Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges

Unraveling the interplay between cardiovascular diseases and alcohol use disorder: A bioinformatics and network-based exploration of shared molecular pathways and key biomarkers validation via western blot analysis

Clinical observations indicate a pronounced exacerbation of Cardiovascular Diseases (CVDs) in individuals grappling with Alcohol Use Disorder (AUD), suggesting an intricate interplay between these maladies. Pinpointing shared risk factors for both conditions has proven elusive. To address this, we pioneered a sophisticated bioinformatics framework and network-based strategy to unearth genes exhibiting aberrant expression patterns in both AUD and CVDs. In heart tissue samples from patients battling both AUD and CVDs, our study identified 76 Differentially Expressed Genes (DEGs) further used for retrieving important Gene Ontology (GO) keywords and metabolic pathways, highlighting mechanisms like proinflammatory cascades, T-cell cytotoxicity, antigen processing and presentation. By using Protein-Protein Interaction (PPI) analysis, we were able to identify key hub proteins that have a significant impact on the pathophysiology of these illnesses. Several hub proteins were identified include PTGS2, VCAM1, CCL2, CXCL8, IL7R, among these only CDH1 was covered in 10 algorithms of cytoHubba plugin. Furthermore, we pinpointed several Transcription Factors (TFs), including SOD2, CXCL8, THBS2, GREM1, CCL2, and PTGS2, alongside potential microRNAs (miRNAs) such as hsa-mir-203a-3p, hsa-mir-23a-3p, hsa-mir-98-5p, and hsa-mir-7-5p, which exert critical regulatory control over gene expression… In vitro study investigates the effect of alcohol on E-cadherin (CDH1) expression in HepG2 and Hep3B cells, showing a significant decrease in expression following ethanol treatment. These findings suggest that alcohol exposure may disrupt cell adhesion, potentially contributing to cellular changes associated with cardiovascular diseases. Our innovative approach has unveiled distinctive biomarkers delineating the dynamic interplay between AUD and various cardiovascular conditions for future therapeutic exploration.

Blood Sample Collection in Randomized Controlled Trials for Biomarker Discovery and Validation: Experience of the PREOPANC-2 Trial

BACKGROUND

This study aimed to investigate the feasibility and yield of blood sample collection in an investigator-initiated nationwide randomized controlled trial (RCT).

METHODS

In the PREOPANC-2 trial, 375 patients with (borderline) resectable pancreatic cancer were randomly assigned to two neoadjuvant regiments in 19 centers in the Netherlands (2018-2021). Blood sample collection was scheduled at seven time points before, during, and after treatment. The primary outcome was the proportion of successfully collected blood samples at each scheduled time point.

RESULTS

Of the 375 randomized patients, 12 were excluded from blood sample collection before any treatment. From the remaining 363 patients, 1513 (87 %) of 1748 blood samples were collected, processed, mailed, and centrally stored. The blood samples were collected before treatment from 347 (96 %) of the 363 patients, after the first neoadjuvant cycle from 322 (94 %) of 343 patients, after neoadjuvant treatment (i.e., before surgery) from 260 (83 %) of 313 patients, and after surgery from 210 (77 %) of 271 patients. During the follow-up visits, blood samples were collected from 147 (82 %) of 179 patients 12 months after randomization and from 83 (77 %) of 108 patients after 24 months. A total of 220 samples (13 %) were missing. The most common causes for missing blood samples were scheduling oversights, unsuccessful blood draw attempts, and mailing failures (151 times, 69 %). Blood sample collection was canceled 69 times (31 %) due to COVID-19.

CONCLUSION

Blood sample collection in the PREOPANC-2 trial had a yield of 96 % before treatment and an overall yield of 87 %. Collection of blood samples for biomarker studies is feasible in a nationwide RCT.

Resistance to Radiotherapy in Cancer

Radiation therapy or radiotherapy is a medical treatment that uses high doses of ionizing radiation to eliminate cancer cells and shrink tumors. It works by targeting the DNA within the tumor cells restricting their proliferation. Radiotherapy has been used for treating cancer for more than 100 years. Along with surgery and chemotherapy, it is one of the three main and most common approaches used in cancer therapy. Nowadays, radiotherapy has become a standard treatment option for a wide range of cancers around the world, including lung, breast, cervical, and colorectal cancers. Around 50% of all patients will require radiotherapy, 60% of whom are treated with curative intent. Moreover, it is commonly used for palliative treatment. Radiotherapy provides 5-year local control and overall survival benefit in 10.4% and 2.4% of all cancer patients, respectively. The highest local control benefit is reported for cervical (33%), head and neck (32%), and prostate (26%) cancers. But no benefit is observed in pancreas, ovary, liver, kidney, and colon cancers. Such relatively low efficiency is related to the development of radiation resistance, which results in cancer recurrence, metastatic dissemination, and poor prognosis. The identification of radioresistance biomarkers allows for improving the treatment outcome. These biomarkers mainly include proteins involved in metabolism and cell signaling pathways.

Mechanistic Insights into the Successful Development of Combination Therapy of Enfortumab Vedotin and Pembrolizumab for the Treatment of Locally Advanced or Metastatic Urothelial Cancer

Antibody-drug conjugates (ADCs) consist of an antibody backbone that recognizes and binds to a target antigen expressed on tumor cells and a small molecule chemotherapy payload that is conjugated to the antibody via a linker. ADCs are one of the most promising therapeutic modalities for the treatment of various cancers. However, many patients have developed resistance to this form of therapy. Extensive efforts have been dedicated to identifying an effective combination of ADCs with other types of anticancer therapies to potentially overcome this resistance. A recent clinical study demonstrated that a combination of the ADC enfortumab vedotin (EV) with the immune checkpoint inhibitor (ICI) pembrolizumab can achieve remarkable clinical efficacy as the first-line therapy for the treatment of locally advanced or metastatic urothelial carcinoma (la/mUC)-leading to the first approval of a combination therapy of an ADC with an ICI for the treatment of cancer patients. In this review, we highlight knowledge and understanding gained from the successful development of EV and the combination therapy of EV with ICI for the treatment of la/mUC. Using urothelial carcinoma as an example, we will focus on dissecting the underlying mechanisms necessary for the development of this type of combination therapy for a variety of cancers.

