Citations

Lepore hemoglobins (Hbs) such as Hb Lepore-Boston-Washington [δ(1-87) β(116-146)], Hb Lepore-Baltimore [δ(1-50) β(86-146)], Hb Lepore-Hollandia [δ(1-22) β(50-146)] are δβ gene fusion/deletion variants (ε- Gγ- Aγ-[δβ] v )(Figure 1). They are controlled by the weak δ-globin gene promoter and synthesized at approximately 10-15% of hemoglobin fractions. This results in a similar phenotype as β +-thalassemia (microcytosis and hypochromia ± mild anemia). Anti-Lepore hemoglobins such as Hb P-Nilotic (β(1-22) δ(50-146) are βδ gene fusion/duplication variants (ε- Gγ- Aγ-δ-[βδ] v-β) associated with normal CBC values. Herein we describe a novel βδβ double fusion variant (ε- Gγ- Aγ-δ-[βδβ] v). Due to high homology between the δ and β genes, this double fusion event encodes an identical protein to Hb Lepore-Hollandia, albeit linked to the β-promoter and therefore expressed at ~40% of hemoglobin fractions, which would be expected to enhance any intrinsic properties of the variant. This novel hemoglobin variant is named Hb Lepore Rochester-MN. Two cases have been identified (Table 1). Case 1 was a 10-month-old Caucasian/Native American infant referred for an abnormal newborn screen and a reported family history of Hb G-Coushatta (β22, GAA>GCA, Glu>Ala; HGVS: c.68A>C, p.E23A). Case 2 was a 3-month-old infant referred for an abnormal newborn screen. CBCs were normal for age including MCV. Cation-exchange high performance liquid chromatography (HPLC) showed a shouldered peak in the Hb A 2 window typical for Hb Lepore (elution time: 3.41-3.46 mins), although the variant percentage was higher than expected, mimicking Hb G-Coushatta (expected elution time: 3.33 mins). Capillary electrophoresis identified a peak in the D zone (Z(D) zone), which is expected for Hb Lepore or Hb G-Coushatta. Intact mass spectrometry was consistent with Hb Lepore-Hollandia at 15836 amu, differing from Hb G-Coushatta (Glu>Ala, 15809 amu). Multiplex ligand probe amplification (MLPA) was normal because the highly variable fusion regions are unsuitable for probe placement. Sanger sequencing of the β-globin gene identified multiple single base substitutions which were challenging to interpret but matched the sequence expected for a complex double crossover event (NM_000518.4:c.28_68delins41 (p.Ser10_Glu23delins14TAVNALWGKVNVDA). The first crossover is located between c.-29 and c.9, the second crossover is located between c.69 and c.92+17. Long-read sequencing was performed (Sequel, Pacific Biosciences) which detected the multiple missense mutations in cis conformation, as was corroborated by the mass spectrometry data. In summary, Hb Lepore Rochester-MN represents a novel double fusion [βδβ] variant class that will pose a diagnostic challenge for laboratories. Although it is likely a result of sequential crossover events (deletion and duplication) between Lepore and anti-Lepore variants, MLPA analysis is normal due to technical constraints and high homology. DNA sequencing (both Sanger and long read methods) yielded complex results that require sophisticated analysis and correlation with protein data including intact mass spectrometry. HPLC and CE data including variant percentage are very similar to Hb G-Coushatta. In addition, although resulting in an identical protein, the variant should not be mistaken for Hb Lepore-Hollandia, which is produced at lower levels and causes a thalassemic phenotype. Hb Lepore Rochester-MN is controlled by the β-promoter and therefore is associated with approximately 40% variant and apparently normal CBC values in the two infants.

