Citations

Abstract INTRODUCTION Immunotherapy with engineered CAR T cells is a promising new therapy for glioblastoma, for which predictive and prognostic biomarkers are needed to inform effective intervention. Recently, our group analyzed standard-of-care (SOC) MRI images of long-term and short-term glioblastoma survivors and identified six intratumoral “habitats” of which “Habitat 6” was correlated with survival at diagnosis in high-grade glioma. Based on the MRI characteristics of “Habitat 6”, viz. high enhancement and high edema, we hypothesized that it could be a marker of tumor immune infiltrates. We are studying longitudinal changes in tumor “habitat” composition on MRIs of subjects with recurrent high-grade glioma treated with CAR T cells engineered to target IL13Ra2. METHODS MRI scans of the brain were acquired in 6 subjects at 3.0 T at baseline and various times before and after initiation of CAR T cell therapy. FLAIR, T1W and T1W-CE MRI images were registered to T2W images and six intratumoral “habitats” were computed as per our recently published methodology. The six habitats generated at the end of the tumor segmentation process were: “Habitat 1” (low FLAIR, low enhancement), “Habitat 2” (high FLAIR, low enhancement), “Habitat 3” (low FLAIR, medium enhancement), “Habitat 4” (high FLAIR, medium enhancement), “Habitat 5” (low FLAIR, high enhancement), and “Habitat 6” (high FLAIR, high enhancement). RESULTS Analysis of temporal changes in the six “habitats” shows an initial increase in both “Habitat 4” and “Habitat 6” following CAR T cell therapy initiation. Subjects with higher absolute volumes of “Habitat 6” at the baseline (pre-treatment) showed longer overall survival. Overall survival is a function of absolute “Habitat 6” volume at baseline, its direction of change immediately post-therapy, the duration of any increase in “Habitat 6” post-treatment, and the “Habitat 6” to “Habitat 4” ratio. Additional subjects are being evaluated to further understand these preliminary observations.

Abstract Glioblastoma (GBM) is the most aggressive primary brain tumor with an average survival of 15 months with standard of care treatment. GBM patients typically present with necrosis surrounded by enhancement on T1-weighted post gadolinium magnetic resonance imaging (T1gd MRI), however some patients present with a significant cystic component. Cysts are caused by different underlying biological mechanisms to necrosis and are important to identify for future clinical investigations. These cystic components can be manually identified through MRI but this process can be time consuming for large patient cohorts. Over the last two decades, our lab has collected serial MRI data of brain tumor patients. With over 70,000 images now in the database and that number increasing daily, it is clear that we have a unique resource for clinical investigation and a need to automate this process. To this end, the aim of this work was to develop and assess the performance of a convolution neural network (CNN) model for automatic detection of cystic GBMs. In this retrospective IRB-approved work, we collected pretreatment MRIs of a patient cohort consisting of 85 patients with a significant cystic component at presentation along with 400 non-cystic GBM, both identified manually through MRI. Image slices with a view of the cystic component were used as positive samples for training. Data were randomly split into training, validation, and test sets using a 70:15:15 ratio. The proportion of positive to negative cases was comparable between sets. Prior to training, we used image augmentation techniques to compensate for the class imbalance in our data. Our results showed that deep learning networks can automatically detect cystic GBMs on MRIs with high accuracy and thus illustrates the potential use of this technique in clinically relevant settings.