The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study

The development of next-generation sequencing (NGS) opens opportunities for new applications such as liquid biopsy, in which tumor mutation genotypes can be determined by sequencing circulating tumor DNA after blood draws. However, with highly diluted samples like those obtained with liquid biopsy, NGS invariably introduces a certain level of misclassification, even with improved technology. Recently, there has been a high demand to use mutation genotypes as biomarkers for predicting prognosis and treatment selection. Many methods have also been proposed to build classifiers based on multiple loci with machine learning algorithms as biomarkers. How the higher misclassification rate introduced by liquid biopsy will affect the performance of these biomarkers has not been thoroughly investigated. In this paper, we report the results from a simulation study focused on the clinical utility of biomarkers when misclassification is present due to the current technological limit of NGS in the liquid biopsy setting. The simulation covers a range of performance profiles for current NGS platforms with different machine learning algorithms and uses actual patient genotypes. Our results show that, at the high end of the performance spectrum, the misclassification introduced by NGS had very little effect on the clinical utility of the biomarker. However, in more challenging applications with lower accuracy, misclassification could have a notable effect on clinical utility. The pattern of this effect can be complex, especially for machine learning-based classifiers. Our results show that simulation can be an effective tool for assessing different scenarios of misclassification.

A review of trials investigating ctDNA-guided adjuvant treatment of solid tumors: The importance of trial design

Circulating tumor DNA (ctDNA) holds promise as a biomarker for guiding adjuvant treatment decisions in solid tumors. This review systematically assembles ongoing and published trials investigating ctDNA-directed adjuvant treatment strategies. A total of 57 phase II/III trials focusing on ctDNA in minimal residual disease (MRD) detection were identified, with a notable increase in initiation over recent years. Most trials target stage II or III colon/colorectal cancer, followed by breast cancer and non-small cell lung cancer. Trial methodologies vary, with some randomizing ctDNA-positive patients between standard-of-care (SoC) treatment and intensified regimens, while others aim to de-escalate therapy in ctDNA-negative patients. Challenges in trial design include the need for randomized controlled trials to establish clinical utility for ctDNA, ensuring adherence to standard treatment in control arms, and addressing the ethical dilemma of withholding treatment in high-risk ctDNA-positive patients. Longitudinal ctDNA surveillance emerges as a strategy to improve sensitivity for recurrence, particularly in less proliferative tumor types. However, ctDNA as longitudinal marker is often not validated yet. Ultimately, designing effective ctDNA interventional trials requires careful consideration of feasibility, meaningful outcomes, and potential impact on patient care.

Two-stage stratified designs with survival outcomes and adjustment for misclassification in predictive biomarkers

Biomarker stratified clinical trial designs are versatile tools to assess biomarker clinical utility and address its relationship with clinical endpoints. Due to imperfect assays and/or classification rules, biomarker status is prone to errors. To account for biomarker misclassification, we consider a two-stage stratified design for survival outcomes with an adjustment for misclassification in predictive biomarkers. Compared to continuous and/or binary outcomes, the test statistics for survival outcomes with an adjustment for biomarker misclassification is much more complicated and needs to take special care. We propose to use the information from the observed biomarker status strata to construct adjusted log-rank statistics for true biomarker status strata. These adjusted log-rank statistics are then used to develop sequential tests for the global (composite) hypothesis and component-wise hypothesis. We discuss the power analysis with the control of the type-I error rate by using the correlations between the adjusted log-rank statistics within and between the design stages. Our method is illustrated with examples of the recent successful development of immunotherapy in nonsmall-cell lung cancer.

Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case of Counting Outcome Data with Inflated Zeros

Precision medicine is revolutionizing health care, particularly by addressing patient variability due to different biological profiles. As traditional treatments may not always be appropriate for certain patient subsets, the rise of biomarker-stratified clinical trials has driven the need for innovative methods. We introduced a Bayesian sequential scheme to evaluate therapeutic interventions in an intensive care unit setting, focusing on complex endpoints characterized by an excess of zeros and right truncation. By using a zero-inflated truncated Poisson model, we efficiently addressed this data complexity. The posterior distribution of rankings and the surface under the cumulative ranking curve (SUCRA) approach provided a comprehensive ranking of the subgroups studied. Different subsets of subgroups were evaluated depending on the availability of biomarker data. Interim analyses, accounting for early stopping for efficacy, were an integral aspect of our design. The simulation study demonstrated a high proportion of correct identification of the subgroup which is the most predictive of the treatment effect, as well as satisfactory false positive and true positive rates. As the role of personalized medicine grows, especially in the intensive care setting, it is critical to have designs that can manage complicated endpoints and that can control for decision error. Our method seems promising in this challenging context.

Quantitative PET-based biomarkers in lymphoma: getting ready for primetime

The use of functional quantitative biomarkers extracted from routine PET-CT scans to characterize clinical responses in patients with lymphoma is gaining increased attention, and these biomarkers can outperform established clinical risk factors. Total metabolic tumour volume enables individualized estimation of survival outcomes in patients with lymphoma and has shown the potential to predict response to therapy suitable for  risk-adapted treatment approaches in clinical trials. The deployment of machine learning tools in molecular imaging research can assist in recognizing complex patterns and, with image classification, in tumour identification and segmentation of data from PET-CT scans. Initial studies using fully automated approaches to calculate metabolic tumour volume and other PET-based biomarkers have demonstrated appropriate correlation with calculations from experts, warranting further testing in large-scale studies. The extraction of computer-based quantitative tumour characterization through radiomics can provide a comprehensive view of phenotypic heterogeneity that better captures the molecular and functional features of the disease. Additionally, radiomics can be integrated with genomic data to provide more accurate prognostic information. Further improvements in PET-based biomarkers are imminent, although their incorporation into clinical decision-making currently has methodological shortcomings that need to be addressed with confirmatory prospective validation in selected patient populations. In this Review, we discuss the current knowledge, challenges and opportunities in the integration of quantitative PET-based biomarkers in clinical trials and the routine management of patients with lymphoma.

Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence

Purpose

For patients receiving lung stereotactic ablative radiotherapy (SABR), evidence suggests that high peritumor density predicts an increased risk of microscopic disease (MDE) and local-regional failure, but only if there is low or heterogenous incidental dose surrounding the tumor (GTV). A data-mining method (Cox-per-radius) has been developed to investigate this dose-density interaction. We apply the method to predict local relapse (LR) and regional failure (RF) in patients with non-small cell lung cancer.

Methods

199 patients treated in a routine setting were collated from a single institution for training, and 76 patients from an external institution for validation. Three density metrics (mean, 90th percentile, standard deviation (SD)) were studied in 1mm annuli between 0.5cm inside and 2cm outside the GTV boundary. Dose SD and fraction of volume receiving less than 30Gy were studied in annuli 0.5-2cm outside the GTV to describe incidental MDE dosage. Heat-maps were created that correlate with changes in LR and RF rates due to the interaction between dose heterogeneity and density at each distance combination. Regions of significant improvement were studied in Cox proportional hazards models, and explored with and without re-fitting in external data. Correlations between the dose component of the interaction and common dose metrics were reported.

Results

Local relapse occurred at a rate of 6.5% in the training cohort, and 18% in the validation cohort, which included larger and more centrally located tumors. High peritumor density in combination with high dose variability (0.5 - 1.6cm) predicts LR. No interactions predicted RF. The LR interaction improved the predictive ability compared to using clinical variables alone (optimism-adjusted C-index; 0.82 vs 0.76). Re-fitting model coefficients in external data confirmed the importance of this interaction (C-index; 0.86 vs 0.76). Dose variability in the 0.5-1.6 cm annular region strongly correlates with heterogeneity inside the target volume (SD; ρ = 0.53 training, ρ = 0.65 validation).

Conclusion

In these real-world cohorts, the combination of relatively high peritumor density and high dose variability predicts increase in LR, but not RF, following lung SABR. This external validation justifies potential use of the model to increase low-dose CTV margins for high-risk patients.

Simulation study for evaluating an adaptive-randomisation Bayesian hybrid trial design with enrichment

Background

As we enter the era of precision medicine, the role of adaptive designs, such as response-adaptive randomisation or enrichment designs in drug discovery and development, has become increasingly important to identify the treatment given to a patient based on one or more biomarkers. Tailoring the ventilation supply technique according to the responsiveness of patients to positive end-expiratory pressure is a suitable setting for such a design.

Methods

In the setting of marker-strategy design, we propose a Bayesian response-adaptive randomisation with enrichment design based on group sequential analyses. This design combines the elements of enrichment design and response-adaptive randomisation. Concerning the enrichment strategy, Bayesian treatment-by-subset interaction measures were used to adaptively enrich the patients most likely to benefit from an experimental treatment while controlling the false-positive rate.The operating characteristics of the design were assessed by simulation and compared to those of alternate designs.

Results

The results obtained allowed the detection of the superiority of one treatment over another and the presence of a treatment-by-subgroup interaction while keeping the false-positive rate at approximately 5\% and reducing the average number of included patients. In addition, simulation studies identified that the number of interim analyses and the burn-in period may have an impact on the performance of the scheme.

Conclusion

The proposed design highlights important objectives of precision medicine, such as determining whether the experimental treatment is superior to another and identifying wheter such an efficacy could depend on patient profile.

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.

Design and reporting of phase III oncology trials with prospective biomarker validation

BACKGROUND

Phase III trials with prospective biomarker validation are essential to drug development in the era of personalized oncology. However, concerns have emerged regarding the design and reporting of phase III trials with prospective biomarker validation.

METHODS

We searched MEDLINE for phase III oncology trials with prospective biomarker validation published in high-impact medical journals from 2011 to 2020. Information regarding trial design and reporting were extracted. Descriptive methods were used to summarize the results.

RESULTS

We identified 45 phase III trials with prospective biomarker validation. There was a trend for increasing use of biomarker validation phase III trials (from 1 trial in 2011 to 12 trials in 2020). For 39 (86.7%) trials, results in biomarker-negative population were either listed as an exploratory subgroup analysis (62.2%) or not mentioned in the methods (24.4%). Twenty-one (46.7%) trials were originally designed without biomarker validation but were then apparently modified to incorporate prospective biomarker validation after trial commencement, albeit only 15 (33.3%) trials reported this change. Treatment effect and primary outcome values in biomarker-negative patients were not reported in 24.4% and 40.0% trials, respectively. For 18 trials with statistically significant results in the overall population, only 7 trials reported a hazard ratio less than 0.8 in the biomarker-negative population.

CONCLUSIONS

Although biomarker validation in phase III trials have been increasingly used in the past decade, issues regarding changes in trial design after commencement without disclosure, underreporting of results in biomarker-negative groups, and recommending treatment in biomarker negative groups despite modest effects require substantial improvement.

Biomarkers of systemic treatment response in people with psoriasis: a scoping review

BACKGROUND

Responses to the systemic treatments commonly used to treat psoriasis vary. Biomarkers that accurately predict effectiveness and safety would enable targeted treatment selection, improved patient outcomes and more cost-effective healthcare.

OBJECTIVES

To perform a scoping review to identify and catalogue candidate biomarkers of systemic treatment response in psoriasis for the translational research community.

METHODS

A systematic search of CENTRAL, Embase, LILACS and MEDLINE was performed for relevant articles published between 1990 and December 2021. Eligibility criteria were studies involving patients with psoriasis (any age, n ≥ 50) reporting biomarkers associated with systemic treatment response. The main outcomes were any measure of systemic treatment efficacy or safety. Data were extracted by one reviewer and checked by a second; studies meeting minimal quality criteria (use of methods to control for confounding) were formally assessed for bias. Candidate biomarkers were identified by an expert multistakeholder group using a majority voting consensus exercise and mapped to relevant cellular and molecular pathways.

RESULTS

Of 71 included studies (67 studying effectiveness outcomes and eight safety outcomes; four studied both), most reported genomic or proteomic biomarkers associated with response to biologics (48 studies). Methodological or reporting limitations frequently compromised the interpretation of findings, including inadequate control for key covariates, lack of adjustment for multiple testing, and selective outcome reporting. We identified candidate biomarkers of efficacy to tumour necrosis factor inhibitors [variation in CARD14, CDKAL1, IL1B, IL12B and IL17RA loci, and lipopolysaccharide-induced phosphorylation of nuclear factor (NF)-κB in type 2 dendritic cells] and ustekinumab (HLA-C*06:02 and variation in an IL1B locus). None were supported by sufficient evidence for clinical use without further validation studies. Candidate biomarkers were found to be involved in the immune cellular crosstalk implicated in psoriasis pathogenesis, most notably antigen presentation, T helper (Th)17 cell differentiation, positive regulation of NF-κB, and Th17 cell activation.

