Clinical Trial Designs for Predictive Marker Validation in Cancer Treatment Trials

The effect of low-intensity resistance exercise training on Serum tumor biomarkers and quality of life in women with breast cancer: A randomized controlled trial

BackgroundAs the role of physical activity in breast cancer management gains increasing recognition, understanding the effects of aerobic exercise on patients' quality of life and biological markers has emerged as a critical area of research to inform clinical practices and improve patient outcomes.ObjectiveThis study aims to investigate the impact of low-intensity resistance exercise training on serum tumor biomarkers and quality of life in women with breast cancer, providing evidence for its potential role as an adjunct therapy in improving clinical outcomes and patient well-being.MethodsThis study was carried out on 70 women between the ages of 18 and 65, who were included in the study while receiving chemotherapy treatment. The subject was divided into low-intensity resistance exercise (Group I) and control (Group II). Demographic characteristics, quality of life, and serum tumor biomarkers were evaluated. Participants in group I underwent a 12-week exercise programme of low-intensity resistance exercises three times a week (three metabolic equivalents, approximately 30 min/session).ResultsThe quality of life has been found to be significantly higher in the low-intensity resistance exercise group (p < 0.05). The serum tumor biomarker levels of CEA, CA15-3, and CA19-9 decreased across all participants. However, the reduction in serum tumor biomarker levels was found to be more pronounced in Group 1 (p < 0.05).ConclusionsLow-intensity resistance exercise has demonstrated a positive effect on the quality of life in women with breast cancer. Within the framework of oncological rehabilitation, aerobic exercise regimens may be preferred due to their role in promoting improvements in serum tumor biomarker levels and contributing to enhanced quality of life.

Criteria for assessing evidence for biomarker-targeted therapies in rare cancers-an extrapolation framework

Background

Advances in targeted therapy development and tumor sequencing technology are reclassifying cancers into smaller biomarker-defined diseases. Randomized controlled trials (RCTs) are often impractical in rare diseases, leading to calls for single-arm studies to be sufficient to inform clinical practice based on a strong biological rationale. However, without RCTs, favorable outcomes are often attributed to therapy but may be due to a more indolent disease course or other biases. When the clinical benefit of targeted therapy in a common cancer is established in RCTs, this benefit may extend to rarer cancers sharing the same biomarker. However, careful consideration of the appropriateness of extending the existing trial evidence beyond specific cancer types is required. A framework for extrapolating evidence for biomarker-targeted therapies to rare cancers is needed to support transparent decision-making.

Objectives

To construct a framework outlining the breadth of criteria essential for extrapolating evidence for a biomarker-targeted therapy generated from RCTs in common cancers to different rare cancers sharing the same biomarker.

Design

A series of questions articulating essential criteria for extrapolation.

Methods

The framework was developed from the core topics for extrapolation identified from a previous scoping review of methodological guidance. Principles for extrapolation outlined in guidance documents from the European Medicines Agency, the US Food and Drug Administration, and Australia's Medical Services Advisory Committee were incorporated.

Results

We propose a framework for assessing key assumptions of similarity of the disease and treatment outcomes between the common and rare cancer for five essential components: prognosis of the biomarker-defined cancer, biomarker test analytical validity, biomarker actionability, treatment efficacy, and safety. Knowledge gaps identified can be used to prioritize future studies.

Conclusion

This framework will allow systematic assessment, standardize regulatory, reimbursement and clinical decision-making, and facilitate transparent discussions between key stakeholders in drug assessment for rare biomarker-defined cancers.

Predicting Phase 1 Lymphoma Clinical Trial Durations Using Machine Learning: An In-Depth Analysis and Broad Application Insights

Lymphoma diagnoses in the US are substantial, with an estimated 89,380 new cases in 2023, necessitating innovative treatment approaches. Phase 1 clinical trials play a pivotal role in this context. We developed a binary predictive model to assess trial adherence to expected average durations, analyzing 1089 completed Phase 1 lymphoma trials from clinicaltrials.gov. Using machine learning, the Random Forest model demonstrated high efficacy with an accuracy of 0.7248 and an ROC-AUC of 0.7677 for lymphoma trials. The difference in the accuracy level of the Random Forest is statistically significant compared to the other alternative models, as determined by a 95% confidence interval on the testing set. Importantly, this model maintained an ROC-AUC of 0.7701 when applied to lung cancer trials, showcasing its versatility. A key insight is the correlation between higher predicted probabilities and extended trial durations, offering nuanced insights beyond binary predictions. Our research contributes to enhanced clinical research planning and potential improvements in patient outcomes in oncology.

Proteomic Profiling of Small-Cell Lung Cancer: A Systematic Review

The accurate diagnosis of small-cell lung cancer (SCLC) is crucial, as treatment strategies differ from those of other lung cancers. This systematic review aims to identify proteins differentially expressed in SCLC compared to normal lung tissue, evaluating their potential utility in diagnosing and prognosing the disease. Additionally, the study identifies proteins differentially expressed between SCLC and large cell neuroendocrine carcinoma (LCNEC), aiming to discover biomarkers distinguishing between these two subtypes of neuroendocrine lung cancers. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across PubMed/MEDLINE, Scopus, Embase, and Web of Science databases. Studies reporting proteomics information and confirming SCLC and/or LCNEC through histopathological and/or cytopathological examination were included, while review articles, non-original articles, and studies based on animal samples or cell lines were excluded. The initial search yielded 1705 articles, and after deduplication and screening, 16 articles were deemed eligible. These studies revealed 117 unique proteins significantly differentially expressed in SCLC compared to normal lung tissue, along with 37 unique proteins differentially expressed between SCLC and LCNEC. In conclusion, this review highlights the potential of proteomics technology in identifying novel biomarkers for diagnosing SCLC, predicting its prognosis, and distinguishing it from LCNEC.

