Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials

Empowering drug off-target discovery with metabolic and structural analysis

Elucidating intracellular drug targets is a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we develop a hierarchic workflow to focus on specific targets based on analysis of metabolomics data and growth rescue experiments. We deploy this framework to understand the intracellular molecular interactions of the multi-valent dihydrofolate reductase-targeting antibiotic compound CD15-3. We analyse global metabolomics data utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets. Overexpression and in vitro activity assays confirm one of the predicted candidates, HPPK (folK), as a CD15-3 off-target. This study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows for discovering off-targets of a metabolic inhibitor.

Conditional lethality profiling reveals anticancer mechanisms of action and drug-nutrient interactions

Chemical screening studies have identified drug sensitivities across hundreds of cancer cell lines but most putative therapeutics fail to translate. Discovery and development of drug candidates in models that more accurately reflect nutrient availability in human biofluids may help in addressing this major challenge. Here we performed high-throughput screens in conventional versus Human Plasma-Like Medium (HPLM). Sets of conditional anticancer compounds span phases of clinical development and include non-oncology drugs. Among these, we characterize a unique dual-mechanism of action for brivudine, an agent otherwise approved for antiviral treatment. Using an integrative approach, we find that brivudine affects two independent targets in folate metabolism. We also traced conditional phenotypes for several drugs to the availability of nucleotide salvage pathway substrates and verified others for compounds that seemingly elicit off-target anticancer effects. Our findings establish generalizable strategies for exploiting conditional lethality in HPLM to reveal therapeutic candidates and mechanisms of action.

A proteome-wide atlas of drug mechanism of action

Defining the cellular response to pharmacological agents is critical for understanding the mechanism of action of small molecule perturbagens. Here, we developed a 96-well-plate-based high-throughput screening infrastructure for quantitative proteomics and profiled 875 compounds in a human cancer cell line with near-comprehensive proteome coverage. Examining the 24-h proteome changes revealed ligand-induced changes in protein expression and uncovered rules by which compounds regulate their protein targets while identifying putative dihydrofolate reductase and tankyrase inhibitors. We used protein-protein and compound-compound correlation networks to uncover mechanisms of action for several compounds, including the adrenergic receptor antagonist JP1302, which we show disrupts the FACT complex and degrades histone H1. By profiling many compounds with overlapping targets covering a broad chemical space, we linked compound structure to mechanisms of action and highlighted off-target polypharmacology for molecules within the library.

High-throughput approaches to uncover synergistic drug combinations in leukemia

We report a comprehensive drug synergy study in acute myeloid leukemia (AML). In this work, we investigate a panel of cell lines spanning both MLL-rearranged and non-rearranged subtypes. The work comprises a resource for the community, with many synergistic drug combinations that could not have been predicted a priori, and open source code for automation and analyses. We base our definitions of drug synergy on the Chou-Talalay method, which is useful for visualizations of synergy experiments in isobolograms, and median-effects plots, among other representations. Our key findings include drug synergies affecting the chromatin state, specifically in the context of regulation of the modification state of histone H3 lysine-27. We report open source high throughput methodology such that multidimensional drug screening can be accomplished with equipment that is accessible to most laboratories. This study will enable preclinical investigation of new drug combinations in a lethal blood cancer, with data analysis and automation workflows freely available to the community.

Collagen-Anchored Interleukin-2 and Interleukin-12 Safely Reprogram the Tumor Microenvironment in Canine Soft-Tissue Sarcomas

PURPOSE

Cytokine therapies such as IL2 and IL12 suffer from impractically small therapeutic windows driven by their on-target, off-tumor activity, limiting their clinical potential despite potent antitumor effects. We previously engineered cytokines that bind and anchor to tumor collagen following intratumoral injection, and sought to test their safety and biomarker activity in spontaneous canine soft-tissue sarcomas (STS).

EXPERIMENTAL DESIGN

Collagen-binding cytokines were canine-ized to minimize immunogenicity and were used in a rapid dose-escalation study in healthy beagles to identify a maximum tolerated dose. Ten client-owned pet dogs with STS were then enrolled into trial, receiving cytokines at different intervals prior to surgical tumor excision. Tumor tissue was analyzed through IHC and NanoString RNA profiling for dynamic changes within treated tumors. Archived, untreated STS samples were analyzed in parallel as controls.

