BACKGROUND:Reanalysis of exome/genome data improves diagnostic yield. However, the value of reanalysis of clinical array comparative genomic hybridisation (aCGH) data has never been investigated. Case-by-case reanalysis can be challenging in busy diagnostic laboratories. METHODS AND RESULTS:We harmonised historical postnatal clinical aCGH results from ~16 000 patients tested via our diagnostic laboratory over ~7 years with current clinical guidance. This led to identification of 37 009 copy number losses (CNLs) including 33 857 benign, 2173 of uncertain significance and 979 pathogenic. We found benign CNLs to be significantly less likely to encompass haploinsufficient genes compared with the pathogenic or CNLs of uncertain significance in our database. Based on this observation, we developed a reanalysis pipeline using up-to-date disease association data and haploinsufficiency scores and shortlisted 207 CNLs of uncertain significance encompassing at least one autosomal dominant disease-gene associated with haploinsufficiency or loss-of-function mechanism. Clinical scientist reviews led to reclassification of 15 CNLs of uncertain significance as pathogenic or likely pathogenic. This was ~0.7% of the starting cohort of 2173 CNLs of uncertain significance and 7.2% of 207 shortlisted CNLs. The reclassified CNLs included first cases of CNV-mediated disease for some genes where all previously described cases involved only point variants. Interestingly, some CNLs could not be reclassified because the phenotypes of patients with CNLs seemed distinct from the known clinical features resulting from point variants, thus raising questions about accepted underlying disease mechanisms. CONCLUSIONS:Reanalysis of clinical aCGH data increases diagnostic yield.

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Key Points Gilteritinib induces 2 distinct marrow responses in FLT3-mutated AML: responses with and without differentiation. Ongoing clonal hematopoiesis is ubiquitous during gilteritinib therapy and may promote genetic evolution and drug resistance.

Approximately 2.4% of the human mitochondrial DNA (mtDNA) genome exhibits common homoplasmic genetic variation. We analyzed 12,975 whole-genome sequences to show that 45.1% of individuals from 1526 mother-offspring pairs harbor a mixed population of mtDNA (heteroplasmy), but the propensity for maternal transmission differs across the mitochondrial genome. Over one generation, we observed selection both for and against variants in specific genomic regions; known variants were more likely to be transmitted than previously unknown variants. However, new heteroplasmies were more likely to match the nuclear genetic ancestry as opposed to the ancestry of the mitochondrial genome on which the mutations occurred, validating our findings in 40,325 individuals. Thus, human mtDNA at the population level is shaped by selective forces within the female germ line under nuclear genetic control, which ensures consistency between the two independent genetic lineages.

AbstractGilteritinib is a potent and selective FLT3 kinase inhibitor with single-agent clinical efficacy in relapsed/refractory FLT3-mutated acute myeloid leukemia (AML). In this context, however, gilteritinib is not curative, and response duration is limited by the development of secondary resistance. To evaluate resistance mechanisms, we analyzed baseline and progression samples from patients treated on clinical trials of gilteritinib. Targeted next-generation sequencing at the time of AML progression on gilteritinib identified treatment-emergent mutations that activate RAS/MAPK pathway signaling, most commonly in NRAS or KRAS. Less frequently, secondary FLT3-F691L gatekeeper mutations or BCR–ABL1 fusions were identified at progression. Single-cell targeted DNA sequencing revealed diverse patterns of clonal selection and evolution in response to FLT3 inhibition, including the emergence of RAS mutations in FLT3-mutated subclones, the expansion of alternative wild-type FLT3 subclones, or both patterns simultaneously. These data illustrate dynamic and complex changes in clonal architecture underlying response and resistance to mutation-selective tyrosine kinase inhibitor therapy in AML.Significance:Comprehensive serial genotyping of AML specimens from patients treated with the selective FLT3 inhibitor gilteritinib demonstrates that complex, heterogeneous patterns of clonal selection and evolution mediate clinical resistance to tyrosine kinase inhibition in FLT3-mutated AML. Our data support the development of combinatorial targeted therapeutic approaches for advanced AML.See related commentary by Wei and Roberts, p. 998.This article is highlighted in the In This Issue feature, p. 983

This study describes the analytical performance of the QuantideX qPCR BCR-ABL IS Kit, the first Food and Drug Administration-cleared assay designed to monitor breakpoint cluster region-Abelson tyrosine-protein kinase 1 (BCR-ABL1) fusion transcripts isolated from peripheral blood specimens from patients with chronic myeloid leukemia. This multiplex real-time quantitative RT-PCR assay amplifies both e13a2 and e14a2 Major BCR-ABL1 transcripts and the reference target ABL1. The test results are provided in international scale (IS) values by incorporating armored RNA-based calibrators that have defined IS values tied directly to the World Health Organization BCR-ABL1 Primary Reference Materials, without the necessity of determining and maintaining conversion factors. For each batch run, the integrated interpretive software evaluates run and specimen quality control metrics (including a sufficient amount of ABL1 control transcripts to ensure a minimal limit of detection) and calculates both molecular response (MR) and %IS values for each specimen. The test has a limit of detection of MR4.7 (0.002%IS) and a linear range from MR0.3 (50%IS) to MR4.7 (0.002%IS) for both Major transcripts. Single-site and multisite precision studies demonstrated a maximum SD of 0.13 MR (30% CV within the assay range between MR0.7 and MR3.7). The performance of this BCR-ABL1 monitoring test meets all of the clinical guideline recommendations for sensitivity and IS reporting for the management of chronic myeloid leukemia patients.

