Biomarker-based Trials Research Articles

54. CancerTrialMatch: A computational resource for the management of clinical trials at a precision oncology center

The routine use of genetic and genomic sequencing in the treatment of cancer has been steadily rising over the past few years, with a corresponding increase in the availability of biomarker-based clinical trials. At the Avera Cancer Institute, biomarker-based clinical trials are often presented as treatment options to oncologists at the molecular tumor board. This necessitated a way to capture structured trial data, and match them to patients based on their disease and sequencing profile in a systematic manner. We developed an open-source web application, CancerTrialMatch, that has the ability to (i) add trials through a semi-automated curation interface, (ii) browse and search trials, (iii) and match patients to biomarker-based trials. This application uses R Shiny to create simple interfaces, a mongo database to store trial data, various R libraries to query data and perform computations, and Docker to manage software installation and application instantiation. The curation interface is semi-automated because querying the clinicaltrials.gov API will return discrete data for many fields, but not for biomarkers or for disease subtypes. The user has to manually input disease type based on the OncoTree classification, as well as biomarker information for mutations, copy numbers, fusions, TMB, MSI/PD-L1 status, RNA expression, and disease-specific markers such as ER/PR/HER2 status. We believe that this application will reduce the person-hours required for trial management for a patient, aid in increasing clinical trial enrollment by systematically providing treatment options, and is thus an important tool in the clinical application of precision oncology.

Disparities in comprehensive genomic profiling in older patients with gastrointestinal malignancies.

646 Background: Limited data guides the management of older adults with cancer, a patient population that continues to be under-represented in clinical trials. Comprehensive Genomic Profiling (CGP) drives the enrollment on biomarker-based trials and may inform treatment selection. This analysis aims to evaluate the use of CGP in older patients with gastrointestinal (GI) malignancies and to compare results of genomic profiling across age groups. Methods: Clinical CGP results derived from Next Generation Sequencing (NGS) of tumor tissue (n=92802) were reviewed for patients with GI malignancies. Genomic alterations (GAs) and complex signatures were identified, based on hybridization-captured, adapter ligation-based sequencing. Association between age group (< 65: n=51652; 65-74: n=28972; 75+: n=12178) and biomarkers of interests was evaluated using Chi-square test, adjusting for multiple testing using Bonferroni correction. Delay-adjusted incidence rates in populations included in the study were acquired from SEER Databases. Results: The majority of patients in the CGP cohort were <65 yrs (55.7%). Patients aged 75 and above were underrepresented as compared to the SEER database incidence rates (15.2% vs. 31.6%, p <0.0005). Overall, the incidence of known or likely pathogenic GAs was similar across all age groups (>99%). An analysis of specific GAs among all the GI cancers analyzed showed an age-associated increase of high tumor mutational burden (≥10 mut/Mb) (TMB-H) (5.6% vs 6.6% vs 10.7% for respective age groups, p<0.0005). The overall incidence of mismatch repair deficiency or microsatellite instability (dMMR/MSI) (3.6% vs 4.0% vs 7.5%, p<0.0005) and DNA damage repair mutations (DDR mut) (13.2% vs 13.9% vs 16.4%, p<0.0005) also increased with age. The magnitude of the CGP findings varied by GI cancer type, as summarized for select significant findings (p <0.0005) in Table. Detailed results for each GI subtype and relevant biomarkers will be presented. Conclusions: This large scale analysis of CGP done for patients with GI cancers showed that genomic profiling is under utilized in older adults who constitute the majority of patients with GI malignancies. These findings may improve access to clinical trials and guide the development of older adult-specific studies.[Table: see text]

Learning from BISCAY: The future of biomarker-based trial design in bladder cancer.

A recent article in Nature Medicine explored the combination of immunotherapy with multiple distinct targeted therapies in patients with metastatic urothelial cancer, employing a biomarker-driven approach. Herein, we discuss the merits of this ambitious study and highlight the need for larger biomarker-based randomized trials to arrive at definitive clinical conclusions.

