Thioredoxin reductase is a major regulator of metabolism in leukemia cells.

Despite the fact that AML is the most common acute leukemia in adults, patient outcomes are poor necessitating the development of novel therapies. We identified that inhibition of Thioredoxin Reductase (TrxR) is a promising strategy for AML and report a highly potent and specific inhibitor of TrxR, S-250. Both pharmacologic and genetic inhibition of TrxR impairs the growth of human AML in mouse models. We found that TrxR inhibition leads to a rapid and marked impairment of metabolism in leukemic cells subsequently leading to cell death. TrxR was found to be a major and direct regulator of metabolism in AML cells through impacts on both glycolysis and the TCA cycle. Studies revealed that TrxR directly regulates GAPDH leading to a disruption of glycolysis and an increase in flux through the pentose phosphate pathway (PPP). The combined inhibition of TrxR and the PPP led to enhanced leukemia growth inhibition. Overall, TrxR abrogation, particularly with S-250, was identified as a promising strategy to disrupt AML metabolism.

Sexually dimorphic impact of the iron-regulating gene, HFE, on survival in glioblastoma.

BACKGROUND: The median survival for patients with glioblastoma (GBM), the most common primary malignant brain tumor in adults, has remained approximately 1 year for more than 2 decades. Recent advances in the field have identified GBM as a sexually dimorphic disease. It is less prevalent in females and they have better survival compared to males. The molecular mechanism of this difference has not yet been established. Iron is essential for many biological processes supporting tumor growth and its regulation is impacted by sex. Therefore, we interrogated the expression of a key component of cellular iron regulation, the HFE (homeostatic iron regulatory) gene, on sexually dimorphic survival in GBM. METHODS: We analyzed TCGA microarray gene expression and clinical data of all primary GBM patients (IDH-wild type) to compare tumor mRNA expression of HFE with overall survival, stratified by sex. RESULTS: In low HFE expressing tumors (below median expression, n = 220), survival is modulated by both sex and MGMT status, with the combination of female sex and MGMT methylation resulting in over a 10-month survival advantage (P < .0001) over the other groups. Alternatively, expression of HFE above the median (high HFE, n = 240) is associated with significantly worse overall survival in GBM, regardless of MGMT methylation status or patient sex. Gene expression analysis uncovered a correlation between high HFE expression and expression of genes associated with immune function. CONCLUSIONS: The level of HFE expression in GBM has a sexually dimorphic impact on survival. Whereas HFE expression below the median imparts a survival benefit to females, high HFE expression is associated with significantly worse overall survival regardless of established prognostic factors such as sex or MGMT methylation.

Phase I Trial of Lithium and Tretinoin for Treatment of Relapsed and Refractory Non-promyelocytic Acute Myeloid Leukemia.

Glycogen synthase kinase-3 (GSK3) inhibitors induce differentiation and growth inhibition of acute myeloid leukemia (AML) cells. Our pre-clinical studies showed GSK3 inhibition leads to sensitization of AML cells to tretinoin-mediated differentiation. We conducted a phase I trial of lithium, a GSK3 inhibitor, plus tretinoin for relapsed, refractory non-promyelocytic AML. Nine patients with median (range) age 65 (42-82) years were enrolled. All subjects had relapsed leukemia after prior therapy, with a median (range) of 3 (1-3) prior therapies. Oral lithium carbonate 300 mg was given 2-3 times daily and adjusted to meet target serum concentration (0.6 to 1.0 mmol/L); tretinoin 22.5 or 45 mg/m2/day (two equally divided doses) was administered orally on days 1-7 and 15-21 of a 28-day cycle. Four patients attained disease stability with no increase in circulating blasts for ≥4 weeks. Median (range) survival was 106 days (60-502). Target serum lithium concentration was achieved in all patients and correlated with GSK3 inhibition in leukemic cells. Immunophenotypic changes associated with myeloid differentiation were observed in five patients. The combination treatment led to a reduction in the CD34+ CD38- AML stem cell population both in vivo and in vitro. The combination of lithium and tretinoin is well-tolerated, induces differentiation of leukemic cells, and may target AML stem cells, but has limited clinical activity in the absence of other antileukemic agents. The results of this clinical trial suggest GSK3 inhibition can result in AML cell differentiation and may be a novel therapeutic strategy in this disease, particularly in combination with other antileukemic agents. Lithium is a weak GSK3 inhibitor and future strategies in AML treatment will probably require more potent agents targeting this pathway or combinations with other antileukemic agents. This trial is registered at ClinicalTrials.gov NCT01820624.

