Biology subjects

Gillies, R. J.

Publications and source records attributed to Gillies, R. J..

12 recordsLinked to original sources

bioRxiv2024

Eco-evolutionary Guided Pathomic Analysis to Predict DCIS Upstaging

Cancers evolve in a dynamic ecosystem. Thus, characterizing cancers ecological dynamics is crucial to understanding cancer evolution and can lead to discovering novel biomarkers to predict disease progression. Ductal carcinoma in situ (DCIS) is an early-stage breast cancer characterized by abnormal epithelial cell growth confined within the milk ducts. In this study, we show that ecological analysis of hypoxia and acidosis biomarkers can significantly improve prediction of DCIS upstaging. First, we developed a novel eco-evolutionary designed approach to define habitats in the tumor intraductal microenvironment based on oxygen diffusion distance. Then, we identified cancer cells with metabolic phenotypes attributed to their habitats, including CA9 for hypoxia responding phenotype, and LAMP2b for acid adapted phenotype. Traditionally these markers have shown limited predictive capabilities for DCIS progression, if any. However, when analyzed from an ecological perspective, their power to differentiate between non-upstaged and upstaged DCIS increased significantly. Second, we discovered distinct niches with spatial patterns of these biomarkers and used the distribution of such niches to predict patient upstaging. The niches were characterized by pattern analysis of both cellular and spatial features. With a 5-fold validation on the biopsy cohort, we trained a random forest classifier to achieve the area under curve (AUC) of 0.74. Our results affirm the importance of tumor ecological features in eco-evolutionary-designed approaches for novel biomarkers discovery. SignificanceOur results affirm the importance of spatial and ecological features in eco-evolutionary-designed biomarkers discovery studies in the era of digital pathology.

cancer biology↗

bioRxiv2023

L-DOS47 enhances response to immunotherapy in pancreatic cancer tumor

Acidosis is an important immunosuppressive mechanism that leads to tumor growth. Therefore, we investigated the neutralization of tumor acidity to improve immunotherapy response. L-DOS47, a new targeted urease immunoconjugate designed to neutralize tumor acidity, has been well tolerated in phase I/IIa trials. L-DOS47 binds CEACAM6, a cell surface protein highly expressed in gastrointestinal cancers, allowing urease to cleave endogenous urea into two NH4+ and one CO2, thereby raising local pH. To test the synergetic effect of neutralizing tumor acidity with immunotherapy, we developed a pancreatic orthotopic murine tumor model (KPC961) expressing human CEACAM6. Our results demonstrate that combining L DOS47 with anti-PD1 significantly increases the efficacy of anti-PD1 monotherapy, reducing tumor growth for up to 4 weeks.

cancer biology↗

bioRxiv2021

Macrophage-derived cholesterol contributes to therapeutic resistance in prostate cancer

Tumor-associated macrophages are key immune cells associated with cancer progression. Here we sought to determine the role of macrophages in castration-resistant prostate cancer (CRPC) using a syngeneic model that reflected the mutational landscape of the disease. A transcriptomic analysis of CRPC tumors following macrophage depletion revealed lower molecular signatures for steroid and bile acid synthesis, indicating potential perturbation of cholesterol metabolism. Since cholesterol is the precursor of the five major classes of steroid hormones, we reasoned that macrophages were regulating androgen biosynthesis within the prostate tumor microenvironment. Indeed, macrophage depletion reduced the levels of androgens within prostate tumors and restricted androgen receptor (AR) nuclear localization in vitro and in vivo. Macrophages were cholesterol rich and had the ability to transfer cholesterol to tumor cells in vitro, and AR nuclear translocation was inhibited by activation of Liver X Receptor (LXR)-{beta}, the master regulator of cholesterol homeostasis. Finally, combining macrophage depletion with androgen deprivation therapy increased survival, supporting the therapeutic potential of targeting macrophages in CRPC.

cancer biology↗

bioRxiv2020

Cycling hypoxia selects for constitutive HIF stabilization

Tumors experience temporal and spatial fluctuations in oxygenation. Hypoxia inducible transcription factors (HIF-) in tumor cells are stabilized in response to low levels of oxygen and induce angiogenesis to re-supply oxygen. HIF- stabilization is typically facultative, induced by hypoxia and reduced by normoxia. In some cancers, however, HIF- stabilization becomes constitutive even under normoxia, a condition known as pseudohypoxia. Herein, we develop a mathematical model that predicts the effects of fluctuating levels of oxygen availability on stabilization of HIF- and its client proteins based on fitness. The model shows that facultative regulation of HIF- always promotes greater cell fitness than constitutive regulation. However, cell fitness is nearly identical regardless of HIF- regulation strategy when there are rapid periodic fluctuations in oxygenation. Furthermore, the model predicts that stochastic changes in oxygenation favor facultative HIF- regulation. We conclude that rapid and regular cycling of oxygenation levels selects for pseudohypoxia.

