Robert W. Cook

Beta A versus beta B: is it merely a matter of expression?

Activins are members of the transforming growth factor (TGF) beta (beta) superfamily of proteins that function in a wide array of physiological processes. Like other TGFbeta ligands, activins are biologically active as dimers. An activin molecule is comprised of two beta-subunits, of which four isoforms have been identified: betaA, betaB, betaC, and betaE. The most widely studied activins to date are activin A (betaA/betaA), activin B (betaB/betaB), and activin AB (betaA/betaB). Inhibin is a naturally occurring activin antagonist that consists of an alpha-subunit disulfide-linked to one of the activin beta-subunits, producing inhibin A (alpha/betaA), or inhibin B (alpha/betaB). The development of assays distinguishing between different forms of activins and inhibins, along with knock-in and knock-out models, have provided evidence that the betaA- and betaB-subunits have independent and separate roles physiologically. Additionally, evaluation of ligand-receptor interactions indicates significant differences in receptor affinity between activin isoforms, as well as between inhibin isoforms. In this review we explore the differences between activin/inhibin betaA- and betaB-subunits, including expression patterns, binding properties, and the specific structural aspects of each. From the growing pool of knowledge regarding activins and inhibins, the emerging data support the hypothesis that betaA- and betaB-subunits are functionally differently.

System Design of a Dynamic Microprocessor

Dynamic microprogramnming (i. e., utilizing a READ/ WRITE microstorage) allows the structure of a computer to be altered to suit a problem at hand and results in major efficiencies (an order of magnitude) in running time for nonarithmetic programs (e. g., compilers).

Development of a Prognostic Genetic Signature to Predict the Metastatic Risk Associated with Cutaneous Melanoma

Purpose: The development of a genetic signature for the identification of high-risk cutaneous melanoma tumors would provide a valuable prognostic tool with value for stage I and II patients who represent a remarkably heterogeneous group with a 3% to 55% chance of disease progression and death 5 years from diagnosis. Experimental Design: A prognostic 28-gene signature was identified by analysis of microarray expression data. Primary cutaneous melanoma tumor tissue was evaluated by RT-PCR for expression of the signature, and radial basis machine (RBM) modeling was performed to predict risk of metastasis. Results: RBM analysis of cutaneous melanoma tumor gene expression reports low risk (class 1) or high risk (class 2) of metastasis. Metastatic risk was predicted with high accuracy in development (ROC = 0.93) and validation (ROC = 0.91) cohorts of primary cutaneous melanoma tumor tissue. Kaplan–Meier analysis indicated that the 5-year disease-free survival (DFS) rates in the development set were 100% and 38% for predicted classes 1 and 2 cases, respectively (P < 0.0001). DFS rates for the validation set were 97% and 31% for predicted classes 1 and 2 cases, respectively (P < 0.0001). Gene expression profile (GEP), American Joint Committee on Cancer stage, Breslow thickness, ulceration, and age were independent predictors of metastatic risk according to Cox regression analysis. Conclusions: The GEP signature accurately predicts metastasis risk in a multicenter cohort of primary cutaneous melanoma tumors. Preliminary Cox regression analysis indicates that the signature is an independent predictor of metastasis risk in the cohort presented. Clin Cancer Res; 21(1); 175–83. ©2015 AACR.

Gene expression profiling for molecular staging of cutaneous melanoma in patients undergoing sentinel lymph node biopsy.

BACKGROUND A gene expression profile (GEP) test able to accurately identify risk of metastasis for patients with cutaneous melanoma has been clinically validated. OBJECTIVE We aimed for assessment of the prognostic accuracy of GEP and sentinel lymph node biopsy (SLNB) tests, independently and in combination, in a multicenter cohort of 217 patients. METHODS Reverse transcription polymerase chain reaction (RT-PCR) was performed to assess the expression of 31 genes from primary melanoma tumors, and SLNB outcome was determined from clinical data. Prognostic accuracy of each test was determined using Kaplan-Meier and Cox regression analysis of disease-free, distant metastasis-free, and overall survivals. RESULTS GEP outcome was a more significant and better predictor of each end point in univariate and multivariate regression analysis, compared with SLNB (P < .0001 for all). In combination with SLNB, GEP improved prognostication. For patients with a GEP high-risk outcome and a negative SLNB result, Kaplan-Meier 5-year disease-free, distant metastasis-free, and overall survivals were 35%, 49%, and 54%, respectively. LIMITATIONS Within the SLNB-negative cohort of patients, overall risk of metastatic events was higher (∼30%) than commonly found in the general population of patients with melanoma. CONCLUSIONS In this study cohort, GEP was an objective tool that accurately predicted metastatic risk in SLNB-eligible patients.