A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer

BACKGROUND

Many scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application of adequate statistical methods is essential, but little is known about the scope of methods used.

METHODS

A PubMed search identified clinical studies among women with breast cancer comparing at least two different treatment groups, one of which chemotherapy or endocrine treatment, by levels of at least one biomarker. Studies presenting original data published in 2019 in one of 15 selected journals were eligible for this review. Clinical and statistical characteristics were extracted by three reviewers and a selection of characteristics for each study was reported.

RESULTS

Of 164 studies identified by the query, 31 were eligible. Over 70 different biomarkers were evaluated. Twenty-two studies (71%) evaluated multiplicative interaction between treatment and biomarker. Twenty-eight studies (90%) evaluated either the treatment effect in biomarker subgroups or the biomarker effect in treatment subgroups. Eight studies (26%) reported results for one predictive biomarker analysis, while the majority performed multiple evaluations, either for several biomarkers, outcomes and/or subpopulations. Twenty-one studies (68%) claimed to have found significant differences in treatment effects by biomarker level. Fourteen studies (45%) mentioned that the study was not designed to evaluate treatment effect heterogeneity.

CONCLUSIONS

Most studies evaluated treatment heterogeneity via separate analyses of biomarker-specific treatment effects and/or multiplicative interaction analysis. There is a need for the application of more efficient statistical methods to evaluate treatment heterogeneity in clinical studies.

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.

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.

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.

REMARK guidelines for tumour biomarker study reporting: a remarkable history

In 2005, several experts in tumor biomarker research publishe the REporting recommendations for Tumor MARKer prognostic studies (REMARK) criteria. Coupled with the subsequent Biospecimen Reporting for Improved Study Quality (BRISQ) criteria, these initiatives provide a framework for transparently reporting of the methods of study conduct and analyses.

Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment

BACKGROUND

Medical advances in the treatment of cancer have allowed the development of multiple approved treatments and prognostic and predictive biomarkers for many types of cancer. Identifying improved treatment strategies among approved treatment options, the study of which is termed comparative effectiveness, using predictive biomarkers is becoming more common. RCTs that incorporate predictive biomarkers into the study design, called prediction-driven RCTs, are needed to rigorously evaluate these treatment strategies. Although researched extensively in the experimental treatment setting, literature is lacking in providing guidance about prediction-driven RCTs in the comparative effectiveness setting.

METHODS

Realistic simulations with time-to-event endpoints are used to compare contrasts of clinical utility and provide examples of simulated prediction-driven RCTs in the comparative effectiveness setting.

RESULTS

Our proposed contrast for clinical utility accurately estimates the true clinical utility in the comparative effectiveness setting while in some scenarios, the contrast used in current literature does not.

DISCUSSION

It is important to properly define contrasts of interest according to the treatment setting. Realistic simulations should be used to choose and evaluate the RCT design(s) able to directly estimate that contrast. In the comparative effectiveness setting, our proposed contrast for clinical utility should be used.

Assessment of the Clinical Utility of Pretreatment DPYD Testing for Patients Receiving Fluoropyrimidine Chemotherapy

PURPOSE

Patients who carry pathogenic variants in DPYD have higher systemic fluoropyrimidine (FP) concentrations and greater risk of severe and fatal FP toxicity. Pretreatment DPYD testing and DPYD-guided FP dosing to reduce toxicity and health care costs is recommended by European clinical oncology guidelines and has been adopted across Europe, but has not been recommended or adopted in the United States. The cochairs of the National Comprehensive Cancer Network Guidelines for colon cancer treatment explained their concerns with recommending pretreatment DPYD testing, particularly the risk that reduced FP doses in DPYD carriers may reduce treatment efficacy.

METHODS

This special article uses previously published frameworks for assessing the clinical utility of cancer biomarker tests, including for germline indicators of toxicity risk, to assess the clinical utility of pretreatment DPYD testing, with a particular focus on the risk of reducing treatment efficacy.

RESULTS

There is no direct evidence of efficacy reduction, and the available indirect evidence demonstrates that DPYD-guided FP dosing results in similar systemic FP exposure and toxicity compared with standard dosing in noncarriers, and is well calibrated to the maximum tolerated dose, strongly suggesting there is minimal risk of efficacy reduction.

CONCLUSION

This article should serve as a call to action for clinicians and clinical guidelines committees in the United States to re-evaluate the clinical utility of pretreatment DPYD testing. If clinical utility has not been demonstrated, further dialogue is needed to clarify what additional evidence is needed and which of the available study designs, also described within this article, would be appropriate. Clinical guideline recommendations for pretreatment DPYD testing would increase clinical adoption and ensure that all patients receive maximally safe and effective FP treatment.

Proceedings From the ASCO/College of American Pathologists Immune Checkpoint Inhibitor Predictive Biomarker Summit

PURPOSE

Immune checkpoint inhibition (ICI) therapy represents one of the great advances in the field of oncology, highlighted by the Nobel Prize in 2018. Multiple predictive biomarkers for ICI benefit have been proposed. These include assessment of programmed death ligand-1 expression by immunohistochemistry, and determination of mutational genotype (microsatellite instability or mismatch repair deficiency or tumor mutational burden) as a reflection of neoantigen expression. However, deployment of these assays has been challenging for oncologists and pathologists alike.