RESULTS

Intratumorally administered collagen-binding IL2 and IL12 were well tolerated by STS-bearing dogs, with only Grade 1/2 adverse events observed (mild fever, thrombocytopenia, neutropenia). IHC revealed enhanced T-cell infiltrates, corroborated by an enhancement in gene expression associated with cytotoxic immune function. We found concordant increases in expression of counter-regulatory genes that we hypothesize would contribute to a transient antitumor effect, and confirmed in mouse models that combination therapy to inhibit this counter-regulation can improve responses to cytokine therapy.

CONCLUSIONS

These results support the safety and activity of intratumorally delivered, collagen-anchoring cytokines for inflammatory polarization of the canine STS tumor microenvironment. We are further evaluating the efficacy of this approach in additional canine cancers, including oral malignant melanoma.

Liposomes targeting the cancer cell-exposed receptor, claudin-4, for pancreatic cancer chemotherapy

BACKGROUND

Claudin-4 (CLDN4), a tight junction protein, is overexpressed in several types of cancer, and is considered a biomarker for cancer-targeted treatment. CLDN4 is not exposed in normal cells, but becomes accessible in cancer cells, in which tight junctions are weakened. Notably, surface-exposed CLDN4 has recently been found to act as a receptor for Clostridium perfringens enterotoxin (CPE) and fragment of CPE (CPE17) that binds to the second domain of CLDN4.

METHODS

Here, we sought to develop a CPE17-containing liposome that targets pancreatic cancers through binding to exposed CLDN4.

RESULTS

Doxorubicin (Dox)-loaded, CPE17-conjugated liposomes (D@C-LPs) preferentially targeted CLDN4-expressing cell lines, as evidenced by greater uptake and cytotoxicity compared with CLDN4-negative cell lines, whereas uptake and cytotoxicity of Dox-loaded liposomes lacking CPE17 (D@LPs) was similar for both CLDN4-positive and negative cell lines. Notably, D@C-LPs showed greater accumulation in targeted pancreatic tumor tissues compared with normal pancreas tissue; in contrast, Dox-loaded liposomes lacking CPE17 (D@LPs) showed little accumulation in pancreatic tumor tissues. Consistent with this, D@C-LPs showed greater anticancer efficacy compared with other liposome formulations and significantly extended survival.

CONCLUSIONS

We expect our findings will aid in the prevention and treatment of pancreatic cancer and provide a framework for identifying cancer-specific strategies that target exposed receptors.

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

BACKGROUND

There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates.

RESULTS

First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors.

CONCLUSIONS

DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA .

The recent progress of deep-learning-based in silico prediction of drug combination

Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.

In Situ Synthesis of a Tumor-Microenvironment-Responsive Chemotherapy Drug

Current chemotherapy still suffers from unsatisfactory therapeutic efficacy, multi-drug resistance, and severe adverse effects, thus necessitating the development of techniques to confine chemotherapy drugs in the tumor microenvironment. Herein, we fabricated nanospheres of mesoporous silica (MS) doped with Cu (MS-Cu) and polyethylene glycol (PEG)-coated MS-Cu (PEG-MS-Cu) as exogenous copper supply systems to tumors. The synthesized MS-Cu nanospheres showed diameters of 30-150 nm with Cu/Si molar ratios of 0.041-0.069. Only disulfiram (DSF) and only MS-Cu nanospheres showed little cytotoxicity in vitro, whereas the combination of DSF and MS-Cu nanospheres showed significant cytotoxicity against MOC1 and MOC2 cells at concentrations of 0.2-1 μg/mL. Oral DSF administration in combination with MS-Cu nanospheres intratumoral or PEG-MS-Cu nanospheres intravenous administration showed significant antitumor efficacy against MOC2 cells in vivo. In contrast to traditional drug delivery systems, we herein propose a system for the in situ synthesis of chemotherapy drugs by converting nontoxic substances into antitumor chemotherapy drugs in a specific tumor microenvironment.

