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Wennerberg, K.

Publications and source records attributed to Wennerberg, K..

At least 19 recordsLinked to original sources

Concurrent AXL inhibition enhances RAS and ERK inhibitor efficacy in KRAS-mutant pancreatic and lung cancer

Resistance limits the clinical efficacy of RAS inhibitors. We applied chemical and genetic screens and identified the AXL receptor tyrosine kinase as a driver of resistance to RAS-ERK inhibition. We determined that combination treatment with the AXL inhibitor bemcentinib (AXLi) together with the RAS(ON) multi-selective tri-complex inhibitor RMC-7977 (RASi) or the ERK-selective inhibitor SCH772984 (ERKi) significantly enhanced growth suppression in human KRAS-mutant pancreatic and lung cancer models. Combined AXLi and RASi treatment of human KRAS-mutant pancreatic cell line-derived xenograft tumors synergistically suppressed ERK activation and MYC expression, and caused tumor regression. Analyses of immunocompetent mouse allograft pancreatic tumor models revealed a largely tumor cell-intrinsic response to inhibitor treatment. We identified an unexpected mechanism whereby KRAS inhibition upregulated the AXL ligand GAS6, activating AXL but inducing an AXL-dependent adaptive resistance mechanism wherein AXL antagonizes RASi efficacy. Our observations support concurrent AXL inhibition as a strategy to enhance RAS inhibitor clinical efficacy. STATEMENT OF SIGNIFICANCEOur findings identify AXL as a driver of resistance to RAS inhibitors, establishing a combination strategy to overcome resistance and enhance RAS inhibitor therapeutic efficacy in KRAS-mutant cancer by maximally inhibiting oncogenic RAS signaling.

cancer biology↗

Single-Cell Transcriptomic Signatures Enable Stratified Combination Therapy for Platinum-Resistant Ovarian Cancer

In high-grade serous carcinoma (HGSC), extensive intra-tumoral heterogeneity hinders complete eradication and remains a major obstacle to developing combination therapies capable of eliminating subpopulations resistant to standard-of-care treatment. Using single-cell RNA sequencing of 72 samples from 54 HGSC patients spanning treatment-naive, post-neoadjuvant chemotherapy and relapse stages, we established a carboplatin-anchored framework that identifies transcriptional signatures of intrinsic (pre-existing) and adaptive (therapy-induced) resistance in individual tumors and prioritizes mechanistically matched drugs to potentiate carboplatin efficacy. Candidate compounds were ranked by integrating orthogonal resources--viability (GDSC, PRISM) and perturbational transcriptomics (L1000, Perturb-seq)--to reduce context bias. Among 64 candidates, three carboplatin adjuvants enhanced long-term efficacy in patient-derived organoids (PDOs), and pevonedistat further significantly reduced tumor burden in orthotopic xenografts. This tiered validation pipeline--from short-term and long-term PDOs and in vivo orthoptic xenografts--establishes a translational framework linking single cell resistance programs to actionable, tumor-specific, carboplatin-anchored combinations for HGSC.

cancer biology↗

Generation of an in vitro 3D multicellular culture model of ovarian high-grade serous carcinoma

The development of translational ovarian cancer models to investigate and overcome treatment resistance accounting for the impact of the tumor microenvironment is critical. Here, we present a protocol to establish a multicellular culture model that retains both genetic complexity and the microenvironment of patient tumors, is amenable for molecular and phenotypic analyses, and high throughput drug testing. We describe steps for culturing and characterizing stromal cells derived from cryopreserved and fresh samples and detail procedures for combining them with organoids. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/686515v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@792c12org.highwire.dtl.DTLVardef@13c6680org.highwire.dtl.DTLVardef@6d73adorg.highwire.dtl.DTLVardef@1c56834_HPS_FORMAT_FIGEXP M_FIG C_FIG Before you beginThis protocol describes the in vitro generation of a complex 3D multicellular model (MC), mimicking relevant cellular and extracellular matrix (ECM) interactions in metastatic ovarian high-grade serous carcinoma (HGSC). First, cultures of stromal cells (cancer-associated fibroblasts (CAF), mesothelial cells and adipocytes) are generated. CAF and mesothelial cell cultures are established from fresh tumor tissues and/or from cryopreserved tissue digest and ascites fluid. Next, the identity of the stromal cells is evaluated using relevant markers, and the validated cultures are expanded and cryopreserved. Adipocytes are isolated from fresh tumor tissues and cultured in suspension for a short period before 3D embedding. Finally, previously established patient-derived cancer organoids1 are combined with relevant components of the tumor microenvironment (TME)2, including Type I collagen (main ECM protein in omental metastases) and stromal cells (Figure 1). The resulting MC model, which is viable for at least 14 days, can be used in various downstream applications. Here, we provide detailed protocols for two of them: high throughput drug sensitivity testing and single-cell RNA sequencing (scRNA-seq). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/686515v1_fig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@13c3a1dorg.highwire.dtl.DTLVardef@10b56f6org.highwire.dtl.DTLVardef@17a6e8forg.highwire.dtl.DTLVardef@f09b27_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Overview of the samples and culture conditions used to establish stromal cell cultures, and their integration with patient-derived organoids to generate the MC model. C_FIG