CONCLUSIONS

This comprehensive catalogue provides a key resource for researchers and reveals a diverse range of biomarker types and outcomes in the included studies. The candidate biomarkers identified require further evaluation in methodologically robust studies to establish potential clinical utility. Future studies should aim to address the common methodological limitations highlighted in this review to expedite discovery and validation of biomarkers for clinical use. What is already known about this topic? Responses to the systemic treatments commonly used to treat psoriasis vary. Biomarkers that accurately predict effectiveness and safety would enable targeted treatment selection, improved patient outcomes and more cost-effective healthcare. What does this study add? This review provides a comprehensive catalogue of investigated biomarkers of systemic treatment response in psoriasis. A diverse range of biomarker types and outcomes was found in the included studies, serving as a key resource for the translational research community.

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.

Quantification of prevalence, clinical characteristics, co-existence, and geographic variations of traditional Chinese medicine diagnostic patterns via latent tree analysis-based differentiation rules among functional dyspepsia patients

BACKGROUND

Traditional Chinese Medicine (TCM) treatment strategies are guided by pattern differentiation, as documented in the eleventh edition of the International Classification of Diseases (ICD). However, no standards for pattern differentiation are proposed to ensure inter-rater agreement. Without standardisation, research on associations between TCM diagnostic patterns, clinical features, and geographical characteristics is also not feasible. This diagnostic cross-sectional study aimed to (i) establish the pattern differentiation rules of functional dyspepsia (FD) using latent tree analysis (LTA); (ii) compare the prevalence of diagnostic patterns in Hong Kong and Hunan; (iii) discover the co-existence of diagnostic patterns; and (iv) reveal the associations between diagnostic patterns and FD common comorbidities.

METHODS

A total of 250 and 150 participants with FD consecutively sampled in Hong Kong and Hunan, respectively, completed a questionnaire on TCM clinical features. LTA was performed to reveal TCM diagnostic patterns of FD and derive relevant pattern differentiation rules. Multivariate regression analyses were performed to quantify correlations between different diagnostic patterns and between diagnostic patterns and clinical and geographical variables.

RESULTS

At least one TCM diagnostic pattern was differentiated in 70.7%, 73.6%, and 64.0% of the participants in the overall (n = 400), Hong Kong (n = 250), and Hunan (n = 150) samples, respectively, using the eight pattern differentiation rules derived. 52.7% to 59.6% of the participants were diagnosed with two or more diagnostic patterns. Cold-heat complex (59.8%) and spleen-stomach dampness-heat (77.1%) were the most prevalent diagnostic patterns in Hong Kong and Hunan, respectively. Spleen-stomach deficiency cold was highly likely to co-exist with spleen-stomach qi deficiency (adjusted odds ratio (AOR): 53.23; 95% confidence interval (CI): 21.77 to 130.16). Participants with severe anxiety tended to have liver qi invading the stomach (AOR: 1.20; 95% CI: 1.08 to 1.33).

CONCLUSIONS

Future updates of the ICD, textbooks, and guidelines should emphasise the importance of clinical and geographical variations in TCM diagnosis. Location-specific pattern differentiation rules should be derived from local data using LTA. In future, patients' pattern differentiation results, local prevalence of TCM diagnostic patterns, and corresponding TCM treatment choices should be accessible to practitioners on online clinical decision support systems to streamline service delivery.

Advancing microfluidic diagnostic chips into clinical use: a review of current challenges and opportunities

Microfluidic diagnostic (μDX) technologies miniaturize sensors and actuators to the length-scales that are relevant to biology: the micrometer scale to interact with cells and the nanometer scale to interrogate biology's molecular machinery. This miniaturization allows measurements of biomarkers of disease (cells, nanoscale vesicles, molecules) in clinical samples that are not detectable using conventional technologies. There has been steady progress in the field over the last three decades, and a recent burst of activity catalyzed by the COVID-19 pandemic. In this time, an impressive and ever-growing set of technologies have been successfully validated in their ability to measure biomarkers in clinical samples, such as blood and urine, with sensitivity and specificity not possible using conventional tests. Despite our field's many accomplishments to date, very few of these technologies have been successfully commercialized and brought to clinical use where they can fulfill their promise to improve medical care. In this paper, we identify three major technological trends in our field that we believe will allow the next generation of μDx to have a major impact on the practice of medicine, and which present major opportunities for those entering the field from outside disciplines: 1. the combination of next generation, highly multiplexed μDx technologies with machine learning to allow complex patterns of multiple biomarkers to be decoded to inform clinical decision points, for which conventional biomarkers do not necessarily exist. 2. The use of micro/nano devices to overcome the limits of binding affinity in complex backgrounds in both the detection of sparse soluble proteins and nucleic acids in blood and rare circulating extracellular vesicles. 3. A suite of recent technologies that obviate the manual pre-processing and post-processing of samples before they are measured on a μDX chip. Additionally, we discuss economic and regulatory challenges that have stymied μDx translation to the clinic, and highlight strategies for successfully navigating this challenging space.

The potential of predictive and prognostic breast MRI (P2-bMRI)

Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria.

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.

Extrapolating evidence for molecularly targeted therapies from common to rare cancers: a scoping review of methodological guidance

OBJECTIVES

Cancer is increasingly classified according to biomarkers that drive tumour growth and therapies developed to target them. In rare biomarker-defined cancers, randomised controlled trials to adequately assess targeted therapies may be infeasible. Extrapolating existing evidence of targeted therapy from common cancers to rare cancers sharing the same biomarker may reduce evidence requirements for regulatory approval in rare cancers. It is unclear whether guidelines exist for extrapolation. We sought to identify methodological guidance for extrapolating evidence from targeted therapies used for common cancers to rare biomarker-defined cancers.

DESIGN

Scoping review.

