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Massey, S. C.

Publications and source records attributed to Massey, S. C..

10 recordsLinked to original sources

Reproducible autosomal gene expression changes with loss of typical X and Y complement across tumor types

Although there are known sex differences in cancer incidence, severity, and treatment, the sex chromosomes are typically excluded from genomic analyses because of the unique technical challenges associated with assessing their copy number, sequence variation, and expression. Here we assess sex chromosome complement in three widely-used human genomics datasets from normal (non-cancerous) tissues, primary tumors, and cancer cell lines and study the effects on genome-wide gene expression. Expected sex chromosome complements based on reported patient sex were observed in non-cancerous tissues, but about half of tumors and cancer cell lines showed loss of typical sex chromosome gene expression across tissue types with three categories: loss of chromosome Y (LOY), loss of chromosome X (LOX) and reactivation of the inactive X chromosome (XaXa). Genes consistently differentially expressed in tumors with loss of chromosome X, loss of chromosome Y, or loss of X chromosome inactivation are associated with the hallmarks of cancer and include both sex-linked and autosomal genes from nearly all chromosomes, druggable genes, and genes with molecular functions relevant to cancer signaling, such as kinase activity. Strikingly, tumors that are X0, including tumors from female patients that have lost an X chromosome and tumors from male patients that have lost a Y chromosome, cluster together by gene expression profile. Patients with tumors that have LOX or LOY had poorer survival outcomes compared to those with tumors that had maintained their sex chromosome complement. Further, LOX and LOY eliminates nearly all of the differential gene expression between tumors from different patient sexes, affecting sex chromosomal and autosomal gene expression. Going forward, considering patient sex as well as the entire genome, including assessment of the sex chromosome complement, will provide additional insights into personalized tumor etiology, progression, treatment, and patient outcome. TeaserLoss of typical sex chromosome complement is present in primary tumors and cancer cell lines across tissue types. We identify consistent autosomal gene expression changes across multiple cancers when the sex chromosome complement is altered, and show that loss of sex chromosomes reduces tumor gene expression differences between patient sexes. We show that loss of sex chromosomes is also associated with poorer patient survival.

cancer biology↗

Complementary role of mathematical modeling in preclinical glioblastoma: differentiating poor drug delivery from drug insensitivity

Glioblastoma is the most malignant primary brain tumor with significant heterogeneity and a limited number of effective therapeutic options. Many investigational targeted therapies have failed in clinical trials, but it remains unclear if this results from insensitivity to therapy or poor drug delivery across the blood-brain barrier. Using well-established EGFR-amplified patient-derived xenograft (PDX) cell lines, we investigated this question using an EGFR-directed therapy. With only bioluminescence imaging, we used a mathematical model to quantify the heterogeneous treatment response across the three PDX lines (GBM6, GBM12, GBM39). Our model estimated the primary cause of intracranial treatment response for each of the lines, and these findings were validated with parallel experimental efforts. This mathematical modeling approach can be used as a useful complementary tool that can be widely applied to many more PDX lines. This has the potential to further inform experimental efforts and reduce the cost and time necessary to make experimental conclusions. Author summaryGlioblastoma is a deadly brain cancer that is difficult to treat. New therapies often fail to surpass the current standard of care during clinical trials. This can be attributed to both the vast heterogeneity of the disease and the blood-brain barrier, which may or may not be disrupted in various regions of tumors. Thus, while some cancer cells may develop insensitivity in the presence of a drug due to heterogeneity, other tumor areas are simply not exposed to the drug. Being able to understand to what extent each of these is driving clinical trial results in individuals may be key to advancing novel therapies. To address this challenge, we used mathematical modeling to study the differences between three patient-derived tumors in mice. With our unique approach, we identified the reason for treatment failure in each patient tumor. These results were validated through rigorous and time-consuming experiments, but our mathematical modeling approach allows for a cheaper, quicker, and widely applicable way to come to similar conclusions.

cancer biology↗

Sex Differences in MRI-Based Metrics of Glioma Invasion and Brain Mechanics

Gliomas are brain tumors characterized by highly variable growth patterns. Magnetic resonance imaging (MRI) is the cornerstone of glioma diagnosis and management planning. However, glioma features on MRI do not directly correlate with tumor cell distribution. Additionally, there is evidence that glioma tumor characteristics and prognosis are sex-dependent. Magnetic resonance elastography (MRE) is an imaging technique that allows interrogation of tissue stiffness in-vivo and has found utility in the imaging of several cancers. We investigate the relationship between MRI features, MRE features, and growth parameters derived from an established mathematical model of glioma proliferation and invasion. Results suggest that both the relationship between tumor volume and tumor stiffness as well as the relationship between the parameters derived from the mathematical model and tumor stiffness are sex-dependent. These findings lend evidence to a growing body of knowledge about the clinical importance of sex in the context of cancer diagnosis, prognosis and treatment.

