Metrics other than potency reveal systematic variation in responses to cancer drugs

Rational design of 2-benzylsulfinyl-benzoxazoles as potent and selective indoleamine 2,3-dioxygenase 1 inhibitors to combat inflammation

Mimicking the transition state of tryptophan (Trp) and O2 in the enzymatic reaction is an effective approach to design indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors. In this study, we firstly assembled a small library of 2-substituted benzo-fused five membered heterocycles and found 2-sulfinyl-benzoxazoles with interesting IDO1 inhibitory activities. Next the inhibitory activity toward IDO1 was gradually improved. Several benzoxazoles showed potent IDO1 inhibitory activity with IC50 of 82-91 nM, and exhibited selectivity between IDO1 and tryptophan 2,3-dioxygenase (TDO2). Enzyme binding studies showed that benzoxazoles are reversible type II IDO1 inhibitors, and modeling studies suggested that the oxygen atom of the sulfoxide in benzoxazoles interacts with the iron atom of the heme group, which mimics the transition state of Fe-O-O-Trp complex. Especially, 10b can effectively inhibit the NO production in lipopolysaccharides (LPS) stimulated RAW264.7 cells, and it also shows good anti-inflammation effect on mice acute inflammation model of croton oil induced ear edema.

Phenotypic, Genomic, and Transcriptomic Heterogeneity in a Pancreatic Cancer Cell Line

OBJECTIVE

To evaluate the suitability of the MIA PaCa-2 cell line for studying pancreatic cancer intratumor heterogeneity, we aim to further characterize the nature of MIA PaCa-2 cells' phenotypic, genomic, and transcriptomic heterogeneity.

MATERIALS AND METHODS

MIA PaCa-2 single-cell clones were established through flow cytometry. For the phenotypic study, we quantified the cellular morphology, proliferation rate, migration potential, and drug sensitivity of the clones. The chromosome copy number and transcriptomic profiles were quantified using SNPa and RNA-seq, respectively.

RESULTS

Four MIA PaCa-2 clones showed distinctive phenotypes, with differences in cellular morphology, proliferation rate, migration potential, and drug sensitivity. We also observed a degree of genomic variations between these clones in form of chromosome copy number alterations and single nucleotide variations, suggesting the genomic heterogeneity of the population, and the intrinsic genomic instability of MIA PaCa-2 cells. Lastly, transcriptomic analysis of the clones also revealed gene expression profile differences between the clones, including the uniquely regulated ITGAV , which dictates the morphology of MIA PaCa-2 clones.

CONCLUSIONS

MIA PaCa-2 is comprised of cells with distinctive phenotypes, heterogeneous genomes, and differential transcriptomic profiles, suggesting its suitability as a model to study the underlying mechanisms behind pancreatic cancer heterogeneity.

A clinically compatible in vitro drug-screening platform identifies therapeutic vulnerabilities in primary cultures of brain metastases

PURPOSE

Brain metastases represent the most common intracranial tumors in adults and are associated with a poor prognosis. We used a personalized in vitro drug screening approach to characterize individual therapeutic vulnerabilities in brain metastases.

METHODS

Short-term cultures of cancer cells isolated from brain metastasis patients were molecularly characterized using next-generation sequencing and functionally evaluated using high-throughput in vitro drug screening to characterize pharmacological treatment sensitivities.

RESULTS

Next-generation sequencing identified matched genetic alterations in brain metastasis tissue samples and corresponding short-term cultures, suggesting that short-term cultures of brain metastases are suitable models for recapitulating the genetic profile of brain metastases that may determine their sensitivity to anti-cancer drugs. Employing a high-throughput in vitro drug screening platform, we successfully screened the cultures of five brain metastases for response to 267 anticancer compounds and related drug response to genetic data. Among others, we found that targeted treatment with JAK3, HER2, or FGFR3 inhibitors showed anti-cancer effects in individual brain metastasis cultures.

CONCLUSION

Our preclinical study provides a proof-of-concept for combining molecular profiling with in vitro drug screening for predictive evaluation of therapeutic vulnerabilities in brain metastasis patients. This approach could advance the use of patient-derived cancer cells in clinical practice and might eventually facilitate decision-making for personalized drug treatment.

Time-of-day effects of cancer drugs revealed by high-throughput deep phenotyping

The circadian clock, a fundamental biological regulator, governs essential cellular processes in health and disease. Circadian-based therapeutic strategies are increasingly gaining recognition as promising avenues. Aligning drug administration with the circadian rhythm can enhance treatment efficacy and minimize side effects. Yet, uncovering the optimal treatment timings remains challenging, limiting their widespread adoption. In this work, we introduce a high-throughput approach integrating live-imaging and data analysis techniques to deep-phenotype cancer cell models, evaluating their circadian rhythms, growth, and drug responses. We devise a streamlined process for profiling drug sensitivities across different times of the day, identifying optimal treatment windows and responsive cell types and drug combinations. Finally, we implement multiple computational tools to uncover cellular and genetic factors shaping time-of-day drug sensitivity. Our versatile approach is adaptable to various biological models, facilitating its broad application and relevance. Ultimately, this research leverages circadian rhythms to optimize anti-cancer drug treatments, promising improved outcomes and transformative treatment strategies.

scDrug+: predicting drug-responses using single-cell transcriptomics and molecular structure

Predicting drug responses based on individual transcriptomic profiles holds promise for refining prognosis and advancing precision medicine. Although many studies have endeavored to predict the responses of known drugs to novel transcriptomic profiles, research into predicting responses for newly discovered drugs remains sparse. In this study, we introduce scDrug+, a comprehensive pipeline that seamlessly integrates single-cell analysis with drug-response prediction. Importantly, scDrug+ is equipped to predict the response of new drugs by analyzing their molecular structures. The open-source tool is available as a Docker container, ensuring ease of deployment and reproducibility. It can be accessed at https://github.com/ailabstw/scDrugplus.

Discovery and Preclinical Characterization of BIIB129, a Covalent, Selective, and Brain-Penetrant BTK Inhibitor for the Treatment of Multiple Sclerosis

Multiple sclerosis (MS) is a chronic disease with an underlying pathology characterized by inflammation-driven neuronal loss, axonal injury, and demyelination. Bruton's tyrosine kinase (BTK), a nonreceptor tyrosine kinase and member of the TEC family of kinases, is involved in the regulation, migration, and functional activation of B cells and myeloid cells in the periphery and the central nervous system (CNS), cell types which are deemed central to the pathology contributing to disease progression in MS patients. Herein, we describe the discovery of BIIB129 (25), a structurally distinct and brain-penetrant targeted covalent inhibitor (TCI) of BTK with an unprecedented binding mode responsible for its high kinome selectivity. BIIB129 (25) demonstrated efficacy in disease-relevant preclinical in vivo models of B cell proliferation in the CNS, exhibits a favorable safety profile suitable for clinical development as an immunomodulating therapy for MS, and has a low projected total human daily dose.

