Durvalumab and guadecitabine in advanced clear cell renal cell carcinoma: results from the phase Ib/II study BTCRC-GU16-043.

Epigenetic modulation is well established in hematologic malignancies but to a lesser degree in solid tumors. Here we report the results of a phase Ib/II study of guadecitabine and durvalumab in advanced clear cell renal cell carcinoma (ccRCC; NCT03308396). Patients received guadecitabine (starting at 60 mg/m2 subcutaneously on days 1-5 with de-escalation to 45 mg/m2 in case of dose limiting toxicity) with durvalumab (1500 mg intravenously on day 8). The study enrolled 57 patients, 6 in phase Ib with safety being the primary objective and 51in phase II, comprising 2 cohorts: 36 patients in Cohort 1 were treatment naive to checkpoint inhibitors (CPI) with 0-1 prior therapies and 15 patients in Cohort 2 were treated with up to two prior systemic therapies including one CPI. The combination of guadecitabine 45 mg/m2 with durvalumab 1500 mg was deemed safe. The primary objective of overall response rate (ORR) in cohort 1 was 22%. Sixteen patients (44%) experienced stable disease (SD). Secondary objectives included overall survival (OS), duration of response, progression-free survival (PFS), clinical benefit rate, and safety as well as ORR for Cohort 2. Median PFS for cohort 1 and cohort 2 were 14.26 and 3.91 months respectively. Median OS was not reached. In cohort 2, one patient achieved a partial response and 60% achieved SD. Asymptomatic neutropenia was the most common adverse event. Even though the trial did not meet the primary objective in cohort 1, the tolerability and PFS signal in CPI naive patients are worth further investigation.

SRGN-Triggered Aggressive and Immunosuppressive Phenotype in a Subset of TTF-1-Negative Lung Adenocarcinomas.

BACKGROUND: Approximately 20% of lung adenocarcinoma (LUAD) is negative for the lineage-specific oncogene Thyroid transcription factor 1 (TTF-1) and exhibits worse clinical outcome with a low frequency of actionable genomic alterations. To identify molecular features associated with TTF-1-negative LUAD, we compared the transcriptomic and proteomic profiles of LUAD cell lines. SRGN , a chondroitin sulfate proteoglycan Serglycin, was identified as a markedly overexpressed gene in TTF-1-negative LUAD. We therefore investigated the roles and regulation of SRGN in TTF-1-negative LUAD. METHODS: Proteomic and metabolomic analyses of 41 LUAD cell lines were done using mass spectrometry. The function of SRGN was investigated in 3 TTF-1-negative and 4 TTF-1-positive LUAD cell lines and in a syngeneic mouse model (n = 5 to 8 mice per group). Expression of SRGN was evaluated in 94 and 105 surgically resected LUAD tumor specimens using immunohistochemistry. All statistical tests were 2-sided. RESULTS: SRGN was markedly overexpressed at mRNA and protein levels in TTF-1-negative LUAD cell lines (P < .001 for both mRNA and protein levels). Expression of SRGN in LUAD tumor tissue was associated with poor outcome (hazard ratio = 4.22, 95% confidence interval = 1.12 to 15.86, likelihood ratio test, P = .03), and with higher expression of Programmed cell death 1 ligand 1 (PD-L1) in tumor cells and higher infiltration of Programmed cell death protein 1-positive lymphocytes. SRGN regulated expression of PD-L1 as well as proinflammatory cytokines, including Interleukin-6, Interleukin-8, and C-X-C motif chemokine 1 in LUAD cell lines; increased migratory and invasive properties of LUAD cells and fibroblasts; and enhanced angiogenesis. SRGN was induced by DNA demethylation resulting from Nicotinamide N-methyltransferase-mediated impairment of methionine metabolism. CONCLUSIONS: Our findings suggest that SRGN plays a pivotal role in tumor-stromal interaction and reprogramming into an aggressive and immunosuppressive tumor microenvironment in TTF-1-negative LUAD.

The Calcium Versus Hemorrhage Trial: Developing Diagnostic Criteria for Chronic Intracranial Susceptibility Lesions Using Single-Energy Computed Tomography, Dual-Energy Computed Tomography, and Quantitative Susceptibility Mapping.