METHODS

To address these issues, ASCO and the College of American Pathologists convened a virtual Predictive Factor Summit from September 14 to 15, 2021. Representatives from the academic community, US Food and Drug Administration, Centers for Medicare and Medicaid Services, National Institutes of Health, health insurance organizations, pharmaceutical companies, in vitro diagnostics manufacturers, and patient advocate organizations presented state-of-the-art predictive factors for ICI, associated problems, and possible solutions.

RESULTS

The Summit provided an overview of the challenges and opportunities for improvement in assay execution, interpretation, and clinical applications of programmed death ligand-1, microsatellite instability-high or mismatch repair deficient, and tumor mutational burden-high for ICI therapies, as well as issues related to regulation, reimbursement, and next-generation ICI biomarker development.

CONCLUSION

The Summit concluded with a plan to generate a joint ASCO/College of American Pathologists strategy for consideration of future research in each of these areas to improve tumor biomarker tests for ICI therapy.

MALDI-TOF MS: application in diagnosis, dereplication, biomolecule profiling and microbial ecology

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has revolutionized scientific research over the past few decades and has provided a unique platform in ongoing technological developments. Undoubtedly, there has been a bloom chiefly in the field of biological sciences with this emerging technology, and has enabled researchers to generate critical data in the field of disease diagnoses, drug development, dereplication. It has received well acceptance in the field of microbial identification even at strain level, as well as diversified field like biomolecule profiling (proteomics and lipidomics) has evolved tremendously. Additionally, this approach has received a lot more attention over conventional technologies due to its high throughput, speed, and cost effectiveness. This review aims to provide a detailed insight regarding the application of MALDI-TOF MS in the context of medicine, biomolecule profiling, dereplication, and microbial ecology. In general, the expansion in the application of this technology and new advancements it has made in the field of science and technology has been highlighted.

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.

Trials in the Key of G: Building Level 1 Evidence on the Real-world Effectiveness of Prostate Biomarkers

A number of genomic classifiers are available to aid in shared decision-making for men with localized prostate cancer; however, there is no high-level evidence assessing their clinical utility. The two randomized controlled trials in this report prospectively evaluate the use of gene expression classifier testing at the time of cancer diagnosis and after surgical treatment.

Circulating tumour cell gene expression and chemosensitivity analyses: predictive accuracy for response to multidisciplinary treatment of patients with unresectable refractory recurrent rectal cancer or unresectable refractory colorectal cancer liver metastases

BACKGROUND

Patients with unresectable recurrent rectal cancer (RRC) or colorectal cancer (CRC) with liver metastases, refractory to at least two lines of traditional systemic therapy, may receive third line intraarterial chemotherapy (IC) and targeted therapy (TT) using drugs selected by chemosensitivity and tumor gene expression analyses of liquid biopsy-derived circulating tumor cells (CTCs).

METHODS

In this retrospective study, 36 patients with refractory unresectable RRC or refractory unresectable CRC liver metastases were submitted for IC and TT with agents selected by precision oncotherapy chemosensitivity assays performed on liquid biopsy-derived CTCs, transiently cultured in vitro, and by tumor gene expression in the same CTC population, as a ratio to tumor gene expression in peripheral mononuclear blood cells (PMBCs) from the same individual. The endpoint was to evaluate the predictive accuracy of a specific liquid biopsy precision oncotherapy CTC purification and in vitro culture methodology for a positive RECIST 1.1 response to the therapy selected.

RESULTS

Our analyses resulted in evaluations of 94.12% (95% CI 0.71-0.99) for sensitivity, 5.26% (95% CI 0.01-0.26) for specificity, a predictive value of 47.06% (95% CI 0.29-0.65) for a positive response, a predictive value of 50% (95% CI 0.01-0.98) for a negative response, with an overall calculated predictive accuracy of 47.22% (95% CI 0.30-0.64).

CONCLUSIONS

This is the first reported estimation of predictive accuracy derived from combining chemosensitivity and tumor gene expression analyses on liquid biopsy-derived CTCs, transiently cultured in vitro which, despite limitations, represents a baseline and benchmark which we envisage will be improve upon by methodological and technological advances and future clinical trials.

Microbiome in cancer: Role in carcinogenesis and impact in therapeutic strategies

Cancer is the world's second-leading cause of death, and the involvement of microbes in a range of diseases, including cancer, is well established. The gut microbiota is known to play an important role in the host's health and physiology. The gut microbiota and its metabolites may activate immunological and cellular pathways that kill invading pathogens and initiate a cancer-fighting immune response. Cancer is a multiplex illness, characterized by the persistence of several genetic and physiological anomalies in malignant tissue, complicating disease therapy and control. Humans have coevolved with a complex bacterial, fungal, and viral microbiome over millions of years. Specific long-known epidemiological links between certain bacteria and cancer have recently been grasped at the molecular level. Similarly, advances in next-generation sequencing technology have enabled detailed research of microbiomes, such as the human gut microbiome, allowing for the finding of taxonomic and metabolomic linkages between the microbiome and cancer. These investigations have found causative pathways for both microorganisms within tumors and bacteria in various host habitats far from tumors using direct and immunological procedures. Anticancer diagnostic and therapeutic solutions could be developed using this review to tackle the threat of anti-cancer medication resistance as well through the wide-ranging involvement of the microbiota in regulating host metabolic and immunological homeostasis. We reviewed the significance of gut microbiota in cancer initiation as well as cancer prevention. We look at certain microorganisms that may play a role in the development of cancer. Several bacteria with probiotic qualities may be employed as bio-therapeutic agents to re-establish the microbial population and trigger a strong immune response to remove malignancies, and further study into this should be conducted.

