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bioRxiv2026

DLL3 expression predicts response and long-term outcome in small cell lung cancer patients treated with tarlatamab

Background: Small cell lung cancer (SCLC) is an aggressive, high-grade neuroendocrine carcinoma (hgNEC) with poor prognosis. Tarlatamab, a DLL3-targeting T cell engager was approved in 2024 for relapsed SCLC, with almost doubled overall survival benefit compared to chemotherapy. However, over half of patients do not respond to tarlatamab and others develop resistance within months. With multiple DLL3-targeting therapies in clinical trials, there are currently no validated predictive biomarkers to identify patients most likely to benefit from tarlatamab. In this study, we evaluate baseline DLL3 IHC level in patients as a predictive biomarker to tarlatamab clinical outcome. Methods: We assembled a cohort of 138 patients within IRB-approved MD Anderson GEMINI database (PA13-0589) with DLL3 expression by CLIA-validated immunohistochemistry (IHC), and those treated with tarlatamab from 7/1/2024 to 3/30/2026. Only those with DLL3 IHC results and treated with tarlatamab were included in this predictive biomarker study. All DLL3 levels were reported as percentage cells with cytoplasmic or surface DLL3 expression, including 24 with additional intensity score and H-score calculation. demographic, clinical, and outcome data were collected. Systemic and intracranial response data were assessed by RECIST and mRANO BM criteria respectively. Longitudinal liquid biopsies were taken from a subset of patients for circulating tumor DNA (ctDNA) and (Cytometry by Time-of-Flight) CyTOF analyses. Results: In the predictive biomarker cohort of 54 patients with SCLC treated with tarlatamab, Median DLL3% level in tarlatamab treated cohort was 75%. Median follow-up time was 9.9 months, median time on tarlatamab treatment (ToT) was 5.8 months. At the data cut-off, 61% (33 out of 54) patients were alive. Kaplan-Meier curve stratified by DLL3 % of <=75% vs. >75 showed significant longer ToT and a trend toward longer median OS (mOS). In DLL3>75% compared to DLL3 <=75%: mToT was 10.1 months vs. 2.9 months (p=0.036), mOS: not reached vs. 9.5 months (p= 0.066). Among 41 patients who completed one cycle of tarlatamab treatment and were eligible for systemic (extra-cranial) response per RECIST criteria; we observed partial response (PR) in 51% (21) patients, stable disease (SD) in 22% (9) patients; and progressive disease (PD) in 27% (11) patients. The median DLL3 % in those with PR vs. PD were 80% vs. 40% (p=0.006); median DLL3% comparing PR and SD were 80% vs. 60% (p=0.051). Among 35 patients evaluable for intracranial response, best objective response prior to any brain radiation were 5 (14%) CR,11(31%) PR, 8 (23%) SD and 11 (31%) PD. 9 received brain radiation (8 SRS, 1 WBRT) during tarlatamab to achieve better intracranial control while systemic control is maintained. DLL3% does not appear to be correlated with intracranial response. Longitudinal ctDNA and CyTOF analyses showed persistent ctDNA positivity, as well as increasing NEUROD1 subtype cells in circulating tumor cells in post-tarlatamab progression samples. We additionally profiled DLL3 expression by IHC in 138 patients with SCLC and found heterogenous DLL3 expression, with Median DLL3% of 70% across different biopsy sites. Conclusions: Our data suggests DLL3 expression in patients with SCLC is heterogeneous and not ubiquitous, with median DLL3 IHC of 70%. DLL3 IHC predicts objective response and long-term outcome with tarlatamab. In addition, dynamic monitoring of ctDNA level and CTCs by CyTOF are suggestive of development of tarlatamab resistance, however further studies with larger cohort of paired longitudinal samples are needed to validate the novel blood-based biomarkers. To our knowledge, this study is the first to validate DLL3 IHC as a predictive biomarker for tarlatamab in SCLC, which leads the way for optimizing treatment selection and combinatorial therapies for the subset of patients less likely to respond to tarlatamab.

cancer biology↗

bioRxiv2017

Translating GWAS Findings Into Therapies For Depression And Anxiety Disorders: Drug Repositioning Using Gene-Set Analyses And Testing For Enrichment Of Psychiatric Drug Classes