DATA SOURCES

Websites of health technology assessment agencies, regulatory bodies, research groups, scientific societies and industry. EBM Reviews-Cochrane Methodology Register and Health Technology Assessment, Embase and MEDLINE databases (1946 to 11 May 2022).

ELIGIBILITY CRITERIA

Papers proposing a framework or recommendations for extrapolating evidence for rare cancers, small populations and biomarker-defined cancers.

DATA EXTRACTION AND SYNTHESIS

We extracted framework details where available and guidance for components of extrapolation. We used these components to structure and summarise recommendations.

RESULTS

We identified 23 papers. One paper provided an extrapolation framework but was not cancer specific. Extrapolation recommendations addressed six distinct components: strategies for grouping cancers as the same biomarker-defined disease; analytical validation requirements of a biomarker test to use across cancer types; strategies to generate control data when a randomised concurrent control arm is infeasible; sources to inform biomarker clinical utility assessment in the absence of prospective clinical evidence; requirements for surrogate endpoints chosen for the rare cancer; and assessing and augmenting safety data in the rare cancer.

CONCLUSIONS

In the absence of an established framework, our recommendations for components of extrapolation can be used to guide discussions about interpreting evidence to support extrapolation. The review can inform the development of an extrapolation framework for biomarker-targeted therapies in rare cancers.

PD-1 Blockade in Solid Tumors with Defects in Polymerase Epsilon

Missense mutations in the polymerase epsilon (POLE) gene have been reported to generate proofreading defects resulting in an ultramutated genome and to sensitize tumors to checkpoint blockade immunotherapy. However, many POLE-mutated tumors do not respond to such treatment. To better understand the link between POLE mutation variants and response to immunotherapy, we prospectively assessed the efficacy of nivolumab in a multicenter clinical trial in patients bearing advanced mismatch repair-proficient POLE-mutated solid tumors. We found that only tumors harboring selective POLE pathogenic mutations in the DNA binding or catalytic site of the exonuclease domain presented high mutational burden with a specific single-base substitution signature, high T-cell infiltrates, and a high response rate to anti-PD-1 monotherapy. This study illustrates how specific DNA repair defects sensitize to immunotherapy. POLE proofreading deficiency represents a novel agnostic biomarker for response to PD-1 checkpoint blockade therapy.

SIGNIFICANCE

POLE proofreading deficiency leads to high tumor mutational burden with high tumor-infiltrating lymphocytes and predicts anti-PD-1 efficacy in mismatch repair-proficient tumors. Conversely, tumors harboring POLE mutations not affecting proofreading derived no benefit from PD-1 blockade. POLE proofreading deficiency is a new tissue-agnostic biomarker for cancer immunotherapy. This article is highlighted in the In This Issue feature, p. 1397.

Clinical relevance of an objective flow cytometry approach based on limit of detection and limit of quantification for measurable residual disease assessment in acute myeloid leukemia. A post-hoc analysis of the GIMEMA AML1310 trial

Using a multiparametric flow cytometry assay, we assessed the predictive power of a threshold calculated applying the criteria of limit of detection (LOD) and limit of quantitation (LOQ) in adult patients with acute myeloid leukemia. This was a post-hoc analysis of 261 patients enrolled in the GIMEMA AML1310 prospective trial. According to the protocol design, using the predefined measurable residual disease (MRD) threshold of 0.035% bone marrow residual leukemic cells (RLC) calculated on mononuclear cells, 154 (59%) of the 261 patients were negative (MRD <0.035%) and 107 (41%) were positive (MRD ≥0.035%). Using LOD and LOQ, we selected the following categories of patients: (i) LODneg if RLC were below the LOD (74; 28.4%); (ii) LODpos-LOQneg if RLC were between the LOD and LOQ (43; 16.5%); and (iii) LOQpos if RLC were above the LOQ (144; 54.4%). Two-year overall survival of these three categories of patients was 75.4%, 79.8% and 66.4%, respectively (P=0.1197). Given their superimposable outcomes, the LODneg and LODpos-LOQneg categories were combined. Two-year overall survival of LODneg/LODpos-LOQneg patients was 77.0% versus 66.4% of LOQpos individuals (P=0.043). This figure was challenged in univariate analysis (P=0.046, hazard ratio=1.6, 95% confidence interval: 1.01-2.54) which confirmed the independent role of the LOD-LOQ approach in determining overall survival. In the AML1310 protocol, using the threshold of 0.035%, 2-year overall survival of patients with MRD <0.035% and MRD ≥0.035% was 74.5% versus 66.4%, respectively (P=0.3521). In conclusion, the use of the LOD-LOQ method results in more sensitive detection of MRD that, in turn, translates into a more accurate recognition of patients with different outcomes.

Personalized Risk-Based Screening Design for Comparative Two-Arm Group Sequential Clinical Trials

Personalized medicine has been emerging to take into account individual variability in genes and environment. In the era of personalized medicine, it is critical to incorporate the patients' characteristics and improve the clinical benefit for patients. The patients' characteristics are incorporated in adaptive randomization to identify patients who are expected to get more benefit from the treatment and optimize the treatment allocation. However, it is challenging to control potential selection bias from using observed efficacy data and the effect of prognostic covariates in adaptive randomization. This paper proposes a personalized risk-based screening design using Bayesian covariate-adjusted response-adaptive randomization that compares the experimental screening method to a standard screening method based on indicators of having a disease. Personalized risk-based allocation probability is built for adaptive randomization, and Bayesian adaptive decision rules are calibrated to preserve error rates. A simulation study shows that the proposed design controls error rates and yields a much smaller number of failures and a larger number of patients allocated to a better intervention compared to existing randomized controlled trial designs. Therefore, the proposed design performs well for randomized controlled clinical trials under personalized medicine.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Building up a clinical microbiota profiling: a quality framework proposal

Extensive characterization of the human microbiota has revealed promising relationships between microbial composition and health or disease, generating interest in biomarkers derived from microbiota profiling. However, microbiota complexity and technical challenges strongly influencing the results limit the generalization of microbiota profiling and question its clinical utility. In addition, no quality management scheme has been adapted to the specificities of microbiota profiling, notably due to the heterogeneity in methods and results. In this review, we discuss possible adaptation of classical quality management tools routinely used in diagnostic laboratories to microbiota profiling and propose a specific framework. Multiple quality controls are needed to cover all steps, from sampling to data processing. Standard operating procedures, primarily developed for wet lab analyses, must be adapted to the use of bioinformatic tools. Finally, requirements for test validation and proficiency testing must take into account expected discrepancies in results due to the heterogeneity of the processes. The proposed quality management framework should support the implementation of routine microbiota profiling by clinical laboratories to support patient care. Furthermore, its use in research laboratories would improve publication reproducibility as well as transferability of methods and results to routine practice.

