Guidelines for the development and incorporation of biomarker studies in early clinical trials of novel agents

Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors

Precision medicine, characterized by the personalized integration of a patient's genetic blueprint and clinical history, represents a dynamic paradigm in healthcare evolution. The emerging field of personalized anesthesia is at the intersection of genetics and anesthesiology, where anesthetic care will be tailored to an individual's genetic make-up, comorbidities and patient-specific factors. Genomics and biomarkers can provide more accurate anesthetic protocols, while artificial intelligence can simplify anesthetic procedures and reduce anesthetic risks, and real-time monitoring tools can improve perioperative safety and efficacy. The aim of this paper is to present and summarize the applications of these related fields in anesthesiology by reviewing them, exploring the potential of advanced technologies in the implementation and development of personalized anesthesia, realizing the future integration of new technologies into clinical practice, and promoting multidisciplinary collaboration between anesthesiology and disciplines such as genomics and artificial intelligence.

Understanding the mechanistic pathways and clinical aspects associated with protein and gene based biomarkers in breast cancer

Cancer is one of the most widespread and severe diseases with a huge mortality rate. In recent years, the second-leading mortality rate of any cancer globally has been breast cancer, which is one of the most common and deadly cancers found in women. Detecting breast cancer in its initial stages simplifies treatment, decreases death risk, and recovers survival rates for patients. The death rate for breast cancer has risen to 0.024 % in some regions. Sensitive and accurate technologies are required for the preclinical detection of BC at an initial stage. Biomarkers play a very crucial role in the early identification as well as diagnosis of women with breast cancer. Currently, a wide variety of cancer biomarkers have been discovered for the diagnosis of cancer. For the identification of these biomarkers from serum or other body fluids at physiological amounts, many detection methods have been developed. In the case of breast cancer, biomarkers are especially helpful in discovering those who are more likely to develop the disease, determining prognosis at the time of initial diagnosis and choosing the best systemic therapy. In this study we have compiled various clinical aspects and signaling pathways associated with protein-based biomarkers and gene-based biomarkers.

The landscape of lncRNAs in gastric cancer: from molecular mechanisms to potential clinical applications

Gastric cancer (GC) is a highly prevalent and deadly malignant neoplasm worldwide. Currently, long non-coding RNAs (lncRNAs) have recently been identified as crucial regulators implicated in GC development and progression. Dysregulated expression of lncRNAs is commonly associated with enhanced tumor migration, invasiveness, and therapy resistance, highlighting their potential as promising targets for clinical applications. This review offers a comprehensive historical overview of lncRNAs in GC, describes the molecular mechanisms, and discusses the prospects and challenges of establishing lncRNAs as precision biomarkers.

Design a Clinical Research Protocol: Influence of Real-World Setting

The design of a clinical research protocol to evaluate new therapies, devices, patient quality of life, and medical practices from scratch is probably one of the greatest challenges for the majority of novice researchers. This is especially true since a high-quality methodology is required to achieve success and effectiveness in academic and hospital research centers. This review discusses the concrete steps and necessary guidelines needed to create and structure a research protocol. Along with the methodology, some administrative challenges (ethics, regulatory and people-management barriers) and possible time-saving recommendations (standardized procedures, collaborative training, and centralization) are discussed.

Novel Biomarkers of Gastric Cancer: Current Research and Future Perspectives

Gastric cancer is a heterogeneous disease with diverse histological and genomic subtypes, making it difficult to demonstrate treatment efficacy in clinical trials. However, recent efforts have been made to identify molecular biomarkers with prognostic and predictive implications to better understand the broad heterogeneity of gastric cancer and develop effective targeted therapies for it. HER2 overexpression, HER2/neu amplification, MSI-H, and PD-L1+ are predictive biomarkers in gastric cancer, and a growing number of clinical trials based on novel biomarkers have demonstrated the efficacy of targeted therapies alone or in combination with conventional chemotherapy. Enrichment design clinical trials of targeted therapies against FGFR2b and claudin 18.2 have demonstrated efficacy in unresectable advanced gastric cancer. Nonetheless, it is essential to continuously validate promising molecular biomarkers and introduce them into clinical practice to optimize treatment selection and improve patient outcomes. In this review, we focused on established (PD-L1, HER2, MSI) and emerging biomarkers (FGFR2, CLDN18.2) in gastric cancer, their clinical significance, detection methods, limitations, and molecular agents that target these biomarkers.

Positional proteomics: is the technology ready to study clinical cohorts?

INTRODUCTION

Positional proteomics provides proteome-wide information on protein termini and their modifications, uniquely enabling unambiguous identification of site-specific, limited proteolysis. Such proteolytic cleavage irreversibly modifies protein sequences resulting in new proteoforms with distinct protease-generated neo-N and C-termini and altered localization and activity. Misregulated proteolysis is implicated in a wide variety of human diseases. Protein termini, therefore, constitute a huge, largely unexplored source of specific analytes that provides a deep view into the functional proteome and a treasure trove for biomarkers.

AREAS COVERED

We briefly review principal approaches to define protein termini and discuss recent advances in method development. We further highlight the potential of positional proteomics to identify and trace specific proteoforms, with a focus on proteolytic processes altered in disease. Lastly, we discuss current challenges and potential for applying positional proteomics in biomarker and pre-clinical research.

EXPERT OPINION

Recent developments in positional proteomics have provided significant advances in sensitivity and throughput. In-depth analysis of proteolytic processes in clinical cohorts thus appears feasible in the near future. We argue that this will provide insights into the functional state of the proteome and offer new opportunities to utilize proteolytic processes altered or targeted in disease as specific diagnostic, prognostic and companion biomarkers.

Lessons and Opportunities for Biomarker-Driven Radiation Personalization in Head and Neck Cancer

Head and neck cancer is notoriously challenging to treat in part because it constitutes an anatomically and biologically diverse group of cancers with heterogeneous prognoses. While treatment can be associated with significant late toxicities, recurrence is often difficult to salvage with poor survival rates and functional morbidity.^1,2 Thus, achieving tumor control and cure at the initial diagnosis is the highest priority. Given the differing outcome expectations (even within a specific sub-site like oropharyngeal carcinoma), there has been growing interest in personalizing treatment: de-escalation in selected cancers to decrease the risk of late toxicity without compromising oncologic outcomes, and intensification for more aggressive cancers to improve oncologic outcomes without causing undue toxicity. This risk stratification is increasingly accomplished using biomarkers, which can represent molecular, clinicopathologic, and/or radiologic data. In this review, we will focus on biomarker-driven radiotherapy dose personalization with emphasis on oropharyngeal and nasopharyngeal carcinoma. This radiation personalization is largely performed on the population level by identifying patients with good prognosis via traditional clinicopathologic factors, although there are emerging studies supporting inter-tumor and intra-tumor level personalization via imaging and molecular biomarkers.