Depression and anxiety disorders are the first and sixth leading cause of disability worldwide according to latest reports from the World Health Organization. Despite their high prevalence and the significant disability resulted, there are limited advances in new drug development. On the other hand, the advent of genome-wide association studies (GWAS) has greatly improved our understanding of the genetic basis underlying psychiatric disorders.\n\nIn this work we employed gene-set analyses of GWAS summary statistics for drug repositioning. We explored five related GWAS datasets, including two on major depressive disorder (MDD-PGC and MDD-CONVERGE, with the latter focusing on severe melancholic depression), one on anxiety disorders, and two on depressive symptoms and neuroticism in the population. We extracted gene-sets associated with each drug from DSigDB and examined their association with each GWAS phenotype. We also performed repositioning analyses on meta-analyzed GWAS data, integrating evidence from all related phenotypes.\n\nImportantly, we showed that the repositioning hits are generally enriched for known psychiatric medications or those considered in clinical trials, except for MDD-PGC. Enrichment was seen for antidepressants and anxiolytics but also for antipsychotics. We also revealed new candidates or drug classes for repositioning, some of which were supported by experimental or clinical studies. For example, the top repositioning hit using meta-analyzed p-values was fendiline, which was shown to produce antidepressant-like effects in mouse models by inhibition of acid sphingomyelinase and reducing ceramide levels. Taken together, our findings suggest that human genomic data such as GWAS are useful in guiding drug discoveries for depression and anxiety disorders.

genetics↗

bioRxiv2022

Optimized cell culture conditions promote ex-vivo manipulation and expansion of primitive hematopoietic stem cells for therapeutic gene editing.

During the last few years, gene editing has emerged as a powerful tool for the therapeutic correction of monogenic diseases. CRISPR/Cas9 applied to hematopoietic stem and progenitor cells (HSPCs) has shown great promise in proof-of-principle preclinical studies to treat haematological disorders, and clinical trials using these tools are now underway. Nonetheless, there remain important challenges that need to be addressed, such as the efficiency of targeting primitive, long-term repopulating HSPCs and expand them in vitro for clinical purposes. Here we have tested the effect exerted by different culture media compositions on the ability of HSPCs to proliferate and undergo homology directed repair-mediated knock-in of a reporter gene, while preserving their stemness features during ex-vivo culture. We tested different combinations of compounds and demonstrated that by supplementing the culture media with inhibitors of histone deacetylases, and/or by fine-tuning its cytokine composition it is possible to achieve high levels of gene targeting in long-term repopulating HSPCs both in vitro and in vivo, with a beneficial balance between preservation of stemness and cell expansion, thus allowing to obtain a significant amount of edited, primitive HSPCs compared to established, state-of-the-art culture conditions. Overall, the implantation of this optimized ex vivo HSPC culture protocol will improve the efficacy, feasibility and applicability of gene editing and will likely provide one step further to unlock the full therapeutic potential of such powerful technology.

bioengineering↗

bioRxiv2022

Targeting BET Proteins downregulates miR-33a to promote synergy with PIM inhibitors in CMML

Preclinical studies in myeloid neoplasms have demonstrated efficacy of Bromodomain and Extra-Terminal protein inhibitors (BETi). However, BETi demonstrate poor single agent activity in clinical trials. Several studies suggest that combination with other anti-cancer inhibitors may enhance the efficacy of BETi. To nominate BETi combination therapies for myeloid neoplasms, we used a chemical screen with therapies currently in clinical cancer development. We identified PIM inhibitors (PIMi) as therapeutically synergistic with BETi in myeloid leukemia models. Mechanistically, we show that PIM kinase is increased after BETi treatment, and that PIM kinase upregulation is sufficient to induce resistance to BETi and sensitize cells to PIMi. Further, we demonstrate that miR-33a downregulation is the underlying mechanism driving PIM1 upregulation. We also show that GM-CSF hypersensitivity, a hallmark of chronic myelomonocytic leukemia (CMML), represents a molecular signature for sensitivity to combination therapy and credential this using patient-derived xenografts supporting the clinical investigation of this combination.

