Distinguishing prognostic and predictive biomarkers: an information theoretic approach

LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features

Despite the revolutionary impact of immune checkpoint blockade (ICB) in cancer treatment, accurately predicting patient responses remains challenging. Here, we analyzed a large dataset of 2,881 ICB-treated and 841 non-ICB-treated patients across 18 solid tumor types, encompassing a wide range of clinical, pathologic and genomic features. We developed a clinical score called LORIS (logistic regression-based immunotherapy-response score) using a six-feature logistic regression model. LORIS outperforms previous signatures in predicting ICB response and identifying responsive patients even with low tumor mutational burden or programmed cell death 1 ligand 1 expression. LORIS consistently predicts patient objective response and short-term and long-term survival across most cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling precise patient stratification. As an accurate, interpretable method using a few readily measurable features, LORIS may help improve clinical decision-making in precision medicine to maximize patient benefit. LORIS is available as an online tool at https://loris.ccr.cancer.gov/ .

Our current understanding of the biological impact of endometrial cancer mtDNA genome mutations and their potential use as a biomarker

Endometrial cancer (EC) is a devastating and common disease affecting women's health. The NCI Surveillance, Epidemiology, and End Results Program predicted that there would be >66,000 new cases in the United States and >13,000 deaths from EC in 2023, and EC is the sixth most common cancer among women worldwide. Regulation of mitochondrial metabolism plays a role in tumorigenesis. In proliferating cancer cells, mitochondria provide the necessary building blocks for biosynthesis of amino acids, lipids, nucleotides, and glucose. One mechanism causing altered mitochondrial activity is mitochondrial DNA (mtDNA) mutation. The polyploid human mtDNA genome is a circular double-stranded molecule essential to vertebrate life that harbors genes critical for oxidative phosphorylation plus mitochondrial-derived peptide genes. Cancer cells display aerobic glycolysis, known as the Warburg effect, which arises from the needs of fast-dividing cells and is characterized by increased glucose uptake and conversion of glucose to lactate. Solid tumors often contain at least one mtDNA substitution. Furthermore, it is common for cancer cells to harbor mixtures of wild-type and mutant mtDNA genotypes, known as heteroplasmy. Considering the increase in cancer cell energy demand, the presence of functionally relevant carcinogenesis-inducing or environment-adapting mtDNA mutations in cancer seems plausible. We review 279 EC tumor-specific mtDNA single nucleotide variants from 111 individuals from different studies. Many transition mutations indicative of error-prone DNA polymerase γ replication and C to U deamination events were present. We examine the spectrum of mutations and their heteroplasmy and discuss the potential biological impact of recurrent, non-synonymous, insertion, and deletion mutations. Lastly, we explore current EC treatments, exploiting cancer cell mitochondria for therapy and the prospect of using mtDNA variants as an EC biomarker.

A prognostic four-gene signature and a therapeutic strategy for hepatocellular carcinoma: Construction and analysis of a circRNA-mediated competing endogenous RNA network

BACKGROUND

Hepatocellular carcinoma (HCC) has a poor long-term prognosis. The competition of circular RNAs (circRNAs) with endogenous RNA is a novel tool for predicting HCC prognosis. Based on the alterations of circRNA regulatory networks, the analysis of gene modules related to HCC is feasible.

METHODS

Multiple expression datasets and RNA element targeting prediction tools were used to construct a circRNA-microRNA-mRNA network in HCC. Gene function, pathway, and protein interaction analyses were performed for the differentially expressed genes (DEGs) in this regulatory network. In the protein-protein interaction network, hub genes were identified and subjected to regression analysis, producing an optimized four-gene signature for prognostic risk stratification in HCC patients. Anti-HCC drugs were excavated by assessing the DEGs between the low- and high-risk groups. A circRNA-microRNA-hub gene subnetwork was constructed, in which three hallmark genes, KIF4A, CCNA2, and PBK, were subjected to functional enrichment analysis.

RESULTS

A four-gene signature (KIF4A, CCNA2, PBK, and ZWINT) that effectively estimated the overall survival and aided in prognostic risk assessment in the The Cancer Genome Atlas (TCGA) cohort and International Cancer Genome Consortium (ICGC) cohort was developed. CDK inhibitors, PI3K inhibitors, HDAC inhibitors, and EGFR inhibitors were predicted as four potential mechanisms of drug action (MOA) in high-risk HCC patients. Subsequent analysis has revealed that PBK, CCNA2, and KIF4A play a crucial role in regulating the tumor microenvironment by promoting immune cell invasion, regulating microsatellite instability (MSI), and exerting an impact on HCC progression.

CONCLUSIONS

The present study highlights the role of the circRNA-related regulatory network, identifies a four-gene prognostic signature and biomarkers, and further identifies novel therapy for HCC.

Is the CTS5 a helpful decision-making tool in the extended adjuvant therapy setting?

PURPOSE

The Clinical Treatment Score post-5 years (CTS5) is an easy-to-use tool estimating the late distant recurrence (LDR) risk in patients with hormone receptor-positive breast cancer after 5 years of endocrine therapy (ET). Apart from evaluating the prognostic value and calibration accuracy of CTS5, the aim of this study is to clarify if this score is able to identify patients at higher risk for LDR who will benefit from extended ET.

METHODS

Prognostic power, calibration, and predictive value of the CTS5 was tested in patients of the prospective ABCSG-06 and -06a trials (n = 1254 and 860 patients, respectively). Time to LDR was analyzed with Cox regression models.

RESULTS

Higher rates of LDR in the years five to ten were observed in high- and intermediate-risk patients compared to low-risk patients (HR 4.02, 95%CI 2.26-7.15, p < 0.001 and HR 1.93, 95%CI 1.05-3.56, p = 0.035). An increasing continuous CTS5 was associated with increasing LDR risk (HR 2.23, 95% CI 1.74-2.85, p < 0.001). Miscalibration of CTS5 in high-risk patients could be observed. Although not reaching significance, high-risk patients benefitted the most from prolonged ET with an absolute reduction of the estimated 5-year LDR of - 6.1% (95%CI - 14.4 to 2.3).