cancer biology↗

bioRxiv2020

Targeting hypoxic habitats with hypoxia pro-drug evofosfamide in preclinical models of sarcoma

Hypoxic regions (habitats) within tumors are heterogeneously distributed and can be widely variant. Hypoxic habitats are generally pan-therapy resistant. For this reason, hypoxia-activated prodrugs (HAPs) have been developed to target these resistant volumes. The HAP evofosfamide (TH-302) has shown promise in preclinical and early clinical trials of sarcoma. However, in a phase III clinical trial, TH-302 did not improve survival in combination with doxorubicin (dox), most likely due to a lack of patient stratification based on hypoxic status. Herein, our goal was to develop deep-learning (DL) models to identify hypoxic habitats, using multiparametric (mp) MRI and co-registered histology, and to non-invasively monitor response to TH-302 in a patient-derived xenograft (PDX) of rhabdomyosarcoma and a syngeneic model of fibrosarcoma (RIF-1). A DL convolutional neural network showed strong correlations (>0.81) between the true hypoxic portion in histology and the predicted hypoxic portion in multiparametric MRI. TH-302 monotherapy or in combination with Dox delayed tumor growth and increased survival in the hypoxic PDX model (p<0.05), but not in the RIF-1 model, which had lower volume of hypoxic habitats. Control studies showed that RIF-1 resistance was due to hypoxia and not to other causes. Notably, PDX tumors developed resistance to TH-302 under prolonged treatment. In conclusion, response to TH-302 can be attributed to differences in hypoxia status prior therapy. Development of non-invasive MR imaging to assess hypoxia is crucial in determining the effectiveness of TH-302 therapy and to follow response. In further studies, our approach can be used to better plan therapeutic schedules to avoid resistance. One Sentence SummaryDevelopment of non-invasive MR imaging to assess hypoxia is crucial in determining the effectiveness of TH-302 therapy and to follow response.

cancer biology↗

bioRxiv2020

T2 heterogeneity provides a sensitive measure of early tumor response to radiotherapy

PurposeExternal beam radiotherapy (XRT) is a widely used cancer treatment, yet responses vary dramatically between patients. These differences are not accounted for in clinical practice, in part due to a lack of sensitive biomarkers of early response. In this work, we test the hypothesis that quantification of intratumor heterogeneity is a sensitive and robust biomarker of early response to XRT. A novel Magnetic Resonance Imaging (MRI) approach is proposed, utilizing histogram analysis of clinically-used T2 relaxation measurements to assess early changes in the tumor heterogeneity following irradiation in murine models of pancreatic cancer, indicative of radiotherapy response. Methods and MaterialsDynamic Magnetic Resonance T2 relaxation imaging was performed every 72h following 10 Gy dose XRT in two murine models of pancreatic cancer. Proposed biomarker of radiotherapy response was compared with tumor growth kinetics, and biological validation was performed through quantitative histology analysis. ResultsQuantification of tumor T2 interquartile range (IQR) as a measure of histogram width showed excellent sensitivity for detection of XRT-induced tumor changes as early as 72h after treatment, outperforming whole tumor T2 and Diffusion weighted MRI metrics. This response was observed both in quantitative T2 maps and in T2-weighted images that are routine in clinical practice. Histological comparison revealed the T2 IQR provides a measure of spatial heterogeneity in tumor cell density, related to radiation-induced necrosis. The early IQR changes were found to presage subsequent tumor volume changes in two distinct pancreatic models, suggesting promise for treatment response prediction. The metric showed excellent test-retest robustness. ConclusionsOur preclinical findings indicate that spatial heterogeneity analysis of T2 MRI can provide a sensitive and readily translatable method for early radiotherapy response assessment in pancreatic cancer. We propose that this will be useful in adaptive radiotherapy, specifically in MRI-guided treatment paradigms.

biophysics↗

bioRxiv2020

The Harsh Microenvironment in Early Breast Cancer Select for a Warburg Phenotype

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center foment the emergence of a Warburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected pre-malignant breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months, and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA-seq analysis of WE clones identified the transcription factors NF[kcy]B and KLF4 as potential inducers of the WE phenotype. NF[kcy]B was highly phosphorylated in the glycolytic clones. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a Warburg phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

cancer biology↗

bioRxiv2020

Hypoxia-related radiomics predict immunotherapy response: A multi-cohort study of NSCLC