Development and comparison of 1-year survival models in patients with primary bone sarcomas: External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model

Background:

Bone sarcomas often present late with advanced stage at diagnosis and an according, varying short-term survival. In 2016, Nandra et al. generated a Bayesian belief network model for 1-year survival in patients with bone sarcomas. The purpose of this study is: (1) to externally validate the prior 1-year Bayesian belief network prediction model for survival in patients with bone sarcomas and (2) to develop a gradient boosting machine model using Nandra et al.’s cohort and evaluate whether the gradient boosting machine model outperforms the Bayesian belief network model when externally validated in an independent Danish population cohort.

Material and Methods:

The training cohort comprised 3493 patients newly diagnosed with bone sarcoma from the institutional prospectively maintained database at the Royal Orthopaedic Hospital, Birmingham, UK. The validation cohort comprised 771 patients with newly diagnosed bone sarcoma included from the Danish Sarcoma Registry during January 1, 2000–June 22, 2016. We performed area under receiver operator characteristic curve analysis, Brier score and decision curve analysis to evaluate the predictive performance of the models.

Results:

External validation of the Bayesian belief network 1-year prediction model demonstrated an area under receiver operator characteristic curve of 68% (95% confidence interval, 62%-73%). Area under receiver operator characteristic curve of the gradient boosting machine model demonstrated: 75% (95% confidence interval: 70%-80%), overall model performance by the Brier score was 0.09 (95% confidence interval: 0.077–0.11) and decision curve analysis demonstrated a positive net benefit for threshold probabilities above 0.5. External validation of the developed gradient boosting machine model demonstrated an area under receiver operator characteristic curve of 63% (95% confidence interval: 57%-68%), and the Brier score was 0.14 (95% confidence interval: 0.12–0.16).

Conclusion:

External validation of the 1-year Bayesian belief network survival model yielded a poor outcome based on a Danish population cohort validation. We successfully developed a gradient boosting machine 1-year survival model. The gradient boosting machine did not outperform the Bayesian belief network model based on external validation in a Danish population-based cohort.

Two-Stage Adaptive Design for Prognostic Biomarker Signatures with a Survival Endpoint

Cancer biomarker discoveries typically involve utilizing patient specimens. In practice, there is often strong desire to preserve high quality biospecimens for studies that are most likely to yield useful information. Previously, we proposed a two-stage adaptive design for binary endpoints which terminates the biomarker study in a futility interim if the model performance is unsatisfactory. In this work, we extend the two-stage design framework to accommodate time-to-event endpoints. The first stage of the procedure involves testing whether the measure of discrimination for survival models (C-index) exceeds a pre-specified threshold. We describe the computation of cross-validated C-index and evaluation of the statistical significance using re-sampling techniques. The second stage involves an independent model validation. Our simulation studies show that under the null hypothesis, the proposed design maintains type I error at the nominal level and has high probabilities of terminating the study early. Under the alternative hypothesis, power of the design is a function of the true event proportion, the sample size, and the targeted improvement in the discriminant measure. We apply the method to design of a prognostic biomarker study in patients with triple-negative breast cancer. Some practical aspects of the proposed method are discussed.

Tumour and normal tissue radiosensitivity

The place of personalized treatments is highly increasing in medical and radiation oncology. During the last decades, a huge number of assays have been developed to predict responses of normal tissues and tumours. These tests have not yet been included into daily clinical practice but the recent developments of radiation oncology are paving the way of personalized strategies including the risk of tumour recurrence and normal tissue reactions. Concerning tumor radiosensitivity prediction, no test are currently used, even if the radiosensitivity index and the genome-based model for adjusting radiotherapy dose assays seem the most promising with level II of evidence. Commercial developments are under progress. Concerning normal tissue radiosensitivity prediction, single nucleotide polymorphims of prostate cancer patients and radiation-induced CD8 T-lymphocyte apoptosis breast and prostate assays are of level I of evidence. They can be proposed before the beginning of radiotherapy in order to propose personalized treatments according to both risks of tumour and normal tissue radiosensitivity. Commercial developments are also under way.

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.

Randomized test-treatment studies with an outlook on adaptive designs

Background

Diagnostic accuracy studies aim to examine the diagnostic accuracy of a new experimental test, but do not address the actual merit of the resulting diagnostic information to a patient in clinical practice. In order to assess the impact of diagnostic information on subsequent treatment strategies regarding patient-relevant outcomes, randomized test-treatment studies were introduced. Various designs for randomized test-treatment studies, including an evaluation of biomarkers as part of randomized biomarker-guided treatment studies, are suggested in the literature, but the nomenclature is not consistent.

Methods

The aim was to provide a clear description of the different study designs within a pre-specified framework, considering their underlying assumptions, advantages as well as limitations and derivation of effect sizes required for sample size calculations. Furthermore, an outlook on adaptive designs within randomized test-treatment studies is given.

