapeutic agents in that its mechanism of action employs, engages, or enhances a functional immune response to tumour cells, rather than aiming principally to physically remove or destroy cancer cells through inherent radio- or chemical toxicity. Importantly, although immunotherapy is commonly thought of as a new treatment modality, the first immunotherapy approaches in fact predate the discovery and development of cytotoxic agents for the treatment of cancer, or even the discovery of X-rays, let alone the therapeutic use of non-ionising radiation. Furthermore, immunotherapy encompasses several subtypes of treatment modality, including vaccination strategies, cell-based therapies using the patient’s own immune cells with or without ex vivo modification, and immunomodulatory agents, of which checkpoint inhibitor therapies have been the most broadly successful to date.

Physiologic role and therapeutic targeting of immune checkpoints

Unopposed immune activation can be at least as damaging as an ineffective response, necessitating a dynamic system of regulatory signals to integrate the prevailing immune stimuli and direct immune responses appropriately. Initial immune activation requires recognition of the target, which itself is a multistep process classically requiring antigen expression by tumour cells, and its processing and presentation to helper T cells by specialised antigen presenting cells (APCs, e.g., dendritic cells) in the context of class II human leukocyte antigen (HLA; Figure 1). Whether a cognate HLA/antigen – T-cell receptor interaction results in T-cell proliferation and activation is determined by the presence of additional co-stimulatory signals, principally delivered by the engagement of CD28 on the T cell by CD80/86 on the APC (Figure 1). Without this vital second signal, the interaction may be biologically interpreted as representing recognition of a non-pathogenic (or ‘self’) antigenic stimulus to which tolerance may develop. However, in the presence of appropriate co-stimulation, an active immune response against the inciting antigen can proceed, with the generation of humoral responses, recruitment of a cytotoxic T-cell response (HLA class I-restricted) and release of numerous cytokines necessary for effector cell proliferation, survival, localisation, and effector function. Many other stimulatory signals are active throughout the immune response phase, including inducible T-cell co-stimulator (ICOS), glucocorticoid-induced TNFR-related protein, and tumour necrosis factor receptor superfamily members 4 (OX40 or CD134) and 9 (4-1BB or CD137), which function in the amplification and maintenance of overall immune activation (Figure 1).

Figure 1
Figure 1

The cellular immune response to cancer is complex and involves a diverse repertoire of immunoregulatory interactions principally involving antigen presenting cells (APC), T cells, and tumour cells. Presentation of distinct antigen epitopes to CD8+ and CD4+ T cells in the context of major histocompatibility complex class I (on APC or tumour cells directly) and class II (on APCs), respectively, facilitates tumour cell recognition, but numerous other molecular interactions (inset boxes) and input from paracrine and humoral factors (cytokines/chemokines, shown with arrowed lines) integrate to determine the ultimate outcome of immune recognition. Elaboration of survival and inflammatory cytokines, such as IL-2 and IFN-γ, can promote a cytotoxic (CD8+) T-cell response with consequent tumour-directed lytic activity mediated by release of cytotoxic granule contents (e.g., perforin and granyzme) as well as triggering of apoptotic pathways by tumouricidal cytokines (e.g., TNF-α and IFN-γ) and death receptor ligation (e.g., FAS:FAS-L). Debris released from apoptotic/necrotic tumour cells may be taken up by APC and presented in a cycle of immunogenic cell death. However, prolonged immune activation is adaptively opposed by upregulation of immunoinhibitory molecules (e.g., CTLA-4, PD-1, TIM3, TIGIT, and CTLA-4), or their ligands, many of which may be subverted by tumour cells in order to escape immune attack. Release of anti-inflammatory, immunoregulatory or Th2-skewed cytokines also contributes to dampening of the cellular response.

To achieve immune homeostasis, numerous negative feedback stimuli act to dampen the immune response, including the well-described negative regulatory molecules cytotoxic T-lymphocyte-associated protein 4 (CTLA-4 or CD152) and programmed death 1 (PD-1 or CD279). Cytotoxic T-lymphocyte-associated protein 4 is expressed on the T-cell surface and competes with CD28 for binding to CD80/86, providing an inhibitory stimulus upon engagement (Figure 1). It is thought that the action of CTLA-4 may be most relevant at the T-cell priming stage in regional secondary lymphoid organs, thus ultimately acting to impair T-cell help and the generation of an effector T-cell population and its egress back into the tumour. Programmed death 1 is a T-cell surface receptor that delivers inhibitory signals upon engagement with its ligands PD-L1/2, and these ligands may be upregulated in the setting of high levels of IFN-γ (termed adaptive immune resistance), but may also be expressed in the tumour microenvironment via oncogenic expression on tumour cells or expression on other stromal elements (Figure 1) (Pardoll, 2012). Programmed death 1 expressing T cells are thought to represent populations that have largely ‘seen’ their antigen in situ (i.e., within the tumour) and are thus considered a more tumour-specific population than T cells arrested at the priming stage by CTLA-4, however, high levels of PD-1 are also associated with an ‘exhausted’ T-cell phenotype (Wherry and Kurachi, 2015). Multiple other inhibitory ‘checkpoints’ have been identified, including lymphocyte activation gene 3 (LAG3 or CD223), and T-cell immunoglobulin 3 (TIM3) and T-cell immunoglobulin and ITIM domain (TIGIT), for which ligands expressed on tumour or stromal cells may act synchronously or sequentially to promote overall physiologic suppression of immune responses (Figure 1).