Structural Basis for a Functional Antagonist in the Transforming Growth Factor β Superfamily

Within the transforming growth factor β superfamily, the agonist-antagonist relationship between activin and inhibin is unique and critical to integrated reproductive function. Activin acts in the pituitary to stimulate follicle-stimulating hormone, and is antagonized by endocrine acting, gonadally derived inhibin. We have undertaken a mutational analysis of the activin βA subunit to determine the precise structural aspects that contribute to inhibin antagonism of activin. By substituting specific amino acid residues in the activin βA subunit with similarly aligned amino acids from the α subunit, we have pinpointed the residues required for activin receptor binding and activity, as well as for inhibin antagonism of activin through its receptors. Additionally, we have identified an activin mutant with a higher affinity for the activin type I receptor that provides structural evidence for the evolution of ligand-receptor interactions within the transforming growth factor β superfamily.

Altered sex hormone concentrations and gonadal mRNA expression levels of activin signaling factors in hatchling alligators from a contaminated Florida lake

Activins and estrogens participate in regulating the breakdown of ovarian germ cell nests and follicle assembly in mammals. In 1994, our group reported elevated frequencies of abnormal, multioocytic ovarian follicles in 6 month old, environmental contaminant-exposed female alligators after gonadotropin challenge. Here, we investigated if maternal contribution of endocrine disrupting contaminants to the egg subsequently alters estrogen/inhibin/activin signaling in hatchling female offspring, putatively predisposing an increased frequency of multioocytic follicle formation. We quantified basal and exogenous gonadotropin-stimulated concentrations of circulating plasma steroid hormones and ovarian activin signaling factor mRNA abundance in hatchling alligators from the same contaminated (Lake Apopka) and reference (Lake Woodruff) Florida lakes, as examined in 1994. Basal circulating plasma estradiol and testosterone concentrations were greater in alligators from the contaminated environment, whereas activin/inhibin betaA subunit and follistatin mRNA abundances were lower than values measured in ovaries from reference lake animals. Challenged, contaminant-exposed animals showed a more robust increase in plasma estradiol concentration following an acute follicle stimulating hormone (FSH) challenge compared with reference site alligators. Aromatase and follistatin mRNA levels increased in response to an extended FSH challenge in the reference site animals, but not in the contaminant-exposed animals. In hatchling alligators, ovarian follicles have not yet formed; therefore, these endocrine differences are likely to affect subsequent ovarian development, including ovarian follicle assembly.

A Gene Signature to Determine Metastatic Behavior in Thymomas

Purpose Thymoma represents one of the rarest of all malignancies. Stage and completeness of resection have been used to ascertain postoperative therapeutic strategies albeit with limited prognostic accuracy. A molecular classifier would be useful to improve the assessment of metastatic behaviour and optimize patient management. Methods qRT-PCR assay for 23 genes (19 test and four reference genes) was performed on multi-institutional archival primary thymomas (n = 36). Gene expression levels were used to compute a signature, classifying tumors into classes 1 and 2, corresponding to low or high likelihood for metastases. The signature was validated in an independent multi-institutional cohort of patients (n = 75). Results A nine-gene signature that can predict metastatic behavior of thymomas was developed and validated. Using radial basis machine modeling in the training set, 5-year and 10-year metastasis-free survival rates were 77% and 26% for predicted low (class 1) and high (class 2) risk of metastasis (P = 0.0047, log-rank), respectively. For the validation set, 5-year metastasis-free survival rates were 97% and 30% for predicted low- and high-risk patients (P = 0.0004, log-rank), respectively. The 5-year metastasis-free survival rates for the validation set were 49% and 41% for Masaoka stages I/II and III/IV (P = 0.0537, log-rank), respectively. In univariate and multivariate Cox models evaluating common prognostic factors for thymoma metastasis, the nine-gene signature was the only independent indicator of metastases (P = 0.036). Conclusion A nine-gene signature was established and validated which predicts the likelihood of metastasis more accurately than traditional staging. This further underscores the biologic determinants of the clinical course of thymoma and may improve patient management.

Choline kinase β mutant mice exhibit reduced phosphocholine, elevated osteoclast activity, and low bone mass

