Bayesian Clinical Trials.

The following is an introduction from the guest editors for the Bayesian Clinical Trials series.

Bayesian Methods in Human Drug and Biological Products Development in CDER and CBER.

The Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER) of the U.S. Food and Drug Administration (FDA) have been leaders in protecting and promoting the U.S. public health by helping to ensure that safe and effective drugs and biological products are available in the United States for those who need them. The null hypothesis significance testing approach, along with other considerations, is typically used to demonstrate the effectiveness of a drug or biological product. The Bayesian framework presents an alternative approach to demonstrate the effectiveness of a treatment. This article discusses the Bayesian framework for drug and biological product development, highlights key settings in which Bayesian approaches may be appropriate, and provides recent examples of the use of Bayesian approaches within CDER and CBER.

FDA Approval Summary: Belumosudil for Adult and Pediatric Patients 12 Years and Older with Chronic GvHD after Two or More Prior Lines of Systemic Therapy.

On July 16, 2021, the FDA approved belumosudil, a kinase inhibitor, for adult and pediatric patients 12 years and older with chronic GvHD (cGvHD) after failure of at least two prior lines of systemic therapy. Approval was based on the results of Study KD025-213, which included 65 patients with cGvHD treated with belumosudil 200 mg daily in an open-label, single-arm cohort. Efficacy was determined by the overall response rate (ORR) through Cycle 7 Day 1, which included complete response (CR) or partial response (PR) according to the 2014 NIH consensus criteria, and durability of response. The ORR through Cycle 7 Day 1 was 75% [95% confidence interval (CI), 63-85]; 6% of patients achieved a CR, and 69% achieved a PR. The median duration of response was 1.9 months (95% CI, 1.2-2.9), and 62% (95% CI, 46-74) of responding patients remained alive without new systemic therapy for at least 12 months from response. The common adverse reactions were infections, asthenia, nausea, diarrhea, dyspnea, cough, edema, hemorrhage, abdominal pain, musculoskeletal pain, headache, phosphate decreased, gamma-glutamyl transferase increased, lymphocytes decreased, and hypertension. Additional study is warranted to confirm safety with long-term use.

Comparison of confidence interval methods for an intra-class correlation coefficient (ICC).

BACKGROUND: The intraclass correlation coefficient (ICC) is widely used in biomedical research to assess the reproducibility of measurements between raters, labs, technicians, or devices. For example, in an inter-rater reliability study, a high ICC value means that noise variability (between-raters and within-raters) is small relative to variability from patient to patient. A confidence interval or Bayesian credible interval for the ICC is a commonly reported summary. Such intervals can be constructed employing either frequentist or Bayesian methodologies. METHODS: This study examines the performance of three different methods for constructing an interval in a two-way, crossed, random effects model without interaction: the Generalized Confidence Interval method (GCI), the Modified Large Sample method (MLS), and a Bayesian method based on a noninformative prior distribution (NIB). Guidance is provided on interval construction method selection based on study design, sample size, and normality of the data. We compare the coverage probabilities and widths of the different interval methods. RESULTS: We show that, for the two-way, crossed, random effects model without interaction, care is needed in interval method selection because the interval estimates do not always have properties that the user expects. While different methods generally perform well when there are a large number of levels of each factor, large differences between the methods emerge when the number of one or more factors is limited. In addition, all methods are shown to lack robustness to certain hard-to-detect violations of normality when the sample size is limited. CONCLUSIONS: Decision rules and software programs for interval construction are provided for practical implementation in the two-way, crossed, random effects model without interaction. All interval methods perform similarly when the data are normal and there are sufficient numbers of levels of each factor. The MLS and GCI methods outperform the NIB when one of the factors has a limited number of levels and the data are normally distributed or nearly normally distributed. None of the methods work well if the number of levels of a factor are limited and data are markedly non-normal. The software programs are implemented in the popular R language.

A narrow repertoire of transcriptional modules responsive to pyogenic bacteria is impaired in patients carrying loss-of-function mutations in MYD88 or IRAK4.