Molecular and immune landscape of hepatocellular carcinoma to guide therapeutic decision-making

Liver cancer, primarily HCC, exhibits highly heterogeneous histological and molecular aberrations across tumors and within individual tumor nodules. Such intertumor and intratumor heterogeneities may lead to diversity in the natural history of disease progression and various clinical disparities across the patients. Recently developed multimodality, single-cell, and spatial omics profiling technologies have enabled interrogation of the intertumor/intratumor heterogeneity in the cancer cells and the tumor immune microenvironment. These features may influence the natural history and efficacy of emerging therapies targeting novel molecular and immune pathways, some of which had been deemed undruggable. Thus, comprehensive characterization of the heterogeneities at various levels may facilitate the discovery of biomarkers that enable personalized and rational treatment decisions, and optimize treatment efficacy while minimizing the risk of adverse effects. Such companion biomarkers will also refine HCC treatment algorithms across disease stages for cost-effective patient management by optimizing the allocation of limited medical resources. Despite this promise, the complexity of the intertumor/intratumor heterogeneity and ever-expanding inventory of therapeutic agents and regimens have made clinical evaluation and translation of biomarkers increasingly challenging. To address this issue, novel clinical trial designs have been proposed and incorporated into recent studies. In this review, we discuss the latest findings in the molecular and immune landscape of HCC for their potential and utility as biomarkers, the framework of evaluation and clinical application of predictive/prognostic biomarkers, and ongoing biomarker-guided therapeutic clinical trials. These new developments may revolutionize patient care and substantially impact the still dismal HCC mortality.

Identification of a Suitable Untargeted Agent for the Clinical Translation of ABY-029 Paired-Agent Imaging in Fluorescence-Guided Surgery

PURPOSE

Non-specific uptake and retention of molecular targeted agents and heterogeneous tissue optical properties diminish the ability to differentiate between tumor and normal tissues using molecular targeted fluorescent agents. Paired-agent imaging (PAI) can increase the diagnostic ability to detect tumor tissue by mitigating these non-specific effects and providing true molecular contrast by co-administration of an untargeted control imaging agent with a targeted agent. This study evaluates the suitability of available clinically translatable untargeted agents for the translation of PAI in fluorescence-guided surgery using an affibody-based targeted imaging agent (ABY-029).

EXPERIMENTAL

DESIGN: Three untargeted agents that fluoresce near 700 nm and exhibit good clinical safety profiles (methylene blue, IRDye 700DX, and IRDye 680LT) were tested in combination with the clinically tested IRDye 800CW-labeled anti-epidermal growth factor receptor (EGFR) affibody molecule, ABY-029 (eIND 122,681). Properties of the untargeted agent important for human use and integrity of PAI were tested: (1) plasma protein binding; (2) fluorescence signal linearity in in vitro whole blood dilution; (3) in vivo pharmacokinetic matching to targeted agent in negative control tissue; and (4) in vivo diagnostic accuracy of PAI vs single agent imaging (SAI) of ABY-029 alone in orthotopic oral head and neck squamous cell carcinomas.

RESULTS

IRDye 680LT outperformed IRDye 700DX and methylene blue with the highest signal linearity (R² = 0.9998 ± 0.0002, 0.9995 ± 0.0004, 0.91 ± 0.02, respectively), the highest fluorescence yield in whole blood at 1 μM (10^{4.42 ± 0.05}, 10^{3.68 ± 0.09}, 10^{1.9 ± 0.2}, respectively), and the most closely matched ABY-029 pharmacokinetics in EGFR-negative tissues (binding potential error percentage = 0.31% ± 0.37%, 10.25% ± 1.30%, and 8.10% ± 5.37%, respectively). The diagnostic ability of PAI with ABY-029 and IRDye 680LT outperformed conventional SAI with an area-under-the-receiver-operating-characteristic curve (AUC) value of 0.964 vs. 0.854, and 0.978 vs. 0.925 in the Odyssey scanning system and Pearl wide field imaging system, respectively.

CONCLUSION

PAI is a highly promising methodology for increasing detection of tumors in fluorescence-guided surgery. Although not yet clinically approved, IRDye 680LT demonstrates promise as an untargeted agent when paired with ABY-029. The clinical translation of PAI to maximize tumor excision, while minimizing normal tissue removal, could improve both patient survival and life quality.

The role of biomarkers in personalized immunotherapy

BACKGROUND

Immune checkpoint inhibitors have revolutionized cancer therapeutic paradigm and substantially improved the survival of patients with advanced malignancies. However, a significant limitation is the wide variability in clinical response.

MAIN TEXT

Several biomarkers have been evaluated in prior and ongoing clinical trials to investigate their prognostic and predictive role of patient response, nonetheless, most have not been comprehensively incorporated into clinical practice. We reviewed published data regarding biomarkers that have been approved by the United States Food and Drug Administration as well as experimental tissue and peripheral blood biomarkers currently under investigation. We further discuss the role of current biomarkers to predict response and response to immune checkpoint inhibitors and the promise of combination biomarker strategies. Finally, we discuss ideal biomarker characteristics, and novel platforms for clinical trial design including enrichment and stratification strategies, all of which are exciting and dynamic to advance the field of precision immuno-oncology.

CONCLUSION

Incorporation and standardization of strategies to guide selection of combination biomarker approaches will facilitate expansion of the clinical benefit of immune checkpoint inhibitor therapy to appropriate subsets of cancer patients.

T-Cell Heterogeneity in Baseline Tumor Samples: Implications for Early Clinical Trial Design and Analysis

Background

In early stage clinical trials, changes to levels of tumor infiltrating lymphocytes (TILs) in the tumor microenvironment (TME) are critical biomarkers of the mechanism of action of novel immunotherapies. However, baseline heterogeneity of tumor samples, both between and within patients, and the resultant impact on the validity of clinical trial data is not well defined. Here we identify and quantify the impact of baseline variables on the heterogeneity of FoxP3+ and proliferating CD8+ T-cells levels (MKi67+CD8A+) in the TME both between and within patients for the purpose of informing clinical trial design and analysis.

Methods

We compared levels of FoxP3+ and MKi67+CD8+ cell densities (counts/mm2) from >1000 baseline tumor samples from clinical trials and commercially available sources. Using multivariate hierarchical regression techniques, we investigated whether inter-person heterogeneity of activated or regulatory T-cells could be attributed to baseline characteristics including demographics, indication, lesion type, tissue of excision, biopsy method, prior cancer treatment, and tissue type i.e., "fresh" or "archival" status. We also sought to characterize within-patient heterogeneity by lesion type and tissue type.

Results

Prior cancer treatment with hormone therapy or chemotherapy that induces immunogenic cell death may alter the TME. Archival tissue is an unreliable substitute for fresh tissue for determining baseline TIL levels. Baseline and on treatment biopsies should be matched by lesion type to avoid bias.