Elucidation of the complex web of stimulatory and inhibitory signals that contribute to the tug-of-war of immune regulation and their dysregulation in cancer presents clear therapeutic opportunities targeting these to enhance anti-tumour immune responses. The impressive proof-of-principle for this approach came with the report in 2010 of a phase III clinical study of CTLA-4 blockade with the monoclonal antibody ipilimumab in patients with metastatic melanoma, which demonstrated enhanced survival in treated patients (Hodi et al, 2010). Although objective responses were infrequent (<11%), checkpoint inhibitor therapies as a class have been characterised by durability of responses in those patients who achieve an objective response, contributing to a notable ‘tail’ in the survival curve of long-term survivors. Importantly, while patient-level data are frequently limited, even summary data indicate that objective responses are not an absolute requirement for a survival benefit from ipilimumab. In the last six years, four engineered monoclonal antibody immune checkpoint inhibitor agents have been approved in more than 50 global markets for six forms of cancer; ipilimumab (anti-CTLA-4), pembrolizumab and nivolumab (anti-PD-1), and atezolizumab (anti-PD-L1), with response rates of up to 40–50% with PD-1-based therapy. Combination strategies, including immune checkpoint inhibitors, with different mechanisms of action have also been approved (anti-CTLA-4 and anti-PD-1) and are associated with higher response rates (exceeding 60%) though toxicity to therapy is a significant issue (Larkin et al, 2015; Postow et al, 2015). Although the greatest strength of combination regimens may lie in converting a proportion of patients destined not to benefit from single-agent checkpoint blockade into long-term survivors, reliable methods to identify these patients before therapy remain elusive. In addition, matured outcome data will be necessary to determine whether combination checkpoint blockade confers superior overall survival outcomes relative to monotherapy approaches such as PD-1 blockade alone.

Despite significant clinical gains in the setting of treatment with immune checkpoint blockade, limitations to this therapeutic strategy have inevitably surfaced as they have for prior generations of novel therapeutic strategies. Treatment with current checkpoint inhibitor monotherapy is not effective in all cancer types, as tumours with lower mutational burden and/or lower immunogenicity may be inherently resistant to this form of therapy. Even in the setting of initial responses in favourable tumour types, resistance may develop. This may be related to redundancy in the very web of activating and inhibitory molecules targeted by immune checkpoint inhibitors (Koyama et al, 2016), though other mechanisms of therapeutic resistance have also been identified including adaptive loss of antigenicity, recognition machinery, and transience of the inflamed tumour microenvironment. On top of this, strong predictive biomarkers of response to immune checkpoint blockade are currently lacking, and toxicity can be a major issue, particularly in combination strategies. All of these factors, as well as an appreciation of the cost of these agents and issues with access to therapy, call for a more comprehensive understanding of the hallmarks of response to immune checkpoint blockade in order to derive more tailored strategies.

Hallmarks of response to immune checkpoint blockade

There is a growing appreciation of the key factors contributing to response and resistance to immune checkpoint blockade, drawing upon features of the tumour itself (including the cancer genome, epigenome, and microenvironment), components of host immunity (both systemic and anti-tumour immunity), and external influences such as the microbiome (Figure 2). The ‘hallmarks of cancer’ described by Hanahan and Weinberg (2011) are tightly related to these responses, though current applications of the hallmarks are rather tumour cell centric. In contrast, a description of the hallmarks of response to immune checkpoint blockade must take into account more global features, recognising that tumours constitute a dynamic milieu and integrate numerous reinforcing and antagonistic signals from both local and systemic conditions. Herein, we describe the four ‘pillars’ and associated hallmarks of response to immune checkpoint blockade, with intimate interactions also noted between each of the pillars (Figure 2).

