Biomarker enrichment strategies: matching trial design to biomarker credentials

Association of maternal blood metabolomics and gestational diabetes mellitus risk: a systematic review and meta-analysis

Gestational diabetes mellitus (GDM) is a common complication of pregnancy that has short- and long-term adverse effects. Therefore, further exploration of the pathophysiology of GDM and related biomarkers is important. In this study, we performed a systematic review and meta-analysis to investigate the associations between metabolites in blood detected via metabolomics techniques and the risk of GDM and to identify possible biomarkers for predicting the occurrence of GDM. We retrieved case‒control and cohort studies of metabolomics and GDM published in PubMed, Embase, and Web of Science through March 29, 2024; extracted metabolite concentrations, odds ratios (ORs), or relative risks (RRs); and evaluated the integrated results with metabolites per-SD risk estimates and 95% CIs for GDM. We estimated the results via the random effects model and the inverse variance method. Our study is registered in PROSPERO (CRD42024539435). We included a total of 28 case‒control and cohort studies, including 17,370 subjects (4,372 GDM patients and 12,998 non-GDM subjects), and meta-analyzed 67 metabolites. Twenty-five of these metabolites were associated with GDM risk. Some amino acids (isoleucine, leucine, valine, alanine, aspartate, etc.), lipids (C16:0, C18:1n-9, C18:1n-7, lysophosphatidylcholine (LPC) (16:0), LPC (18:0), and palmitoylcarnitine), and carbohydrates and energy metabolites (glucose, pyruvate, lactate, 2-hydroxybutyrate, 3-hydroxybutyrate) were discovered to be associated with increased GDM risk (hazard ratio 1.06-2.77). Glutamine, histidine, C14:0, and sphingomyelin (SM) (34:1) were associated with lower GDM risk (hazard ratio 0.75-0.84). These findings suggest that these metabolites may play essential roles in GDM progression, and serve as biomarkers, contributing to the early diagnosis and prediction of GDM.

Exceptional responses to systemic treatment in metastatic breast cancer: clinical features and long-term outcomes

BACKGROUND

Interest in metastatic solid tumors patients achieving exceptionally durable responses to systemic treatment is progressively increasing; however, available evidence still remains limited. This study characterizes patients with metastatic breast cancer (mBC) achieving an exceptional response, with a focus on patients discontinuing systemic treatment.

METHODS

In this retrospective monocentric study, patients with mBC achieving exceptional responses (2021-2023) were identified; clinical features, hormone receptor (HR) and HER2 status, and radiological responses were collected. Exceptional response was defined as complete (CR) or partial response (PR) lasting for more than twice the expected progression-free survival (PFS). No evidence of disease (NED) was defined as radiological absence of disease achieved integrating locoregional treatments.

RESULTS

We identified 58 exceptional responders: 31 HER2+ (53.5 %), 16 HR+ /HER2- (27.6 %), and 11 HR-/HER2- (19.0 %). 5-year PFS was 89.1 % and 5-year OS was 94.6 % overall, and numerically better in HR-/HER2- mBC (5-year PFS/OS: 100 %) compared to HER2+ (90.2 %/93.5 %) and HR+ /HER2- (80.8 %/93.8 %) mBC. Best radiological response was CR/NED in 69.0 % and PR in 31.0 % of patients. CR/NED status was significantly associated with better outcomes compared to PR (5-year OS: 100 % vs. 83.0 %, p = 0.004). Eleven patients (9 with CR/NED, 2 with PR) discontinued treatment in absence of disease progression; subsequent progression was observed only in one patient with PR.

CONCLUSION

mBC patients achieving exceptional responses exhibit favorable long-term survival outcomes, particularly if achieving CR/NED. These findings highlight the importance of further research to refine management strategies and explore the potential for systemic treatment discontinuation in these patients.

5-Fluorouracil metabolic pathway genes predict recurrence risk following adjuvant S-1 therapy: Results of an ancillary analysis from a phase III trial of resected biliary tract cancer (JCOG1202A1)

BACKGROUND

S-1, an oral fluoropyrimidine derivative, is standard adjuvant therapy for resected biliary tract cancer (BTC), based on the results of the JCOG1202, a phase III trial evaluating the survival benefit with adjuvant S-1 following curative resection for BTC compared to surgery alone. This multicenter ancillary study of the JCOG1202 aimed to evaluate the prognostic impact of the 5-fluorouracil (5-FU) metabolic pathway genes including thymidine phosphorylase (TP) and dihydropyrimidine dehydrogenase (DPD).

METHODS

The 5-FU metabolic pathway genes were measured in tumor cells from formalin-fixed paraffin-embedded resected specimens from 183 patients (surgery alone: n = 94; adjuvant S-1: n = 89). We randomly divided them into training (n = 96) and validation sets (n = 87) for evaluating the interaction between gene levels and RFS benefits in the treatment arm.

RESULTS

RFS benefits of adjuvant S-1 were observed in the low DPD (HR = 0.440 and 0.748, respectively in the training and validation sets) and the low TP groups (HR = 0.709 and 0.602, respectively). Clinicopathological characteristics were well balanced between low and high DPD populations. More advanced stage tumors were observed in high TP populations as compared to those in low TP populations (p = .0332).

CONCLUSION

The results suggest the RFS benefit of adjuvant S-1 in resected BTC patients with low DPD and low TP gene expressions.

Artificial intelligence in oncology: ensuring safe and effective integration of language models in clinical practice

In this Personal View, we address the latest advancements in automatic text analysis with artificial intelligence (AI) in medicine, with a focus on its implications in aiding treatment decisions in medical oncology. Acknowledging that a majority of hospital medical content is embedded in narrative format, natural language processing has become one of the most dynamic research fields for developing clinical decision support tools. In addition, large language models have recently reached unprecedented performance, notably when answering medical questions. Emerging applications include prognosis estimation, treatment recommendations, multidisciplinary tumor board recommendations and matching patients to recruiting clinical trials. Altogether, we advocate for a forward-looking approach in which the community efficiently initiates global prospective clinical evaluations of promising AI-based decision support systems. Such assessments will be essential to validate and evaluate potential biases, ensuring these innovations can be effectively and safely translated into practical tools for oncological practice. We are at a pivotal moment, where continued advancements in patient care must be pursued with scientific rigor.