A Distinct DNA Methylation Shift in a Subset of Glioma CpG Island Methylator Phenotypes during Tumor Recurrence.

Glioma diagnosis is based on histomorphology and grading; however, such classification does not have predictive clinical outcome after glioblastomas have developed. To date, no bona fide biomarkers that significantly translate into a survival benefit to glioblastoma patients have been identified. We previously reported that the IDH mutant G-CIMP-high subtype would be a predecessor to the G-CIMP-low subtype. Here, we performed a comprehensive DNA methylation longitudinal analysis of diffuse gliomas from 77 patients (200 tumors) to enlighten the epigenome-based malignant transformation of initially lower-grade gliomas. Intra-subtype heterogeneity among G-CIMP-high primary tumors allowed us to identify predictive biomarkers for assessing the risk of malignant recurrence at early stages of disease. G-CIMP-low recurrence appeared in 9.5% of all gliomas, and these resembled IDH-wild-type primary glioblastoma. G-CIMP-low recurrence can be characterized by distinct epigenetic changes at candidate functional tissue enhancers with AP-1/SOX binding elements, mesenchymal stem cell-like epigenomic phenotype, and genomic instability. Molecular abnormalities of longitudinal G-CIMP offer possibilities to defy glioblastoma progression.

Models of epigenetic age capture patterns of DNA methylation in glioma associated with molecular subtype, survival, and recurrence.

Background: Models of epigenetic aging (epigenetic clocks) have been implicated as potentially useful markers for cancer risk and prognosis. Using 2 previously published methods for modeling epigenetic age, Horvath's clock and epiTOC, we investigated epigenetic aging patterns related to World Health Organization grade and molecular subtype as well as associations of epigenetic aging with glioma survival and recurrence. Methods: Epigenetic ages were calculated using Horvath's clock and epiTOC on 516 lower-grade glioma and 141 glioblastoma cases along with 136 nontumor (normal) brain samples. Associations of tumor epigenetic age with patient chronological age at diagnosis were assessed with correlation and linear regression, and associations were validated in an independent cohort of 203 gliomas. Contribution of epigenetic age to survival prediction was assessed using Cox proportional hazards modeling. Sixty-three samples from 18 patients with primary-recurrent glioma pairs were also analyzed and epigenetic age difference and rate of epigenetic aging of primary-recurrent tumors were correlated to time to recurrence. Results: Epigenetic ages of gliomas were near-universally accelerated using both Horvath's clock and epiTOC compared with normal tissue. The 2 independent models of epigenetic aging were highly associated with each other and exhibited distinct aging patterns reflective of molecular subtype. EpiTOC was found to be a significant independent predictor of survival. Epigenetic aging of gliomas between primary and recurrent tumors was found to be highly variable and not significantly associated with time to recurrence. Conclusions: We demonstrate that epigenetic aging reflects coherent modifications of the epigenome and can potentially provide additional prognostic power for gliomas.

Epidemiology of Intracranial Gliomas.

Gliomas are the most common primary intracranial neoplasms, which cause significant mortality and morbidity that is disproportionate to their relatively rare incidence. Many potential risk factors for glioma have been studied to date, but only few provide explanation for the number of brain tumor cases identified. The most significant findings include increased risk due to exposure to ionizing radiation and decreased risk with the history of allergy or atopic diseases. The potential effect of the cellular phone usage has been evaluated extensively, but the results remain inconclusive. A very small proportion of gliomas can be attributed to inherited genetic disorders. Additionally, recent analyses using the genome-wide association study design have identified several inherited genomic risk variants.