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

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

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

High DND1 Level Indicates a Poor Prognostic Factor in Prostate Cancer

Background

Dead end 1 (DND1) plays a vital role during oncogenesis and cancer progression by regulating the mRNA content via competitive combination with miRNA, but what function it exerts in prostate cancer has been unclear. The purpose of this paper is to explore the correlation between DND1 expression levels and clinical characteristics in prostate cancer (PCa) patients.

Materials and Methods

To assess the expression of DND1 in tumor specimens compared with paired paracancerous tissues, the sample from 83 patients was analyzed by immunohistochemistry. The Cancer Genome Atlas (TCGA) database was used to verify our results. Subsequently, we statistically analyzed the relationship between DND1 expression and the clinical prognosis of PCa patients.

Results

Compared with paracancerous tissues, DND1 has a higher expression level in prostate cancer. The overexpression of DND1 in protein level was significantly associated with the higher clinical stage (P = 0.006), ISUP grading group (P < 0.001), seminal vesicle invasion (P = 0.006), and PSA density (P = 0.002). Furthermore, the overexpression of DND1 indicates a poor clinical prognosis in prostate cancer patients.

Conclusion

High-level expression of DND1 was associated with tumor progression and poor clinical prognosis. Hence, DND1 may become a potential prognostic biomarker for PCa.

A call to action: molecular pathology in Brazil

Background

Adoption of molecular pathology in Brazil is currently very limited. Of note, there are no programs for training new molecular pathologists in the country; thus, documents compiling nationally applicable information on molecular pathology are few.

Methods

A selected panel of Brazilian experts in fields related to molecular pathology were provided with a series of relevant questions to address prior to the multi-day conference. Within this conference, each narrative was discussed and edited by the entire group, through numerous drafts and rounds of discussion until a consensus was achieved.

Results

The panel proposes specific and realistic recommendations for implementing molecular pathology in cancer care in Brazil. In creating these recommendations, the authors strived to address all barriers to the widespread use and impediments to access mentioned previously within this manuscript.

Conclusion

This manuscript provides a review of molecular pathology principles as well as the current state of molecular pathology in Brazil. Additionally, the panel proposes practical and actionable recommendations for the implementation of molecular pathology throughout the country in order to increase awareness of the importance molecular pathology in Brazil.

Guidelines for cellular and molecular pathology content in clinical trial protocols: the SPIRIT-Path extension

The 2013 SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) Statement provides evidence-based recommendations for the minimum content to be included in a clinical trial protocol. Assessment of biospecimens is often required for trial eligibility or as part of an outcome evaluation, and precision molecular approaches are increasingly used in trial design. However, cellular and molecular pathology practices within trials have not been codified or formalised. We developed international consensus reporting guidelines for cellular and molecular pathology content in clinical trial protocols (the SPIRIT-Path extension) using an international Delphi process, which assesses candidate items generated from a previous systematic review, followed by an expert consensus meeting. 74 individuals from five continents responded, including clinicians, statisticians, laboratory scientists, patient advocates, funders, industry representatives, journal editors, and regulators. The SPIRIT-Path guidelines recommend 14 additional items (seven extensions to the SPIRIT checklist and seven elaborations) that should be addressed in trial protocols containing pathology content, alongside the SPIRIT 2013 Statement items. SPIRIT-Path recommends that protocols should document the individuals, processes, and standards for all cellular and molecular pathology components of the trial, including all stages of the specimen pathway and any digital pathology methods, with specific consideration of the value of trial data and biological tissues for additional translational studies.

Recommendations for cellular and molecular pathology input into clinical trials: a systematic review and meta-aggregation

The SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013 Statement was developed to provide guidance for inclusion of key methodological components in clinical trial protocols. However, these standards do not include guidance specific to pathology input in clinical trials. This systematic review aims to synthesise existing recommendations specific to pathology practice in clinical trials for implementation in trial protocol design. Articles were identified from database searches and deemed eligible for inclusion if they contained: (1) guidance and/or a checklist, which was (2) pathology-related, with (3) content relevant to clinical trial protocols or could influence a clinical trial protocol design from a pathology perspective and (4) were published in 1996 or later. The quality of individual papers was assessed using the AGREE-GRS (Appraisal of Guidelines for REsearch & Evaluation - Global Rating Scale) tool, and the confidence in cumulative evidence was evaluated using the GRADE-CERQual (Grading of Recommendations Assessment, Development and Evaluation-Confidence in Evidence from Reviews of Qualitative research) approach. Extracted recommendations were synthesised using the best fit framework method, which includes thematic analysis followed by a meta-aggregative approach to synthesis within the framework. Of the 10 184 records screened and 199 full-text articles reviewed, only 40 guidance resources met the eligibility criteria for inclusion. Recommendations extracted from 22 guidance documents were generalisable enough for data synthesis. Seven recommendation statements were synthesised as follows: (1) multidisciplinary collaboration in trial design with early involvement of pathologists, particularly with respect to the use of biospecimens and associated biomarker/analytical assays and in the evaluation of pathology-related parameters; (2) funding and training for personnel undertaking trial work; (3) selection of an accredited laboratory with suitable facilities to undertake scheduled work; (4) quality assurance of pathology-related parameters; (5) transparent reporting of pathology-related parameters; (6) policies regarding informatics and tracking biospecimens across trial sites; and (7) informed consent for specimen collection and retention for future research.

Does biomarker use in oncology improve clinical trial failure risk? A large-scale analysis

Purpose

To date there has not been an extensive analysis of the outcomes of biomarker use in oncology.

Methods

Data were pooled across four indications in oncology drawing upon trial outcomes from www.clinicaltrials.gov: breast cancer, non‐small cell lung cancer (NSCLC), melanoma and colorectal cancer from 1998 to 2017. We compared the likelihood drugs would progress through the stages of clinical trial testing to approval based on biomarker status. This was done with multi‐state Markov models, tools that describe the stochastic process in which subjects move among a finite number of states.

Results

Over 10000 trials were screened, which yielded 745 drugs. The inclusion of biomarker status as a covariate significantly improved the fit of the Markov model in describing the drug trajectories through clinical trial testing stages. Hazard ratios based on the Markov models revealed the likelihood of drug approval with biomarkers having nearly a fivefold increase for all indications combined. A 12, 8 and 7‐fold hazard ratio was observed for breast cancer, melanoma and NSCLC, respectively. Markov models with exploratory biomarkers outperformed Markov models with no biomarkers.

Conclusion

This is the first systematic statistical evidence that biomarkers clearly increase clinical trial success rates in three different indications in oncology. Also, exploratory biomarkers, long before they are properly validated, appear to improve success rates in oncology. This supports early and aggressive adoption of biomarkers in oncology clinical trials.