Results

The need to integrate adaptive design procedures in randomized test-treatment studies is apparent. The derivation of effect sizes induces that sample size calculation will always be based on rather vague assumptions resulting in over- or underpowered study results. Therefore, it might be advantageous to conduct a sample size re-estimation based on a nuisance parameter during the ongoing trial.

Conclusions

Due to their increased complexity, compared to common treatment trials, the implementation of randomized test-treatment studies poses practical challenges including a huge uncertainty regarding study parameters like the expected outcome in specific subgroups or disease prevalence which might affect the sample size calculation. Since research on adaptive designs within randomized test-treatment studies is limited so far, further research is recommended.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12874-021-01293-y).

Defining Clinical Utility of Tumor Biomarker Tests: A Clinician's Viewpoint

Tumor biomarker tests (TBTs) are used to guide therapeutic strategies for patients with cancer. However, the regulatory environment for TBTs in the United States is inconsistent and, in general, TBTs are poorly valued. The National Academy of Medicine has recommended that TBTs should not be used in general practice until they are shown to have analytical validity and clinical utility. The latter term, first coined by the Evaluation of Genomic Applications in Practice and Prevention Initiative, has been widely stated but is indeterminately defined. In considering whether a TBT has clinical utility, several factors need to be considered: (1) What is the intended use of the TBT? (2) What are the end points that are used to determine clinical utility? (3) How substantial does the difference in end points between groups defined by the TBT need to be to determine therapeutic strategies? (4) What is the risk tolerance of the stakeholders? and (5) Who are the stakeholders that make the decision? For all these factors, the data used to consider clinical utility must be derived from level I evidence studies. In conclusion, there is no strict definition of clinical utility for a TBT. However, consideration of these factors will lead to more objective conclusions. Doing so will facilitate value-based decisions regarding whether a TBT should be used to guide patient care.

ctDNA and Adjuvant Therapy for Colorectal Cancer: Time to Re-Invent Our Treatment Paradigm

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. While there have been significant developments in the treatments for patients with metastatic CRC in recent years, improving outcomes in the adjuvant setting has been more challenging. Recent technological advances in circulating tumour DNA (ctDNA) assay with the ability to detect minimal residual disease (MRD) after curative intent surgery will fundamentally change how we assess recurrence risk and conduct adjuvant trials. Studies in non-metastatic CRC have now demonstrated the prognostic impact of ctDNA analysis after curative intent surgery over and above current standard of care clinicopathological criteria. This ability of ctDNA analysis to stratify patients into low- and very-high-risk groups provides a window of opportunity to personalise adjuvant treatment where escalation/de-escalation of adjuvant systemic therapy could potentially increase cure rates and also reduce treatment-related physical and financial toxicity. Emerging data suggest that conversion of ctDNA from detectable to undetectable after adjuvant chemotherapy may reflect treatment efficacy. This real-time assessment of treatment benefit could be used as a surrogate endpoint for adjuvant novel drug development. Several ctDNA-based randomized adjuvant trials are ongoing internationally to confirm the clinical utility of ctDNA in colorectal cancer.

High-Dimensional Precision Medicine From Patient-Derived Xenografts

The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.

Oral potentially malignant disorders: A scoping review of prognostic biomarkers

This scoping review aimed to map evidence regarding biomarkers for malignant transformation of oral potentially malignant disorders (OPMD). Seventy-three longitudinal studies investigating prognostic biomarkers for OPMD malignant transformation were included, encompassing 5612 disorders and 108 biomarkers, of which 72 were investigated by immunohistochemistry. Most biomarkers were assessed in one or two studies, while five (p53, Ki-67, podoplanin, p16, and DNA ploidy) were analyzed in five or more studies. All studies investigating podoplanin (n = 8) reported a significant association between positive/high immunoexpression and malignant transformation. Similarly, all studies assessing DNA ploidy (n = 5) found that aneuploidy or gross genomic aberrations were significantly associated with malignant transformation. Included studies often presented mixed data from different OPMD subtypes, inadequate description of population characteristics, and lack of adjusted analysis for confounding factors. One hundred and eight biomarkers were identified and, from these, podoplanin immunoexpression and DNA ploidy were considered promising candidates for future long-term clinical research.

Biomarker development for axial spondyloarthritis

The term axial spondyloarthritis (axSpA) encompasses a heterogeneous group of diseases that have variable presentations, extra-articular manifestations and clinical outcomes, and that will respond differently to treatments. The prototypical type of axSpA, ankylosing spondylitis, is thought to be caused by interaction between the genetically primed host immune system and gut microbiota. Currently used biomarkers such as HLA-B27 status, C-reactive protein and erythrocyte sedimentation rate have, at best, moderate diagnostic and predictive value. Improved biomarkers are needed for axSpA to assist with early diagnosis and to better predict treatment responses and long-term outcomes. Advances in a range of 'omics' technologies and statistical approaches, including genomics approaches (such as polygenic risk scores), microbiome profiling and, potentially, transcriptomic, proteomic and metabolomic profiling, are making it possible for more informative biomarker sets to be developed for use in such clinical applications. Future developments in this field will probably involve combinations of biomarkers that require novel statistical approaches to analyse and to produce easy to interpret metrics for clinical application. Large publicly available datasets from well-characterized case-cohort studies that use extensive biological sampling, particularly focusing on early disease and responses to medications, are required to establish successful biomarker discovery and validation programmes.