Azacitidine (AZA) with Nivolumab (Nivo), and AZA with Nivo + Ipilimumab (Ipi) in Relapsed/Refractory (R/R) Acute Myeloid Leukemia: Clinical and Immune Biomarkers of Response

Azacitidine (AZA) with Nivolumab (Nivo), and AZA with Nivo + Ipilimumab (Ipi) in Relapsed/Refractory (R/R) Acute Myeloid Leukemia: Clinical and Immune Biomarkers of Response

Progressive supranuclear palsy and multiple system atrophy: clinicopathological concepts and therapeutic challenges.

This update discusses novel aspects on clinicopathological concepts and therapeutic challenges in progressive supranuclear palsy (PSP) and multiple system atrophy (MSA), arising from publications of the last 1.5 years. The clinical criteria for diagnosis of PSP have been revised. Clinical variability of pathologically defined PSP and MSA makes the development of mature biomarkers for early diagnosis and biomarker-based trial design indispensable. Novel molecular techniques for biomarker supported diagnosis of PSP and MSA and for monitoring disease progression are being studied. Research in the pathophysiology of both diseases generates gradual progress in the understanding of the underlying processes. Several promising disease-modifying therapeutic approaches for PSP and MSA are now moving into clinical trials. Recent research generates insights in the pathophysiological relevant processes and raises hope for earlier clinical diagnosis and disease-modifying therapies of patients with PSP and MSA.

P036 EXPLORING EARLY-RESPONSIVE PHARMACODYNAMIC BIOMARKERS IN PEDIATRIC INFLAMMATORY BOWEL DISEASE (IBD)

The generation of high-quality pilot data that leads to “go/no-go decisions” regarding new or repurposed drugs is often problematic, particularly with a high placebo response rate. In short duration trials, pharmacodynamic (PD) biomarkers can demonstrate proof-of-concept of drug action that may or may not be bridged to clinical outcomes. We sought to identify early-changing PD biomarkers in patients with IBD. Clinical scores and serum were obtained at baseline and after 1–2 weeks of corticosteroids in 5 IBD patients. Using the SOMAscan assay, 45 published chronic steroid and infliximab-responsive proteins were evaluated. All were log-transformed and normality verified using the Shapiro-Wilk test. Pre- to post- treatment comparisons were performed using a paired t-test (p<0.01). Label-free proteome profiling using mass spectrometry was performed. Thirteen proteins responded consistently to 1–2 weeks of corticosteroids. These included biomarkers previously shown to be responsive in pediatric IBD patients after 2 doses of infliximab (CCL23, carnosinase, p-cadherin, CCL25, testican-2, AGT, and FAM3B) and after longer courses of corticosteroid therapy (AGT, afamin, GHBP, CCL22, MMP12, PROT C, Ly9). Four were detected by label-free mass spectrometry (AGT, afamin, PROT C, and carnosinase). Most are involved in regulation of inflammation, immune homeostasis, and extracellular matrix binding. In this small sample, change in some biomarkers correlated with short-term clinical response (carnosinase, testican-2, FAM3B). We have identified treatment-responsive proteins in IBD patients after several weeks of steroids, after 2 doses of infliximab, and within 1–2 weeks of treatment with steroids; these biomarkers may be useful for screening trials of anti-inflammatory drugs. For selection of candidates to study in larger, controlled trials, short-duration biomarker-based trials would be beneficial for patients.

Optimal Interim Decision Rules Based on a Binary Surrogate Outcome for Adaptive Biomarker-Based Trials in Oncology

ABSTRACTAdaptive enrichment designs represent a promising approach to evaluate targeted therapies, for example, in oncology. They allow selection of the most promising target population in an interim analysis and then combination of the data from the two trial stages for the final proof of efficacy. Application of these designs is motivated by the assumption that there might be a biomarker-defined subgroup of patients with an increased treatment benefit as compared to the total patient population. If the primary outcome is a time-to-event variable and the respective event takes a relatively long time to be observed, it could be beneficial to select the most promising patient population based on an earlier available binary surrogate, for example, response, to save time and costs. We propose an adaptive enrichment design which allows us to implement such a trial setting. For this design, optimal decision rules are derived minimizing the expected loss incurred due to a false interim decision. These rules are compared to ad hoc rules in terms of selection probability and power within a simulation study which is motivated by a clinical trial example. Furthermore, the impact of the correlation between surrogate and primary outcome on power is investigated. Supplementary materials for this article are available online.