cancer biology↗

Conserved cell state dynamics reveal targetable resistance patterns in ovarian high-grade serous carcinoma

Core homeostatic programs of tissues, reflected in gene expression modules, can persist through oncogenesis. To reveal how cell states of normal fallopian tube epithelia (FTE) transform into intra-tumoral heterogeneity in ovarian high-grade serous carcinoma (HGSC), we applied a continuous, multi-state approach on single-cell transcriptomes of treatment-naive tumors (n=50) and normal FTE (n=14). We found the gene modules conserved from normal FTE to cancer to be more diverse than those altered during tumorigenesis, wherein the pseudotime-late TNF/NF-{kappa}B-associated module was linked to poor prognosis. The modules showed biases in subclonal structures and homologous-recombination deficiency, suggesting that genetic drivers employ existing epithelial programs for oncogenic phenotypes. Finally, organoid experiments validated actionable non-genetic drivers of these cell states, allowing chemosensitization by modulating apoptosis and NF-{kappa}B pathways. In summary, we identified prognostic, continuous cell states that reconstitute fallopian tube dynamics in HGSC and offer actionable targets to improve platinum response.

cancer biology↗

Complex I Drives Glutamine-Dependent TCA Cycle to Support Viability of MYChigh Breast Cancer Cells

In many cancers, stably elevated MYC levels drive persistent and concerted activation of cell growth promoting anabolic programs and the cell cycle in ways that are distinct from normal cells. Therefore, synthetic-lethal strategies to target MYC reprograming of these pathways may identify new selective anticancer therapies for the treatment of MYChigh tumors. Here, we identify enhanced mitochondrial respiration as a hallmark of MYC overexpressing cancer cells. Mitochondrial respiration sustains the TCA cycle by regenerating NAD+ through complex I-mediated oxidation of NADH. Metabolic carbon tracing analysis revealed that MYC shifts TCA cycles carbon source from glucose to glutamine. Inhibition of the glutamine-fueled TCA cycle using NAD+-depleting complex I inhibitors resulted in MYC-dependent synthetic lethality in breast cancer cells. In mouse models of MYChigh tumors, persistent inhibition of tumor growth was achieved through combined inhibition of complex I and glutaminolysis. Our results suggest that the high respiration rate observed in MYChigh cells supports glutamine carbon-enriched TCA cycle, rendering MYChigh tumors selectively vulnerable to inhibitors of mitochondrial respiration and glutaminolysis.

cancer biology↗

DEK::NUP214 acts as an XPO1-dependent transcriptional activator of essential leukemia genes

The t(6;9)(p22.3;q34.1) translocation/DEK::NUP214 fusion protein defines a distinct subgroup of younger AML patients classified as a separate disease entity by the World Health Organization. DEK is a nuclear factor with multifunctional roles, including gene regulation, while its fusion partner, NUP214, plays a pivotal role in nuclear export by interacting with transport receptors such as XPO1. However, the precise mechanism by which DEK::NUP214 drives leukemia remains unclear. A comprehensive multi-omics comparison of 57 AML primary samples (including whole genome sequencing, targeted sequencing, transcriptomics, and drug screening with > 500 compounds) revealed that t(6;9) cases display a selective response to XPO1 inhibitors (Selinexor & Eltanexor) and a distinct transcriptomic signature characterized by the overexpression of FOXC1 and HOX genes that are key leukemia mediators. CUT&RUN experiments demonstrated the direct binding of DEK::NUP214 to the promoters of FOXC1 and HOXA/B clusters. Strikingly, the expression of these genes and the binding of DEK::NUP214 to their regulatory regions were selectively reduced upon XPO1 inhibition in t(6;9) cells. Altogether, these results identified a novel function of DEK::NUP214 as an XPO1-dependent transcriptional activator of key leukemia drivers and provide a rationale to explore the use of XPO1 inhibitors in this patient population.

