Molecular subgroups of T-cell acute lymphoblastic leukemia in adults treated according to pediatric-based GMALL protocols.

In contrast to B-cell precursor acute lymphoblastic leukemia (ALL), molecular subgroups are less well defined in T-lineage ALL. Comprehensive studies on molecular T-ALL subgroups have been predominantly performed in pediatric ALL patients. Currently, molecular characteristics are rarely considered for risk stratification. Herein, we present a homogenously treated cohort of 230 adult T-ALL patients characterized on transcriptome, and partly on DNA methylation and gene mutation level in correlation with clinical outcome. We identified nine molecular subgroups based on aberrant oncogene expression correlating to four distinct DNA methylation patterns. The subgroup distribution differed from reported pediatric T-ALL cohorts with higher frequencies of prognostic unfavorable subgroups like HOXA or LYL1/LMO2. A small subset (3%) of HOXA adult T-ALL patients revealed restricted expression of posterior HOX genes with aberrant activation of lncRNA HOTTIP. With respect to outcome, TLX1 (n = 44) and NKX2-1 (n = 4) had an exceptionally favorable 3-year overall survival (3y-OS) of 94%. Within thymic T-ALL, the non TLX1 patients had an inferior but still good prognosis. To our knowledge this is the largest cohort of adult T-ALL patients characterized by transcriptome sequencing with meaningful clinical follow-up. Risk classification based on molecular subgroups might emerge and contribute to improvements in outcome.

Diagnosis and Treatment of Perioperative Allergic Complications: A Practical Review.

Background: Reported drug allergies are commonly encountered by surgeons and can lead to uncertainty in selecting an appropriate agent due to concerns of associated risks with related and cross-reactive drugs. This uncertainty can ultimately lead to increased infection rates. Methods: A literature review was conducted in PubMed using a combination of the terms "allergy," "allergic reaction," "anaphylaxis," and "surgery," "surgical," or "operating room" for articles published within the last 10 years. Publications identified with these search terms were then filtered for review articles, sorted by "best match," and a maximum of 100 articles were manually reviewed for each combination of search terms. Results: Search results yielded 46,484 articles, 676 of which were ultimately included for manual review, based on selection criteria. Specifically, articles selected for inclusion focused on surgical allergic reactions that were either related to mechanism of action, causative agent for the allergic reaction, timing of allergic reaction, or recommendations for appropriate management. Conclusions: Allergic reactions can be a common occurrence in the operative room. Knowledge of likely causative agents, timing of a reaction to various agents, and appropriate management in the immediate and delayed setting can improve outcomes and safety for plastic surgery patients.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances.

INTRODUCTION: Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks. AREAS COVERED: This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets. EXPERT OPINION: Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

Netrank: network-based approach for biomarker discovery.

BACKGROUND: Integrating multi-omics data is fast becoming a powerful approach for predicting disease progression and treatment outcomes. In light of that, we introduce a modified version of the NetRank algorithm, a network-based algorithm for biomarker discovery that incorporates the protein associations, co-expressions, and functions with its phenotypic association to differentiate different types of cancer. NetRank is introduced here as a robust feature selection method for biomarker selection in cancer prediction. We assess the robustness and suitability of the RNA gene expression data through scanning genomic data for 19 cancer types with more than 3000 patients from The Cancer Genome Atlas (TCGA). RESULTS: The results of evaluating different cancer type profiles from the TCGA data demonstrate the strength of our approach to identifying interpretable biomarker signatures for cancer outcome prediction. NetRank's biomarkers segregate most cancer types with an area under the curve (AUC) above 90% using compact signatures. CONCLUSION: In this paper we provide a fast and efficient implementation of NetRank, with a case study from The Cancer Genome Atlas, to assess the performance. We incorporated complete functionality for pre and post-processing for RNA-seq gene expression data with functions for building protein-protein interaction networks. The source code of NetRank is freely available (at github.com/Alfatlawi/Omics-NetRank) with an installable R library. We also deliver a comprehensive practical user manual with examples and data attached to this paper.

An interaction-based drug discovery screen explains known SARS-CoV-2 inhibitors and predicts new compound scaffolds.