Adavosertib plus gemcitabine for platinum-resistant or platinum-refractory recurrent ovarian cancer: a double-blind, randomised, placebo-controlled, phase 2 trial

BACKGROUND

The Wee1 (WEE1hu) inhibitor adavosertib and gemcitabine have shown preclinical synergy and promising activity in early phase clinical trials. We aimed to determine the efficacy of this combination in patients with ovarian cancer.

METHODS

In this double-blind, randomised, placebo-controlled, phase 2 trial, women with measurable recurrent platinum-resistant or platinum-refractory high-grade serous ovarian cancer were recruited from 11 academic centres in the USA and Canada. Women were eligible if they were aged 18 years or older, had an Eastern Cooperative Oncology Group performance status of 0-2, a life expectancy of more than 3 months, and normal organ and marrow function. Women with ovarian cancer of non-high-grade serous histology were eligible for enrolment in a non-randomised exploratory cohort. Eligible participants with high-grade serous ovarian cancer were randomly assigned (2:1), using block randomisation (block size of three and six) and no stratification, to receive intravenous gemcitabine (1000 mg/m2 on days 1, 8, and 15) with either oral adavosertib (175 mg) or identical placebo once daily on days 1, 2, 8, 9, 15, and 16, in 28-day cycles until disease progression or unacceptable toxicity. Patients and the team caring for each patient were masked to treatment assignment. The primary endpoint was progression-free survival. The safety and efficacy analysis population comprised all patients who received at least one dose of treatment. The trial is registered with ClinicalTrials.gov, NCT02151292, and is closed to accrual.

FINDINGS

Between Sept 22, 2014, and May 30, 2018, 124 women were enrolled, of whom 99 had high-grade serous ovarian cancer and were randomly assigned to adavosertib plus gemcitabine (65 [66%]) or placebo plus gemcitabine (34 [34%]). 25 women with non-high-grade serous ovarian cancer were enrolled in the exploratory cohort. After randomisation, five patients with high-grade serous ovarian cancer were found to be ineligible (four in the experimental group and one in the control group) and did not receive treatment. Median age for all treated patients (n=119) was 62 years (IQR 54-67). Progression-free survival was longer with adavosertib plus gemcitabine (median 4·6 months [95% CI 3·6-6·4] with adavosertib plus gemcitabine vs 3·0 months [1·8-3·8] with placebo plus gemcitabine; hazard ratio 0·55 [95% CI 0·35-0·90]; log-rank p=0·015). The most frequent grade 3 or worse adverse events were haematological (neutropenia in 38 [62%] of 61 participants in the adavosertib plus gemcitabine group vs ten [30%] of 33 in the placebo plus gemcitabine group; thrombocytopenia in 19 [31%] of 61 in the adavosertib plus gemcitabine group vs two [6%] of 33 in the placebo plus gemcitabine group). There were no treatment-related deaths; two patients (one in each group in the high-grade serous ovarian cancer cohort) died while on study medication (from sepsis in the experimental group and from disease progression in the control group).

INTERPRETATION

The observed clinical efficacy of a Wee1 inhibitor combined with gemcitabine supports ongoing assessment of DNA damage response drugs in high-grade serous ovarian cancer, a TP53-mutated tumour type with high replication stress. This therapeutic approach might be applicable to other tumour types with high replication stress; larger confirmatory studies are required.

FUNDING

US National Cancer Institute Cancer Therapy Evaluation Program, Ontario Institute for Cancer Research, US Department of Defense, Princess Margaret Cancer Foundation, and AstraZeneca.

A review of the use of next generation sequencing methodologies to identify biomarkers of resistance to CDK4/6 inhibitors in ER+/HER2- breast cancer

The development of cyclin-dependent kinases (CDK) 4 and 6 inhibitors represented a substantial breakthrough in the treatment of estrogen receptor positive (ER+), human epidermal growth factor receptor 2 (HER2) negative metastatic breast cancer. These drugs showed a significant clinical benefit in pivotal clinical trials. However, resistance eventually occurs, leading to disease progression. Next Generation Sequencing methodologies have been employed to investigate predictive biomarkers of response or resistance to CDK4/6 inhibitors. Whole exome and targeted sequencing of solid and liquid biopsies have revealed several possible genomic alterations associated with resistance. Notably, genomic alterations identified by DNA-sequencing did not fully recapitulate the entire landscape of resistance to CDK4/6 inhibitors. Gene expression analysis, such as RNA-Seq methodologies, have provided insights into transcriptional profiles and may need further application. Herein, we report the main findings derived from the use of NGS analysis in the context of resistance to CDK4/6 inhibitors in ER + breast cancer.

The value of interventional radiology in clinical trial teams: experience from the BATTLE lung cancer trials

AIM

To report on the multidisciplinary approach, focusing specifically on the role of the interventional radiologist (IR), used to support the Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) and BATTLE-2 trials.

MATERIALS AND METHODS

Patients who underwent percutaneous image-guided biopsy for the BATTLE and BATTLE-2 trials were reviewed. A radiology-based, three-point, lesion-scoring system was developed and used by two IRs. Lesions were given a score of 3 (most likely to yield sufficient material for biomarker analysis) if they met the following criteria: size >2 cm, solid mass, demonstrated imaging evidence of viability, and were technically easy to sample. Lesions not meeting all four criteria were scored 2 with the missing criteria noted as negative factors. Lesions considered to have risks that outweighed potential benefits receive a score of 1 and were not biopsied. Univariate and multivariate analyses were performed to evaluate the score's ability to predict successful yield for biomarker adequacy.

RESULTS

A total of 555 biopsies were performed. The overall yield for analysis of the required biomarkers was 86.1% (478/555), and 84% (268/319) and 88.9% (210/236) for BATTLE and BATTLE-2, respectively (p=0.09). Lesions receiving a score of 3 were adequate for biomarker analysis in 89% of cases. Lesions receiving a score of 2 with more than two negative factors were adequate for molecular analysis in 69.2% (IR1, p=0.03) and 74% (IR2, p=0.04) of cases. The two IRs scored 78.4% of the lesions the same indicating moderate agreement (kappa=0.55; 95% confidence interval [CI]: 0.48, 0.61).

CONCLUSIONS

IRs add value to clinical trial teams by optimising lesions selected for biopsy and biomarker analysis.

Clinical implication of prognostic and predictive biomarkers for castration-resistant prostate cancer: a systematic review

Background

Diagnosis of metastatic castrate resistant prostate cancer (mCRPC) with current biomarkers is difficult and often results in unnecessary invasive procedures as well as over-diagnosis and over-treatment. There are a number of prognostic biomarkers for CRPC, but there are no validated predictive biomarkers to guide in clinical decision-making. Specific biomarkers are needed that enable to understand the natural history and complex biology of this heterogeneous malignancy, identify early response to treatment outcomes and to identify the population of men most likely to benefit from the treatment. In this systematic review, we discuss the existing literature for the role of biomarkers in CRPC and how they aid in the prognosis, treatment selection and survival outcomes.

