A new analysis that used plasma proteomics, cardiac imaging, and event surveillance of participants in a longitudinal cohort study identified 52 circulating proteins with significant links to aortic valve (AV) hemodynamics and the risk for AV-related hospitalizations. Two of these biomarkers had particularly robust associations, researchers said.
METHODOLOGY:
The analysis included 11,430 participants (mean age 60 years, 46% male, and 21% Black) from the community-based Atherosclerosis Risk in Communities (ARIC) study who attended study visit 3 (mid-life) and 4899 participants (mean age 76 years, 43% male, and 18% Black) who attended study visit 5 (late-life).
Researchers accessed hospital discharge codes to identify AV events; carried out detailed cardiac phenotyping; assessed changes in AV peak velocity (AVmax), AV area, and dimensionless index (DI); and analyzed more than 4800 plasma proteins.
Mendelian randomization (MR) identified proteins with potential causal effects of aortic stenosis (AS) to prioritize as potential targets to prevent AS or slow its progression.
Researchers assessed the generalizability of their findings in a replication cohort of 3413 participants in the community-based observational Cardiovascular Health Study (CHS).
Over a median follow-up of 22 years, 912 ARIC participants were hospitalized with an AV diagnosis or intervention.
TAKEAWAY:
Researchers identified 52 circulating proteins associated with both AV hemodynamics and AS severity according to echocardiography (AVmax and DI) when assessed cross-sectionally in late-life (visit 5) and with incident AV-related hospitalization when ascertained in mid-life (visit 3).
Of the 52 candidate proteins, six were significantly associated with moderate or severe AS, one of which was matrix metalloproteinase (MMP12); higher MMP12 levels were robustly associated with incident AV hospitalizations, worse AV hemodynamics, greater increase in AVmax over 6 years in late-life, greater degree of AV calcification in late-life, and greater expression in calcific than normal or fibrotic AV tissue.
Another candidate marker was complement C1q tumor necrosis factor–related protein 1 (C1QTNF1), which was also robustly associated with AV hemodynamics and the risk for incident AV events. MR analysis identified a consistent potential causal effect of C1QTNF1 on both AS and higher AVmax.
The analysis also confirmed the AS prognostic relevance of growth differentiation factor 15 and found higher circulating leptin levels were associated with less hemodynamic AS and a lower risk for AV hospitalization.
In the external replication analysis, magnitudes of association of the 52 proteins with incident moderate or severe AS were generally similar to those for incident AV-related hospitalization in ARIC.
IN PRACTICE:
“These findings highlight the potential of MMP12 as a novel circulating biomarker of AS risk and C1QTNF1 as a new putative target to prevent CAVD [calcific aortic valve disease] progression,” the authors concluded.
In an accompanying editorial, Brian R. Lindman, MD, Structural Heart and Valve Center, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, called the work “highly innovative” and said it “undoubtedly opens new avenues for discovery,” adding that understanding the biology and potential predictive markers “could be useful for screening for AS.” He cautioned, however, that it’s unclear whether findings will “prove meaningful for clinical action or pharmacologic targeting.”
Biomarkers of Alzheimer’s disease are present early, and their levels evolve over time, according to a large study by researchers at Beijing’s Innovation Center for Neurological Disorders. The biological markers of Alzheimer’s progression included amyloid-beta (Aβ)42, the ratio of Aβ42 to Aβ40, phosphorylated tau 181, total tau, neurofilament light chain, and hippocampal volume. Notably, these signs’ appearance and persistence changed with time.
As cognitive impairment progressed, the team reports, the changes in CSF biomarker levels in the Alzheimer’s disease group initially accelerated and then slowed.
The study was published this week in The New England Journal of Medicine. Jianping Jia, MD, PhD, is the lead author.
It is estimated that over 50 million people worldwide have some form of dementia, and that by 2050 this number will climb to over 150 million. While new drugs are being introduced that finally address the underlying biology of the disease, these are few and don’t have much effect. It is believed that early diagnosis will aid drug development and treatment.
Preclinical Alzheimer’s disease is characterized by normal cognitive function and abnormal levels of specific cerebrospinal fluid (CSF) biomarkers. This preclinical stage is typically followed by mild cognitive impairment, which sometimes progresses to clinically apparent dementia.