Translation initiation factor eIF1A rescues hygromycin B sensitivity caused by deleting the carboxy-terminal tail in the GPN-loop GTPase Npa3

The essential yeast protein GPN-loop GTPase 1 (Npa3) plays a critical role in RNA polymerase II (RNAPII) assembly and subsequent nuclear import. We previously identified a synthetic lethal interaction between a mutant lacking the carboxy-terminal 106-amino acid tail of Npa3 (npa3ΔC) and a bud27Δ mutant. As the prefoldin-like Bud27 protein participates in ribosome biogenesis and translation, we hypothesized that Npa3 may also regulate these biological processes. We investigated this proposal by using Saccharomyces cerevisiae strains episomally expressing either wild-type Npa3 or hypomorphic mutants (Npa3ΔC, Npa3K16R, and Npa3G70A). The Npa3ΔC mutant fully supports RNAPII nuclear localization and activity. However, the Npa3K16R and Npa3G70A mutants only partially mediate RNAPII nuclear targeting and exhibit a higher reduction in Npa3 function. Cell proliferation in these strains displayed an increased sensitivity to protein synthesis inhibitors hygromycin B and geneticin/G418 (npa3G70A > npa3K16R > npa3ΔC > NPA3 cells) but not to transcriptional elongation inhibitors 6-azauracil, mycophenolic acid or 1,10-phenanthroline. In all three mutant strains, the increase in sensitivity to both aminoglycoside antibiotics was totally rescued by expressing NPA3. Protein synthesis, visualized by quantifying puromycin incorporation into nascent-polypeptide chains, was markedly more sensitive to hygromycin B inhibition in npa3ΔC, npa3K16R, and npa3G70A than NPA3 cells. Notably, high-copy expression of the TIF11 gene, that encodes the eukaryotic translation initiation factor 1A (eIF1A) protein, completely suppressed both phenotypes (of reduced basal cell growth and increased sensitivity to hygromycin B) in npa3ΔC cells but not npa3K16R or npa3G70A cells. We conclude that Npa3 plays a critical RNAPII-independent and previously unrecognized role in translation initiation.

Overcoming limitations in current measures of drug response may enable AI-driven precision oncology

Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we identify a fundamental limitation in standard measures of drug sensitivity that hinders the development of personalized prediction models - they focus on absolute effects but do not capture relative differences between cancer subtypes. Our work suggests that using z-scored drug response measures mitigates these limitations and leads to meaningful predictions, opening the door for sophisticated ML precision oncology models.

Characterizing heterogeneous single-cell dose responses computationally and experimentally using threshold inhibition surfaces and dose-titration assays

Single cancer cells within a tumor exhibit variable levels of resistance to drugs, ultimately leading to treatment failures. While tumor heterogeneity is recognized as a major obstacle to cancer therapy, standard dose-response measurements for the potency of targeted kinase inhibitors aggregate populations of cells, obscuring intercellular variations in responses. In this work, we develop an analytical and experimental framework to quantify and model dose responses of individual cancer cells to drugs. We first explore the connection between population and single-cell dose responses using a computational model, revealing that multiple heterogeneous populations can yield nearly identical population dose responses. We demonstrate that a single-cell analysis method, which we term a threshold inhibition surface, can differentiate among these populations. To demonstrate the applicability of this method, we develop a dose-titration assay to measure dose responses in single cells. We apply this assay to breast cancer cells responding to phosphatidylinositol-3-kinase inhibition (PI3Ki), using clinically relevant PI3Kis on breast cancer cell lines expressing fluorescent biosensors for kinase activity. We demonstrate that MCF-7 breast cancer cells exhibit heterogeneous dose responses with some cells requiring over ten-fold higher concentrations than the population average to achieve inhibition. Our work reimagines dose-response relationships for cancer drugs in an emerging paradigm of single-cell tumor heterogeneity.

UNNT: A novel Utility for comparing Neural Net and Tree-based models

The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages over deep learning models, such as a convolutional neural network (CNN), for single drug response problems. However, comparing models is not a trivial task. To make training and comparing CNNs and XGBoost more accessible to users, we developed an open-source library called UNNT (A novel Utility for comparing Neural Net and Tree-based models). The case studies, in this manuscript, focus on cancer drug response datasets however the application can be used on datasets from other domains, such as chemistry.

Design, synthesis, and evaluation of dual EGFR/AURKB inhibitors as anticancer agents for non-small cell lung cancer

The epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are first-line agents for mutant EGFR-positive (mEGFR+) NSCLC. However, secondary resistant mutations develop following therapy that prevent EGFR-TKI binding. The EGFR-TKIs are rendered ineffective in NSCLC expressing EGFR resistant mutations (rmEGFR+). Mutations in Kirsten rat sarcoma virus protein (mKRAS) support persistent signaling downstream of EGFR regardless of EGFR-TKI earlier in the signaling cascade. The EGFR-TKIs are ineffective in mKRAS+ NSCLC. Thus, newer anticancer agents are needed for rmEGFR+ and mKRAS+ NSCLC. Aurora kinase B (AURKB) is a mitosis related kinase that is overexpressed in NSCLC and supports cancer cell proliferation and survival. Literature reports have suggested that AURKB inhibitors if given concurrently with an EGFR-TKI could overcome EGFR-TKI resistance in mKRAS+ NSCLC and rmEGFR + NSCLC, and showed improved anticancer effects compared to current single-targeted EGFR-TKIs. Molecular modeling was used to identify similarities between the kinase pockets of EGFR and AURKB. An overlap was observed for the inactive conformation of EGFR and the active conformation of AURKB. Compounds 3-7 were synthesized as dual EGFR/AURKB inhibitors for mKRAS+ and rmEGFR+ NSCLC. Compounds 5, 6 and 7 were identified as dual EGFR/AURKB inhibitors. Compound 5 demonstrated modest micromolar inhibition of rmEGFR+ NSCLC. All investigated compounds showed moderate inhibition of mKRAS+ NSCLC cells. Compound 7 demonstrated single-digit micromolar inhibition of mKRAS+ NSCLC.

Hsp70-Bim incoherent feedforward loop contributes to cell-fate heterogeneity and fractional killing

BACKGROUND AND PURPOSE

Although chemotherapeutics or molecular targeted drugs often elicit profound initial responses, fractional killing capable of driving acquired resistance can persist. Identifying stress-induced negative feedback or an incoherent feedforward loop (IFFL), which may contribute to fractional killing, is urgently needed.

EXPERIMENTAL APPROACH

Mathematical modelling was used to identify how and to what extent a recently reported Hsp70-Bim protein-protein interaction (PPI) contributes to the adaptation of the Bcl-2 network. Experimental validation was made by using a specific inhibitor of Hsp70-Bim PPI, S1g-2, as chemical tool. Bifurcation analysis and stochastic simulation were used for the theoretical study of the impact of Hsp70-Bim PPI on cell-fate heterogeneity and factional killing.

KEY RESULTS

The Hsp70-Bim-AKT circuit forms an IFFL that greatly contributes to the adaptation of the Bcl-2-regulated apoptosis network, thus leading to fractional killing. This adaptive programme enhances noise-induced cell-fate heterogeneity by shifting from a saddle-node to a saddle-collision transition scenario.

CONCLUSION AND IMPLICATIONS

Hsp70-Bim IFFL serves as a molecular pathway induced by DNA damaging drugs or tyrosine kinase inhibitors that enabled fractional killing, whereby acquired resistance emerges. A synergistic strategy is unveiled for overcoming fractional killing by suppressing Hsp70-Bim PPI.