The recent outbreak of the COVID-19 pandemic caused by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) has shown the necessity for fast and broad drug discovery methods to enable us to react quickly to novel and highly infectious diseases. A well-known SARS-CoV-2 target is the viral main 3-chymotrypsin-like cysteine protease (Mpro), known to control coronavirus replication, which is essential for the viral life cycle. Here, we applied an interaction-based drug repositioning algorithm on all protein-compound complexes available in the protein database (PDB) to identify Mpro inhibitors and potential novel compound scaffolds against SARS-CoV-2. The screen revealed a heterogeneous set of 692 potential Mpro inhibitors containing known ones such as Dasatinib, Amodiaquine, and Flavin mononucleotide, as well as so far untested chemical scaffolds. In a follow-up evaluation, we used publicly available data published almost two years after the screen to validate our results. In total, we are able to validate 17% of the top 100 predictions with publicly available data and can furthermore show that predicted compounds do cover scaffolds that are yet not associated with Mpro. Finally, we detected a potentially important binding pattern consisting of 3 hydrogen bonds with hydrogen donors of an oxyanion hole within the active side of Mpro. Overall, these results give hope that we will be better prepared for future pandemics and that drug development will become more efficient in the upcoming years.

FAT1 expression in T-cell acute lymphoblastic leukemia (T-ALL) modulates proliferation and WNT signaling.

FAT atypical cadherin 1 (FAT1), a transmembrane protein, is frequently mutated in various cancer types and has been described as context-dependent tumor suppressor or oncogene. The FAT1 gene is mutated in 12-16% of T-cell acute leukemia (T-ALL) and aberrantly expressed in about 54% of T-ALL cases contrasted with absent expression in normal T-cells. Here, we characterized FAT1 expression and profiled the methylation status from T-ALL patients. In our T-ALL cohort, 53% of patient samples were FAT1 positive (FAT1pos) compared to only 16% FAT1 positivity in early T-ALL patient samples. Aberrant expression of FAT1 was strongly associated with FAT1 promotor hypomethylation, yet a subset, mainly consisting of TLX1-driven T-ALL patient samples showed methylation-independent high FAT1 expression. Genes correlating with FAT1 expression revealed enrichment in WNT signaling genes representing the most enriched single pathway. FAT1 knockdown or knockout led to impaired proliferation and downregulation of WNT pathway target genes (CCND1, MYC, LEF1), while FAT1 overexpressing conveyed a proliferative advantage. To conclude, we characterized a subtype pattern of FAT1 gene expression in adult T-ALL patients correlating with promotor methylation status. FAT1 dependent proliferation and WNT signaling discloses an impact on deeper understanding of T-ALL leukemogenesis as a fundament for prospective therapeutic strategies.

Open Versus Endoscopic Carpal Tunnel Release: A Comparison of Opioid Prescription Patterns and Occupational Therapy Referrals.

BACKGROUND: Endoscopic and open carpal tunnel releases (ECTR and OCTR) are safe and effective operations. We compared the approaches in terms of postoperative opioid refills and occupational therapy (OT) referrals. METHODS: We conducted a retrospective study of patients with carpal tunnel syndrome (CTS) treated with ECTR or OCTR. Patients with isolated idiopathic CTS were included; patients undergoing simultaneous bilateral carpal tunnel release (CTR), revision CTR, and additional procedures at time of CTR were excluded. Outcomes included number of patients requiring an opioid refill and/or an OT referral within 6 months of surgery. RESULTS: A total of 1125 patients met inclusion criteria. Endoscopic release was performed in 634 (56%) cases and open release in 491 (44%). Unadjusted analysis revealed no difference in number of patients requiring refills (6.0% vs 7.1%, P = .44), mean number of refills among those requiring one (1.29 vs 1.23, P = .69), total oral morphine equivalents (45.1 vs 44.7, P = .84), number of patients calling regarding pain (12.8% vs 14.7%, P = .36), OT referrals (12.1% vs 11.4%, P = .71), or average number of OT visits (4.5 vs 4.2, P = .74) for endoscopic and open techniques, respectively. Adjusted analysis revealed lower age, lower body mass index, and history of muscle relaxant as predictors of opioid refills, and in contrast to the unadjusted analysis, operating surgeon and surgical technique were predictors of referral to OT. CONCLUSION: Endoscopic CTR and OCTR did not differ in terms of unadjusted postoperative patient calls for pain, number of opioid refills, or OT referrals. After correcting for individual surgeon practice, endoscopic was associated with decreased odds of requiring postoperative OT.

NetRank Recovers Known Cancer Hallmark Genes as Universal Biomarker Signature for Cancer Outcome Prediction.