Exploring the clinical implications and applications of exosomal miRNAs in gliomas: a comprehensive study

Gliomas are aggressive brain tumors associated with poor prognosis and limited treatment options due to their invasive nature and resistance to current therapeutic modalities. Research suggests that exosomal microRNAs have emerged as key players in intercellular communication within the tumor microenvironment, influencing tumor progression and therapeutic responses. Exosomal microRNAs (miRNAs), small non-coding RNAs, are crucial in glioma development, invasion, metastasis, angiogenesis, and immune evasion by binding to target genes. This comprehensive review examines the clinical relevance and implications of exosomal miRNAs in gliomas, highlighting their potential as diagnostic biomarkers, therapeutic targets and prognosis biomarker. Additionally, we also discuss the limitations of current exsomal miRNA treatments and address challenges and propose future directions for leveraging exosomal miRNAs in precision oncology for glioma management.

From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations for Developing Precision Medicine in the ICU

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.

Enhancing cancer immunotherapy: Exploring strategies to target the PD-1/PD-L1 axis and analyzing the associated patent, regulatory, and clinical trial landscape

Cancer treatment modalities and their progression is guided by the specifics of cancer, including its type and site of localization. Surgery, radiation, and chemotherapy are the most often used conventional treatments. Conversely, emerging treatment techniques include immunotherapy, hormone therapy, anti-angiogenic therapy, dendritic cell-based immunotherapy, and stem cell therapy. Immune checkpoint inhibitors' anticancer properties have drawn considerable attention in recent studies in the cancer research domain. Programmed Cell Death Protein-1 (PD-1) and its ligand (PD-L1) checkpoint pathway are key regulators of the interactions between activated T-cells and cancer cells, protecting the latter from immune destruction. When the ligand PD-L1 attaches to the receptor PD-1, T-cells are prevented from destroying cells that contain PD-L1, including cancer cells. The PD-1/PD-L1 checkpoint inhibitors block them, boosting the immune response and strengthening the body's defenses against tumors. Recent years have seen incredible progress and tremendous advancement in developing anticancer therapies using PD-1/PD-L1 targeting antibodies. While immune-related adverse effects and low response rates significantly limit these therapies, there is a need for research on methods that raise their efficacy and lower their toxicity. This review discusses various recent innovative nanomedicine strategies such as PLGA nanoparticles, carbon nanotubes and drug loaded liposomes to treat cancer targeting PD-1/PD-L1 axis. The biological implications of PD-1/PD-L1 in cancer treatment and the fundamentals of nanotechnology, focusing on the novel strategies used in nanomedicine, are widely discussed along with the corresponding guidelines, clinical trial status, and the patent landscape of such formulations.

The potential of miRNA-based approaches in glioblastoma: An update in current advances and future perspectives

Glioblastoma (GBM) is the most common malignant central nervous system tumor. The emerging field of epigenetics stands out as particularly promising. Notably, the discovery of micro RNAs (miRNAs) has paved the way for advancements in diagnosing, treating, and prognosticating patients with brain tumors. We aim to provide an overview of the emergence of miRNAs in GBM and their potential role in the multifaceted management of this disease. We discuss the current state of the art regarding miRNAs and GBM. We performed a narrative review using the MEDLINE/PUBMED database to retrieve peer-reviewed articles related to the use of miRNA approaches for the treatment of GBMs. MiRNAs are intrinsic non-coding RNA molecules that regulate gene expression mainly through post-transcriptional mechanisms. The deregulation of some of these molecules is related to the pathogenesis of GBM. The inclusion of molecular characterization for the diagnosis of brain tumors and the advent of less-invasive diagnostic methods such as liquid biopsies, highlights the potential of these molecules as biomarkers for guiding the management of brain tumors such as GBM. Importantly, there is a need for more studies to better examine the application of these novel molecules. The constantly changing characterization and approach to the diagnosis and management of brain tumors broaden the possibilities for the molecular inclusion of novel epigenetic molecules, such as miRNAs, for a better understanding of this disease.

Isotonic design for single-arm biomarker stratified trials

In single-arm trials with a predefined subgroup based on baseline biomarkers, it is often assumed that a biomarker defined subgroup, the biomarker positive subgroup, has the same or higher response to treatment compared to its complement, the biomarker negative subgroup. The goal is to determine if the treatment is effective in each of the subgroups or in the biomarker positive subgroup only or not effective at all. We propose the isotonic stratified design for this problem. The design has a joint set of decision rules for biomarker positive and negative subjects and utilizes joint estimation of response probabilities using assumed monotonicity of response between the biomarker negative and positive subgroups. The new design reduces the sample size requirement when compared to running two Simon's designs in each biomarker positive and negative. For example, the new design requires 23%-35% fewer patients than running two Simon's designs for scenarios we considered. Alternatively, the new design allows evaluating the response probability in both biomarker negative and biomarker positive subgroups using only 40% more patients needed for running Simon's design in the biomarker positive subgroup only.

Tumor-neutrophil cross talk orchestrates the tumor microenvironment to determine the bladder cancer progression

The immune landscape of bladder cancer progression is not fully understood, and effective therapies are lacking in advanced bladder cancer. Here, we visualized that bladder cancer cells recruited neutrophils by secreting interleukin-8 (IL-8); in turn, neutrophils played dual functions in bladder cancer, including hepatocyte growth factor (HGF) release and CCL3highPD-L1high super-immunosuppressive subset formation. Mechanistically, c-Fos was identified as the mediator of HGF up-regulating IL-8 transcription in bladder cancer cells, which was central to the positive feedback of neutrophil recruitment. Clinically, compared with serum IL-8, urine IL-8 was a better biomarker for bladder cancer prognosis and clinical benefit of immune checkpoint blockade (ICB). Additionally, targeting neutrophils or hepatocyte growth factor receptor (MET) signaling combined with ICB inhibited bladder cancer progression and boosted the antitumor effect of CD8+ T cells in mice. These findings reveal the mechanism by which tumor-neutrophil cross talk orchestrates the bladder cancer microenvironment and provide combination strategies, which may have broad impacts on patients suffering from malignancies enriched with neutrophils.