CONCLUSION

The CTS5 is a reliable prognostic tool that is well calibrated in the lower and intermediate risk groups with a substantial difference of expected versus observed LDR rates in high-risk patients. While a numerical trend in favoring prolonged ET for patients with a higher CTS5 was found, a significantly predictive value for the score could not be confirmed.

CLINICAL TRIAL REGISTRATION

ABCSG-06 trial (NCT00309491), ABCSG-06A7 1033AU/0001 (NCT00300508).

Marginal versus conditional rate estimation for count and recurrent event data with an estimand framework

Count and recurrent event endpoints are common key efficacy endpoints in clinical research. For example, in clinical research of pulmonary diseases such as chronic obstructive pulmonary disease (COPD) or asthma, the reduction of the occurrence of a recurrent event, pulmonary exacerbation (PEx) caused by acute respiratory symptoms, is often used to measure the treatment effect. The occurrence of PEx is often analyzed with nonlinear models, such as Poisson regression or Negative Binomial regression. It is observed that model-estimated within-group PEx rates are often lower than the descriptive statistics of within-group PEx rates. Motivated by this observation, we explore their relationship mathematically and demonstrate that it is due to the difference between conditional PEx rates and population-level PEx rates (marginal rates). Our findings corroborate the recent FDA guidance (2023) [1], which discusses considerations for covariate adjustment in nonlinear models, and that conditional or subgroup treatment effects with covariate adjustment may differ from marginal treatment effects. In this article, we demonstrate how covariate adjustment impacts the estimation of event rates and rate ratios with both closed form and simulation studies. Additionally, following the ICH E9 addendum on the estimand framework [2], we discuss the estimand framework for count and recurrent event data.

BZW1 is a prognostic and immunological biomarker in pancreatic adenocarcinoma

Pancreatic adenocarcinoma is the most common malignant tumor of the digestive system and is called the "king of cancer" because it has been labeled with high malignancy, rapid progression, poor survival, and poor prognosis. Previously, it was reported that the basic leucine zipper and W2 domains 1 (BZW1) is involved in the progression of many tumors. However, its research in digestive system tumors such as pancreatic cancer is rarely studied. To explore potential biomarkers related to survival and prognosis of pancreatic cancer and provide a new targeted therapy for it. We first analyzed the mRNA and protein expression of BZW1 in pancreatic cancer. We then explored the correlation of BZW1 with survival prognosis and immune infiltration in pancreatic cancer patients. Finally, we explored BZW1-related gene enrichment analysis, including protein-protein interaction networks, gene ontology functional enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The mRNA and protein expression of the BZW1 gene in pancreatic cancer tissues were higher than those in adjacent normal tissues, and pancreatic cancer patients with high BZW1 expression had a poor prognosis. In addition, the expression of BZW1 was positively or negatively correlated with different immune cells of pancreatic cancer, such as CD4 + T lymphocytes, CD8 + T lymphocytes, B cells, macrophages, neutrophils, etc. Correlation enrichment analysis showed that we obtained 50 available experimentally determined BZW1-binding proteins and 100 targeted genes related to BZW1, and the intersection genes were eukaryotic translation termination factor 1 and Guanine nucleotide binding protein, alpha inhibiting activity polypeptide 3. Moreover, there was a positive correlation between BZW1 and eukaryotic translation termination factor 1 and Guanine nucleotide binding protein, alpha inhibiting activity polypeptide 3 genes in pancreatic cancer. Gene ontology enrichment analysis showed BZW1 was mainly related to biological processes such as "mRNA processing," "RNA splicing," "regulation of translational initiation," and "activation of innate immune response." The results of Kyoto Encyclopedia of Genes and Genomes pathway analysis further indicated that BZW1 may be involved in pancreatic carcinogenesis through the "spliceosome" and "ribosome." The BZW1 gene may be a potential immunotherapy target and a promising prognostic marker for pancreatic cancer.

Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials

The identification and estimation of heterogeneous treatment effects in biomedical clinical trials are challenging, because trials are typically planned to assess the treatment effect in the overall trial population. Nevertheless, the identification of how the treatment effect may vary across subgroups is of major importance for drug development. In this work, we review some existing simulation work and perform a simulation study to evaluate recent methods for identifying and estimating the heterogeneous treatments effects using various metrics and scenarios relevant for drug development. Our focus is not only on a comparison of the methods in general, but on how well these methods perform in simulation scenarios that reflect real clinical trials. We provide the R package benchtm that can be used to simulate synthetic biomarker distributions based on real clinical trial data and to create interpretable scenarios to benchmark methods for identification and estimation of treatment effect heterogeneity.

Using biomarkers to allocate patients in a response-adaptive clinical trial

In this paper, we discuss a response adaptive randomization method, and why it should be used in clinical trials for rare diseases compared to a randomized controlled trial with equal fixed randomization. The developed method uses a patient's biomarkers to alter the allocation probability to each treatment, in order to emphasize the benefit to the trial population. The method starts with an initial burn-in period of a small number of patients, who with equal probability, are allocated to each treatment. We then use a regression method to predict the best outcome of the next patient, using their biomarkers and the information from the previous patients. This estimated best treatment is assigned to the next patient with high probability. A completed clinical trial for the effect of catumaxomab on the survival of cancer patients is used as an example to demonstrate the use of the method and the differences to a controlled trial with equal allocation. Different regression procedures are investigated and compared to a randomized controlled trial, using efficacy and ethical measures.

A robust mRNA signature obtained via recursive ensemble feature selection predicts the responsiveness of omalizumab in moderate-to-severe asthma

BACKGROUND

Not being well controlled by therapy with inhaled corticosteroids and long-acting β2 agonist bronchodilators is a major concern for severe-asthma patients. The current treatment option for these patients is the use of biologicals such as anti-IgE treatment, omalizumab, as an add-on therapy. Despite the accepted use of omalizumab, patients do not always benefit from it. Therefore, there is a need to identify reliable biomarkers as predictors of omalizumab response.