Leveraging molecular datasets for biomarker-based clinical trial design in glioblastoma

Biomarkers can improve clinical trial efficiency, but designing and interpreting biomarker-driven trials require knowledge of relationships among biomarkers, clinical covariates, and endpoints. We investigated these relationships across genomic subgroups of glioblastoma (GBM) within our institution (DF/BWCC), validated results in The Cancer Genome Atlas (TCGA), and demonstrated potential impacts on clinical trial design and interpretation. We identified genotyped patients at DF/BWCC, and clinical associations across 4 common GBM genomic biomarker groups were compared along with overall survival (OS), progression-free survival (PFS), and survival post-progression (SPP). Significant associations were validated in TCGA. Biomarker-based clinical trials were simulated using various assumptions. Epidermal growth factor receptor (EGFR)(+) and p53(-) subgroups were more likely isocitrate dehydrogenase (IDH) wild-type. Phosphatidylinositol-3 kinase (PI3K)(+) patients were older, and patients with O6-DNA methylguanine-methyltransferase (MGMT)-promoter methylation were more often female. OS, PFS, and SPP were all longer for IDH mutant and MGMT methylated patients, but there was no independent prognostic value for other genomic subgroups. PI3K(+) patients had shorter PFS among IDH wild-type tumors, however, and no DF/BWCC long-term survivors were either EGFR(+) (0% vs 7%, P = .014) or p53(-) (0% vs 10%, P = .005). The degree of biomarker overlap impacted the efficiency of Bayesian-adaptive clinical trials, while PFS and OS distribution variation had less impact. Biomarker frequency was proportionally associated with sample size in all designs. We identified several associations between GBM genomic subgroups and clinical or molecular prognostic covariates and validated known prognostic factors in all survival periods. These results are important for biomarker-based trial design and interpretation of biomarker-only and nonrandomized trials.

Precision oncology: A new era of cancer clinical trials

Traditionally, site of disease and anatomic staging have been used to define patient populations to be studied in individual cancer clinical trials. In the past decade, however, oncology has become increasingly understood on a cellular and molecular level, with many cancer subtypes being described as a function of biomarkers or tumor genetic mutations. With these changes in the science of oncology have come changes to the way we design and perform clinical trials. Increasingly common are trials tailored to detect enhanced efficacy in a patient subpopulation, e.g. patients with a known biomarker value or whose tumors harbor a specific genetic mutation. Here, we provide an overview of traditional and newer biomarker-based trial designs, and highlight lessons learned through implementation of several ongoing and recently completed trials.

Biomarker-based trials in neuro-oncology.

Treatment of brain tumors is increasingly informed by biomarkers. One use is to appropriately group tumors with similar genetic/genomic characteristics and to design trials separately for the individual groups. The biomarker's use is to predict patient response so that the most effective treatment can be selected for patients based on their tumor characteristics. Trial designs that recruit unselected patients are poorly suited for identifying treatments effective only in subsets of patients given the relatively small numbers of patients available for trials. Investigators are beginning to use different designs that better account for tumor heterogeneity. In this article, an overview of the role of biomarkers in brain tumor trials is presented in the context of existing clinical trials as well as trials that may be launched within the next several years.