Facts and Hopes on Biomarkers for Successful Early Clinical Immunotherapy Trials: Innovative Patient Enrichment Strategies

Despite the clinical validation and unequivocal benefit to patients, the development of cancer immunotherapies is facing some key challenges and the attrition rate in early phases of development remains high. Identifying the appropriate patient population that would benefit most from the drug is on the critical path for successful clinical development. We believe that a systematic implementation of patient enrichment strategies early in the drug development process and trial design, is the basis for an innovative, more efficient, and leaner clinical development to achieve earlier a clear proof of concept or proof of failure. In this position article, we will describe and propose key considerations for the implementation of patient enrichment strategies as an opportunity to provide decision-enabling data earlier in the drug development process. We introduce an innovative multidimensional tool for immuno-oncology drug development that focuses on facilitating the identification and prioritization of enrichment-relevant biomarkers, based on the drug mechanism of action. To illustrate its utility, we discuss patient enrichment examples and use a case in the field of cancer immunotherapy, together with technical and regulatory considerations. Overall, we propose to implement fit for purpose enrichment strategies for all investigational drugs as early as possible in the development process. We believe that this will increase the success rate of immuno-oncology clinical trials, and eventually bring new and better medicines to patients faster.

Efficient testing of the biomarker positive and negative subgroups in a biomarker-stratified trial

In many randomized placebo-controlled trials with a biomarker defined subgroup, it is believed that this subgroup has the same or higher treatment effect compared with its complement. These subgroups are often referred to as the biomarker positive and negative subgroups. Most biomarker-stratified pivotal trials are aimed at demonstrating a significant treatment effect either in the biomarker positive subgroup or in the overall population. A major shortcoming of this approach is that the treatment can be declared effective in the overall population even though it has no effect in the biomarker negative subgroup. We use the isotonic assumption about the treatment effects in the two subgroups to construct an efficient way to test for a treatment effect in both the biomarker positive and negative subgroups. A substantial reduction in the required sample size for such a trial compared with existing methods makes evaluating the treatment effect in both the biomarker positive and negative subgroups feasible in pivotal trials especially when the prevalence of the biomarker positive subgroup is less than 0.5.

A promising subgroup identification method based on a genetic algorithm for censored survival data

Modern precision medicine requires drug development to account for patients' heterogeneity, as only a subgroup of the patient population is likely to benefit from the targeted therapy. In this paper, we propose a novel method for subgroup identification based on a genetic algorithm. The proposed method can detect promising subgroups defined by predictive biomarkers in which the treatment effects are much higher than the population average. The main idea is to search for the subgroup with the greatest predictive ability in the entire subgroup space via a genetic algorithm. We design a real-valued representation of subgroups that evolves according to a genetic algorithm and derive an objective function that properly evaluates the predictive ability of the subgroups. Compared with model- or tree-based subgroup identification methods, the distinctive search strategy of this new approach offers an improved capability to explore subgroups defined by multiple predictive biomarkers. By embedding a resampling scheme, the multiplicity and complexity issues inherent in subgroup identification methods can be addressed flexibly. We evaluate the performance of the proposed method in comparison with two other methods using simulation studies and a real-world example. The results show that the proposed method exhibits good properties in terms of multiplicity and complexity control, and the subgroups identified are much more accurate. Although we focus on the implementation of censored survival data, this method could easily be extended for the realization of continuous and categorical endpoints.

Clinical trial considerations for pediatric cancer drug development

Oncology has been one of the most active therapeutic areas in medicinal products development. Despite this fact, few drugs have been approved for use in pediatric cancer patients when compared to the number approved for adults with cancer. This disparity could be attributed to the fact that many oncology drugs have had orphan drug designation and were exempt from Pediatric Research Equity Act (PREA) requirements. On August 18, 2017, the RACE for Children Act, i.e. Research to Accelerate Cures and Equity Act, was signed into law as Title V of the 2017 FDA Reauthorization Act (FDARA) to amend the PREA. Pediatric investigation is now required if the drug or biological product is intended for the treatment of an adult cancer and directed at a molecular target that FDA determines to be "substantially relevant to the growth or progression of a pediatric cancer." This paper discusses the specific considerations in clinical trial designs and statistical methodologies to be implemented in oncology pediatric clinical programs.

The Future Glioblastoma Clinical Trials Landscape: Early Phase 0, Window of Opportunity, and Adaptive Phase I-III Studies

PURPOSE OF REVIEW

Innovative clinical trial designs for glioblastoma (GBM) are needed to expedite drug discovery. Phase 0, window of opportunity, and adaptive designs have been proposed, but their advanced methodologies and underlying biostatistics are not widely known. This review summarizes phase 0, window of opportunity, and adaptive phase I-III clinical trial designs in GBM tailored to physicians.

RECENT FINDINGS

Phase 0, window of opportunity, and adaptive trials are now being implemented for GBM. These trials can remove ineffective therapies earlier during drug development and improve trial efficiency. There are two ongoing adaptive platform trials: GBM Adaptive Global Innovative Learning Environment (GBM AGILE) and the INdividualized Screening trial of Innovative GBM Therapy (INSIGhT). The future clinical trials landscape in GBM will increasingly involve phase 0, window of opportunity, and adaptive phase I-III studies. Continued collaboration between physicians and biostatisticians will be critical for implementing these trial designs.

A randomized controlled trial of desvenlafaxine-induced structural brain changes in the treatment of persistent depressive disorder

The anatomical changes that antidepressant medications induce in the brain and through which they exert their therapeutic effects remain largely unknown. We randomized 61 patients with Persistent Depressive Disorder (PDD) to receive either desvenlafaxine or placebo in a 12-week trial and acquired anatomical MRI scans in 42 of those patients at baseline before randomization and immediately at the end of the trial. We also acquired MRIs once in 39 age- and sex-matched healthy controls. We assessed whether the serotonin-norepinephrine reuptake inhibitor, desvenlafaxine, differentially changed cortical thickness during the trial compared with placebo. Patients relative to controls at baseline had thinner cortices across the brain. Although baseline thickness was not associated with symptom severity, thicker baseline cortices predicted greater reduction in symptom severity in those treated with desvenlafaxine but not placebo. We did not detect significant treatment-by-time effects on cortical thickness. These findings suggest that baseline thickness may serve as predictive biomarkers for treatment response to desvenlafaxine. The absence of treatment-by-time effects may be attributable either to use of insufficient desvenlafaxine dosing, a lack of desvenlafaxine efficacy in treating PDD, or the short trial duration.