Integrated characterization of hepatobiliary tumor organoids provides a potential landscape of pharmacogenomic interactions

Despite considerable efforts to identify human liver cancer genomic alterations that might unveil druggable targets, the systematic translation of multiomics data remains challenging. Here, we report success in long-term culture of 64 patient-derived hepatobiliary tumor organoids (PDHOs) from a Chinese population. A divergent response to 265 metabolism- and epigenetics-related chemicals and 36 anti-cancer drugs is observed. Integration of the whole genome, transcriptome, chromatin accessibility profiles, and drug sensitivity results of 64 clinically relevant drugs defines over 32,000 genome-drug interactions. RUNX1 promoter mutation is associated with an increase in chromatin accessibility and a concomitant gene expression increase, promoting a cluster of drugs preferentially sensitive in hepatobiliary tumors. These results not only provide an annotated PDHO biobank of human liver cancer but also suggest a systematic approach for obtaining a comprehensive understanding of the gene-regulatory network of liver cancer, advancing the applications of potential personalized medicine.

R-Loop Accumulation in Spliceosome Mutant Leukemias Confers Sensitivity to PARP1 Inhibition by Triggering Transcription-Replication Conflicts

RNA splicing factor (SF) gene mutations are commonly observed in patients with myeloid malignancies. Here we showed that SRSF2- and U2AF1-mutant leukemias are preferentially sensitive to PARP inhibitors (PARPi), despite being proficient in homologous recombination repair. Instead, SF-mutant leukemias exhibited R-loop accumulation that elicited an R-loop-associated PARP1 response, rendering cells dependent on PARP1 activity for survival. Consequently, PARPi induced DNA damage and cell death in SF-mutant leukemias in an R-loop-dependent manner. PARPi further increased aberrant R-loop levels, causing higher transcription-replication collisions and triggering ATR activation in SF-mutant leukemias. Ultimately, PARPi-induced DNA damage and cell death in SF-mutant leukemias could be enhanced by ATR inhibition. Finally, the level of PARP1 activity at R-loops correlated with PARPi sensitivity, suggesting that R-loop-associated PARP1 activity could be predictive of PARPi sensitivity in patients harboring SF gene mutations. This study highlights the potential of targeting different R-loop response pathways caused by spliceosome gene mutations as a therapeutic strategy for treating cancer.

SIGNIFICANCE

Spliceosome-mutant leukemias accumulate R-loops and require PARP1 to resolve transcription-replication conflicts and genomic instability, providing rationale to repurpose FDA-approved PARP inhibitors for patients carrying spliceosome gene mutations.

Nanoscale Surface Topography of Polyethylene Glycol-Coated Nanoparticles Composed of Bottlebrush Block Copolymers Prolongs Systemic Circulation and Enhances Tumor Uptake

Improving the performance of nanocarriers remains a major challenge in the clinical translation of nanomedicine. Efforts to optimize nanoparticle formulations typically rely on tuning the surface density and thickness of stealthy polymer coatings, such as poly(ethylene glycol) (PEG). Here, we show that modulating the surface topography of PEGylated nanoparticles using bottlebrush block copolymers (BBCPs) significantly enhances circulation and tumor accumulation, providing an alternative strategy to improve nanoparticle coatings. Specifically, nanoparticles with rough surface topography achieve high tumor cell uptake in vivo due to superior tumor extravasation and distribution compared to conventional smooth-surfaced nanoparticles based on linear block copolymers. Furthermore, surface topography profoundly impacts the interaction with serum proteins, resulting in the adsorption of fundamentally different proteins onto the surface of rough-surfaced nanoparticles formed from BBCPs. We envision that controlling the nanoparticle surface topography of PEGylated nanoparticles will enable the design of improved nanocarriers in various biomedical applications.

Clinical forecasting of acute myeloid leukemia using ex vivo drug-sensitivity profiling

Current treatment selection for acute myeloid leukemia (AML) patients depends on risk stratification based on cytogenetic and genomic markers. However, the forecasting accuracy of treatment response remains modest, with most patients receiving intensive chemotherapy. Recently, ex vivo drug screening has gained traction in personalized treatment selection and as a tool for mapping patient groups based on relevant cancer dependencies. Here, we systematically evaluated the use of drug sensitivity profiling for predicting patient survival and clinical response to chemotherapy in a cohort of AML patients. We compared computational methodologies for scoring drug efficacy and characterized tools to counter noise and batch-related confounders pervasive in high-throughput drug testing. We show that ex vivo drug sensitivity profiling is a robust and versatile approach to patient prognostics that comprehensively maps functional signatures of treatment response and disease progression. In conclusion, ex vivo drug profiling can assess risk for individual AML patients and may guide clinical decision-making.

Repurposed drugs and their combinations prevent morbidity-inducing dermonecrosis caused by diverse cytotoxic snake venoms

Morbidity from snakebite envenoming affects approximately 400,000 people annually. Tissue damage at the bite-site often leaves victims with catastrophic life-long injuries and is largely untreatable by current antivenoms. Repurposed small molecule drugs that inhibit specific snake venom toxins show considerable promise for tackling this neglected tropical disease. Using human skin cell assays as an initial model for snakebite-induced dermonecrosis, we show that the drugs 2,3-dimercapto-1-propanesulfonic acid (DMPS), marimastat, and varespladib, alone or in combination, inhibit the cytotoxicity of a broad range of medically important snake venoms. Thereafter, using preclinical mouse models of dermonecrosis, we demonstrate that the dual therapeutic combinations of DMPS or marimastat with varespladib significantly inhibit the dermonecrotic activity of geographically distinct and medically important snake venoms, even when the drug combinations are delivered one hour after envenoming. These findings strongly support the future translation of repurposed drug combinations as broad-spectrum therapeutics for preventing morbidity caused by snakebite.

Drug Responses in Plexiform Neurofibroma Type I (PNF1) Cell Lines Using High-Throughput Data and Combined Effectiveness and Potency

Background: Neurofibromatosis type 1 (NF1) is a genetic disorder characterized by heterozygous germline NF1 gene mutations that predispose patients to developing plexiform neurofibromas, which are benign but often disfiguring tumors of the peripheral nerve sheath induced by loss of heterozygosity at the NF1 locus. These can progress to malignant peripheral nerve sheath tumors (MPNSTs). There are no approved drug treatments for adults with NF1-related inoperable plexiform neurofibromas, and only one drug (selumetinib), which is an FDA-approved targeted therapy for the treatment of symptomatic pediatric plexiform neurofibromas, highlighting the need for additional drug screening and development. In high-throughput screening, the effectiveness of drugs against cell lines is often assessed by measuring in vitro potency (AC50) or the area under the curve (AUC). However, the variability of dose-response curves across drugs and cell lines and the frequency of partial effectiveness suggest that these measures alone fail to provide a full picture of overall efficacy. Methods: Using concentration-response data, we combined response effectiveness (EFF) and potency (AC50) into (a) a score characterizing the effect of a compound on a single cell line, S = log[EFF/AC50], and (b) a relative score, ΔS, characterizing the relative difference between a reference (e.g., non-tumor) and test (tumor) cell line. ΔS was applied to data from high-throughput screening (HTS) of a drug panel tested on NF1-/- tumor cells, using immortalized non-tumor NF1+/- cells as a reference. Results: We identified drugs with sensitivity, targeting expected pathways, such as MAPK-ERK and PI3K-AKT, as well as serotonin-related targets, among others. The ΔS technique used here, in tandem with a supplemental ΔS web tool, simplifies HTS analysis and may provide a springboard for further investigations into drug response in NF1-related cancers. The tool may also prove useful for drug development in a variety of other cancers.