Notably, changes in biomarker levels can begin 15 to 20 years before clinical signs of Alzheimer’s disease. Changes in CSF biomarkers. such as levels of amyloid-beta (Aβ), total tau, phosphorylated tau 181, and neurofilament light chain (NfL) have been indicators in preclinical Alzheimer’s disease that become abnormal sequentially rather than simultaneously.
There’s also a difference between familial and sporadic Alzheimer’s disease. In the sporadic disease a person’s clinical course, beginning with normal cognition and progressing to Alzheimer’s disease, “cannot be predicted,” the authors write. Also, they note, past studies have had an “Underrepresentation of Asian populations… In addition, the relatively short follow-up periods in previous studies do not reflect the lengthy trajectory over decades of biomarker alterations leading to the onset of Alzheimer’s disease.”
This team compared 648 people eventually diagnosed with Alzheimer’s with an equal number who did not develop the disease. All were enrolled in the China Cognition and Aging Study from January 2000 through December 2020. A subgroup of these patients underwent cerebrospinal fluid (CSF) testing, cognitive assessments, and brain imaging at two-year–to–three-year intervals.
The first sign the researchers saw was positive amyloid testing—18 years or 14 years prior to diagnosis depending on the test used. Differences in tau were also detected, followed by a marker of trouble in how neurons communicate. A few years later, differences in brain shrinkage and cognitive test scores between the two groups became apparent.
Specific findings were that CSF and imaging biomarkers in the Alzheimer’s disease group diverged from the cognitively normal group specific points: amyloid-beta (Aβ)42, 18 years; the ratio of Aβ42 to Aβ40, 14 years; phosphorylated tau 181, 11 years; total tau, 10 years; neurofilament light chain, nine years; hippocampal volume, eight years; and cognitive decline, six years.
Researchers identified several genes in blood whose activity is related to suicidal thoughts and actions in men with psychiatric disorders.
The genetic findings, combined with app-based questionnaires, may help clinicians predict which patients are likely to attempt suicide.
Researchers have been seeking a way to objectively measure a person’s risk for suicide.
More than 41,000 Americans commit suicide each year. That’s more than twice the number killed annually by homicide. Most people who end their own lives have a mental disorder such as depression, schizophrenia, or bipolar disorder.
Efforts to reduce suicides have focused on identifying and treating those at risk. However, asking people if they’re suicidal isn’t always a reliable approach. Finding a way to objectively measure a person’s risk for suicide is thus an important area of research. Some researchers are developing questionnaires that measure the likelihood of someone committing suicide. Others are looking for biological markers of people who are suicidal.
A study led by researchers at Indiana University School of Medicine combined these approaches. Their work was funded by an NIH Director’s New Innovator Award and the U.S. Department of Veterans Affairs. Results appeared online in Molecular Psychiatry on August 18, 2015.
The researchers studied 217 male psychiatric patients at the Indianapolis VA Medical Center during multiple visits several months apart. The scientists measured the men’s thoughts of suicide through extensive interviews and took blood samples. They identified 37 patients whose thoughts of suicide increased between visits. Those patients’ blood samples were analyzed to find genes with changes in activity, or expression, between visits. Those genes were ranked based on prior research linking them to suicide risk. The researchers then measured the expression of these top-ranked genes in blood samples from 26 men who had committed suicide.
The team also developed 2 apps that use questionnaires to measure risk factors for suicide. The first collects details on a patients’ emotional state. The second asks about factors known to influence suicide risk, such as life events, stress, and mental health. Both could predict thoughts of suicide more than 85% of the time. These questionnaires were then combined with the most predictive gene biomarkers to create a universal predictive measure called UP-Suicide.
The team tested UP-Suicide in a separate group of 108 psychiatric patients to examine its ability to predict thoughts of suicide and a group of 157 patients to examine ability to predict future hospitalizations. The tool predicted which patients would go on to have serious suicidal thoughts with 92% accuracy. It also predicted with 71% accuracy which patients would be hospitalized for suicidal behaviors in the year following testing. The tool was even more accurate for patients with bipolar disorder, with 98% and 94% accuracy, respectively.
“We believe that widespread adoption of risk prediction tests based on these findings during health care assessments will enable clinicians to intervene with lifestyle changes or treatments that can save lives,” says lead researcher Dr. Alexander B. Niculescu.