cancer biology↗

bioRxiv2023

ME3BP-7 is a targeted cytotoxic agent that rapidly kills pancreatic cancer cells expressing high levels of monocarboxylate transporter MCT1

Nearly 30% of Pancreatic ductal adenocarcinoma (PDAC)s exhibit a marked overexpression of Monocarboxylate Transporter 1 (MCT1) offering a unique opportunity for therapy. However, biochemical inhibitors of MCT1 have proven unsuccessful in clinical trials. In this study we present an alternative approach using 3-Bromopyruvate (3BP) to target MCT1 overexpressing PDACs. 3BP is a cytotoxic agent that is known to be transported into cells via MCT1, but its clinical usefulness has been hampered by difficulties in delivering the drug systemically. We describe here a novel microencapsulated formulation of 3BP (ME3BP-7), that is effective against a variety of PDAC cells in vitro and remains stable in serum. Furthermore, systemically administered ME3BP-7 significantly reduces pancreatic cancer growth and metastatic spread in multiple orthotopic models of pancreatic cancer with manageable toxicity. ME3BP-7 is, therefore, a prototype of a promising new drug, in which the targeting moiety and the cytotoxic moiety are both contained within the same single small molecule. One Sentence SummaryME3BP-7 is a novel formulation of 3BP that resists serum degradation and rapidly kills pancreatic cancer cells expressing high levels of MCT1 with tolerable toxicity in mice.

cancer biology↗

bioRxiv2024

Multi-omic integration with human DRG proteomics highlights TNFα signalling as a relevant sexually dimorphic pathway

The peripheral nervous system (PNS) plays a critical role in pathological conditions, including chronic pain disorders, that manifest differently in men and women. To investigate this sexual dimorphism at the molecular level, we integrated quantitative proteomic profiling of human dorsal root ganglia (hDRG) and peripheral nerve tissue into the expanding omics framework of the PNS. Using data-independent acquisition (DIA) mass spectrometry, we characterized a comprehensive proteomic profile, validating tissue-specific differences between the hDRG and peripheral nerve. Through multi-omic analyses and in vitro functional assays, we identified sex-specific molecular differences, with TNF signalling emerging as a key sexually dimorphic pathway with higher prominence in males. Genetic evidence from genome-wide association studies (GWAS) further supports the functional relevance of TNF signalling in the periphery, while clinical trial data and meta-analyses indicate a sex-dependent response to TNF inhibitors. Collectively, these findings underscore a functionally sexual dimorphism in the PNS, with direct implications for sensory and pain-related clinical translation.

neuroscience↗

bioRxiv2018

DeepTCR: a deep learning framework for revealing structural concepts within TCR Repertoire

Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks, such as in image and vocal recognition 1,2. The ability to learn complex patterns in data has tremendous implications in the genomics and immunology worlds, where sequence motifs become learned features that can be used to predict functionality, guiding our understanding of disease and basic biology 3-6. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system, where complex structural patterns in the TCR can be used to model its antigenic interaction. We present DeepTCR, a broad collection of unsupervised and supervised deep learning methods able to uncover structure in highly complex and large TCR sequencing data by learning a joint representation of a given TCR by its CDR3 sequences, V/D/J gene usage, and HLA background in which the T-cells reside. We demonstrate the utility of deep learning to provide an improved featurization of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCRs in both unsupervised and supervised learning tasks, understanding immunotherapy-related shaping of repertoire in the murine setting, and predicting response to checkpoint blockade immunotherapy from pre-treatment tumor biopsies in a clinical trial of melanoma. Our results show the flexibility and capacity for deep neural networks to handle the complexity of high-dimensional TCR genomic data for both descriptive and predictive purposes across basic science and clinical research.