Hepatocellular Carcinoma: Reasons for Phase III Failure and Novel Perspectives on Trial Design

Hepatocellular carcinoma (HCC) is a major health problem. Most patients with HCC experience a recurrence after resection/ablation or are diagnosed at advanced stages. Sorafenib remains the only approved systemic drug for these patients. Molecular therapies targeting signaling cascades involved in hepatocarcinogenesis have been explored in phase III clinical trials. However, none of the drugs tested have shown positive results in the first-line (brivanib, sunitinib, erlotinib, and linifanib) or second-line (brivanib, everolimus) setting after sorafenib progression. Reasons for failure are heterogeneous and include lack of understanding of critical drivers of tumor progression/dissemination, liver toxicity, flaws in trial design, or marginal antitumoral potency. These trials are also challenging time to progression as a surrogate endpoint of survival. Trials ongoing testing drugs head-to-head versus sorafenib in "all comers" might have difficulties in achieving superior results in the first line. Novel trials are also designed testing drugs in biomarker-based subpopulations of patients with HCC. Most common mutations, however, are undruggable, such as p53 and CTNNB1. Two types of studies are proposed: (i) phase II pivotal proof-of-concept studies testing drugs blocking potential oncogenic addiction loops, such as the one testing MEK inhibitors in RAS(+) patients or amplification of FGF19 as a target; and (ii) phase II to III studies using biomarker-based trial enrichment for defining HCC subpopulations, such as the case of enriching for MET-positive tumors. These strategies have been deemed successful in breast, melanoma, and lung cancers, and are expected to change the landscape of trial design of HCC.

A phase II trial design with direct assignment option for initial marker validation.

34 Background: Phase II clinical trials aim to identify promising experimental regimens for further testing in phase III trials. Testing targeted therapies with predictive biomarkers mandates efficient trial designs. Current biomarker-based trial designs, including the enrichment, all-comers, and adaptive designs, randomize patients to receive treatment or not throughout the entire duration of the trial. Recognizing the need for randomization yet acknowledging the possibility of promising but nonconclusive results after a preplanned interim analysis (IA), we propose a two-stage phase II design that allows for the possibility of direct assignment (i.e., stop randomization and assign all patients to the experimental arm in stage II) based on IA results. Methods: Using simulations, we compared properties of the direct assignment option design to a 1:1 randomized phase II design and assessed the impact of the timing of IA (after 33%, 50%, or 67% of accrual) and number of IA (one versus two with option for direct assignment at the first and second) over a range of response rate ratios (between 1.0 and 3.0). Results: Between 12% and 30% of the trials (out of 6,000 simulated trials) adopt direct assignment in stage II, with direct adoption depending on the treatment effect size and specified type I error rate (TIER). The direct assignment option design has minimal loss in power (&lt;1.8%) and minimal increase in T1ER (&lt;2.1%) compared to a 1:1 randomized design. The maximum loss in power across possible timings of IA was &lt;1.2%. For the direct assignment option design, there was a 20%-50% increase in the number of patients treated on the experimental (vs. control) arm for the 1 IA case, and 40%-100% increase for the 2 IA case. Conclusions: Testing predictive biomarkers in clinical trials requires new design strategies. In the spectrum of phase II designs from adaptive to balanced randomized all-comers or enrichment designs, the direct assignment design provides a middle ground with desirable statistical properties that may appeal to both clinicians and patients.

Oral low dose and topical tamoxifen for breast cancer prevention: modern approaches for an old drug