Leveraging Exosomes as the Next-Generation Bio-Shuttles: The Next Biggest Approach against Th17 Cell Catastrophe

In recent years, the launch of clinical-grade exosomes is rising expeditiously, as they represent a new powerful approach for the delivery of advanced therapies and for diagnostic purposes for various diseases. Exosomes are membrane-bound extracellular vesicles that can act as biological messengers between cells, in the context of health and disease. In comparison to several lab-based drug carriers, exosome exhibits high stability, accommodates diverse cargo loads, elicits low immunogenicity and toxicity, and therefore manifests tremendous perspectives in the development of therapeutics. The efforts made to spur exosomes in drugging the untreatable targets are encouraging. Currently, T helper (Th) 17 cells are considered the most prominent factor in the establishment of autoimmunity and several genetic disorders. Current reports have indicated the importance of targeting the development of Th17 cells and the secretion of its paracrine molecule, interleukin (IL)-17. However, the present-day targeted approaches exhibit drawbacks, such as high cost of production, rapid transformation, poor bioavailability, and importantly, causing opportunistic infections that ultimately hamper their clinical applications. To overcome this hurdle, the potential use of exosomes as vectors seem to be a promising approach for Th17 cell-targeted therapies. With this standpoint, this review discusses this new concept by providing a snapshot of exosome biogenesis, summarizes the current clinical trials of exosomes in several diseases, analyzes the prospect of exosomes as an established drug carrier and delineates the present challenges, with an emphasis on their practical applications in targeting Th17 cells in diseases. We further decode the possible future scope of exosome bioengineering for targeted drug delivery against Th17 cells and its catastrophe.

Design, synthesis, and evaluation of a mitoxantrone probe (MXP) for biological studies

Mitoxantrone (MX) is a robust chemotherapeutic with well-characterized applications in treating certain leukemias and advanced breast and prostate cancers. The canonical mechanism of action associated with MX is its ability to intercalate DNA and inhibit topoisomerase II, giving it the designation of a topoisomerase II poison. Years after FDA approval, investigations have unveiled novel protein-binding partners, such as methyl-CpG-binding domain protein (MBD2), PIM1 serine/threonine kinase, RAD52, and others that may contribute to the therapeutic profile of MX. Moreover, recent proteomic studies have revealed MX's ability to modulate protein expression, illuminating the complex cellular interactions of MX. Although mechanistically relevant, the differential expression across the proteome does not address the direct interaction with potential binding partners. Identification and characterization of these MX-binding cellular partners will provide the molecular basis for the alternate mechanisms that influence MX's cytotoxicity. Here, we describe the design and synthesis of a MX-biotin probe (MXP) and negative control (MXP-NC) that can be used to define MX's cellular targets and expand our understanding of the proteome-wide profile for MX. In proof of concept studies, we used MXP to successfully isolate a recently identified protein-binding partner of MX, RAD52, in a cell lysate pulldown with streptavidin beads and western blotting.

Graphical abstract Draft

Highlights

An 8-step synthesis was used to generate a biotinylated-mitoxantrone probe (MXP).A pulldown of MXP demonstrated selectivity for RAD52, but not Replication Protein A.Western blot confirmed the identity of the isolated protein, RAD52.

Targeting P21-Activated Kinase-1 for Metastatic Prostate Cancer

Metastatic prostate cancer (mPCa) has limited therapeutic options and a high mortality rate. The p21-activated kinase (PAK) family of proteins is important in cell survival, proliferation, and motility in physiology, and pathologies such as infectious, inflammatory, vascular, and neurological diseases as well as cancers. Group-I PAKs (PAK1, PAK2, and PAK3) are involved in the regulation of actin dynamics and thus are integral for cell morphology, adhesion to the extracellular matrix, and cell motility. They also play prominent roles in cell survival and proliferation. These properties make group-I PAKs a potentially important target for cancer therapy. In contrast to normal prostate and prostatic epithelial cells, group-I PAKs are highly expressed in mPCA and PCa tissue. Importantly, the expression of group-I PAKs is proportional to the Gleason score of the patients. While several compounds have been identified that target group-I PAKs and these are active in cells and mice, and while some inhibitors have entered human trials, as of yet, none have been FDA-approved. Probable reasons for this lack of translation include issues related to selectivity, specificity, stability, and efficacy resulting in side effects and/or lack of efficacy. In the current review, we describe the pathophysiology and current treatment guidelines of PCa, present group-I PAKs as a potential druggable target to treat mPCa patients, and discuss the various ATP-competitive and allosteric inhibitors of PAKs. We also discuss the development and testing of a nanotechnology-based therapeutic formulation of group-I PAK inhibitors and its significant potential advantages as a novel, selective, stable, and efficacious mPCa therapeutic over other PCa therapeutics in the pipeline.