Abstract It has been widely acknowledged that limited access to the human brain, along with the importance of its structures, presents significant challenges in obtaining brain tumor tissue. This poses difficulties for clinical decision-making, as healthcare professionals often have to rely solely on imaging to address complex problems. There is currently no definitive method to make these decisions other than relying on informed yet subjective human judgment. That said, imaging abnormalities are a result of a presence of particular tumor-related biology. Because of this, and the heterogeneity frequently observed in brain tumors, we have developed an image-localized biopsy protocol that has been employed for research purposes at Mayo Clinic Arizona for over five years. This multidisciplinary approach involves the collaborative efforts from neurosurgeons, neuro-oncologists, scrub-techs, nurses, radiologists, as well as multiple research labs, all working within a well-established pipeline. The implementation of this protocol has allowed us to obtain image-localized biopsies during surgical procedures, enabling the correlation of local imaging findings with corresponding tissue features through the utilization of machine learning models. The primary objective of this endeavor is to enhance our understanding of the underlying biological mechanisms of brain tumors based on patients' imaging data. This protocol has been successful for our research purposes, and with minor adjustments, it can be readily adopted by any hospital in order to collect more data about these incredibly heterogeneous diseases. While the use of neuronavigation during surgery has become commonplace in many medical institutions, localization of tissue samples is not. Utilizing pre-existing infrastructure in the operating room is a small burden with big potential positive outcomes. Sample locations can be useful to clinicians during tumor board discussions, to better assess disease heterogeneity. These minor refinements in the surgical workflow have the capacity to provide clinicians with additional clinically-relevant data without disrupting patient care.

Abstract Glioblastoma (GBM) is the most aggressive and most common primary malignant brain tumor in adults, with a poor median survival time of 15 months. One of the key challenges in treating GBM is its highly heterogeneous nature, with multiple distinct subtypes that have been shown to occur on both inter- and intra-patient levels. Three main classifications, known as classical/proliferative, mesenchymal and proneural have become commonly demonstrated phenotypes. The cell cycle is a fundamental and highly conserved process that controls faithful division of cells; dysregulation of the cell cycle is known to be a key driver in many cancers. However, how the cell cycle is differently regulated between these subtypes has not been well classified in vivo. We investigate these three GBM subtypes using a recently published single nucleus RNAseq (snRNAseq) data set. We compare cell cycle regulation/dysregulation among these three subtypes using Tricycle, an R/Bioconductor package that utilises dimension reduction via principal component analysis and transfer learning to infer cell cycle position from any snRNAseq data set. We find that the classical GBM subtype has the highest proportion of actively dividing cells (cells in: S/G2/M phases), while the mesenchymal and proneural subtypes have a very low proportion of actively dividing cells. This supports the idea of a proliferation-migration dichotomy between GBM subtypes. We use this proportion of actively proliferating cells to calibrate a minimal spatiotemporal mathematical model for GBM tumor growth that accounts for the differences in cell cycle regulation between these three GBM subtypes.

Abstract High-grade glioma continues to have dismal survival owing in part to its intra- and inter-patient heterogeneity. Standard clinical protocol collects tumor samples with the aim of providing or confirming a diagnosis and determining the status of a few key genes (e.g. IDH1, MGMT). However, this protocol is unable to capture the diversity within tumor regions, immune expression or normal cell abundances that play key roles in the development of the disease. To overcome this, during surgery we collect image-localized multi-regional biopsies to characterize this disease heterogeneity. Data collection is ongoing, and we currently have 202 samples from 58 patients with available bulk RNA-Seq. With a single-cell reference dataset from Columbia University, we used CIBERSORTx, a deconvolution method, to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell subpopulations for each sample. We used Cox proportional hazard models with first-order statistics (mean, minimum, maximum) of abundances within patients to determine whether these are prognostic, then used TCGA RNA-Seq data to validate these findings. We found that one glioma proneural subtype was significantly beneficial for patient survival relative to other glioma subtypes across all statistics (and showed significance in TCGA). Proliferative and mesenchymal glioma subtypes also showed significance for one or two of the calculated statistics. Oligodendrocyte progenitor cell abundances were consistently significantly beneficial for patient survival in our cohort, while an increased abundance of abnormal (reactive) astrocytes is associated with poor prognosis. We also ran these analyses within invasive margin and core tumor regions to determine location-specific population abundances associated with patient survival. In conclusion, understanding the in vivo diversity of cellular subpopulations within high grade glioma is important for treatment stratification and patient benefit.