METHODS

Two novel computational algorithms, machine-learning based Recursive Ensemble Feature Selection (REFS) and rule-based algorithm Logic Explainable Networks (LEN), were used on open accessible mRNA expression data from moderate-to-severe asthma patients to identify genes as predictors of omalizumab response.

RESULTS

With REFS, the number of features was reduced from 28,402 genes to 5 genes while obtaining a cross-validated accuracy of 0.975. The 5 responsiveness predictive genes encode the following proteins: Coiled-coil domain- containing protein 113 (CCDC113), Solute Carrier Family 26 Member 8 (SLC26A), Protein Phosphatase 1 Regulatory Subunit 3D (PPP1R3D), C-Type lectin Domain Family 4 member C (CLEC4C) and LOC100131780 (not annotated). The LEN algorithm found 4 identical genes with REFS: CCDC113, SLC26A8 PPP1R3D and LOC100131780. Literature research showed that the 4 identified responsiveness predicting genes are associated with mucosal immunity, cell metabolism, and airway remodeling.

CONCLUSION AND CLINICAL RELEVANCE

Both computational methods show 4 identical genes as predictors of omalizumab response in moderate-to-severe asthma patients. The obtained high accuracy indicates that our approach has potential in clinical settings. Future studies in relevant cohort data should validate our computational approach.

A flexible approach for predictive biomarker discovery

An endeavor central to precision medicine is predictive biomarker discovery; they define patient subpopulations which stand to benefit most, or least, from a given treatment. The identification of these biomarkers is often the byproduct of the related but fundamentally different task of treatment rule estimation. Using treatment rule estimation methods to identify predictive biomarkers in clinical trials where the number of covariates exceeds the number of participants often results in high false discovery rates. The higher than expected number of false positives translates to wasted resources when conducting follow-up experiments for drug target identification and diagnostic assay development. Patient outcomes are in turn negatively affected. We propose a variable importance parameter for directly assessing the importance of potentially predictive biomarkers and develop a flexible nonparametric inference procedure for this estimand. We prove that our estimator is double robust and asymptotically linear under loose conditions in the data-generating process, permitting valid inference about the importance metric. The statistical guarantees of the method are verified in a thorough simulation study representative of randomized control trials with moderate and high-dimensional covariate vectors. Our procedure is then used to discover predictive biomarkers from among the tumor gene expression data of metastatic renal cell carcinoma patients enrolled in recently completed clinical trials. We find that our approach more readily discerns predictive from nonpredictive biomarkers than procedures whose primary purpose is treatment rule estimation. An open-source software implementation of the methodology, the uniCATE R package, is briefly introduced.

Clinical-genomic determinants of immune checkpoint blockade response in head and neck squamous cell carcinoma

BACKGROUNDRecurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) is generally an incurable disease, with patients experiencing median survival of under 10 months and significant morbidity. While immune checkpoint blockade (ICB) drugs are effective in approximately 20% of patients, the remaining experience limited clinical benefit and are exposed to potential adverse effects and financial costs. Clinically approved biomarkers, such as tumor mutational burden (TMB), have a modest predictive value in HNSCC.METHODSWe analyzed clinical and genomic features, generated using whole-exome sequencing, in 133 ICB-treated patients with R/M HNSCC, of whom 69 had virus-associated and 64 had non-virus-associated tumors.RESULTSHierarchical clustering of genomic data revealed 6 molecular subtypes characterized by a wide range of objective response rates and survival after ICB therapy. The prognostic importance of these 6 subtypes was validated in an external cohort. A random forest-based predictive model, using several clinical and genomic features, predicted progression-free survival (PFS), overall survival (OS), and response with greater accuracy than did a model based on TMB alone. Recursive partitioning analysis identified 3 features (systemic inflammatory response index, TMB, and smoking signature) that classified patients into risk groups with accurate discrimination of PFS and OS.CONCLUSIONThese findings shed light on the immunogenomic characteristics of HNSCC tumors that drive differential responses to ICB and identify a clinical-genomic classifier that outperformed the current clinically approved biomarker of TMB. This validated predictive tool may help with clinical risk stratification in patients with R/M HNSCC for whom ICB is being considered.FUNDINGFundación Alfonso Martín Escudero, NIH R01 DE027738, US Department of Defense CA210784, The Geoffrey Beene Cancer Research Center, The MSKCC Population Science Research Program, the Jayme Flowers Fund, the Sebastian Nativo Fund, and the NIH/NCI Cancer Center Support Grant P30 CA008748.

Associations between KIR/KIR-ligand genotypes and clinical outcome for patients with advanced solid tumors receiving BEMPEG plus nivolumab combination therapy in the PIVOT-02 trial

Bempegaldesleukin (BEMPEG), a CD122-preferential IL2 pathway agonist, has been shown to induce proliferation and activation of NK cells. NK activation is dependent on the balance of inhibitory and excitatory signals transmitted by NK receptors, including Fc-gamma receptors (FCγRs) and killer immunoglobulin-like receptors (KIRs) along with their KIR-ligands. The repertoire of KIRs/KIR-ligands an individual inherits and the single-nucleotide polymorphisms (SNPs) of FCγRs can influence NK function and affect responses to immunotherapies. In this retrospective analysis of the single-arm PIVOT-02 trial, 200 patients with advanced solid tumors were genotyped for KIR/KIR-ligand gene status and FCγR SNP status and evaluated for associations with clinical outcome. Patients with inhibitory KIR2DL2 and its ligand (HLA-C1) observed significantly greater tumor shrinkage (TS, median change -13.0 vs. 0%) and increased PFS (5.5 vs. 3.3 months) and a trend toward improved OR (31.2 vs. 19.5%) compared to patients with the complementary genotype. Furthermore, patients with KIR2DL2 and its ligand together with inhibitory KIR3DL1 and its ligand (HLA-Bw4) had improved OR (36.5 vs. 19.6%), greater TS (median change -16.1 vs. 0%), and a trend toward prolonged PFS (8.4 vs. 3.6 months) as compared to patients with the complementary genotype. FCγR polymorphisms did not influence OR/PFS/TS.These data show that clinical response to BEMPEG plus nivolumab treatment in the PIVOT-02 trial may be associated with the repertoire of KIR/KIR-ligands an individual inherits. Further investigation and validation of these results may enable KIR/KIR-ligand genotyping to be utilized prospectively for identifying patients likely to benefit from certain cancer immunotherapy regimens.