PURPOSE: Chronic susceptibility lesions in the brain can be either hemorrhagic (potentially dangerous) or calcific (usually not dangerous) but are difficult to discriminate on routine imaging. We proposed to develop quantitative diagnostic criteria for single-energy computed tomography (SECT), dual-energy computed tomography (DECT), and quantitative susceptibility mapping (QSM) to distinguish hemorrhage from calcium. MATERIALS AND METHODS: Patients with positive susceptibility lesions on routine T2*-weighted magnetic resonance of the brain were recruited into this prospective imaging clinical trial, under institutional review board approval and with informed consent. The SECT, DECT, and QSM images were obtained, the lesions were identified, and the regions of interest were defined, with the mean values recorded. Criteria for quantitative interpretation were developed on the first 50 patients, and then applied to the next 45 patients. Contingency tables, scatter plots, and McNemar test were applied to compare classifiers. RESULTS: There were 95 evaluable patients, divided into a training set of 50 patients (328 lesions) and a validation set of 45 patients (281 lesions). We found the following classifiers to best differentiate hemorrhagic from calcific lesions: less than 68 Hounsfield units for SECT, calcium level of less than 15 mg/mL (material decomposition value) for DECT, and greater than 38 ppb for QSM. There was general mutual agreement among the proposed criteria. The proposed criteria outperformed the current published criteria. CONCLUSIONS: We provide the updated criteria for the classification of chronic positive susceptibility brain lesions as hemorrhagic versus calcific for each major clinically available imaging modality. These proposed criteria have greater internal consistency than the current criteria and should likely replace it as gold standard.

Quantile Graphical Models: Bayesian Approaches.

Graphical models are ubiquitous tools to describe the interdependence between variables measured simultaneously such as large-scale gene or protein expression data. Gaussian graphical models (GGMs) are well-established tools for probabilistic exploration of dependence structures using precision matrices and they are generated under a multivariate normal joint distribution. However, they suffer from several shortcomings since they are based on Gaussian distribution assumptions. In this article, we propose a Bayesian quantile based approach for sparse estimation of graphs. We demonstrate that the resulting graph estimation is robust to outliers and applicable under general distributional assumptions. Furthermore, we develop efficient variational Bayes approximations to scale the methods for large data sets. Our methods are applied to a novel cancer proteomics data dataset where-in multiple proteomic antibodies are simultaneously assessed on tumor samples using reverse-phase protein arrays (RPPA) technology.

A pilot study of pembrolizumab in smoldering myeloma: report of the clinical, immune, and genomic analysis.

Multiple myeloma is, in most patients, an incurable cancer. Its precursors can be identified with routine tests setting the stage for early intervention to prevent active myeloma. We investigated the efficacy and safety of pembrolizumab, an antiprogrammed cell death 1 antibody, in smoldering myeloma patients with intermediate/high risk of progression to symptomatic myeloma. Thirteen patients were treated with a median number of 8 cycles. One patient achieved a stringent complete response with bone marrow next-generation sequencing negativity at 10-4 that is ongoing at 27 months (8%); 11 had stable disease (85%), and 1 progressed (8%). Three patients discontinued therapy due to immune-related adverse events: 2 with transaminitis and 1 due to tubulointerstitial nephritis. Immune profiling of bone marrow samples at baseline showed markers associated with a preexisting immune response in the responder compared with nonresponders and features of increased T-cell exhaustion in nonresponders. Consistent with this, transcriptome sequencing of bone marrow samples at baseline revealed an increased interferon-γ signature in the responder compared with the nonresponders. In summary, our results suggest that smoldering myeloma may be immunogenic in a subset of patients, and therapies that enhance antitumor T-cell responses may be effective in preventing its progression. This trial was registered at www.clinicaltrials.gov as #NCT02603887.

Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging.

