Biology subjects

Heckman, C. A.

Publications and source records attributed to Heckman, C. A..

10 recordsLinked to original sources

bioRxiv2026

Impact of Regularization Methods and Outlier Removal on Unsupervised Sample Classification

BackgroundHigh-content assays (HCAs) have problems distinguishing biologically significant effects from the incidental effects of non-repeatable technical factors. Non-repeatable results are attributed to variations in the cell culture environment and the numerous, heterogeneous descriptors evaluated. The aim here was to determine whether preprocessing operations impacted the reproducibility of class assignments of experimental data. MethodsBatch effects that could affect reproducibility, i.e., signal/noise ratio, instrumental conditions, and segmentation, were controlled variables. The remaining batch effects, variations in materials, personnel, and culture environment could not be controlled. Descriptors values were measured directly from images. Exploratory factor analysis was used to solve the identifiable and interpretable feature, factor 4. In each of five trials, one sample was treated with the same chemical mixture (EXP) and another with the solvent vehicle alone (CON). ResultsRepeated CON and EXP samples showed significant differences among factor 4 means in data regularized within each trial. The mean of Trial 3 CON differed significantly from all other CON samples. These differences disappeared upon regularization to comprehensive databases. Among repeated EXPs, the Trial 2 mean differed from three other EXPs, but regularization to comprehensive databases had little effect. However, classification patterns were unchanged after regularization to any comprehensive database derived by the same protocol. After regularization to datasets derived by two different protocols, the classification pattern differed but only reflected elevation of differences that had been marginal to statistical significance. Outlier removal was deleterious. Even with the most sparing definition of outliers, over 3% of a single samples contents were removed from most trials. Elimination based on the overall within-trial distributions caused type I and type II errors. ConclusionsNon-repeatable factor 4 means in repeated trials had negligible influence on classification outcomes, so repeatability may not be a good indicator of assay quality. Irreducible batch effects, combined with small sample sizes and skewed distributions of descriptors values, may account for non-repeatability. As the current results are based on real-world data, they suggest that non-repeatability is an uncorrectable feature of these assays. Classification patterns are not affected by several irreducible technical factors, namely materials, personnel, and non-repeatable environmental variables.

bioinformatics↗

bioRxiv2026

Dynamics of Leukemic Blast and Immune Cell Populations in Acute Myeloid Leukemia

Most patients with acute myeloid leukemia (AML) initially respond to standard chemotherapy. However, relapse and refractory disease remain common. The responses to targeted therapies are often transient and the efficacy of immunotherapy is limited. Although single-cell RNA sequencing (scRNA-seq) studies have provided insights into the cellular diversity and immune landscape of AML, many have primarily focused on limited, or newly diagnosed patient cohorts, leaving cellular dynamics across advanced disease incompletely defined. Here, we profiled 72 samples from AML patients across different disease stages using scRNA-seq and compared these against healthy donor samples. We observed selective enrichment of immature progenitor populations, along with widespread upregulation of oxidative phosphorylation in AML. The immune microenvironment of AML was characterized by CD8+ effector memory T cell expansion with reduced IL2-STAT5 and increased mTORC1 pathways and exhaustion markers, suggesting a functional imbalance. Several AML-specific genes were identified providing potential therapeutic opportunities. Cell communication analysis revealed reduced HLA interactions in relapsed/refractory samples compared to diagnosis samples, suggesting impaired antigen presentation and defective T cell priming. Together, these results improve the understanding of cellular and immune changes in AML during disease progression and provide a basis for new therapeutic strategies.

cancer biology↗

bioRxiv2024

Targeting AML Resistance with LY3009120-Sapanisertib and Ruxolitinib-Ulixertinib Combinations Demonstrate Superior Efficacy in FLT3, TP53, and MUC4 mutations

Acute myeloid leukemia (AML) is a genetically heterogeneous malignancy characterized by the clonal expansion of myeloid precursor cells. Despite the advent of venetoclax-based regimens, resistance mechanisms remain a major clinical challenge, particularly in patients with high-risk mutations such as TP53, MUC4, HLA-B and FLT3. This study aims to investigate the efficacy of drug combinations for the treatment of AML using both, AML cell lines and zebrafish embryo xenograft model. Specifically, we focus on two drug combinations; the pan-RAF inhibitor LY3009120 combined with the mTOR inhibitor sapanisertib (designated as LS), and the JAK1/2 inhibitor ruxolitinib combined with the ERK inhibitor ulixertinib (designated as RU). The study integrates real-time cell viability assays, xenograft imaging, and genomic analyses to assess drug efficacy and explore correlations between treatment responses and mutational profiles, particularly TP53, MUC4, HLA-B and FLT3 mutations. Both combinations demonstrated superior efficacy compared to venetoclax-based therapy, with LS notably reducing viability in MOLM-16 and SKM-1 cells, and RU showing comparable efficacy with a favorable safety profile. In zebrafish embryos, LS combination effectively inhibited the proliferation of xenografted human AML cells, as evidenced by decreased fluorescence signals, indicating cell death. The RU combination also disrupted survival of cancer cells, showing promise as a therapeutic strategy. Furthermore, a correlation was identified between drug response and mutational profiles, with TP53, MUC4, HLA-B and FLT3 mutations significantly influencing sensitivity to the LS and RU combinations. These findings support the further development of LS and RU as effective alternatives to current clinical regimens, with implications for personalized AML treatment.