Leveraging external data in the design and analysis of clinical trials in neuro-oncology

Integration of external control data, with patient-level information, in clinical trials has the potential to accelerate the development of new treatments in neuro-oncology by contextualising single-arm studies and improving decision making (eg, early stopping decisions). Based on a series of presentations at the 2020 Clinical Trials Think Tank hosted by the Society of Neuro-Oncology, we provide an overview on the use of external control data representative of the standard of care in the design and analysis of clinical trials. High-quality patient-level records, rigorous methods, and validation analyses are necessary to effectively leverage external data. We review study designs, statistical methods, risks, and potential distortions in using external data from completed trials and real-world data, as well as data sources, data sharing models, ongoing work, and applications in glioblastoma.

Toward the Next Generation of High-Grade Glioma Clinical Trials in the Era of Precision Medicine

ABSTRACT

In the era of precision medicine, there is a desire to harness our improved understanding of genomic and molecular underpinnings of gliomas to develop therapies that can be tailored to individual patients and tumors. With the rapid development of novel therapies, there has been a growing need to develop smart clinical trials that are designed to efficiently test promising agents, identify therapies likely to benefit patients, and discard ineffective therapies. We review clinical trial design in gliomas and developments designed to address the unique challenges of precision medicine. To provide an overview of this topic, we examine considerations for endpoints and response assessment, biomarkers, and novel clinical trial designs such as adaptive platform trials in the testing of new therapies for glioma patients.

Predictive and Diagnostic Biomarkers of Anastomotic Leakage: A Precision Medicine Approach for Colorectal Cancer Patients

Development of an anastomotic leak (AL) following intestinal surgery for the treatment of colorectal cancers is a life-threatening complication. Failure of the anastomosis to heal correctly can lead to contamination of the abdomen with intestinal contents and the development of peritonitis. The additional care that these patients require is associated with longer hospitalisation stays and increased economic costs. Patients also have higher morbidity and mortality rates and poorer oncological prognosis. Unfortunately, current practices for AL diagnosis are non-specific, which may delay diagnosis and have a negative impact on patient outcome. To overcome these issues, research is continuing to identify AL diagnostic or predictive biomarkers. In this review, we highlight promising candidate biomarkers including ischaemic metabolites, inflammatory markers and bacteria. Although research has focused on the use of blood or peritoneal fluid samples, we describe the use of implantable medical devices that have been designed to measure biomarkers in peri-anastomotic tissue. Biomarkers that can be used in conjunction with clinical status, routine haematological and biochemical analysis and imaging have the potential to help to deliver a precision medicine package that could significantly enhance a patient's post-operative care and improve outcomes. Although no AL biomarker has yet been validated in large-scale clinical trials, there is confidence that personalised medicine, through biomarker analysis, could be realised for colorectal cancer intestinal resection and anastomosis patients in the years to come.

Design and Analysis of Cancer Clinical Trials for Personalized Medicine

Biomarkers play a key role in the development of personalized medicine. Cancer clinical trials with biomarker should be appropriately designed and analyzed reflecting the various factors, such as the phase of trials, the type of biomarker, the study objectives, and whether the used biomarker is already validated or not. In this paper, we demonstrate design and analysis of two phase II cancer clinical trials, one with a predictive biomarker and the other with a prognostic biomarker. A statistical testing method and its sample size calculation method are presented for each of the trials. We assume that the primary endpoint of these trials is a time to event variable, but this concept can be used for any type of endpoint with associated testing methods. The test statistics and their sample size formulas are derived using the large sample approximation based on the martingale central limit theorem. Using simulations, we find that the test statistics control the type I error rate accurately and the sample sizes calculated using the formulas maintain the statistical power specified at the design stage.

Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs

We design two‐stage confirmatory clinical trials that use adaptation to find the subgroup of patients who will benefit from a new treatment, testing for a treatment effect in each of two disjoint subgroups. Our proposal allows aspects of the trial, such as recruitment probabilities of each group, to be altered at an interim analysis. We use the conditional error rate approach to implement these adaptations with protection of overall error rates. Applying a Bayesian decision‐theoretic framework, we optimize design parameters by maximizing a utility function that takes the population prevalence of the subgroups into account. We show results for traditional trials with familywise error rate control (using a closed testing procedure) as well as for umbrella trials in which only the per‐comparison type 1 error rate is controlled. We present numerical examples to illustrate the optimization process and the effectiveness of the proposed designs.

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.

The Epithelial-Mesenchymal Transcription Factor Slug Predicts Survival Benefit of Up-Front Surgery in Head and Neck Cancer

Simple Summary

In preclinical studies, the epithelial-to-mesenchymal transition (EMT)-related transcription factor Slug indicated radio- and chemoresistance in head and neck squamous cell carcinoma (HNSCC). Here we show that Slug is a biomarker associated with treatment failure in HNSCC patients treated with primary radio- or radiochemotherapy, but not in patients undergoing upfront surgery and postoperative radio- or chemoradiotherapy. Slug may thus serve as a predictive biomarker to identify HNSCC patients who will benefit from upfront surgery. Slug status is an immunohistochemical (IHC) parameter that is easy to determine. If the predictive value observed here can be confirmed in validation studies with independent data, Slug immunohistochemistry may have significant clinical relevance in treatment planning for HNSCC patients.