Loss of function of the kinase IRAK4 or the adaptor MyD88 in humans interrupts a pathway critical for pathogen sensing and ignition of inflammation. However, patients with loss-of-function mutations in the genes encoding these factors are, unexpectedly, susceptible to only a limited range of pathogens. We employed a systems approach to investigate transcriptome responses following in vitro exposure of patients' blood to agonists of Toll-like receptors (TLRs) and receptors for interleukin 1 (IL-1Rs) and to whole pathogens. Responses to purified agonists were globally abolished, but variable residual responses were present following exposure to whole pathogens. Further delineation of the latter responses identified a narrow repertoire of transcriptional programs affected by loss of MyD88 function or IRAK4 function. Our work introduces the use of a systems approach for the global assessment of innate immune responses and the characterization of human primary immunodeficiencies.

Analysis of the expression of human tumor antigens in ovarian cancer tissues.

Biomarkers for early detection of cancer have great clinical diagnostic potential. Numerous reports have documented the generation of humoral immune responses that are triggered in response to changes in protein expression patterns in tumor tissues and these biomarkers are referred to as tumor associated antigens (TAAs). Using a high-throughput technology, we previously identified 65 proteins as diagnostically useful TAAs by profiling the humoral immune responses in ovarian cancer (OVCA) patients. Here we determined the expression status of some of those TAAs in tissues from OVCA patients. The protein expression patterns of 4 of those 65 antigens, namely NASP, RCAS1, Nijmegen breakage syndrome1 (NBS1) and eIF5A, along with p53 and Her2 (known molecular prognosticators) and two proteins that interact with NBS1, MRE11 and RAD50, were assessed by immunohistochemistry (IHC). NASP and RCAS1 proteins were more frequently expressed in ovarian cancer tissues than with normal ovarian tissue and serous cystadenomas and MRE11 was less frequently expressed. When evaluated simultaneously, only NASP and MRE11 remained statistically significant with sensitivity of 66% and specificity of 89%. None of these proteins' expression levels were prognostic for survival. Together, our results indicate that occurrence of humoral immune responses against some of these TAAs in OVCA patients is triggered by antigen protein overexpression.

Depression, cortisol, and suppressed cell-mediated immunity in metastatic breast cancer.

Cancer treatment is known to have significant immuno-suppressive/dysregulatory effects. Psychological distress and depression, which often accompany cancer diagnosis and treatment, can also suppress or dysregulate endocrine and immune function. Cell-mediated immunity (CMI) is critical for protection against a host of pathogens to which cancer patients may be particularly susceptible. CMI is also important for defense against some tumors. This study explored relationships among depressive symptoms, cortisol secretion, and CMI responses in 72 women with metastatic breast cancer. Depressive symptoms were assessed with the Center for Epidemiologic Studies-Depression Scale (CES-D). Saliva was sampled throughout the day over a 3-day period to obtain a physiologic index of diurnal cortisol concentrations and rhythmicity, which is associated with breast cancer survival time. CMI for specific antigens was measured following intradermal administration of seven commonly encountered antigens (tuberculin, tetanus, diphtheria, Streptococcus, Candida, Trichophyton, and Proteus). Analyses adjusting for relevant medical and treatment variables indicated that women reporting more depressive symptoms showed suppressed immunity as measured by lower average induration size. Women with higher mean diurnal cortisol concentrations also showed suppressed immunity as indicated by a decreased number of antigens to which positive reactions were measured. This study highlights the relationships among depression, stress, and immune function in the context of advanced breast cancer.

Autoantibody approach for serum-based detection of head and neck cancer.