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.

Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso

In clinical trials, identification of prognostic and predictive biomarkers has became essential to precision medicine. Prognostic biomarkers can be useful for the prevention of the occurrence of the disease, and predictive biomarkers can be used to identify patients with potential benefit from the treatment. Previous researches were mainly focused on clinical characteristics, and the use of genomic data in such an area is hardly studied. A new method is required to simultaneously select prognostic and predictive biomarkers in high dimensional genomic data where biomarkers are highly correlated. We propose a novel approach called PPLasso, that integrates prognostic and predictive effects into one statistical model. PPLasso also takes into account the correlations between biomarkers that can alter the biomarker selection accuracy. Our method consists in transforming the design matrix to remove the correlations between the biomarkers before applying the generalized Lasso. In a comprehensive numerical evaluation, we show that PPLasso outperforms the traditional Lasso and other extensions on both prognostic and predictive biomarker identification in various scenarios. Finally, our method is applied to publicly available transcriptomic and proteomic data.

Under the Umbrella of Clinical Pharmacology: Inflammatory Bowel Disease, Infliximab and Adalimumab, and a Bridge to an Era of Biosimilars

Monoclonal antibodies (MAbs) have revolutionized the treatment of many chronic inflammatory diseases, including inflammatory bowel disease (IBD). IBD is a term that comprises two quite similar, yet distinctive, disorders-Crohn's disease (CD) and ulcerative colitis (UC). Two blockbuster MAbs, infliximab (IFX) and adalimumab (ADL), transformed the pharmacological approach of treating CD and UC. However, due to the complex interplay of pharmacology and immunology, MAbs face challenges related to their immunogenicity, effectiveness, and safety. To ease the burden of IBD and other severe diseases, biosimilars have emerged as a cost-effective alternative to an originator product. According to the current knowledge, biosimilars of IFX and ADL in IBD patients are shown to be as safe and effective as their originators. The future of biosimilars, in general, is promising due to the potential of making the health care system more sustainable. However, their use is accompanied by misconceptions regarding their effectiveness and safety, as well as by controversy regarding their interchangeability. Hence, until a scientific consensus is achieved, scientific data on the long-term effectiveness and safety of biosimilars are needed.

Model-free screening for variables with treatment interaction

Precision medicine is a medical paradigm that focuses on making effective treatment decision based on individual patient characteristics. When there are a large amount of patient information, such as patient’s genetic information, medical records and clinical measurements, available, it is of interest to select the covariates which have interactions with the treatment, for example, in determining the individualized treatment regime where only a subset of covariates with treatment interactions involves in decision making. We propose a marginal feature ranking and screening procedure for measuring interactions between the treatment and covariates. The method does not require imposing a specific model structure on the regression model and is applicable in a high dimensional setting. Theoretical properties in terms of consistency in ranking and selection are established. We demonstrate the finite sample performance of the proposed method by simulation and illustrate the applications with two real data examples from clinical trials.

Beyond the Golden Hour: Treating Acute Stroke in the Platinum 30 Minutes

BACKGROUND

To emphasize treatment speed for time-sensitive conditions, emergency medicine has developed not only the concept of the golden hour, but also the platinum half-hour. Patients with acute stroke treated within the first half-hour of onset have not been previously characterized.

METHODS

In this cohort study, we analyzed patients enrolled in the FAST-MAG (Field Administration of Stroke Therapy-Magnesium) trial, testing paramedic prehospital start of neuroprotective agent ≤2 hours of onset. The features of all acute cerebral ischemia, and intracranial hemorrhage patients with treatment starting at ≤30 m of last known well were compared with later-treated patients.

RESULTS

Among 1680 patients, 203 (12.1%) received study agents within 30 minutes of last known well. Among platinum half-hour patients, median onset-to-treatment time was 28 minutes (interquartile range, 25-30), and final diagnoses were acute cerebral ischemia in 71.8% (ischemic stroke, 61.5%, TIA 10.3%); intracranial hemorrhage in 26.1%; and mimic in 2.5%. Clinical features among platinum half-hour patients were largely similar to later-treated patients and included age 69 (interquartile range, 57-79), 44.8% women, prehospital Los Angeles Motor Scale median 4 (3-5), and early-postarrival National Institutes of Health Stroke Scale deficit 8 (interquartile range, 3-18). Platinum half-hour acute cerebral ischemia patients did have more severe prehospital motor deficits and younger age; platinum half-hour intracranial hemorrhage patients had more severe motor deficits, were more often female, and less often of Hispanic ethnicity. Outcomes at 3 m in platinum half-hour patients were comparable to later-treated patients and included freedom-from-disability (modified Rankin Scale score, 0-1) in 35.5%, functional independence (modified Rankin Scale score, 0-2) in 53.2%, and mortality in 17.7%.

CONCLUSIONS

Prehospital initiation permits treatment start within the platinum half-hour after last known well in a substantial proportion of acute ischemic and hemorrhagic stroke patients, accounting for more than 1 in 10 enrolled in a multicenter trial. Hyperacute platinum half-hour patients were largely similar to later-treated patients and are an attainable target for treatment in prehospital stroke trials.

Current and Emerging Prognostic Biomarkers in Endometrial Cancer

Endometrial cancer is the most common gynaecological malignancy in high income countries and its incidence is rising. Whilst most women with endometrial cancer are diagnosed with highly curable disease and have good outcomes, a significant minority present with adverse clinico-pathological characteristics that herald a poor prognosis. Prognostic biomarkers that reliably select those at greatest risk of disease recurrence and death can guide management strategies to ensure that patients receive appropriate evidence-based and personalised care. The Cancer Genome Atlas substantially advanced our understanding of the molecular diversity of endometrial cancer and informed the development of simplified, pragmatic and cost-effective classifiers with prognostic implications and potential for clinical translation. Several blood-based biomarkers including proteins, metabolites, circulating tumour cells, circulating tumour DNA and inflammatory parameters have also shown promise for endometrial cancer risk assessment. This review provides an update on the established and emerging prognostic biomarkers in endometrial cancer.