bioinformatics↗

bioRxiv2018

CDEK: Clinical Drug Experience Knowledgebase

The Clinical Drug Experience Knowledgebase (CDEK) is a database and web platform of active pharmaceutical ingredients with evidence of clinical testing as well as the organizations involved in their research and development. CDEK was curated by disambiguating intervention and organization names from ClinicalTrials.gov and cross-referencing these entries with other prominent drug databases. Approximately 43% of active pharmaceutical ingredients in the CDEK database were sourced from ClinicalTrials.gov and cannot be found in any other prominent compound-oriented database. The contents of CDEK are structured around three pillars: active pharmaceutical ingredients (n = 22,292), clinical trials (n = 127,223), and organizations (n = 24,728). The envisioned use of the CDEK is to support the investigation of many aspects of drug development, including discovery, repurposing opportunities, chemo- and bio-informatics, clinical and translational research, and regulatory sciences.\n\nDatabase URL: http://cdek.wustl.edu

bioinformatics↗

bioRxiv2021

Molecular features of exceptional response to neoadjuvant anti-androgen therapy in high-risk localized prostate cancer

High-risk localized prostate cancer (HRLPC) is associated with a substantial risk of recurrence and prostate cancer-specific mortality1. Recent clinical trials have shown that intensifying anti-androgen therapies administered prior to prostatectomy can induce pathologic complete responses (pCR) or minimal residual disease (MRD) (<5 mm), together termed exceptional response, although the molecular determinants of these clinical outcomes are largely unknown. Here, we performed whole exome (WES) and whole transcriptome sequencing (RNA-seq) on pre-treatment multi-regional tumor biopsies from exceptional responders (ER: pCR and MRD patients) and non-responders (NR: pathologic T3 or lymph node positive disease) treated with intensive anti-androgen therapies prior to prostatectomy. SPOP mutation and SPOPL copy number loss were exclusively observed in ER, while TP53 mutation and PTEN copy number loss were exclusively observed in NR. These alterations were clonal in all tumor phylogenies per patient. Additionally, transcriptional programs involving androgen signaling and TGF{beta} signaling were enriched in ER and NR, respectively. The presence of these alterations in routine biopsies from patients with HRLPC may inform the prospective identification of responders to neoadjuvant anti-androgen therapies to improve clinical outcomes and stratify other patients to alternative biologically informed treatment strategies.

cancer biology↗

bioRxiv2021

Highly-Neutralizing COVID-19-Convalescent-Plasmas Potently Block SARS-CoV-2 Replication and Pneumonia in Syrian Hamsters.

Despite various attempts to treat SARS-CoV-2-infected patients with COVID-19-convalescent plasmas, neither appropriate approach nor clinical utility has been established. We examined the efficacy of administration of highly-neutralizing COVID-19-convalescent plasma (hn-plasmas) and such plasma-derived IgG administration using the Syrian hamster COVID-19 model. Two hn-plasmas, which were in the best 1% of 340 neutralizing-activity-determined convalescent plasma samples, were intraperitoneally administered to SARS-CoV-2-infected hamsters, resulting in significant reduction of viral titers in lungs by up to 32-fold as compared to the viral titers in hamsters receiving control non-neutralizing plasma, while with two moderately neutralizing plasmas (mn-plasmas) administered, viral titer reduction was by up to 6-fold. IgG fractions purified from the two hn-plasmas also reduced viral titers in lungs than those from the two mn-plasmas. The severity of lung lesions seen in hamsters receiving hn-plasmas was minimal to moderate as assessed using micro-computerized tomography, which histological examination confirmed. Western blotting revealed that all four COVID-19-convalescent-plasmas variably contained antibodies against SARS-CoV-2 components including the receptor-binding domain and S1 domain. The present data strongly suggest that administering potent-neutralizing-activity-confirmed COVID-19-convalescent plasmas would be efficacious in treating patients with COVID-19. ImportanceConvalescent plasmas obtained from patients, who recovered from a specific infection, have been used as agents to treat other patients infected with the very pathogen. To treat using convalescent plasmas, despite that more than 10 randomized-controlled-clinical-trials have been conducted and more than 100 studies are currently ongoing, the effects of convalescent plasma against COVID-19 remained uncertain. On the other hand, certain COVID-19 vaccines have been shown to reduce the clinical COVID-19 onset by 94-95%, for which the elicited SARS-CoV-2-neutralizing antibodies are apparently directly responsible. Here, we demonstrate that highly-neutralizing-effect-confirmed convalescent plasmas significantly reduce the viral titers in the lung of SARS-CoV-2-infected Syrian hamsters and block the development of virally-induced lung lesions. The present data provide a proof-of-concept that the presence of highly-neutralizing antibody in COVID-19-convalescent plasmas is directly responsible for the reduction of viral replication and support the use of highly-neutralizing antibody-containing plasmas in COVID-19 therapy with convalescent plasmas.