Current drug development and trial designs in neuro-oncology: report from the first American Society of Clinical Oncology and Society for Neuro-Oncology Clinical Trials Conference

Successful drug development for people with cancers of the CNS has been challenging. There are multiple barriers to successful drug development including biological factors, rarity of the disease, and ineffective use of clinical trials. Based upon a series of presentations at the First Central Nervous System Clinical Trials Conference hosted by the American Society of Clinical Oncology and the Society for Neuro-Oncology, we provide an overview on drug development and novel trial designs in neuro-oncology. This Review discusses the challenges of therapeutic development in neuro-oncology and proposes strategies to improve the drug discovery process by enriching the pipeline of promising therapies, optimising trial design, incorporating biomarkers, using external data, and maximising efficacy and reproducibility of clinical trials.

New insights into adaptive enrichment designs

The transition towards personalized medicine is happening and the new experimental framework is raising several challenges, from a clinical, ethical, logistical, regulatory, and statistical perspective. To face these challenges, innovative study designs with increasing complexity have been proposed. In particular, adaptive enrichment designs are becoming more attractive for their flexibility. However, these procedures rely on an increasing number of parameters that are unknown at the planning stage of the clinical trial, so the study design requires particular care. This review is dedicated to adaptive enrichment studies with a focus on design aspects. While many papers deal with methods for the analysis, the sample size determination and the optimal allocation problem have been overlooked. We discuss the multiple aspects involved in adaptive enrichment designs that contribute to their advantages and disadvantages. The decision-making process of whether or not it is worth enriching should be driven by clinical and ethical considerations as well as scientific and statistical concerns.

Utility of Plasma Protein Biomarkers and Mid-infrared Spectroscopy for Diagnosing Fracture-related Infections: A Pilot Study

OBJECTIVES

To compare a large panel of plasma protein inflammatory biomarkers and mid-infrared (MIR) spectral patterns in patients with confirmed fracture-related infections (FRIs) with those in controls without infection.

DESIGN

Prospective case-control study.

SETTING

Academic, Level 1 trauma center.

PATIENTS

Thirteen patients meeting confirmatory FRI criteria were matched to 13 controls based on age, time after surgery, and fracture region.

INTERVENTION

Plasma levels of 49 proteins were measured using enzyme-linked immunosorbent assay techniques. Fourier transform infrared spectroscopy of dried films was used to obtain MIR spectra of plasma samples.

MAIN OUTCOME MEASUREMENTS

The main outcome measurements included plasma protein levels and MIR spectra of samples.

RESULTS

Multivariate analysis-based predictive model developed using enzyme-linked immunosorbent assay-based biomarkers had sensitivity, specificity, and accuracy of 69.2% ± 0.0%, 99.9% ± 1.0%, and 84.5% ± 0.6%, respectively, with platelet-derived growth factor-AB/BB, C-reactive protein, and MIG selected as the minimum number of variables explaining group differences ( P < 0.05). Sensitivity, specificity, and accuracy of the predictive model based on MIR spectra were 69.9% ± 6.2%, 71.9% ± 5.9%, and 70.9% ± 4.8%, respectively, with 6 wavenumbers as explanatory variables ( P < 0.05).

CONCLUSIONS

This pilot study demonstrates the feasibility of using a select panel of plasma proteins and Fourier transform infrared spectroscopy to diagnose FRIs. Preliminary data suggest that the measurement of these select proteins and MIR spectra may be potential clinical tools to detect FRIs. Further investigation of these biomarkers in a larger cohort of patients is warranted.

LEVEL OF EVIDENCE

Diagnostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology

Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of these tests, hindering optimal treatment. Recently, deep learning (DL) has been applied to predict molecular test results from digitized images of tissue slides. Furthermore, treatment response and prognosis can be predicted from tissue slides using DL. In this review, we summarized DL-based studies regarding the prediction of genetic mutation, microsatellite instability, tumor mutational burden, molecular subtypes, gene expression, treatment response, and prognosis directly from hematoxylin- and eosin-stained tissue slides. Although performance needs to be improved, these studies clearly demonstrated the feasibility of DL-based prediction of key molecular features in cancer tissues. With the accumulation of data and technical advances, the performance of the DL system could be improved in the near future. Therefore, we expect that DL could provide cost- and time-effective alternative tools for patient stratification in the era of precision oncology.

Bias correction based on weighted likelihood for conditional estimation of subgroup effects in randomized clinical trials

Currently, many confirmatory randomized clinical trials (RCTs) with predictive markers have taken the all-comers approach because of the difficulty in developing predictive markers that are biologically compelling enough to apply the enrichment approach to restrict the patient population to a marker-defined subgroup. However, such a RCT with weak marker credentials can conclude that the new treatment is efficacious only in the subgroup, especially when the primary analysis demonstrates some treatment efficacy in the subgroup, but the overall treatment efficacy is not significant under a control of study-wise alpha rate. In this article, we consider conditional estimation of subgroup treatment effects, given the negative result in testing the overall treatment efficacy in the trial. To address the problem of unstable estimation due to the truncation in the distribution of the test statistic on overall treatment efficacy, we propose a new approach based on a weighted likelihood for the truncated distribution. The weighted likelihood can be derived by invoking a randomized test with a smooth critical function for the overall test. Our approach allows for point and interval estimations of the conditional effects consistently based on the standard maximum likelihood inference. Numerical evaluations, including simulations and application to real clinical trials, and guidelines for implementing our methods with R-codes, are provided.

New Insights into Clinical and Mechanistic Heterogeneity of the Acute Respiratory Distress Syndrome: Summary of the Aspen Lung Conference 2021

Clinical and molecular heterogeneity are common features of human disease. Understanding the basis for heterogeneity has led to major advances in therapy for many cancers and pulmonary diseases such as cystic fibrosis and asthma. Although heterogeneity of risk factors, disease severity, and outcomes in survivors are common features of the acute respiratory distress syndrome (ARDS), many challenges exist in understanding the clinical and molecular basis for disease heterogeneity and using heterogeneity to tailor therapy for individual patients. This report summarizes the proceedings of the 2021 Aspen Lung Conference, which was organized to review key issues related to understanding clinical and molecular heterogeneity in ARDS. The goals were to review new information about ARDS phenotypes, to explore multicellular and multisystem mechanisms responsible for heterogeneity, and to review how best to account for clinical and molecular heterogeneity in clinical trial design and assessment of outcomes. The report concludes with recommendations for future research to understand the clinical and basic mechanisms underlying heterogeneity in ARDS to advance the development of new treatments for this life-threatening critical illness.