Biomarkers of non-communicable chronic disease: an update on contemporary methods

Chronic diseases constitute a major global burden with significant impact on health systems, economies, and quality of life. Chronic diseases include a broad range of diseases that can be communicable or non-communicable. Chronic diseases are often associated with modifications of normal physiological levels of various analytes that are routinely measured in serum and other body fluids, as well as pathological findings, such as chronic inflammation, oxidative stress, and mitochondrial dysfunction. Identification of at-risk populations, early diagnosis, and prediction of prognosis play a major role in preventing or reducing the burden of chronic diseases. Biomarkers are tools that are used by health professionals to aid in the identification and management of chronic diseases. Biomarkers can be diagnostic, predictive, or prognostic. Several individual or grouped biomarkers have been used successfully in the diagnosis and prediction of certain chronic diseases, however, it is generally accepted that a more sophisticated approach to link and interpret various biomarkers involved in chronic disease is necessary to improve our current procedures. In order to ensure a comprehensive and unbiased coverage of the literature, first a primary frame of the manuscript (title, headings and subheadings) was drafted by the authors working on this paper. Second, based on the components drafted in the preliminary skeleton a comprehensive search of the literature was performed using the PubMed and Google Scholar search engines. Multiple keywords related to the topic were used. Out of screened papers, only 190 papers, which are the most relevant, and recent articles were selected to cover the topic in relation to etiological mechanisms of different chronic diseases, the most recently used biomarkers of chronic diseases and finally the advances in the applications of multivariate biomarkers of chronic diseases as statistical and clinically applied tool for the early diagnosis of chronic diseases was discussed. Recently, multivariate biomarkers analysis approach has been employed with promising prospect. A brief discussion of the multivariate approach for the early diagnosis of the most common chronic diseases was highlighted in this review. The use of diagnostic algorithms might show the way for novel criteria and enhanced diagnostic effectiveness inpatients with one or numerous non-communicable chronic diseases. The search for new relevant biomarkers for the better diagnosis of patients with non-communicable chronic diseases according to the risk of progression, sickness, and fatality is ongoing. It is important to determine whether the newly identified biomarkers are purely associations or real biomarkers of underlying pathophysiological processes. Use of multivariate analysis could be of great importance in this regard.

Prognostic and predictive impact of MGMT promoter methylation status in high risk grade II glioma

BACKGROUND

MGMT promoter methylation has been associated with favorable prognosis and survival outcomes in patients with glioblastoma and WHO grade III glioma. However, the effects of promoter methylation of MGMT in patients with WHO grade II gliomas have not been established. The purpose of the current study is to evaluate the prognostic impact and predictive values of MGMT methylation in patients with grade II glioma.

METHODS

The National Cancer Database (NCDB) was queried (2004-2016) for patients with newly diagnosed grade II glioma. Demographics and clinical characteristics of these patients were examined. Statistics included Kaplan-Meier overall survival (OS) analysis alongside Cox proportional hazards modeling.

RESULTS

A total of 11,223 patients met the selection criteria; 1252 patients (11%) had MGMT testing. Of the patients who had MGMT testing, 58.5% were MGMT methylated (mMGMT), and 43.5% were MGMT unmethylated (uMGMT). mMGMT patients had greater median overall survival (77.3 months) than both uMGMT patients (42.6 months) and patients with no MGMT status reported (61.9 months (p < 0.001 for both). mMGMT was also associated with improved OS, when compared to patients with uMGMT, for patients receiving adjuvant chemoradiation or adjuvant radiation therapy.

CONCLUSIONS

This is the largest study to date demonstrating both the prognostic and predictive impact of MGMT methylation on patients with grade II glioma. The current results show that mMGMT is a prognostic factor and possibly a predictive biomarker for grade II glioma patients. MGMT methylation status can be used to determine and stratify patients by risk levels, and thus select patients for treatment intensification.

IMPORTANCE OF STUDY

The present study is the largest to date examining the prognostic and predictive significance of MGMT methylation (mMGMT) in patients with WHO grade II glioma. The results suggest that mMGMT is prognostic with increasing overall survival rates for patients with mMGMT compared to uMGMT patients. The results also suggest that mMGMT is predictive as shown by improved overall survival in patients receiving gross total resection, adjuvant chemoradiation or adjuvant radiation therapy, but no difference was observed in patients receiving adjuvant chemotherapy or no adjuvant treatment.

Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research

The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.

Perspectives on the diagnostic, predictive and prognostic markers of neuroendocrine neoplasms (Review)

Neuroendocrine neoplasms (NENs) are a heterogeneous group of rare tumors with different types of physiology and prognosis. Therefore, prognostic information, including morphological differentiation, grade, tumor stage and primary location, are invaluable and contribute to the formulation of treatment decisions. Biomarkers that are currently used, including chromogranin A (CgA), serotonin and neuron-specific enolase, are singular parameters that cannot be used to accurately predict variables associated with tumor growth, including proliferation, metabolic rate and metastatic potential. In addition, site-specific biomarkers, such as insulin and gastrin, cannot be applied to all types of NENs. The clinical application of broad-spectrum markers, as it is the case for CgA, remains controversial despite being widely used. Due to limitations of the currently available mono-analyte biomarkers, recent studies were conducted to explore novel parameters for NEN diagnosis, prognosis, therapy stratification and evaluation of treatment response. Identification of prognostic factors for predicting NEN outcome is a critical requirement for the planning of adequate clinical management. Advances in ‘liquid’ biopsies and genomic analysis techniques, including microRNA, circulating tumor DNA or circulating tumor cells and sophisticated biomathematical analysis techniques, such as NETest or molecular image-based biomarkers, are currently under investigation as potentially novel tools for the management of NENs in the future. Despite these recent findings yielding promising observations, further research is necessary. The present review therefore summarizes the existing knowledge and recent advancements in the exploration of biochemical markers for NENs, with focus on gastroenteropancreatic-neuroendocrine tumors.