BACKGROUND: Undersampling of gliomas at first biopsy is a major clinical problem, as accurate grading determines all subsequent treatment. We submit a technological solution to reduce the problem of undersampling by estimating a marker of tumor proliferation (Ki-67) using MR imaging data as inputs, against a stereotactic histopathology gold standard. METHODS: MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, in untreated glioma patients in a prospective clinical trial. Stereotactic biopsies were harvested from each patient immediately prior to surgical resection. For each biopsy, an imaging description (23 parameters) was developed, and the Ki-67 index was recorded. Machine learning models were built to estimate Ki-67 from imaging inputs, and cross validation was undertaken to determine the error in estimates. The best model was used to generate graphical maps of Ki-67 estimates across the whole brain. RESULTS: Fifty-two image-guided biopsies were collected from 23 evaluable patients. The random forest algorithm best modeled Ki-67 with 4 imaging inputs (T2-weighted, fractional anisotropy, cerebral blood flow, Ktrans). It predicted the Ki-67 expression levels with a root mean square (RMS) error of 3.5% (R2 = 0.75). A less accurate predictive result (RMS error 5.4%, R2 = 0.50) was found using conventional imaging only. CONCLUSION: Ki-67 can be predicted to clinically useful accuracies using clinical imaging data. Advanced imaging (diffusion, perfusion, and permeability) improves predictive accuracy over conventional imaging alone. Ki-67 predictions, displayed as graphical maps, could be used to guide biopsy, resection, and/or radiation in the care of glioma patients.

Concomitant targeting of the mTOR/MAPK pathways: novel therapeutic strategy in subsets of RICTOR/KRAS-altered non-small cell lung cancer.

Despite a therapeutic paradigm shift into targeted-driven medicinal approaches, resistance to therapy remains a hallmark of lung cancer, driven by biological and molecular diversity. Using genomic and expression data from advanced non-small cell lung cancer (NSCLC) patients enrolled in the BATTLE-2 clinical trial, we identified RICTOR alterations in a subset of lung adenocarcinomas and found RICTOR expression to carry worse overall survival. RICTOR-altered cohort was significantly enriched in KRAS/MAPK axis mutations, suggesting a co-oncogenic driver role in these molecular settings. Using NSCLC cell lines, we showed that, distinctly in KRAS mutant backgrounds, RICTOR blockade impairs malignant properties and generates a compensatory enhanced activation of the MAPK pathway, exposing a unique therapeutic vulnerability. In vitro and in vivo concomitant pharmacologic inhibition of mTORC1/2 and MEK1/2 resulted in synergistic responses of anti-tumor effects. Our study provides evidence of a distinctive therapeutic opportunity in a subset of NSCLC carrying concomitant RICTOR/KRAS alterations.

Hepatocellular Carcinoma Tumor Dose Response After 90Y-radioembolization With Glass Microspheres Using 90Y-SPECT/CT-Based Voxel Dosimetry.

PURPOSE: To investigate hepatocellular carcinoma tumor dose-response characteristics based on voxel-level absorbed doses (D) and biological effective doses (BED) using quantitative 90Y-single-photon emission computed tomography (SPECT)/computed tomography (CT) after 90Y-radioembilization with glass microspheres. We also investigated the relationship between normal liver D and toxicities. METHODS AND MATERIALS: 90Y-radioembolization activity distributions for 34 patients were based on quantitative 90Y-bremsstrahlung SPECT/CT. D maps were generated using a local-deposition algorithm. Contrast-enhanced CT or magnetic resonance imaging scans of the liver were registered to 90Y-SPECT/CT, and all tumors larger than 2.5 cm diameter (53 tumors) were segmented. Tumor mean D and BED (Dmean and BEDmean) and dose volume coverage from 0% to 100% in 10% steps (D0-D100 and BED0-BED100) were extracted. Tumor response was evaluated on follow-up using World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), and modified RECIST (mRECIST) criteria. Differences in dose metrics for responders and nonresponders were assessed using the Mann-Whitney U test. A univariate logistic regression model was used to determine tumor dose metrics that correlated with tumor response. Correlations among tumor size, tumor Dmean, and tumor dose heterogeneity (defined as the coefficient of variation) were assessed. RESULTS: The objective response rates were 14 of 53, 15 of 53, and 30 of 53 for WHO, RECIST, and mRECIST criteria, respectively. WHO and RECIST response statuses did not correlate with D or BED. For mRECIST responders and nonresponders, D and BED were significantly different for Dmean, D20 to D80, BEDmean, and BED0 to BED80. Threshold doses (and the 95% confidence interval) for 50% probability of mRECIST response (D50%) were 160 Gy (123-196 Gy) for Dmean and 214 Gy (146-280 Gy) for BEDmean. Tumor dose heterogeneity significantly correlated with tumor volume. No statistically significant association between Dmean to normal liver and complications related to bilirubin, albumin, or ascites was observed. CONCLUSIONS: Hepatocellular carcinoma tumor dose-response curves after 90Y-radioembolization with glass microspheres showed Dmean of 160 Gy and BEDmean of 214 Gy for D50% with a positive predictive value of ∼70% and a negative predictive value of ∼62%. No complications were observed in our patient cohort for normal liver Dmean less than 44 Gy.