Checkpoint blockade immunotherapy provides improved long-term survival in a subset of advanced stage non-small cell lung cancer (NSCLC) patients. However, highly predictive biomarkers of immunotherapy response are unmet clinical need. In this study, we utilized pre-treatment clinical factors and quantitative image-based biomarkers (radiomics) to identify a parsimonious model that predicts survival outcomes among NSCLC patients treated with immunotherapy. The NSCLC patients treated with single or double agent immunotherapy were included in three different cohorts: Training (N = 180), test (N = 90) and validation (N = 62) cohorts. The models were created based on overall survival (OS) and were additionally assessed for progression-free survival (PFS). Including most predictive radiomic features and clinical covariates, Classification and Regression Tree analysis was applied to stratify patients into survival risk-groups in the training cohort. The risk groups were later generated in the test and validation cohorts. Four independent NSCLC cohorts (total N = 446) were utilized for further validation of the radiomic signature. The biological underpinnings of the most informative radiomics were assessed using gene expression data from a radiogenomics dataset and validated by immunohistochemistry data (IHC). A parsimonious clinical-radiomics model was found to be significantly associated with OS and PFS after stratifying patients into groups of low-, moderate-, high-, and very-high risk of death and progression. This trained model was further tested and validated in two independent cohorts. When the extreme phenotypes were compared, the very-high risk group was found to be associated with extremely poor OS in both the test (hazard ratio [HR] = 5.35, 95% confidence interval [CI]: 2.14 - 13.36; 1-year OS = 11.1%) and validation (HR = 13.81, 95% CI: 2.58 - 73.93; 1-year OS = %47.6) cohorts when compared to the low risk group (HRs = 1.00; 1-year OS = 85.0% & 80.2%). Similar findings were observed for PFS. The final radiomic feature (GLCM inverse difference) was associated with OS in four independent NSCLC cohorts and was found to be positively associated with the hypoxia-related carbonic anhydrase, CAIX, by gene expression profiling and immunohistochemistry. We validated a novel clinical-radiomics model that is associated with OS and PFS among NSCLC patients treated with immunotherapy and identified a highly vulnerable subset of patients that are unlikely to respond to immunotherapy. The most informative radiomic feature was associated with CAIX, a marker of tumor hypoxia, tumor acidosis, and treatment resistance.

bioinformatics↗

bioRxiv2020

Frequency-dependent interactions determine outcome of competition between two breast cancer cell lines

Tumors are highly dynamic ecosystems in which diverse cancer cell subpopulations compete for space and resources. These complex, often non-linear interactions govern continuous spatial and temporal changes in the size and phenotypic properties of these subpopulations. Because intra-tumoral blood flow is often chaotic, competition for resources may be a critical selection factor in progression and prognosis. Here, we quantify resource competition using 3D spheroid cultures with MDA-MB-231 and MCF-7 breast cancer cells. We hypothesized that MCF-7 cells, which primarily rely on efficient aerobic glucose metabolism, would dominate the population under normal pH and low glucose conditions; and MDA-MB-231 cells, which exhibit high levels of glycolytic metabolism, would dominate under low pH and high glucose conditions. In spheroids with single populations, MCF-7 cells exhibited equal or superior intrinsic growth rates (density-independent measure of success) and carrying capacities (density-dependent measure of success) when compared to MDA-MB-231 cells under all pH and nutrient conditions. Despite these advantages, when grown together, MCF-7 cells do not always outcompete MDA-MB-231 cells. MDA-MB-231 cells outcompete MCF-7 cells in low glucose conditions and coexistence is achieved in low pH conditions. Under all conditions, MDA-MB-231 has a stronger competitive effect (frequency-dependent interaction) on MCF-7 cells than vice-versa. This, and the inability of growth rate or carrying capacity when grown individually to predict the outcome of competition, suggests a reliance on frequency-dependent interactions and the need for competition assays. We frame these results in a game-theoretic (frequency-dependent) model of cancer cell interactions and conclude that competition assays can demonstrate critical density-independent, density-dependent and frequency-dependent interactions that likely contribute to in vivo outcomes. HighlightsDemonstrate how mixed-culture spheroids must be used to characterize competition between two cancer cell lines. Competition alters growth dynamics of cancer cells. Competition growth models can be used to quantify density-independent, density-dependent and frequency-dependent effects on competition. Competition affects tumor progression and structure, making it key to understanding tumor development and evolution.