Predictive biomarkers for systemic therapy of hepatocellular carcinoma

Introduction: Hepatocellular carcinoma (HCC) is the most common primary liver cancer and the third cancer-related cause of death worldwide. In recent years, several systemic therapy drugs including sorafenib, lenvatinib, regorafenib, cabozantinib, ramucicurab, nivilumab, and pembrolizumab have been approved by FDA for advanced HCC. However, their insufficient efficacy, toxicity, and drug resistance require clinically applicable and validated predictive biomarkers.Areas covered: Our review covers the recent advancements in the identification of proteomic/genomic/epigenomic/transcriptomic biomarkers for predicting HCC treatment efficacy with the use of multi-kinase inhibitors (MKIs), CDK4/6 inhibitors, and immune checkpoint inhibitors (ICIs). Alpha-fetoprotein, des-carboxyprothrombin, vascular endothelial growth factor, angiopoietin-2, and dysregulated MTOR, VEGFR2, c-KIT, RAF1, PDGFRβ have the potential of proteomic/genomic biomarkers for sorafenib treatment. Alanine aminotransferase, aspartate aminotransferase, and albumin-bilirubin grade can predict the efficacy of other MKIs. Rb, p16, and Ki-67, and genes involved in cell cycle regulation, CDK1-4, CCND1, CDKN1A, and CDKN2A have been proposed for CD4/6 inhibitors, while dysregulated TERT, CTNNB1, TP53 FGF19, and TP53 are found to be predictors for ICI efficacy.Expert opinion: There are still limited clinically applicable and validated predictive biomarkers to identify HCC patients who benefit from systemic therapy. Further prospective biomarker validation studies for HCC personalized systemic therapy are required.

Using knockoffs for controlled predictive biomarker identification

One of the key challenges of personalized medicine is to identify which patients will respond positively to a given treatment. The area of subgroup identification focuses on this challenge, that is, identifying groups of patients that experience desirable characteristics, such as an enhanced treatment effect. A crucial first step towards the subgroup identification is to identify the baseline variables (eg, biomarkers) that influence the treatment effect, which are known as predictive variables. Many subgroup discovery algorithms return importance scores that capture the variables' predictive strength. However, a major limitation of these scores is that they do not answer the core question: "Which variables are actually predictive?" With our work we answer this question by using the knockoff framework, which is a general framework for controlling the false discovery rate when performing prognostic variable selection. In contrast, our work is the first that uses knockoffs for predictive variable selection. We introduce two novel knockoff filters: one parametric, building on variable importance scores derived from a penalized linear regression model, and one non-parametric, building on causal forest variable importance scores. We conduct extensive simulations to validate performance of the proposed methodology and we also apply the proposed methods to data from a randomized clinical trial.

HYAL4-V1/Chondroitinase (Chase) Drives Gemcitabine Resistance and Predicts Chemotherapy Failure in Patients with Bladder Cancer

PURPOSE

Gemcitabine-based chemotherapy regimens are first-line for several advanced cancers. Because of better tolerability, gemcitabine + cisplatin is a preferred neoadjuvant, adjuvant, and/or palliative chemotherapy regimen for advanced bladder cancer. Nevertheless, predicting treatment failure and overcoming resistance remain unmet clinical needs. We discovered that splice variant (V1) of HYAL-4 is a first-in-class eukaryotic chondroitinase (Chase), and CD44 is its major substrate. V1 is upregulated in bladder cancer and drives a malignant phenotype. In this study, we investigated whether V1 drives chemotherapy resistance.

EXPERIMENTAL DESIGN

V1 expression was measured in muscle-invasive bladder cancer (MIBC) specimens by qRT-PCR and IHC. HYAL-4 wild-type (Wt) and V1 were stably expressed or silenced in normal urothelial and three bladder cancer cell lines. Transfectants were analyzed for chemoresistance and associated mechanism in preclinical models.

RESULTS

V1 levels in MIBC specimens of patients who developed metastasis, predicted response to gemcitabine + cisplatin adjuvant/salvage treatment and disease-specific mortality. V1-expressing bladder cells were resistant to gemcitabine but not to cisplatin. V1 expression neither affected gemcitabine influx nor the drug-efflux transporters. Instead, V1 increased gemcitabine metabolism and subsequent efflux of difluorodeoxyuridine, by upregulating cytidine deaminase (CDA) expression through increased CD44-JAK2/STAT3 signaling. CDA inhibitor tetrahydrouridine resensitized V1-expressing cells to gemcitabine. While gemcitabine (25-50 mg/kg) inhibited bladder cancer xenograft growth, V1-expressing tumors were resistant. Low-dose combination of gemcitabine and tetrahydrouridine abrogated the growth of V1 tumors with minimal toxicity.

CONCLUSIONS

V1/Chase drives gemcitabine resistance and potentially predicts gemcitabine + cisplatin failure. CDA inhibition resensitizes V1-expressing tumors to gemcitabine. Because several chemotherapy regimens include gemcitabine, our study could have broad significance.

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values

BACKGROUND

Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets.

METHODS

The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included. Autoencoder was used to extract representative features for k-means clustering. Genetic algorithms (GA) were employed to derive a parsimonious 5-gene class prediction model. The class model was then applied to external datasets (n = 780) to evaluate its prognostic and predictive performance.