microbiology↗

bioRxiv2025

Sulbactam-durlobactam susceptibility among cefiderocol heteroresistant Acinetobacter

The ATTACK clinical trial for treatment of carbapenem-resistant Acinetobacter baumannii-calcoaceticus complex (CRAB) isolates determined treatment with sulbactam-durlobactam to be efficacious and safe. However, other newly introduced {beta}-lactam antibiotics, including the novel cephalosporin cefiderocol, have been compromised upon clinical introduction by a type of antibiotic resistance called heteroresistance, in which only a small subpopulation of total cells exhibit phenotypic resistance. Therefore, we sought to test for sulbactam-durlobactam heteroresistance, as well as whether sulbactam-durlobactam was effective against cefiderocol heteroresistant CRAB isolates. We did not observe heteroresistance (or conventional resistance) to sulbactam-durlobactam among the 107 carbapenem-resistant Acinetobacter isolates tested, consistent with the efficacy of this new antibiotic in the ATTACK trial. Further, sulbactam-durlobactam was active against cefiderocol heteroresistant CRAB, highlighting that this antibiotic may be prioritized in relation to cefiderocol in treating CRAB infections.

microbiology↗

bioRxiv2022

A circulating biomarker of facioscapulohumeral muscular dystrophy clinical severity, valid in skeletal muscle and blood

Facioscapulohumeral muscular dystrophy (FSHD) is incurable. DUX4 mis-expression is believed to underlie FSHD pathogenesis, alongside PAX7 target gene repression, yet clinical trials lack robust biomarkers of severity. FSHD entails fatty replacement of muscle, accelerated by inflammation, we thus performed RNA-sequencing on both an MRI guided inflamed (TIRM+) and non-inflamed (TIRM-) muscle biopsies from clinically-characterised FSHD patients, alongside peripheral blood mononucleated cells (PBMCs). PAX7 target gene repression in TIRM-muscle associates with severity. DUX4 target gene biomarkers associate with lower limb fat fraction and D4Z4 repeat length, but not severity. PAX7 target gene repression in muscle correlates with levels in matched PBMCs. A refined biomarker computed in PBMCs associates with severity in FSHD patients, and also validates as a classifier of severity in an independent set of 54 FSHD patient blood samples. In summary, we present a minimally-invasive, circulating, transcriptomic biomarker of FSHD clinical severity valid in muscle and blood.

bioinformatics↗

bioRxiv2023

Epigenomic signatures as circulating and predictive biomarkers in sarcomatoid renal cell carcinoma

Renal cell carcinoma with sarcomatoid differentiation (sRCC) is associated with poor survival and heightened response to immune checkpoint inhibitors (ICIs). Two major barriers to improving outcomes for sRCC are (1) a limited understanding of its gene regulatory programs and (2) difficulty identifying sarcomatoid differentiation on tumor biopsies due to spatial heterogeneity. To address these challenges, we characterized the epigenomic landscape of sRCC by profiling 107 epigenomic libraries in tissue and plasma samples from 50 patients with RCC and healthy volunteers. We identified highly recurrent epigenomic reprogramming, as assessed by histone modifications and DNA methylation, that distinguishes sRCC from non-sarcomatoid RCC. Computational analysis of RCC epigenomic profiles and CRISPRa experiments implicated the transcription factor FOSL1 in activating sRCC-associated gene regulatory programs. Analysis of two randomized clinical trials identified FOSL1 expression as a predictive biomarker of response to ICIs in RCC. Finally, we demonstrate that epigenomic signatures of sRCC are detectable in patient plasma, establishing an approach for blood-based diagnosis of this clinically important phenotype. These findings provide a framework for the discovery and non-invasive detection of epigenomic correlates of tumor histology via liquid biopsy.

genomics↗

bioRxiv2025

Enhanced Activity of Apramycin and Apramycin-Based Combinations Against Mycobacteroides abscessus