Differential modulation of cellular phenotype and drug sensitivity by extracellular matrix proteins in primary and metastatic pancreatic cancer cells

Pancreatic ductal adenocarcinoma (PDAC) is reported to be the third highest cause of cancer-related deaths in the United States. PDAC is known for its high proportion of stroma, which accounts for 90% of the tumor mass. The stroma is made up of extracellular matrix (ECM) and nonmalignant cells such as inflammatory cells, cancer-associated fibroblasts, and lymphatic and blood vessels. Here, we decoupled the effects of the ECM on PDAC cell lines by culturing cells on surfaces coated with different ECM proteins. Our data show that the primary tumor-derived cell lines have different morphology depending on the ECM proteins on which they are cultured, while metastatic lesion-derived PDAC lines' morphology does not change with respect to the different ECM proteins. Similarly, ECM proteins modulate the proliferation rate and the gemcitabine sensitivity of the primary tumor PDAC cell lines, but not the metastatic PDAC lines. Lastly, transcriptomics analysis of the primary tumor PDAC cells cultured on different ECM proteins reveals the regulation of various pathways, such as cell cycle, cell-adhesion molecules, and focal adhesion, including the regulation of several integrin genes that are essential for ECM recognition.

CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose-response curves

Dose-response curves are key metrics in pharmacology and biology to assess phenotypic or molecular actions of bioactive compounds in a quantitative fashion. Yet, it is often unclear whether or not a measured response significantly differs from a curve without regulation, particularly in high-throughput applications or unstable assays. Treating potency and effect size estimates from random and true curves with the same level of confidence can lead to incorrect hypotheses and issues in training machine learning models. Here, we present CurveCurator, an open-source software that provides reliable dose-response characteristics by computing p-values and false discovery rates based on a recalibrated F-statistic and a target-decoy procedure that considers dataset-specific effect size distributions. The application of CurveCurator to three large-scale datasets enables a systematic drug mode of action analysis and demonstrates its scalable utility across several application areas, facilitated by a performant, interactive dashboard for fast data exploration.

Belinostat loaded lipid-polymer hybrid nanoparticulate delivery system for breast cancer: improved pharmacokinetics and biodistribution in a tumor model

Despite various treatment modalities for breast cancer, it still persists as one of the most diagnosed types of cancer in females. The recent investigations in the epigenetics of breast cancer reveal several aberrations in the expression levels of various HDAC enzymes. Henceforth, the present work entails the formulation and characterization of a lipid polymer-based hybrid nanoparticulate (LPN) system for delivery of an epigenetic modulator drug, Belinostat, for its clinical application in breast cancer. The size of Belinostat nanoparticles prepared using a modified hot homogenization method was found to be 166.6 ± 19.95 nm with an encapsulation efficiency of 94.5 ± 5.1%. In vitro characterization for cytotoxicity, cellular uptake, and protein expression in two different breast cancer cells, 4T1 and MCF 7, revealed the superiority of the formulation in comparison with the free drug in MCF 7 cells. Subsequently, the behaviour of the formulation in in vivo settings of healthy and breast cancer xenograft bearing animals was analyzed using pharmacokinetic and biodistribution studies. The results revealed that the formulation demonstrated multi-fold improvement in the pharmacokinetic parameters in tumor bearing animals when compared with the free drug while no difference in pharmacokinetic behaviour was observed in healthy animals indicating the altered biodistribution and specificity of the formulation in breast tumor. This was confirmed by the biodistribution studies exhibiting 20-fold improved uptake and retention of the nanoparticulate formulation in tumor tissues of the animal model at the end of 4 h. Thus, the developed LPN system holds potential to act as a novel drug delivery system for Belinostat with several advantages over the free drug.

Mathematical Modeling Identifies Optimum Palbociclib-fulvestrant Dose Administration Schedules for the Treatment of Patients with Estrogen Receptor-positive Breast Cancer

Cyclin-dependent kinases 4/6 (CDK4/6) inhibitors such as palbociclib are approved for the treatment of metastatic estrogen receptor-positive (ER+) breast cancer in combination with endocrine therapies and significantly improve outcomes in patients with this disease. However, given the large number of possible pairwise drug combinations and administration schedules, it remains unclear which clinical strategy would lead to best survival. Here, we developed a computational, cell cycle-explicit model to characterize the pharmacodynamic response to palbociclib-fulvestrant combination therapy. This pharmacodynamic model was parameterized, in a Bayesian statistical inference approach, using in vitro data from cells with wild-type estrogen receptor (WT-ER) and cells expressing the activating missense ER mutation, Y537S, which confers resistance to fulvestrant. We then incorporated pharmacokinetic models derived from clinical data into our computational modeling platform. To systematically compare dose administration schedules, we performed in silico clinical trials based on integrating our pharmacodynamic and pharmacokinetic models as well as considering clinical toxicity constraints. We found that continuous dosing of palbociclib is more effective for lowering overall tumor burden than the standard, pulsed-dose palbociclib treatment. Importantly, our mathematical modeling and statistical analysis platform provides a rational method for comparing treatment strategies in search of optimal combination dosing strategies of other cell-cycle inhibitors in ER+ breast cancer.

SIGNIFICANCE

We created a computational modeling platform to predict the effects of fulvestrant/palbocilib treatment on WT-ER and Y537S-mutant breast cancer cells, and found that continuous treatment schedules are more effective than the standard, pulsed-dose palbociclib treatment schedule.

Biological production of epicoccamide-aglycone and its cytotoxicity

Epicoccamide (EPC) is an O-d-mannosylated acyltetramic acid of Epicoccum origin and is a bolaamphiphilic fungal polyketide. EPC displays weak toxicity against Staphylococcus aureus and HeLa cell lines. The EPC biosynthetic gene cluster was previously identified in Epicoccum nigrum and knockout of the glycosyltransferase gene (epcB) abolished EPC production. EPC-aglycone was expected in the epcB knockout but was not found. This study demonstrates that extractive culture using the hydrophobic resin Diaion HP-20 resulted in the production of EPC-aglycone, which was isolated using chromatographic separation techniques, and its structural identity was substantiated by chemical analyses. EPC-aglycone displayed strong antibacterial activity against Staphylococcus aureus, with the minimal inhibitory concentration of 1 μg/mL (64 μg/mL for EPC). EPC-aglycone displayed higher levels of growth inhibition against HeLa cell line (the half inhibitory concentration, 19 μM) and WI-38 (15 μM) cell line than EPC (76 μM and 38 μM vs. HeLa and WI-38, respectively). The dose-response curve fit of growth inhibition indicated that EPC-aglycone adopted a shallow curve (low slope factor), which was different from that of EPC, suggesting that their cellular targets are distinct from each other. This study substantiates that the d-mannose attachment is the final step in EPC biosynthesis, showcasing a glycosylation-mediated modulation of the biological activity of simple acyltetramic acid. This study also highlights the usefulness of extractive cultures in mining cryptic microbial natural products.

Targeted Investigational Oncology Agents in the NCI-60: A Phenotypic Systems-based Resource

The NCI-60 human tumor cell line panel has proved to be a useful tool for the global cancer research community in the search for novel chemotherapeutics. The publicly available cell line characterization and compound screening data from the NCI-60 assay have significantly contributed to the understanding of cellular mechanisms targeted by new oncology agents. Signature sensitivity/resistance patterns generated for a given chemotherapeutic agent against the NCI-60 panel have long served as fingerprint presentations that encompass target information and the mechanism of action associated with the tested agent. We report the establishment of a new public NCI-60 resource based on the cell line screening of a large and growing set of 175 FDA-approved oncology drugs (AOD) plus >825 clinical and investigational oncology agents (IOA), representing a diverse set (>250) of therapeutic targets and mechanisms. This data resource is available to the public (https://ioa.cancer.gov) and includes the raw data from the screening of the IOA and AOD collection along with an extensive set of visualization and analysis tools to allow for comparative study of individual test compounds and multiple compound sets.