Gene expression can serve as a powerful predictor for disease progression and other phenotypes. Consequently, microarrays, which capture gene expression genome-wide, have been used widely over the past two decades to derive biomarker signatures for tasks such as cancer grading, prognosticating the formation of metastases, survival, and others. Each of these signatures was selected and optimized for a very specific phenotype, tissue type, and experimental set-up. While all of these differences may naturally contribute to very heterogeneous and different biomarker signatures, all cancers share characteristics regardless of particular cell types or tissue as summarized in the hallmarks of cancer. These commonalities could give rise to biomarker signatures, which perform well across different phenotypes, cell and tissue types. Here, we explore this possibility by employing a network-based approach for pan-cancer biomarker discovery. We implement a random surfer model, which integrates interaction, expression, and phenotypic information to rank genes by their suitability for outcome prediction. To evaluate our approach, we assembled 105 high-quality microarray datasets sampled from around 13,000 patients and covering 13 cancer types. We applied our approach (NetRank) to each dataset and aggregated individual signatures into one compact signature of 50 genes. This signature stands out for two reasons. First, in contrast to other signatures of the 105 datasets, it is performant across nearly all cancer types and phenotypes. Second, It is interpretable, as the majority of genes are linked to the hallmarks of cancer in general and proliferation specifically. Many of the identified genes are cancer drivers with a known mutation burden linked to cancer. Overall, our work demonstrates the power of network-based approaches to compose robust, compact, and universal biomarker signatures for cancer outcome prediction.

A multiscale model of the regulation of aquaporin 2 recycling.

The response of cells to their environment is driven by a variety of proteins and messenger molecules. In eukaryotes, their distribution and location in the cell are regulated by the vesicular transport system. The transport of aquaporin 2 between membrane and storage region is a crucial part of the water reabsorption in renal principal cells, and its malfunction can lead to Diabetes insipidus. To understand the regulation of this system, we aggregated pathways and mechanisms from literature and derived three models in a hypothesis-driven approach. Furthermore, we combined the models to a single system to gain insight into key regulatory mechanisms of Aquaporin 2 recycling. To achieve this, we developed a multiscale computational framework for the modeling and simulation of cellular systems. The analysis of the system rationalizes that the compartmentalization of cAMP in renal principal cells is a result of the protein kinase A signalosome and can only occur if specific cellular components are observed in conjunction. Endocytotic and exocytotic processes are inherently connected and can be regulated by the same protein kinase A signal.

Impact of Opioid Restriction Legislation on Prescribing Practices for Outpatient Plastic and Reconstructive Surgery.

BACKGROUND: Overprescription of opioids for acute postoperative pain, plastic surgery procedures included, is contributing to the pervasive opioid epidemic in the United States. This study examines the effect of a statewide legislation limiting postoperative opioids on opioid prescription behavior among providers following outpatient plastic surgery procedures at a high-volume academic center. METHODS: Retrospective review of all outpatient surgical encounters between June 1, 2016, and November 30, 2018, was performed. Encounters were grouped into two cohorts: prepolicy and postpolicy. Primary outcomes included total oral morphine equivalents prescribed on the day of surgery and proportion of patients prescribed greater than 210 oral morphine equivalents. Secondary outcomes included proportion of patients requiring an opioid refill within 30 days following surgery, and number of refills required. RESULTS: The mean oral morphine equivalents prescribed on the day of surgery was reduced from 271.8 to 150.37 oral morphine equivalents ( p < 0.001) following implementation of the legislation, with an associated decrease in the standard deviation of oral morphine equivalents prescribed from 225.35 to 196.71 ( p < 0.001), suggesting a decrease in the variability of prescriber practices. Time series analysis demonstrated the decrease in oral morphine equivalents remained significant when accounting for baseline level of change in opioid prescription patterns. CONCLUSION: This study provides evidence that legislation at the state level restricting postoperative opioid prescriptions is associated with a decrease in opioid prescriptions without an increase in the need for refills in the acute postoperative setting following outpatient plastic surgery procedures.

An alternative CYB5A transcript is expressed in aneuploid ALL and enriched in relapse.