cancer biology↗

bioRxiv2024

Exploring Mechanisms of Action in Combinatorial Therapy through Stability/Solubility Alterations: Advancing AML Treatment

Acute myeloid leukemia (AML) remains a formidable clinical challenge due to genetic heterogeneity, high relapse rates, and toxicities associated with conventional chemotherapies. Rationally designed drug combinations offer improved efficacy, yet their selection is often empirical and lacks molecular mechanistic understanding. Here, we present CoPISA workflow (Proteome Integral Solubility/Stability Alteration Analysis for Combinations), a high-throughput proteomics workflow that captures protein solubility/stability alterations unique to combinatorial drug treatments, revealing mechanisms unattainable through single-drug analyses. Applying CoPISA to two rationally designed AML drug pairs, LY3009120-sapanisertib (LS) and ruxolitinib-ulixertinib (RU), we mapped primary (lysate) and secondary (living cell) protein target landscapes. Notably, our analysis uncovered an emergent mechanistic principle, "conjunctional targeting" (i.e., conjunctional inhibition), wherein cooperative drug actions induce treatment-specific targets not achievable individually, analogous to an AND-gate logic model. LS-specific AND-gate proteins converged on SUMOylation, chromatin condensation, and VEGF-linked adhesion, while RU-specific targets disrupted DNA-damage checkpoints, mitochondrial bioenergetics, and RNA-splicing machinery, collectively implicating synthetic-lethal vulnerabilities. Additionally, the post-translational modifications (PTMs) profiling of differential soluble proteins confirms several combination-induced modifications (e.g., acetylation, dimethylation, phosphorylation) on key AML proteins, such as NPM1. Network interrogation of AML-associated proteins showed that a high percentage of targeted proteins are unique to the combinations, including frequently mutated drivers DNMT3A, NPM1, and TP53. CoPISA exposes how drug pairs enact multi-axis pressure on AML cells through conjunctional targeting, a mechanistic layer beyond classical synergy. By pinpointing combination-exclusive protein targets and signaling pathways, CoPISA provides a blueprint for precision-guided regimen design in AML and other heterogeneous cancers. Data are available via ProteomeXchange with identifier PXD066812.

biochemistry↗

bioRxiv2024

Multi-omics data integration reveals molecular mechanisms of carfilzomib resistance in multiple myeloma

Multiple myeloma represents a complex hematological malignancy, characterized by its wide array of genetic and clinical events. The introduction of proteasome inhibitors, such as carfilzomib or bortezomib, into the therapeutic landscape has notably enhanced the quality of life and survival rates for patients suffering from this disease. Nonetheless, a significant obstacle in the long-term efficacy of this treatment is the inevitable development of resistance to PIs, posing a substantial challenge in managing the disease effectively. Our study investigates the molecular mechanisms behind carfilzomib resistance by analyzing multi-omics profiles from four multiple myeloma cell lines: AMO-1, KMS-12-PE, RPMI-8226 and OPM-2, together with their carfilzomib-resistant variants. We uncovered a significant downregulation of metabolic pathways linked to strong mitochondrial dysfunction in resistant cells. Further examination of patient samples identified key genes - ABCB1, RICTOR, PACSIN1, KMT2D, WEE1 and GATM - potentially crucial for resistance, guiding us towards promising carfilzomib combination therapies to circumvent resistance mechanisms. The response profiles of tested compounds have led to the identification of a network of gene interactions in resistant cells. We identified two already approved drugs, benidipine and tacrolimus, as potential partners for combination therapy with carfilzomib to counteract resistance. This discovery enhances the clinical significance of our findings.

cancer biology↗

bioRxiv2024

Morphological single-cell analysis of peripheral blood mononuclear cells from 390 healthy blood donors with Blood Cell Painting

The morphological diversity of blood immune cells of healthy individuals, critical for recognizing disease-related phenotypes, remains largely uncharacterized. To address this gap, we developed Blood Cell Painting (BCP): a high content, high throughput fluorescence imaging assay for peripheral blood mononuclear cells. We generated a BCP Atlas with images of 50 million cells from 390 healthy blood donors, identifying 18 distinct immune cell morphology clusters. A genome-wide association study of BCP-derived imaging-based cellular features revealed 93 significant associations across 30 genetic loci. These loci include genes linked to mast cell function, inflammation, immune signaling, mitochondrial maintenance and circadian immune modulation. We also observed correlations between immune cell morphological features and clinical traits, such as respiratory conditions and healthcare visits related to contraceptive management, potentially reflecting hormonal influences on immune cell phenotypes. As a proof of concept for clinical application, acute myeloid leukemia subtypes were distinguished by BCP. Our study establishes BCP as a versatile method for immune cell profiling to uncover genetic, phenotypic and clinical determinants of immune cell morphology in health and disease.