Targeting pan-essential pathways in cancer with cytotoxic chemotherapy: challenges and opportunities

Cytotoxic chemotherapy remains a key modality in cancer treatment. These therapies, successfully used for decades, continue to transform the lives of cancer patients daily. With the high attrition rate of current oncology drug development, combined with the knowledge that most new therapies do not displace standard-of-care treatments and that many healthcare systems cannot afford these new therapies; cytotoxic chemotherapies will remain an important component of cancer therapy for many years to come. The clinical value of these therapies is often under-appreciated within the pre-clinical cancer research community, where this diverse class of agents are often grouped together as non-specific cellular poisons killing tumor cells based solely upon proliferation rate; however, this is inaccurate. This review article seeks to reaffirm the importance of focusing research efforts upon improving our basic understanding of how these drugs work, discussing their ability to target pan-essential pathways in cancer cells, the relationship of this to the chemotherapeutic window, and highlighting basic science approaches that can be employed towards refining their use.

Growth Factor Immobilization to Synthetic Hydrogels: Bioactive bFGF-Functionalized Polyisocyanide Hydrogels

With its involvement in cell proliferation, migration and differentiation basic fibroblast growth factor (bFGF) has great potential for tissue engineering purposes. So far, however, clinical translation of soluble bFGF-based therapies is unsuccessful, because the required effective doses are often supraphysiological, which may cause adverse effects. An effective solution is growth factor immobilization, whereby bFGF retains its bioactivity at increased efficacy. Studied carriers include films, solid scaffolds, and particles, as well as natural and synthetic hydrogels. However, these synthetic hydrogels poorly resemble the characteristics of the native extracellular matrix (ECM). In this work, bFGF is covalently conjugated to the synthetic, but highly biocompatible, polyisocyanide-based hydrogel (PIC-bFGF), which closely mimics the architecture and mechanical properties of the ECM. The growth factor conjugation protocol is straightforward and readily extrapolated to other growth factors or proteins. The PIC-bFGF hydrogel shows a prolonged bioactivity up to 4 weeks although no clear effects on the ECM metabolism are observed. Beyond the future potential of the PIC-bFGF hydrogel toward various tissue engineering applications, this work underlines that simple biological conjugation procedures are a powerful strategy to induce additional bioactivity in 3D synthetic cell culture matrices.

Metabolic modulation of mitochondrial mass during CD4+ T cell activation

Mitochondrial biogenesis initiates within hours of T cell receptor (TCR) engagement and is critical for T cell activation, function, and survival; yet, how metabolic programs support mitochondrial biogenesis during TCR signaling is not fully understood. Here, we performed a multiplexed metabolic chemical screen in CD4+ T lymphocytes to identify modulators of metabolism that impact mitochondrial mass during early T cell activation. Treatment of T cells with pyrvinium pamoate early during their activation blocks an increase in mitochondrial mass and results in reduced proliferation, skewed CD4+ T cell differentiation, and reduced cytokine production. Furthermore, administration of pyrvinium pamoate at the time of induction of experimental autoimmune encephalomyelitis, an experimental model of multiple sclerosis in mice, prevented the onset of clinical disease. Thus, modulation of mitochondrial biogenesis may provide a therapeutic strategy for modulating T cell immune responses.

Pan-Cancer Pharmacogenomic Analysis of Patient-Derived Tumor Cells Using Clinically Relevant Drug Exposures

As a result of tumor heterogeneity and solid cancers harboring multiple molecular defects, precision medicine platforms in oncology are most effective when both genetic and pharmacologic determinants of a tumor are evaluated. Expandable patient-derived xenograft (PDX) mouse tumor and corresponding PDX culture (PDXC) models recapitulate many of the biological and genetic characteristics of the original patient tumor, allowing for a comprehensive pharmacogenomic analysis. Here, the somatic mutations of 23 matched patient tumor and PDX samples encompassing four cancers were first evaluated using next-generation sequencing (NGS). 19 antitumor agents were evaluated across 78 patient-derived tumor cultures using clinically relevant drug exposures. A binarization threshold sensitivity classification determined in culture (PDXC) was used to identify tumors that best respond to drug in vivo (PDX). Using this sensitivity classification, logic models of DNA mutations were developed for 19 antitumor agents to predict drug response. We determined that the concordance of somatic mutations across patient and corresponding PDX samples increased as variant allele frequency increased. Notable individual PDXC responses to specific drugs, as well as lineage-specific drug responses were identified. Robust responses identified in PDXC were recapitulated in vivo in PDX-bearing mice and logic modeling determined somatic gene mutation(s) defining response to specific antitumor agents. In conclusion, combining NGS of primary patient tumors, high-throughput drug screen using clinically relevant doses, and logic modeling, can provide a platform for understanding response to therapeutic drugs targeting cancer.

An Investigation of Structure-Activity Relationships and Cell Death Mechanisms of the Marine Alkaloids Discorhabdins in Merkel Cell Carcinoma Cells

A library of naturally occurring and semi-synthetic discorhabdins was assessed for their effects on Merkel cell carcinoma (MCC) cell viability. The set included five new natural products and semi-synthetic compounds whose structures were elucidated with NMR, HRMS, and ECD techniques. Several discorhabdins averaged sub-micromolar potency against the MCC cell lines tested and most of the active compounds showed selectivity towards virus-positive MCC cell lines. An investigation of structure-activity relationships resulted in an expanded understanding of the crucial structural features of the discorhabdin scaffold. Mechanistic cell death assays suggested that discorhabdins, unlike many other MCC-active small molecules, do not induce apoptosis, as shown by the lack of caspase activation, annexin V staining, and response to caspase inhibition. Similarly, discorhabdin treatment failed to increase MCC intracellular calcium and ROS levels. In contrast, the rapid loss of cellular reducing potential and mitochondrial membrane potential suggested that discorhabdins induce mitochondrial dysfunction leading to non-apoptotic cell death.

Challenges and Opportunities in Developing Targeted Therapies for Triple Negative Breast Cancer

Triple negative breast cancer (TNBC) is a heterogeneous group of breast cancers characterized by their lack of estrogen receptors, progesterone receptors, and the HER2 receptor. They are more aggressive than other breast cancer subtypes, with a higher mean tumor size, higher tumor grade, the worst five-year overall survival, and the highest rates of recurrence and metastasis. Developing targeted therapies for TNBC has been a major challenge due to its heterogeneity, and its treatment still largely relies on surgery, radiation therapy, and chemotherapy. In this review article, we review the efforts in developing targeted therapies for TNBC, discuss insights gained from these efforts, and highlight potential opportunities going forward. Accumulating evidence supports TNBCs as multi-driver cancers, in which multiple oncogenic drivers promote cell proliferation and survival. In such multi-driver cancers, targeted therapies would require drug combinations that simultaneously block multiple oncogenic drivers. A strategy designed to generate mechanism-based combination targeted therapies for TNBC is discussed.