BackgroundMycobacteroides abscessus are rapidly growing non-tuberculous mycobacteria that cause chronic lung and soft tissue infections. Treatment options are often severely limited due to intrinsic resistance to most antimicrobials. Amikacin has historically been a mainstay of combination treatment regimens. However, irreversible hearing loss and vestibular toxicity have led to a search for alternative agents. Apramycin is a novel aminoglycoside currently in phase I clinical trials that may offer lower potential for ototoxic and renal toxic side effects. ObjectivesThe goal of this study was to compare apramycins in vitro activity with amikacin and other aminoglycosides against a large collection of M. abscessus clinical isolates, both alone and in combination with clofazimine or linezolid. We also tested the activity of apramycin against a more limited collection of other species of rapidly growing mycobacteria. MethodsAnalysis was performed using reference broth microdilution minimal inhibitory concentration testing, inkjet printer-assisted checkerboard assays, and time-kill assays. ResultsAgainst M. abscessus, the MIC50/90 for apramycin (2 {micro}g/mL) was 8-fold lower than for amikacin (16 {micro}g/mL). Plazomicin was inactive, and organisms were rarely susceptible to tobramycin. Synergy was not detected by checkerboard assay. In time-kill studies, clofazimine modestly potentiated activity of apramycin and. to a lesser extent, amikacin. Apramycin and amikacin showed delayed bacterial killing that either achieved or approached a bactericidal threshold. Apramycin was similarly potent against other rapidly growing mycobacteria tested. ConclusionsApramycin exhibits more potent in vitro activity against a diverse set of M. abscessus and other rapidly growing mycobacteria than approved aminoglycosides.

microbiology↗

bioRxiv2026

A foundational in vivo platform for predicting human health outcomes

Rodents remain the workhorse of preclinical drug development, yet often fail to predict human clinical outcomes. Existing alternatives are similarly constrained. Cells in culture cannot recapitulate whole-organism physiology, and larger mammals cannot be studied at comparable throughput. Here we present a scalable, information-dense platform that can predict a drugs long-term human clinical outcomes from 24 hours of rodent behavior. A novel home-cage system continuously records behavior, generating thousands of features per hour. Models are trained on human clinical trial data to map these features onto outcomes including gastrointestinal adverse events, cardiac toxicity, neuropsychiatric side effects, and long-term weight loss. In addition to being an order of magnitude faster, the platform provides more accurate clinical predictions than standard long-term preclinical experiments. The approach readily extends to other outcomes, enabling rodents to serve as quantitative models for human clinical prediction.

pharmacology and toxicology↗

bioRxiv2026

DS5: An Open-Source Framework for Standardized High-Throughput Drug Screening Data Storage, Analysis, and Drug Prioritization

Background. High-throughput drug screening (HTS) is increasingly used in precision oncology across cell lines, patient-derived models such as organoids (PDO) and xenograft organoids (PDxO), and ex vivo patient samples, with multiple active clinical trials incorporating HTS for individualizing treatment. Yet HTS efforts predominantly rely on ad hoc, spreadsheet-based workflows, with no standardized infrastructure for storing, analyzing, and comparing screens across patients, cohorts, or institutions, limiting reproducibility and slowing clinical data exchange. Existing tools address isolated pieces of the workflow, handling either data management or analysis and visualization, but none integrate standardized storage, reproducible dose-response analysis, individualized drug prioritization, and clinician and molecular tumor board friendly reporting in a single open-source framework. Results. We present DS5, an open-source Python framework that operates on an HDF5-based DS5 file format that unifies standardized storage, quality control, dose-response analysis, cohort-level drug prioritization, and automated reporting in a single lightweight package. DS5 enforces raw-data immutability, integrates RxNorm-based drug name standardization, and scales from small clinical cohorts to large pharmacogenomics datasets on a standard laptop. We validated DS5 against two public benchmarks spanning over 428,000 drug, cell line pairs, recovering published LN IC50 values from GDSC2 (Pearson r = 0.973) and Emax values from CTRP (r = 0.961). Using a published breast cancer PDxO dataset (45 compounds by 16 models), we reproduced GR-based profiles via a custom-metric extension and used DS5's cohort-level prioritization to predict in vivo tumor response with ROC AUC = 0.907 across 30 drug PDX pairs. Finally, we demonstrate the utilities of DS5 in an ongoing pediatric brain tumor precision oncology initiative, where its cohort-normalized drug rankings and automated reports directly support molecular tumor board discussions. Conclusion. DS5 provides the cancer basic and translational research community with a standardized file format and reproducible computational analysis methods to support functional drug screening experiments; and its reporting features facilitate bridging raw drug screening measurements to translational decisions. DS5 is released open-source under the permissible MIT license.