cancer biology↗

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

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

bioinformatics↗

Synthetic lethal interaction between WEE1 and PKMYT1 is a target for multiple low dose treatment of high-grade serous ovarian carcinoma

Ovarian cancer is driven by genetic alterations that necessitate protective DNA damage and replication stress responses through cell cycle control and genome maintenance. This creates specific vulnerabilities that may be exploited therapeutically. WEE1 kinase is a key cell cycle control kinase, and it has emerged as a promising cancer therapy target. However, adverse effects have limited its clinical progress, especially, when tested in combination with chemotherapies. A strong genetic interaction between WEE1 and PKMYT1 led us to hypothesize that a multiple low dose approach utilising joint WEE1 and PKMYT1 inhibition would allow exploitation of the synthetic lethality. In the present study, we found that the combination of WEE1 and PKMYT1 inhibition exhibited synergistic effects in eradicating ovarian cancer cells and ovarian organoid models at a low dose. The WEE1 and PKMYT1 inhibition synergistically promoted activation of CDK1 by decreasing the phosphorylation levels of T14 and Y15 residues. Furthermore, the combined treatment exacerbated DNA replication stress and replication catastrophe, leading to increase of the genomic instability and inflammatory STAT1 signalling activation. Finally, the multiple low dosing was well tolerated in mice. These findings suggest a new multiple low dose approach to harness the potency of WEE1 inhibition through the synthetic lethal interaction with PKMYT1 that may contribute to the development of new treatments for ovarian cancer.

cancer biology↗

A synthetic lethal dependency on casein kinase 2 in response to replication-perturbing drugs in RB1-deficient ovarian and breast cancer cells

Treatment of patients with high-grade serous ovarian carcinoma (HGSOC) and triple-negative breast cancer (TNBC) includes platinum-based drugs, gemcitabine, and PARP inhibitors. However, resistance to these therapies develops in most cases, highlighting the need for novel therapeutic approaches and biomarkers to guide the optimal treatment choice. Using a CRISPR loss-of-function screen for carboplatin sensitizers in the HGSOC cell line OVCAR8, we identified CSNK2A2, the gene encoding for the alpha () catalytic subunit of casein kinase 2 (CK2). Expanding on this finding, we confirmed that the CK2 inhibitors silmitasertib and SGC-CK2-1 sensitized many, but not all, TNBC and HGSOC cell lines to the drugs that perturb DNA replication, including platinum drugs, gemcitabine, and PARP inhibitors. We identified RB1 tumor suppressor deficiency as a prerequisite context for the CK2 inhibition-mediated sensitization to these therapeutics. In RB1-deficient cells, CK2 inhibition resulted in accumulation of cells in S phase of the cell cycle, associated with micronuclei formation, and accelerated PARP inhibitor-induced aneuploidy and mitotic cell death. Patient HGSOC organoids that lacked RB1 expression displayed an enhanced long-term response to carboplatin and PARP inhibitor niraparib when combined with silmitasertib, suggesting RB1-stratified efficacy in patients. As RB1 deficiency affects up to 25% of HGSOC and 40% of TNBC cases, CK2 inhibition, proven safe from previous clinical exploration with silmitasertib, is a promising approach to overcome resistance to standard therapeutics in large strata of patients.

cancer biology↗

Pharmacological PP2A reactivation overcomes multikinase inhibitor tolerance across brain tumor cell models