Toward Precision Medicine in ADHD

Attention-Deficit Hyperactivity Disorder (ADHD) is a complex and heterogeneous neurodevelopmental condition for which curative treatments are lacking. Whilst pharmacological treatments are generally effective and safe, there is considerable inter-individual variability among patients regarding treatment response, required dose, and tolerability. Many of the non-pharmacological treatments, which are preferred to drug-treatment by some patients, either lack efficacy for core symptoms or are associated with small effect sizes. No evidence-based decision tools are currently available to allocate pharmacological or psychosocial treatments based on the patient's clinical, environmental, cognitive, genetic, or biological characteristics. We systematically reviewed potential biomarkers that may help in diagnosing ADHD and/or stratifying ADHD into more homogeneous subgroups and/or predict clinical course, treatment response, and long-term outcome across the lifespan. Most work involved exploratory studies with cognitive, actigraphic and EEG diagnostic markers to predict ADHD, along with relatively few studies exploring markers to subtype ADHD and predict response to treatment. There is a critical need for multisite prospective carefully designed experimentally controlled or observational studies to identify biomarkers that index inter-individual variability and/or predict treatment response.

Clinical Benefit Scales and Trial Design: Some Statistical Issues

Recently developed clinical-benefit outcome scales by the European Society for Medical Oncology (ESMO) and the American Society of Clinical Oncology (ASCO) allow standardized objective evaluation of outcomes of randomized clinical trials. However, incorporation of clinical-benefit outcome scales into trial designs highlights a number of statistical issues: the relationship between minimal clinical benefit and the target treatment-effect alternative used in the trial design, designing trials to assess long-term benefit, potential problems with using a trial endpoint that is not overall survival, and how to incorporate subgroup analyses into the trial design. Using the ESMO Magnitude of Clinical Benefit Scale as a basis for discussion, we review what these issues are and how they can guide the choice of trial-design target effects, appropriate endpoints, and pre-specified subgroup analyses to increase the chances that the resulting trial outcomes can be appropriately evaluated for clinical benefit.

Bayesian two-stage sequential enrichment design for biomarker-guided phase II trials for anticancer therapies

Biomarker-guided phase II trials have become increasingly important for personalized cancer treatment. In this paper, we propose a Bayesian two-stage sequential enrichment design for such biomarker-guided trials. We assumed that all patients were dichotomized as marker positive or marker negative based on their biomarker status; the positive patients were considered more likely to respond to the targeted drug. Early stopping rules and adaptive randomization methods were embedded in the design to control the number of patients receiving inferior treatment. At the same time, a Bayesian hierarchical model was used to borrow information between the positive and negative control arms to improve efficiency. Simulation results showed that the proposed design achieved higher empirical power while controlling the type I error and assigned more patients to the superior treatment arms. The operating characteristics suggested that the design has good performance and may be useful for biomarker-guided phase II trials for evaluating anticancer therapies.

A State-of-the-Art Roadmap for Biomarker-Driven Drug Development in the Era of Personalized Therapies

Advances in biotechnology have enabled us to assay human tissue and cells to a depth and resolution that was never possible before, redefining what we know as the “biomarker”, and how we define a “disease”. This comes along with the shift of focus from a “one-drug-fits-all” to a “personalized approach”, placing the drug development industry in a highly dynamic landscape, having to navigate such disruptive trends. In response to this, innovative clinical trial designs have been key in realizing biomarker-driven drug development. Regulatory approvals of cancer genome sequencing panels and associated targeted therapies has brought personalized medicines to the clinic. Increasing availability of sophisticated biotechnologies such as next-generation sequencing (NGS) has also led to a massive outflux of real-world genomic data. This review summarizes the current state of biomarker-driven drug development and highlights examples showing the utility and importance of the application of real-world data in the process. We also propose that all stakeholders in drug development should (1) be conscious of and efficiently utilize real-world evidence and (2) re-vamp the way the industry approaches drug development in this era of personalized medicines.

How Do We Predict a Patient's Disease Course and Whether They Will Respond to Specific Treatments?

Gastroenterologists will be all too familiar with the difficult decisions that managing inflammatory bowel disease often presents. How aggressively should I treat this patient? Do I expect them to have a mild or aggressive form of disease? Do they need a biologic? If so, which one? And when should I start it? The reality is that the answers that would be right for one patient might be disastrous for another. The growing therapeutic armamentarium will only make these decisions more difficult, and yet, we have seen how other specialties have begun to use the molecular heterogeneity in their diseases to provide some answers. Here, we review the progress that has been made in predicting the future for any given patient with inflammatory bowel disease-whether that is the course of disease that they will experience or whether or not they will respond to, or indeed tolerate, a particular therapy.

From Basic Science to Clinical Translation in Kidney Cancer: A Report from the Second Kidney Cancer Research Summit

The second Kidney Cancer Research Summit was held virtually in October 2020. The meeting gathered worldwide experts in the field of kidney cancer, including basic, translational, and clinical scientists as well as patient advocates. Novel studies were presented, addressing areas of unmet need related to different topics. These include novel metabolic targets, promising immunotherapeutic regimens, predictive genomic and transcriptomic biomarkers, and variant histologies of renal cell carcinoma (RCC). With the development of pioneering technologies, and an unprecedented commitment to kidney cancer research, the field has tremendously evolved. This perspective aims to summarize the different sessions of the conference, outline major advances in the understanding of RCC and discuss current challenges faced by the field.

A Problematic Biomarker Trial Design

Efficient biomarker-driven randomized clinical trials are a key tool for implementing precision oncology. A commonly used biomarker phase III design is focused on testing the treatment effect in biomarker-positive and overall study populations. This approach may result in recommending new treatments to biomarker-negative patients when these treatments have no benefit for these patients.

High-grade glioma therapy: adding flexibility in trial design to improve patient outcomes

INTRODUCTION

Outcomes for patients with high grade gliomas have changed little over the past thirty years. This realization prompted renewed efforts to increase flexibility in the design and conduct of clinical brain tumor trials.

AREAS COVERED

This manuscript reviews the development of clinical trial methods, challenges and considerations of flexible clinical trial designs, approaches to improve identification and testing of active agents for high grade gliomas, and evaluation of their delivery to the central nervous system.

EXPERT OPINION

Flexibility can be introduced in clinical trials in several ways. Flexible designs tout smaller sample sizes, adaptive modifications, fewer control arms, and inclusion of multiple arms in one study. Unfortunately, modifications in study designs cannot address two challenges that are largely responsible for the lack of progress in treating high grade gliomas: 1) the identification of active pharmaceutical agents and 2) the delivery of these agents to brain tumor tissue in therapeutic concentrations. To improve the outcomes of patients with high grade gliomas efforts must be focused on the pre-clinical screening of drugs for activity, the ability of these agents to achieve therapeutic concentrations in non-enhancing tumors, and a willingness to introduce novel compounds in minimally pre-treated patient populations.