Integrative multi-omics and drug-response characterization of patient-derived prostate cancer primary cells

Prostate cancer (PCa) is the second most prevalent malignancy in males across the world. A greater knowledge of the relationship between protein abundance and drug responses would benefit precision treatment for PCa. Herein, we establish 35 Chinese PCa primary cell models to capture specific characteristics among PCa patients, including gene mutations, mRNA/protein/surface protein distributions, and pharmaceutical responses. The multi-omics analyses identify Anterior Gradient 2 (AGR2) as a pre-operative prognostic biomarker in PCa. Through the drug library screening, we describe crizotinib as a selective compound for malignant PCa primary cells. We further perform the pharmacoproteome analysis and identify 14,372 significant protein-drug correlations. Surprisingly, the diminished AGR2 enhances the inhibition activity of crizotinib via ALK/c-MET-AKT axis activation which is validated by PC3 and xenograft model. Our integrated multi-omics approach yields a comprehensive understanding of PCa biomarkers and pharmacological responses, allowing for more precise diagnosis and therapies.

Oscillatory ERK Signaling and Morphology Determine Heterogeneity of Breast Cancer Cell Chemotaxis via MEK-ERK and p38-MAPK Signaling Pathways

Chemotaxis, regulated by oscillatory signals, drives critical processes in cancer metastasis. Crucial chemoattractant molecules in breast cancer, CXCL12 and EGF, drive the activation of ERK and Akt. Regulated by feedback and crosstalk mechanisms, oscillatory signals in ERK and Akt control resultant changes in cell morphology and chemotaxis. While commonly studied at the population scale, metastasis arises from small numbers of cells that successfully disseminate, underscoring the need to analyze processes that cancer cells use to connect oscillatory signaling to chemotaxis at single-cell resolution. Furthermore, little is known about how to successfully target fast-migrating cells to block metastasis. We investigated to what extent oscillatory networks in single cells associate with heterogeneous chemotactic responses and how targeted inhibitors block signaling processes in chemotaxis. We integrated live, single-cell imaging with time-dependent data processing to discover oscillatory signal processes defining heterogeneous chemotactic responses. We identified that short ERK and Akt waves, regulated by MEK-ERK and p38-MAPK signaling pathways, determine the heterogeneous random migration of cancer cells. By comparison, long ERK waves and the morphological changes regulated by MEK-ERK signaling, determine heterogeneous directed motion. This study indicates that treatments against chemotaxis in consider must interrupt oscillatory signaling.

Fractionation and Characterization of Triterpenoids from Vaccinium vitis-idaea L. Cuticular Waxes and Their Potential as Anticancer Agents

Fruit and leaf cuticular waxes are valuable source materials for the isolation of triterpenoids that can be applied as natural antioxidants and anticancer agents. The present study aimed at the semi-preparative fractionation of triterpenoids from cuticular wax extracts of Vaccinium vitis-idaea L. (lingonberry) leaves and fruits and the evaluation of their cytotoxic potential. Qualitative and quantitative characterization of obtained extracts and triterpenoid fractions was performed using HPLC-PDA method, followed by complementary analysis by GC-MS. For each fraction, cytotoxic activities towards the human colon adenocarcinoma cell line (HT-29), malignant melanoma cell line (IGR39), clear renal carcinoma cell line (CaKi-1), and normal endothelial cells (EC) were determined using MTT assay. Furthermore, the effect of the most promising samples on cancer spheroid growth and viability was examined. This study allowed us to confirm that particular triterpenoid mixtures from lingonberry waxes may possess stronger cytotoxic activities than crude unpurified extracts. Fractions containing triterpenoid acids plus fernenol, complexes of oleanolic:ursolic acids, and erythrodiol:uvaol were found to be the most potent therapeutic candidates in the management of cancer diseases. The specificity of cuticular wax extracts of lingonberry leaves and fruits, leading to different purity and anticancer potential of obtained counterpart fractions, was also enclosed. These findings contribute to the profitable utilization of lingonberry cuticular waxes and provide considerable insights into the anticancer effects of particular triterpenoids and pharmacological interactions.

Sunitinib efficacy with minimal toxicity in patient-derived retinoblastoma organoids

BACKGROUND

Recurrence of retinoblastoma (RB) following chemoreduction is common and is often managed with local (intra-arterial/intravitreal) chemotherapy. However, some tumors are resistant to even local administration of maximum feasible drug dosages, or effective tumor control and globe preservation may be achieved at the cost of vision loss due to drug-induced retinal toxicity. The aim of this study was to identify drugs with improved antitumor activity and more favorable retinal toxicity profiles via screening of potentially repurposable FDA-approved drugs in patient-derived tumor organoids.

METHODS

Genomic profiling of five RB organoids and the corresponding parental tissues was performed. RB organoids were screened with 133 FDA-approved drugs, and candidate drugs were selected based on cytotoxicity and potency. RNA sequencing was conducted to generate a drug signature from RB organoids, and the effects of drugs on cell cycle progression and proliferative tumor cone restriction were examined. Drug toxicity was assessed with human embryonic stem cell-derived normal retinal organoids. The efficacy/toxicity profiles of candidate drugs were compared with those of drugs in clinical use.

RESULTS

RB organoids maintained the genomic features of the parental tumors. Sunitinib was identified as highly cytotoxic against both classical RB1-deficient and novel MYCN-amplified RB organoids and inhibited proliferation while inducing differentiation in RB. Sunitinib was a more effective suppressor of proliferative tumor cones in RB organoids and had lower toxicity in normal retinal organoids than either melphalan or topotecan.

CONCLUSION

The efficacy and retinal toxicity profiles of sunitinib suggest that it could potentially be repurposed for local chemotherapy of RB.

Real-time luminescence enables continuous drug-response analysis in adherent and suspension cell lines

The drug-induced proliferation (DIP) rate is a metric of in vitro drug response that avoids inherent biases in commonly used metrics such as 72 h viability. However, DIP rate measurements rely on direct cell counting over time, a laborious task that is subject to numerous challenges, including the need to fluorescently label cells and automatically segment nuclei. Moreover, it is incredibly difficult to directly count cells and accurately measure DIP rates for cell populations in suspension. As an alternative, we use real-time luminescence measurements derived from the cellular activity of NAD(P)H oxidoreductase to efficiently estimate drug response in both adherent and suspension cell populations to a panel of known anticancer agents. For the adherent cell lines, we collect both luminescence reads and direct cell counts over time simultaneously to assess their congruency. Our results demonstrate that the proposed approach significantly speeds up data collection, avoids the need for cellular labels and image segmentation, and opens the door to significant advances in high-throughput screening of anticancer drugs.

De novo serine biosynthesis from glucose predicts sex-specific response to antifolates in non-small cell lung cancer cell lines

Lung cancer is the leading cause of cancer-related death. Intriguingly, males with non-small cell lung cancer (NSCLC) have a higher mortality rate than females. Here, we investigated the role of serine metabolism as a predictive marker for sensitivity to the antifolate pemetrexed in male and female NSCLC cell lines. Using [13C6] glucose tracing in NSCLC cell lines, we found that a subset of male cells generated significantly more serine from glucose than female cells. Higher serine biosynthesis was further correlated with increased sensitivity to pemetrexed in male cells only. Concordant sex differences in metabolic gene expression were evident in NSCLC and pan-cancer transcriptome datasets, suggesting a potential mechanism with wide-reaching applicability. These data were further validated by integrating antifolate drug cytotoxicity and metabolic pathway transcriptome data from pan-cancer cell lines. Together, these findings highlight the importance of considering sex differences in cancer metabolism to improve treatment for all patients.

