Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs

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.

Calcineurin promotes adaptation to chronic stress through two distinct mechanisms

Adaptation to environmental stress requires coordination between stress-defense programs and cell cycle progression. The immediate response to many stressors has been well characterized, but how cells survive in challenging environments long term is unknown. Here, we investigate the role of the stress-activated phosphatase calcineurin (CN) in adaptation to chronic CaCl2 stress in Saccharomyces cerevisiae. We find that prolonged exposure to CaCl2 impairs mitochondrial function and demonstrate that cells respond to this stressor using two CN-dependent mechanisms-one that requires the downstream transcription factor Crz1 and another that is Crz1 independent. Our data indicate that CN maintains cellular fitness by promoting cell cycle progression and preventing CaCl2-induced cell death. When Crz1 is present, transient CN activation suppresses cell death and promotes adaptation despite high levels of mitochondrial loss. However, in the absence of Crz1, prolonged activation of CN prevents mitochondrial loss and further cell death by upregulating glutathione biosynthesis genes thereby mitigating damage from reactive oxygen species. These findings illustrate how cells maintain long-term fitness during chronic stress and suggest that CN promotes adaptation in challenging environments by multiple mechanisms.

WEE1 inhibition delays resistance to CDK4/6 inhibitor and antiestrogen treatment in estrogen receptor-positive breast cancer

Although endocrine therapies and Cdk4/6 inhibitors have produced significantly improved outcomes for patients with estrogen receptor positive (ER+) breast cancer, continuous application of these drugs often results in resistance. We hypothesized that cancer cells acquiring drug resistance might increase their dependency on negative regulators of the cell cycle. Therefore, we investigated the effect of inhibiting WEE1 on delaying the development of resistance to palbociclib and fulvestrant. We treated ER+ MCF7 breast cancer cells with palbociclib alternating with a combination of fulvestrant and a WEE1 inhibitor AZD1775 for 12 months. We found that the alternating treatment prevented the development of drug resistance to palbociclib and fulvestrant compared to monotherapies. Furthermore, we developed a mathematical model that can simulate cell proliferation under monotherapy, combination or alternating drug treatments. Finally, we showed that the mathematical model can be used to minimize the number of fulvestrant plus AZD1775 treatment periods while maintaining its efficacy.

The SRC family kinase inhibitor NXP900 demonstrates potent antitumor activity in squamous cell carcinomas

NXP900 is a selective and potent SRC family kinase (SFK) inhibitor, currently being dosed in a phase 1 clinical trial, that locks SRC in the "closed" conformation, thereby inhibiting both kinase-dependent catalytic activity and kinase-independent functions. In contrast, several multi-targeted kinase inhibitors that inhibit SRC, including dasatinib and bosutinib, bind their target in the active "open" conformation, allowing SRC and other SFKs to act as a scaffold to promote tumorigenesis through non-catalytic functions. NXP900 exhibits a unique target selectivity profile with sub-nanomolar activity against SFK members over other kinases. This results in highly potent and specific SFK pathway inhibition. Here, we demonstrate that esophageal squamous cell carcinomas and head and neck squamous cell carcinomas are exquisitely sensitive to NXP900 treatment in cell culture and in vivo, and we identify a patient population that could benefit from treatment with NXP900.

Mechanistic modeling of cell viability assays with in silico lineage tracing

Data from cell viability assays, which measure cumulative division and death events in a population and reflect substantial cellular heterogeneity, are widely available. However, interpreting such data with mechanistic computational models is hindered because direct model/data comparison is often muddled. We developed an algorithm that tracks simulated division and death events in mechanistically detailed single-cell lineages to enable such a model/data comparison and suggest causes of cell-cell drug response variability. Using our previously developed model of mammalian single-cell proliferation and death signaling, we simulated drug dose response experiments for four targeted anti-cancer drugs (alpelisib, neratinib, trametinib and palbociclib) and compared them to experimental data. Simulations are consistent with data for strong growth inhibition by trametinib (MEK inhibitor) and overall lack of efficacy for alpelisib (PI-3K inhibitor), but are inconsistent with data for palbociclib (CDK4/6 inhibitor) and neratinib (EGFR inhibitor). Model/data inconsistencies suggest (i) the importance of CDK4/6 for driving the cell cycle may be overestimated, and (ii) that the cellular balance between basal (tonic) and ligand-induced signaling is a critical determinant of receptor inhibitor response. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions. Variations in mother cells prior to drug treatment all impinging on ERK pathway activity are associated with the rapidly dividing phenotype and trametinib resistance. This work lays a foundation for the application of mechanistic modeling to large-scale cell viability assay datasets and better understanding determinants of cellular heterogeneity in drug response.

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.

Computational Modeling of Drug Response Identifies Mutant-Specific Constraints for Dosing panRAF and MEK Inhibitors in Melanoma

PURPOSE

This study explores the potential of pre-clinical in vitro cell line response data and computational modeling in identifying the optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential.

RESULTS

In a drug combination screen of 43 melanoma cell lines, we identified specific dosage landscapes of panRAF and MEK inhibitors for NRAS vs. BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma (mean Bliss score of 0.27 in NRAS vs. 0.1 in BRAF mutants). Computational modeling and follow-up molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated the in vivo translatability of in vitro dose-response maps by predicting tumor growth in xenografts with high accuracy in capturing cytostatic and cytotoxic responses. We analyzed the pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients.

CONCLUSION

Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Overall, this work presents a framework to aid dose selection in drug combinations.

Optimization of a human induced pluripotent stem cell-derived sensory neuron model for the in vitro evaluation of taxane-induced neurotoxicity

Human induced pluripotent stem cell-derived sensory neuron (iPSC-dSN) models are a valuable resource for the study of neurotoxicity but are affected by poor replicability and reproducibility, often due to a lack of optimization. Here, we identify experimental factors related to culture conditions that substantially impact cellular drug response in vitro and determine optimal conditions for improved replicability and reproducibility. Treatment duration and cell seeding density were both found to be significant factors, while cell line differences also contributed to variation. A replicable dose-response in viability was demonstrated after 48-h exposure to docetaxel or paclitaxel. Additionally, a replicable dose-dependent reduction in neurite outgrowth was demonstrated, demonstrating the applicability of the model for the examination of additional phenotypes. Overall, we have established an optimized iPSC-dSN model for the study of taxane-induced neurotoxicity.

Establishment of the microscope incubation system and its application in evaluating tumor treatment effects through real-time live cellular imaging

Introduction: Long-term imaging of live cells is commonly used for the study of dynamic cell behaviors. It is crucial to keep the cell viability during the investigation of physiological and biological processes by live cell imaging. Conventional incubators that providing stable temperature, carbon dioxide (CO2) concentration, and humidity are often incompatible with most imaging tools. Available commercial or custom-made stage-top incubators are bulky or unable to provide constant environmental conditions during long time culture. Methods: In this study, we reported the development of the microscope incubation system (MIS) that can be easily adapted to any inverted microscope stage. Incremental PID control algorithm was introduced to keep stable temperature and gas concentration of the system. Moreover, efficient translucent materials were applied for the top and bottom of the incubator which make it possible for images taken during culture. Results: The MIS could support cell viability comparable to standard incubators. When used in real time imaging, the MIS was able to trace single cell migration in scratch assay, T cell mediated tumor cells killing in co-culture assay, inflation-collapse and fusion of organoids in 3D culture. And the viability and drug responses of cells cultured in the MIS were able to be calculated by a label-free methods based on long term imaging. Discussion: We offer new insights into monitoring cell behaviors during long term culture by using the stage adapted MIS. This study illustrates that the newly developed MIS is a viable solution for long-term imaging during in vitro cell culture and demonstrates its potential in cell biology, cancer biology and drug discovery research where long-term real-time recording is required.