An Overview of Phase 2 Clinical Trial Designs

Clinical trials are studies to test new treatments in humans. Typically, these treatments are evaluated over several phases to assess their safety and efficacy. Phase 1 trials are designed to evaluate the safety and tolerability of a new treatment, typically with a small number of patients (eg, 20-80), generally spread across several dose levels. Phase 2 trials are designed to determine whether the new treatment has sufficiently promising efficacy to warrant further investigation in a large-scale randomized phase 3 trial, as well as to further assess safety. These studies usually involve a few hundred patients. This article provides an overview of some of the most commonly used phase 2 designs for clinical trials and emphasizes their critical elements and considerations. Key references to some of the most commonly used phase 2 designs are given to allow the reader to explore in more detail the critical aspects when planning a phase 2 trial. A comparison of 3 potential designs in the context of the NRG-HN002 trial is presented to complement the discussion about phase 2 trials.

Incorporating covariates information in adaptive clinical trials for precision medicine

Precision medicine is the systematic use of information that pertains to an individual patient to select or optimize that patient's preventative and therapeutic care. Recent studies have classified biomarkers into predictive and prognostic biomarkers based on their roles in clinical studies. To design a clinical trial for precision medicine, predictive biomarkers and prognostic biomarkers should both be included. In statistical analysis, biomarkers are mathematically treated as covariates. We first classify covariates into predictive and prognostic covariates according to their roles. We then provide a brief review of recent advances in adaptive designs that incorporate covariates. However, the literature includes no designs that incorporate both prognostic covariates and predictive covariates simultaneously. In this paper, we propose a new family of covariate-adjusted response-adaptive (CARA) designs that incorporate both prognostic and predictive covariates and the responses. It is important to note that the predictive biomarkers and prognostic biomarkers play different roles in the new designs. The advantages of the proposed methods are demonstrated via numerical studies, and some further statistical issues are also discussed.

Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach

BACKGROUND

Studies correlating specific genetic mutations and treatment response are ongoing to establish an effective treatment strategy for gastric cancer (GC). To facilitate this research, a cost- and time-effective method to analyze the mutational status is necessary. Deep learning (DL) has been successfully applied to analyze hematoxylin and eosin (H and E)-stained tissue slide images.

AIM

To test the feasibility of DL-based classifiers for the frequently occurring mutations from the H and E-stained GC tissue whole slide images (WSIs).

METHODS

From the GC dataset of The Cancer Genome Atlas (TCGA-STAD), wild-type/mutation classifiers for CDH1, ERBB2, KRAS, PIK3CA, and TP53 genes were trained on 360 × 360-pixel patches of tissue images.

RESULTS

The area under the curve (AUC) for the receiver operating characteristic (ROC) curves ranged from 0.727 to 0.862 for the TCGA frozen WSIs and 0.661 to 0.858 for the TCGA formalin-fixed paraffin-embedded (FFPE) WSIs. The performance of the classifier can be improved by adding new FFPE WSI training dataset from our institute. The classifiers trained for mutation prediction in colorectal cancer completely failed to predict the mutational status in GC, indicating that DL-based mutation classifiers are incompatible between different cancers.

CONCLUSION

This study concluded that DL could predict genetic mutations in H and E-stained tissue slides when they are trained with appropriate tissue data.

Severe Pulmonary Arterial Hypertension Is Characterized by Increased Neutrophil Elastase and Relative Elafin Deficiency

BACKGROUND

Preclinical evidence implicates neutrophil elastase (NE) in pulmonary arterial hypertension (PAH) pathogenesis, and the NE inhibitor elafin is under early therapeutic investigation.

RESEARCH QUESTION

Are circulating NE and elafin levels abnormal in PAH and are they associated with clinical severity?

STUDY DESIGN AND METHODS

In an observational Stanford University PAH cohort (n = 249), plasma NE and elafin levels were measured in comparison with those of healthy control participants (n = 106). NE and elafin measurements were then related to PAH clinical features and relevant ancillary biomarkers. Cox regression models were fitted with cubic spline functions to associate NE and elafin levels with survival. To validate prognostic relationships, we analyzed two United Kingdom cohorts (n = 75 and n = 357). Mixed-effects models evaluated NE and elafin changes during disease progression. Finally, we studied effects of NE-elafin balance on pulmonary artery endothelial cells (PAECs) from patients with PAH.

RESULTS

Relative to control participants, patients with PAH were found to have increased NE levels (205.1 ng/mL [interquartile range (IQR), 123.6-387.3 ng/mL] vs 97.6 ng/mL [IQR, 74.4-126.6 ng/mL]; P < .0001) and decreased elafin levels (32.0 ng/mL [IQR, 15.3-59.1 ng/mL] vs 45.5 ng/mL [IQR, 28.1-92.8 ng/mL]; P < .0001) independent of PAH subtype, illness duration, and therapies. Higher NE levels were associated with worse symptom severity, shorter 6-min walk distance, higher N-terminal pro-type brain natriuretic peptide levels, greater right ventricular dysfunction, worse hemodynamics, increased circulating neutrophil levels, elevated cytokine levels, and lower blood BMPR2 expression. In Stanford patients, NE levels of > 168.5 ng/mL portended increased mortality risk after adjustment for known clinical predictors (hazard ratio [HR], 2.52; CI, 1.36-4.65, P = .003) or prognostic cytokines (HR, 2.63; CI, 1.42-4.87; P = .001), and the NE level added incremental value to established PAH risk scores. Similar prognostic thresholds were identified in validation cohorts. Longitudinal NE changes tracked with clinical trends and outcomes. PAH PAECs exhibited increased apoptosis and attenuated angiogenesis when exposed to NE at the level observed in patients' blood. Elafin rescued PAEC homeostasis, yet the required dose exceeded levels found in patients.

INTERPRETATION

Blood levels of NE are increased while elafin levels are deficient across PAH subtypes. Higher NE levels are associated with worse clinical disease severity and outcomes, and this target-specific biomarker could facilitate therapeutic development of elafin.