The path to a better biomarker: application of a risk management framework for the implementation of PD-L1 and TILs as immuno-oncology biomarkers in breast cancer clinical trials and daily practice

Immune checkpoint inhibitor therapies targeting PD-1/PD-L1 are now the standard of care in oncology across several hematologic and solid tumor types, including triple negative breast cancer (TNBC). Patients with metastatic or locally advanced TNBC with PD-L1 expression on immune cells occupying ≥1% of tumor area demonstrated survival benefit with the addition of atezolizumab to nab-paclitaxel. However, concerns regarding variability between immunohistochemical PD-L1 assay performance and inter-reader reproducibility have been raised. High tumor-infiltrating lymphocytes (TILs) have also been associated with response to PD-1/PD-L1 inhibitors in patients with breast cancer (BC). TILs can be easily assessed on hematoxylin and eosin-stained slides and have shown reliable inter-reader reproducibility. As an established prognostic factor in early stage TNBC, TILs are soon anticipated to be reported in daily practice in many pathology laboratories worldwide. Because TILs and PD-L1 are parts of an immunological spectrum in BC, we propose the systematic implementation of combined PD-L1 and TIL analyses as a more comprehensive immuno-oncological biomarker for patient selection for PD-1/PD-L1 inhibition-based therapy in patients with BC. Although practical and regulatory considerations differ by jurisdiction, the pathology community has the responsibility to patients to implement assays that lead to optimal patient selection. We propose herewith a risk-management framework that may help mitigate the risks of suboptimal patient selection for immuno-therapeutic approaches in clinical trials and daily practice based on combined TILs/PD-L1 assessment in BC. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Natural agents inhibit colon cancer cell proliferation and alter microbial diversity in mice

The current study was undertaken to investigate the effect of differentially formulated polyphenolic compound Essential Turmeric Oil-Curcumin (ETO-Cur), and Tocotrienol-rich fraction (TRF) of vitamin E isomers on colorectal cancer (CRC) cells that produce aggressive tumors. Combinations of ETO-Cur and TRF were used to determine the combinatorial effects of ETO-Cur and TRF-mediated inhibition of growth of CRC cells in vitro and HCT-116 cells xenograft in SCID mice. 16S rRNA gene sequence profiling was performed to determine the outcome of gut microbial communities in mice feces between control and ETO-Cur-TRF groups. Bacterial identifications were validated by performing SYBR-based Real Time (RT) PCR. For metagenomics analysis to characterize the microbial communities, multiple software/tools were used, including Quantitative Insights into Microbial Ecology (QIIME) processing tool. We found ETO-Cur and TRF to synergize and that the combination of ETO-Cur-TRF significantly inhibited growth of HCT-116 xenografts in SCID mice. This was associated with a marked alteration in microbial communities and increased microbial OTU (operation taxonomic unit) number. The relative abundance of taxa was increased and the level of microbial diversity after 34 days of combinatorial treatment was found to be 44% higher over the control. Shifting of microbial family composition was observed in ETO-Cur-TRF treated mice as evidenced by marked reductions in Bacteroidaceae, Ruminococcaceae, Clostridiales, Firmicutes and Parabacteroids families, compared to controls. Interestingly, during the inhibition of tumor growth in ETO-Cur treated mice, probiotic Lactobacillaceae and Bifidobacteriaceae were increased by 20-fold and 6-fold, respectively. The relative abundance of anti-inflammatory Clostridium XIVa was also increased in ETO-Cur-TRF treated mice when compared with the control. Our data suggest that ETO-Cur-TRF show synergistic effects in inhibiting colorectal cancer cell proliferation in vitro and in mouse xenografts in vivo, and might induce changes in microbial diversity in mice.

GRADE guidelines: 21 part 1. Study design, risk of bias, and indirectness in rating the certainty across a body of evidence for test accuracy

OBJECTIVES

This article provides updated GRADE guidance about how authors of systematic reviews and health technology assessments and guideline developers can assess the results and the certainty of evidence (also known as quality of the evidence or confidence in the estimates) of a body of evidence addressing test accuracy (TA).

STUDY DESIGN AND SETTING

We present an overview of the GRADE approach and guidance for rating certainty in TA in clinical and public health and review the presentation of results of a body of evidence regarding tests. Part 1 of the two parts in this 21st guidance article about how to apply GRADE focuses on understanding study design issues in test accuracy, provide an overview of the domains, and describe risk of bias and indirectness specifically.

RESULTS

Supplemented by practical examples, we describe how raters of the evidence using GRADE can evaluate study designs focusing on tests and how they apply the GRADE domains risk of bias and indirectness to a body of evidence of TA studies.

CONCLUSION

Rating the certainty of a body of evidence using GRADE in Cochrane and other reviews and World Health Organization and other guidelines dealing with in TA studies helped refining our approach. The resulting guidance will help applying GRADE successfully for questions and recommendations focusing on tests.

Optimal Decision Criteria for the Study Design and Sample Size of a Biomarker-Driven Phase III Trial

BACKGROUND

The design and sample size of a phase III study for new medical technologies were historically determined within the framework of frequentist hypothesis testing. Recently, drug development using predictive biomarkers, which can predict efficacy based on the status of biomarkers, has attracted attention, and various study designs using predictive biomarkers have been suggested. Additionally, when choosing a study design, considering economic factors, such as the risk of development, expected revenue, and cost, is important.