Genome-wide CRISPR-Cas9 knockout screens identify DNMT1 as a druggable dependency in sonic hedgehog medulloblastoma

Sonic hedgehog subgroup of medulloblastoma (SHH-MB) is characterized by aberrant activation of the SHH signaling pathway. An inhibition of the positive SHH regulator Smoothened (SMO) has demonstrated promising clinical efficacy. Yet, primary and acquired resistance to SMO inhibitors limit their efficacy. An understanding of underlying molecular mechanisms of resistance to therapy is warranted to bridge this unmet need. Here, we make use of genome-wide CRISPR-Cas9 knockout screens in murine SMB21 and human DAOY cells, in order to unravel genetic dependencies and drug-related genetic interactors that could serve as alternative therapeutic targets for SHH-MB. Our screens reinforce SMB21 cells as a faithful model system for SHH-MB, as opposed to DAOY cells, and identify members of the epigenetic machinery including DNA methyltransferase 1 (DNMT1) as druggable targets in SHH-dependent tumors. We show that Dnmt1 plays a crucial role in normal murine cerebellar development and is required for SHH-MB growth in vivo. Additionally, DNMT1 pharmacological inhibition alone and in combination with SMO inhibition effectively inhibits tumor growth in murine and human SHH-MB cell models and prolongs survival of SHH-MB mouse models by inhibiting SHH signaling output downstream of SMO. In conclusion, our data highlight the potential of inhibiting epigenetic regulators as a novel therapeutic avenue in SMO-inhibitor sensitive as well as resistant SHH-MBs.

Computational modeling of drug response identifies mutant-specific constraints for dosing panRAF and MEK inhibitors in melanoma

Purpose

This study explores the potential of preclinical in vitro cell line response data and computational modeling in identifying optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential.

Results

In a drug combination screen of 43 melanoma cell lines, we identified unique dosage landscapes of panRAF and MEK inhibitors for NRAS vs BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma. Computational modeling and molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated in vivo translatability of in vitro dose-response maps by accurately predicting tumor growth in xenografts. Then, we analyzed pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients.

Conclusion

Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range.

Assessments of prostate cancer cell functions highlight differences between a pan-PI3K/mTOR inhibitor, gedatolisib, and single-node inhibitors of the PI3K/AKT/mTOR pathway

Metastatic castration-resistant prostate cancer (mCRPC) is characterized by loss of androgen receptor (AR) sensitivity and oncogenic activation of the PI3K/AKT/mTOR (PAM) pathway. Loss of the PI3K regulator PTEN is frequent during prostate cancer (PC) initiation, progression, and therapeutic resistance. Co-targeting the PAM/AR pathways is a promising mCRPC treatment strategy but is hampered by reciprocal negative feedback inhibition or feedback relief. Most PAM inhibitors selectively spare (or weakly inhibit) one or more key nodes of the PAM pathway, potentiating drug resistance depending on the PAM pathway mutation status of patients. We posited that gedatolisib, a uniformly potent inhibitor of all class I PI3K isoforms, as well as mTORC1 and mTORC2, would be more effective than inhibitors targeting single PAM pathway nodes in PC cells. Using a combination of functional and metabolic assays, we evaluated a panel of PC cell lines with different PTEN/PIK3CA status for their sensitivity to multi-node PAM inhibitors (PI3K/mTOR: gedatolisib, samotolisib) and single-node PAM inhibitors (PI3Kα: alpelisib; AKT: capivasertib; mTOR: everolimus). Gedatolisib induced anti-proliferative and cytotoxic effects with greater potency and efficacy relative to the other PAM inhibitors, independent of PTEN/PIK3CA status. The superior effects of gedatolisib were likely associated with more effective inhibition of critical PAM-controlled cell functions, including cell cycle, survival, protein synthesis, oxygen consumption rate, and glycolysis. Our results indicate that potent and simultaneous blockade of all class I PI3K isoforms, mTORC1, and mTORC2 could circumvent PTEN-dependent resistance. Gedatolisib, as a single agent and in combination with other therapies, reported promising preliminary efficacy and safety in various solid tumor types. Gedatolisib is currently being evaluated in a Phase 1/2 clinical trial in combination with darolutamide in patients with mCRPC previously treated with an AR inhibitor, and in a Phase 3 clinical trial in combination with palbociclib and fulvestrant in patients with HR+/HER2- advanced breast cancer.

A Novel AMPK Inhibitor Sensitizes Pancreatic Cancer Cells to Ferroptosis Induction

Cancer cells must develop strategies to adapt to the dynamically changing stresses caused by intrinsic or extrinsic processes, or therapeutic agents. Metabolic adaptability is crucial to mitigate such challenges. Considering metabolism as a central node of adaptability, it is focused on an energy sensor, the AMP-activated protein kinase (AMPK). In a subtype of pancreatic ductal adenocarcinoma (PDAC) elevated AMPK expression and phosphorylation is identified. Using drug repurposing that combined screening experiments and chemoproteomic affinity profiling, it is identified and characterized PF-3758309, initially developed as an inhibitor of PAK4, as an AMPK inhibitor. PF-3758309 shows activity in pre-clinical PDAC models, including primary patient-derived organoids. Genetic loss-of-function experiments showed that AMPK limits the induction of ferroptosis, and consequently, PF-3758309 treatment restores the sensitivity toward ferroptosis inducers. The work established a chemical scaffold for the development of specific AMPK-targeting compounds and deciphered the framework for the development of AMPK inhibitor-based combination therapies tailored for PDAC.

Acquired Temozolomide Resistance Instructs Patterns of Glioblastoma Behavior in Gelatin Hydrogels

Acquired drug resistance in glioblastoma (GBM) presents a major clinical challenge and is a key factor contributing to abysmal prognosis, with less than 15 months median overall survival. Aggressive chemotherapy with the frontline therapeutic, temozolomide (TMZ), ultimately fails to kill residual highly invasive tumor cells after surgical resection and radiotherapy. Here, a 3D engineered model of acquired TMZ resistance is reported using two isogenically matched sets of GBM cell lines encapsulated in gelatin methacrylol hydrogels. Response of TMZ-resistant versus TMZ-sensitive GBM cell lines within the gelatin-based extracellular matrix platform is benchmarked and drug response at physiologically relevant TMZ concentrations is further validated. The changes in drug sensitivity, cell invasion, and matrix-remodeling cytokine production are shown as the result of acquired TMZ resistance. This platform lays the foundation for future investigations targeting key elements of the GBM tumor microenvironment to combat GBM's devastating impact by advancing the understanding of GBM progression and treatment response to guide the development of novel treatment strategies.

Chemogenomics for NR1 nuclear hormone receptors

Nuclear receptors (NRs) regulate transcription in response to ligand binding and NR modulation allows pharmacological control of gene expression. Although some NRs are relevant as drug targets, the NR1 family, which comprises 19 NRs binding to hormones, vitamins, and lipid metabolites, has only been partially explored from a translational perspective. To enable systematic target identification and validation for this protein family in phenotypic settings, we present an NR1 chemogenomic (CG) compound set optimized for complementary activity/selectivity profiles and chemical diversity. Based on broad profiling of candidates for specificity, toxicity, and off-target liabilities, sixty-nine comprehensively annotated NR1 agonists, antagonists and inverse agonists covering all members of the NR1 family and meeting potency and selectivity standards are included in the final NR1 CG set. Proof-of-concept application of this set reveals effects of NR1 members in autophagy, neuroinflammation and cancer cell death, and confirms the suitability of the set for target identification and validation.

Cytoskeletal gene alterations linked to sorafenib resistance in hepatocellular carcinoma

BACKGROUND

Although sorafenib has been consistently used as a first-line treatment for advanced hepatocellular carcinoma (HCC), most patients will develop resistance, and the mechanism of resistance to sorafenib needs further study.

METHODS

Using KAS-seq technology, we obtained the ssDNA profiles within the whole genome range of SMMC-7721 cells treated with sorafenib for differential analysis. We then intersected the differential genes obtained from the analysis of hepatocellular carcinoma patients in GSE109211 who were ineffective and effective with sorafenib treatment, constructed a PPI network, and obtained hub genes. We then analyzed the relationship between the expression of these genes and the prognosis of hepatocellular carcinoma patients.