cell biology↗

bioRxiv2023

Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in patients with advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in scarce patient cells. Here, we developed scTherapy, a machine learning model that leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors.

bioinformatics↗

bioRxiv2023

Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling

Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that the combinations of ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective with the least toxicity and best synergistic effects on blasts. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment. Key PointsO_LIRuxolitinib-ulixertinib and sapanisertib-LY3009120 have the best synergistic effects on AML, with the least toxicity. C_LIO_LIThis studys combinations destroy blasts without harming other healthy cells, unlike standard chemotherapy, which is less specific. C_LI

cancer biology↗

bioRxiv2021

How Filopodia Respond to Calcium in the Absence of a Calcium-binding Structural Protein: They Use Rapid Transit

During directional locomotion, cells must reorient themselves in response to attractive or repulsive cues. Filopodia are narrow actin-based protrusions whose prevalence at the leading edge of a migrating cell is related to the persistence of locomotion. Although there is a marked absence of calcium-binding components in their structure, they responded to store-operated calcium entry (SOCE). Here, we used a two-phase protocol to determine how they responded. In the first phase, extracellular calcium was removed and ER calcium lowered by blocking reuptake through the calcium pump. This was known to activate stromal interacting molecule (STIM) and cause its microtubule-mediated translocation to the cell surface. In the second phase, extracellular calcium and calcium influx into the ER were restored. ER depletion caused filopodia to increase, followed by a spontaneous decrease that was blocked by inhibiting endocytosis. The intracellular calcium concentration increased during depletion, while the size of the exchangeable compartment of vesicles, measured by fluid-phase marker uptake, shrank. When SOCE mediators and the aquaporin, AQP4, were localized, STIM and transient receptor potential canonical (TPRC) channels occupied vesicular profiles side-by-side in linear arrays. STIM1 was translocated, as expected. TRPC1 was initially in a rapidly recycling pool (RRP) where it partially colocalized with Vamp2. Calcium restoration caused TRPC1 exocytosis, while STIM1 reverted toward its original pattern associated with the ER. The exchangeable compartment was restored and this enabled filopodia extension, which was blocked by inhibitors of TRPC1/4/5 and endocytosis. That vesicle recycling was essential for extension during calcium readdition was indicated by reversal of the effect of endocytosis inhibitors in the depletion and readdition phases. The results suggest that SOCE regulates the size of the RRP in epithelial cells, and vesicle recycling is the immediate mechanism affecting filopodia extension. The conclusions are discussed in light of factors regulating protrusion formation, namely surface tension and vesicle trafficking.

cell biology↗

bioRxiv2019

Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer

High-throughput drug sensitivity screening has been utilized for facilitating the discovery of drug combinations in cancer. Many existing studies adopted a dose-response matrix design, aiming for the characterization of drug combination sensitivity and synergy. However, there is lack of consensus on the definition of sensitivity and synergy, leading to the use of different mathematical models that do not necessarily agree with each other. We proposed a cross design to enable a more cost-effective testing of sensitivity and synergy for a drug pair. We developed a drug combination sensitivity score (CSS) to summarize the drug combination dose-response curves. Using a high-throughput drug combination dataset, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as Elastic Net, Random Forests and Support Vector Machines, the CSS can also be predicted with high accuracy. Furthermore, we defined a synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic drug combinations. The cross drug combination design coupled with the CSS scoring facilitated the evaluation of drug combination sensitivity and synergy using the same scale, with minimal experimental material that is required. Our approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening. The R scripts for calculating and predicting CSS are available at https://github.com/amalyutina/CSS.\n\nAuthor summaryBeing a complex disease, cancer is one of the main death causes worldwide. Although new treatment strategies have been achieved with cancers, they still have limited efficacy. Even when there is an initial treatment response, cancer cells can develop drug resistance thus cause disease recurrence. To achieve more effective and safe therapies to treat cancer, patients critically need multi-targeted drug combinations that will kill cancer cells at reduced dosages and thereby avoid side effects that are often associated with the standard treatment. However, the increasing number of possible drug combinations makes a pure experimental approach unfeasible, even with automated drug screening instruments. Therefore, we have proposed a new experimental set up to get the drug combination sensitivity data cost-efficiently and developed a score to quantify the efficiency of the drug combination, called drug combination sensitivity score (CSS). Using public datasets, we have shown that the CSS robustness and its highly predictive nature with an accuracy comparable to the experimental replicates. We have also defined a CSS-based synergy score as a metric of drug interaction and justified its relevance. Thus, we expect the proposed computational techniques to be easily applicable and beneficial in the field of drug combination discovery.

pharmacology and toxicology↗