Multiplexed and reproducible high content screening of live and fixed cells using Dye Drop

High-throughput measurement of cells perturbed using libraries of small molecules, gene knockouts, or different microenvironmental factors is a key step in functional genomics and pre-clinical drug discovery. However, it remains difficult to perform accurate single-cell assays in 384-well plates, limiting many studies to well-average measurements (e.g., CellTiter-Glo®). Here we describe a public domain Dye Drop method that uses sequential density displacement and microscopy to perform multi-step assays on living cells. We use Dye Drop cell viability and DNA replication assays followed by immunofluorescence imaging to collect single-cell dose-response data for 67 investigational and clinical-grade small molecules in 58 breast cancer cell lines. By separating the cytostatic and cytotoxic effects of drugs computationally, we uncover unexpected relationships between the two. Dye Drop is rapid, reproducible, customizable, and compatible with manual or automated laboratory equipment. Dye Drop improves the tradeoff between data content and cost, enabling the collection of information-rich perturbagen-response datasets.

Pharmacogenomic landscape of head and neck squamous cell carcinoma informs precision oncology therapy

Head and neck squamous cell carcinoma (HNSCC) is a common and frequently lethal cancer with few therapeutic options. In particular, there are few effective targeted therapies. Development of highly effective therapeutic strategies tailored to patients with HNSCC remains a pressing challenge. To address this, we present a pharmacogenomic study to facilitate precision treatments for patients with HNSCC. We established a large collection of 56 HNSCC patient-derived cells (PDCs), which recapitulated the molecular features of the original tumors. Pharmacological assessment of HNSCCs was conducted using a three-tiered high-throughput drug screening using 2248 compounds across these PDC models and an additional 18 immortalized cell lines. We integrated genomic, transcriptomic, and pharmacological analysis to predict biomarkers, gene-drug associations, and validated biomarkers. These results supported drug repurposing for multiple HNSCC subtypes, including the JAK2 inhibitor fedratinib, for low KRT18-expressing HNSCC cases, and the topoisomerase inhibitor mitoxantrone, for IL6R-activated HNSCC cases. Our results demonstrated concordance between susceptibility predictions from the PDCs and the matched patients' responses to standard clinical medication. Moreover, we identified and experimentally confirmed that high expression of ITGB1 elicited therapeutic resistance to docetaxel and high SOD1 expression conferred resistance to afatinib. We further validated ITGB1 as a predictive biomarker for the efficacy of docetaxel therapy in a phase 2 clinical trial. In summary, our study shows that this HNSCC cell resource, as well as the resulting pharmacogenomic profiles, is effective for biomarker discovery and for guiding precision oncology therapies in HNSCCs.

Growth‐rate model predicts in vivo tumor response from in vitro data

A major challenge in oncology drug development is to elucidate why drugs that show promising results in cancer cell lines in vitro fail in mouse studies or human trials. One of the fundamental steps toward solving this problem is to better predict how in vitro potency translates into in vivo efficacy. A common approach to infer whether a model will respond in vivo is based on in vitro half‐maximal inhibitory concentration values (IC₅₀), but yields limited quantitative comparison between cell lines and drugs, potentially because cell division and death rates differ between cell lines and in vivo models. Other methods based either on mechanistic modeling or machine learning require molecular insights or extensive training data, limiting their use for early drug development. To address these challenges, we propose a mathematical model integrating in vitro growth rate inhibition values with pharmacokinetic parameters to estimate in vivo drug response. Upon calibration with a drug‐specific factor, our model yields precise estimates of tumor growth rate inhibition for in vivo studies based on in vitro data. We then demonstrate how our model can be used to study dosing schedules and perform sensitivity analyses. In addition, it provides meaningful metrics to assess association with genotypes and guide clinical trial design. By relying on commonly collected data, our approach shows great promise for optimizing drug development, better characterizing the efficacy of novel molecules targeting proliferation, and identifying more robust biomarkers of sensitivity while limiting the number of in vivo experiments.

Multiparametric quantitative phase imaging for real-time, single cell, drug screening in breast cancer

Quantitative phase imaging (QPI) measures the growth rate of individual cells by quantifying changes in mass versus time. Here, we use the breast cancer cell lines MCF-7, BT-474, and MDA-MB-231 to validate QPI as a multiparametric approach for determining response to single-agent therapies. Our method allows for rapid determination of drug sensitivity, cytotoxicity, heterogeneity, and time of response for up to 100,000 individual cells or small clusters in a single experiment. We find that QPI EC50 values are concordant with CellTiter-Glo (CTG), a gold standard metabolic endpoint assay. In addition, we apply multiparametric QPI to characterize cytostatic/cytotoxic and rapid/slow responses and track the emergence of resistant subpopulations. Thus, QPI reveals dynamic changes in response heterogeneity in addition to average population responses, a key advantage over endpoint viability or metabolic assays. Overall, multiparametric QPI reveals a rich picture of cell growth by capturing the dynamics of single-cell responses to candidate therapies.

Arylamine N-acetyltransferase 1 deficiency inhibits drug-induced cell death in breast cancer cells: switch from cytochrome C-dependent apoptosis to necroptosis

Purpose

Arylamine N-acetyltransferase 1 (NAT1) deficiency has been associated with drug resistance and poor outcomes in breast cancer patients. The current study aimed to investigate drug resistance in vitro using normal breast cancer cell lines and NAT1-deficient cell lines to understand the changes induced by the lack of NAT1 that resulted in poor drug response.

Methods

The response to seven chemotherapeutic agents was quantified following NAT1 deletion using CRISPR-Cas 9 in MDA-MB-231 and T-47D cells. Apoptosis was monitored by annexin V staining and caspase 3/7 activity. Cytochrome C release and caspase 8 and 9 activities were measured by Western blots. Caspase 8 was inhibited using Z-IETD-FMK and necroptosis was inhibited using necrostatin and necrosulfonamide.

Results

Compared to parental cells, NAT1 depleted cells were resistant to drug treatment. This could be reversed following NAT1 rescue of the NAT1 deleted cells. Release of cytochrome C in response to treatment was decreased in the NAT1 depleted cells, suggesting suppression of the intrinsic apoptotic pathway. In addition, NAT1 knockout resulted in a decrease in caspase 8 activation. Treatment with necrosulfonamide showed that NAT1 deficient cells switched from intrinsic apoptosis to necroptosis when treated with the anti-cancer drug cisplatin.

Conclusions

NAT1 deficiency can switch cell death from apoptosis to necroptosis resulting in decreased response to cytotoxic drugs. The absence of NAT1 in patient tumours may be a useful biomarker for selecting alternative treatments in a subset of breast cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-022-06668-3.

Aneuploidy Rate and Stemness in Low-Level Mosaic Human Embryonic Stem Cells in the Presence/Absence of Bortezomib, Paclitaxel, and Lapatinib

Human embryonic stem cells (hESCs) are predisposed to aneuploidy through continual passages. Some reports indicate more sensitivity of aneuploid hESCs cells to anticancer drugs. The present study was designed to investigate the cytotoxicity of three anticancer drugs (including bortezomib, paclitaxel, and lapatinib) and their effect on aneuploidy rate in hESCs. To create a low-level mosaic cell line, normal hESCs (80%) and trisomic hESCs for chromosomes 12 and 17 (20%) were mixed. The effect of the 3 mentioned anticancer drugs on the chromosomal status was assessed by metaphase spread analysis after selection of the nontoxic conditions. Expression of pluripotency genes was analyzed, and an alkaline phosphatase test was performed to assess pluripotency preservation. Our data showed that treatment with bortezomib, paclitaxel, and lapatinib was nontoxic at 0.01, 0.01, and 0.2 μM concentrations, respectively. Alkaline phosphatase and pluripotency gene expression analyses revealed maintenance of pluripotency following treatment with above-noted nontoxic concentrations. Aneuploid cells were dominant in treated and control groups with a minimum abundance of 70%, with no significant differences between groups. Drug treatments had no negative effect on pluripotency. Insensitivity of aneuploid cells in treatment groups could be related to the specific characteristics of each cell line in response to the drug and the proliferative superiority of cells with trisomies 12 and 17.