Abstract OBJECTIVES Dogma suggests that for brain tumors, regions of enhancement on T1-weighted gadolinium contrast enhanced (T1Gd) magnetic resonance imaging (MRI) correlate with intravenously delivered drug distribution as enhancement indicates a compromised blood-brain barrier (BBB). However, poor response to intravenous therapies highlights the importance of the diffuse disease beyond enhancing regions. This study investigated whether imaging features can provide an accurate prediction of drug distribution. METHODS Eight brain tumor patients (7 gliomas and 1 metastatic adenocarcinoma) were included in this Phase 0 trial. Presurgery T1-weighted, T1Gd, T2-weighted gadolinium contrast enhanced (T2Gd), and T2-weighted Fluid Attenuated Inversion Recovery (T2FLAIR) MRIs were acquired. All images underwent bias correction using the N4 algorithm, standardization of intensities, and registration. Prior to incision, patients received both an antibiotic cefazolin (6% BBB penetrance) and levetiracetam (80% BBB penetrance), an anti-seizure drug. Subsequently, multiple blood samples and image-guided biopsies were taken and analyzed for drug concentration using liquid chromatography mass spectrometry. Biopsy drug levels are reported as Brain-Plasma Ratio (BPR), the ratio of biopsy concentration relative to plasma concentration. Mean image intensity was extracted from an 8x8 mm window surrounding each biopsy location. Regression analysis was performed to determine which combination of image types were linearly predictive of BPR for both drugs. Correlations were also analyzed according to the biopsy location radiographic appearance. RESULTS Regression analysis revealed that T2Gd intensity was linearly predictive of cefazolin BPR and FLAIR intensity was linearly predictive of levetiracetam BPR (p=0.009 and 0.041, respectively). Grouping samples according the the radiographic appearance revealed that levetiracetam BPR had a similar pattern of values to that of FLAIR intensities and cefazolin BPR had a similar pattern to T2, further supporting the regression analysis results. CONCLUSIONS Local concentrations of drug may be related to T2-weighted signals (T2Gd and T2FLAIR) rather than the gadolinium distribution on T1Gd images.

Abstract Background: Large deletions involving the beta globin complex are relatively rare. They can be categorized generally into five groups by deletion size and/or location: 1) beta zero thalassemia (BZT); 2) delta beta thalassemia (DBT); 3) hereditary persistence of fetal hemoglobin (HPFH); 4) gamma delta beta thalassemia (GDBT); and 5) epsilon gamma delta beta thalassemia (EGDBT). These deletions are not well understood but often have significant clinical impact, either when present alone or in combination with other hemoglobin mutations. In this study, we analyze phenotypic and molecular data on a large number of cases with deletions in the beta globin gene complex to better classify these five groups of deletions as they occur in isolation. Methods: A query of the routine clinical testing patient files from the Mayo Clinic Metabolic Hematology and Molecular Genetics Laboratories from 2010 to 2015 identified 179 patients with a deletion confirmed by a Multiplex Ligation-dependent Probe Amplification (MLPA) assay. Twenty-four probes sets were placed from the 5' locus control region (LCR) to the 3' hypersensitivity region, spanning the beta globin gene complex. Using a Luminex LX200 flow cytometer, a gene dosage ratio was calculated for each probe set using the median fluorescent intensity value collected. The size and location of the deletion and patient phenotype were compared. Results: Of the 179 total cases, the following large