Abstract Identification of key phenotypic regions such as necrosis, contrast enhancement, and edema on magnetic resonance imaging (MRI) is important for understanding disease evolution and treatment response in patients with glioma. Manual delineation is time intensive and not feasible for a clinical workflow. Automating phenotypic region segmentation overcomes many issues with manual segmentation, however, current glioma segmentation datasets focus on pre-treatment, diagnostic scans, where treat-ment effects and surgical cavities are not present. Thus, existing automatic segmenta-tion models are not applicable to post-treatment imaging that is used for longitudinal evaluation of care. Here, we present a comparison of three-dimensional convolutional neural networks (nnU-Net architecture) trained on large temporally defined pre-treatment, post-treatment, and mixed cohorts. We used a total of 1563 imaging timepoints from 854 patients curated from 13 different institutions as well as diverse public data sets to understand the capabilities and limitations of automatic segmenta-tion on glioma images with different phenotypic and treatment appearance. We as-sessed the performance of models using Dice coefficients on test cases from each group comparing predictions with manual segmentations generated by trained techni-cians. We demonstrate that training a combined model can be as effective as models trained on just one temporal group. The results highlight the importance of a diverse training set, that includes images from the course of disease and with effects from treatment, in the creation of a model that can accurately segment glioma MRIs at multiple treatment time points.

Abstract Image-based mathematical modeling is emerging as an advantageous tool to integrate into the care routine for patients with brain tumors. These tools can be used for a variety of tasks including predicting tumor growth, indicating spatial genomic alterations, and guiding radiation therapy. Often, the methods to produce these maps are developed in silos, using cohorts of retrospective patients and switching to a per patient map calculation can be an afterthought. Automatic pipelines are needed to enable creating mathematical maps in a clinically-relevant workflow. To this end, we developed a pipeline to create an image-based mathematical map of edema abundance based on T2-weighted (T2W) magnetic resonance imaging that includes preprocessing steps, abnormality and tissue type segmentation, brain extraction, map generation, and conversion between image file formats. The python-based pipeline was designed to take in T1-weighted (T1W), T1W with gadolinium contrast (T1Gd), T2W, and fluid attenuated inversion recovery (FLAIR) DICOM images for a single patient as input and convert them to NIfTIs for downstream steps. The pipeline preprocessed images by correcting bias field fluctuations using the N4 algorithm and normalizing intensities. The T1W, T1Gd, and FLAIR are then registered to the T2W. T1Gd and FLAIR are used in a deep learning algorithm to delineate the brain and abnormality including enhancing tumor, necrosis, and peripheral edema. Normal tissue segmentations are performed using the statistical parametric mapping (SPM) segmentation routine. The segmentations and preprocessed images are all utilized in the edema mathematical model. The resulting edema map is then reformatted as a DICOM and output for further use by clinicians and clinical imaging systems. Our future goals include integrating our pipeline in a clinical workstation and sending final maps to our hospital’s picture archiving and communication system (PACS) to allow for deployment of these models for patient care in a clinically efficient timeline.

Abstract BACKGROUND Treatment of high-grade glioma (HGG) remains a challenge. Several groups have developed ex vivo drug sensitivity assays to evaluate susceptibility of the tumor tissue to specific chemotherapeutics. This tool, in addition to molecular analysis of tumors might provide another layer of confidence in choosing treatments for patients with high-grade gliomas. METHODS 3D PredictTM platform (Kiyatec, Greenville, SC, USA) was tested at three academic medical centers in the United States. Twelve patients with newly diagnosed and recurrent HGGs were included. Tumor spheroids created ex vivo were exposed to twelve pre-selected drugs. Responses were reported as: no response, moderate response and response. We analyzed: demographics, histopathology, stage of disease, success rate and results of the ex vivo assay and clinical implications. RESULTS Majority of patients were women (67%). Median age was 58.5. Histology: GBM-83%, HGGs-17%. Eighty three percent of the samples represented recurrent disease. Sixty percent represented first recurrence while the remaining 40% represented 2nd or 3rd recurrence. Eight out of 12 tissue samples (67%) were successfully tested ex vivo. In six cases (75%), the information was used for immediate clinical decision making. In 2 cases, the decision was deferred until the next progression of the disease. The drugs chosen for treatment based on assay results included: olaparib (the assay was positive for response to rucaparib), temozolomide, lomustine, carboplatin and irinotecan. CONCLUSIONS This platform is easy to use in a standard clinical setting, the success rate for creating viable spheroids for drug sensitivity testing is high and the information obtained can be useful in clinical practice. Addition of other agents to the assay, correlation with molecular testing of the tumor tissue and attempt to assess response to drug combinations should be considered in further development of this technology. Prospective clinical trial is needed to validate this approach for routine clinical use.