High expression of O6-methylguanine-DNA methyltransferase (MGMT) in glioblastoma is associated with resistance to temozolomide, as tumor cells lacking MGMT activity are significantly more sensitive to the cytotoxic effects of temozolomide. The MGMT promoter methylation status (MGMTpms) is typically determined as MGMT-methylated or MGMT-unmethylated. Some single-center studies have reported results ranging from 70–95% detection rates using MRI. We aim to further validate these findings using a multi-institutional data set. We hypothesize that transfer learning based features when integrated via machine learning may lead to non-invasive determination of MGMTpms. A total of 270 patients were included across the 3 institutions (Hospital of the University of Pennsylvania (HUP), Jefferson University Hospital (JUH); the TCIA). JUH and TCIA datasets comprised conventional modalities (T1,T2,T2-FLAIR,T1-Gd), whereas HUP dataset had additional modalities (DSC,DTI) as well. We used transfer learning and adapted a convolutional neural network (CNN) model pre-trained on 1.2 million 3-channel images of the ImageNet to extract deep learning features from the given images. A support vector machine multivariately integrated these features towards a non-invasive marker of MGMTpms. The cross-validated accuracy of our MGMT marker in classifying the mutation status in individual patients was 86.95%, 81.56%, and 82.43%, respectively, in HUP, JUH, and TCIA. Our marker revealed MGMT-methylated tumors with lower neovascularization and cell density, when compared with MGMT-unmethylated tumors. MGMT-unmethylated tumors were found to be more lateralized to the right hemisphere, when compared with MGMT-methylated tumors. Our findings suggest that transfer learning features when integrated via machine learning allow robust prediction of MGMTpms on mpMRI acquired within multiple institutions. The proposed non-invasive MGMT marker may contribute to (i) MGMTpms determination for patients with inadequate tissue/inoperable tumors, (ii) stratification of patients into clinical trials, (iii) patient selection for targeted therapy, and (iv) personalized treatment planning.

Molecular profiling of glioblastoma has revealed complex cytogenetic, epigenetic, and molecular abnormalities that are necessary for diagnosis, prognosis, and treatment. Our neuro-oncology group has developed a data-driven, institutional consensus guideline for efficient and optimal workup of glioblastomas based on our routine performance of molecular testing. We describe our institution's testing algorithm, assay development, and genetic findings in glioblastoma, to illustrate current practices and challenges in neuropathology related to molecular and genetic testing. We have found that coordination of test requisition, tissue handling, and incorporation of results into the final pathologic diagnosis by the neuropathologist improve patient care. Here, we present analysis of O6-methylguanine-DNA-methyltransferase promoter methylation and next-generation sequencing results of 189 patients, obtained utilizing our internal processes led by the neuropathology team. Our institutional pathway for neuropathologist-driven molecular testing has streamlined the management of glioblastoma samples for efficient return of results for incorporation of genomic data into the pathological diagnosis and optimal patient care.

2051 Background: MGMT promoter methylation is associated with better prognosis and increased benefit from temozolomide in patients with GBM. The methylation status of the MGMT promoter is typically determined by tissue-based polymerase chain reaction assays, which can be limited by inadequate specimen or assay failures. We hypothesized that multivariate analysis of quantitative imaging (QI) features, extracted from multi-parametric MRI (mpMRI), could enable the non-invasive determination of MGMT promoter methylation status. Methods: We performed a retrospective cohort study of 111 GBM patients at the University of Pennsylvania whose tumors underwent MGMT methylation testing (pyrosequencing across 4 CpG sites in the MGMT promoter) and for whom pre-operative structural mpMRI data (T1, T1-Gd, T2, and T2 FLAIR) were available. For each enhancing and non-enhancing tumor sub-region and its peritumoral edema/invasion, we extracted a diverse set of QI features comprising volumetric, morphologic, and texture characteristics, histogram-based signal profiling, and spatial distribution patterns. These features were multivariately integrated via a support vector machine to construct a non-invasive marker of MGMT promoter methylation that was quantitatively evaluated using a 10-fold cross-validation (CV). Results: 40 patients (36%) were positive for MGMT promoter methylation and 71 (64%) were negative. The accuracy of the non-invasive MGMT methylation marker was 88.28% [Specificity = 97.0%, Sensitivity = 75.0%, Area under the curve (AUC) = 0.80]. The most predictive features were consistently selected across the 10-fold CV. Conclusions: Multivariate integrative analysis of QI features extracted from mpMRI yields an accurate, non-invasive marker of MGMT promoter methylation status in GBM. If validated in larger datasets, this marker may allow for early stratification of newly diagnosed GBM trial candidates by MGMT methylation status, non-invasive MGMT methylation testing in patients for whom tissue is inadequate, and potential monitoring of MGMT methylation status during treatment.