METHODS

Here, we propose a method to determine the optimal phase III design and sample size and judge whether the phase III study will be conducted using the expected net present value (eNPV). The eNPV is defined using the probability of success of the study calculated based on historical data, the revenue that will be obtained after the success of the phase III study, and the cost of the study. Decision procedures of the optimal phase III design and sample size considering historical data obtained up to the start of the phase III study were considered using numerical examples.

RESULTS

Based on the numerical examples, the optimal study design and sample size depend on the mean treatment effect in the biomarker-positive and biomarker-negative populations obtained from historical data, the between-trial variance of response, the prevalence of the biomarker-positive population, and the threshold value of probability of success required to go to phase III study.

CONCLUSIONS

Thus, the design and sample size of a biomarker-driven phase III study can be appropriately determined based on the eNPV.

Noncoding RNAs and Liquid Biopsy in Lung Cancer: A Literature Review

Lung cancer represents a genetically heterogeneous disease with low survival rates. Recent data have evidenced key roles of noncoding RNAs in lung cancer initiation and progression. These functional RNA molecules that can act as both oncogenes and tumor suppressors may become future biomarkers and more efficient therapeutic targets. In the precision medicine era, circulating nucleic acids have the potential to reshape the management and prognosis of cancer patients. Detecting genomic alterations and level variations of circulating nucleic acids in liquid biopsy samples represents a noninvasive method for portraying tumor burden. Research is currently trying to validate the potential role of liquid biopsy in lung cancer screening, prognosis, monitoring of disease progression, and treatment response. However, this method requires complex detection assays, and implementation of plasma genotyping in clinical practice continues to be hindered by discrepancies that arise when compared to tissue genotyping. Understanding the genomic landscape of lung cancer is essential in order to provide useful and innovative research in the age of patient-tailored therapy. In this landscape, the noncoding RNAs play a crucial role due to their target genes that dramatically influence the tumor microenvironment and the response to therapy. This article addresses present and future possible roles of liquid biopsy in lung cancer. It also discusses how the complex role of noncoding RNAs in lung tumorigenesis could influence the management of this pathology.

Designing precision medicine trials to yield a greater population impact

Traditionally, a clinical trial is conducted comparing treatment to standard care for all patients. However, it could be inefficient given patients' heterogeneous responses to treatments, and rapid advances in the molecular understanding of diseases have made biomarker-based clinical trials increasingly popular. We propose a new targeted clinical trial design, termed as Max-Impact design, which selects the appropriate subpopulation for a clinical trial and aims to optimize population impact once the trial is completed. The proposed design not only gains insights on the patients who would be included in the trial but also considers the benefit to the excluded patients. We develop novel algorithms to construct enrollment rules for optimizing population impact, which are fairly general and can be applied to various types of outcomes. Simulation studies and a data example from the SWOG Cancer Research Network demonstrate the competitive performance of our proposed method compared to traditional untargeted and targeted designs.

Phase III Precision Medicine Clinical Trial Designs That Integrate Treatment and Biomarker Evaluation

Recent advances in biotechnology and cancer genomics have afforded enormous opportunities for development of more effective anticancer therapies. A key thrust of this modern drug development paradigm is successful identification of predictive biomarkers that can distinguish patients who might be sensitive to new targeted therapies. To respond to this challenge, a number of phase III cancer trial designs integrating biomarker-based objectives have been proposed and implemented in oncology drug development. In this article, we provide an updated review of commonly used biomarker-based randomized clinical trial designs, with a particular focus on design efficiency. When the efficacy of a new therapy may be limited to a biomarker-defined subgroup, the choice of an appropriate randomized clinical trial design should be guided by the strength of the biomarker's credentials. If compelling evidence indicates that a targeted therapy is beneficial only in a particular biomarker-defined subgroup, an enrichment design should be used. If there is strong evidence that the treatment is likely to be more beneficial in the biomarker-positive patients but a meaningful benefit is also possible in the biomarker-negative patients, then a properly powered biomarker-stratified design (eg, a subgroup-specific or Marker Sequential Test strategy) would provide the most rigorous determination of the sensitive populations. If the evidence supporting the predictive value of the biomarker is weak and the treatment is expected to work in the overall population, then a fallback design could be used. Careful selection of an appropriate phase III design strategy that integrates evaluation of a new anticancer therapy and its companion diagnostic is critical to the success of precision medicine in oncology.

IHC Color Histograms for Unsupervised Ki67 Proliferation Index Calculation

Automated image analysis tools for Ki67 breast cancer digital pathology images would have significant value if integrated into diagnostic pathology workflows. Such tools would reduce the workload of pathologists, while improving efficiency, and accuracy. Developing tools that are robust and reliable to multicentre data is challenging, however, differences in staining protocols, digitization equipment, staining compounds, and slide preparation can create variabilities in image quality and color across digital pathology datasets. In this work, a novel unsupervised color separation framework based on the IHC color histogram (IHCCH) is proposed for the robust analysis of Ki67 and hematoxylin stained images in multicentre datasets. An "overstaining" threshold is implemented to adjust for background overstaining, and an automated nuclei radius estimator is designed to improve nuclei detection. Proliferation index and F1 scores were compared between the proposed method and manually labeled ground truth data for 30 TMA cores that have ground truths for Ki67+ and Ki67- nuclei. The method accurately quantified the PI over the dataset, with an average proliferation index difference of 3.25%. To ensure the method generalizes to new, diverse datasets, 50 Ki67 TMAs from the Protein Atlas were used to test the validated approach. As the ground truth for this dataset is PI ranges, the automated result was compared to the PI range. The proposed method correctly classified 74 out of 80 TMA images, resulting in a 92.5% accuracy. In addition to these validations experiments, performance was compared to two color-deconvolution based methods, and to six machine learning classifiers. In all cases, the proposed work maintained more consistent (reproducible) results, and higher PI quantification accuracy.