BACKGROUND: B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is a genetically heterogenous malignancy with poor prognosis in relapsed adult patients. The genetic basis for relapse in aneuploid subtypes such as near haploid (NH) and high hyperdiploid (HeH) BCP-ALL is only poorly understood. Pathogenic genetic alterations remain to be identified. To this end, we investigated the dynamics of genetic alterations in a matched initial diagnosis-relapse (ID-REL) BCP-ALL cohort. Here, we firstly report the identification of the novel genetic alteration CYB5Aalt, an alternative transcript of CYB5A, in two independent cohorts. METHODS: We identified CYB5alt in the RNAseq-analysis of a matched ID-REL BCP-ALL cohort with 50 patients and quantified its expression in various molecular BCP-ALL subtypes. Findings were validated in an independent cohort of 140 first diagnosis samples from adult BCP-ALL patients. Derived from patient material, the alternative open reading frame of CYB5Aalt was cloned (pCYB5Aalt) and pCYB5Aalt or the empty vector were stably overexpressed in NALM-6 cells. RNA sequencing was performed of pCYB5Aalt clones and empty vector controls followed by differential expression analysis, gene set enrichment analysis and complementing cell death and viability assays to determine functional implications of CYB5Aalt. RESULTS: RNAseq data analysis revealed non-canonical exon usage of CYB5Aalt starting from a previously undescribed transcription start site. CYB5Aalt expression was increased in relapsed BCP-ALL and its occurrence was specific towards the shared gene expression cluster of NH and HeH BCP-ALL in independent cohorts. Overexpression of pCYB5Aalt in NALM-6 cells induced a distinct transcriptional program compared to empty vector controls with downregulation of pathways related to reported functions of CYB5A wildtype. Interestingly, CYB5A wildtype expression was decreased in CYB5Aalt samples in silico and in vitro. Additionally, pCYB5Aalt NALM-6 elicited a more resistant drug response. CONCLUSIONS: Across all age groups, CYB5Aalt was the most frequent secondary genetic event in relapsed NH and HeH BCP-ALL. In addition to its high subgroup specificity, CYB5Aalt is a novel candidate to be potentially implicated in therapy resistance in NH and HeH BCP-ALL. This is underlined by overexpressing CYB5Aalt providing first evidence for a functional role in BCL2-mediated apoptosis.

Deep Learning Improves Pancreatic Cancer Diagnosis Using RNA-Based Variants.

For optimal pancreatic cancer treatment, early and accurate diagnosis is vital. Blood-derived biomarkers and genetic predispositions can contribute to early diagnosis, but they often have limited accuracy or applicability. Here, we seek to exploit the synergy between them by combining the biomarker CA19-9 with RNA-based variants. We use deep sequencing and deep learning to improve differentiating pancreatic cancer and chronic pancreatitis. We obtained samples of nucleated cells found in peripheral blood from 268 patients suffering from resectable, non-resectable pancreatic cancer, and chronic pancreatitis. We sequenced RNA with high coverage and obtained millions of variants. The high-quality variants served as input together with CA19-9 values to deep learning models. Our model achieved an area under the curve (AUC) of 96% in differentiating resectable cancer from pancreatitis using a test cohort. Moreover, we identified variants to estimate survival in resectable cancer. We show that the blood transcriptome harbours variants, which can substantially improve noninvasive clinical diagnosis.

Automated and robust organ segmentation for 3D-based internal dose calculation.

PURPOSE: In this work, we address image segmentation in the scope of dosimetry using deep learning and make three main contributions: (a) to extend and optimize the architecture of an existing convolutional neural network (CNN) in order to obtain a fast, robust and accurate computed tomography (CT)-based organ segmentation method for kidneys and livers; (b) to train the CNN with an inhomogeneous set of CT scans and validate the CNN for daily dosimetry; and (c) to evaluate dosimetry results obtained using automated organ segmentation in comparison with manual segmentation done by two independent experts. METHODS: We adapted a performant deep learning approach using CT-images to delineate organ boundaries with sufficiently high accuracy and adequate processing time. The segmented organs were consequently used as binary masks for further convolution with a point spread function to retrieve the activity values from quantitatively reconstructed SPECT images for "volumetric"/3D dosimetry. The resulting activities were used to perform dosimetry calculations with the kidneys as source organs. RESULTS: The computational expense of the algorithm was sufficient for clinical daily routine, required minimum pre-processing and performed with acceptable accuracy a Dice coefficient of [Formula: see text] for liver segmentation and of [Formula: see text] for kidney segmentation, respectively. In addition, kidney self-absorbed doses calculated using automated segmentation differed by [Formula: see text] from dosimetry performed by two medical physicists in 8 patients. CONCLUSION: The proposed approach may accelerate volumetric dosimetry of kidneys in molecular radiotherapy with 177Lu-labelled radiopharmaceuticals such as 177Lu-DOTATOC. However, even though a fully automated segmentation methodology based on CT images accelerates organ segmentation and performs with high accuracy, it does not remove the need for supervision and corrections by experts, mostly due to misalignments in the co-registration between SPECT and CT images. Trial registration EudraCT, 2016-001897-13. Registered 26.04.2016, www.clinicaltrialsregister.eu/ctr-search/search?query=2016-001897-13 .