This study presents the oxygenic photogranule (OPG) process, a light-driven process for wastewater treatment, developed based on photogranulation of filamentous cyanobacteria, nonphototrophic bacteria, and microalgae. Unlike other biogranular processes requiring airlift or upflow-based mixing, the OPG process was operated in stirred-tank reactors without aeration. Reactors were seeded with hydrostatically grown photogranules and operated in a sequencing-batch mode for five months to treat wastewater. The new reactor biomass propagated with progression of photogranulation under periodic light/dark cycles. Due to effective biomass separation from water, the system was operated with short settling time (10 min) with effective decoupling of hydraulic and solids retention times (0.75 d vs 21-42 d). During quasi-steady state, the diameter of the OPGs ranged between 0.1 and 4.5 mm. The reactors produced effluents with average total chemical oxygen demand less than 30 mg/L. Nitrogen removal (28-71%) was achieved by bioassimilation and nitrification/denitrification pathways. Oxygen needed for the oxidation of organic matter and nitrification was produced by OPGs at a rate of 12.6 ± 2.4 mg O2/g biomass-h. The OPG system presents a new biogranule process, which can potentially use simple mixing and natural light to treat wastewater.

The promoter methylation status of the gene encoding for the repair enzyme O6-methylguanine-DNA methyltransferase (MGMT) indicates increased efficacy of current standard of care therapy, which is concomitant adjuvant chemoradiotherapy with temozolomide. The MGMT promoter methylation status (MGMTpms) is typically determined as MGMT-methylated or MGMT-unmethylated by tissue-based polymerase chain reaction assays, which can be limited by inadequate specimen or assay failures. Thus, we investigate the hypothesis that integration of subtle, yet distinctive, quantitative imaging phenomic (QIP) features using machine learning may lead to non-invasive determination of MGMTpms. We identified a retrospective cohort of 122 (46 MGMT-methylated) pathology-proven de novo glioblastoma patients with available baseline pre-operative multi-parametric magnetic resonance imaging (mpMRI) data (T1, T1-Gd, T2, T2-FLAIR, DSC, DTI). MGMTpms was obtained through MGMT methylation testing (pyrosequencing across 4 CpG sites in the MGMT promoter). Following delineation of distinct abnormal sub-regions (enhancing, non-enhancing, edematous), comprehensive and diverse QIP features were extracted using the Cancer Imaging Phenomics Toolkit (CaPTk, www.cbica.upenn.edu/captk), capturing intensity, volume, morphology, statistics, and texture of each sub-region. A support vector machine multivariately integrated these features towards a non-invasive marker of MGMTpms. The cross-validated accuracy of our MGMT marker in classifying the mutation status in individual patients was 84.43% (sensitivity=80.43%, specificity=86.84%, area under the curve [AUC]=0.85). Our marker revealed MGMT-methylated tumors with lower neovascularization and cell density, when compared with MGMT-unmethylated tumors, and a distinct spatial distribution pattern between MGMT-methylated and MGMT-unmethylated tumors, with the latter being more lateralized to the right hemisphere. Multivariate integrative analysis of QIP features extracted from mpMRI yields an accurate, non-invasive marker of MGMTpms in glioblastoma. The proposed non-invasive MGMT marker may contribute to (i) MGMTpms determination for patients with inadequate tissue/inoperable tumors, (ii) stratification of patients into clinical trials, (iii) patient selection for targeted therapy, and (iv) personalized treatment planning. *equal contribution

Heritable platelet function disorders (PFDs) are genetically heterogeneous and poorly characterized. Pathogenic variants in RASGRP2, which encodes calcium and diacylglycerol-regulated guanine exchange factor I (CalDAG-GEFI), have been reported previously in 3 pedigrees with bleeding and reduced platelet aggregation responses. To better define the phenotype associated with pathogenic RASGRP2 variants, we compared high-throughput sequencing and phenotype data from 2042 cases in pedigrees with unexplained bleeding or platelet disorders to data from 5422 controls. Eleven cases harbored 11 different, previously unreported RASGRP2 variants that were biallelic and likely pathogenic. The variants included 5 high-impact variants predicted to prevent CalDAG-GEFI expression and 6 missense variants affecting the CalDAG-GEFI CDC25 domain, which mediates Rap1 activation during platelet inside-out αIIbβ3 signaling. Cases with biallelic RASGRP2 variants had abnormal mucocutaneous, surgical, and dental bleeding from childhood, requiring ≥1 blood or platelet transfusion in 78% of cases. Platelets displayed reduced aggregation in response to adenosine 5'-diphosphate and epinephrine, but variable aggregation defects with other agonists. There were no other consistent clinical or laboratory features. These data enable definition of human CalDAG-GEFI deficiency as a nonsyndromic, recessive PFD associated with a moderate or severe bleeding phenotype and complex defects in platelet aggregation.