A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models.

PURPOSE: Radiomics is the extraction of multidimensional imaging features, which when correlated with genomics, is termed radiogenomics. However, radiogenomic biological validation is not sufficiently described in the literature. We seek to establish causality between differential gene expression status and MRI-extracted radiomic-features in glioblastoma. EXPERIMENTAL DESIGN: Radiogenomic predictions and validation were done using the Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data glioblastoma patients (n = 93) and orthotopic xenografts (OX; n = 40). Tumor phenotypes were segmented, and radiomic-features extracted using the developed radiome-sequencing pipeline. Patients and animals were dichotomized on the basis of Periostin (POSTN) expression levels. RNA and protein levels confirmed RNAi-mediated POSTN knockdown in OX. Total RNA of tumor cells isolated from mouse brains (knockdown and control) was used for microarray-based expression profiling. Radiomic-features were utilized to predict POSTN expression status in patient, mouse, and interspecies. RESULTS: Our robust pipeline consists of segmentation, radiomic-feature extraction, feature normalization/selection, and predictive modeling. The combination of skull stripping, brain-tissue focused normalization, and patient-specific normalization are unique to this study, providing comparable cross-platform, cross-institution radiomic features. POSTN expression status was not associated with qualitative or volumetric MRI parameters. Radiomic features significantly predicted POSTN expression status in patients (AUC: 76.56%; sensitivity/specificity: 73.91/78.26%) and OX (AUC: 92.26%; sensitivity/specificity: 92.86%/91.67%). Furthermore, radiomic features in OX were significantly associated with patients with similar POSTN expression levels (AUC: 93.36%; sensitivity/specificity: 82.61%/95.74%; P = 02.021E-15). CONCLUSIONS: We determined causality between radiomic texture features and POSTN expression levels in a preclinical model with clinical validation. Our biologically validated radiomic pipeline also showed the potential application for human-mouse matched coclinical trials.

TUSC2 Immunogene Therapy Synergizes with Anti-PD-1 through Enhanced Proliferation and Infiltration of Natural Killer Cells in Syngeneic Kras-Mutant Mouse Lung Cancer Models.

Expression of the multikinase inhibitor encoded by the tumor suppressor gene TUSC2 (also known as FUS1) is lost or decreased in non-small cell lung carcinoma (NSCLC). TUSC2 delivered systemically by nanovesicles has mediated tumor regression in clinical trials. Because of the role of TUSC2 in regulating immune cells, we assessed TUSC2 efficacy on antitumor immune responses alone and in combination with anti-PD-1 in two Kras-mutant syngeneic mouse lung cancer models. TUSC2 alone significantly reduced tumor growth and prolonged survival compared with anti-PD-1. When combined, this effect was significantly enhanced, and correlated with a pronounced increases in circulating and splenic natural killer (NK) cells and CD8+ T cells, and a decrease in regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and T-cell checkpoint receptors PD-1, CTLA-4, and TIM-3. TUSC2 combined with anti-PD-1 induced tumor infiltrating more than NK and CD8+ T cells and fewer MDSCs and Tregs than each agent alone, both in subcutaneous tumor and in lung metastases. NK-cell depletion abrogated the antitumor effect and Th1-mediated immune response of this combination, indicating that NK cells mediate TUSC2/anti-PD-1 synergy. Release of IL15 and IL18 cytokines and expression of the IL15Rα chain and IL18R1 were associated with NK-cell activation by TUSC2. Immune response-related gene expression in the tumor microenvironment was altered by combination treatment. These data provide a rationale for immunogene therapy combined with immune checkpoint blockade in the treatment of NSCLC. Cancer Immunol Res; 6(2); 163-77. ©2018 AACR.

Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers.

Multiple omic profiles have been generated for many cancer types; however, comprehensive assessment of their prognostic values across cancers is limited. We conducted a pan-cancer prognostic assessment and presented a multi-omic kernel machine learning method to systematically quantify the prognostic values of high-throughput genomic, epigenomic, and transcriptomic profiles individually, integratively, and in combination with clinical factors for 3,382 samples across 14 cancer types. We found that the prognostic performance varied substantially across cancer types. mRNA and miRNA expression profile frequently performed the best, followed by DNA methylation profile. Germline susceptibility variants displayed low prognostic performance consistently across cancer types. The integration of omic profiles with clinical variables can lead to substantially improved prognostic performance over the use of clinical variables alone in half of cancer types examined. Moreover, we showed that the kernel machine learning method consistently outperformed existing prognostic signatures, suggesting that including a large number of omic biomarkers may provide substantial improvement in prognostic assessment. Our study provides a comprehensive portrait of omic architecture for tumor prognosis across cancers, and highlights the prognostic value of genome-wide omic biomarker aggregation, which may facilitate refined prognostic assessment in the era of precision oncology.

Fusobacterium Nucleatum Subspecies Animalis Influences Proinflammatory Cytokine Expression and Monocyte Activation in Human Colorectal Tumors.

Chronic infection and associated inflammation have long been suspected to promote human carcinogenesis. Recently, certain gut bacteria, including some in the Fusobacterium genus, have been implicated in playing a role in human colorectal cancer development. However, the Fusobacterium species and subspecies involved and their oncogenic mechanisms remain to be determined. We sought to identify the specific Fusobacterium spp. and ssp. in clinical colorectal cancer specimens by targeted sequencing of Fusobacterium 16S ribosomal RNA gene. Five Fusobacterium spp. were identified in clinical colorectal cancer specimens. Additional analyses confirmed that Fusobacterium nucleatum ssp. animalis was the most prevalent F. nucleatum subspecies in human colorectal cancers. We also assessed inflammatory cytokines in colorectal cancer specimens using immunoassays and found that expression of the cytokines IL17A and TNFα was markedly increased but IL21 decreased in the colorectal tumors. Furthermore, the chemokine (C-C motif) ligand 20 was differentially expressed in colorectal tumors at all stages. In in vitro co-culture assays, F. nucleatum ssp. animalis induced CCL20 protein expression in colorectal cancer cells and monocytes. It also stimulated the monocyte/macrophage activation and migration. Our observations suggested that infection with F. nucleatum ssp. animalis in colorectal tissue could induce inflammatory response and promote colorectal cancer development. Further studies are warranted to determine if F. nucleatum ssp. animalis could be a novel target for colorectal cancer prevention and treatment. Cancer Prev Res; 10(7); 398-409. ©2017 AACR.

Novel algorithmic approach predicts tumor mutation load and correlates with immunotherapy clinical outcomes using a defined gene mutation set.