Because the team studied only male psychiatric patients, further research will be needed to understand how well this approach can predict suicidal thoughts and behaviors in other populations, such as women and those who aren’t psychiatric patients.
Research and treatment of psychiatric disorders are stymied by a lack of biomarkers – objective biological or physiological markers that can help diagnose, track, predict, and treat diseases.
Traditionally, psychiatric disorders such as depression have been diagnosed based on symptoms according to subjective assessments. The identification of biomarkers to aid in diagnosis and treatment selection would greatly advance treatments.
Finding biomarkers of mental illnesses, rather than relying on symptoms, may provide a more precise means of diagnosis, and thereby aligning psychiatric diagnosis with other medical diagnosesYihong Yang
In the current study, the investigators used brain imaging data from the Adolescent Brain Cognitive Development (ABCD) Study of nearly 12,000 children aged 9 to 10 at the beginning of the study. Modern neuroimaging techniques, including resting-state functional connectivity (rsFC) analysis, allow researchers to investigate the organization of brain circuits through their interaction with one another over time. Yihong Yang, PhD, senior author of the study, at the Neuroimaging Research Branch, National Institute on Drug Abuse, said, “Using a functional MRI (fMRI) dataset, we identified a brain connectivity variate that is positively correlated with cognitive functions and negatively correlated with psychopathological measures.”
Cognition has long been studied in the context of mental disorders, and recent research has pointed to shared neurobiology between the two, as supported in this new study. This brain-based variate predicted how many psychiatric disorders were identified in participants at the time of the scan and over the following two years. It also predicted the transition of diagnosis across disorders over the two-year follow-up period.” Dr. Yang added, “These findings provide evidence for a transdiagnostic brain-based measure that underlies individual differences in developing psychiatric disorders in early adolescence.”
John Krystal, MD, Editor of Biological Psychiatry, said of the work, “Mental illness in adolescence has emerged as a cardinal public health challenge in the post-Covid era. More than ever before, we would benefit from better ways to identify adolescents at risk. This study uses data from the landmark ABCD Study to illustrate how neuroimaging data could illuminate risk for mental illness across the spectrum of diagnoses.” Dr. Yang added, “Finding biomarkers of mental illnesses, rather than relying on symptoms, may provide a more precise means of diagnosis, and thereby aligning psychiatric diagnosis with other medical diagnoses.”
Circulating tumor DNA (ctDNA) is considered to be a surrogate marker for minimal residual disease (MRD) and a predictive marker to determine the risk of colon cancer recurrence.
Patients with colon cancer with detectable ctDNA post–curative-intent therapy (MRD-positive) are at a remarkable risk of recurrence (95% to 100%) if not offered systemic therapy, which suggests that MRD positivity is not just a high-risk marker for recurrence but also a measure of persistent disease.
ctDNA-guided management can be considered to identify the cohort of patients with stage II colon cancer who may not benefit from adjuvant therapy, based on the DYNAMIC study.
Surgery followed by adjuvant chemotherapy based on clinical and pathologic risk stratification is the recommended approach for all patients with stage II (ie, high-risk stage II) and stage III colon cancer. Despite this approach, approximately 15% and 30% of patients with stage II and III disease, respectively, may experience recurrence, while 40% to 50% of the patients are cured by surgery alone.1-3 Biomarker-guided follow-up and personalized treatment are vital for patients with resected colon cancer to aid in clinical decision-making and appropriate risk stratification.
