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1. Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma

   https://doi.org/10.1038/s41598-021-83141-z  

   Hu, Leland S. ; Wang, Lujia ; Hawkins-Daarud, Andrea ; Eschbacher, Jennifer M. ; Singleton, Kyle W. ; Jackson, Pamela R. ; Clark-Swanson, Kamala ; Sereduk, Christopher P. ; Peng, Sen ; Wang, Panwen ; et al ( December 2021 , Scientific Reports)

   null (Ed.)

   Abstract Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor—a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making. 

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2. Longitudinal Patterns in Clinical and Imaging Measurements Predict Residual Survival in Glioblastoma Patients

   Smedley, Nova F ; Ellingson, Benjamin M ; Cloughesy, Timothy F ; Hsu, William ( January 2018 , Scientific reports)

   The growing amount of longitudinal data for a large population of patients has necessitated the application of algorithms that can discover patterns that inform patient management. This study demonstrates how temporal patterns generated from a combination of clinical and imaging measurements improve residual survival prediction in glioblastoma patients. Temporal patterns were identified with sequential pattern mining using data from 304 patients. Along with patient covariates, the patterns were incorporated as features in logistic regression models to predict 2-, 6-, or 9-month residual survival at each visit. The modeling approach that included temporal patterns achieved test performances of 0.820, 0.785, and 0.783 area under the receiver operating characteristic curve for predicting 2-, 6-, and 9-month residual survival, respectively. This approach significantly outperformed models that used tumor volume alone (p < 0.001) or tumor volume combined with patient covariates (p < 0.001) in training. Temporal patterns involving an increase in tumor volume above 122 mm3/day, a decrease in KPS across multiple visits, moderate neurologic symptoms, and worsening overall neurologic function suggested lower residual survival. These patterns are readily interpretable and found to be consistent with known prognostic indicators, suggesting they can provide early indicators to clinicians of changes in patient state that inform management decisions. 

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3. A patient-designed tissue-engineered model of the infiltrative glioblastoma microenvironment

   https://doi.org/10.1038/s41698-022-00290-8  

   Cornelison, R. C. ; Yuan, J. X. ; Tate, K. M. ; Petrosky, A. ; Beeghly, G. F. ; Bloomfield, M. ; Schwager, S. C. ; Berr, A. L. ; Stine, C. A. ; Cimini, D. ; et al ( July 2022 , npj Precision Oncology)

   Glioblastoma is an aggressive brain cancer characterized by diffuse infiltration. Infiltrated glioma cells persist in the brain post-resection where they interact with glial cells and experience interstitial fluid flow. We use patient-derived glioma stem cells and human glial cells (i.e., astrocytes and microglia) to create a four-component 3D model of this environment informed by resected patient tumors. We examine metrics for invasion, proliferation, and putative stemness in the context of glial cells, fluid forces, and chemotherapies. While the responses are heterogeneous across seven patient-derived lines, interstitial flow significantly increases glioma cell proliferation and stemness while glial cells affect invasion and stemness, potentially related to CCL2 expression and differential activation. In a screen of six drugs, we find in vitro expression of putative stemness marker CD71, but not viability at drug IC₅₀, to predict murine xenograft survival. We posit this patient-informed, infiltrative tumor model as a novel advance toward precision medicine in glioblastoma treatment.

    

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4. Targeting EYA2 tyrosine phosphatase activity in glioblastoma stem cells induces mitotic catastrophe

   https://doi.org/10.1084/jem.20202669  

   Zhang, Guoxin ; Dong, Zhen ; Gimple, Ryan C. ; Wolin, Arthur ; Wu, Qiulian ; Qiu, Zhixin ; Wood, Lisa M. ; Shen, Jia Z. ; Jiang, Li ; Zhao, Linjie ; et al ( November 2021 , Journal of Experimental Medicine)