BACKGROUND: While clinical outcomes following immunotherapy have shown an association with tumor mutation load using whole exome sequencing (WES), its clinical applicability is currently limited by cost and bioinformatics requirements. METHODS: We developed a method to accurately derive the predicted total mutation load (PTML) within individual tumors from a small set of genes that can be used in clinical next generation sequencing (NGS) panels. PTML was derived from the actual total mutation load (ATML) of 575 distinct melanoma and lung cancer samples and validated using independent melanoma (n = 312) and lung cancer (n = 217) cohorts. The correlation of PTML status with clinical outcome, following distinct immunotherapies, was assessed using the Kaplan-Meier method. RESULTS: PTML (derived from 170 genes) was highly correlated with ATML in cutaneous melanoma and lung adenocarcinoma validation cohorts (R2 = 0.73 and R2 = 0.82, respectively). PTML was strongly associated with clinical outcome to ipilimumab (anti-CTLA-4, three cohorts) and adoptive T-cell therapy (1 cohort) clinical outcome in melanoma. Clinical benefit from pembrolizumab (anti-PD-1) in lung cancer was also shown to significantly correlate with PTML status (log rank P value < 0.05 in all cohorts). CONCLUSIONS: The approach of using small NGS gene panels, already applied to guide employment of targeted therapies, may have utility in the personalized use of immunotherapy in cancer.

Selective Internal Radiation Therapy With Yttrium-90 Glass Microspheres: Biases and Uncertainties in Absorbed Dose Calculations Between Clinical Dosimetry Models.

PURPOSE: To quantify differences that exist between dosimetry models used for 90Y selective internal radiation therapy (SIRT). METHODS AND MATERIALS: Retrospectively, 37 tumors were delineated on 19 post-therapy quantitative 90Y single photon emission computed tomography/computed tomography scans. Using matched volumes of interest (VOIs), absorbed doses were reported using 3 dosimetry models: glass microsphere package insert standard model (SM), partition model (PM), and Monte Carlo (MC). Univariate linear regressions were performed to predict mean MC from SM and PM. Analysis was performed for 2 subsets: cases with a single tumor delineated (best case for PM), and cases with multiple tumors delineated (typical clinical scenario). Variability in PM from the ad hoc placement of a single spherical VOI to estimate the entire normal liver activity concentration for tumor (T) to nontumoral liver (NL) ratios (TNR) was investigated. We interpreted the slope of the resulting regression as bias and the 95% prediction interval (95%PI) as uncertainty. MCNLsingle represents MC absorbed doses to the NL for the single tumor patient subset; other combinations of calculations follow a similar naming convention. RESULTS: SM was unable to predict MCTsingle or MCTmultiple (p>.12, 95%PI >±177 Gy). However, SMsingle was able to predict (p<.012) MCNLsingle, albeit with large uncertainties; SMsingle and SMmultiple yielded biases of 0.62 and 0.71, and 95%PI of ±40 and ± 32 Gy, respectively. PMTsingle and PMTmultiple predicted (p<2E-6) MCTsingle and MCTmultiple with biases of 0.52 and 0.54, and 95%PI of ±38 and ± 111 Gy, respectively. The TNR variability in PMTsingle increased the 95%PI for predicting MCTsingle (bias = 0.46 and 95%PI = ±103 Gy). The TNR variability in PMTmultiple modified the bias when predicting MCTmultiple (bias = 0.32 and 95%PI = ±110 Gy). CONCLUSIONS: The SM is unable to predict mean MC tumor absorbed dose. The PM is statistically correlated with mean MC, but the resulting uncertainties in predicted MC are large. Large differences observed between dosimetry models for 90Y SIRT warrant caution when interpreting published SIRT absorbed doses. To reduce uncertainty, we suggest the entire NL VOI be used for TNR estimates when using PM.

PCAN: Probabilistic correlation analysis of two non-normal data sets.

Most cancer research now involves one or more assays profiling various biological molecules, e.g., messenger RNA and micro RNA, in samples collected on the same individuals. The main interest with these genomic data sets lies in the identification of a subset of features that are active in explaining the dependence between platforms. To quantify the strength of the dependency between two variables, correlation is often preferred. However, expression data obtained from next-generation sequencing platforms are integer with very low counts for some important features. In this case, the sample Pearson correlation is not a valid estimate of the true correlation matrix, because the sample correlation estimate between two features/variables with low counts will often be close to zero, even when the natural parameters of the Poisson distribution are, in actuality, highly correlated. We propose a model-based approach to correlation estimation between two non-normal data sets, via a method we call Probabilistic Correlations ANalysis, or PCAN. PCAN takes into consideration the distributional assumption about both data sets and suggests that correlations estimated at the model natural parameter level are more appropriate than correlations estimated directly on the observed data. We demonstrate through a simulation study that PCAN outperforms other standard approaches in estimating the true correlation between the natural parameters. We then apply PCAN to the joint analysis of a microRNA (miRNA) and a messenger RNA (mRNA) expression data set from a squamous cell lung cancer study, finding a large number of negative correlation pairs when compared to the standard approaches.