Figure 2
Figure 2

The core pillars and thematic hallmarks of anti-tumour immunity governing response to immune checkpoint blockade. Extensive research has identified numerous tumour-centric domains (shown in blue), including both static (existing genomic aberrations) and dynamic (epigenomic, metabolic and microenvironmental) features, which moderate anti-tumour immune responses and have impact on the efficacy of immune checkpoint blockade. Relevant metrics of overall immunocompetence, and systemic factors regulating the balance between immunotolerant and inflammatory states (e.g., innate and adaptive cell abundance and composition, immune cell circulation/sequestration, cytokine levels; shown in brown) are gradually being quantified. Environmental factors previously not implicated in directly modulating the anti-tumour response are now recognised to impact on immune checkpoint response (shown in green); principal among these sources of immunomodulation is the gut microbiota, while environmental stresses (e.g., thermal stress) and other tumour-remote immune-pathogen interactions may produce humoral factors that impact upon the specific anti-tumour response.

Tumour genome and epigenome

We have gained a tremendous amount of information on cancer genomics over the past few decades through the use of next-generation sequencing techniques, which has helped to usher in the age of precision medicine, although how best to use this data in the clinic remains unclear.

Genomic alterations in cancer may have divergent roles—potentially enhancing anti-tumour immunity in some instances and conferring resistance in others. A prime example of how mutations may enhance responses comes from evidence that tumour types with higher average mutational loads (such as melanoma and non-small cell lung cancer) have a much higher response to treatment with immune checkpoint blockade than those with a lower mutational burden, likely related to a proportionally higher burden of immunogenic cancer-specific ‘neoantigens’ (Van Allen et al, 2015; McGranahan et al, 2016). In addition to this, subtypes of cancer with specific genomic alterations leading to increased mutational burden may also demonstrate enhanced responses to immune checkpoint blockade, such as microsatellite unstable colorectal cancers resulting from mutational loss or epigenetic silencing of DNA mismatch repair genes and resultant genomic instability (Le et al, 2015). Similarly, it has been noted that several mutagen exposures – such as UV light in melanoma, and tobacco smoke in non-small cell lung cancer – display strong co-associations with mutational burden and checkpoint blockade immunotherapy response (Rizvi et al, 2015). However, simply harbouring high mutational levels is not the complete story, as neoantigen proteins must be expressed, processed, and of sufficient binding characteristics in the context of HLA to be immunogenic although evidence suggests that the predictive value of neoantigen load is not driven by the small proportion of neoantigens with high predicted HLA-binding affinity (Van Allen et al, 2015). Detailed exomic analysis of a cohort of melanoma patients treated with CTLA-4 blockade revealed a shared repertoire of tetrapeptide neoantigen sequences in patients who derived clinical benefit; the immunogenicity of several neoantigen peptides was confirmed using patient-derived lymphocytes in vitro (Snyder et al, 2014). Importantly, the association with response was stronger for the neoantigen signature than for overall mutational burden, consistent with the notion that overall mutational burden increases the likelihood that a tumour is immunogenic, but that it may not be an absolute requirement for checkpoint blockade response. Importantly, a number of other types of antigens exist in cancer (cancer germline antigens, differentiation antigens, over-expressed antigens, and viral antigens), which can help to elicit anti-tumour immune responses (Blankenstein et al, 2012).

In contrast to the potentially pro-immunogenic impact of genomic alterations, there is a growing body of evidence regarding other genomic and epigenomic alterations in tumours that may impair immune responses and facilitate resistance to immune checkpoint blockade. Constitutive mitogen-activated protein kinase (MAPK) activation by mutations in the BRAF oncogene (and other MAPK pathway mutations) contributes to immune evasion by altering expression of tumour-associated antigens and major histocompatibility complex expression (Boni et al, 2010). Loss of expression of the tumour suppressor gene PTEN (either by mutations or copy number alterations) is also associated with impaired response to immune checkpoint blockade (Peng et al, 2016). Several studies have shown that immunotherapy resistance may originate in more than one compartment of the tumour microenvironment, with signals derived from tumour cells preventing immune infiltration (e.g., Wnt-β-catenin, PPAR-γ, FGFR3) while the dynamic interplay of anti-tumour immune attack adaptively moulds the landscape of immunomodulatory elements present over time (Spranger et al, 2013, 2015; Sweis et al, 2016). In line with evidence that interferon signatures play a significant role in the response to immune checkpoint blockade and may potentially act as clinical biomarkers (Ribas et al, 2015), JAK1/2 mutations have been identified in patients resistant to PD-1 blockade, acting via disruption of tumour-inhibitory interferon signalling (Zaretsky et al, 2016). Notably, the list of genomic alterations demonstrated to modify response to immune checkpoint blockade grows on a daily basis.