Strategy toward Kinase-Selective Drug Discovery

Kinase drug selectivity is the ground challenge in cancer research. Due to the structurally similar kinase drug pockets, off-target inhibitor toxicity has been a major cause for clinical trial failures. The pockets are similar but not identical. Here, we describe a transformation invariant protocol to identify distinct geometric features in the drug pocket that can distinguish one kinase from all others. We integrate available experimental structures with the artificial intelligence-based structural kinome, performing a kinome-wide structural bioinformatic analysis to establish the structural principles of kinase drug selectivity. We generate the structural landscape from the experimental kinase-ligand complexes and propose a binary network that encapsulates the information. The results show that all kinases contain binary units that are shared by less than seven other kinases in the kinome. 331 kinases contain unique binary units that may distinguish them from all others. The structural features encoded by these binary units in the network represent the inhibitor-accessible geometric space that may capture the kinome-wide selectivity. Our proposed binary network with the unsupervised clustering can serve as a general structural bioinformatic protocol for extracting the distinguishing structural features for any protein from their families. We apply the binary network to epidermal growth factor receptor tyrosine kinase inhibitor selectivity by targeting the gate area and the AKT1 serine/threonine kinase selectivity by binding to the αC-helix region and the allosteric pocket. Finally, we develop the cross-platform software, KDS (Kinase Drug Selectivity), for customized visualization and analysis of the binary networks in the human kinome (https://github.com/CBIIT/KDS).

The abscopal effect: Implications for drug discovery in autoimmunity

The emergence of novel targeted therapies and the tools that increase the stability and delivery of drugs have greatly improved treatment outcomes in autoimmune diseases (ADs). Recently-developed strategies deplete specific deleterious T- and B-cell subsets, interrupt receptor-ligand interactions, and/or inhibit the secretion or activity of inflammatory mediators linked to tissue damage. Although generally efficient, these lines of intervention have limitations, with documented cases of drug-resistance and undesired side effects. They are also difficult to apply to non-organ-specific ADs, where the trigger and effector antigens are unknown and in which autoimmune activity is widely spread throughout the body. The potential of cellular modulators that act at a distance from the affected site, by abscopal effect, as described in the case of cancer radio- and immuno-therapy might be especially efficient in the context of ADs. Future research to discover small molecule- and peptide-based treatments will need to explore potential drugs with abscopal effects that could elicit potent immune tolerance and clinical quiescence to restore quality of life of affected patients.

Therapeutic potential of CDK11 in cancer

Human cyclin-dependent kinases (CDKs) direct the progression of the cell cycle and transcription. They are deregulated in tumours, and despite their involvement in the regulation of basic cellular processes, many CDKs are promising targets for cancer therapy. CDK11 is an essential gene for the growth of many malignancies; however, its primary cellular function has been obscure, and the mode-of-action of OTS964, the first CDK11 inhibitor and antiproliferative compound, has been unknown. A recent study has shown that OTS964 prevents spliceosome activation, revealing a key role of CDK11 in the regulation of pre-mRNA splicing. In light of these findings, we discuss the therapeutic potential of CDK11 in cancer.