CDK5 Regulates Paclitaxel Sensitivity in Ovarian Cancer Cells by Modulating AKT Activation, p21Cip1- and p27Kip1-Mediated G1 Cell Cycle Arrest and Apoptosis.

Cyclin-dependent kinase 5 (CDK5) is a cytoplasmic serine/ threonine kinase. Knockdown of CDK5 enhances paclitaxel sensitivity in human ovarian cancer cells. This study explores the mechanisms by which CDK5 regulates paclitaxel sensitivity in human ovarian cancers. Multiple ovarian cancer cell lines and xenografts were treated with CDK5 small interfering RNA (siRNA) with or without paclitaxel to examine the effect on cancer cell viability, cell cycle arrest and tumor growth. CDK5 protein was measured by immunohistochemical staining of an ovarian cancer tissue microarray to correlate CDK5 expression with overall patient survival. Knockdown of CDK5 with siRNAs inhibits activation of AKT which significantly correlates with decreased cell growth and enhanced paclitaxel sensitivity in ovarian cancer cell lines. In addition, CDK5 knockdown alone and in combination with paclitaxel induced G1 cell cycle arrest and caspase 3 dependent apoptotic cell death associated with post-translational upregulation and nuclear translocation of TP53 and p27(Kip1) as well as TP53-dependent transcriptional induction of p21(Cip1) in wild type TP53 cancer cells. Treatment of HEYA8 and A2780 wild type TP53 xenografts in nu/nu mice with CDK5 siRNA and paclitaxel produced significantly greater growth inhibition than either treatment alone. Increased expression of CDK5 in human ovarian cancers correlates inversely with overall survival. CDK5 modulates paclitaxel sensitivity by regulating AKT activation, the cell cycle and caspase-dependent apoptosis. CDK5 inhibition can potentiate paclitaxel activity in human ovarian cancer cells.

Characteristics and outcomes of patients with multiple myeloma who develop therapy-related myelodysplastic syndrome, chronic myelomonocytic leukemia, or acute myeloid leukemia.

BACKGROUND: Patients with multiple myeloma (MM) have had significant improvements in outcomes. An increased risk of therapy-related myeloid neoplasms (t-MNs) has also developed. Little is known about the characteristics and outcomes of these patients. PATIENTS AND METHODS: Patients with MM treated at our institution from 1993 to 2011 were reviewed. Forty-seven patients were diagnosed with t-MN. Our primary objective was to evaluate the interval to t-MN, response to treatment, and overall survival (OS). RESULTS: The median patient age at the MM diagnosis was 65 years. Of the 47 patients, 32 (68.0%) initially received conventional chemotherapeutic agents, 7 (14.9%), novel agents (eg, lenalidomide, thalidomide, bortezomib), and 8 (17.0%), a combination. Twenty patients (42.6%) underwent high-dose chemotherapy and autologous hematopoietic stem cell transplantation. The median interval from the MM diagnosis to t-MN was 7 years (95% CI, 5.0-28.0). Of the 47 patients, 33 (70.2%) developed therapy-related myelodysplastic syndrome (t-MDS), 11 (23.4%) acute myeloid leukemia (t-AML), and 3 (6.4%) chronic myelomonocytic leukemia (t-CMML). The median age at the t-MN diagnosis was 65 years. Of the 47 patients, 26 (78.8%) with t-MDS, 9 (81.8%) with t-AML, and 1 (33.3%) with t-CMML had complex/high-risk cytogenetics. The median OS for all 47 patients after the t-MN diagnosis was 6.3 months (95% CI, 4.0-8.7). CONCLUSION: The development of t-MN in patients with MM is associated with poor outcomes. These patients, in general, have complex cytogenetic abnormalities and short complete remission and OS times. A better understanding of the disease biology and novel therapeutic approaches are warranted.

