TB-3 miR-33a depletion accelerate medulloblastoma generation and invasion

Background and purposes: Lipid metabolism have been shown to be associated with tumorigenicity in various malignancies. The purpose of this study was to investigate the association of miR-33, a key regulator of lipid metabolism, in tumorigenicity and progression of medulloblastoma. Methods: Incidence of medulloblastoma and histopathological findings were compared between ptch1+/- mice and ptch1+/- miR-33a-/- mice. Tumors extracted from these mice were transplanted subcutaneously in nude mice (n=14 for ptch1+/-, n=19 for ptch1+/- miR-33a-/-) and in C57BL/6 mice (n=12 for each). Gene expression profile was compared between tumors from ptch1+/- mice and those from ptch1+/- miR-33a-/- mice. Results: Knockout of miR-33a in ptch1+/- transgenic mouse model increased the incidence of spontaneous generation of medulloblastoma from 34.5% to 84.0% (p< 0.001) at 12 months. Histopathological analysis showed infiltrative tumor borders in ptch1+/- miR-33a-/- tumors as compared with ptch1+/- ones. Tumor formation was observed in 21.4% for ptch1+/- tumors and 68.4% for ptch1+/- miR-33a-/- tumors in nude mice (p= 0.008). It was observed in 0% and 16.7% in immune competent mice. RNA sequencing detected that SCD1 and SREBF1 was upregulated in tumors from miR-33a knockout mice. Discussion: Our results demonstrated that depletion of miR-33a accelerated medulloblastoma generation and invasion. miR-33a may also be important for immune evasion. SCD1, which is reported to play a role in tumor stem cell maintenance and metastasis, can be a potential therapeutic target for medulloblastoma.

NIMG-30. PATHOLOGICAL VALIDATION OF CONTRAST ENHANCEMENT AT AUTOPSY BETWEEN PATIENTS TREATED AND UNTREATED WITH CHEMOTHERAPY AND RADIATION

Tumor heterogeneity in glioblastoma complicates delineation of active tumor using standard MR imaging. Pseudo-progression following treatment with chemotherapy and radiation (chemoRT) further complicates how tumors appear. T1-weighted subtraction maps (T1S) have been used to better identify subtly enhancing regions containing infiltrative tumor. This study examines the differences in tumor appearance between patients treated with chemoRT compared to a cohort opting out at autopsy, to understand how chemoRT changes contrast enhancement on MRI. Ten patients diagnosed with glioblastoma were recruited for whole brain donation for this study. Three patients received no treatment (chemoRT-), and seven received a combination of chemoRT and additional treatments (chemoRT+), including but not limited to bevacizumab (Bev) and tumor treating fields (TTF). Large tissue samples were taken at autopsy from whole brain samples sliced axially to align with the last clinical MRI using patient-specific 3D-printed slicing jigs. All tissue samples were hematoxylin and eosin (HE) stained and digitized at 40X resolution (27 total samples). The whole slide images (WSI) were annotated to outline regions containing necrosis without pseudopalisading cells, tumor with pseudopalisading necrosis, and infiltrative tumor. T1S were created for each patient by subtracting intensity normalized T1-weighted images from T1 post-contrast images (T1C). The annotated WSIs were aligned and resampled into MRI space using a custom software. A mixed effect model was used to compare the T1S intensity between chemoRT +/- cohorts within each pathological annotation, incorporating a random effect for subject. Mean T1S intensity was greater in untreated subjects when compared to chemoRT-subjects within each pathological annotation, including necrosis without pseudopalisading cells, tumor with pseudopalisading necrosis, and infiltrative tumor (p< 0.001). We show that chemoRT reduces the contrast enhancement in all aspects of pathologically validated tumor compartments, including infiltrative tumor. Future research is needed to examine if other patterns are evident in additional MR sequences.

NIMG-42. MP-MRI-BASED TUMOR PROBABILITY MAPS TRAINED USING AUTOPSY TISSUE SAMPLES AS GROUND TRUTH NON-INVASIVELY PREDICT INFILTRATIVE TUMOR BEYOND THE CONTRAST ENHANCING REGION