BackgroundGlioblastoma is characterized by hyperactivation of kinase signaling pathways. Regardless, most glioblastoma clinical trials targeting kinase signaling have failed. We hypothesized that overcoming the glioblastoma kinase inhibitor tolerance requires efficient shut-down of phosphorylation-dependent signaling rewiring by simultaneous inhibition of multiple critical kinases combined with reactivation of Protein Phosphatase 2A (PP2A). MethodsLive-cell imaging and colony growth assays were used to determine long-term impact of therapy effects on ten brain tumor cell models. Immunoblotting, MS-phosphoproteomics, and Seahorse metabolic assay were used for analysis of therapy-induced signaling rewiring. BH3 profiling was used to understand the mitochondrial apoptosis mechanisms. Medulloblastoma models were used to expand the importance to other brain cancer. Intracranial xenografts were used to validate the in vivo therapeutic impact of the triplet therapy. ResultsCollectively all tested ten glioblastoma and medulloblastoma cell models were effectively eradicated by the newly discovered triplet therapy combining inhibition of AKT and PDK1-4 kinases with pharmacological PP2A reactivation. Mechanistically, the brain tumor cell selective lethality of the triplet therapy could be explained by its combinatorial effects on therapy-induced signaling rewiring, OXPHOS, and apoptosis priming. The brain-penetrant triplet combination had a significant in vivo efficacy in intracranial glioblastoma and medulloblastoma models. ConclusionThe results confirm highly heterogenous responses of brain cancer cells to mono - and doublet combination therapies targeting phosphorylation-dependent signaling. However, the brain cancer cells cannot escape the triplet therapy targeting of AKT, PDK1-4, and PP2A. The results encourage evaluation of brain tumor PP2A status for design of future kinase inhibitor combination trials. Key PointsO_LIDevelopment of triplet kinase-phosphatase targeting therapy strategy for overcoming therapy tolerance across brain tumor models. C_LIO_LIIdentification of interplay between therapy-induced signaling rewiring, OXPHOS, and BH3 protein-mediated apoptosis priming as a cause for kinase inhibitor tolerance in brain cancers. C_LIO_LIValidation of the results in intracranial in vivo models with orally bioavailable and brain penetrant triplet therapy combination. C_LI Importance of the StudyBased on current genetic knowledge, glioblastoma should be particularly suitable target for kinase inhibitor therapies, However, in glioblastoma alone over 180 clinical trials with kinase inhibitors have failed. In this manuscript, we recapitulate this clinical observation by demonstrating broad tolerance of brain cancer cell models to kinase inhibitors even when combined with reactivation of PP2A. However, we discover that the therapy-induced signaling rewiring, and therapy tolerance, can be overcome by triplet targeting of AKT, PDK1-4 and PP2A. We provide strong evidence for the translatability of the findings by orally dosed brain penetrant triplet therapy combination in intracranial brain cancer models. The results encourage biomarker profiling of brain tumors for their PP2A status for clinical trials with combination of AKT and PDK1-4 inhibitors. Further, the results indicate that rapidly developing PP2A reactivation therapies will constitute an attractive future therapy option for brain tumors when combined with multi-kinase inhibition.

cancer biology↗

A platform for efficient establishment, expansion and drug response profiling of high-grade serous ovarian cancer organoids

The broad research use of organoids from high-grade serous ovarian carcinoma (HGSC) has been hampered by low culture success rates and limited availability of fresh tumor material. Here we describe a method for generation and long-term expansion of HGSC organoids with efficacy markedly improved over previous reports (55% vs. 23-38%). We established organoids from cryopreserved material, demonstrating the feasibility of using viably biobanked tissue for HGSC organoid derivation. Genomic, histologic and single-cell transcriptomic analyses revealed that organoids recapitulated genetic and phenotypic features of original tumors. Organoid drug responses correlated with clinical treatment outcomes, although in culture conditions-dependent manner and only in organoids maintained in human plasma-like medium (HPLM). Organoids from consenting patients are available to the research community through a public biobank and organoid genomic data explorable through an interactive online tool. Taken together, this resource facilitates the application of HGSC organoids in basic and translational ovarian cancer research.

cancer biology↗

Triplet kinase-phosphatase targeting to overcome kinase inhibitor tolerance in brain tumor cells