Methods

We performed a literature search on PubMed and EMBASE databases from January 2015 through February 2020 in accordance to Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Articles were assessed to identify relevant observational studies and randomized controlled trials regarding biomarkers which aid in identifying progression to mCRPC as well as predictive biomarkers which help in treatment selection.

Results

We identified 3640 number of hits of which 58 articles were found to be relevant. Here we addressed biomarkers in the context of prognosis, prediction and patient selection of therapy. These biomarkers were found to be effective as prognostic or predictive factors under variety of conditions. The higher levels for all these biomarkers were associated with shorter median OS and sometimes PFS. Lower amounts of biomarkers in serum or urine were associated with prolonged survival outcomes, longer time to CRPC development or CRPC progression and longer median follow-up irrespective of any therapy.

Conclusion

We observed that the biomarkers included in our study predicted clinically relevant survival outcomes and treatment exposure. Though the current biomarkers are prognostic when measured prior to initiating treatment, not all are validated as predictive markers in post treatment setting. A greater understanding of biomarkers in CRPC is need of the hour for development of more personalized approach to maximize benefit and minimize harm in men with CRPC.

Biomarkers in critical care nutrition

The goal of nutrition support is to provide the substrates required to match the bioenergetic needs of the patient and promote the net synthesis of macromolecules required for the preservation of lean mass, organ function, and immunity. Contemporary observational studies have exposed the pervasive undernutrition of critically ill patients and its association with adverse clinical outcomes. The intuitive hypothesis is that optimization of nutrition delivery should improve ICU clinical outcomes. It is therefore surprising that multiple large randomized controlled trials have failed to demonstrate the clinical benefit of restoring or maximizing nutrient intake. This may be in part due to the absence of biological markers that identify patients who are most likely to benefit from nutrition interventions and that monitor the effects of nutrition support. Here, we discuss the need for practical risk stratification tools in critical care nutrition, a proposed rationale for targeted biomarker development, and potential approaches that can be adopted for biomarker identification and validation in the field.

Biostatistical and Logistical Considerations in the Development of Basket and Umbrella Clinical Trials

Oncology clinical trials are undergoing transformation to evaluate targeted therapies addressing a wider variety of biologically defined cancer subgroups. Multiarm basket and umbrella trials conducted under master protocols have become more prominent mechanisms for the clinical evaluation of promising new biologically driven anticancer therapies that are integral to precision oncology medicine. These new trial designs permit efficient clinical evaluation of multiple therapies in a variety of histologically and biologically defined cancers. These complex trials require extensive planning and attention to many factors, including choice of biomarker assay platform, mechanism for processing clinicopathologic and biomarker data to assign patients to substudies, and statistical design, monitoring, and analysis of substudies. Trial teams have expanded to include expertise in the interface between biology, clinical oncology, bioinformatics, and statistics. Strategies for the design, conduct, and analysis of these complex trials will continue to evolve to meet new challenges and opportunities in precision oncology medicine.

On precision dosing of oral small molecule drugs in oncology

Personalization of oral small molecule anticancer drug doses based on individual patient blood drug levels, also known as therapeutic drug monitoring (TDM), has the potential to significantly improve the effectiveness of treatment by maximizing drug efficacy and minimize toxicity. However, this option has not yet been widely embraced by the oncology community. Some reasons for this include increased logistical complexity of dose individualization, the lack of clinical laboratories that measure small molecule drug concentrations in support of patient care, and the lack of reimbursement of costs. However, the main obstacle may be the lack of studies clearly demonstrating that monitoring of oral small molecule anticancer drug levels actually improves clinical outcomes. Without unequivocal evidence in support of TDM-guided dose individualization, especially demonstration of improved survival with TDM in randomized controlled trials, wide acceptance of this approach by oncologists and reimbursement by insurance companies is unlikely, and patients may continue to suffer as a result of receiving incorrect drug doses. This article reviews the current status of TDM of oral small molecule drugs in oncology and intends to provide strategic insights into the design of studies for evaluating the utility of TDM in this clinical context.

Ovarian Cancer Translational Activity of the Multicenter Italian Trial in Ovarian Cancer (MITO) Group: Lessons Learned in 10 Years of Experience

Ovarian cancer is the most lethal gynecological cancer, and despite years of research, with the exception of a BRCA mutation driving the use of PARP inhibitors, no new prognostic/predictive biomarkers are clinically available. Improvement in biomarker selection and validation may derive from the systematic inclusion of translational analyses into the design of clinical trials. In the era of personalized medicine, the prospective centralized collection of high-quality biological material, expert pathological revision, and association to well-controlled clinical data are important or even essential added values to clinical trials. Here, we present the academic experience of the MITO (Multicenter Italian Trial in Ovarian Cancer) group, including gynecologists, pathologists, oncologists, biostatisticians, and translational researchers, whose effort is dedicated to the care and basic/translational research of gynecologic cancer. In our ten years of experience, we have been able to collect and process, for translational analyses, formalin-fixed, paraffin-embedded blocks from more than one thousand ovarian cancer patients. Standard operating procedures for collection, shipping, and processing were developed and made available to MITO researchers through the coordinating center’s web-based platform. Clinical data were collected through dedicated electronic case report forms hosted in a web-based electronic platform and stored in a central database at the trial’s coordinating center, which performed all the analyses related to the proposed translational researches. During this time, we improved our strategies of block management from retrospective to prospective collection, up to the design of a prospective collection with a quality check for sample eligibility before patients’ accrual. The final aim of our work is to share our experience by suggesting a guideline for the process of centralized collection, revision processing, and storing of formalin-fixed, paraffin-embedded blocks for translational purposes.

Investigating Rates of Food and Drug Administration Approvals and Guidances in Drug Development: A Structural Breakpoint/Cointegration Timeseries Analysis

BACKGROUND

The number of original and supplemental ANDAs, BLAs, NDAs, and Biosimilars FDA drug/biologic approvals (Approvals) has risen dramatically in the recent years, incidentally, so has the number of issued FDA guidances (Guidances). It is hypothesized that if the structures of the two timeseries are similar and/or concomitantly co-evolving, then there is a relationship between the two variables that may be worthy of further investigation.

METHODS

Structural breakpoint (SBP) and cointegration (CI) analyses are used to provide insights into the relatedness of the two timeseries (Approvals, Guidances). Various descriptive statistics (e.g., nonparametric correlation testing, decomposition, unit testing, stationarity, and maximum order of integration) were also performed to better understand the nature of the timeseries understudy.