Tamoxifen is a drug that has been in worldwide use for the treatment of estrogen receptor (ER)-positive breast cancer for over 30 years; it has been used in both the metastatic and adjuvant settings. Tamoxifen's approval for breast cancer risk reduction dates back to 1998, after results from the Breast Cancer Prevention Trial, co-sponsored by the National Cancer Institute and the National Surgical Adjuvant Breast and Bowel Project, showed a 49% reduction in the incidence of invasive, ER-positive breast cancer in high-risk women. Despite these positive findings, however, the public's attitude toward breast cancer chemoprevention remains ambivalent, and the toxicities associated with tamoxifen, particularly endometrial cancer and thromboembolic events, have hampered the drug's uptake by high-risk women who should benefit from its preventive effects. Among the strategies to overcome such obstacles to preventive tamoxifen, two novel and potentially safer modes of delivery of this agent are discussed in this paper. Low-dose tamoxifen, expected to confer fewer adverse events, is being investigated in both clinical biomarker-based trials and observational studies. A series of systemic biomarkers (including lipid and insulin-like growth factor levels) and tissue biomarkers (including Ki-67) are known to be favorably affected by conventional tamoxifen dosing and have been shown to be modulated in a direction consistent with a putative anti-cancer effect. These findings suggest possible beneficial clinical preventive effects by low-dose tamoxifen regimens and they are supported by observational studies. An alternative approach is topical administration of active tamoxifen metabolites directly onto the breast, the site where the cancer is to be prevented. Avoidance of systemic administration is expected to reduce the distribution of drug to tissues susceptible to tamoxifen-induced toxicity. Clinical trials of topical tamoxifen with biological endpoints are still ongoing whereas pharmacokinetic studies have already shown that appropriate formulations of drug successfully penetrate the skin to reach breast tissue, where a preventive effect is sought.

Failure of Biomarkers in Clinical Trials of Alzheimer's Disease: Blaming the Messenger?

Surrogate markers of disease activity have accelerated the development of treatments in diseases as diverse as HIV infection and multiple sclerosis, where viral load and brain MRI white matter lesion burden, respectively, have essentially replaced clinical outcomes for drug development. These are just two examples of the power of surrogate markers for testing therapeutic strategies for indolent diseases like Alzheimer’s disease (AD). Since a typical Phase III AD treatment trial based on conventional clinical outcome measures requires over 400 subjects followed for a minimum of 18 months at a cost of several million dollars, the possibility of conducting biomarker-based trials with a smaller number of subjects and shorter duration of treatment is highly attractive. The markers that have shown the most promise as trial outcome measures include MRI-derived brain atrophy rates [1], nuclear medicine studies of brain amyloid burden [2], and cerebrospinal fluid (CSF) levels of biochemical correlates of the classical AD lesions, namely amyloid-β42 (Aβ42) and tau [3]. Despite the modest

Bayesian adaptive design for targeted therapy development in lung cancer — a step toward personalized medicine

With the advancement in biomedicine, many biologically targeted therapies have been developed. These targeted agents, however, may not work for everyone. Biomarker profiles can be used to identify effective targeted therapies. Our goals are to characterize the molecular signature of individual tumors, offer the best-fit targeted therapies to patients in a study, and identify promising agents for future development. We propose an outcome-based adaptive randomization trial design for patients with advanced stage non-small cell lung cancer. All patients have baseline biopsy samples taken for biomarker assessment prior to randomization to treatments. The primary endpoint of this study is the disease control rate at 8 weeks after randomization. The Bayesian probit model is used to characterize the disease control rate. Patients are adaptively randomized to one of four treatments with the randomization rate based on the updated disease control rate from the accumulated data in the trial. For each biomarker profile, high-performing treatments have higher randomization rates, and vice versa. An early stopping rule is implemented to suspend low-performing treatments from randomization. Based on extensive simulation studies, with a total of 200 evaluable patients, our trial has desirable operating characteristics to: (1) identify effective agents with a high probability; (2) suspend ineffective agents; and (3) treat more patients with effective agents that correspond to their biomarker profiles. Our trial design continues to update and refine the estimates as the trial progresses. This biomarker-based trial requires biopsible tumors and a two-week turn around time for biomarker profiling before randomization. Additionally, in order to learn from the interim data and adjust the randomization rate accordingly, the outcome-based adaptive randomization design is applicable only for trials when the endpoint can be assessed in a relative short period of time. Bayesian adaptive randomization trial design is a smart, novel, and ethical design. In conjunction with an early stopping rule, it can be used to efficiently identify effective agents, eliminate ineffective ones, and match effective treatments with patients' biomarker profiles. The proposed design is suitable for the development of targeted therapies and provides a rational design for personalized medicine.