In addition to genomic alterations, epigenomic alterations in tumour cells may also have a profound effect on anti-tumour immune responses. Epigenetic chromatin modifications function physiologically to silence (or activate) genes in an orchestrated fashion during key developmental processes, however aberrant epigenomic alterations often exist in cancer, and can contribute to oncogenesis and also to immune evasion (Jones and Baylin, 2007). Epigenetic downregulation of antigen expression and silencing of immune-related genes may negatively impact immunotherapy response (Heninger et al, 2015), and early studies combining epigenetic modifiers, such as hypomethylating agents and histone deacetylase inhibitors, with immune checkpoint inhibitors have shown promising results (Wrangle et al, 2013).

Tumour microenvironment

The most extensively discussed component of the tumour microenvironment (TME) other than cancer cells themselves is tumour infiltrating lymphocytes (TIL). The presence of TIL has long been known to confer a favourable prognosis (Galon et al, 2006), and a greater appreciation of the complexity of immune infiltrates with regard to phenotype and distribution of the infiltrating leukocytes is mounting. Traditional metrics of TIL density and distribution (e.g., central vs peripheral) and gross enumeration of the T-cell infiltrate by CD3 and CD8 markers can now be readily supplemented with detailed characterisation of numerous surface markers, expression of immunomodulatory molecules, and quantification of T-cell clonotypes. Studies incorporating these techniques have revealed a broad range of infiltrating lymphocytes far beyond the dichotomous effector and regulatory T lymphocyte subsets, and have highlighted their complex regulatory potential as well as potential plasticity (Iida et al, 2011; Djenidi et al, 2015). Additional information has been gained by studying spatial relationships of TIL to tumour and stromal cells, yielding insight into the physical limitations to intercellular functional interactions. This has been demonstrated in the context of response to PD-1 blockade, in which not only density of CD8 T-cell infiltrate, but also location at the invasive margin and proximity of PD-1 expression to PD-L1 expression, were important factors associated with treatment response (Tumeh et al, 2014).

Tumours not only contain cancer cells, but also harbour a rich microenvironment composed of blood vessels, APCs, neutrophils, myeloid derived suppressor cells, tumour-associated macrophages and fibroblasts, components of the extracellular matrix, and soluble factors (such as cytokines and growth factors), all of which may assist or hinder anti-tumour immune responses. This is particularly evident when considering response to immune checkpoint inhibitors where the ability to exclude infiltrating immune cells from the TME can ‘make or break’ an anti-tumour immune response. On the basis of this, tumours have been classified into several cancer-immune phenotypes including ‘inflamed’ or ‘non-inflamed’ (Spranger and Gajewski, 2013), with more recent reports describing tumours as ‘immune-deserts’, ‘immune-excluded’, or ‘inflamed’ (Chen and Mellman, 2017). This type of classification motivates extensive research to identify predictive microenvironmental biomarkers that transcend existing markers such as PD-L1. Numerous therapeutic approaches targeting non-tumour cell stromal elements and functions are currently being tested in preclinical models and clinical trials, either as monotherapies or in combination with immune checkpoint blockade. Key examples include molecules inhibiting generation of the immunosuppressive metabolite indoleamine-2,3-dioxygenase (e.g., NCT02471846 and NCT 02073123), antagonists of the tumour-associated macrophage stimulating CSF1R (e.g., NCT02713529, NCT02526017, and NCT02323191), and ongoing development of agonist agents of the stimulator of interferon genes, aiming to favourably skew the TME towards an inflamed phenotype. Early studies of combination immune checkpoint and angiogenesis inhibition have showed promise from this multi-targeted approach in patients with advanced melanoma and renal cell cancer (Hodi et al, 2014; Wallin et al, 2016). Future advances in such strategies will be based on a deeper unravelling of the microenvironmental interactions to identify targetable nodes in the network.

Host immunity

Central to the efficacy of immune checkpoint blockade is preserved host immunity, predicated upon adequate number, availability, and activity of other innate and adaptive immune cell types. Systemic immunity is dynamic, shaped by prior antigenic stimuli, and influenced by interactions both within and outside the host, as diverse as invading microbial pathogens at topologically ‘external’ body surfaces (e.g., skin and gut), and interactions with tumours themselves. During the development and progression of cancer, components of dying tumour cells are taken up by APCs, which present processed antigen in the context of HLA to helper (CD4+) and cytotoxic (CD8+) T lymphocytes. This results in a cascade of events that leads ultimately to activation and expansion or anergy, depending on numerous modulating factors, principally the availability of appropriate co-stimulatory – or inhibitory – ligand-receptor engagement. This forms the foundation of the cancer immunity cycle described by Chen and Mellman (2013), and involves contributions from numerous other cells of the innate (e.g., NK cells) and adaptive (e.g., B lymphocytes) immune system. That a quantitatively and qualitatively intact overall immune system is important in cancer control is clear from the generally higher rates of many cancer types, including several without known viral aetiology, in immunosuppressed patients (Grulich et al, 2007). Although germline polymorphisms in immune-related genes are known to impact cancer predisposition and immune function in other settings such as haematopoietic transplantation outcomes (Delgado et al, 2010), whether polymorphisms predictive of checkpoint inhibitor efficacy or toxicity will be identified is currently unknown (though is highly likely).

