Resistance of myeloma to lenalidomide is an emerging clinical problem, and though it has been associated in part with activation of Wnt/β-catenin signaling, the mediators of this phenotype remained ...undefined. Lenalidomide-resistant models were found to overexpress the hyaluronan (HA)-binding protein CD44, a downstream Wnt/β-catenin transcriptional target. Consistent with a role of CD44 in cell adhesion-mediated drug resistance (CAM-DR), lenalidomide-resistant myeloma cells were more adhesive to bone marrow stroma and HA-coated plates. Blockade of CD44 with monoclonal antibodies, free HA or CD44 knockdown reduced adhesion and sensitized to lenalidomide. Wnt/β-catenin suppression by FH535 enhanced the activity of lenalidomide, as did interleukin-6 neutralization with siltuximab. Notably, all-trans retinoic acid (ATRA) downregulated total β-catenin, cell-surface and total CD44, reduced adhesion of lenalidomide-resistant myeloma cells and enhanced the activity of lenalidomide in a lenalidomide-resistant in vivo murine xenograft model. Finally, ATRA sensitized primary myeloma samples from patients that had relapsed and/or refractory disease after lenalidomide therapy to this immunomodulatory agent ex vivo. Taken together, our findings support the hypotheses that CD44 and CAM-DR contribute to lenalidomide resistance in multiple myeloma, that CD44 should be evaluated as a putative biomarker of sensitivity to lenalidomide, and that ATRA or other approaches that target CD44 may overcome clinical lenalidomide resistance.
Patients with type II diabetes mellitus (DM) have an increased risk of adenomatous colorectal (CRC) polyps and CRC cancer. The use of the anti-hyperglycemic agent metformin is associated with a ...reduced incidence of cancer-related deaths.
We retrospectively evaluated the medical records of 4758 patients seen at a single institution and determined that 424 patients were identified by their physicians as having type II DM and CRC cancer. Data were subsequently acquired determining the subject's age, body mass index (BMI), and disease date of diagnosis, stage, site of cancer, treatment, and survival.
Patients with type II DM and CRC cancer treated with metformin as one of their diabetic medications had a survival of 76.9 months (95% CI=61.4-102.4) as compared with 56.9 months in those patients not treated with metformin (95% CI=44.8-68.8), P=0.048. By using a multivariable Cox regression model adjusted for age, sex, race, BMI, and initial stage of disease, we demonstrated that type II diabetic patients treated with metformin had a 30% improvement in overall survival (OS) when compared with diabetic patients treated with other diabetic agents.
Colorectal cancer patients with DM treated with metformin as part of their diabetic therapy appear to have a superior OS.
Insulin/insulin-like growth factor-1 signalling may underlie the promoting effect of type 2 diabetes on cancer. This study examined the association of diabetes, including steroid-induced diabetes ...(SID), and the impact of anti-diabetic medication on clinical outcomes of multiple myeloma (MM).
A retrospective review was conducted of 1240 MM patients. Overall survival (OS) and MM disease status prior to death were analysed.
Diabetic patients had a significantly shorter OS than non-diabetic patients (median: 65.4 vs 98.7 months). In multivariate analysis, SID was a significant predictor of decreased OS, along with age, comorbidity, MM stage, and cytogenetic abnormalities. Analyzing only the diabetic MM patients, Cox regression showed that metformin predicted an increased OS, whereas use of insulin/analogues predicted a decreased OS. Competing risk analysis showed that DM was associated with increased cumulative incidence of death with progressive MM. Among the diabetics, multivariate regression showed that insulin/analogues were associated with increased, but metformin with decreased death with progressive MM. Potential immortal time bias was evaluated by landmark analyses.
DM, SID in particular, is associated with poor clinical outcomes in MM. Insulin/analogues are associated with poor outcomes, whereas metformin is associated with improved outcomes. No conclusion about causal relationships can be made at this time. Managing hyperglycaemia with non-insulin regimens should be investigated in randomised trials.
Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, ...but the degree to which cellular density can be quantitatively estimated from imaging is unknown. The purpose of this study was to discover the best MR imaging and processing techniques to make quantitative and spatially specific estimates of cellular density.
We collected stereotactic biopsies in a prospective imaging clinical trial targeting untreated patients with gliomas at our institution undergoing their first resection. The data included preoperative MR imaging with conventional anatomic, diffusion, perfusion, and permeability sequences and quantitative histopathology on biopsy samples. We then used multiple machine learning methodologies to estimate cellular density using local intensity information from the MR images and quantitative cellular density measurements at the biopsy coordinates as the criterion standard.
The random forest methodology estimated cellular density with
= 0.59 between predicted and observed values using 4 input imaging sequences chosen from our full set of imaging data (T2, fractional anisotropy, CBF, and area under the curve from permeability imaging). Limiting input to conventional MR images (T1 pre- and postcontrast, T2, and FLAIR) yielded slightly degraded performance (
= 0.52). Outputs were also reported as graphic maps.
Cellular density can be estimated with moderate-to-strong correlations using MR imaging inputs. The random forest machine learning model provided the best estimates. These spatially specific estimates of cellular density will likely be useful in guiding both diagnosis and treatment.
Gliomas are highly heterogeneous tumors, and optimal treatment depends on identifying and locating the highest grade disease present. Imaging techniques for doing so are generally not validated ...against the histopathologic criterion standard. The purpose of this work was to estimate the local glioma grade using a machine learning model trained on preoperative image data and spatially specific tumor samples. The value of imaging in patients with brain tumor can be enhanced if pathologic data can be estimated from imaging input using predictive models.