Brain tumor biobanking in the precision medicine era: building a high-quality resource for translational research in neuro-oncology.

The growth of precision medicine has made access to biobanks with high-quality, well-annotated neuro-oncology biospecimens critical. Developing and maintaining neuro-oncology biobanks is best accomplished through multidisciplinary collaboration between clinicians and researchers. Balancing the needs and leveraging the skills of all stakeholders in this multidisciplinary effort is of utmost importance. Collaboration with a multidisciplinary team of clinicians, health care team members, and institutions, as well as patients and their families, is essential for access to participants in order to obtain informed consent, collect samples under strict standard operating procedures, and accurate and relevant clinical annotation. Once a neuro-oncology biobank is established, development and implementation of policies related to governance and distribution of biospecimens (both within and outside the institution) is of critical importance for sustainability. Proper implementation of a governance process helps to ensure that the biospecimens and data can be utilized in research with the largest potential benefit. New NIH and peer-reviewed journal policies related to public sharing of 'omic' data generated from stored biospecimens create new ethical challenges that must be addressed in developing informed consents, protocols, and standard operating procedures. In addition, diversification of sources of funding for the biobanks is needed for long-term sustainability.

Protein Markers Predict Survival in Glioma Patients.

Glioblastoma multiforme (GBM) is a genomically complex and aggressive primary adult brain tumor, with a median survival time of 12-14 months. The heterogeneous nature of this disease has made the identification and validation of prognostic biomarkers difficult. Using reverse phase protein array data from 203 primary untreated GBM patients, we have identified a set of 13 proteins with prognostic significance. Our protein signature predictive of glioblastoma (PROTGLIO) patient survival model was constructed and validated on independent data sets and was shown to significantly predict survival in GBM patients (log-rank test: p = 0.0009). Using a multivariate Cox proportional hazards, we have shown that our PROTGLIO model is distinct from other known GBM prognostic factors (age at diagnosis, extent of surgical resection, postoperative Karnofsky performance score (KPS), treatment with temozolomide (TMZ) chemoradiation, and methylation of the MGMT gene). Tenfold cross-validation repetition of our model generation procedure confirmed validation of PROTGLIO. The model was further validated on an independent set of isocitrate dehydrogenase wild-type (IDHwt) lower grade gliomas (LGG)-a portion of these tumors progress rapidly to GBM. The PROTGLIO model contains proteins, such as Cox-2 and Annexin 1, involved in inflammatory response, pointing to potential therapeutic interventions. The PROTGLIO model is a simple and effective predictor of overall survival in glioblastoma patients, making it potentially useful in clinical practice of glioblastoma multiforme.

A Novel Glycogen Synthase Kinase-3 Inhibitor Optimized for Acute Myeloid Leukemia Differentiation Activity.

Standard therapies used for the treatment of acute myeloid leukemia (AML) are cytotoxic agents that target rapidly proliferating cells. Unfortunately, this therapeutic approach has limited efficacy and significant toxicity and the majority of AML patients still die of their disease. In contrast to the poor prognosis of most AML patients, most individuals with a rare subtype of AML, acute promyelocytic leukemia, can be cured by differentiation therapy using regimens containing all-trans retinoic acid. GSK3 has been previously identified as a therapeutic target in AML where its inhibition can lead to the differentiation and growth arrest of leukemic cells. Unfortunately, existing GSK3 inhibitors lead to suboptimal differentiation activity making them less useful as clinical AML differentiation agents. Here, we describe the discovery of a novel GSK3 inhibitor, GS87. GS87 was discovered in efforts to optimize GSK3 inhibition for AML differentiation activity. Despite GS87's dramatic ability to induce AML differentiation, kinase profiling reveals its high specificity in targeting GSK3 as compared with other kinases. GS87 demonstrates high efficacy in a mouse AML model system and unlike current AML therapeutics, exhibits little effect on normal bone marrow cells. GS87 induces potent differentiation by more effectively activating GSK3-dependent signaling components including MAPK signaling as compared with other GSK3 inhibitors. GS87 is a novel GSK3 inhibitor with therapeutic potential as a differentiation agent for non-promyelocytic AML. Mol Cancer Ther; 15(7); 1485-94. ©2016 AACR.