The host T-cell repertoire, within which a subset of potentially tumour-reactive T cells resides, is largely shaped during development and early childhood while other components of the immune system remain more malleable throughout adult life. Host immunity moulds the tumour landscape, exemplified by the concept of immune editing as described by Schreiber and colleagues, through which immune action shapes the tumour composition to arrive at the parallel fates of equilibrium, elimination, or escape (Schreiber et al, 2011). However, it is becoming increasingly clear that this relationship is bidirectional; tumours may themselves profoundly influence the systemic environment through secretion of immunosuppressive cytokines and tumour-associated exosomes, which have been shown to be immunosuppressive (Meehan and Vella, 2016) and prime secondary locations for future metastasis (Peinado et al, 2012).

Environment

Importantly, factors within the broader environment (both outside and inside the host) may shape anti-tumour and overall immune responses. Perhaps the most poignant example of this is the microbiome, with recent evidence demonstrating a critical link between the gut microbiome and anti-tumour immunity. These interactions have significant implications in the setting of immune checkpoint blockade, as there is evidence that modulating the gut microbiome can enhance – or may even be a prerequisite for – therapeutic responses to these agents in preclinical models (Sivan et al, 2015; Vetizou et al, 2015). This has recently been studied in patients, with data suggesting that differential bacterial ‘signatures’ exist in responders vs non-responders to immune checkpoint blockade (namely, anti-PD-1 therapy) in a cohort of patients with metastatic melanoma (Gopalakrishnan et al, 2017). This finding needs to be validated in larger cohorts and across cancer types, but provides formative evidence regarding the influence of environmental factors on tumour and host immunity.

Indeed, other external pressures, such as diet and stress, can also impact the host and anti-tumour immunity (Kokolus et al, 2013), with hints that these factors might also modulate responses to immune checkpoint blockade although the complex mechanisms behind these influences are still being elucidated. Nonetheless, it is clear that we are gaining a more holistic and comprehensive view of the influences on anti-tumour immunity, which tightly relate to our understanding of the factors affecting therapeutic immune checkpoint blockade.

Implications for immune monitoring and novel strategies to overcome resistance to immune checkpoint blockade

On the basis of a deeper understanding of these pillars and hallmarks and the complex interactions between them, we will ultimately be able to refine strategies to monitor and enhance responses to immune checkpoint blockade. Importantly, the insights gained from the study of checkpoint inhibitor agents in current clinical use will have direct relevance to other forms of immunotherapy in active development, such as immunostimulatory checkpoint agonists and adoptive cell therapy.

The pillars (and hallmarks) of response to immune checkpoint blockade should be considered when designing immune monitoring strategies for these forms of therapy, and must take into account aspects of the tumour, the TME, overall immune competence, and environmental influences. This is already being done in some regards, with interrogation of specific genomic alterations and total mutational load as well as examination of the tumour for CD8 infiltrate density and PD-L1 expression. Evidence is emerging that monitoring host immune responses (e.g., via phenotypic markers, such as ICOS on T cells) (Ng Tang et al, 2013), may help predict responses to immune checkpoint blockade, and that the microbiome may serve as a predictive factor for long-term benefit to other forms of immunotherapy (Taur et al, 2014). Standardised approaches for each of these strategies are not yet developed and represent an area of unmet need in the field; additional intricacies will undoubtedly arise when monitoring combination therapies pairing immune checkpoint blockade with immunostimulatory agents (e.g., agonistic antibodies targeting 4-1BB or OX40), cell-based therapies, or molecular-targeted agents.

In addition to implications for monitoring responses, an understanding of these pillars and hallmarks also provides a framework for understanding and overcoming mechanisms of therapeutic resistance to immune checkpoint inhibitors. Numerous (epi)genomic, microenvironmental, and immune mechanisms of resistance to immune checkpoint blockade have been identified (Sharma et al, 2017), spurring the development of even more numerous multi-drug strategies targeting them. There is growing interest in better understanding the role of chronic inflammation, diet, and stress on general and tumour-specific immunity but much work is required to extract actionable elements from this knowledge.