Multimodal perception links cellular state to decision making in single cells

Individual cells take decisions that are adapted to their internal state and surroundings, but how cells can reliably do this remains unclear. Using multiplexed quantification of signaling responses and markers of the cellular state, we find that signaling nodes in a network display adaptive information processing, which leads to heterogeneous growth factor responses and enables nodes to capture partially non-redundant information about the cellular state. Collectively, as a multimodal percept, this gives individual cells a large information processing capacity to accurately place growth factor concentration within the context of their cellular state and make cellular state-dependent decisions. We propose that heterogeneity and complexity in signaling networks have co-evolved to enable specific and context-aware cellular decision making in a multicellular setting.

Adjuvant Discovery via a High Throughput Screen using Human Primary Mononuclear Cells

Motivation

Vaccines are a key biomedical intervention to prevent the spread of infectious diseases, but their efficacy can be limited by insufficient immunogenicity and nonuniform reactogenic profiles. Adjuvants are molecules that potentiate vaccine responses by inducing innate immune activation. However, there are a limited number of adjuvants in approved vaccines, and current approaches for preclinical adjuvant discovery and development are inefficient. To enhance adjuvant identification, we developed a protocol based on in vitro screening of human primary leukocytes.

Summary

We describe a methodology utilizing high-throughput and high-content screening for novel adjuvant candidates that was used to screen a library of ~2,500 small molecules via a 384-well quantitative combined cytokine and flow cytometry immunoassay in primary human peripheral blood mononuclear cells (PBMCs) from 4 healthy adult study participants. Hits were identified based on their induction of soluble cytokine (TNF, IFNg and IL-10) secretion and PBMC maturation (CD 80/86, Ox40, and HLA-DR) in at least two of the four donors screened. From an initial set of 197 compounds identified using these biomarkers-an 8.6% hit rate-we downselected to five scaffolds that demonstrated robust efficacy and potency in vitro and evaluated the top hit, vinblastine sulfate, for adjuvanticity in vivo. Vinblastine sulfate significantly enhanced murine humoral responses to recombinant SARS-CoV-2 spike protein, including a four-fold enhancement of IgG titer production when compared to treatment with the spike antigen alone. Overall, we outline a methodology for discovering immunomodulators with adjuvant potential via high-throughput screening of PBMCs in vitro that yielded a lead compound with in vivo adjuvanticity.

Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin

Simple Summary

Using a collection of publicly available drug screening resources, we identified different partners of genes associated with either sensitivity or resistance to 90 anti-cancer therapies. When subsequently applying these signatures to multiple datasets, we found that these predictive models could predict a large range of drug responses in patient samples. In particular, we discovered a new gene signature to identify breast cancer tumors that are likely to respond to cisplatin in the absence of BRCA1 mutations. This work constitutes an important advance to accelerate the application of platinum-based therapies in patient groups that are not routinely treated with these drugs. In the future, this approach may help to guide the choice of drugs based on the molecular profile of the tumors.

Abstract

The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.

Ex vivo organotypic cultures for synergistic therapy prioritization identify patient-specific responses to combined MEK and Src inhibition in colorectal cancer

Translating preclinical studies to effective treatment protocols and identifying specific therapeutic responses in individuals with cancer is challenging. This may arise due to the complex genetic makeup of tumor cells and the impact of their multifaceted tumor microenvironment on drug response. To find new clinically relevant drug combinations for colorectal cancer (CRC), we prioritized the top five synergistic combinations from a large in vitro screen for ex vivo testing on 29 freshly resected human CRC tumors and found that only the combination of mitogen-activated protein kinase kinase (MEK) and proto-oncogene tyrosine-protein kinase Src (Src) inhibition was effective when tested ex vivo. Pretreatment phosphorylated Src (pSrc) was identified as a predictive biomarker for MEK and Src inhibition only in the absence of KRAS^G12 mutations. Overall, we demonstrate the potential of using ex vivo platforms to identify drug combinations and discover MEK and Src dual inhibition as an effective drug combination in a predefined subset of individuals with CRC.

Independent Drug Action in Combination Therapy: Implications for Precision Oncology

Combination therapies are superior to monotherapy for many cancers. This advantage was historically ascribed to the ability of combinations to address tumor heterogeneity, but synergistic interaction is now a common explanation as well as a design criterion for new combinations. We review evidence that independent drug action, described in 1961, explains the efficacy of many practice-changing combination therapies: it provides populations of patients with heterogeneous drug sensitivities multiple chances of benefit from at least one drug. Understanding response heterogeneity could reveal predictive or pharmacodynamic biomarkers for more precise use of existing drugs and realize the benefits of additivity or synergy.

Polyurethane Foam Rafts Supported In Vitro Cultures of Rindera graeca Roots for Enhanced Production of Rinderol, Potent Proapoptotic Naphthoquinone Compound

Unique phytochemical profile of plants belonging to Boraginaceae family provides a prolific resource of lipophilic pigments from the group of naphthoquinone derivatives. To overcome low compound content, the major obstacle of plant-based production, immobilization of Rindera graeca roots in in vitro cultures was implemented for efficient production of rinderol, novel furanonaphthoquinone derivative with anticancer properties. Chromatographic procedures revealed rinderol presence in extracts of all investigated root lines, derived both from root biomass and post-culture medium. Unexpectedly, in the second stage of the experiment, rinderol production was ceased in control, unmodified culture systems. On the contrary, roots immobilized on PUF rafts uniformly and stably produced rinderol, and its highest amount was noted for transformed root lines after 42 days of cultivation (222.98 ± 10.47 µg/flask). PUF occurred to be the main place of compound accumulation. Moreover, investigation of rinderol biological activity revealed its fast-acting cell death induction in HeLa cervical cancer cells at relatively low concentrations. Presented results revealed successful application of R. graeca roots immobilization on PUF rafts for production and in situ product removal of rinderol, novel lipophilic furanonaphthoquinone with suggested proapoptotic activity.

Using mathematical modeling to estimate time-independent cancer chemotherapy efficacy parameters

One of the primary cancer treatment modalities is chemotherapy. Unfortunately, traditional anti-cancer drugs are often not selective and cause damage to healthy cells, leading to serious side effects for patients. For this reason more targeted therapeutics and drug delivery methods are being developed. The effectiveness of new treatments is initially determined via in vitro cell viability assays, which determine the IC 50  of the drug. However, these assays are known to result in estimates of IC 50  that depend on the measurement time, possibly resulting in over- or under-estimation of the IC 50 . Here, we test the possibility of using cell growth curves and fitting of mathematical models to determine the IC 50  as well as the maximum efficacy of a drug ( ε max ). We measured cell growth of MCF-7 and HeLa cells in the presence of different concentrations of doxorubicin and fit the data with a logistic growth model that incorporates the effect of the drug. This method leads to measurement time-independent estimates of IC 50  and ε max , but we find that ε max  is not identifiable. Further refinement of this methodology is needed to produce uniquely identifiable parameter estimates.