FINDINGS

A total of 12 datasets involving 1613 patients were included. Two classes were identified in the discovery cohort (n = 685). Class 1 was characterized by immunosuppression with higher mortality than class 2 (21.8% [70/321] vs. 12.1% [44/364]; p < 0.01 for Chi-square test). A 5-gene class model (C14orf159, AKNA, PILRA, STOM and USP4) was developed with GA. In external validation cohorts, the 5-gene class model (AUC: 0.707; 95% CI: 0.664 - 0.750) performed better in predicting mortality than sepsis response signature (SRS) endotypes (AUC: 0.610; 95% CI: 0.521 - 0.700), and performed equivalently to the APACHE II score (AUC: 0.681; 95% CI: 0.595 - 0.767). In the dataset E-MTAB-7581, the use of hydrocortisone was associated with increased risk of mortality (OR: 3.15 [1.13, 8.82]; p = 0.029) in class 2. The effect was not statistically significant in class 1 (OR: 1.88 [0.70, 5.09]; p = 0.211).

INTERPRETATION

Our study identified two classes of sepsis that showed different mortality rates and responses to hydrocortisone therapy. Class 1 was characterized by immunosuppression with higher mortality rate than class 2. We further developed a 5-gene class model to predict class membership.

FUNDING

The study was funded by the National Natural Science Foundation of China (Grant No. 81,901,929).

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.

muTarget: A platform linking gene expression changes and mutation status in solid tumors

Large oncology repositories have paired genomic and transcriptomic data for all patients. We used these data to perform two independent analyses: to identify gene expression changes related to a gene mutation and to identify mutations altering the expression of a selected gene. All data processing steps were performed in the R statistical environment. RNA-sequencing and mutation data were acquired from The Cancer Genome Atlas (TCGA). The DESeq2 algorithm was applied for RNA-seq normalization, and transcript variants were annotated with AnnotationDbi. MuTect2-identified somatic mutation data were utilized, and the MAFtools Bioconductor program was used to summarize the data. The Mann-Whitney U test was used for differential expression analysis. The established database contains 7876 solid tumors from 18 different tumor types with both somatic mutation and RNA-seq data. The utility of the approach is presented via three analyses in breast cancer: gene expression changes related to TP53 mutations, gene expression changes related to CDH1 mutations and mutations resulting in altered progesterone receptor (PGR) expression. The breast cancer database was split into equally sized training and test sets, and these data sets were analyzed independently. The highly significant overlap of the results (chi-square statistic = 16 719.7 and P < .00001) validates the presented pipeline. Finally, we set up a portal at http://www.mutarget.com enabling the rapid identification of novel mutational targets. By linking somatic mutations and gene expression, it is possible to identify biomarkers and potential therapeutic targets in different types of solid tumors. The registration-free online platform can increase the speed and reduce the development cost of novel personalized therapies.

Biomarkers of COVID-19 and technologies to combat SARS-CoV-2

Due to the unprecedented public health crisis caused by COVID-19, our first contribution to the newly launching journal, Advances in Biomarker Sciences and Technology, has abruptly diverted to focus on the current pandemic. As the number of new COVID-19 cases and deaths continue to rise steadily around the world, the common goal of healthcare providers, scientists, and government officials worldwide has been to identify the best way to detect the novel coronavirus, named SARS-CoV-2, and to treat the viral infection - COVID-19. Accurate detection, timely diagnosis, effective treatment, and future prevention are the vital keys to management of COVID-19, and can help curb the viral spread. Traditionally, biomarkers play a pivotal role in the early detection of disease etiology, diagnosis, treatment and prognosis. To assist myriad ongoing investigations and innovations, we developed this current article to overview known and emerging biomarkers for SARS-CoV-2 detection, COVID-19 diagnostics, treatment and prognosis, and ongoing work to identify and develop more biomarkers for new drugs and vaccines. Moreover, biomarkers of socio-psychological stress, the high-technology quest for new virtual drug screening, and digital applications are described.

Overestimation of Late Distant Recurrences in High-Risk Patients With ER-Positive Breast Cancer: Validity and Accuracy of the CTS5 Risk Score in the TEAM and IDEAL Trials

PURPOSE

Most distant recurrences (DRs) in women with hormone receptor-positive breast cancer occur after 5 years from diagnosis. The Clinical Treatment Score post-5 years (CTS5) estimates DRs after 5 years of adjuvant endocrine therapy (AET). The aim of this study was to externally validate the CTS5 as a prognostic/predictive tool.

METHODS

The CTS5 categorizes patients who have been disease free for 5 years into low, intermediate, and high risk and calculates an absolute risk for developing DRs between 5 and 10 years. Discrimination and calibration were assessed using data from the TEAM and IDEAL trials. The predictive value of the CTS5 was tested with data from the IDEAL trial.

RESULTS

A total of 5,895 patients from the TEAM trial and 1,591 patients from the IDEAL trial were included. When assessing the CTS5 discrimination, significantly more DRs were found at 10 years after diagnosis in the CTS5 high- and intermediate-risk groups than in the low-risk group (hazard ratio, 5.7 [95% CI, 3.6 to 8.8] and 2.8 [95% CI, 1.7 to 4.4], respectively). In low- and intermediate-risk patients, the CTS5-predicted DR rates were higher, although not statistically significantly so, than observed rates. However, in high-risk patients, the CTS5-predicted DR rates were significantly higher than observed rates (29% v 19%, respectively; P < .001). The CTS5 was not predictive for extended AET duration.

CONCLUSION

The CTS5 score as applied to patients treated in the TEAM and IDEAL cohorts discriminates between risk categories but overestimates the risk of late DRs in high-risk patients. Therefore, the numerical risk assessment from the CTS5 calculator in its current form should be interpreted with caution when used in daily clinical practice, particularly in high-risk patients.

Neuropathic Pain: From Mechanisms to Treatment

Neuropathic pain caused by a lesion or disease of the somatosensory nervous system is a common chronic pain condition with major impact on quality of life. Examples include trigeminal neuralgia, painful polyneuropathy, postherpetic neuralgia, and central poststroke pain. Most patients complain of an ongoing or intermittent spontaneous pain of, for example, burning, pricking, squeezing quality, which may be accompanied by evoked pain, particular to light touch and cold. Ectopic activity in, for example, nerve-end neuroma, compressed nerves or nerve roots, dorsal root ganglia, and the thalamus may in different conditions underlie the spontaneous pain. Evoked pain may spread to neighboring areas, and the underlying pathophysiology involves peripheral and central sensitization. Maladaptive structural changes and a number of cell-cell interactions and molecular signaling underlie the sensitization of nociceptive pathways. These include alteration in ion channels, activation of immune cells, glial-derived mediators, and epigenetic regulation. The major classes of therapeutics include drugs acting on α₂δ subunits of calcium channels, sodium channels, and descending modulatory inhibitory pathways.

