Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor associated with a poor median survival of 15–18 months, yet there is wide heterogeneity across and within patients. This ...heterogeneity has been the source of significant clinical challenges facing patients with GBM and has hampered the drive toward more precision or personalized medicine approaches to treating these challenging tumors. Over the last two decades, the field of Mathematical Neuro-oncology has grown out of desire to use (often patient-specific) mathematical modeling to better treat GBMs. Here, we will focus on a series of clinically relevant results using patient-specific mathematical modeling. The core model at the center of these results incorporates two hallmark features of GBM, proliferation
(
ρ
)
and invasion (
D
), as key parameters. Based on routinely obtained magnetic resonance images, each patient’s tumor can be characterized using these two parameters. The Proliferation-Invasion (PI) model uses
ρ
and
D
to create patient-specific growth predictions. The PI model, its predictions, and parameters have been used in a number of ways to derive biological insight. Beyond predicting growth, the PI model has been utilized to identify patients who benefit from different surgery strategies, to prognosticate response to radiation therapy, to develop a treatment response metric, and to connect clinical imaging features and genetic information. Demonstration of the PI model’s clinical relevance supports the growing role for it and other mathematical models in routine clinical practice.
Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing ...effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.
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 lack O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation.
Using a cohort of 90 newly-diagnosed GBM patients treated according to the standard of care, 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, methylated tumors were more diffusely invasive than unmethylated tumors, suggesting 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 patients with these tumors may benefit from additional adjuvant TMZ cycles, even for those without MGMT methylation.
The skin represents an important barrier for pathogens and is known to produce fatty acids that are toxic toward Gram-positive bacteria. A screen of fatty acids as growth inhibitors of Staphylococcus ...aureus revealed structure-specific antibacterial activity. Fatty acids like oleate (18:1Δ9) were nontoxic, whereas palmitoleate (16:1Δ9) was a potent growth inhibitor. Cells treated with 16:1Δ9 exhibited rapid membrane depolarization, the disruption of all major branches of macromolecular synthesis, and the release of solutes and low-molecular-weight proteins into the medium. Other cytotoxic lipids, such as glycerol ethers, sphingosine, and acyl-amines blocked growth by the same mechanisms. Nontoxic 18:1Δ9 was used for phospholipid synthesis, whereas toxic 16:1Δ9 was not and required elongation to 18:1Δ11 prior to incorporation. However, blocking fatty acid metabolism using inhibitors to prevent acyl-acyl carrier protein formation or glycerol-phosphate acyltransferase activity did not increase the toxicity of 18:1Δ9, indicating that inefficient metabolism did not play a determinant role in fatty acid toxicity. Nontoxic 18:1Δ9 was as toxic as 16:1Δ9 in a strain lacking wall teichoic acids and led to growth arrest and enhanced release of intracellular contents. Thus, wall teichoic acids contribute to the structure-specific antimicrobial effects of unsaturated fatty acids. The ability of poorly metabolized 16:1 isomers to penetrate the cell wall defenses is a weakness that has been exploited by the innate immune system to combat S. aureus.
Extracellular fatty acid incorporation into the phospholipids of Staphylococcus aureus occurs via fatty acid phosphorylation. We show that fatty acid kinase (Fak) is composed of two dissociable ...protein subunits encoded by separate genes. FakA provides the ATP binding domain and interacts with two distinct FakB proteins to produce acyl-phosphate. The FakBs are fatty acid binding proteins that exchange bound fatty acid/acyl-phosphate with fatty acid/acyl-phosphate presented in detergent micelles or liposomes. The Δ fakA and Δ fakB1 Δ fakB2 strains were unable to incorporate extracellular fatty acids into phospholipid. FakB1 selectively bound saturated fatty acids whereas FakB2 preferred unsaturated fatty acids. Affymetrix array showed a global perturbation in the expression of virulence genes in the Δ fakA strain. The severe deficiency in α-hemolysin protein secretion in Δ fakA and Δ fakB1 Δ fakB2 mutants coupled with quantitative mRNA measurements showed that fatty acid kinase activity was required to support virulence factor transcription. These data reveal the function of two conserved gene families, their essential role in the incorporation of host fatty acids by Gram-positive pathogens, and connects fatty acid kinase to the regulation of virulence factor transcription in S. aureus .
Summary
Acyl‐CoA and acyl‐acyl carrier protein (ACP) synthetases activate exogenous fatty acids for incorporation into phospholipids in Gram‐negative bacteria. However, Gram‐positive bacteria utilize ...an acyltransferase pathway for the biogenesis of phosphatidic acid that begins with the acylation of sn‐glycerol‐3‐phosphate by PlsY using an acyl‐phosphate (acyl‐PO4) intermediate. PlsX generates acyl‐PO4 from the acyl‐ACP end‐products of fatty acid synthesis. The plsX gene of Staphylococcus aureus was inactivated and the resulting strain was both a fatty acid auxotroph and required de novo fatty acid synthesis for growth. Exogenous fatty acids were only incorporated into the 1‐position and endogenous acyl groups were channeled into the 2‐position of the phospholipids in strain PDJ39 (ΔplsX). Extracellular fatty acids were not elongated. Removal of the exogenous fatty acid supplement led to the rapid accumulation of intracellular acyl‐ACP and the abrupt cessation of fatty acid synthesis. Extracts from the ΔplsX strain exhibited an ATP‐dependent fatty acid kinase activity, and the acyl‐PO4 was converted to acyl‐ACP when purified PlsX is added. These data reveal the existence of a novel fatty acid kinase pathway for the incorporation of exogenous fatty acids into S. aureus phospholipids.
Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the ...development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome.
We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity.
WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes.
This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.
Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet ...the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.
Transient receptor potential (TRP) channels are a superfamily of cation transmembrane proteins that are expressed in many tissues and respond to many sensory stimuli. TRP channels play a role in ...sensory signaling for taste, thermosensation, mechanosensation, and nociception. Activation of TRP channels (e.g., TRPM5) in taste receptors by food/chemicals (e.g., capsaicin) is essential in the acquisition of nutrients, which fuel metabolism, growth, and development. Pain signals from these nociceptors are essential for harm avoidance. Dysfunctional TRP channels have been associated with neuropathic pain, inflammation, and reduced ability to detect taste stimuli. Humans have long recognized the relationship between taste and pain. However, the mechanisms and relationship among these taste-pain sensorial experiences are not fully understood. This article provides a narrative review of literature examining the role of TRP channels on taste and pain perception. Genomic variability in the
gene has been associated with alterations in various pain conditions. Moreover, polymorphisms of the
gene have been associated with alterations in salty taste sensitivity and salt preference. Studies of genetic variations in
genes or modulation of TRP pathways may increase our understanding of the shared biological mediators of pain and taste, leading to therapeutic interventions to treat many diseases.
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