deletions were identified: beta gene (HBB) (n = 47), delta (HBD) through HBB (n = 105), A-gamma (HBG2) through HBB (n = 20), and locus control region (LCR) through HBB (n = 7). One case had a deletion involving the LCR epsilon with the rest of the complex left intact. A subset (n = 60) of cases had compound hemoglobin mutations that altered the phenotype. The BZT cases had relatively high Hb A2 levels and variable Hb F levels consistent with promotor region loss. The main differences between DBT and HPFH included Hb F and Hb A2 levels. GDBT cases presented with median Hb F levels higher than that observed in DBTs, normal Hb A2, and microcytic anemia. EGDBT cases had variable features according to age of the patient and Hb F level; severe microcytic anemia was observed in neonates, milder microcytic anemia in young children, and microcytosis without anemia in an adult case. The phenotypic features of 119 patients with isolated large deletions are compiled in table 1. Conclusion: In general, all five categories of large deletions in an isolated heterozygous state can present with microcytic anemia and are typically benign with the exception of transient severe microcytic anemia in neonatal EGDBT cases. Although phenotypes associated with large deletions involving the beta globin gene complex are frequently distinctive, significant phenotypic overlap can be seen in a subset of cases. These cases require molecular analysis due to their clinical importance when in combination with another beta globin gene complex mutation for an adequate diagnosis and treatment approach. Table 1. Deletion type Age n HbF (%) HbA2 (%) Hb (g/dL) MCV (fL) RBC (10^12/L) RDW (%) MCH (pg/cell) BZT 20 6.3 (0.6-94.4) 6.8 (3.4-11.6) 11.1 (8.3-14.5) 65.4 (60.8-77.2) 5.4 (4.2-6.2) 19.2 (16.6-21.2) 20.9 (18.3-25.7) DBT 56 10.6 (2.7-22.4) 2.7 (2.5-3.1) 11.7 (8.6-14.4) 68.9 (61.3-83.5) 5.3 (4.1-7.3) 21.4 (18.2-26.8) 21.6 (19.9-39.2) HPFH 23 25.9 (17.6-39.7) 2.0 (1.5-2.4) 11.6 (8.1-16.7) 78.4 (60.2-101.9) 4.4 (3.0-6.3) 17.5 (14.1-22.3) 25.4 (17.6-29.7) GDBT 14 13.3 (8.2-19.0) 2.6 (1.8-2.7) 11.0 (8.6-14.1) 72.5 (57.9-82.1) 5.1 (3.5-6.2) 20.6 (17.4-23.5) 22 (17.9-25.1) EGDBT* 28 Y 1 0.3 3 13.3 59.4 6.9 15.4 19.2 1-4 Y 3 0.9 (0-1.6) 3.2 (2.9-3.5) 9.5 (8.8-13.3) 57.8 (57.6-59.4) 5.2 (4.9-6.9) 16.6 (15.4-17.4) 18.5 (18.1-19.2) <6 month 2 21.4 (14.8-27.9) 2.6 (2.2-2.9) 6.3 (6.0-6.6) 61.3 (59.9-62.6) 3.4 (3.3-3.3) 21.5 (21.2-21.7) 18.4 (18.1-18.7) medians, (min, max); *stratified by age Disclosures No relevant conflicts of interest to declare.

Abstract Background: The control of hemoglobin F (Hb F) expression has proven an elusive puzzle with several postulated but not mutually exclusive hypotheses. A silencing mechanism hypothesis, which predicts the existence of a regulatory element that suppresses HbF expression, has been supported by recent investigations into a 3.5 kb region upstream from the delta globin gene (HBD). To test this hypothesis, we have investigated our historical case files in a tertiary care high-throughput clinical laboratory and compared the patient phenotype (Hgb, MCV, MCH, RDW, Hb F %, and flow cytometry Hb F distribution) with the status of this 3.5 kb region. Methods: A query of clinical testing patient files from Mayo Clinic Metabolic Hematology Laboratory yielded 179 patients confirmed by Multiplex Ligation-dependent Probe Amplification (MLPA) to contain large deletions located within the epsilon through beta globin genes. Of these, 27 unrelated patients with breakpoints between the pseudobeta (HBBP1) and HBD genes were identified. Four additional MLPA probe pairs were placed in this region and the patient phenotypes were compared. Results: The interior two (of