Background: Bone homeostasis requires constant remodeling by bone-resorbing osteoclasts and bone-forming osteoblasts. Results: Choline kinase β (CHKB)-deficient mice have reduced bone mass, increased osteoclast number, and impaired osteoblast function. Conclusion: CHKB activity controls bone homeostasis via regulation of osteoclasts and osteoblasts. Significance: This is the first report implicating a role for CHKB in bone homeostasis. The maintenance of bone homeostasis requires tight coupling between bone-forming osteoblasts and bone-resorbing osteoclasts. However, the precise molecular mechanism(s) underlying the differentiation and activities of these specialized cells are still largely unknown. Here, we identify choline kinase β (CHKB), a kinase involved in the biosynthesis of phosphatidylcholine, as a novel regulator of bone homeostasis. Choline kinase β mutant mice (flp/flp) exhibit a systemic low bone mass phenotype. Consistently, osteoclast numbers and activity are elevated in flp/flp mice. Interestingly, osteoclasts derived from flp/flp mice exhibit reduced sensitivity to excessive levels of extracellular calcium, which could account for the increased bone resorption. Conversely, supplementation of cytidine 5′-diphosphocholine in vivo and in vitro, a regimen that bypasses CHKB deficiency, restores osteoclast numbers to physiological levels. Finally, we demonstrate that, in addition to modulating osteoclast formation and function, loss of CHKB corresponds with a reduction in bone formation by osteoblasts. Taken together, these data posit CHKB as a new modulator of bone homeostasis.

Current clinical practice: differential management of uveal melanoma in the era of molecular tumor analyses

Objective Assess current clinical practices for uveal melanoma (UM) and the impact of molecular prognostic testing on treatment decisions. Design Cross-sectional survey and sequential medical records review. Participants Ophthalmologists who treat UM. Methods (A) Medical records review of all Medicare beneficiaries tested by UM gene expression profile in 2012, conducted under an institutional review board-approved protocol. (B) 109 ophthalmologists specializing in the treatment of UM were invited to participate in 24-question survey in 2012; 72 were invited to participate in a 23-question survey in 2014. Main outcome measures Responses analyzed by descriptive statistics, frequency analyses (percentages, Tukey, histograms), and Fisher’s exact test. Descriptive presentation of essay answers. Results The review of Medicare medical records included 191 evaluable patients, 88 (46%) with documented medical treatment actions or institutional policies related to surveillance plans. Of these 88, all gene expression profiling (GEP) Class 1 UM patients were treated with low-intensity surveillance. All GEP Class 2 UM patients were treated with high-intensity surveillance (P<0.0001 versus Class 1). There were 36 (19%) with information concerning referrals after initial diagnosis. Of these 36, all 23 Class 2 patients were referred to medical oncology; however, none of the 13 Class 1 patients were referred (P<0.0001 versus Class 1). Only Class 2 patients were recommended for adjunctive treatment regimens. 2012 survey: 50 respondents with an annual median of 35 new UM patients. The majority of respondents (82%) performed molecular analysis of UM tumors after fine needle biopsy (FNAB); median: 15 FNAB per year; 2014 survey: 35 respondents with an annual median of 30 new UM patients. The majority offered molecular analyses of UM tumor samples to most patients. Patients with low metastatic risk (disomy 3 or GEP Class 1) were generally assigned to less frequent (every 6 or 12 months) and less intensive clinical visits. Patients with high metastatic risk (monosomy 3 or GEP Class 2) were assigned to more frequent surveillance with hepatic imaging and liver function testing every 3–6 months. High-risk patients were considered more suitable for adjuvant treatment protocols. Conclusion The majority of ophthalmologists treating UM have adopted molecular diagnostic tests for the purpose of designing risk-appropriate treatment strategies.

Clinical impact of a 31-gene expression profile test for cutaneous melanoma in 156 prospectively and consecutively tested patients

Abstract Objective: DecisionDx-Melanoma* is a 31-gene expression profile test that predicts the risk of metastasis in patients with primary cutaneous melanoma (CM). This study was designed to ascertain clinical management changes determined by the test outcome, which classifies CM patients being at low (Class 1) or high (Class 2) risk for recurrence. Research design and methods: Medical charts were reviewed from 156 CM patients from six institutions (three dermatology and three surgical oncology practices) who were consecutively tested between May 2013 and December 2015. Clinical management data that were compiled and compared before and after receipt of the 31-gene expression test result included frequency of physical exams, frequency and modality of imaging, and referrals to surgical and medical oncologists. Results: Forty-two percent of patients were Stage I, 47% were Stage II and 8% were Stage III. Overall, 95 patients (61%) were Class 1 and 61 (39%) were Class 2. Documented changes in management were observed in 82 (53%) patients, with the majority of Class 2 patients (77%) undergoing management changes compared to 37% of Class 1 patients (p < 0.0001 by Fisher’s exact test). The majority (77/82, 94%) of these changes were concordant with the risk indicated by the test result (p < 0.0001 by Fisher’s exact test), with increased management intensity for Class 2 patients and reduced management intensity for Class 1 patients. Conclusions: Molecular risk classification by gene expression profiling has clinical impact and influences physicians to direct clinical management of CM patients. The vast majority of the changes implemented after the receipt of test results were reflective of the low or high recurrence risk associated with the patient’s molecular classification. Because follow-up data was not collected for this patient cohort, the study is limited for the assessment of the impact of gene expression profile based management changes on healthcare resource utilization and patient outcome.