RESULTS

Structural breaks were identified with the following dates: Approvals (1983, 1989, 1996, 2004, and 2012) and Guidances (1995 and 2012). Approvals and Guidances were (medium) correlative, nonstationary, and cointegrated with a maximum order of integration of one (I(1)). Descriptive statistical markers suggest additional similarities (e.g., seasonal variation) between the two timeseries.

CONCLUSIONS

To the author's knowledge, this is the first work to empirically investigate Guidances and their relationship with Approvals. The similarity in the structure of the timeseries (e.g., seasonal variation, SBPs and CI) suggests a deeper relationship between Guidances and Approvals, including the existence of a "long-run" equilibrium (wherein one or more exogenous factors restrain the divergence) between the two variables. This work offers an exciting opportunity for further research into the processes influencing the rates of Approvals and Guidances. A discussion on the limitations of the approach is also presented.

Biomarker-Driven Oncology Clinical Trials: Key Design Elements, Types, Features, and Practical Considerations

In this precision oncology era, where molecular profiling at the individual patient level becomes increasingly accessible and affordable, more and more clinical trials are now driven by biomarkers, with an overarching objective to optimize and personalize disease management. As compared with the conventional clinical development paradigms, where the key is to evaluate treatment effects in histology-defined populations, the choices of biomarker-driven clinical trial designs and analysis plans require additional considerations that are heavily dependent on the nature of biomarkers (eg, prognostic or predictive, integral or integrated) and the credential of biomarkers' performance and clinical utility. Most recently, another major paradigm change in biomarker-driven trials is to conduct multi-agent and/or multihistology master protocols or platform trials. These trials, although they may enjoy substantial infrastructure and logistical advantages, also face unique operational and conduct challenges. Here we provide a concise overview of design options for both the setting of single-biomarker/single-disease and the setting of multiple-biomarker/multiple-disease types. We focus on explaining the trial design and practical considerations and rationale of when to use which designs, as well as how to incorporate various adaptive design components to provide additional flexibility, enhance logistical efficiency, and optimize resource allocation. Lessons learned from real trials are also presented for illustration.

A computational solution to improve biomarker reproducibility during long-term projects

Biomarkers are fundamental to basic and clinical research outcomes by reporting host responses and providing insight into disease pathophysiology. Measuring biomarkers with research-use ELISA kits is universal, yet lack of kit standardization and unexpected lot-to-lot variability presents analytic challenges for long-term projects. During an ongoing two-year project measuring plasma biomarkers in cancer patients, control concentrations for one biomarker (PF) decreased significantly after changes in ELISA kit lots. A comprehensive operations review pointed to standard curve shifts with the new kits, an analytic variable that jeopardized data already collected on hundreds of patient samples. After excluding other reasonable contributors to data variability, a computational solution was developed to provide a uniform platform for data analysis across multiple ELISA kit lots. The solution (ELISAtools) was developed within open-access R software in which variability between kits is treated as a batch effect. A defined best-fit Reference standard curve is modelled, a unique Shift factor “S” is calculated for every standard curve and data adjusted accordingly. The averaged S factors for PF ELISA kit lots #1–5 ranged from -0.086 to 0.735, and reduced control inter-assay variability from 62.4% to <9%, within quality control limits. S factors calculated for four other biomarkers provided a quantitative metric to monitor ELISAs over the 10 month study period for quality control purposes. Reproducible biomarker measurements are essential, particularly for long-term projects with valuable patient samples. Use of research-use ELISA kits is ubiquitous and judicious use of this computational solution maximizes biomarker reproducibility.

Implementing Liquid Biopsies in Clinical Trials: State of Affairs, Opportunities, and Challenges

A primary goal of personalized medicine is to develop tumor-specific biomarkers to aid in treatment selection and to better evaluate response to targeted therapies. The assessment of circulating blood markers as surrogate real-time biopsies of disease status, termed liquid biopsies, has been under investigation. There are many different types of liquid biopsies each with different functionalities and limitations. These include tumor markers, circulating tumor cells, cell-free DNA, and extracellular vesicles including exosomes. Multiple clinical trials have evaluated liquid biopsies as prognostic biomarkers with positive results. Additional studies are underway to evaluate liquid biopsies as predictive biomarkers, pharmacodynamic biomarkers, and surrogate efficacy endpoints for treatment response evaluation. There are several challenges in and barriers to implementation of liquid biopsies into clinical trials and subsequently into routine clinical practice, which are addressed in this review.

The emerging role of cell-free DNA as a molecular marker for cancer management

An increasing number of studies demonstrate the potential use of cell-free DNA (cfDNA) as a surrogate marker for multiple indications in cancer, including diagnosis, prognosis, and monitoring. However, harnessing the full potential of cfDNA requires (i) the optimization and standardization of preanalytical steps, (ii) refinement of current analysis strategies, and, perhaps most importantly, (iii) significant improvements in our understanding of its origin, physical properties, and dynamics in circulation. The latter knowledge is crucial for interpreting the associations between changes in the baseline characteristics of cfDNA and the clinical manifestations of cancer. In this review we explore recent advancements and highlight the current gaps in our knowledge concerning each point of contact between cfDNA analysis and the different stages of cancer management.

Gynecological Cancers Translational, Research Implementation, and Harmonization: Gynecologic Cancer InterGroup Consensus and Still Open Questions

In the era of personalized medicine, the introduction of translational studies in clinical trials has substantially increased their costs, but provides the possibility of improving the productivity of trials with a better selection of recruited patients. With the overall goal of creating a roadmap to improve translational design for future gynecological cancer trials and of defining translational goals, a main discussion was held during a brainstorming day of the Gynecologic Cancer InterGroup (GCIG) Translational Research Committee and overall conclusions are here reported. A particular emphasis was dedicated to the new frontier of the immunoprofiling of gynecological cancers. The discussion pointed out that to maximize patients' benefit, translational studies should be integral to clinical trial design with standardization and optimization of procedures including a harmonization program of Standard Operating Procedures. Pathology-reviewed sample collection should be mandatory and ensured by dedicated funding. Biomarker validation and development should be made public and transparent to ensure rapid progresses with positive outcomes for patients. Guidelines/templates for patients' informed consent are needed. Importantly for the public, recognized goals are to increase the involvement of advocates and to improve the reporting of translational data in a forum accessible to patients.