cancer biology↗

bioRxiv2019

Collagen Production and Niche Engineering: A Novel Strategy for Cancer Cells to Survive Acidosis and Evolve

Ductal Carcinoma in situ (DCIS) is an avascular disease characterized by profound acidosis. Pre-malignant cells within this niche must adapt to acidosis to survive and thrive. A component of this acid-adaptation involves extracellular matrix remodeling leading to niche construction and remodeling. Using discovery proteomics, we identified that collagen producing enzyme PLODs are upregulated in acid-adapted breast cancer cells. Second harmonic generation microscopy showed significant collagen deposition within DCIS lesions of patients. In vitro analyses identified that acid-adaptation involves production of rare collagens that can be regulated by Ras and SMAD pathway. Secretome analysis showed upregulation ECM remodeling enzymes such as TGM2 and LOXL2. Comparison of acid induced collagens in vitro and in patient data showed correlation between rare collagens production and survival of patients. We conclude acidosis induces collagen production by cancer cells and promote growth independent of basal membrane attachment. The independently produced collagen can be used for niche construction and engineering as an adaptation strategy of cancer cells to survive and evolve.

cancer biology↗

bioRxiv2019

Intermittent hormonal therapy shows similar outcome than SOC in ER+ breast cancer preclinical model.

Clinical breast cancers in which at least 10% of cells express the estrogen receptor are labeled as \"ER positive.\" First line therapy for these patients is typically continuous administration of anti-estrogen drugs at maximum tolerated dose (MTD) until progression. In the vast majority of patients, resistance to hormone therapy evolves in the breast cancer cells within 2 years leading to treatment failure and tumor progression. In prior studies, we have demonstrated continuous application of MTD chemotherapy results in evolutionary dynamics (termed \"competitive release\") that accelerates proliferation of treatment-resistance populations. In contrast, evolution-informed application of treatment reduces drug administration to maintain substantial populations of therapy-sensitive cells to reduce proliferation of resistant phenotypes. Prior pre-clinical and clinical studies have shown this strategy can delay or prevent proliferation of resistant cells and prolong time to progression (TTP). We hypothesize that similar dynamics may be observed in hormonal therapy of ER+ breast cancers. Here we address two important dynamics. First, we consider a clinical scenario in which symptoms are sufficiently severe or life-threatening to require rapid and substantial tumor reduction. Can this be achieved while retaining evolutionary dynamics to subsequently delay proliferation of resistance? A second, related question is defining the cost of resistance to anti-estrogen therapy. Here, we investigated the evolutionary dynamics of resistance to anti-estrogen therapy using ER+ MCF-7 orthotropic xenografts treated with both continuous Tamoxifen as well as cycles in which estrogen stimulation is combined with estrogen suppression. As expected, continuous administration of anti-estrogen drugs successfully suppressed tumor growth. However we found that brief interruptions in drug administration permitted equal tumor control while administering up to 50% less drug and maintaining cell phenotypes that retained high levels of ER expression and lower levels of MDR1 expression. In follow-on experiments combining hormonal and chemo-therapies; we obtained similar tumor control to hormonal therapy alone but with more necrosis and significantly lower ER expression in the surviving population.

cancer biology↗

bioRxiv2017

Combining radiomics and mathematical modeling to elucidate mechanisms of resistance to immune checkpoint blockade in non-small cell lung cancer

Immune therapies have shown promise in a number of cancers, and clinical trials using the anti-PD-L1/PD-1 checkpoint inhibitor in lung cancer have been successful for a number of patients. However, some patients either do not respond to the treatment or have cancer recurrence after an initial response. It is not clear which patients might fall into these categories or what mechanisms are responsible for treatment failure. To explore the different underlying biological mechanisms of resistance, we created a spatially explicit mathematical model with a modular framework. This construction enables different potential mechanisms to be turned on and off in order to adjust specific tumor and tissue interactions to match a specific patient's disease. In parallel, we developed a software suite to identify significant computed tomography (CT) imaging features correlated with outcome using data from an anti-PDL-1 checkpoint inhibitor clinical trial for lung cancer and a tool that extracts these features from both patient CT images and \"virtual CT\" images created from the cellular density profile of the model. The combination of our two toolkits provides a framework that feeds patient data through an iterative pipeline to identify predictive imaging features associated with outcome, whilst at the same time proposing hypotheses about the underlying resistance mechanisms.

cancer biology↗