Abstract

EMT promotes radio- and chemotherapy resistance in HNSCC in vitro. As EMT has been correlated to the transcription factor Slug in tumor specimens from HNSCC patients, we assessed whether Slug overexpression predicts radio- and chemotherapy resistance and favors upfront surgery in HNSCC patients. Slug expression was determined by IHC scoring in tumor specimens from patients with incident HNSCC. Patients were treated with either definitive radiotherapy or chemoradiotherapy (primary RT/CRT) or upfront surgery with or without postoperative RT or CRT (upfront surgery/PORT). Treatment failure rates and overall survival (OS) were compared between RT/CRT and upfront surgery/PORT in Slug-positive and Slug-negative patients. Slug IHC was positive in 91/354 HNSCC patients. Primary RT/CRT showed inferior response rates (univariate odds ratio (OR) for treatment failure, 3.6; 95% CI, 1.7 to 7.9; p = 0.001) and inferior 5-year OS (univariate, p < 0.001) in Slug-positive patients. The independent predictive value of Slug expression status was confirmed in a multivariable Cox model (p = 0.017). Slug-positive patients had a 3.3 times better chance of survival when treated with upfront surgery/PORT versus primary RT/CRT. For HNSCC patients, Slug IHC represents a novel and feasible predictive biomarker to support upfront surgery.

Is It Possible to Personalize the Diagnosis and Treatment of Breast Cancer during Pregnancy?

The main goal of precision medicine in patients with breast cancer is to tailor the treatment according to the particular genetic makeup and the genetic changes in the cancer cells. Breast cancer occurring during pregnancy (BCP) is a complex and difficult clinical problem. Although it is not very common, both maternal and fetal outcome must be always considered when planning treatment. Pregnancy represents a significant barrier to the implementation of personalized treatment for breast cancer. Tailoring therapy mainly takes into account the stage of pregnancy, the subtype of cancer, the stage of cancer, and the patient’s preference. Results of the treatment of breast cancer in pregnancy are as yet not very satisfactory because of often delayed diagnosis, and it usually has an unfavorable outcome. Treatment of patients with pregnancy-associated breast cancer should be centralized. Centralization may result in increased experience in diagnosis and treatment and accumulated data may help us to optimize the treatment approaches, modify general treatment recommendations, and improve the survival and quality of life of the patients.

Sample size re-estimation in adaptive enrichment design

Clinical trial participants are often heterogeneous, which is a fundamental problem in the rapidly developing field of precision medicine. Participants heterogeneity causes considerable difficulty in the current phase III trial designs. Adaptive enrichment designs provide a flexible and intuitive solution. At the interim analysis, we enrich the subgroup of trial participants who have a higher likelihood to benefit from the new treatment. However, it is critical to control the level of the test size and maintain adequate power after enrichment of certain subgroup of participants. We develop two adaptive enrichment strategies with sample size re-estimation and verify their feasibility and practicability through extensive simulations and sensitivity analyses. The simulation studies show that the proposed methods can control the overall type I error rate and exhibit competitive improvement in terms of statistical power and expected sample size. The proposed designs are exemplified with a real trial application.

Biomarkers for Aging Identified in Cross-sectional Studies Tend to Be Non-causative

Biomarkers are important tools for diagnosis, prognosis, and identification of the causal factors of physiological conditions. Biomarkers are typically identified by correlating biological measurements with the status of a condition in a sample of subjects. Cross-sectional studies sample subjects at a single timepoint, whereas longitudinal studies follow a cohort through time. Identifying biomarkers of aging is subject to unique challenges. Individuals who age faster have intrinsically higher mortality rates and so are preferentially lost over time, in a phenomenon known as cohort selection. In this article, we use simulations to show that cohort selection biases cross-sectional analysis away from identifying causal loci of aging, to the point where cross-sectional studies are less likely to identify loci that cause aging than if loci had been chosen at random. We go on to show this bias can be corrected by incorporating correlates of mortality identified from longitudinal studies, allowing cross-sectional studies to effectively identify the causal factors of aging.

Xenograft-based, platform-independent gene signatures to predict response to alkylating chemotherapy, radiation, and combination therapy for glioblastoma

BACKGROUND

Predictive molecular biomarkers to select optimal treatment for patients with glioblastoma and other cancers are lacking. New strategies are needed when large randomized trials with correlative molecular data are not feasible.

METHODS

Gene signatures (GS) were developed from 31 orthotopic glioblastoma patient-derived xenografts (PDXs), treated with standard therapies, to predict benefit from radiotherapy (RT-GS), temozolomide (Chemo-GS), or the combination (ChemoRT-GS). Independent validation was performed in a heterogeneously treated clinical cohort of 502 glioblastoma patients with overall survival as the primary endpoint. Multivariate Cox analysis was used to adjust for confounding variables and evaluate interactions between signatures and treatment.

RESULTS

PDX models recapitulated the clinical heterogeneity of glioblastoma patients. RT-GS, Chemo-GS, and ChemoRT-GS were correlated with benefit from treatment in the PDX models. In independent clinical validation, higher RT-GS scores were associated with increased survival only in patients receiving RT (P = 0.0031, hazard ratio [HR] = 0.78 [0.66-0.92]), higher Chemo-GS scores were associated with increased survival only in patients receiving chemotherapy (P < 0.0001, HR = 0.66 [0.55-0.8]), and higher ChemoRT-GS scores were associated with increased survival only in patients receiving ChemoRT (P = 0.0001, HR = 0.54 [0.4-0.74]). RT-GS and ChemoRT-GS had significant interactions with treatment on multivariate analysis (P = 0.0009 and 0.02, respectively), indicating that they are bona fide predictive biomarkers.

CONCLUSIONS

Using a novel PDX-driven methodology, we developed and validated 3 platform-independent molecular signatures that predict benefit from standard of care therapies for glioblastoma. These signatures may be useful to personalize glioblastoma treatment in the clinic and this approach may be a generalizable method to identify predictive biomarkers without resource-intensive randomized trials.

Meta-Analysis of PD-L1 Expression As a Predictor of Survival After Checkpoint Blockade

PURPOSE

Programmed cell death receptor ligand 1 (PD-L1) expression is the most studied biomarker to predict the efficacy of immune checkpoint inhibitors (ICIs), but its clinical significance is controversial. We estimated the distribution of PD-L1 expression scores (ie, tumor proportion score or combined proportion score) and the relationship between PD-L1 levels and ICIs’ impact on overall survival (OS).