   Glioblastoma ranks among the most lethal of primary brain malignancies, with glioblastoma stem cells (GSCs) at the apex of tumor cellular hierarchies. Here, to discover novel therapeutic GSC targets, we interrogated gene expression profiles from GSCs, differentiated glioblastoma cells (DGCs), and neural stem cells (NSCs), revealing EYA2 as preferentially expressed by GSCs. Targeting EYA2 impaired GSC maintenance and induced cell cycle arrest, apoptosis, and loss of self-renewal. EYA2 displayed novel localization to centrosomes in GSCs, and EYA2 tyrosine (Tyr) phosphatase activity was essential for proper mitotic spindle assembly and survival of GSCs. Inhibition of the EYA2 Tyr phosphatase activity, via genetic or pharmacological means, mimicked EYA2 loss in GSCs in vitro and extended the survival of tumor-bearing mice. Supporting the clinical relevance of these findings, EYA2 portends poor patient prognosis in glioblastoma. Collectively, our data indicate that EYA2 phosphatase function plays selective critical roles in the growth and survival of GSCs, potentially offering a high therapeutic index for EYA2 inhibitors. 

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5. In vivo imaging using surface enhanced spatially offset raman spectroscopy (SESORS): balancing sampling frequency to improve overall image acquisition

   https://doi.org/10.1038/s44303-024-00011-9  

   Nicolson, Fay ; Andreiuk, Bohdan ; Lee, Eunah ; O’Donnell, Bridget ; Whitley, Andrew ; Riepl, Nicole ; Burkhart, Deborah L. ; Cameron, Amy ; Protti, Andrea ; Rudder, Scott ; et al ( April 2024 , npj Imaging)

   In the field of optical imaging, the ability to image tumors at depth with high selectivity and specificity remains a challenge. Surface enhanced resonance Raman scattering (SERRS) nanoparticles (NPs) can be employed as image contrast agents to specifically target cells in vivo; however, this technique typically requires time-intensive point-by-point acquisition of Raman spectra. Here, we combine the use of “spatially offset Raman spectroscopy” (SORS) with that of SERRS in a technique known as “surface enhanced spatially offset resonance Raman spectroscopy” (SESORRS) to image deep-seated tumors in vivo. Additionally, by accounting for the laser spot size, we report an experimental approach for detecting both the bulk tumor, subsequent delineation of tumor margins at high speed, and the identification of a deeper secondary region of interest with fewer measurements than are typically applied. To enhance light collection efficiency, four modifications were made to a previously described custom-built SORS system. Specifically, the following parameters were increased: (i) the numerical aperture (NA) of the lens, from 0.2 to 0.34; (ii) the working distance of the probe, from 9 mm to 40 mm; (iii) the NA of the fiber, from 0.2 to 0.34; and (iv) the fiber diameter, from 100 µm to 400 µm. To calculate the sampling frequency, which refers to the number of data point spectra obtained for each image, we considered the laser spot size of the elliptical beam (6 × 4 mm). Using SERRS contrast agents, we performed in vivo SESORRS imaging on a GL261-Luc mouse model of glioblastoma at four distinct sampling frequencies: par-sampling frequency (12 data points collected), and over-frequency sampling by factors of 2 (35 data points collected), 5 (176 data points collected), and 10 (651 data points collected). In comparison to the previously reported SORS system, the modified SORS instrument showed a 300% improvement in signal-to-noise ratios (SNR). The results demonstrate the ability to acquire distinct Raman spectra from deep-seated glioblastomas in mice through the skull using a low power density (6.5 mW/mm²) and 30-times shorter integration times than a previous report (0.5 s versus 15 s). The ability to map the whole head of the mouse and determine a specific region of interest using as few as 12 spectra (6 s total acquisition time) is achieved. Subsequent use of a higher sampling frequency demonstrates it is possible to delineate the tumor margins in the region of interest with greater certainty. In addition, SESORRS images indicate the emergence of a secondary tumor region deeper within the brain in agreement with MRI and H&E staining. In comparison to traditional Raman imaging approaches, this approach enables improvements in the detection of deep-seated tumors in vivo through depths of several millimeters due to improvements in SNR, spectral resolution, and depth acquisition. This approach offers an opportunity to navigate larger areas of tissues in shorter time frames than previously reported, identify regions of interest, and then image the same area with greater resolution using a higher sampling frequency. Moreover, using a SESORRS approach, we demonstrate that it is possible to detect secondary, deeper-seated lesions through the intact skull.

    

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