P-TEFb promotes cell survival upon p53 activation by suppressing intrinsic apoptosis pathway

Positive transcription elongation factor b (P-TEFb) is the crucial player in RNA polymerase II (Pol II) pause release that has emerged as a promising target in cancer. Because single-agent therapy may fail to deliver durable clinical response, targeting of P-TEFb shall benefit when deployed as a combination therapy. We screened a comprehensive oncology library and identified clinically relevant antimetabolites and Mouse double minute 2 homolog (MDM2) inhibitors as top compounds eliciting p53-dependent death of colorectal cancer cells in synergy with selective inhibitors of P-TEFb. While the targeting of P-TEFb augments apoptosis by anti-metabolite 5-fluorouracil, it switches the fate of cancer cells by the non-genotoxic MDM2 inhibitor Nutlin-3a from cell-cycle arrest to apoptosis. Mechanistically, the fate switching is enabled by the induction of p53-dependent pro-apoptotic genes and repression of P-TEFb-dependent pro-survival genes of the PI3K-AKT signaling cascade, which stimulates caspase 9 and intrinsic apoptosis pathway in BAX/BAK-dependent manner. Finally, combination treatments trigger apoptosis of cancer cell spheroids. Together, co-targeting of P-TEFb and suppressors of intrinsic apoptosis could become a viable strategy to eliminate cancer cells.

Using patient-derived organoids to predict locally advanced or metastatic lung cancer tumor response: A real-world study

Predicting the clinical response to chemotherapeutic or targeted treatment in patients with locally advanced or metastatic lung cancer requires an accurate and affordable tool. Tumor organoids are a potential approach in precision medicine for predicting the clinical response to treatment. However, their clinical application in lung cancer has rarely been reported because of the difficulty in generating pure tumor organoids. In this study, we have generated 214 cancer organoids from 107 patients, of which 212 are lung cancer organoids (LCOs), primarily derived from malignant serous effusions. LCO-based drug sensitivity tests (LCO-DSTs) for chemotherapy and targeted therapy have been performed in a real-world study to predict the clinical response to the respective treatment. LCO-DSTs accurately predict the clinical response to treatment in this cohort of patients with advanced lung cancer. In conclusion, LCO-DST is a promising precision medicine tool in treating of advanced lung cancer.

Targeting the 'Undruggable' Driver Protein, KRAS, in Epithelial Cancers: Current Perspective

This review summarizes recent development in synthetic drugs and biologics targeting intracellular driver genes in epithelial cancers, focusing on KRAS, and provides a current perspective and potential leads for the field. Compared to biologics, small molecule inhibitors (SMIs) readily penetrate cells, thus being able to target intracellular proteins. However, SMIs frequently suffer from pleiotropic effects, off-target cytotoxicity and invariably elicit resistance. In contrast, biologics are much larger molecules limited by cellular entry, but if this is surmounted, they may have more specific effects and less therapy-induced resistance. Exciting breakthroughs in the past two years include engineering of non-covalent KRAS G12D-specific inhibitor, probody bispecific antibodies, drug-peptide conjugate as MHC-restricted neoantigen to prompt immune response by T-cells, and success in the adoptive cell therapy front in both breast and pancreatic cancers.

OGG1 competitive inhibitors show important off-target effects by directly inhibiting efflux pumps and disturbing mitotic progression

One of the most abundant DNA lesions induced by Reactive oxygen species (ROS) is 8-oxoG, a highly mutagenic lesion that compromises genetic instability when not efficiently repaired. 8-oxoG is specifically recognized by the DNA-glycosylase OGG1 that excises the base and initiates the Base Excision Repair pathway (BER). Furthermore, OGG1 has not only a major role in DNA repair but it is also involved in transcriptional regulation. Cancer cells are particularly exposed to ROS, thus challenging their capacity to process oxidative DNA damage has been proposed as a promising therapeutic strategy for cancer treatment. Two competitive inhibitors of OGG1 (OGG1i) have been identified, TH5487 and SU0268, which bind to the OGG1 catalytic pocket preventing its fixation to the DNA. Early studies with these inhibitors show an enhanced cellular sensitivity to cytotoxic drugs and a reduction in the inflammatory response. Our study uncovers two unreported off-targets effects of these OGG1i that are independent of OGG1. In vitro and in cellulo approaches have unveiled that OGG1i TH5487 and SU0268, despite an unrelated molecular structure, are able to inhibit some members of the ABC family transporters, in particular ABC B1 (MDR1) and ABC G2 (BCRP). The inhibition of these efflux pumps by OGG1 inhibitors results in a higher intra-cellular accumulation of various fluorescent probes and drugs, and largely contributes to the enhanced cytotoxicity observed when the inhibitors are combined with cytotoxic agents. Furthermore, we found that SU0268 has an OGG1-independent anti-mitotic activity-by interfering with metaphase completion-resulting in a high cellular toxicity. These two off-target activities are observed at concentrations of OGG1i that are normally used for in vivo studies. It is thus critical to consider these previously unreported non-specific effects when interpreting studies using TH5487 and SU0268 in the context of OGG1 inhibition. Additionally, our work highlights the persistent need for new specific inhibitors of the enzymatic activity of OGG1.