Dr. Midhun Malla
Recent Data Support the Predictive and Prognostic Utility of ctDNA as a Biomarker
ctDNA is considered a surrogate marker for MRD and a predictive marker to determine the risk of recurrence. Tumor-informed and tumor-agnostic testing strategies are currently available for ctDNA assessment.3 Both have demonstrated improved specificity and sensitivity of ctDNA as a strong predictive and prognostic biomarker compared with carcinoembryonic antigen across multiple studies.4-8 There is strong evidence to suggest that MRD status outperforms known clinicopathologic factors in predicting relapse. However, it is important to note that improved sensitivity and specificity are associated with serial testing of ctDNA, while single timepoint testing has similar sensitivity and specificity to carcinoembryonic antigen. Patients with colon cancer with detectable ctDNA post–curative-intent therapy, defined as MRD-positive, are at a remarkable risk of recurrence (95% to 100%) if not offered systemic therapy, which suggests that MRD positivity is not just a high-risk marker for recurrence but also a measure of persistent disease. Furthermore, the growth rate of ctDNA is also prognostic of outcomes, with exponential growth associated with significantly inferior survival outcomes compared with a slow rise in ctDNA.7 The optimal timing of ctDNA is essential for a reliable test with improved sensitivity. Historically, ctDNA testing was advised to be performed at least 4 weeks following surgery, as the increased shed of cell-free DNA in the immediate postoperative period could lead to false-negative results. However, a real-world analysis of patients who received commercial, tumor-informed ctDNA testing at 2 to 4 weeks postoperatively demonstrated similar sensitivity for MRD detection compared with 4 to 8 weeks postoperatively despite the presence of detectable cell-free DNA levels.9
The updated results of the GALAXY arm of the CIRCULATE-Japan study, which prospectively followed serial ctDNA in patients with stage I-III colorectal cancer (CRC), were presented at the European Society for Medical Oncology Congress 2023.10 Detectable postoperative ctDNA was the most significant prognostic factor for disease recurrence (HR 10.44). Adjuvant chemotherapy prolonged disease-free survival (DFS) in patients who were MRD-positive compared with no therapy (38.6% vs 16.1%; P < .01). Furthermore, no significant difference in DFS at 2 years was observed in patients with ctDNA-negative stage I-III CRC, either with or without adjuvant chemotherapy (88.3% vs 89.9%; P = .156).
How Can ctDNA Be Used in Routine Practice to Personalize Treatment?
Prospective clinical trials on ctDNA-guided management of colon cancer using adjuvant therapy is the perceivable next step after establishing ctDNA as a strong biomarker (Table 1).11-12 The DYNAMIC trial led by Tie et al compared tumor-informed, ctDNA-guided adjuvant treatment to standard treatment in patients with stage II colon cancer postoperatively.11 The primary endpoint of recurrence-free survival (RFS) at 2 years with ctDNA-directed management was noninferior to standard management, reminiscent of the fact that a ctDNA-guided approach can perhaps limit the use of adjuvant chemotherapy without compromising RFS in stage II colon cancer.11 PEGASUS is a multicenter prospective trial that used ctDNA-guided (tumor-agnostic) adjuvant treatment for patients with high-risk stage II or stage III colon cancer.12 Patients who were MRD-positive were treated with CAPOX for 3 months, while patients that were MRD-negative were treated with capecitabine for 6 months. Out of 135 patients, 35 patients had detectable postoperative ctDNA (26%). Patients with persistent ctDNA despite CAPOX received FOLFIRI. Seroconversion after CAPOX or FOLFIRI was observed in 14 out of 35 patients (40%) after a median follow-up of 21.2 months. Interestingly, patients with MRD-negative stage III and high-risk stage II disease demonstrated a low relapse rate of 7% despite de-escalated treatment with capecitabine. It is important to note that the false-positive test results associated with plasma-based assay can lead to overtreatment; 7 patients demonstrated persistent ctDNA positivity with no evidence of disease recurrence after the completion of the CAPOX and FOLFIRI treatment regimens.12
Can ctDNA Identify Patients Who May Not Derive Benefit From Adjuvant Therapy Without Compromising RFS?
Tumor-informed, ctDNA-guided treatment can be considered to help determine the cohort of patients with stage II colon cancer who may not benefit from adjuvant therapy based on existing prospective data. However, we need long-term follow-up and prospective confirmatory data from larger phase 3 trials to consolidate this evidence. Multiple prospective trials focused on ctDNA-guided management have been ongoing in the MRD space of adjuvant treatment of colon cancer (Table 2). Unfortunately, the COBRA study (NCT04068103) has been terminated because of higher-than-expected false-positive ctDNA results with tumor-agnostic assay, likely attributed to methylation and epigenomic markers that could have limited the sensitivity of the assay. An ideal assay should provide greater than 90% sensitivity and specificity, with minimal false-positive and -negative test results; this is onerous given the setting of limited ctDNA quantity across limited-stage (I, II, III) colon cancer.
