Biology subjects

Tyner, J. W.

Publications and source records attributed to Tyner, J. W..

6 recordsLinked to original sources

bioRxiv2025

A Rapid Gene Expression Profiler Classifies AML Tumor Responsiveness to Standard Therapies

The emergence of transcriptional signatures that define cell types and pathways has made it possible to guide cancer therapy selection through gene expression profiling. We developed a rapid qPCR-based platform to profile cell state, stemness, and BCL2 family gene expression as a companion diagnostic test for acute myeloid leukemia (AML). We validated the stability and utility of the signatures across multiple measurement platforms and using patient samples from two centers. Integrating these signatures with clinical features enables an expedient means to predict the likelihood of patient responses to two standard-of-care therapies: intensive chemotherapy and hypomethylating agent plus venetoclax (HMA+Ven). For patients treated with HMA+Ven, expression levels of the promonocyte-like signature and BCL2 add predictive value for response and overall survival in multivariable models that include genetic features. The incorporation of the rapid profiler into the prospective evaluation of newly diagnosed AML patients may enhance treatment stratification and improve outcomes.

cancer biology↗

bioRxiv2024

Transcriptional and phenotypic heterogeneity underpinning venetoclax resistance in AML

The venetoclax BCL2 inhibitor in combination with hypomethylating agents represents a cornerstone of induction therapy for older AML patients, unfit for intensive chemotherapy. Like other targeted therapies, venetoclax-based therapies suffer from innate and acquired resistance. While several mechanisms of resistance have been identified, the heterogeneity of resistance mechanism across patient populations is poorly understood. Here we utilized integrative analysis of transcriptomic and ex-vivo drug response data in AML patients to identify four transcriptionally distinct VEN resistant clusters (VR_C1-4), with distinct phenotypic, genetic and drug response patterns. VR_C1 was characterized by enrichment for differentiated monocytic- and cDC-like blasts, transcriptional activation of PI3K-AKT-mTOR signaling axis, and energy metabolism pathways. They showed sensitivity to mTOR and CDK inhibition. VR_C2 was enriched for NRAS mutations and associated with distinctive transcriptional suppression of HOX expression. VR_C3 was characterized by enrichment for TP53 mutations and higher infiltration by cytotoxic T cells. This cluster showed transcriptional expression of erythroid markers, suggesting tumor cells mimicking erythroid differentiation, activation of JAK-STAT signaling, and sensitivity to JAK inhibition, which in a subset of cases synergized with venetoclax. VR_C4 shared transcriptional similarities with venetoclax-sensitive patients, with modest over-expression of interferon signaling. They were also characterized by high rates of DNMT3A mutations. Finally, we projected venetoclax-resistance states onto single cells profiled from a patient who relapsed under venetoclax therapy capturing multiple resistance states in the tumor and shifts in their abundance under venetoclax selection, suggesting that single tumors may consist of cells mimicking multiple VR_Cs contributing to intra-tumor heterogeneity. Taken together, our results provide a strategy to evaluate inter- and intra-tumor heterogeneity of venetoclax resistance mechanisms and provide insights into approaches to navigate further management of patients who failed therapy with BCL2 inhibitors.

cancer biology↗

bioRxiv2023

Computational modeling of methylation impact of AML drivers reveals new pathways and refines AML risk-stratification

Decades before its clinical onset, epigenetic changes start to accumulate in the progenitor cells of Acute Myelogenous Leukemia (AML). Delineating these changes can improve risk-stratification for patients and shed insights into AML etiology, dynamics and mechanisms. Towards this goal, we extracted "epigenetic signatures" through two parallel machine learning approaches: a supervised regression model using frequently mutated genes as labels and an unsupervised topic modeling approach to factorize covarying epigenetic changes into a small number of "topics". First, we created regression models for DNMT3A and TET2, the two most frequently mutated epigenetic drivers in AML. Our model differentiated wild-type vs. mutant genotypes based on their downstream epigenetic impacts with very high accuracy: AUROC 0.9 and 0.8, respectively. Methylation loci frequently selected by the models recapitulated known downstream pathways and identified several novel recurrent targets. Second, we used topic modeling to systematically factorize the high dimensional methylation profiles to a latent space of 15 topics. We annotated identified topics with biological and clinical features such as mutation status, prior malignancy and ELN criteria. Topic modeling successfully deconvoluted the combined effects of multiple upstream epigenetic drivers into individual topics including relatively infrequent cytogenetic events, improving the methylation-based subtyping of AML. Furthermore, they revealed complimentary and synergistic interactions between drivers, grouped them based on the similarity of their downstream methylation impact and linked them to prognostic criteria. Our models identify new signatures and methylation pathways, refine risk-stratification and inform detection and drug response studies for AML patients. KEY POINTSO_LISupervised and unsupervised models reveal new methylation pathways of AML driver events and validate previously known associations. C_LIO_LIIndividual DNMT3A and TET2 signatures are accurate and robust, despite the complex genetic and epigenetic make-up of samples at diagnosis. C_LIO_LIUnsupervised topic modeling factorizes covarying methylation changes and isolates methylation signatures caused by rare mutations. C_LIO_LITopic modeling reveals a group of mutations with similar downstream methylation impacts and mapped to adverse-risk class by ELN. C_LIO_LITopic modeling uncovers methylation signatures of infrequent cytogenetic events, significantly improving methylation-based subtyping. C_LIO_LIOur models can be leveraged to build predictive models for AML-risk. C_LIO_LIOur models show that cytogenetic events, such as t(15;17) have widespread trans downstream methylation impacts. C_LI