The proposed pillars and hallmarks provide a foundation on which to build as we gain volumes of information from preclinical studies, clinical trials, and biomarker assessment in patients on standard-of-care therapy. Ultimately, integration of such data sets will inform optimal therapeutic strategies incorporating immune checkpoint blockade (and other forms of immunotherapy) in this age of cancer precision medicine.

Biomarkers for Identifying Risk of Immune Reconstitution Inflammatory Syndrome


In the last 10–20 years, access to antiretroviral therapy (ART) has improved worldwide, resulting in substantial reduction in HIV-associated mortality and increased life expectancy, especially in low and middle-income countries. However, immune reconstitution inflammatory syndrome (IRIS), the clinical deterioration in patients with HIV initiating ART, is a common complication of ART initiation. The manifestations of IRIS depend on the type of opportunistic infection. With HIV-1 as the strongest predisposing factor to tuberculosis (TB) and TB as the commonest cause of death in HIV-1 infected persons in Africa, the otherwise beneficial dual therapy for HIV-1 and TB is frequently complicated by the occurrence of TB-immune reconstitution inflammatory syndrome (TB-IRIS) (Walker et al., 2015). Two forms of TB-IRIS are recognized: paradoxical, which occurs in patients established on anti-tuberculosis therapy before ART, but who develop recurrent or new TB symptoms and clinical features after ART initiation; and unmasking TB-IRIS in patients not receiving treatment for TB when ART is started, but who present with active TB within 3 months of starting ART (Meintjes et al., 2008). Paradoxical TB-IRIS affects approximately 15.7% of all HIV-1-infected patients commencing ART while on TB treatment, and up to 52% in some populations, causing considerable morbidity and mortality (Namale et al., 2015).

While the clinical features are relatively well-described, specific diagnostic tools and treatments for TB-IRIS are lacking. The diagnosis of IRIS is clinical, and excluding other causes for a clinical deterioration, such as other opportunistic infections and drug-resistance, is challenging, especially in a resource-limited setting. While risk factors for IRIS have been identified, such as low CD4 count pre-ART initiation and the presence of a disseminated opportunistic infection, there are no biomarkers that predict which patients will develop IRIS. The identification of biomarkers for IRIS prediction may help elucidate the mechanism of IRIS pathogenesis, which may in turn facilitate the development of specific therapies and additionally, allow high-risk patients that would benefit from specific preventative strategies to be identified.

A large number of investigations have addressed the roles played by different aspects of the immune response in contributing to TB-IRIS pathogenesis, reviewed in Lai et al. (2015a)). A recent unbiased whole-blood transcriptomic profiling of HIV-TB co-infected patients commencing ART showed that inflammation in TB-IRIS is driven by innate immune signaling and activation of the inflammasome, which triggers the activation of transcription factors leading to hypercytokinemia, resulting in systemic inflammation (Lai et al., 2015b). Other recent work also suggests that extracellular matrix destruction by matrix metalloproteinases may play a role in paradoxical TB-IRIS (Tadokera et al., 2014, Shruthi Ravimohan, 2015). Immunosuppressive corticosteroid therapy improves symptoms and reduces hospital admissions but is not without adverse events, and is potentially detrimental in cases of drug-resistant TB (Meintjes et al., 2010). Therefore therapeutic strategies that offer greater immune specificity should be explored.

The CADIRIS study, a double-blind, randomized, placebo-controlled trial, investigated the use of maraviroc (a CCR5 antagonist) for IRIS prevention, based on the hypothesis that inflammatory cytokines and chemokines mediate the influx of CCR5-expressing immune cells in IRIS and CCR5 blockade would prevent these inflammatory cells leaving the circulation, reducing local inflammatory reactions leading to IRIS. The study recruited HIV-infected participants with advanced immunosuppression (CD4 count <100/μl) from five clinical sites in Mexico and one in South Africa and followed them for 1 year. Patients were assigned to receive either maraviroc (600 mg twice daily) or placebo in addition to ART, the primary outcome being time to an IRIS event by 24 weeks. Maraviroc had no significant effect on development of IRIS after ART initiation. While this CCR5 inhibitor has proven antiviral activity, safety and tolerability as part of an ART regimen, its use as an immune-modulator to prevent IRIS appears un-warranted (Sierra-Madero et al., 2014).