[Predictive assays for responses of tumors and normal tissues in radiation oncology]

The impact of curative radiotherapy depends mainly on the total dose delivered homogenously in the target volume. Tumor sensitivity to radiotherapy may be particularly inconstant depending on location, histology, somatic genetic parameters and the capacity of the immune system to infiltrate the tumor. In addition, the dose delivered to the surrounding healthy tissues may reduce the therapeutic ratio of many radiation treatments. In a same population treated in one center with the same technique, it appears that individual radiosensitivity clearly exists, namely in terms of late side effects that are in principle non-reversible. This review details the different radiobiological approaches that have been developed to better predict the tumor response but also the radiation-induced late effects.

A Bayesian adaptive marker-stratified design for molecularly targeted agents with customized hierarchical modeling

It is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker-stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Under the Bayesian framework, the beta-binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis. However, this conventional model ignores the fact that the biomarker used in the MSD is, in general, predictive only for the MTA. The response rates for the standard treatment can be approximately consistent across different subgroups stratified by the biomarker. In this paper, we proposed a Bayesian hierarchical model incorporating this biomarker information into consideration. The proposed model uses a hierarchical prior to borrow strength across different subgroups of patients receiving the standard treatment and, therefore, improve the efficiency of the design. Prior informativeness is determined by solving a "customized" equation reflecting the physician's professional opinion. We developed a Bayesian adaptive design based on the proposed hierarchical model to guide the treatment allocation and test the subgroup treatment effect as well as the predictive marker effect. Simulation studies and a real trial application demonstrate that the proposed design yields desirable operating characteristics and outperforms the existing designs.

Molecular biomarkers in bladder preservation therapy for muscle-invasive bladder cancer

Although muscle-invasive bladder cancer is commonly treated with radical cystectomy, a standard alternative is bladder preservation therapy, consisting of maximum transurethral bladder tumour resection followed by radiotherapy with concurrent chemotherapy. Although no successfully completed randomised comparisons are available, the two treatment paradigms seem to have similar long-term outcomes; however, clinicopathologic parameters can be insufficient to provide clear guidance in the selection of one treatment over the other. Recent advances in the molecular understanding of bladder cancer have led to the identification of new predictive biomarkers that ultimately might help guide the tailored selection of therapy on the basis of the intrinsic biology of the tumour. In this Review, we discuss the existing evidence for molecular alterations and genomic signatures as prognostic or predictive biomarkers for bladder preservation therapy. If validated in prospective clinical trials, such biomarkers could enable the identification of subgroups of patients who are more likely to benefit from one treatment over another, and guide the use of combination therapies that include other modalities, such as immunotherapy, which might act synergistically with radiotherapy.

Pharmaceutical Advances and Proteomics Researches

Proteomics enables understanding the composition, structure, function and interactions of the entire protein complement of a cell, a tissue, or an organism under exactly defined conditions. Some factors such as stress or drug effects will change the protein pattern and cause the present or absence of a protein or gradual variation in abundances. The aim of this study is to explore relationship between proteomics application and drug discovery. "proteomics", "Application", and "pharmacology were the main keywords that were searched in PubMed (PubMed Central), Web of Science, and Google Scholar. The titles that were stablished by 2019, were studied and after study of the appreciated abstracts, the full texts of the 118 favor documents were extracted. Changes in the proteome provide a snapshot of the cell activities and physiological processes. Proteomics shows the observed protein changes to the causal effects and generate a complete three-dimensional map of the cell indicating their exact location. Proteomics is used in different biological fields and is applied in medicine, agriculture, food microbiology, industry, and pharmacy and drug discovery. Biomarker discovery, follow up of drug effect on the patients, and in vitro and in vivo proteomic investigation about the drug treated subjects implies close relationship between proteomics advances and application and drug discovery and development. This review overviews and summarizes the applications of proteomics especially in pharmacology and drug discovery.

Auxiliary variable-enriched biomarker-stratified design

Clinical trials in the era of precision medicine require assessment of biomarkers to identify appropriate subgroups of patients for targeted therapy. In a biomarker-stratified design (BSD), biomarkers are measured on all patients and used as stratification variables. However, such a trial can be both inefficient and costly, especially when the prevalence of the subgroup of primary interest is low and the cost of assessing the biomarkers is high. Efficiency can be improved and costs reduced by using enriched biomarker-stratified designs, in which patients of primary interest, typically the biomarker-positive patients, are oversampled. We consider a special type of enrichment design, an auxiliary variable-enriched design (AEBSD), in which enrichment is based on some inexpensive auxiliary variable that is positively correlated with the true biomarker. The proposed AEBSD reduces the total cost of the trial compared with a standard BSD when the prevalence rate of true biomarker positivity is small and the positive predictive value (PPV) of the auxiliary biomarker is larger than the prevalence rate. In addition, for an AEBSD, we can immediately randomize the patients selected in the screening process without waiting for the result of the true biomarker test, reducing the treatment waiting time. We propose an adaptive Bayesian method to adjust the assumed PPV while the trial is ongoing. Numerical studies and an example illustrate the approach. An R package is available.