Challenges with Novel Clinical Trial Designs: Master Protocols

The 2018 Accelerating Anticancer Agent Development (AAADV) Workshop assembled a panel of experts for an in-depth discussion session to present "Challenges with Novel Clinical Trial Designs." This panel offered assessments of the challenges faced by industry, the FDA, investigators, institutional review boards, and patients. The panel focused on master protocols, which include umbrella trials, platform trials, and basket trials. Umbrella trials and platform trials share many commonalities, whereas basket trials are more distinct. Umbrella and platform trials are generally designed with multiple arms where patients of the same histology or other unifying characteristics are enrolled into different arms and multiple investigational agents are evaluated in a single protocol. In contrast, basket studies generally enroll patients with different tumor types based on the presence of a specific mutation or biomarker regardless of histology; these trials may include expansion cohorts. These novel designs offer the promise of expedited drug assessment and approval, but they also place new challenges on all the stakeholders involved in the drug development process. Only by identifying the challenges of these complex, innovative clinical trial designs and highlighting challenges from each perspective can we begin to address these challenges. The 2018 AAADV Workshop convened a panel of experts from relevant disciplines to highlight the challenges that are created by master protocols, and, where appropriate, offer strategies to address these challenges.

Elevated Serum Megakaryocyte Potentiating Factor as a Predictor of Poor Survival in Patients with Mesothelioma and Primary Lung Cancer

Background

There is an urgent need for a companion assay to work with mesothelin-targeted therapeutic agents and for noninvasive and accurate prognostication of malignant mesothelioma (MM) patients. We report the development and validation of a blood-based assay for megakaryocyte potentiating factor (MPF) and the evaluation of its effectiveness for prognosis in MM and lung cancer patients.

Methods

Using electrochemiluminescence technology, we developed a sensitive MPF assay and performed both analytical and clinical validations. Further, the effectiveness of the MPF assay in predicting prognosis was evaluated for 95 MM and 272 lung cancer patients.

Results

We performed comprehensive analytical and clinical validation, including precision and accuracy, interference, preanalytical variables, sensitivity, and specificity for mesothelioma. In MM patients, increased serum MPF is a predictor of poor survival with a hazard ratio (HR) = 2.46 (log-rank P = 0.003; n = 95). In refractory MM patients, increased MPF is a strong predictor of poor outcome with an HR = 6.12 (log-rank P = 0.0007; n = 57). In a lung cancer patient cohort, increased MPF is a predictor of poor survival, with an HR = 1.57 (log-rank P = 0.003; n = 272).

Conclusions

The MPF assay has robust technical characteristics, with strong analytic and clinical validation. Clinical studies indicate that increased serum MPF is a predictor of poor survival for MM patients, throughout the course of the disease. Increased MPF is also associated with poor overall survival for patients with newly diagnosed lung cancer.

Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas

Adenocarcinomas and active granulomas can both have a spiculated appearance on computed tomography (CT) and both are often fluorodeoxyglucose (FDG) avid on positron emission tomography (PET) scan, making them difficult to distinguish. Consequently, patients with benign granulomas are often subjected to invasive surgical biopsies or resections. In this study, quantitative vessel tortuosity (QVT), a novel CT imaging biomarker to distinguish between benign granulomas and adenocarcinomas on routine non-contrast lung CT scans is introduced. Our study comprised of CT scans of 290 patients from two different institutions, one cohort for training (N = 145) and the other (N = 145) for independent validation. In conjunction with a machine learning classifier, the top informative and stable QVT features yielded an area under receiver operating characteristic curve (ROC AUC) of 0.85 in the independent validation set. On the same cohort, the corresponding AUCs for two human experts including a radiologist and a pulmonologist were found to be 0.61 and 0.60, respectively. QVT features also outperformed well known shape and textural radiomic features which had a maximum AUC of 0.73 (p-value = 0.002), as well as features learned using a convolutional neural network AUC = 0.76 (p-value = 0.028). Our results suggest that QVT features could potentially serve as a non-invasive imaging biomarker to distinguish granulomas from adenocarcinomas on non-contrast CT scans.

Arrayed microfluidic chip for detection of circulating tumor cells and evaluation of drug potency

Circulating tumor cells (CTCs) from peripheral blood of cancer patients are considered as one of the most promising pharmacodynamic (PD) biomarkers due to its non-invasive property in disease diagnosis and prognosis. However, the detection of extremely low number of CTCs in patient blood requires methods with high sensitivity and accuracy. We fabricated an arrayed geometrically enhanced mixing (GEM) chip with a "dislocation herringbone" layout based on cell immunoaffinity. By optimizing the injection and rinsing flow rate, an average cell capture rate of 87.02% and an average capture purity of 99.58% were achieved using the human lung adenocarcinoma cell lines H1975. In addition, we determined the specificity, precision, accuracy, and detection limit of our chip. The results demonstrated the chip was stable, accurate and reliable for the "liquid biopsy" of lung cancer cells using the peripheral blood of patients. Our chip can also be used to evaluate the potency of different drugs against tumor cells in parallel due to the presence of four independent microchannels.

Development of a translational medicine protocol for an NCTN genitourinary clinical trial: Critical steps, common pitfalls and a basic guide to translational clinical research

Translational medicine (TM) components of prospective clinical trials provide an invaluable opportunity to test hypotheses that contribute to our knowledge of human disease biology and/or the mechanism of action of a given therapeutic intervention. Our ability to sample tumors and their microenvironment, and the depth and breadth of biological information that can be extracted from them, has increased exponentially in recent years. This information is critical to guide the next steps clinical research if we are to accelerate the pace of progress in cancer treatment. Thus, TM studies should be considered key components of any clinical trial. However, TM studies are costly and biologic sampling can impose significant morbidity on our patients. Therefore, TM investigators should be engaged early in the design process (similar to a statistician) to ensure that the most imperative research questions are rigourosly defined, that the obtained specimens can be used to answer them and that the results will serve as the foundation for additional studies. In this review, we focus on TM studies in the context of the National Cancer Institute's National Clinical Trials Network trials and offer a description of the genesis of TM components, methods in sample acquisition and biomarker research, and a guide to funding mechanisms, in order to provide a blueprint for future TM research protocols. While TM studies can take many forms, the research discussed primarily focusses on basic and translational research involving molecular, cellular, and immunobiology.

Biomarkers in early-phase trials: fundamental issues

Biomarkers are frequently being included in early-phase clinical trials. This article is meant to introduce clinical investigators to the fundamentals of choosing a biomarker test for use in an early phase trial. Steps to consider are briefly outlined including defining the role of the biomarker in the early phase trial; selecting a fit-for-purpose biomarker test and laboratory; describing the test procedures; carrying out analytical validation testing appropriate for the research objectives and the risk involved in the trial; implementing the test in the trial; and planning for the future. Examples illustrate analytical validation approaches in the context of typical biomarker roles. The importance of collaboration between clinical investigators and laboratory researchers is emphasized.