Sarilumab and adalimumab differential effects on bone remodelling and cardiovascular risk biomarkers, and predictions of treatment outcomes

Background

Interleukin-6 (IL-6) is a pleiotropic cytokine that plays a key role in the pathogenesis of rheumatoid arthritis. Sarilumab is a human monoclonal antibody that binds membrane-bound and soluble IL-6 receptor-α to inhibit IL-6 signalling. The aim of this study was to compare the effects of sarilumab and adalimumab (a tumour necrosis factor alpha inhibitor) monotherapy on levels of circulating biomarkers associated with the acute-phase response, bone remodelling, atherothrombosis, anaemia of chronic disease and markers purported to reflect synovial lymphoid and myeloid cell infiltrates, as well as the potential of these biomarkers to differentially predict clinical and patient-reported outcomes with sarilumab vs. adalimumab.

Methods

In this post hoc analysis, serum samples were analysed at baseline and prespecified post-treatment timepoints up to week 24 in adults with moderate-to-severe active rheumatoid arthritis intolerant of or inadequate responders to methotrexate from the MONARCH trial (NCT02332590).

Results

Greater reductions in C-reactive protein (CRP; − 94.0% vs. –24.0%), serum amyloid A (SAA; − 83.2% vs. –17.4%), total receptor activator of nuclear factor-κB ligand (RANKL; − 18.3% vs. 10.5%) and lipoprotein (a) (− 41.0% vs. –2.8%) were observed at week 24 with sarilumab vs. adalimumab, respectively (adjusted p < 0.0001). Greater increases in procollagen type 1 N-terminal propeptide (P1NP) were observed with sarilumab vs. adalimumab at week 24 (22.8% vs. 6.2%, p = 0.027). Patients with high baseline SAA, CRP and matrix metalloproteinase-3 (MMP-3) were more likely to achieve clinical efficacy, including American College of Rheumatology 20% improvement criteria and Disease Activity Score (28 joints)-CRP < 3.2, and report improvements in patient-reported outcomes, including Health Assessment Questionnaire-Disability Index and pain visual analogue scale, with sarilumab than adalimumab.

Conclusion

Sarilumab was associated with greater positive effects on bone remodelling and decreases in biomarkers of the acute-phase response, synovial inflammation and cardiovascular risk vs. adalimumab. High baseline concentrations of SAA, CRP and MMP-3 are predictive of clinical and patient-reported outcome responses to sarilumab treatment and prospective validation is warranted to confirm these results.

Trial registration

ClinicalTrials.gov, NCT02332590. Registered on 5 January 2015

Information Theoretic Multi-Target Feature Selection via Output Space Quantization

A key challenge in information theoretic feature selection is to estimate mutual information expressions that capture three desirable terms—the relevancy of a feature with the output, the redundancy and the complementarity between groups of features. The challenge becomes more pronounced in multi-target problems, where the output space is multi-dimensional. Our work presents an algorithm that captures these three desirable terms and is suitable for the well-known multi-target prediction settings of multi-label/dimensional classification and multivariate regression. We achieve this by combining two ideas—deriving low-order information theoretic approximations for the input space and using quantization algorithms for deriving low-dimensional approximations of the output space. Under the above framework we derive a novel criterion, Group-JMI-Rand, which captures various high-order target interactions. In an extensive experimental study we showed that our suggested criterion achieves competing performance against various other information theoretic feature selection criteria suggested in the literature.

The Use of (Network) Meta-Analysis in Clinical Oncology

Meta-analysis is important in oncological research to provide a more reliable answer to a clinical research question that was assessed in multiple studies but with inconsistent results. Pair-wise meta-analysis can be applied when comparing two treatments at once, whereas it is possible to compare multiple treatments at once with network meta-analysis (NMA). After careful systematic review of the literature and quality assessment of the identified studies, there are several assumptions in the use of meta-analysis. First, the added value of meta-analysis should be evaluated by examining the comparability of study populations. Second, the appropriate comparator in meta-analysis should be chosen according to the types of comparisons made in individual studies: (1) Experimental and comparator arms are different treatments (A vs. B); (2) Substitution of a conventional treatment by an experimental treatment (A+B vs. A+C); or (3) Addition of an experimental treatment (A+B vs. B). Ideally there is one common comparator treatment, but when there are multiple common comparators, the most efficacious comparator is preferable. Third, treatments can only be adequately pooled in meta-analysis or merged into one treatment node in NMA when considering likewise mechanism of action and similar setting in which treatment is indicated. Fourth, for both pair-wise meta-analysis and NMA, adequate assessment of heterogeneity should be performed and sub-analysis and sensitivity analysis can be applied to objectify a possible confounding factor. Network inconsistency, as statistical manifestation of violating the transitivity assumption, can best be evaluated by node-split modeling. NMA has advantages over pair-wise meta-analysis, such as clarification of inconsistent outcomes from multiple studies including multiple common comparators and indirect effect calculation of missing direct comparisons between important treatments. Also, NMA can provide increased statistical power and cross-validation of the observed treatment effect of weak connections with reasonable network connectivity and sufficient sample-sizes. However, inappropriate use of NMA can cause misleading results, and may emerge when there is low network connectivity, and therefore low statistical power. Furthermore, indirect evidence is still observational and should be interpreted with caution. NMA should therefore preferably be conducted and interpreted by both expert clinicians in the field and an experienced statistician. Finally, the use of meta-analysis can be extended to other areas, for example the identification of prognostic and predictive factors. Also, the integration of evidence from both meta-analysis and expert opinion can improve the construction of prognostic models in real-world databases.

Translational Metabolomics: Current Challenges and Future Opportunities

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.