Patients with gliomas were enrolled in a prospective clinical imaging trial between 2013 and 2016. MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, followed by image-guided stereotactic biopsy before resection. An imaging description was developed for each biopsy, and multiclass machine learning models were built to predict the World Health Organization grade. Models were assessed on classification accuracy, Cohen κ, precision, and recall.
Twenty-three patients (with 7/9/7 grade II/III/IV gliomas) had analyzable imaging-pathologic pairs, yielding 52 biopsy sites. The random forest method was the best algorithm tested. Tumor grade was predicted at 96% accuracy (κ = 0.93) using 4 inputs (T2, ADC, CBV, and transfer constant from dynamic contrast-enhanced imaging). By means of the conventional imaging only, the overall accuracy decreased (89% overall, κ = 0.79) and 43% of high-grade samples were misclassified as lower-grade disease.
We found that local pathologic grade can be predicted with a high accuracy using clinical imaging data. Advanced imaging data improved this accuracy, adding value to conventional imaging. Confirmatory imaging trials are justified.
Clinical brain MR imaging registration algorithms are often made available by commercial vendors without figures of merit. The purpose of this study was to suggest a rational performance comparison ...methodology for these products.
Twenty patients were imaged on clinical 3T scanners by using 4 sequences: T2-weighted, FLAIR, susceptibility-weighted angiography, and T1 postcontrast. Fiducial landmark sites (
= 1175) were specified throughout these image volumes to define identical anatomic locations across sequences. Multiple registration algorithms were applied by using the T2 sequence as a fixed reference. Euclidean error was calculated before and after each registration and compared with a criterion standard landmark registration. The Euclidean effectiveness ratio is the fraction of Euclidean error remaining after registration, and the statistical effectiveness ratio is similar, but accounts for dispersion and noise.
Before registration, error values for FLAIR, susceptibility-weighted angiography, and T1 postcontrast were 2.07 ± 0.55 mm, 2.63 ± 0.62 mm, and 3.65 ± 2.00 mm, respectively. Postregistration, the best error values for FLAIR, susceptibility-weighted angiography, and T1 postcontrast were 1.55 ± 0.46 mm, 1.34 ± 0.23 mm, and 1.06 ± 0.16 mm, with Euclidean effectiveness ratio values of 0.493, 0.181, and 0.096 and statistical effectiveness ratio values of 0.573, 0.352, and 0.929 for rigid mutual information, affine mutual information, and a commercial GE registration, respectively.
We demonstrate a method for comparing the performance of registration algorithms and suggest the Euclidean error, Euclidean effectiveness ratio, and statistical effectiveness ratio as performance metrics for clinical registration algorithms. These figures of merit allow registration algorithms to be rationally compared.
Operable thoracic esophageal/gastroesophageal junction carcinoma (EC) is often treated with chemoradiation and surgery but tumor responses are unpredictable and heterogeneous. We hypothesized that ...aldehyde dehydrogenase-1 (ALDH-1) could be associated with response.
The labeling indices (LIs) of ALDH-1 by immunohistochemistry in untreated tumor specimens were established in EC patients who had chemoradiation and surgery. Univariate logistic regression and 3-fold cross validation were carried out for the training (67% of patients) and validation (33%) sets. Non-clinical experiments in EC cells were performed to generate complimentary data.
Of 167 EC patients analyzed, 40 (24%) had a pathologic complete response (pathCR) and 27 (16%) had an extremely resistant (exCRTR) cancer. The median ALDH-1 LI was 0.2 (range, 0.01–0.85). There was a significant association between pathCR and low ALDH-1 LI (p ≤ 0.001; odds-ratio OR = 0.432). The 3-fold cross validation led to a concordance index (C-index) of 0.798 for the fitted model. There was a significant association between exCRTR and high ALDH-1 LI (p ≤ 0.001; OR = 3.782). The 3-fold cross validation led to the C-index of 0.960 for the fitted model. In several cell lines, higher ALDH-1 LIs correlated with resistant/aggressive phenotype. Cells with induced chemotherapy resistance upregulated ALDH-1 and resistance conferring genes (SOX9 and YAP1). Sorted ALDH-1+ cells were more resistant and had an aggressive phenotype in tumor spheres than ALDH-1− cells.
Our clinical and non-clinical data demonstrate that ALDH-1 LIs are predictive of response to therapy and further research could lead to individualized therapeutic strategies and novel therapeutic targets for EC patients.
•ALDH-1, a stem cell biomarker, was examined in 167 untreated adenocarcinoma of the esophagus.•ALDH-1 expression levels correlated with resistance or response to chemoradiation•Non-clinical experiments correlated with clinical observations.•First demonstration of ALDH-1 as a predictive biomarker.
Advances in neuromedicine have emerged from endeavors to elucidate the distinct genetic factors that influence the changes in brain structure that underlie various neurological conditions. We present ...a framework for examining the extent to which genetic factors impact imaging phenotypes described by voxel-wise measurements organized into collections of functionally relevant regions of interest (ROIs) that span the entire brain. Statistically, the integration of neuroimaging and genetic data is challenging. Because genetic variants are expected to impact different regions of the brain, an appropriate method of inference must simultaneously account for spatial dependence and model uncertainty. Our proposed framework combines feature extraction using generalized principal component analysis to account for inherent short- and long-range structural dependencies with Bayesian model averaging to effectuate variable selection in the presence of multiple genetic variants. The methods are demonstrated on a cocaine dependence study to identify ROIs associated with genetic factors that impact diffusion parameters.
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