Therapeutic resistance to kinase inhibitors constitutes a major unresolved clinical challenge in cancer and especially in glioblastoma. Multi-kinase inhibitors may be used for simultaneous targeting of multiple target kinase and thereby potentially overcome kinase inhibitor resistance. However, in most cases identification of the target kinases mediating therapeutic effects of multi-kinase inhibitors has been challenging. To tackle this important problem, we developed an Actionable Targets of Multi-kinase Inhibitors (AToMI) strategy and used it for characterization of glioblastoma target kinases of staurosporine derivatives displaying synergy with protein phosphatase 2A (PP2A) reactivation. AToMI consists of interchangeable modules combining drug-kinase interaction assay, siRNA high-throughput screening, bioinformatics analysis and validation screening with more selective target kinase inhibitors. As a result, AToMI analysis revealed AKT and mitochondrial pyruvate dehydrogenase kinase PDK1 and PDK4 as kinase targets of staurosporine derivatives UCN-01, CEP-701, and K252a that synergized with PP2A activation across heterogeneous glioblastoma cells. Based on these proof-of-principle results we propose that application and further development of AToMI for clinically applicable multi-kinase inhibitors could provide significant benefits in overcoming the challenge of lack of knowledge of target specificity of multi-kinase inhibitors.

cancer biology↗

PI3Kβ inhibition restores ALK inhibitor sensitivity in ALK-rearranged lung cancer

For non-small cell lung cancer (NSCLC) patients with ALK-rearranged tumors, treatment with ALK inhibitors can improve outcomes. However, clinical resistance typically develops over time, and in the majority of cases resistance mechanisms are ALK-independent. We generated tumor cell cultures from multiple regions of an ALK-rearranged clinical tumor specimen, and deployed functional drug screens to identify modulators of resistance to ALK inhibitors. This identified a role for PI3K{beta} and EGFR in regulating resistance to ALK inhibition. Furthermore, inhibition of ALK elicited activation of EGFR, and inhibition of PI3K{beta} rescued EGFR-mediated ALK inhibitor resistance. In ALK-rearranged primary cultures, cell lines and in vivo xenograft models, combined inhibition of ALK and PI3K{beta} prevented compensatory MAPK and PI3K-AKT pathway reactivation and selectively targeted the cancer cells. The combinatorial effect was seen even in the background of TP53 mutations and in epithelial-mesenchymal transformed cells. In conclusion, combinatorial ALK and PI3K{beta} inhibitor treatment carries promise as a treatment for ALK-rearranged NSCLC.

cancer biology↗

COMPARE, an ultra-fast and robust suite for multiparametric screening, identifies phenotypic drug responses in acute myeloid leukemia

Multiparametric phenotypic screening of cells, for example assessing their responses to small molecules or knockdown/knockout of specific genes, is a powerful approach to understanding cellular systems and identifying potential new therapeutic strategies. However, automated tools for analyzing similarities and differences between a large number of tested conditions have not been readily available. Methods designed for clustering cells cannot identify differences between samples effectively. We introduce O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW for ultra-fast and robust analysis of multiparametric high-throughput screening. Applying a mass-aware gridding algorithm using hypercubes, O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW performs automatic and effective similarity comparison for hundreds to thousands of tests and provides information about the treatment effect. Particularly for screening data, O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW is equipped with modules to remove various sources of bias. Benchmarking tests show that O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW can circumvent batch effects and perform a similarity analysis substantially faster than conventional analysis tools. Applying O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW to high-throughput flow cytometry screening data, we were able to distinguish subtle phenotypic drug responses in a human sample and a genetically engineered mouse model with acute myeloid leukemia (AML). O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW revealed groups of drugs with similar responses even though their mechanisms are distinct from each other. In another screening, O_SCPLOWCOMPAC_SCPLOWRO_SCPLOWEC_SCPLOW effectively circumvented batch effects and grouped samples from AML and myelodysplastic syndrome (MDS) patients using clinical flow cytometry data.

cancer biology↗

Minimal information for Chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments

Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of MICHA (Minimal Information for Chemosensitivity Assays), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents, and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets, and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies, as well as six recently conducted COVID-19 studies. With the MICHA webserver and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.

bioinformatics↗

CIP2A interacts with TopBP1 and is selectively essential for DNA damage-induced basal-like breast cancer tumorigenesis