METHODS

We reconstructed, pooled, and analyzed individual-level data on 7,617 patients with cancer from 14 randomized clinical trials. The effects of ICIs were quantified using differences in 24-month restricted mean survival times (ΔRMSTs; ie, the increase in life expectancy truncated at 2 years associated with ICI therapy). In a simulation study, we compared standard randomized clinical trial designs with a trial design that leverages meta-analytic results like ours.

RESULTS

Approximately 93% of patients had a PD-L1 expression ≤ 5% (66% of patients) or > 50% (27% of patients). OS improves with ICIs regardless of PD-L1 expression level, which predicts the benefits’ magnitude. For patients with non–small-cell lung cancer (NSCLC), ΔRMSTs ranged from 1.4 months (95% probability interval [PI], 0.7 to 2.2 months) for PD-L1 expression ≤ 1% to 4.1 months (95% PI, 3.2 to 5.2 months) for PD-L1 expression > 80%. For patients with non-NSCLC tumors, ΔRMSTs ranged from 0.8 months (95% PI, −0.1 to 1.7 months) to 2.3 months (95% PI, 1.3 to 4.4 months), again for PD-L1 expression levels of ≤ 1% and > 80%, respectively. Simulations suggested that designs tailored to meta-analytic results can detect the effects of ICIs in PD-L1 subgroups with higher probability (> 15%) than standard designs.

CONCLUSION

The practice of dichotomizing the range of PD-L1 expression scores is inadequate for patient stratification. Meta-analytic estimates of the distribution of PD-L1 scores and subgroup-specific treatment effects can improve the designs of future trials of ICIs.

Design and analysis of biomarker-integrated clinical trials with adaptive threshold detection and flexible patient enrichment

We propose a new adaptive threshold detection and enrichment design in which the biomarker threshold is adaptively estimated and updated by optimizing a trade-off between the size of the biomarker positive population and the magnitude of the treatment effect in that population. Enrichment is based on an enrollment criterion that accounts for the uncertainty in estimation of the threshold. Early termination for futility is allowed based on predictive success probability. Valid testing and estimation techniques for the treatment effect overall and inpatient subgroups are studied. Simulations and an example demonstrate advantages of the proposed design over existing designs.

Principles of Good Clinical Trial Design

Clinical trials are a fundamental component of medical research and serve as the main route to obtain evidence of the safety and efficacy of treatment before its approval. A trial's ability to provide the intended evidence hinges on appropriate design, background knowledge, trial rationale to sample size, and interim monitoring rules. In this article, we present some general design principles for investigators and their research teams to consider when planning to conduct a trial.

Precision Medicine and the Role of Biomarkers of Radiotherapy Response in Breast Cancer

Radiotherapy remains an important treatment modality in nearly two thirds of all cancers, including the primary curative or palliative treatment of breast cancer. Unfortunately, largely due to tumor heterogeneity, tumor radiotherapy response rates can vary significantly, even between patients diagnosed with the same tumor type. Although in recent years significant technological advances have been made in the way radiation can be precisely delivered to tumors, it is proving more difficult to personalize radiotherapy regimens based on cancer biology. Biomarkers that provide prognostic or predictive information regarding a tumor's intrinsic radiosensitivity or its response to treatment could prove valuable in helping to personalize radiation dosing, enabling clinicians to make decisions between different treatment options whilst avoiding radiation-induced toxicity in patients unlikely to gain therapeutic benefit. Studies have investigated numerous ways in which both patient and tumor radiosensitivities can be assessed. Tumor molecular profiling has been used to develop radiosensitivity gene signatures, while the assessment of specific intracellular or secreted proteins, including circulating tumor cells, exosomes and DNA, has been performed to identify prognostic or predictive biomarkers of radiation response. Finally, the investigation of biomarkers related to radiation-induced toxicity could provide another means by which radiotherapy could become personalized. In this review, we discuss studies that have used these methods to identify or develop prognostic/predictive signatures of radiosensitivity, and how such assays could be used in the future as a means of providing personalized radiotherapy.

Phase II Feasibility and Biomarker Study of Neoadjuvant Trastuzumab and Pertuzumab With Chemoradiotherapy for Resectable Human Epidermal Growth Factor Receptor 2-Positive Esophageal Adenocarcinoma: TRAP Study

PURPOSE

Approximately 15% to 43% of esophageal adenocarcinomas (EACs) are human epidermal growth factor receptor 2 (HER2) positive. Because dual-agent HER2 blockade demonstrated a survival benefit in breast cancer, we conducted a phase II feasibility study of trastuzumab and pertuzumab added to neoadjuvant chemoradiotherapy (nCRT) in patients with EAC.

PATIENTS AND METHODS

Patients with resectable HER2-positive EAC received standard nCRT with carboplatin and paclitaxel and 41.4 Gy of radiotherapy, with 4 mg/kg of trastuzumab on day 1, 2 mg/kg per week during weeks 2 to 6, and 6 mg/kg per week during weeks 7, 10, and 13 and 840 mg of pertuzumab every 3 weeks. The primary end point was feasibility, defined as ≥ 80% completion of treatment with both trastuzumab and pertuzumab. An exploratory comparison of survival with a propensity score-matched cohort receiving standard nCRT was performed, as were exploratory pharmacokinetic and biomarker analyses.

RESULTS

Of the 40 enrolled patients (78% men; median age, 63 years), 33 (83%) completed treatment with trastuzumab and pertuzumab. No unexpected safety events were observed. R0 resection was achieved in all patients undergoing surgery, with pathologic complete response in 13 patients (34%). Three-year progression-free and overall survival (OS) were 57% and 71%, respectively (median follow-up, 32.1 months). Compared with the propensity score-matched cohort, a significantly longer OS was observed with HER2 blockade (hazard ratio, 0.58; 95% CI, 0.34 to 0.97). Results of pharmacokinetic analysis and activity on [18F]fluorodeoxyglucose positron emission tomography scans did not correlate with survival or pathologic response. Patients with HER2 3+ overexpression or growth factor receptor-bound protein 7 (Grb7) -positive tumors at baseline demonstrated significantly better survival (P = .007) or treatment response (P = .016), respectively.

CONCLUSION

Addition of trastuzumab and pertuzumab to nCRT in patients with HER2-positive EAC is feasible and demonstrates potentially promising activity compared with historical controls. HER2 3+ overexpression and Grb7 positivity are potentially predictive for survival and treatment response, respectively.