Epidemiology of gliomas.

Gliomas are the most common type of primary intracranial tumors. Some glioma subtypes cause significant mortality and morbidity that are disproportionate to their relatively rare incidence. A very small proportion of glioma cases can be attributed to inherited genetic disorders. Many potential risk factors for glioma have been studied to date, but few provide explanation for the number of brain tumors identified. The most significant of these factors includes increased risk due to exposure to ionizing radiation, and decreased risk with history of allergy or atopic disease. The potential effect of exposure to cellular phones has been studied extensively, but the results remain inconclusive. Recent genomic analyses, using the genome-wide association study (GWAS) design, have identified several inherited risk variants that are associated with increased glioma risk. The following chapter provides an overview of the current state of research in the epidemiology of intracranial glioma.

Pharmacogenomic approach to identify drug sensitivity in small-cell lung cancer.

There are currently no molecular targeted approaches to treat small-cell lung cancer (SCLC) similar to those used successfully against non-small-cell lung cancer. This failure is attributable to our inability to identify clinically-relevant subtypes of this disease. Thus, a more systematic approach to drug discovery for SCLC is needed. In this regard, two comprehensive studies recently published in Nature, the Cancer Cell Line Encyclopedia and the Cancer Genome Project, provide a wealth of data regarding the drug sensitivity and genomic profiles of many different types of cancer cells. In the present study we have mined these two studies for new therapeutic agents for SCLC and identified heat shock proteins, cyclin-dependent kinases and polo-like kinases (PLK) as attractive molecular targets with little current clinical trial activity in SCLC. Remarkably, our analyses demonstrated that most SCLC cell lines clustered into a single, predominant subgroup by either gene expression or CNV analyses, leading us to take a pharmacogenomic approach to identify subgroups of drug-sensitive SCLC cells. Using PLK inhibitors as an example, we identified and validated a gene signature for drug sensitivity in SCLC cell lines. This gene signature could distinguish subpopulations among human SCLC tumors, suggesting its potential clinical utility. Finally, circos plots were constructed to yield a comprehensive view of how transcriptional, copy number and mutational elements affect PLK sensitivity in SCLC cell lines. Taken together, this study outlines an approach to predict drug sensitivity in SCLC to novel targeted therapeutics.

Computational identification of multi-omic correlates of anticancer therapeutic response.

BACKGROUND: A challenge in precision medicine is the transformation of genomic data into knowledge that can be used to stratify patients into treatment groups based on predicted clinical response. Although clinical trials remain the only way to truly measure drug toxicities and effectiveness, as a scientific community we lack the resources to clinically assess all drugs presently under development. Therefore, an effective preclinical model system that enables prediction of anticancer drug response could significantly speed the broader adoption of personalized medicine. RESULTS: Three large-scale pharmacogenomic studies have screened anticancer compounds in greater than 1000 distinct human cancer cell lines. We combined these datasets to generate and validate multi-omic predictors of drug response. We compared drug response signatures built using a penalized linear regression model and two non-linear machine learning techniques, random forest and support vector machine. The precision and robustness of each drug response signature was assessed using cross-validation across three independent datasets. Fifteen drugs were common among the datasets. We validated prediction signatures for eleven out of fifteen tested drugs (17-AAG, AZD0530, AZD6244, Erlotinib, Lapatinib, Nultin-3, Paclitaxel, PD0325901, PD0332991, PF02341066, and PLX4720). CONCLUSIONS: Multi-omic predictors of drug response can be generated and validated for many drugs. Specifically, the random forest algorithm generated more precise and robust prediction signatures when compared to support vector machines and the more commonly used elastic net regression. The resulting drug response signatures can be used to stratify patients into treatment groups based on their individual tumor biology, with two major benefits: speeding the process of bringing preclinical drugs to market, and the repurposing and repositioning of existing anticancer therapies.