PLIP 2021: expanding the scope of the protein-ligand interaction profiler to DNA and RNA.

With the growth of protein structure data, the analysis of molecular interactions between ligands and their target molecules is gaining importance. PLIP, the protein-ligand interaction profiler, detects and visualises these interactions and provides data in formats suitable for further processing. PLIP has proven very successful in applications ranging from the characterisation of docking experiments to the assessment of novel ligand-protein complexes. Besides ligand-protein interactions, interactions with DNA and RNA play a vital role in many applications, such as drugs targeting DNA or RNA-binding proteins. To date, over 7% of all 3D structures in the Protein Data Bank include DNA or RNA. Therefore, we extended PLIP to encompass these important molecules. We demonstrate the power of this extension with examples of a cancer drug binding to a DNA target, and an RNA-protein complex central to a neurological disease. PLIP is available online at https://plip-tool.biotec.tu-dresden.de and as open source code. So far, the engine has served over a million queries and the source code has been downloaded several thousand times.

Differential Effects of Trp53 Alterations in Murine Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) development is a multi-step process resulting in the accumulation of genetic alterations. Despite its high incidence, there are currently no mouse models that accurately recapitulate this process and mimic sporadic CRC. We aimed to develop and characterize a genetically engineered mouse model (GEMM) of Apc/Kras/Trp53 mutant CRC, the most frequent genetic subtype of CRC. METHODS: Tumors were induced in mice with conditional mutations or knockouts in Apc, Kras, and Trp53 by a segmental adeno-cre viral infection, monitored via colonoscopy and characterized on multiple levels via immunohistochemistry and next-generation sequencing. RESULTS: The model accurately recapitulates human colorectal carcinogenesis clinically, histologically and genetically. The Trp53 R172H hotspot mutation leads to significantly increased metastatic capacity. The effects of Trp53 alterations, as well as the response to treatment of this model, are similar to human CRC. Exome sequencing revealed spontaneous protein-modifying alterations in multiple CRC-related genes and oncogenic pathways, resulting in a genetic landscape resembling human CRC. CONCLUSIONS: This model realistically mimics human CRC in many aspects, allows new insights into the role of TP53 in CRC, enables highly predictive preclinical studies and demonstrates the value of GEMMs in current translational cancer research and drug development.

Structure-based drug repositioning: Potential and limits.

Drug repositioning, the assignment of new therapeutic purposes to known drugs, is an established strategy with many repurposed drugs on the market and many more at experimental stage. We review three use cases, a herpes drug with benefits in cancer, a cancer drug with potential in autoimmune disease, and a selective and an unspecific drug binding the same target (GPCR). We explore these use cases from a structural point of view focusing on a deep understanding of the underlying drug-target interactions. We review tools and data needed for such a drug-centric structural repositioning approach. Finally, we show that the availability of data on targets is an important limiting factor to realize the full potential of structural drug-repositioning.

Structure-based drug repositioning explains ibrutinib as VEGFR2 inhibitor.

Many drugs are promiscuous and bind to multiple targets. On the one hand, these targets may be linked to unwanted side effects, but on the other, they may achieve a combined desired effect (polypharmacology) or represent multiple diseases (drug repositioning). With the growth of 3D structures of drug-target complexes, it is today possible to study drug promiscuity at the structural level and to screen vast amounts of drug-target interactions to predict side effects, polypharmacological potential, and repositioning opportunities. Here, we pursue such an approach to identify drugs inactivating B-cells, whose dysregulation can function as a driver of autoimmune diseases. Screening over 500 kinases, we identified 22 candidate targets, whose knock out impeded the activation of B-cells. Among these 22 is the gene KDR, whose gene product VEGFR2 is a prominent cancer target with anti-VEGFR2 drugs on the market for over a decade. The main result of this paper is that structure-based drug repositioning for the identified kinase targets identified the cancer drug ibrutinib as micromolar VEGFR2 inhibitor with a very high therapeutic index in B-cell inactivation. These findings prove that ibrutinib is not only acting on the Bruton's tyrosine kinase BTK, against which it was designed. Instead, it may be a polypharmacological drug, which additionally targets angiogenesis via inhibition of VEGFR2. Therefore ibrutinib carries potential to treat other VEGFR2 associated disease. Structure-based drug repositioning explains ibrutinib's anti VEGFR2 action through the conservation of a specific pattern of interactions of the drug with BTK and VEGFR2. Overall, structure-based drug repositioning was able to predict these findings at a fraction of the time and cost of a conventional screen.