Differentiation of low-attenuation intracranial hemorrhage and calcification using dual-energy computed tomography in a phantom system.

OBJECTIVES: Calcific and hemorrhagic intracranial lesions with attenuation levels of less than 100 Hounsfield units (HUs) cannot currently be reliably differentiated by single-energy computed tomography (SECT). The proper differentiation of these lesion types would have a multitude of clinical applications. A phantom model was used to test the ability of dual-energy CT (DECT) to differentiate such lesions. MATERIALS AND METHODS: Agar gel-bound ferric oxide and hydroxyapatite were used to model hemorrhage and calcification, respectively. Gel models were scanned using SECT and DECT and organized into SECT attenuation-matched pairs at 16 attenuation levels between 0 and 100 HU. Dual-energy CT data were analyzed using 3-dimensional (3D) Gaussian mixture models (GMMs), as well as a simplified threshold plane metric derived from the 3D GMM, to assign voxels to hemorrhagic or calcific categories. Accuracy was calculated by comparing predicted voxel assignments with actual voxel identities. RESULTS: We measured 6032 voxels from each gel model, for a total of 193,024 data points (16 matched model pairs). Both the 3D GMM and its more clinically implementable threshold plane derivative yielded similar results, with higher than 90% accuracy at matched SECT attenuation levels of 50 HU and greater. CONCLUSIONS: Hemorrhagic and calcific lesions with attenuation levels between 50 and 100 HU were differentiable using DECT in a clinically relevant phantom system with higher than 90% accuracy. This method warrants further testing for potential clinical applications.

Targeting MET kinase with the small-molecule inhibitor amuvatinib induces cytotoxicity in primary myeloma cells and cell lines.

BACKGROUND: MET is a receptor tyrosine kinase that is activated by the ligand HGF and this pathway promotes cell survival, migration, and motility. In accordance with its oncogenic role, MET is constitutively active, mutated, or over-expressed in many cancers. Corollary to its impact, inhibition of MET kinase activity causes reduction of the downstream signaling and demise of cells. In myeloma, a B-cell plasma malignancy, MET is neither mutated nor over-expressed, however, HGF is increased in plasma or serum obtained from myeloma patients and this was associated with poor prognosis. The small-molecule, amuvatinib, inhibits MET receptor tyrosine kinase. Based on this background, we hypothesized that targeting the HGF/MET signaling pathway is a rational approach to myeloma therapy and that myeloma cells would be sensitive to amuvatinib. METHODS: Expression of MET and HGF mRNAs in normal versus malignant plasma cells was compared during disease progression. Cell death and growth as well as MET signaling pathway were assessed in amuvatinib treated primary myeloma cells and cell lines. RESULTS: There was a progressive increase in the transcript levels of HGF (but not MET) from normal plasma cells to refractory malignant plasma cells. Amuvatinib readily inhibited MET phosphorylation in primary CD138+ cells from myeloma patients and in concordance, increased cell death. A 48-hr amuvatinib treatment in high HGF-expressing myeloma cell line, U266, resulted in growth inhibition. Levels of cytotoxicity were time-dependent; at 24, 48, and 72 h, amuvatinib (25 µM) resulted in 28%, 40%, and 55% cell death. Consistent with these data, there was an amuvatinib-mediated decrease in MET phosphorylation in the cell line. Amuvatinib at concentrations of 5, 10, or 25 µM readily inhibited HGF-dependent MET, AKT, ERK and GSK-3-beta phosphorylation. MET-mediated effects were not observed in myeloma cell line that has low MET and/or HGF expression. CONCLUSIONS: These data suggest that at the cellular level MET/HGF pathway inclines with myeloma disease progression. Amuvatinib, a small molecule MET kinase inhibitor, is effective in inducing growth inhibition and cell death in myeloma cell lines as well as primary malignant plasma cells. These cytostatic and cytotoxic effects were associated with an impact on MET/HGF pathway.