RESULTS

In this study, we identified 7 hub ERGs (ACTB, CFL1, ACTG1, ACTN1, WDR1, TAGLN2, HSPA8) related to drug resistance, and these genes are associated with the cytoskeleton.

CONCLUSIONS

The cytoskeleton is associated with sorafenib resistance in hepatocellular carcinoma. Using KAS-seq to analyze the early changes in tumor cells treated with drugs is feasible for studying the drug resistance of tumors, which provides reference significance for future research.

Gedatolisib shows superior potency and efficacy versus single-node PI3K/AKT/mTOR inhibitors in breast cancer models

The PI3K, AKT, and mTOR (PAM) pathway is frequently dysregulated in breast cancer (BC) to accommodate high catabolic and anabolic activities driving tumor growth. Current therapeutic options for patients with hormone receptor (HR) + / HER2- advanced BC (ABC) include PAM inhibitors that selectively inhibit only one PAM pathway node, which can lead to drug resistance as cells rapidly adapt to maintain viability. We hypothesized that gedatolisib, which potently inhibits all Class I PI3K isoforms, as well as mTORC1 and mTORC2, may be more effective in BC cells than single-node PAM inhibitors by limiting adaptive resistances. By using multiple functional assays, a panel of BC cell lines was evaluated for their sensitivity to four different PAM inhibitors: gedatolisib (pan-PI3K/mTOR inhibitor), alpelisib (PI3Kα inhibitor), capivasertib (AKT inhibitor), and everolimus (mTORC1 inhibitor). Gedatolisib exhibited more potent and efficacious anti-proliferative and cytotoxic effects regardless of the PAM pathway mutational status of the cell lines compared to the single-node PAM inhibitors. The higher efficacy of gedatolisib was confirmed in three-dimensional culture and in BC PDX models. Mechanistically, gedatolisib decreased cell survival, DNA replication, cell migration and invasion, protein synthesis, glucose consumption, lactate production, and oxygen consumption more effectively than the other PAM inhibitors tested. These results indicate that inhibition of multiple PAM pathway nodes by a pan-PI3K/mTOR inhibitor like gedatolisib may be more effective at inducing anti-tumor activity than single-node PAM inhibitors. A global Phase 3 study is currently evaluating gedatolisib plus fulvestrant with and without palbociclib in patients with HR+/HER2- ABC.

Comparing in vitro cytotoxic drug sensitivity in colon and pancreatic cancer using 2D and 3D cell models: Contrasting viability and growth inhibition in clinically relevant dose and repeated drug cycles

BACKGROUND

In vitro drug screening that is more translatable to the in vivo tumor environment can reduce both time and cost of cancer drug development. Here we address some of the shortcomings in screening and show how treatment with 5-fluorouracil (5-FU) in 2D and 3D culture models of colorectal cancer (CRC) and pancreatic ductal adenocarcinomas (PDAC) give different responses regarding growth inhibition.

METHODS

The sensitivity of the cell lines at clinically relevant 5-FU concentrations was monitored over 4 days of treatment in both 2D and 3D cultures for CRC (SW948 and HCT116) and PDAC (Panc-1 and MIA-Pa-Ca-2) cell lines. The 3D cultures were maintained beyond this point to enable a second treatment cycle at Day 14, following the timeline of a standard clinical 5-FU regimen.

RESULTS

Evaluation after one cycle did not reveal significant growth inhibition in any of the CRC or PDAC 2D models. By the end of the second cycle of treatment the CRC spheroids reached 50% inhibition at clinically achievable concentrations in the 3D model, but not in the 2D model. The PDAC models were not sensitive to clinical doses even after two cycles. High content viability metrics point to even lower response in the resistant PDAC models.

CONCLUSION

This study reveals the limitations of testing drugs in 2D cancer models and short exposure in 3D models, and the importance of using appropriate growth inhibition analysis. We found that screening with longer exposure and several cycles of treatment in 3D models suggests a more reliable way to assess drug sensitivity.

Assessment of targeted therapy opportunities in sinonasal cancers using patient-derived functional tumor models

Malignant tumors derived from the epithelium lining the nasal cavity region are termed sinonasal cancers, a highly heterogeneous group of rare tumors accounting for 3 - 5 % of all head and neck cancers. Progress with next-generation molecular profiling has improved our understanding of the complexity of sinonasal cancers and resulted in the identification of an increasing number of distinct tumor entities. Despite these significant developments, the treatment of sinonasal cancers has hardly evolved since the 1980s, and an advanced sinonasal cancer presents a poor prognosis as targeted therapies are usually not available. To gain insights into potential targeted therapeutic opportunities, we performed a multiomics profiling of patient-derived functional tumor models to identify molecular characteristics associated with pharmacological responses in the different subtypes of sinonasal cancer.

METHODS

Patient-derived ex vivo tumor models representing four distinct sinonasal cancer subtypes: sinonasal intestinal-type adenocarcinoma, sinonasal neuroendocrine carcinoma, sinonasal undifferentiated carcinoma and SMARCB1 deficient sinonasal carcinoma were included in the analyses. Results of functional drug screens of 160 anti-cancer therapies were integrated with gene panel sequencing and histological analyses of the tumor tissues and the ex vivo cell cultures to establish associations between drug sensitivity and molecular characteristics including driver mutations.

RESULTS

The different sinonasal cancer subtypes display considerable differential drug sensitivity. Underlying the drug sensitivity profiles, each subtype was associated with unique molecular features. The therapeutic vulnerabilities correlating with specific genomic background were extended and validated with in silico analyses of cancer cell lines representing different human cancers and with reported case studies of sinonasal cancers treated with targeted therapies.

CONCLUSION

The results demonstrate the importance of understanding the differential biology and the molecular features associated with the different subtypes of sinonasal cancers. Patient-derived ex vivo tumor models can be a powerful tool for investigating these rare cancers and prioritizing targeted therapeutic strategies for future clinical development and personalized medicine.

Optimizing Solid Tumor Treatment with Antibody-drug Conjugates Using Agent-Based Modeling: Considering the Role of a Carrier Dose and Payload Class

INTRODUCTION

Antibody-drug conjugates (ADCs) show significant clinical efficacy in the treatment of solid tumors, but a major limitation to their success is poor intratumoral distribution. Adding a carrier dose improves both distribution and overall drug efficacy of ADCs, but the optimal carrier dose has not been outlined for different payload classes.

OBJECTIVE

In this work, we study two carrier dose regimens: 1) matching payload potency to cellular delivery but potentially not reaching cells farther away from blood vessels, or 2) dosing to tumor saturation but risking a reduction in cell killing from a lower amount of payload delivered per cell.

METHODS

We use a validated computational model to test four different payloads conjugated to trastuzumab to determine the optimal carrier dose as a function of target expression, ADC dose, and payload potency.

RESULTS

We find that dosing to tumor saturation is more efficacious than matching payload potency to cellular delivery for all payloads because the increase in the number of cells targeted by the ADC outweighs the loss in cell killing on targeted cells. An important exception exists if the carrier dose reduces the payload uptake per cell to the point where all cell killing is lost. Likewise, receptor downregulation can mitigate the benefits of a carrier dose.