The 2020 BMT CTN Myeloma Intergroup Workshop on Immune Profiling and Minimal Residual Disease Testing in Multiple Myeloma

The fifth annual Blood and Marrow Transplant Clinical Trials Network Myeloma Intergroup Workshop on Immune Profiling and Minimal Residual Disease Testing in Multiple Myeloma was conducted as one of the American Society of Hematology Annual Meeting Scientific Workshops on Thursday December 3, 2020. This workshop focused on four main topics: (1) integrating minimal residual disease into clinical trial design and practice; (2) the molecular and immunobiology of disease evolution and progression in myeloma; (3) adaptation of next-generation sequencing, next-generation flow cytometry, and cytometry by time of flight techniques; and (4) chimeric antigen receptor T-cell and other cellular therapies for myeloma. In this report, we provide a summary of the workshop presentations and discuss future directions in the field.

Predicting fracture healing with blood biomarkers: the potential to assess patient risk of fracture nonunion

Fracture non-union is a significant orthopaedic problem affecting a substantial number of patients yearly. Treatment of nonunions is devastating to patients and costly to the healthcare system. Unfortunately, the diagnosis of non-union is typically made in a reactionary fashion by an orthopaedic surgeon based on clinical assessment and radiographic features several months into treatment. For this reason, investigators have been trying to develop prediction algorithms; however, these have relied on population-based approaches and lack the predictive capability necessary to make individual treatment decisions. There is also a growing body of literature focussed on identifying blood biomarkers that are associated with non-union. This review describes the research that has been done in this area. Further studies of patient-centered, precision medicine approaches will likely improve fracture non-union diagnostic/prognostic capabilities.

Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD-Disease Outcome and Response to Therapy

Inflammatory bowel diseases [IBD] are a heterogeneous spectrum with two extreme phenotypes, Crohn's disease [CD] and ulcerative colitis [UC], which both represent numerous phenotypical variations. Hence, we should no longer approach all IBD patients similarly, but rather aim to rethink clinical classifications and modify treatment algorithms to usher in a new era of precision medicine in IBD. This scientific ECCO workshop aims to provide a state-of-the-art overview on prognostic and predictive markers, shed light on key questions in biomarker development, propose best practices in IBD biomarker development [including trial design], and discuss the potential for multi-omic data integration to help drive further advances to make precision medicine a reality in IBD.

Systematic review of combinations of targeted or immunotherapy in advanced solid tumors

With rapid advances in our understanding of cancer, there is an expanding number of potential novel combination therapies, including novel-novel combinations. Identifying which combinations are appropriate and in which subpopulations are among the most difficult questions in medical research. We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review of trials of novel-novel combination therapies involving immunotherapies or molecular targeted therapies in advanced solid tumors. A MEDLINE search was conducted using a modified Cochrane Highly Sensitive Search Strategy for published clinical trials between July 1, 2017, and June 30, 2020, in the top-ranked medical and oncology journals. Trials were evaluated according to a criterion adapted from previously published Food and Drug Administration guidance and other key considerations in designing trials of combinations. This included the presence of a strong biological rationale, the use of a new established or emerging predictive biomarker prospectively incorporated into the clinical trial design, appropriate comparator arms of monotherapy or supportive external data sources and a primary endpoint demonstrating a clinically meaningful benefit. Of 32 identified trials, there were 11 (34%) trials of the novel-novel combination of anti-programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and anti-cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) therapy, and 10 (31%) trials of anti-PD-1/PD-L1 and anti-vascular endothelial growth factor (VEGF) combination therapy. 20 (62.5%) trials were phase II trials, while 12 (37.5%) were phase III trials. Most (72%) trials lacked significant preclinical evidence supporting the development of the combination in the given indication. A majority of trials (69%) were conducted in biomarker unselected populations or used pre-existing biomarkers within the given indication for patient selection. Most studies (66%) were considered to have appropriate comparator arms or had supportive external data sources such as prior studies of monotherapy. All studies were evaluated as selecting a clinically meaningful primary endpoint. In conclusion, designing trials to evaluate novel-novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design. Designing trials to evaluate novel-novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design.

What's new in pulmonary hypertension clinical research: lessons from the best abstracts at the 2020 American Thoracic Society International Conference

In this conference paper, we review the 2020 American Thoracic Society International Conference session titled, "What's New in Pulmonary Hypertension Clinical Research: Lessons from the Best Abstracts". This virtual mini-symposium took place on 21 October 2020, in lieu of the annual in-person ATS International Conference which was cancelled due to the COVID-19 pandemic. Seven clinical research abstracts were selected for presentation in the session, which encompassed five major themes: (1) standardizing diagnosis and management of pulmonary hypertension, (2) improving risk assessment in pulmonary arterial hypertension, (3) evaluating biomarkers of disease activity, (4) understanding metabolic dysregulation across the spectrum of pulmonary hypertension, and (5) advancing knowledge in chronic thromboembolic pulmonary hypertension. Focusing on these five thematic contexts, we review the current state of knowledge, summarize presented research abstracts, appraise their significance and limitations, and then discuss relevant future directions in pulmonary hypertension clinical research.

Prostate Cancer Biomarkers: From diagnosis to prognosis and precision-guided therapeutics

Prostate cancer (PCa) is one of the most commonly diagnosed malignancies and among the leading causes of cancer-related death worldwide. It is a highly heterogeneous disease, ranging from remarkably slow progression or inertia to highly aggressive and fatal disease. As therapeutic decision-making, clinical trial design and outcome highly depend on the appropriate stratification of patients to risk groups, it is imperative to differentiate between benign versus more aggressive states. The incorporation of clinically valuable prognostic and predictive biomarkers is also potentially amenable in this process, in the timely prevention of metastatic disease and in the decision for therapy selection. This review summarizes the progress that has so far been made in the identification of the genomic events that can be used for the classification, prediction and prognostication of PCa, and as major targets for clinical intervention. We include an extensive list of emerging biomarkers for which there is enough preclinical evidence to suggest that they may constitute crucial targets for achieving significant advances in the management of the disease. Finally, we highlight the main challenges that are associated with the identification of clinically significant PCa biomarkers and recommend possible ways to overcome such limitations.

Translational precision medicine: an industry perspective

In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.

Cardiac-sparing radiotherapy for locally advanced non-small cell lung cancer

BACKGROUND

We have carried out a study to determine the scope for reducing heart doses in photon beam radiotherapy of locally advanced non-small cell lung cancer (LA-NSCLC).