[ctDNA] is on the path to revolutionize the adjuvant treatment of patients with colon cancer by minimizing the risk of both under- and overtreatment.
Ongoing Education and Clinical Trial Efforts Continue to Improve ctDNA Use
ctDNA continues to evolve as a tool to assess MRD and treatment response monitoring and is on the path to revolutionize the adjuvant treatment of patients with colon cancer by minimizing the risk of both under- and overtreatment. Ideal testing assay with excellent sensitivity and specificity; interpretation and application of testing results in the clinical context; patient anxiety associated with a positive test; availability; and health care reimbursement across the world are some of the challenges that remain toward its incorporation into clinical practice. Although both tumor-informed and -uninformed assays are available commercially, tumor-informed assay seems to have stronger data, with the availability of larger datasets to date, as well as improved sensitivity and specificity of the assay. Ongoing educational efforts through international organizations such as ASCO and the European Society for Medical Oncology, along with the involvement of patient advocate societies, continue to be immensely helpful in improving the interpretation and application of ctDNA testing. Ongoing clinical trials can help answer the question of whether ctDNA can be a surrogate marker for DFS and whether earlier initiation of treatment for patients at high risk for recurrence can lead to prolonged survival outcomes.
Checkpoint-blockade immunotherapy has transformed cancer therapeutics but still benefits only a subset of patients. The development of more-robust biomarkers of response could change that.
Immune-checkpoint inhibitors (ICIs) that block the immunoinhibitory receptor PD-1 and its ligand PD-L1 or the immunomodulatory receptor CTLA-4 have had a transformational impact on the care of patients with cancer, offering curative potential for patients who until recently had no suitable therapeutic options. Despite the growing number of regulatory approvals for use of these drugs in a number of different malignancies, it is now becoming clear that many patients who receive ICIs do not benefit from treatment but remain at risk for potentially serious immune-related adverse events. Expanding the benefit of ICIs to more patients and limiting the impact of their adverse effects will require better biomarkers of response and toxicity.
Although high tumor mutational burden (TMB), presence of tumor microsatellite instability (MSI) and mismatch-repair-deficient (dMMR) status, as well as high PD-L1 expression, in tumor cells are well established biomarkers, they are not perfect. For example, some patients with PD-L1-negative tumors do respond to ICI treatment. In the CheckMate 227 trial, the combination of nivolumab (anti-PD-1) plus ipilimumab (anti-CTLA-4) yielded comparable overall survival benefits in patients with non–small-cell lung cancer whose tumors were above or below the PD-L1 expression threshold of 1%. Moreover, differences in defining high PD-L1 and TMB thresholds, as well as variability in sensitivity of detection platforms, can influence patient classification. Notably, TMB estimates have recently been shown to be affected by ancestry, with misclassified TMB-high patients not benefiting from ICI treatment.
The US Food and Drug Administration has also approved specific companion diagnostics to determine TMB-high and MSI-high/dMMR status as tumor-agnostic biomarkers of the response to pembrolizumab (anti-PD-1). Although these tests enable more patients to access this drug, the efficacy of these biomarkers in predicting response varies across different tumor types. Multiple analyses suggest that these biomarkers, at least at particular cut-offs, may not be universally associated with response across tumor types and may not necessarily be generalizable for patients with a specific tumor type, and point to the need for tumor-type-specific composite biomarkers that integrate multiple parameters.
As ICIs are tested for more indications, more trial datasets also exist with the potential to both identify and validate potential determinants of response. However, integrating these data has proven challenging due to heterogeneity in trial inclusion criteria, the types of samples collected, workflows for sampling and data processing, as well as assay selection. Dedicated sites managed by research agencies exist for the deposition of sequencing results, but standardizing these data and obtaining the relevant clinical metadata necessary for useful interpretation can be difficult. Repositories for other types of data commonly generated in ICI trials, such as immunohistochemistry and flow cytometry results, are lacking or not consistently used. The Cancer Immune Monitoring and Analysis Centers–Cancer Immunologic Data Commons (CIMAC-CIDC) Network, which was established by the US National Cancer Institute, is one ongoing partnership aimed at harmonizing methods and big data for potential immunotherapy biomarkers.