CONCLUSIONS

Because tumor saturation and in vitro potency can be measured early in ADC design, these results provide insight into maximizing ADC efficacy and demonstrate the benefits of using simulation to guide ADC design.

heredERA Breast Cancer: a phase III, randomized, open-label study evaluating the efficacy and safety of giredestrant plus the fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection in patients with previously untreated HER2-positive, estrogen receptor-positive locally advanced or metastatic breast cancer

BACKGROUND

HER2-positive, estrogen receptor-positive breast cancer (HER2+, ER+ BC) is a distinct disease subtype associated with inferior response to chemotherapy plus HER2-targeted therapy compared with HER2+, ER-negative BC. Bi-directional crosstalk leads to cooperation of the HER2 and ER pathways that may drive treatment resistance; thus, simultaneous co-targeting may optimize treatment impact and survival outcomes in patients with HER2+, ER+ BC. First-line (1L) treatment for patients with HER2+ metastatic BC (mBC) is pertuzumab, trastuzumab, and taxane chemotherapy. In clinical practice, dual HER2 blockade plus a fixed number of chemotherapy cycles are given as induction therapy to maximize tumor response, with subsequent HER2-targeted maintenance treatment given as a more tolerable regimen for long-term disease control. For patients whose tumors co-express ER, maintenance endocrine therapy (ET) can be added, but uptake varies due to lack of data from randomized clinical trials investigating the superiority of maintenance ET plus dual HER2 blockade versus dual HER2 blockade alone. Giredestrant, a novel oral selective ER antagonist and degrader, shows promising clinical activity and manageable safety across phase I-II trials of patients with ER+, HER2-negative BC, with therapeutic potential in those with HER2 co-expression.

METHODS

This phase III, randomized, open-label, two-arm study aims to recruit 812 patients with HER2+, ER+  locally advanced (LA)/mBC into the induction phase (fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection [PH FDC SC] plus a taxane) to enable 730 patients to be randomized 1:1 to the maintenance phase (giredestrant plus PH FDC SC or PH FDC SC [plus optional ET]), stratified by disease site (visceral versus non-visceral), type of LA/metastatic presentation (de novo versus recurrent), best overall response to induction therapy (partial/complete response versus stable disease), and intent to give ET (yes versus no). The primary endpoint is investigator-assessed progression-free survival. Secondary endpoints include overall survival, objective response rate, clinical benefit rate, duration of response, safety, and patient-reported outcomes.

DISCUSSION

heredERA BC will address whether giredestrant plus dual HER2 blockade is superior to dual HER2 blockade alone, to inform the use of this combination in clinical practice for maintenance 1L treatment of patients with HER2+, ER+ LA/mBC.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT05296798; registered on March 25, 2022. Protocol version 3.0 (November 18, 2022).

SPONSOR

F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124 4070, Basel, Switzerland.

Cancer cells forgo translating mRNA transcribed from genes of nonspecialized tasks

The coupling of transcription and translation enables prokaryotes to regulate mRNA stability and reduce nonfunctional transcripts. Eukaryotes evolved other means to perform these functions. Here, we quantify the disparity between gene expression and protein levels and attempt to explain its origins. We collected publicly available simultaneous measurements of gene expression, protein level, division rate, and growth inhibition of breast cancer cells under drug perturbation. We used the cell lines as entities with shared origin, different evolutionary trajectories, and cancer hallmarks to define tasks subject to specializing and trading-off. We observed varying average mRNA and protein correlation across cell lines, and it was consistently higher for the gene products in the cancer hallmarks. The enrichment of hallmark gene products signifies the resources invested in it as a task. Enrichment based on mRNA or protein abundance corresponds to the relative resources dedicated to transcription and translation. The differences in gene- and protein-based enrichment correlated with nominal division rates but not growth inhibition under drug perturbations. Comparing the range of enrichment scores of the hallmarks within each cell signifies the resources dedicated to each. Cells appear to have a wider range of enrichment in protein synthesis relative to gene transcription. The difference and range of enrichment of the hallmark genes and proteins correlated with cell division and inhibition in response to drug treatments. We posit that cancer cells may express the genes coding for seemingly nonspecialized tasks but do not translate them to the corresponding proteins. This trade-off may cost the cells under normal conditions but confer benefits during stress.

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.

Improving the power of drug toxicity measurements by quantitative nuclei imaging

Imaging-based anticancer drug screens are becoming more prevalent due to development of automated fluorescent microscopes and imaging stations, as well as rapid advancements in image processing software. Automated cell imaging provides many benefits such as their ability to provide high-content data, modularity, dynamics recording and the fact that imaging is the most direct way to access cell viability and cell proliferation. However, currently most publicly available large-scale anticancer drugs screens, such as GDSC, CTRP and NCI-60, provide cell viability data measured by assays based on colorimetric or luminometric measurements of NADH or ATP levels. Although such datasets provide valuable data, it is unclear how well drug toxicity measurements can be integrated with imaging data. Here we explored the relations between drug toxicity data obtained by XTT assay, two quantitative nuclei imaging methods and trypan blue dye exclusion assay using a set of four cancer cell lines with different morphologies and 30 drugs with different mechanisms of action. We show that imaging-based approaches provide high accuracy and the differences between results obtained by different methods highly depend on drug mechanism of action. Selecting AUC metrics over IC50 or comparing data where significantly drugs reduced cell numbers noticeably improves consistency between methods. Using automated cell segmentation protocols we analyzed mitochondria activity in more than 11 thousand drug-treated cells and showed that XTT assay produces unreliable data for CDK4/6, Aurora A, VEGFR and PARP inhibitors due induced cell size growth and increase in individual mitochondria activity. We also explored several benefits of image-based analysis such as ability to monitor cell number dynamics, dissect changes in total and individual mitochondria activity from cell proliferation, and ability to identify chromatin remodeling drugs. Finally, we provide a web tool that allows comparing results obtained by different methods.

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.

Sialyl-Tn serves as a potential therapeutic target for ovarian cancer

BACKGROUND

Ovarian cancer remains the deadliest of the gynecologic cancers in the United States. There have been limited advances in treatment strategies that have seen marked increases in overall survival. Thus, it is essential to continue developing and validating new treatment strategies and markers to identify patients who would benefit from the new strategy. In this report, we sought to further validate applications for a novel humanized anti-Sialyl Tn antibody-drug conjugate (anti-STn-ADC) in ovarian cancer.

METHODS

We aimed to further test a humanized anti-STn-ADC in sialyl-Tn (STn) positive and negative ovarian cancer cell line, patient-derived organoid (PDO), and patient-derived xenograft (PDX) models. Furthermore, we sought to determine whether serum STn levels would reflect STn positivity in the tumor samples enabling us to identify patients that an anti-STn-ADC strategy would best serve. We developed a custom ELISA with high specificity and sensitivity, that was used to assess whether circulating STn levels would correlate with stage, progression-free survival, overall survival, and its value in augmenting CA-125 as a diagnostic. Lastly, we assessed whether the serum levels reflected what was observed via immunohistochemical analysis in a subset of tumor samples.

RESULTS

Our in vitro experiments further define the specificity of the anti-STn-ADC. The ovarian cancer PDO, and PDX models provide additional support for an anti-STn-ADC-based strategy for targeting ovarian cancer. The custom serum ELISA was informative in potential triaging of patients with elevated levels of STn. However, it was not sensitive enough to add value to existing CA-125 levels for a diagnostic. While the ELISA identified non-serous ovarian tumors with low CA-125 levels, the sample numbers were too small to provide any confidence the STn ELISA would meaningfully add to CA-125 for diagnosis.

CONCLUSIONS

Our preclinical data support the concept that an anti-STn-ADC may be a viable option for treating patients with elevated STn levels. Moreover, our STn-based ELISA could complement IHC in identifying patients with whom an anti-STn-based strategy might be more effective.

Drug repurposing screening and mechanism analysis based on human colorectal cancer organoids

Colorectal cancer (CRC) is a highly heterogeneous cancer and exploring novel therapeutic options is a pressing issue that needs to be addressed. Here, we established human CRC tumor-derived organoids that well represent both morphological and molecular heterogeneities of original tumors. To efficiently identify repurposed drugs for CRC, we developed a robust organoid-based drug screening system. By combining the repurposed drug library and computation-based drug prediction, 335 drugs were tested and 34 drugs with anti-CRC effects were identified. More importantly, we conducted a detailed transcriptome analysis of drug responses and divided the drug response signatures into five representative patterns: differentiation induction, growth inhibition, metabolism inhibition, immune response promotion, and cell cycle inhibition. The anticancer activities of drug candidates were further validated in the established patient-derived organoids-based xenograft (PDOX) system in vivo. We found that fedratinib, trametinib, and bortezomib exhibited effective anticancer effects. Furthermore, the concordance and discordance of drug response signatures between organoids in vitro and pairwise PDOX in vivo were evaluated. Our study offers an innovative approach for drug discovery, and the representative transcriptome features of drug responses provide valuable resources for developing novel clinical treatments for CRC.