Currently, no effective tool exists for screening or early diagnosis of head and neck squamous cell carcinoma (HNSCC). Here, we describe an approach for cancer detection based on analysis of patterns of serum immunoreactivity against a panel of biomarkers selected using microarray-based serologic profiling and specialized bioinformatics. We biopanned phage display libraries derived from three different HNSCC tissues to generate 5,133 selectively cloned tumor antigens. Based on their differential immunoreactivity on protein microarrays against serum immunoglobulins from 39 cancer and 41 control patients, we reduced the number of clones to 1,021. The performance of a neural network model (Multilayer Perceptron) for cancer classification on a data set of 80 HNSCC and 78 control samples was assessed using 10-fold cross-validation repeated 100 times. A panel of 130 clones was found to be adequate for building a classifier with sufficient sensitivity and specificity. Using these 130 markers on a completely new and independent set of 80 samples, an accuracy of 84.9% with sensitivity of 79.8% and specificity of 90.1% was achieved. Similar performance was achieved by reshuffling of the data set and by using other classification models. The performance of this classification approach represents a significant improvement over current diagnostic accuracy (sensitivity of 37% to 46% and specificity of 24%) in the primary care setting. The results shown here are promising and show the potential use of this approach toward eventual development of diagnostic assay with sufficient sensitivity and specificity suitable for detection of early-stage HNSCC in high-risk populations.

Pathways to implementation of serum proteomics for cancer.

Proteomic and genomic technologies have been developed that can simultaneously detect large panels of cancer biomarkers in body fluids such as serum, plasma, sputum, saliva or urine. These approaches provide great promise for the early detection of cancer, but have thrust the field into the era of diagnostic multianalyte-based cancer tests with few, if any, models for the implementation of such tests. These multianalyte tests may be based on the detection of serum antibodies to tumor antigens, the presence of cancer-related proteins in serum or the presence of tumor-specific genomic changes that appear in plasma as free DNA. The application of noninvasive diagnostic approaches to detect early stage cancer will provide the physician with greater presymptomatic periods for clinical intervention, but it is uncertain how the various forces will impact their implementation in a patient care setting. Utilization will be balanced by medical follow-up pathways, commercial/reimbursement factors and regulatory issues that influence implementation of new devices in the marketplace.

Diagnostic markers of ovarian cancer by high-throughput antigen cloning and detection on arrays.

A noninvasive screening test would significantly facilitate early detection of epithelial ovarian cancer. This study used a combination of high-throughput selection and array-based serologic detection of many antigens indicative of the presence of cancer, thereby using the immune system as a biosensor. This high-throughput selection involved biopanning of an ovarian cancer phage display library using serum immunoglobulins from an ovarian cancer patient as bait. Protein macroarrays containing 480 of these selected antigen clones revealed 65 clones that interacted with immunoglobulins in sera from 32 ovarian cancer patients but not with sera from 25 healthy women or 14 patients having other benign or malignant gynecologic diseases. Sequence analysis data of these 65 clones revealed 62 different antigens. Among the markers, we identified some known antigens, including RCAS1, signal recognition protein-19, AHNAK-related sequence, nuclear autoantogenic sperm protein, Nijmegen breakage syndrome 1 (Nibrin), ribosomal protein L4, Homo sapiens KIAA0419 gene product, eukaryotic initiation factor 5A, and casein kinase II, as well as many previously uncharacterized antigenic gene products. Using these 65 antigens on protein microarrays, we trained neural networks on two-color fluorescent detection of serum IgG binding and found an average sensitivity and specificity of 55% and 98%, respectively. In addition, the top 6 of the most specific clones resulted in an average sensitivity and specificity of 32% and 94%, respectively. This global approach to antigenic profiling, epitomics, has applications to cancer and autoimmune diseases for diagnostic and therapeutic studies. Further work with larger panels of antigens should provide a comprehensive set of markers with sufficient sensitivity and specificity suitable for clinical testing in high-risk populations.

Epitomics: global profiling of immune response to disease using protein microarrays.

The immune system retains memory of current and past infections and can sense the presence of cancer by elaborating autoantibodies to tumor proteins. In the presence of an autoimmune disease, the immune system is an efficient, natural biosensor. Therefore we exploit the immune system through a high-throughput process to isolate disease-specific epitopes for diagnostic and therapeutic purposes. These cloned disease-specific antigens are robotically spotted onto protein microarrays and interrogated with serum from the subjects under analyses. These arrays deliver personalized profiles of antigenic exposures and therapeutic targets for personalized immunotherapy. The immune system is the ultimate biosensor, superior to anything a human could create and ready to be exploited for biotechnology and biomedicine.