bioinformatics↗

bioRxiv2022

A clinical stage LMW-DS drug inhibits cell infection by coronaviruses and modulates reactive cytokine release from microglia

Most coronaviruses infect animals including bats, birds and mammals, which act as hosts and reservoirs for the viruses, but the viruses can sometimes move host species and infect humans. Coronoviruses were first identified as human pathogens in the 1960s and now there are seven types known to infect humans. Whilst four of these types cause mild-to-moderate respiratory disease, the other three may cause more severe and possibly even fatal disease in vulnerable individuals particularly, with the most recent SARS-CoV-2 pandemic being associated with severe acute respiratory syndrome (SARS) in many infected people. The aim of the present study was to evaluate the potential of a unique low molecular weight dextran sulphate (LMW-DS) clinical stage drug, ILB(R), to inhibit infection of human cells by the NL63 coronavirus assessed by immunofluorescence of viral particles, and also to see if the drug directly blocked the interaction of the SARS-CoV-2 viral spike protein with the ACE2 receptor. Furthermore, we evaluated if ILB(R) could modulate the downstream consequences of viral infection including the reactive cytokine release from human microglia induced by various SARS-CoV-2 variant spike proteins. We demonstrated that ILB(R) blocked ACE2:spike protein interaction and inhibited coronaviral infection. ILB(R) also attenuated the omicron-induced release of pro-inflammatory cytokines, including TNF, from human microglia, indicating control of post-viral neuroinflammation. In conclusion, given the safety profile of ILB(R) established in a number of Phase I and Phase II clinical trials, these results highlight the potential of ILB(R) to treat patients infected with coronaviruses to both limit infectivity and attenuate the progression to severe disease. There is now an opportunity to translate these findings quickly by the clinical investigation of drug efficacy.

immunology↗

bioRxiv2018

Classes of ITD predict outcomes in patients with AML treated with FLT3 inhibitors

Recurrent internal tandem duplication (ITD) mutations are observed in various cancers including acute myeloid leukemia (AML). ITD mutations of Fms-like tyrosine kinase 3 (FLT3) receptor increase kinase activity, and are associated with poor prognostic outcomes. Currently, several small-molecule FLT3 inhibitors (FLT3i) are in clinical trials for targeted therapy of high-risk FLT3-ITD-positive AML. However, the variability of survival following FLT3i treatment suggests that the mere presence of FLT3-ITD mutations in a patient might not guarantee effective clinical response to targeted inhibition of FLT3 kinase. Motivated by the heterogeneity of FLT3-ITD mutations, we sought to investigate the effects of FLT3-ITD structural features on response to treatment in AML patients. To this end, we developed HeatITup (HEAT diffusion for Internal Tandem dUPlication), an algorithm to efficiently and accurately identify ITDs and classify them based on their nucleotide composition into newly defined categories of \"typical\" or \"atypical\". Typical ITDs insert sequences are entirely endogenous to the FLT3 locus whereas atypical ITDs contain nucleotides exogenous to the wildtype FLT3. We applied HeatITup to our cohort of de novo and relapsed AML patients. Individuals with AML carrying typical ITDs benefited significantly more from FLT3i than patients with atypical ITDs, regardless of whether FLT3i was used after initial induction or at relapse. Furthermore, analysis of the TCGA AML cohort demonstrated improved survival for patients with typical ITDs treated with induction chemotherapy. These results underscore the importance of structural discernment of complex somatic mutations such as ITDs in progressing towards personalized treatment for AML patients.

cancer biology↗