Sucrose Treatment Enhances the Electrotransfer of DNA by Activating Phospholipase A2

Our previous study discovered that sucrose and other non-reducing sugars (e.g., trehalose and raffinose) could be used to improve the electrotransfer (ET) of molecular cargo, including DNA, mRNA, and ribonucleoprotein in various cell lines and primary human cells in vitro and in vivo. To understand the molecular mechanisms of this improvement, we used RNA sequencing technology to analyze changes in the cell transcriptome after sucrose treatment. The results from our analysis demonstrated that the sucrose treatment upregulated phospholipase A2 and V-ATPase gene families, which could potentially influence the acidity of intracellular vesicles through augmenting vesicle fusion and the influx of proton, respectively. To determine how this upregulation affects ET efficiency, we treated cells with pharmaceutical inhibitors of phospholipase A2 and V-ATPase. The data demonstrated that the treatment with the phospholipase A2 inhibitor could reverse the ET improvement elicited by the sucrose treatment. The V-ATPase inhibitor treatment either had little influence or further enhanced the effect of the sucrose treatment on the ET efficiency. These observations provide a molecular explanation for our previous findings, demonstrating that the sucrose treatment primarily enhanced the ET efficiency by promoting vesicle trafficking and fusion through the activation of phospholipase A2.

Calcineurin promotes adaptation to chronic stress through two distinct mechanisms

Adaptation to environmental stress requires coordination between stress-defense programs and cell cycle progression. The immediate response to many stressors has been well characterized, but how cells survive in challenging environments long-term is unknown. Here, we investigate the role of the stress-activated phosphatase calcineurin (CN) in adaptation to chronic CaCl2 stress in Saccharomyces cerevisiae. We find that prolonged exposure to CaCl2 impairs mitochondrial function and demonstrate that cells respond to this stressor using two CN-dependent mechanisms - one that requires the downstream transcription factor Crz1 and another that is Crz1-independent. Our data indicate that CN maintains cellular fitness by promoting cell cycle progression and preventing CaCl2-induced cell death. When Crz1 is present, transient CN activation suppresses cell death and promotes adaptation despite high levels of mitochondrial loss. However, in the absence of Crz1, prolonged activation of CN prevents mitochondrial loss and further cell death by upregulating glutathione (GSH) biosynthesis genes thereby mitigating damage from reactive oxygen species. These findings illustrate how cells maintain long-term fitness during chronic stress and suggest that CN promotes adaptation in challenging environments by multiple mechanisms.

Making drugs from T cells: The quantitative pharmacology of engineered T cell therapeutics

Engineered T cells have emerged as highly effective treatments for hematological cancers. Hundreds of clinical programs are underway in efforts to expand the efficacy, safety, and applications of this immuno-therapeutic modality. A primary challenge in developing these "living drugs" is the complexity of their pharmacology, as the drug product proliferates, differentiates, traffics between tissues, and evolves through interactions with patient immune systems. Using publicly available clinical data from Chimeric Antigen Receptor (CAR) T cells, we demonstrate how mathematical models can be used to quantify the relationships between product characteristics, patient physiology, pharmacokinetics and clinical outcomes. As scientists work to develop next-generation cell therapy products, mathematical models will be integral for contextualizing data and facilitating the translation of product designs to clinical strategy.

Functional genomic screens with death rate analyses reveal mechanisms of drug action

A common approach for understanding how drugs induce their therapeutic effects is to identify the genetic determinants of drug sensitivity. Because 'chemo-genetic profiles' are performed in a pooled format, inference of gene function is subject to several confounding influences related to variation in growth rates between clones. In this study, we developed Method for Evaluating Death Using a Simulation-assisted Approach (MEDUSA), which uses time-resolved measurements, along with model-driven constraints, to reveal the combination of growth and death rates that generated the observed drug response. MEDUSA is uniquely effective at identifying death regulatory genes. We apply MEDUSA to characterize DNA damage-induced lethality in the presence and absence of p53. Loss of p53 switches the mechanism of DNA damage-induced death from apoptosis to a non-apoptotic death that requires high respiration. These findings demonstrate the utility of MEDUSA both for determining the genetic dependencies of lethality and for revealing opportunities to potentiate chemo-efficacy in a cancer-specific manner.

High-content analysis identified synergistic drug interactions between INK128, an mTOR inhibitor, and HDAC inhibitors in a non-small cell lung cancer cell line

BACKGROUND

The development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations.

METHODS

To address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses.

RESULTS

By examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails.

CONCLUSIONS

In conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response.

CRISPR/Cas9-edited ROS1 + non-small cell lung cancer cell lines highlight differential drug sensitivity in 2D vs 3D cultures while reflecting established resistance profiles

INTRODUCTION

The study of resistance-causing mutations in oncogene-driven tumors is fundamental to guide clinical decisions. Several point mutations affecting the ROS1 kinase domain have been identified in the clinical setting, but their impact requires further exploration, particularly in improved pre-clinical models. Given the scarcity of solid pre-clinical models to approach rare cancer subtypes like ROS1 + NSCLC, CRISPR/Cas9 technology allows the introduction of mutations in patient-derived cell lines for which resistant variants are difficult to obtain due to the low prevalence of cases within the clinical setting.

METHODS

In the SLC34A2-ROS1 rearranged NSCLC cell line HCC78, we knocked-in through CRISPR/Cas9 technology three ROS1 drug resistance-causing mutations: G2032R, L2026M and S1986Y. Such variants are located in different functional regions of the ROS1 kinase domain, thus conferring TKI resistance through distinct mechanisms. We then performed pharmacological assays in 2D and 3D to assess the cellular response of the mutant lines to crizotinib, entrectinib, lorlatinib, repotrectinib and ceritinib. In addition, immunoblotting assays were performed in 2D-treated cell lines to determine ROS1 phosphorylation and MAP kinase pathway activity. The area over the curve (AOC) defined by the normalized growth rate (NGR_fit) dose-response curves was the variable used to quantify the cellular response towards TKIs.

RESULTS

Spheroids derived from ROS1G2032R cells were significantly more resistant to repotrectinib (AOC fold change = - 7.33), lorlatinib (AOC fold change = - 6.17), ceritinib (AOC fold change = - 2.8) and entrectinib (AOC fold change = - 2.02) than wild type cells. The same cells cultured as a monolayer reflected the inefficacy of crizotinib (AOC fold change = - 2.35), entrectinib (AOC fold change = - 2.44) and ceritinib (AOC fold change = - 2.12) in targeting the ROS1 G2032R mutation. ROS1L2026M cells showed also remarkable resistance both in monolayer and spheroid culture compared to wild type cells, particularly against repotrectinib (spheroid AOC fold change = - 2.19) and entrectinib (spheroid AOC fold change = - 1.98). ROS1S1986Y cells were resistant only towards crizotinib in 2D (AOC fold change = - 1.86). Overall, spheroids showed an increased TKI sensitivity compared to 2D cultures, where the impact of each mutation that confers TKI resistance could be clearly distinguished. Western blotting assays qualitatively reflected the patterns of response towards TKI observed in 2D culture through the levels of phosphorylated-ROS1. However, we observed a dose-response increase of phosphorylated-Erk1/2, suggesting the involvement of the MAPK pathway in the mediation of apoptosis in HCC78 cells.