the four added) probes between HBBP1 and HBD stratified all 27 cases. The other two (flanking) probes were never discriminatory, thus refining the area of interest to 2.4 kb. Sixteen cases showed breakpoints within hg19 g.5260154-5259135 (5' region) and eleven cases showed breakpoints within hg19 g.5259136-5257692 (3' region) upstream of HBD. Patients who displayed a phenotype of HPFH (n = 9) contained breakpoints in the 5' region whereas those with a delta beta thalassemia (DBT) phenotype (n = 11) were associated with breakpoints in the 3' region. A subset (n = 7) with breakpoints in the 5' region had indefinite phenotypic features, but of these, all but one showed homocellular Hb F distribution. Conclusion: Our molecular and phenotypic correlation of 27 patients with large deletional breakpoints between HBBP1 and HBD supports the hypothesis of a silencing element located upstream of HBD and further narrows the area that segregates many HPFH and DBT patient phenotypes from 3.5 kb to 2.4 kb. Several potential silencing effectors with binding sites in this region include HDAC1, GATA1 and H3K27me3; interestingly, the BCL11A binding site may be outside of the regulatory area. Table 1. Phenotypic data of patients with deletions between HBBP1 and HBD. Case Age/Sex Hb F Hb A2 Hb X Hgb RBC MCV MCH MCHC RDW Hb F Flow (Y) (%) (%) (%) (g/dL) ( 10^12/L) (fL) (pg/cell) (hg/cell) (%) 3' Region 1 47 M 25.5 2.1 16.7 6.3 81.4 26.7 32.8 15.7 H 2 5 M 28.3 2.4 12.5 4.9 74.6 25.4 34 15.9 H 3 24 F 30.3 1.7 11.6 4.23 92.7 27.4 29.6 15.2 H 4 9 F 36.3 2.2 S = 62 13.1 5.2 69.9 NA NA 14.8 5 24 F 37.3 2.1 C = 61 12.9 5.1 72.9 25.5 35 14.3 6 3 M 39.7 2.1 12.4 4.4 88.7 28.1 31.7 16.3 7 27 F 40.2 3.4 S = 56 12.2 4.4 79.7 27.8 34.9 15 8 16 M 43.7 2.2 S = 54 15.9 6.8 71.5 23.5 32.9 14.1 9 7m M 100.0* 0.0 12.8 4.8 76 26.5 34.9 16.8 3' Region 10 28 F 20.9 2 10.11 4.4 70.9 22.7 32.1 19 H 11 64 M 21.4 2.3 10.51 4.1 81.1 25.8 31.8 18.1 12 2.5 M 24 2.1 10.21 5.3 59.4 19.3 32.5 20.9 H 13 58 F 24 2.1 9.51 4.4 72.2 21.5 29.8 17.7 H 14 14 F 25.4 1.5 10.81 4.9 69.4 22 31.8 17.6 H 15 52 F 26.9 1.8 8.42 3.6 72 23.5 32.7 19.4 T 16 34 M 38.7 1.7 S = 60 11.4 4.7 71.6 NA NA 21.6 5' Region 17 24 F 2.7 2.9 12.3 6.1 62.2 20 32.2 18.2 18 32 F 6.1 2.7 10.2 5.1 61.3 19.9 32.5 19.3 19 27 F 7.2 2.9 11 5.1 72.9 21.7 29.8 19.9 20 29 F 7.8 2.7 10.2 4.7 72.2 21.7 30 20.5 21 15 M 7.8 2.5 12.7 6.2 64.7 20.4 31.5 21.2 22 42 M 7.8 3.1 10.6 4.9 75.1 21.5 28.6 23.7 23 60 M 10.1 2.5 11 4.5 74.8 24.7 33 23.4 24 41 M 10.7 2.8 8.6 4.1 73.3 21.2 29 26.8 25 30 F 11.5 2.7 12.1 5.5 67.4 21.9 32.5 21.8 26 25 F 13.2 2.8 12.1 5.3 68.4 22.8 33.3 21.6 27 3 F 18.3 2.6 11.9 5.79 63.6 20.6 32.3 22.7 T Normal values <1 2-3.3 0 H = homocellular; T= heterocellular; I = indeterminate; *homozygous deletion -- = no deletion; + = deletion present (all alpha deletions were single -3.7 kb 'rightward' deletions) 1 = Suspected Fe deficiency; 2 = recent transfusion Disclosures No relevant conflicts of interest to declare.

Abstract Background : Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. Methods : Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). Results : Among males, tumor (T1Gd) radius was a predictor of overall survival (HR=1.027, p=0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR=1.011, p<0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p=0.010 t-test), but tumor size was not correlated with female overall survival (p=0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p=0.004, F p=0.001, t-test). Additionally, extent of resection, tumor laterality, and IDH1 mutation status were also found to have sex-specific effects on overall survival. Conclusion : Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.