Infiltrative glioma beyond contrast enhancement on MRI is often difficult to identify with conventional imaging. In this study, we use large-format autopsy samples aligned to multi-parametric MRI to test the hypothesis that radio-pathomic machine learning models are able to accurately identify areas of infiltrative tumor beyond the contrast enhancing region. At autopsy, 140 tissue samples from 62 brain cancer patients were collected from brain slices sectioned to align with the patients’ last clinical MRI prior to death. Cell, extra-cellular fluid (ECF), and cytoplasm densities were computed from digitized, hematoxylin and eosin-stained samples, and a subset of 20 slides from 9 patients were annotated for tumor presence by a pathologist-trained technician. In-house custom software was used to align the tissue samples to the patients’ last clinical imaging, which included pre- and post-contrast T1, FLAIR, and ADC images. Bagging random forest models were then trained to predict cellularity, ECF, and cytoplasm density using 5-by-5 voxel tiles from each MRI as input. A 2/3-1/3 train-test split was used to validate model generalizability. A naïve Bayes classifier was trained to predict tumor class using cellularity, ECF, and cytoplasm segmentations within the annotation data set, again using a 2/3-1/3 train-test split to validate performance. The random forest models each accurately predicted cellularity, ECF, and cytoplasm density within the test data set, with root-mean-squared error values for each falling within one standard deviation of the ground truth. The histology-based tumor prediction model accurately predicted tumor, with a test set ROC AUC of 0.86. When using whole brain cellularity, ECF, and cytoplasm predictions from the random forest models as inputs for the naïve Bayes classifier, tumor probability maps identified regions of infiltrative tumor beyond contrast enhancement. Our results suggest that radio-pathomic maps of tumor probability accurately identify regions of infiltrative tumor beyond currently accepted MRI signatures.

Quantitative Mapping of Individual Voxels in the Peritumoral Region of Glioblastoma to Distinguish Between Tumor Infiltration and Edema

Purpose: The peritumoral region (PTR) in glioblastoma (GBM) represents a combination of infiltrative tumor and vasogenic edema, which are indistinguishable on magnetic resonance imaging (MRI). We developed a radiomic signature by using imaging data from low grade glioma (LGG) (marker of tumor) and PTR of brain metastasis (BM) (marker of edema) and applied it on the GBM PTR to generate probabilistic maps. Methods: 270 features were extracted from T1-weighted, T2-weighted, and apparent diffusion coefficient maps in over 3.5 million voxels of LGG (36 segments) and BM (45 segments) scanned in a 1.5 T MRI. A support vector machine classifier was used to develop the radiomics model from approximately 50% voxels (downsampled to 10%) and validated with the remaining. The model was applied to over 575,000 voxels of the PTR of 10 patients with GBM to generate a quantitative map using Platt scaling (infiltrative tumor vs. edema). Results: The radiomics model had an accuracy of 0.92 and 0.79 in the training and test set, respectively (LGG vs. BM). When extrapolated on the GBM PTR, 9 of 10 patients had a higher percentage of voxels with a tumor-like signature over radiological recurrence areas. In 7 of 10 patients, the areas under curves (AUC) were >0.50 confirming a positive correlation. Including all the voxels from the GBM patients, the infiltration signature had an AUC of 0.61 to predict recurrence.Conclusion: A radiomic signature can demarcate areas of microscopic tumors from edema in the PTR of GBM, which correlates with areas of future recurrence.

SURG-07. DETECTION OF GLIOMA INFILTRATION AT THE TUMOR MARGIN USING QUANTITATIVE STIMULATED RAMAN SCATTERING MICROSCOPY

BACKGROUND Differentiating neoplastic tumor tissues from normal brain at the infiltrative tumor margin remains challenging. Stimulated Raman scattering microscopy (SRM) is a rapid, label-free technique that provides microscopic imaging of unprocessed tissues. However, it has never been studied as a tool to enhance extent of resection. METHODS In this single center study, patients with WHO II-IV gliomas undergoing surgical resection were included. Margin samples were taken with corresponding stereotactic coordinates. Each tumor margin sample was analyzed using (1) H&E, (2) SRM, and (3) immunohistochemical (IHC) stains for IDH1-R132H or p53. Specimens were imaged fresh with SRM, inked for orientation and placed in formalin for routine processing. Stained slides were scored by 3-neuro-pathologists using a semiquantitative 2-tiered scoring system for the presence or absence of residual tumor. SRM microscopy images were segmented into cellularity. The intraobserver measure of agreement between modalities for each pathologist was calculated using Cohen’s kappa. RESULTS A total of 31 patients and 179 margin specimens were acquired. Postoperative MRI confirmed that 169 (94%) of glioma margin sample coordinates were indeed at the tumor margins (10 of 179 were not true margins). All 10 samples which were not true margins were identified as having residual tumor by SRM. IHC semiquantitative scoring of margin specimen confirmed 72 of 128 samples (56%) had residual tumor. Similarly, H&E-scoring confirmed 82 of 169 samples (49%) and 82 of 167 samples (49%) by SRM-scoring had residual tumor. Intraobserver agreements between all 3 modalities confirmed agreement: IHC-SRM was near perfect (K-0.84, CI-0.750-0.94); IHC-H&E substantial, (K-0.67 CI-0.54-0.80); SRM-H&E agreement substantial (K-0.72 CI-0.62-0.83). Margin samples with residual tumor demonstrated higher tissue cellularity when compared with samples without tumor (p< 0.0001). CONCLUSIONS SRM allows for identification of residual microscopic disease at the infiltrative tumor margin and therefore may be a promising tool to enhance extent of resection.