Well-conducted clinical trials, even if their outcome is negative, are an enormously valuable resource for further studies, such as identifying correlates of risk and/or protection. In this issue of EBioMedicine, Musselwhite and colleagues (Musselwhite et al., 2016) investigate plasma biomarkers predictive of IRIS in samples banked at enrolment from HIV-infected patients entering the CADIRIS trial. With the hypothesis that the risk of IRIS is most likely already present before starting ART, and can be predicted from measuring biomarkers in plasma samples collected before starting ART, they assessed twenty biomarkers in an exploratory way, and retrospectively associated them with the risk of developing IRIS. Of the 267 patients with banked plasma samples, 62 developed IRIS within 6 months of ART initiation, 31% of them TB-IRIS specifically, within median of 13 days of ART. The results indicate that baseline concentrations of vitamin D and higher concentrations of D-dimer, as well as markers of T cell and monocyte activation (interferon-γ and sCD14) were independently associated with risk of IRIS in general. Vitamin D deficiency was prevalent. Higher vitamin D levels were associated with protection against IRIS events, suggesting vitamin D plays an immune-modulatory role. However, vitamin D and D-dimer concentrations were not associated with TB-IRIS specifically, perhaps due to lack of power for this sub-analysis. TB-IRIS was associated with higher concentrations of CRP, sCD14, and interferon-γ and lower hemoglobin than other forms of IRIS and these parameters were used in a composite score to predict TB-IRIS over Other IRIS, with an area under the curve of 0.85 (CI 0.79-0.92) on Receiver Operator Characteristics (ROC) analysis.

The strength of this study lies in reasonable power to assess predictors of IRIS and the availability of plasma samples prior to starting ART on two different continents, contributing to the generalizability of the findings. Interesting comparisons are drawn between TB-IRIS and other causes of IRIS, demonstrating heterogeneity in IRIS pathophysiology. As patients with CD4 counts ≥100/μl and those with critical illness (e.g. severe laboratory abnormalities, CNS infections) were excluded, generalizability of the findings to these groups is unknown. Further work is required to confirm the findings in these and other at-risk patient populations.

References

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Source:http://www.ebiomedicine.com

Blood test to predict likelihood of disease years in the future


Blood test
Scientists look for signature patterns of biomarkers

A simple blood test that can tell how well a person is likely to age is on the horizon after scientists cracked blood “signature” patterns which predict ill health.

The breakthrough means doctors will be able to assess the likelihood of dementia, cardiovascular disease and a range of other conditions years before patients show any symptoms.

Researchers at Boston University learnt to recognise combinations of specific “biomarkers”, or chemicals found in the blood, of 5,000 people in a study.

 We can now detect and measure thousands of biomarkers from a small amount of bloodBoston University

They then matched these to the participants’ health outcomes over a period of eight years.

They found specific patterns associated with disease and disability-free aging, as well as patterns associated with the threat of several diseases.

While various techniques already exist for predicting specific conditions, such as heart disease, the new approach will enable doctors to paint a comprehensive picture of their patient’s overall future health.

It also promises to give people the chance to change their lifestyles or begin preventative treatment to stave off diseases flagged up as a risk by their blood composition.

The team identified 26 different signatures, finding that around half the people in the study shared an average signature of 19 biomarkers, while other groups had different patterns that deviated from the norm.

“These signatures depict differences in how people age, and they show promise in predicting healthy ageing, changes in cognitive and physical function, survival and age-related diseases like disease, stroke, type 2 diabetes and cancer,” the research team said.

“We can now detect and measure thousands of biomarkers from a small amount of blood with the idea of eventually being able to predict who is at risk of a wide range of diseases long before any clinical signs become apparent.”

Companies are already offering blood tests which claim to estimate a person’s lifespan, however this is done using DNA analysis and does not give insights into the likelihood of developing specific conditions.

The technique works by measuring structures at the end of a person’s chromosomes, called telomeres, which scientists believe are an important indicator of the speed at which a person is ageing.

The researchers behind the new study, which is published in the journal Ageing Cell, say the biomarker signature technique could also be used to speed up long and laborious drug trials.

Prof Paola Sebastiani said that rather than waiting “years and years” for clinical outcomes, instead trials may be able to rely on biomarker signatures far earlier to detect the effect of a trial medicine.

Pathways Clinical Decision Support for Appropriate Use of Key Biomarkers


Abstract

Purpose: Breast cancer diagnostics have the ability to predict disease recurrence and the benefit of chemotherapy. This study measures the use of a diagnostic assay, Oncotype DX, when embedded in a breast cancer decision support algorithm and, on the basis of the assay results, the use of chemotherapy in the adjuvant setting.

Methods: UPMC CancerCenter retrospectively reviewed patients with estrogen receptor–positive, human epidermal growth factor receptor 2 (HER2)Neu–negative disease with zero to three positive nodes navigated in the Via Pathways decision support portal during a 12-month period. The breast algorithm prompted input of the assay recurrence score (RS) and then recommended hormonal therapy alone (HT) for low RS, or chemotherapy followed by HT for high RS. The patient’s RS was correlated with the treatment decision.