cancer biology↗

bioRxiv2022

Dual Targeting of FLT3 and LSD1 Disrupts the MYC Super-Enhancer Complex in Acute Myeloid Leukemia

Mutations in Fms-like tyrosine kinase 3 (FLT3) are common drivers in acute myeloid leukemia (AML) yet FLT3 inhibitors only provide modest clinical benefit. Prior work has shown that inhibitors of lysine-specific demethylase 1 (LSD1) enhance kinase inhibitor activity in AML. Here we show that combined LSD1 and FLT3 inhibition induces synergistic cell death in FLT3-mutant AML. Multi-omic profiling revealed that the drug combination disrupts STAT5, LSD1, and GFI1 binding at the MYC blood super-enhancer, suppressing super-enhancer activation as well as MYC expression and activity. The drug combination simultaneously results in the accumulation of repressive H3K9me1 methylation, an LSD1 substrate, at MYC target genes. We validated these findings in 72 primary AML samples with the nearly every sample demonstrating synergistic responses to the drug combination. Collectively, these studies provide preclinical rationale for the investigation of dual FLT3/LSD1 inhibition in a clinical trial.

cancer biology↗

bioRxiv2019

Variable Retention of Differentiation-specific DNA Replication Timing in Human Pediatric Leukemia

Human B-lineage precursor acute lymphoid leukemias (BCP-ALLs) comprise a group of genetically and clinically distinct disease entities with features of differentiation arrest at known stages of normal B-lineage differentiation. We previously showed BCP-ALL cells display unique and clonally heritable DNA-replication timing (RT) programs; i.e., programs describing the variable order of replication of megabase-scale chromosomal units of DNA in different cell types. To determine the extent to which BCP-ALL RT programs mirror or deviate from specific stages of normal human B-cell differentiation, we transplanted immunodeficient mice with quiescent normal human CD34+ cord blood cells and obtained RT signatures of the regenerating B-lineage populations. We then compared these with RT signatures for leukemic cells from a large cohort of BCP-ALL patients. The results identify BCP-ALL subtype-specific features that resemble specific stages of B-cell differentiation and features that appear associated with relapse. These results suggest the genesis of BCP-ALL involves alterations in RT that reflect clinically relevant leukemia-specific genetic and/or epigenetic changes. SUMMARYGenome-wide DNA replication timing profiles of >100 pediatric leukemic samples and normally differentiating human B-lineage cells isolated from xenografted immunodeficient mice were generated. Comparison of these identified potentially clinically relevant features that both match and deviate from the normal profiles.

cancer biology↗

bioRxiv2018

RNAi screening of the tyrosine kinome in primary patient samples of acute myeloid leukemia

Targeted therapy has proven to be successful in improving outcomes across multiple cancer types. However, many challenges still remain for implementation of this strategy in most patient cohorts, especially with the challenges of identifying the specific mutations or abnormalities in a heterogeneous tumor that are functionally significant. Previously, we developed a functional screening assay, RNAi-assisted protein target identification (RAPID) technology, which evaluates the viability of tumor cells after exposure to siRNAs against members of the tyrosine kinome and NRAS/KRAS. Here, we publish the comprehensive results of this screen for 332 primary AML patient samples. Data from these screening efforts have already helped identify previously unknown therapeutic targets, and will continue to provide insights into better treatment strategies for these patients.

cancer biology↗