MATERIALS AND METHODS

Baseline VMAT plans were created for 20 LA-NSCLC patients following the IDEAL-CRT isotoxic protocol, and were re-optimized after adding an objective limiting heart mean dose (MDHeart). Reductions in MDHeart achievable without breaching limits on target coverage or normal tissue irradiation were determined. The process was repeated for objectives limiting the heart volume receiving ≥ 50 Gy (VHeart-50-Gy) and left atrial wall volume receiving ≥ 63 Gy (VLAwall-63-Gy).

RESULTS

Following re-optimization, mean MDHeart, VHeart-50-Gy and VLAwall-63-Gy values fell by 4.8 Gy and 2.2% and 2.4% absolute respectively. On the basis of associations observed between survival and cardiac irradiation in an independent dataset, the purposefully-achieved reduction in MDHeart is expected to lead to the largest improvement in overall survival. It also led to useful knock-on reductions in many measures of cardiac irradiation including VHeart-50-Gy and VLAwall-63-Gy, providing some insurance against survival being more strongly related to these measures than to MDHeart. The predicted hazard ratio (HR) for death corresponding to the purposefully-achieved mean reduction in MDHeart was 0.806, according to which a randomized trial would require 1140 patients to test improved survival with 0.05 significance and 80% power. In patients whose baseline MDHeart values exceeded the median value in a published series, the average MDHeart reduction was particularly large, 8.8 Gy. The corresponding predicted HR is potentially testable in trials recruiting 359 patients enriched for greater MDHeart values.

CONCLUSIONS

Cardiac irradiation in RT of LA-NSCLC can be reduced substantially. Of the measures studied, reduction of MDHeart led to the greatest predicted increase in survival, and to useful knock-on reductions in other cardiac irradiation measures reported to be associated with survival. Potential improvements in survival can be trialled more efficiently in a population enriched for patients with greater baseline MDHeart levels, for whom larger reductions in heart doses can be achieved.

Altered expression of EGFR and miR-34a derived from serum and tumoral tissue was associated with glioblastoma multiform

OBJECTIVE

Glioblastoma multiform (GBM) is the most prevalent and invasive brain malignancy in adults. There are ongoing researches to introduce novel and non-invasive potential biomarkers for the early detection of GBM.

METHODS

Here we compared the expression of EGFR, miR-34a, and miR-19a between tumoral and adjacent non-cancerous tissues (ANCTs) of 50 GBM patients and also compared their expression levels in serum samples of GBM patients with serum samples of 50 control subjects.

RESULTS

The expression level of the EGFR gene was elevated in GBM tissues in comparison to the corresponding ANCTs (P < 0.0001) and also was higher in the serum sample of patients compared with control serum (P < 0.0001). The miR-34a was significantly downregulated in serum samples as well as tissues obtained from GBM patients compared with the corresponding controls (expression ratio = 0.57 and 0.4, P = 0.02 and 0.001 respectively).

CONCLUSIONS

Dysregulation of the EGFR gene and miR-34a in serum samples of GBM patients compared with the control subjects promises the emergence of non-invasive biomarkers for early detection of GBM which need confirmative studies with a large sample size.

Moving Molecular Profiling to Routine Clinical Practice: A Way Forward?

Molecular profiling of a patient's tumor to guide targeted treatment selection offers the potential to advance patient care by improving outcomes and minimizing toxicity (by avoiding ineffective treatments). However, current development of molecular profile (MP) panels is often based on applying institution-specific or subjective algorithms to nonrandomized patient cohorts. Consequently, obtaining reliable evidence that molecular profiling is offering clinical benefit and is ready for routine clinical practice is challenging. In particular, we discuss here the problems with interpreting for clinical utility nonrandomized studies that compare outcomes in patients treated based on their MP vs those treated with standard of care, studies that compare the progression-free survival (PFS) seen on a MP-directed treatment to the PFS seen for the same patient on a previous standard treatment (PFS ratio), and multibasket trials that evaluate the response rates of targeted therapies in specific molecularly defined subpopulations (regardless of histology). We also consider some limitations of randomized trial designs. A two-step strategy is proposed in which multiple mutation-agent pairs are tested for activity in one or more multibasket trials in the first step. The results of the first step are then used to identify promising mutation-agent pairs that are combined in a molecular panel that is then tested in the step-two strategy-design randomized clinical trial (the molecular panel-guided treatment for the selected mutations vs standard of care). This two-step strategy should allow rigorous evidence-driven identification of mutation-agent pairs that can be moved into routine clinical practice.

Fluid Biomarkers in Clinical Trials for Alzheimer's Disease: Current and Future Application

Alzheimer's disease (AD) research is entering a unique moment in which enormous information about the molecular basis of this disease is being translated into therapeutics. However, almost all drug candidates have failed in clinical trials over the past 30 years. These many trial failures have highlighted a need for the incorporation of biomarkers in clinical trials to help improve the trial design. Fluid biomarkers measured in cerebrospinal fluid and circulating blood, which can reflect the pathophysiological process in the brain, are becoming increasingly important in AD clinical trials. In this review, we first succinctly outline a panel of fluid biomarkers for neuropathological changes in AD. Then, we provide a comprehensive overview of current and future application of fluid biomarkers in clinical trials for AD. We also summarize the many challenges that have been encountered in efforts to integrate fluid biomarkers in clinical trials, and the barriers that have begun to be overcome. Ongoing research efforts in the field of fluid biomarkers will be critical to make significant progress in ultimately unveiling disease-modifying therapies in AD.

Lost in application: Measuring hypoxia for radiotherapy optimisation

The history of radiotherapy is intertwined with research on hypoxia. There is level 1a evidence that giving hypoxia-targeting treatments with radiotherapy improves locoregional control and survival without compromising late side-effects. Despite coming in and out of vogue over decades, there is now an established role for hypoxia in driving molecular alterations promoting tumour progression and metastases. While tumour genomic complexity and immune profiling offer promise, there is a stronger evidence base for personalising radiotherapy based on hypoxia status. Despite this, there is only one phase III trial targeting hypoxia modification with full transcriptomic data available. There are no biomarkers in routine use for patients undergoing radiotherapy to aid management decisions, and a roadmap is needed to ensure consistency and provide a benchmark for progression to application. Gene expression signatures address past limitations of hypoxia biomarkers and could progress biologically optimised radiotherapy. Here, we review recent developments in generating hypoxia gene expression signatures and highlight progress addressing the challenges that must be overcome to pave the way for their clinical application.