Despite saturated genetic profiling of breast cancers, oncogenic drivers for the clinically challenging basal-like breast cancer (BLBC) subtype are still poorly understood. Here, we demonstrate that CIP2A is selectively essential for DNA damage-induced initiation of mouse BLBC tumors, but not of other cancer types. Mechanistically, CIP2A was discovered genome-widely the closest functional homologue for DNA-damage proteins TopBP1, RHNO, POLQ, NBN and PARP1. CIP2A directly interacts with the ATR-activation domain of TopBP1, and dampens both, chromatin binding of TopBP1 and RAD51, and G2/M checkpoint in DNA-damaged cells. CIP2A also drives BLBC-associated proliferative MYC and E2F1 signaling. Consistently with high DNA-damage response activity BLBCs, and CIP2As novel role in checkpoint signaling, CIP2A was found essential for DNA-damaged, and BRCA-mutant BLBC cells. Selective role for CIP2A as BLBC driver was supported by association of high CIP2A expression with poor patient prognosis only in BLBC, but not in other breast cancer types. Therapeutically, small molecule reactivators of PP2A (SMAPs) phenocopy CIP2A-dependent DNA damage response, and inhibit in vivo growth of patient-derived BLBC xenograft. In summary, we discover sub-type selective essential role for CIP2A in BLBC initiation and maintenance that can be explained by its newly discovered association with DNA-damage response, coordinated with regulation of proliferative signaling. The results also identify therapeutic strategy for CIP2A-dependent BLBCs.

cancer biology↗

Functional diagnostics using fresh uncultured lung tumor cells to guide personalized treatments

Functional profiling of a cancer patients tumor cells holds potential to tailor personalized cancer treatment. Here we report the utility of Fresh Uncultured Tumor-derived EpCAM+ epithelial Cells (FUTC) for ex vivo drug response interrogation. Analysis of murine Kras mutant FUTCs demonstrated pharmacological and adaptive signaling profiles comparable to subtype-matched cultured cells. Applying FUTC profiling on non-small cell lung cancer patient samples, we generated robust drug response data in 18 of 19 cases, where the cells exhibited targeted drug sensitivities corresponding to their oncogenic drivers. In one of these cases, an EGFR mutant lung adenocarcinoma patient refractory to osimertinib, FUTC profiling was used to guide compassionate treatment. FUTC profiling identified selective sensitivity to disulfiram and the combination of carboplatin plus etoposide and the patient received substantial clinical benefit from the treatment with these agents. We conclude that FUTC profiling provides a robust, rapid, and actionable assessment of personalized cancer treatment options.

cancer biology↗

Patient-tailored design of AML cell subpopulation-selective drug combinations

The extensive primary and secondary drug resistance in acute myeloid leukemia (AML) requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to optimally target disease-driving AML cell subpopulations. However, the large number of AML-relevant drug combinations makes the testing impossible in scarce primary patient cells. This combinatorial problem is further exacerbated by the translational challenge of how to design such personalized and selective drug combinations that do not only show synergistic effect in overall AML cell killing but also result in minimal toxic side effects on non-malignant cells. To solve these challenges, we implemented a systematic computational-experimental approach for identifying potential drug combinations that have a desired synergy-efficacy-toxicity balance. Our mechanism-agnostic approach combines single-cell RNA-sequencing (scRNA-seq) with ex vivo single-agent viability testing in primary patient cells. The data integration and predictive modelling are carried out at a single-cell resolution by means of a machine learning model that makes use of compound-target interaction networks to narrow down the massive search space of potentially effective drug combinations. When applied to two diagnostic and two refractory AML patient cases, each having a different genetic background, our integrated approach predicted a number of patient-specific combinations that were shown to result not only in synergistic cancer cell inhibition but were also capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Overall, 53% of the 59 predicted combinations were experimentally confirmed to show synergy, and 83% were non-antagonistic, as validated with viability assays, which is a significant improvement over the success rate of randomly guessing a synergistic drug combination (5%). Importantly, 67% of the predicted combinations showed low toxicity to non-malignant cells, as validated with flow-based population assays, suggesting their selective killing of AML cell populations. Our data-driven approach provides an unbiased means for systematic prioritization of patient-specific drug combinations that selectively inhibit AML cells and avoid co-inhibition of non-malignant cells, thereby increasing their likelihood for clinical translation. The approach uses only a limited number of patient primary cells, and it is widely applicable to hematological cancers that are accessible for scRNA-seq profiling and ex vivo compound testing.

systems biology↗