CONCLUSION

In this study we knock-in for the first time in a ROS1 + patient-derived cell line, three different known resistance-causing mutations using CRISPR/Cas9 in the endogenous translocated ROS1 alleles. Pharmacological assays performed in 2D and 3D cell culture revealed that spheroids are more sensitive to TKIs than cells cultured as a monolayer. This direct comparison between two culture systems could be done thanks to the implementation of normalized growth rates (NGR) to uniformly quantify drug response between 2D and 3D cell culture. Overall, this study presents the added value of using spheroids and positions lorlatinib and repotrectinib as the most effective TKIs against the studied ROS1 resistance point mutations.

Merremia emarginata Extract Potentiates the Inhibition of Human Colon Cancer Cells (HT-29) via the Modulation of Caspase-3/Bcl-2-Mediated Pathways

Background This study investigates Merremia emarginata's curative effectiveness against colon cancer cells. M. emarginata, often known as Elika jemudu, is a Convolvulaceae family plant. The inhibitory ability of anticancer herbal extracts against cancer cell growth and mediators is tested.  Aim This study aims to evaluate the potent anticancer activity of M. emarginata against colon cancer cell line (HT-29). Materials and methods M. emarginata leaves were gathered and processed using solvent extraction. Anticancer activity on colon cancer cells was measured using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) test and cysteine aspartic acid protease-3 (caspase 3), B-cell lymphoma 2 (Bcl-2), and B-cell lymphoma-extra large (Bcl-xL) mRNA expressions. The data was reported as the mean ± SD of three separate experiments done in triplicate. The statistical analysis was carried out using one-way analysis of variance (ANOVA), with a p-value less than 0.05 indicating statistical significance. Results The cell viability test showed a gradual decrease in cell growth and proliferation as the concentration increased. The ethanolic extract of M. emarginata was found to be cytotoxic against colon caller cell lines. The extract was able to induce apoptosis of cancer as revealed by Bcl-2, Bcl-xL, and caspase-3 (p<0.05 and p<0.001) signaling pathways. Conclusion M. emarginata extracts showed good anticancer activity against colon cancer cell lines. Further work is required to establish and identify the chemical constituent responsible for its anticancer activity.

Patient derived tumoroids of high grade neuroendocrine neoplasms for more personalized therapies

There are no therapeutic predictive biomarkers or representative preclinical models for high-grade gastroenteropancreatic neuroendocrine neoplasms (GEP-NEN), a highly aggressive, fatal, and heterogeneous malignancy. We established patient-derived (PD) tumoroids from biobanked tissue samples of advanced high-grade GEP-NEN patients and applied this model for targeted rapid ex vivo pharmacotyping, next-generation sequencing, and perturbational profiling. We used tissue-matched PD tumoroids to profile individual patients, compared ex vivo drug response to patients' clinical response to chemotherapy, and investigated treatment-induced adaptive stress responses.PD tumoroids recapitulated biological key features of high-grade GEP-NEN and mimicked clinical response to cisplatin and temozolomide ex vivo. When we investigated treatment-induced adaptive stress responses in PD tumoroids in silico, we discovered and functionally validated Lysine demethylase 5 A and interferon-beta, which act synergistically in combination with cisplatin. Since ex vivo drug response in PD tumoroids matched clinical patient responses to standard-of-care chemotherapeutics for GEP-NEN, our rapid and functional precision oncology approach could expand personalized therapeutic options for patients with advanced high-grade GEP-NEN.

Modulators of the Nrf2 Signaling Pathway Enhance the Cytotoxic Effect of Standard Chemotherapeutic Drugs on Organoids of Metastatic Colorectal Cancer

The activity of known modulators of the Nrf2 signaling pathway (bardoxolone and brusatol) was studied on cultures of tumor organoids of metastatic colorectal cancer previously obtained from three patients. The effect of modulators was studied both as monotherapy and in combination with standard chemotherapy drugs used to treat colorectal cancer. The Nrf2 inhibitor brusatol and the Nrf2 activator bardoxolone have antitumor activity. Moreover, bardoxolone and brusatol also significantly enhance the effect of the chemotherapy drugs 5-fluorouracil, oxaliplatin, and irinotecan metabolite SN-38. Thus, bardoxolone and brusatol can be considered promising candidates for further preclinical and clinical studies in the treatment of colorectal cancer.

A functional personalised oncology approach against metastatic colorectal cancer in matched patient derived organoids

Globally, colorectal cancer (CRC) is the third most frequently occurring cancer. Progression on to an advanced metastatic malignancy (metCRC) is often indicative of poor prognosis, as the 5-year survival rates of patients decline rapidly. Despite the availability of many systemic therapies for the management of metCRC, the long-term efficacies of these regimens are often hindered by the emergence of treatment resistance due to intratumoral and intertumoral heterogeneity. Furthermore, not all systemic therapies have associated biomarkers that can accurately predict patient responses. Hence, a functional personalised oncology (FPO) approach can enable the identification of patient-specific combinatorial vulnerabilities and synergistic combinations as effective treatment strategies. To this end, we established a panel of CRC patient-derived organoids (PDOs) as clinically relevant biological systems, of which three pairs of matched metCRC PDOs were derived from the primary sites (ptCRC) and metastatic lesions (mCRC). Histological and genomic characterisation of these PDOs demonstrated the preservation of histopathological and genetic features found in the parental tumours. Subsequent application of the phenotypic-analytical drug combination interrogation platform, Quadratic Phenotypic Optimisation Platform, in these pairs of PDOs identified patient-specific drug sensitivity profiles to epigenetic-based combination therapies. Most notably, matched PDOs from one patient exhibited differential sensitivity patterns to the rationally designed drug combinations despite being genetically similar. These findings collectively highlight the limitations of current genomic-driven precision medicine in guiding treatment strategies for metCRC patients. Instead, it suggests that epigenomic profiling and application of FPO could complement the identification of novel combinatorial vulnerabilities to target synchronous ptCRC and mCRC.

Multiomic profiling of breast cancer cells uncovers stress MAPK-associated sensitivity to AKT degradation

More than 50% of human tumors display hyperactivation of the serine/threonine kinase AKT. Despite evidence of clinical efficacy, the therapeutic window of the current generation of AKT inhibitors could be improved. Here, we report the development of a second-generation AKT degrader, INY-05-040, which outperformed catalytic AKT inhibition with respect to cellular suppression of AKT-dependent phenotypes in breast cancer cell lines. A growth inhibition screen with 288 cancer cell lines confirmed that INY-05-040 had a substantially higher potency than our first-generation AKT degrader (INY-03-041), with both compounds outperforming catalytic AKT inhibition by GDC-0068. Using multiomic profiling and causal network integration in breast cancer cells, we demonstrated that the enhanced efficacy of INY-05-040 was associated with sustained suppression of AKT signaling, which was followed by induction of the stress mitogen-activated protein kinase (MAPK) c-Jun N-terminal kinase (JNK). Further integration of growth inhibition assays with publicly available transcriptomic, proteomic, and reverse phase protein array (RPPA) measurements established low basal JNK signaling as a biomarker for breast cancer sensitivity to AKT degradation. Together, our study presents a framework for mapping the network-wide signaling effects of therapeutically relevant compounds and identifies INY-05-040 as a potent pharmacological suppressor of AKT signaling.

Organoids as a biomarker for personalized treatment in metastatic colorectal cancer: drug screen optimization and correlation with patient response

BACKGROUND

The inability to predict treatment response of colorectal cancer patients results in unnecessary toxicity, decreased efficacy and survival. Response testing on patient-derived organoids (PDOs) is a promising biomarker for treatment efficacy. The aim of this study is to optimize PDO drug screening methods for correlation with patient response and explore the potential to predict responses to standard chemotherapies.

METHODS

We optimized drug screen methods on 5-11 PDOs per condition of the complete set of 23 PDOs from patients treated for metastatic colorectal cancer (mCRC). PDOs were exposed to 5-fluorouracil (5-FU), irinotecan- and oxaliplatin-based chemotherapy. We compared medium with and without N-acetylcysteine (NAC), different readouts and different combination treatment set-ups to capture the strongest association with patient response. We expanded the screens using the optimized methods for all PDOs. Organoid sensitivity was correlated to the patient's response, determined by % change in the size of target lesions. We assessed organoid sensitivity in relation to prior exposure to chemotherapy, mutational status and sidedness.