biophysics↗

Tbr2-expressing retinal ganglion cells are ipRGCs

The mammalian retina contains more than 40 retinal ganglion cell (RGC) subtypes based on their unique morphologies, functions, and molecular profiles. Among them, intrinsically photosensitive RGCs (ipRGCs) are the first specified RGC type that emerged from a common pool of retinal progenitor cells. Previous work has shown that T-box transcription factor T-brain 2 (Tbr2) is essential for the formation and maintenance of ipRGCs, and Tbr2-expressing RGCs activate Opn4 expression upon native ipRGC loss, suggesting that Tbr2+ RGCs can serve as a reservoir for ipRGCs. However, the identity of Tbr2+ RGCs has not been fully vetted, and the developmental and molecular mechanisms underlying the formation of native and reservoir ipRGCs remain unclear. Here, we showed that Tbr2-expressing retinal neurons include RGCs and GABAergic displaced amacrine cells (dACs). Using genetic sparse labeling, we demonstrated that the majority of Tbr2+ RGCs are intrinsically photosensitive and morphologically indistinguishable from known ipRGC types and have identical retinofugal projections. Additionally, we found a minor fraction of Pou4f1-expressing Tbr2+ RGCs marks a unique OFF RGC subtype. Most of the Tbr2+ RGCs can be ablated by anti-melanopsin-SAP toxin in adult retinas, supporting that Tbr2+ RGCs contain reservoir ipRGCs that express melanopsin at varying levels. When Tbr2 is deleted in adult retinas, Opn4 expression is diminished followed by the death of Tbr2-deficient cells, suggesting that Tbr2 is essential for both Opn4 expression and ipRGC survival. Finally, Tbr2 extensively occupies multiple T-elements in the Opn4 locus, indicating a direct regulatory role for Tbr2 on Opn4 transcription. Significance statementMelanopsin/Opn4-expressing intrinsically photosensitive retinal ganglion cells (ipRGCs) play fundamental roles in non-image forming vision. Previously we identified Tbr2 as the key transcription regulator for the development and maintenance of ipRGCs. To reveal the full identity of Tbr2-expressing retinal neurons and how Tbr2 acts, we generated a novel mouse line to genetically label and study Tbr2-expressing cells. Our in-depth characterizations firmly established that most Tbr2+ RGCs are indeed ipRGCs and that Tbr2 regulates Opn4 transcription, thus place Tbr2-Opn4 transcription regulatory hierarchy as the primary component in the development and maintenance of the non-image forming visual system.

neuroscience↗

Quantifying glioblastoma drug response dynamics incorporating resistance and blood brain barrier penetrance from experimental data

Many drugs investigated for the treatment of glioblastoma (GBM) have had poor clinical outcomes, as their efficacy is dependent on adequate delivery to sensitive tumor cell populations, which is limited by the blood-brain barrier (BBB). Further complicating evaluation of therapeutic efficacy, tumors can become resistant to anti-cancer drugs, and it can be difficult to gauge the extent to which BBB limitations and resistance each contribute to a drugs failure. To address this question, we developed a minimal mathematical model to characterize these elements of overall drug response, informed by time-series bioluminescence imaging data from a treated patient-derived xenograft (PDX) experimental model. By fitting this mathematical model to a preliminary dataset in a series of nonlinear regression steps, we estimated parameter values for individual PDX subjects that correspond to the dynamics seen in experimental data. Using these estimates, we performed a parameter sensitivity analysis using Latin hypercube sampling and partial rank correlation coefficients. Results from this analysis combined with simulation results suggest that BBB permeability may play a slightly larger role in therapeutic efficacy than drug resistance. Our model and fitting technique to estimate parameters from data may be a useful tool in aiding further exploration of these challenges in future studies of drug efficacy with larger datasets.

cancer biology↗

From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response

Glioblastomas are aggressive primary brain tumors known for their inter- and intratumor heterogeneity. This disease is uniformly fatal, with intratumor heterogeneity the major reason for treatment failure and recurrence. Just like the nature vs nurture debate, heterogeneity can arise from heritable or environmental influences. Whilst it is impossible to clinically separate observed behavior of cells from their environmental context, using a mathematical framework combined with multiscale data gives us insight into the relative roles of variation from inherited and environmental sources.\n\nTo better understand the implications of intratumor heterogeneity on therapeutic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor kinetics and the individual cellular behavior. We track single cells as agents, cell density on a coarser scale, and growth factor diffusion and dynamics on a finer scale over time and space. Our model parameters were fit utilizing serial MRI imaging and cell tracking data from ex vivo tissue slices acquired from a growth-factor driven glioblastoma murine model.\n\nWhen fitting our model to serial imaging only, there was a spectrum of equally-good parameter fits corresponding to a wide range of phenotypic behaviors. This wide spectrum of in silico tumors also had a wide variety of responses to an application of an antiproliferative treatment. Recurrent tumors were generally less proliferative than pre-treatment tumors as measured via the model simulations and validated from human GBM patient histology. When fitting our model using imaging and cell scale data, we determined that heritable heterogeneity is required to capture the observed migration behavior. Further, we found that all tumors increased in size after an anti-migratory treatment, and some tumors were larger after a combination treatment than with an anti-proliferative treatment alone. Together our results emphasize the need to understand the underlying phenotypes and tumor heterogeneity in designing therapeutic regimens.