In addition to trial-intrinsic differences, restricted access to datasets further complicates biomarker-validation efforts. Although many clinical research journals, including Nature Medicine, require inclusion of data availability or sharing statements in published papers, data access is still often limited and results that are shared may not be fully clinically annotated, which greatly reduces their utility for analysis and validation. For better leveraging of the correlative big data generated in ICI trials, improved strategies must be developed for efficient sharing and harmonization of all major data types while maintaining patient confidentiality. Portals that aggregate trial datasets and permit query-only analysis could be one option.
Numerous other genomic and non-genomic determinants of ICI response have been proposed, and they are often non-redundant. For example, both an intratumoral T cell–inflamed gene-expression profile and TMB have been shown to independently predict the response to pembrolizumab across multiple types of solid tumors. Prospective validation of some of these biomarkers is already underway. In a recent phase 2 trial, patients with advanced soft-tissue sarcomas and intratumoral tertiary lymphoid structures were shown to have better clinical outcomes after pembrolizumab treatment than those of patients without such structures, which suggests that careful selection of patients with tumor types generally considered less responsive to ICIs could actually lead to clinical benefit. Trials such as this one, ideally randomized with direct comparisons to ‘all-comers’ arms, and arms focused on different biomarker combinations, could refine the scope of ICIs while also helping to establish standardized approaches for measuring specific biomarkers.
It is critical that future biomarker-driven trials be thoughtfully designed to maximize the types of correlative data that can be reasonably obtained and analyzed from patient samples, as well as the diversity of the patient population, given the potential effect of ancestry. In particular, determinants of response that are less invasive than tumor-based biomarkers, such as blood TMB and serum IL-8, should be a priority for prospective validation.
The future for ICIs is undeniably bright, with promising recent results in the neoadjuvant setting and for inhibitors of targets beyond PD-1–PD-L1 and CTLA-4, as well as approvals for use in combination with other types of therapy. Intensifying efforts to enhance data standardization, sharing of existing trial datasets, and prospective validation of candidate biomarkers in diverse populations will be crucial for the development of more-effective biomarkers of response to and toxicity of ICIs and to expand the impact of immune-checkpoint-blockade therapies to many more patients with cancer.
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
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
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.
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.
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.
Lai, R.P., Meintjes, G., Wilkinson, K.A., Graham, C.M., Marais, S., Van der Plas, H., Deffur, A., Schutz, C., Bloom, C., Munagala, I., Anguiano, E., Goliath, R., Maartens, G., Banchereau, J., Chaussabel, D., O’Garra, A., and Wilkinson, R.J. HIV-tuberculosis-associated immune reconstitution inflammatory syndrome is characterized by Toll-like receptor and inflammasome signalling. Nat. Commun.2015; 6: 8451
Musselwhite, Laura W., BBA, Ellenberg, Susan S., Tierney, Ann, Belaunzaran-Zamudio, Pablo F., Rupert, Adam, Lederman, Michael M., Sanne, Ian, Madero, Juan G. Sierra, and Sereti, Irini. Vitamin D, d-dimer, interferon γ, and sCD14 levels are independently associated with immune reconstitution inflammatory syndrome: a prospective, international study. EBioMedicine. 2016; 4: 115–123
Namale, P.E., Abdullahi, L.H., Fine, S., Kamkuemah, M., Wilkinson, R.J., and Meintjes, G. Paradoxical TB-IRIS in HIV-infected adults: a systematic review and meta-analysis. Future Microbiol. 2015; 10: 1077–1099
Sierra-Madero, J.G., Ellenberg, S., Rassool, M.S., Tierney, A., Belaunzaran-Zamudio, P.F., Lopez-Martinez, A., Pineirua-Menendez, A., Montaner, L.J., Azzoni, L., Benitez, C.R., Sereti, I., Andrade-Villanueva, J., Mosqueda-Gomez, J.L., Rodriguez, B., Sanne, I., Lederman, M.M., and team Cs. A randomized, double-blind, placebo-controlled clinical trial of a chemokine receptor 5 (CCR5) antagonist to decrease the occurrence of immune reconstitution inflammatory syndrome in HIV-infection: the CADIRIS study. Lancet HIV. 2014; 1: e60–e67
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.
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.
ARYAN'S BLOG 