RESULTS

Drug screen optimization involved excluding N-acetylcysteine from the medium and biphasic curve fitting for 5-FU & oxaliplatin combination screens. CellTiter-Glo measurements were comparable with CyQUANT and did not affect the correlation with patient response. Furthermore, the correlation improved with application of growth rate metrics, when 5-FU & oxaliplatin was screened in a ratio, and 5-FU & SN-38 using a fixed dose of SN-38. Area under the curve was the most robust drug response curve metric. After optimization, organoid and patient response showed a correlation coefficient of 0.58 for 5-FU (n = 6, 95% CI -0.44,0.95), 0.61 for irinotecan- (n = 10, 95% CI -0.03,0.90) and 0.60 for oxaliplatin-based chemotherapy (n = 11, 95% CI -0.01,0.88). Median progression-free survival of patients with resistant PDOs to oxaliplatin-based chemotherapy was significantly shorter than sensitive PDOs (3.3 vs 10.9 months, p = 0.007). Increased resistance to 5-FU in patients with prior exposure to 5-FU/capecitabine was adequately reflected in PDOs (p = 0.003).

CONCLUSIONS

Our study emphasizes the critical impact of the screening methods for determining correlation between PDO drug screens and mCRC patient outcomes. Our 5-step optimization strategy provides a basis for future research on the clinical utility of PDO screens.

An inflamed tumor cell subpopulation promotes chemotherapy resistance in triple negative breast cancer

Individual cancers are composed of heterogeneous tumor cells with distinct phenotypes and genotypes, with triple negative breast cancers (TNBC) demonstrating the most heterogeneity among breast cancer types. Variability in transcriptional phenotypes could meaningfully limit the efficacy of monotherapies and fuel drug resistance, although to an unknown extent. To determine if transcriptional differences between tumor cells lead to differential drug responses we performed single cell RNA-seq on cell line and PDX models of breast cancer revealing cell subpopulations in states associated with resistance to standard-of-care therapies. We found that TNBC models contained a subpopulation in an inflamed cellular state, often also present in human breast cancer samples. Inflamed cells display evidence of heightened cGAS/STING signaling which we demonstrate is sufficient to cause tumor cell resistance to chemotherapy. Accordingly, inflamed cells were enriched in human tumors taken after neoadjuvant chemotherapy and associated with early recurrence, highlighting the potential for diverse tumor cell states to promote drug resistance.

Identifying squalene epoxidase as a metabolic vulnerability in high-risk osteosarcoma using an artificial intelligence-derived prognostic index

BACKGROUND

Osteosarcoma (OSA) presents a clinical challenge and has a low 5-year survival rate. Currently, the lack of advanced stratification models makes personalized therapy difficult. This study aims to identify novel biomarkers to stratify high-risk OSA patients and guide treatment.

METHODS

We combined 10 machine-learning algorithms into 101 combinations, from which the optimal model was established for predicting overall survival based on transcriptomic profiles for 254 samples. Alterations in transcriptomic, genomic and epigenomic landscapes were assessed to elucidate mechanisms driving poor prognosis. Single-cell RNA sequencing (scRNA-seq) unveiled genes overexpressed in OSA cells as potential therapeutic targets, one of which was validated via tissue staining, knockdown and pharmacological inhibition. We characterized changes in multiple phenotypes, including proliferation, colony formation, migration, invasion, apoptosis, chemosensitivity and in vivo tumourigenicity. RNA-seq and Western blotting elucidated the impact of squalene epoxidase (SQLE) suppression on signalling pathways.

RESULTS

The artificial intelligence-derived prognostic index (AIDPI), generated by our model, was an independent prognostic biomarker, outperforming clinicopathological factors and previously published signatures. Incorporating the AIDPI with clinical factors into a nomogram improved predictive accuracy. For user convenience, both the model and nomogram are accessible online. Patients in the high-AIDPI group exhibited chemoresistance, coupled with overexpression of MYC and SQLE, increased mTORC1 signalling, disrupted PI3K-Akt signalling, and diminished immune infiltration. ScRNA-seq revealed high expression of MYC and SQLE in OSA cells. Elevated SQLE expression correlated with chemoresistance and worse outcomes in OSA patients. Therapeutically, silencing SQLE suppressed OSA malignancy and enhanced chemosensitivity, mediated by cholesterol depletion and suppression of the FAK/PI3K/Akt/mTOR pathway. Furthermore, the SQLE-specific inhibitor FR194738 demonstrated anti-OSA effects in vivo and exhibited synergistic effects with chemotherapeutic agents.

CONCLUSIONS

AIDPI is a robust biomarker for identifying the high-risk subset of OSA patients. The SQLE protein emerges as a metabolic vulnerability in these patients, providing a target with translational potential.

Repurposing proteasome inhibitors for improved treatment of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is associated with poor prognosis and limited treatment options due to the lack of important receptors (estrogen receptor [ER], progesterone receptor [PR], and human epidermal growth factor receptor 2 [HER2]) used for targeted therapy. However, high-throughput in vitro drug screening of cell lines is a powerful tool for identifying effective drugs for a disease. Here, we determine the intrinsic chemosensitivity of TNBC cell lines to proteasome inhibitors (PIs), thereby identifying potentially potent 2-drug combinations for TNBC. Eight TNBC cell lines (BT-549, CAL-148, HCC1806, HCC38, HCC70, MDA-MB-436, MDA-MB-453, and MDA-MB-468) and two controls (MCF-10A and MCF-7) were first exposed to 18 drugs (11 PIs and 7 clinically relevant chemotherapeutic agents) as monotherapy, followed by prediction of potent 2-drug combinations using the IDACombo pipeline. The synergistic effects of the 2-drug combinations were evaluated with SynergyFinder in four TNBC cell lines (CAL-148, HCC1806, HCC38, and MDA-MB-468) and three controls (BT-474, MCF-7, and T47D) in vitro, followed by further evaluation of tumor regression in zebrafish tumor models established using HCC1806 and MCF-7 cells. Monotherapy identified nine effective drugs (bortezomib, carfilzomib, cisplatin, delanzomib, docetaxel, epoxomicin, MLN-2238, MLN-9708, and nedaplatin) across all cell lines. PIs (e.g., bortezomib, delanzomib, and epoxomicin) were highly potent drugs in TNBC cells, of which bortezomib and delanzomib inhibited the chymotrypsin-like activity of the 20 S proteasome by 100% at 10 µM. Moreover, several potent 2-drug combinations (e.g., bortezomib+nedaplatin and epoxomicin+epirubicin) that killed virtually 100% of cells were also identified. Although HCC1806- and MCF-7-derived xenografts treated with bortezomib+nedaplatin and carboplatin+paclitaxel were smaller, HCC1806 cells frequently metastasized to the trunk region. Taken together, we show that PIs used in combination with platinum agents or topoisomerase inhibitors exhibit increased efficiency with almost 100% inhibition in TNBC cell lines, indicating that PIs are therefore promising compounds to use as combination therapy for TNBC.