Results: During this time period, 643 patients had ER-positive, HER2Neu-negative disease with zero to three positive nodes. Of those, 596 (92.7%) had diagnostic testing to determine chemotherapy plus HT versus HT alone, and 47 had chemotherapy followed by HT without an RS. For node-negative patients classified with low or high RS, pathway treatment adherence rates were 99.7% and 96.6%, respectively; node-positive patients had 95.7% and 87.5% adherence rates, respectively.

Conclusion: This analysis demonstrates the use of a clinical pathway to measure the adoption of a diagnostic test, the Oncotype DX breast assay, and the use of the appropriate therapy on the basis of the RS. As more diagnostics are established to aid in the personalized treatment of diseases, pathways may be important in maintaining clinician awareness of the appropriate disease presentations where these tests should be used, measuring usage of these tests, and tracking the treatment decisions on the basis of test results.

More Alpha-Synuclein in Spinal Fluid Linked to Faster Cognitive Decline


Alpha-synuclein — the protein that clumps in the cells of Parkinson’s patients — is currently the major focus of Parkinson’s biomarker studies. Researchers are analyzing biosamples (spinal fluid, blood, tissue) to make a connection between alpha-synuclein and risk, onset or progression of Parkinson’s disease (PD). The latest findings, published in The American Journal of Pathology, report that patients with higher levels in spinal fluid experienced faster cognitive decline.

In a project funded by The Michael J. Fox Foundation (MJFF), Jing Zhang, MD, PhD, and his team at the University of Washington in Seattle examined samples and data from PD patients obtained in the DATATOP study. Led by the Parkinson’s Study Group in the late 1980s, the deprenyl and tocopherol antioxidative therapy of parkinsonism (DATATOP) study collected samples and clinical data from PD subjects for up to eight years.

In this latest analysis, researchers compared alpha-synuclein levels to scores from tests of cognition, such as verbal learning and memory, visuospatial memory and processing speed, among 304 PD patients. They found that patients with higher levels of alpha-synuclein in spinal fluid had faster cognitive decline.

“This is a surprising conclusion,” says Mark Frasier, PhD, MJFF vice president of research programs. “One would think that people with more cognitive problems would have less alpha-synuclein in spinal fluid because more would be caught up in the brain causing those problems.”

Zhang’s group also reported that while alpha-synuclein levels decreased significantly over two years, that decline could not predict motor symptoms. These findings join a list of observations about how alpha-synuclein in spinal fluid relates to PD. Initial analysis from the MJFF-sponsored Parkinson’s Progression Markers Initiative (PPMI) reported last year that PD patients had lower alpha-synuclein levels in spinal fluid compared to controls. They also found that patients with posture/gait disturbance averaged lower alpha-synuclein than patients with tremor-dominant PD.

Further investigation into alpha-synuclein continues in PPMI and other studies. Zhang and his coauthors cited PPMI as a potential source for validation of their cognition findings. Since PPMI includes healthy controls, researchers could test whether those results are PD-specific or seen in healthy aging adults with cognitive decline, too.

To accelerate research around PD biomarkers, MJFF spearheaded an effort to make data and samples from varied Parkinson’s studies available to investigators. The Foundation also offers funding to use the data and samples, such as to Zhang for the DATATOP analysis.

Tobacco smoke biomarkers and cancer risk among male smokers in the Shanghai Cohort Study.


Tobacco smoke constituent metabolites are established biomarkers of cigarette smoke exposure. ► This paper demonstrates that some of these metabolites are also biomarkers of cancer risk in male smokers from Shanghai. ► The biomarkers of cancer risk are total cotinine, total NNAL, PheT, and total NNN.

Abstract

Metabolites of tobacco smoke constituents can be quantified in urine and other body fluids providing a realistic measure of carcinogen and toxicant dose in a smoker. Many previous studies have demonstrated that these metabolites – referred to as biomarkers in this paper – are related to tobacco smoke exposure. The studies reviewed here were designed to answer another question: are these substances also biomarkers of cancer risk? Using a prospective study design comparing biomarker levels in cancer cases and controls, all of whom were smokers, the results demonstrate that several of these biomarkers – total cotinine, total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), r-1-,t-2,3,c-4-tetrahydroxy-1,2,3,4-tetrahydrophenanthrene (PheT), and total N′-nitrosonornicotine (NNN) – are biomarkers of cancer risk. Therefore, these biomarkers have the potential to become part of a cancer risk prediction algorithm for smokers.

 

Source: cancer letters