cancer biology↗

Quantifying glioblastoma drug penetrance from experimental data

Poor clinical trial outcomes for glioblastoma (GBM) can be attributed to multiple possible causes. GBM is heterogeneous, such that there is a chance of treatment-resistant cells coming to predominate the tumor, and due to the blood brain barrier (BBB) it is also possible that therapy was inadequately delivered to the tumor. Mathematically modeling the dynamics of therapeutic response in patient-derived xenografts (PDX) and fitting the mathematical model to bioluminescence imaging flux data, we may be able to assess the degree to which both drug resistance and drug penetrance are driving varied responses to these therapies.

cancer biology↗

Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma

Temozolomide (TMZ) has been the standard-of-care chemotherapy for glioblastoma (GBM) patients for more than a decade. Despite this long time in use, significant questions remain regarding how best to optimize TMZ therapy for individual patients. Understanding the relationship between TMZ response and factors such as number of adjuvant TMZ cycles, patient age, patient sex, and image-based tumor features, might help predict which GBM patients would benefit most from TMZ, particularly for those whose tumors are not MGMT methylated. Using a cohort of 90 newly-diagnosed GBM patients treated according to the Stupp protocol, we examined the relationships between several patient and tumor characteristics and volumetric and survival outcomes during adjuvant chemotherapy. Volumetric changes in MR imaging abnormalities during adjuvant therapy were used to assess TMZ response. T1Gd volumetric response is associated with younger patient age, increased number of TMZ cycles, longer time to nadir volume, and decreased tumor invasiveness. Moreover, increased adjuvant TMZ cycles corresponded with improved volumetric response only among more nodular tumors, and this volumetric response was associated with improved survival outcomes. Finally, in a subcohort of patients with known MGMT methylation status, MGMT methylated tumors were more diffusely invasive than unmethylated tumors, suggesting that the improved response in nodular tumors is not driven by a preponderance of MGMT methylated tumors. Our finding that less diffusely invasive tumors are associated with greater volumetric response to TMZ suggests that patients with these tumors may benefit from additional cycles of adjuvant TMZ, even for those without MGMT methylation.

cell biology↗

ENvironmental Dynamics Underlying Responsive Extreme Survivors (ENDURES) of Glioblastoma: a Multi-disciplinary Team-based, Multifactorial Analytical Approach

Although glioblastoma is a fatal primary brain cancer with a short median survival of 15 months, a small number of patients survive more than 5 years after diagnosis; they are known as extreme survivors (ES). Due to their rarity, very little is known about what differentiates these outliers from other glioblastoma patients. For the purpose of identifying unknown drivers of extreme survivorship in glioblastoma, we developed the ENDURES consortium (ENvironmental Dynamics Underlying Responsive Extreme Survivors of glioblastoma). This consortium is a multicenter collaborative network of investigators focused on the integration of multiple types of clinical data and the creation of patient-specific models of tumor growth informed by radiographic and histological parameters. Leveraging our combined resources, the goals of the ENDURES consortium are two-fold: (1) to build a curated, searchable, multilayered repository housing clinical and outcome data on a large cohort of ES patients with glioblastoma and (2) to leverage the ENDURES repository for new insights on tumor behavior and novel targets for prolonging survival for all glioblastoma patients. In this article, we review the available literature and discuss what is already known about ES. We then describe the creation of our consortium and some of our preliminary results.\n\nFundingThis review was financially supported by a grant from the James S. McDonnell Foundation\n\nConflicts of InterestThe authors have declared that no conflicts of interest exist.\n\nAuthorshipConceptualized consortium: LW, RG, KME, PC, and KRS. Built consortium: SKJ, PK, NR, JS, KME, PC, and KRS. Wrote the manuscript: SKJ, PW, SCM, PK, AP, and KME. Reviewed and edited the manuscript: LFGC, MMM, AHD, PRJ, and LSH. Contributed to writing, provided feedback, and approved of final manuscript: All authors.\n\nLink to website for ENDUREShttp://mathematicalneurooncology.org/?page_id=2125

cancer biology↗

Hybrid approach for parameter estimation in agent-based models

Agent-based models are valuable in cancer research to show how different behaviors emerge from individual interactions between cells and their environment. However, calibrating such models can be difficult, especially if the parameters that govern the underlying interactions are hard to measure experimentally. Herein, we detail a new method to converge on parameter sets that fit an agent-based model to multiscale data using a model of glioblastoma as an example.

systems biology↗