Biomarkers and personalized medicine: current status and further perspectives with special focus on dermatology

Biomarkers are of increasingly high importance in medicine, particularly in the realm of 'personalized medicine'. They are valuable for predicting prognosis and dose selection. Moreover, they may be helpful in detecting therapeutic and adverse responses and in patient stratification based on efficacy or safety prediction. Thus, biomarkers are essential tools for the selection of appropriate patients for treatment with certain drugs to and enable personalized medicine, that is 'providing the right treatment to the right patient, at the right dose at the right time'. Currently, there are six drugs approved for dermatological indications with recommended or mandatory biomarker testing. Most of them are used to treat melanoma and human immunodeficiency virus infection. In contrast to the few fully validated biomarkers, many exploratory biomarkers and biomarker candidates have potential applications. Prognostic biomarkers are of particular significance for malignant conditions. Similarly, diagnostic biomarkers are important in autoimmune diseases. Disease severity biomarkers are helpful tools in the treatment for inflammatory skin diseases. Identification, qualification and implementation of the different kinds of biomarkers are challenging and frequently necessitate collaborative efforts. This is particularly true for stratification biomarkers that require a companion diagnostic marker that is co-developed with a certain drug. In this article general definitions and requirements for biomarkers as well as for the impact of biomarkers in dermatology are reviewed and opportunities and challenges are discussed.

Underreporting of Research Biopsies from Clinical Trials in Oncology

Purpose: Research biopsies are frequently incorporated within clinical trials in oncology and are often a mandatory requirement for trial enrollment. However, limited information is available regarding the extent and completeness of research biopsy reporting.Experimental Design: We identified a cohort of therapeutic clinical trials where research biopsies were performed between January 2005 and October 2010 from an IR database at our institution. Clinical trial protocols were compared with the highest level of corresponding publication as a manuscript or registry report.Results: A total of 866 research biopsies were performed across 46 clinical trials, with a median of 8 patients biopsied/trial and 19 biopsies collected/trial. After a median follow-up time of 4.3 years from trial completion, 36 of 46 trials (78%) reported trial results: published manuscripts (n = 35), or registry report (n = 1). A total of 635 conducted biopsies were reported in 18 of the 46 trials (39%). Six (33%) of these 18 trials underreported the number of biopsies performed. Of 33 trials with mandatory research biopsies, 13 (39%) trials reported on these biopsies. Biopsy complications occurred in 8 trials [n = 39 patients, 6 grade 3/4 adverse events (AE)] but only 1 trial reported these. Factors associated with biopsy reporting included a larger number of biopsies (P ≤ 0.001) and serial biopsies (P < 0.001). Twelve of 16 (75%) trials with >12 biopsies performed reported on these biopsies compared with only 20% (6/30) that performed ≤12 biopsies.Conclusions: Despite ethical obligations to report research biopsies, the majority (61%) of trials do not report results from research biopsies. Complications are rarely reported in these studies. Improved reporting of results and AEs from research biopsies is needed. Clin Cancer Res; 23(21); 6450-7. ©2017 AACR.

Radiomics: the bridge between medical imaging and personalized medicine

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

Microvascular and lymphovascular tumour invasion are associated with poor prognosis and metastatic spread in renal cell carcinoma: a validation study in clinical practice

OBJECTIVE

To validate microvascular (MVI) and lymphovascular (LVI) invasion as prognostic factors in patients with renal cell carcinoma (RCC).

PATIENTS AND METHODS

Data of patients with RCC who underwent radical or nephron-sparing surgery were prospectively collected from three academic centres. The occurrence of MVI and LVI was determined with standard staining protocols by experienced pathologists at the time of diagnosis. The association of MVI and LVI with clinicopathological data, metastatic spread, and cancer-specific survival (CSS) were evaluated with Fisher's exact tests, binary logistic regression analyses, and univariable and multivariable Cox proportional hazard regression models.

RESULTS

MVI was present in 201 of 747 patients (26.9%) and was associated with advanced Tumour-Node-Metastasis (TNM) stages, high Fuhrman grades, and sarcomatoid features (all P < 0.001). MVI was associated with a higher rate of metastatic spread. LVI was present in 32 of 573 patients (5.5%) and was associated with advanced TNM stages, high Fuhrman grade, and sarcomatoid features (all P < 0.001). Two-thirds of LVI-positive patients died (P < 0.001). Both LVI and MVI were significantly associated with CSS in all patients, clear cell RCC (ccRCC), and localised RCC in univariable analysis (all P < 0.001). On multivariable analysis, presence of MVI was identified as an independent prognostic factor (hazard ratio 2.09; P = 0.001). Moreover, MVI [odds ratio (OR) 2.7; P = 0.001] and not macrovascular invasion (P = 0.895) was an independent predictor of sychronuous metastatic spread. LVI was the strongest factor associated with sychronous metastatic spread (OR 4.73, 95% confidence interval 1.84-12.14; P = 0.001) in all patients and in the subgroup of patients with ccRCC (P = 0.001).

CONCLUSIONS

The present study validated LVI and MVI as prognostic factors for poor outcome in RCC. These findings endorse an evaluation of both variables in the clinical routine setting to facilitate survival prognostication in follow-up protocols and for assignment to adjuvant treatment trials.

Burdensome Research Procedures in Trials: Why Less Is More

A large volume of trials involve invasive, nontherapeutic research procedures, like organ biopsy or sham surgeries, that can pose risks comparable with the experimental treatment itself but that have no direct benefit for volunteers. Though such procedures can enhance the value of clinical investigations, recent studies suggest that many studies involving invasive, nontherapeutic research procedures are not well planned and reported; some studies suggest that their results are often not utilized in the planning of new investigations. This commentary offers recommendations for how investigators, sponsors, and ethics committees might improve evaluation and implementation of studies involving invasive nontherapeutic procedures. We conclude by urging more demanding scientific standards for the rationale, design, and reporting of burdensome, nontherapeutic research procedures-particularly where they involve risk of serious complications.

Data-Based Radiation Oncology: Design of Clinical Trials in the Toxicity Biomarkers Era

The ability to stratify patients using a set of biomarkers, which predict that toxicity risk would allow for radiotherapy (RT) modulation and serve as a valuable tool for precision medicine and personalized RT. For patients presenting with tumors with a low risk of recurrence, modifying RT schedules to avoid toxicity would be clinically advantageous. Indeed, for the patient at low risk of developing radiation-associated toxicity, use of a hypofractionated protocol could be proposed leading to treatment time reduction and a cost-utility advantage. Conversely, for patients predicted to be at high risk for toxicity, either a more conformal form or a new technique of RT, or a multidisciplinary approach employing surgery could be included in the trial design to avoid or mitigate RT when the potential toxicity risk may be higher than the risk of disease recurrence. In addition, for patients at high risk of recurrence and low risk of toxicity, dose escalation, such as a greater boost dose, or irradiation field extensions could be considered to improve local control without severe toxicities, providing enhanced clinical benefit. In cases of high risk of toxicity, tumor control should be prioritized. In this review, toxicity biomarkers with sufficient evidence for clinical testing are presented. In addition, clinical trial designs and predictive models are described for different clinical situations.