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Acute myeloid leukemia is a poor-prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and fail to consistently predict therapeutic response. Here, we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measure the proteome and phosphoproteome of 210 patients and combine them with genomic and transcriptomic measurements to identify four proteogenomic subtypes that complement existing genetic subtypes. We build a predictor to classify samples into subtypes and map them to a "landscape" that identifies specific drug response patterns. We then build a drug response prediction model to identify drugs that target distinct subtypes and validate our findings on cell lines representing various stages of quizartinib resistance. Our results show how multiomics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

HTSplotter: An end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening

In biomedical research, high-throughput screening is often applied as it comes with automatization, higher-efficiency, and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay. The data analysis consists of data cleaning and structuring, as well as further data processing and visualisation, which, due to the amount of data, can easily become laborious, time-consuming and error-prone. Therefore, several tools have been developed to aid researchers in this process, but these typically focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens. To meet these needs, we developed HTSplotter, a web tool and Python module that performs automatic data analysis and visualization of visualization of eitherendpoint or real-time assays from different high-throughput screening experiments: drug, drug combination, genetic perturbagen and genetic-chemical perturbagen screens. HTSplotter implements an algorithm based on conditional statements to identify experiment types and controls. After appropriate data normalization, including growth rate normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism assessment by the Bliss independence (BI), Zero Interaction Potency (ZIP) and Highest Single Agent (HSA) methods. All results are exported as a text file and plots are saved in a PDF file. The main advantage of HTSplotter over other available tools is the automatic analysis of genetic-chemical perturbagen screens and real-time assays where growth rate and perturbagen effect results are plotted over time. In conclusion, HTSplotter allows for the automatic end-to-end data processing, analysis and visualisation of various high-throughput in vitro cell culture screens, offering major improvements in terms of versatility, efficiency and time over existing tools.

Robust scoring of selective drug responses for patient-tailored therapy selection

Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient's responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.

The Role of Growth Factor Delivery Systems on Cellular Activities of Dental Stem Cells: A Systematic Review (Part II)

OBJECTIVE

The current systematic review aims to provide the available ex vivo evidence evaluating the biological interactions of dental stem cells (DSCs) and growth factor delivery systems.

METHODS

Following the Preferred Reporting Items for a Systematic Reviews and Meta-Analyses (PRISMA) guidelines, systematic search was conducted in the electronic databases (PubMed/Medline, Scopus, Web of Science, and Google Scholar) up to January 2022. Studies evaluating the biological interactions of DSCs and growth factor delivery systems were included. The outcome measures were cell cytocompatibility, mineralization, and differentiation.

RESULTS

Sixteen studies were selected for the qualitative synthesis. The following growth factor delivery systems exhibit adequate cytocompatibility, enhanced mineralization, and osteo/odontoblast differentiation potential of DSCs: 1) Fibroblast growth factor (FGF-2)-loaded-microsphere and silk fibroin, 2) Bone morphogenic protein-2 (BMP-2)-loaded-microsphere and mesoporous calcium silicate scaffold, 3) Transforming growth factor Beta 1 (TGF-ß1)-loaded-microsphere, glass ionomer cement (GIC), Bio-GIC and liposome, 4) TGF-ß1-loaded-nanoparticles/scaffold, 5) Vascular endothelial growth factor (VEGF)-loaded-fiber and hydrogel, 6) TGF-ß1/VEGF-loaded-nanocrystalline calcium sulfate/hydroxyapatite/calcium sulfate, 7) Epidermal growth factor-loaded- nanosphere, 8) Stem cell factor/DSCs-loaded-hydrogel and Silk fibroin, 9) VEGF/BMP-2/DSCs-loaded-Three-dimensional matrix, 10) VEGF/DSCs-loaded-microsphere/hydrogel, and 11) BMP-2/DSCs and VEGF/DSCs-loaded-Collagen matrices. The included delivery systems showed viability, except for Bio-GIC on day 3. The choice of specific growth factors and delivery systems (i.e., BMP-2-loaded-microsphere and VEGF-loaded-hydrogel) resulted in a greater gene expression.

CONCLUSIONS

This study, with low-level evidence obtained from ex vivo studies, suggests that growth factor delivery systems induce cell proliferation, mineralization, and differentiation toward a therapeutic potential in regenerative endodontics.

Effects of Growth Factors on the Differentiation of Dental Stem Cells: A Systematic Review and Meta-analysis (Part I)

INTRODUCTION

To evaluate the biological interaction between dental stem cells (DSCs) and different growth factors in the field of regenerative endodontics.

METHODS

A systematic search was conducted in the electronic databases up to October 2021. This study followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Ex vivo studies evaluating the biological interactions of DSCs and growth factors were included. The meta-analysis was performed according to the type of growth factor. The outcomes were cell viability/ proliferation and mineralization. Standardized mean differences (SMDs) were estimated using the random-effect maximum-likelihood method (P < .05). Additional analysis was performed to find any potential source of heterogeneity.

RESULTS

Twenty articles were included in the systematic review; meta-analysis was performed for fibroblast growth factor-2 (FGF-2) and Transforming growth factor-ß1 (TGF-β1) (n = 5). Results showed that use of FGF-2 significantly increased cell proliferation on day 1-(SMD = 3.56, P = 0.00), 3-(SMD = 9.04, P = 0.00), 5-(SMD = 8.37, P = 0.01), and 7 (SMD=8.51, P=0.00) than the control group. TGF-ß1 increased alkaline phosphatase (ALP) activity more than control only on day 3 (SMD = 3.68, P = 0.02). TGF-β1 had no significant effect on cell proliferation on days 1 and 3 (P > 0.05) and on ALP activity on days 5 and 7 (P > 0.05). Meta-regression analysis showed that different covariates (i.e., cell type, passage number, and growth factors' concentration) could significantly influence the effect sizes at different follow- ups (P < 0.05).

CONCLUSION

Specific growth factors might enhance the proliferation and mineralization of DSCs; however, the obtained evidence was weak. Due to the high heterogeneity among the included studies, other growth factors' inhibitory/stimulatory effects on DSCs could not be evaluated.

Drug combinations screening using a Bayesian ranking approach based on dose-response models

Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia.

Measuring Drug Response with Single-Cell Growth Rate Quantification

Intratumoral heterogeneity is a substantial cause of drug resistance development during chemotherapy or other drug treatments for cancer. Therefore, monitoring and measuring cell exposure and response to drugs at the single-cell level are crucial. Previous research suggested that the single-cell growth rate can be used to investigate drug-cell interactions. However, currently established methods for quantifying single-cell growth are limited to isolated or monolayer cells. Here, we introduce a technique that accurately measures both 2D and 3D cell growth rates using label-free ratiometric stimulated Raman scattering (SRS) microscopy. We use deuterated amino acids, leucine, isoleucine, and valine, as tracers and measure the C-D SRS signal from deuterium-labeled proteins and the C-H SRS signal from unlabeled proteins simultaneously to determine the cell growth rate at the single-cell level. The technique offers single-cell level drug sensitivity measurement with a shorter turnaround time (within 12 h) than most traditional assays. The submicrometer resolution of the imaging technique allows us to examine the effects of chemotherapeutic drugs, including kinase inhibitors, mitotic inhibitors, and topoisomerase II inhibitors, on both the cell growth rate and morphology. The capability of quantifying 3D cell growth rates provides insight into a deeper understanding of the cell-drug interaction in the actual tumor environment.

Single-organoid analysis reveals clinically relevant treatment-resistant and invasive subclones in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal diseases, characterized by a treatment-resistant and invasive nature. In line with these inherent aggressive characteristics, only a subset of patients shows a clinical response to the standard of care therapies, thereby highlighting the need for a more personalized treatment approach. In this study, we comprehensively unraveled the intra-patient response heterogeneity and intrinsic aggressive nature of PDAC on bulk and single-organoid resolution. We leveraged a fully characterized PDAC organoid panel (N = 8) and matched our artificial intelligence-driven, live-cell organoid image analysis with retrospective clinical patient response. In line with the clinical outcomes, we identified patient-specific sensitivities to the standard of care therapies (gemcitabine-paclitaxel and FOLFIRINOX) using a growth rate-based and normalized drug response metric. Moreover, the single-organoid analysis was able to detect resistant as well as invasive PDAC organoid clones, which was orchestrates on a patient, therapy, drug, concentration and time-specific level. Furthermore, our in vitro organoid analysis indicated a correlation with the matched patient progression-free survival (PFS) compared to the current, conventional drug response readouts. This work not only provides valuable insights on the response complexity in PDAC, but it also highlights the potential applications (extendable to other tumor types) and clinical translatability of our approach in drug discovery and the emerging era of personalized medicine.