A Mini Review of Novel Topoisomerase II Inhibitors as Future Anticancer Agents

Several reviews of inhibitors of topoisomerase II have been published, covering research before 2018. Therefore, this review is focused primarily on more recent publications with relevant points from the earlier literature. Topoisomerase II is an established target for anticancer drugs, which are further subdivided into poisons and catalytic inhibitors. While most of the topoisomerase II-based drugs in clinical use are mostly topoisomerase II poisons, their mechanism of action has posed severe concern due to DNA damaging potential, including the development of multi-drug resistance. As a result, we are beginning to see a gradual paradigm shift towards non-DNA damaging agents, such as the lesser studied topoisomerase II catalytic inhibitors. In addition, this review describes some novel selective catalytic topoisomerase II inhibitors. The ultimate goal is to bring researchers up to speed by curating and delineating new scaffolds as the leads for the optimization and development of new potent, safe, and selective agents for the treatment of cancer.

Patient-derived models: Advanced tools for precision medicine in neuroblastoma

Neuroblastoma is a childhood cancer derived from the sympathetic nervous system. High-risk neuroblastoma patients have a poor overall survival and account for ~15% of childhood cancer deaths. There is thus a need for clinically relevant and authentic models of neuroblastoma that closely resemble the human disease to further interrogate underlying mechanisms and to develop novel therapeutic strategies. Here we review recent developments in patient-derived neuroblastoma xenograft models and in vitro cultures. These models can be used to decipher mechanisms of metastasis and treatment resistance, for drug screening, and preclinical drug testing. Patient-derived neuroblastoma models may also provide useful information about clonal evolution, phenotypic plasticity, and cell states in relation to neuroblastoma progression. We summarize current opportunities for, but also barriers to, future model development and application. Integration of patient-derived models with patient data holds promise for the development of precision medicine treatment strategies for children with high-risk neuroblastoma.

Death by a Thousand Cuts â€" Combining Kinase Inhibitors for Selective Target Inhibition and Rational Polypharmacology

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. The high sequence and structural conservation of the catalytic kinase domain complicates the development of specific kinase inhibitors. As a consequence, most kinase inhibitors also inhibit off-target kinases which complicates the interpretation of phenotypic responses. Additionally, inhibition of off-targets may cause toxicity in patients. Therefore, highly selective kinase inhibition is a major goal in both biomedical research and clinical practice. Currently, efforts to improve selective kinase inhibition are dominated by the development of new kinase inhibitors. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target activities. We have developed a multicompound-multitarget scoring (MMS) method framework that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables rational polypharmacology by allowing optimization of inhibitor combinations against multiple selected on-targets and off-targets. Using MMS with previously published chemogenomic kinase inhibitor datasets we determine inhibitor combinations that achieve potent activity against a target kinase and that are more selective than the most selective single inhibitor against that target. We validate the calculated effect and selectivity of a combination of inhibitors using the in cellulo NanoBRET assay. The MMS framework is generalizable to other pharmacological targets where compound specificity is a challenge and diverse compound libraries are available.

Oncogene-like addiction to aneuploidy in human cancers

Most cancers exhibit aneuploidy, but its functional significance in tumor development is controversial. Here, we describe ReDACT (Restoring Disomy in Aneuploid cells using CRISPR Targeting), a set of chromosome engineering tools that allow us to eliminate specific aneuploidies from cancer genomes. Using ReDACT, we created a panel of isogenic cells that have or lack common aneuploidies, and we demonstrate that trisomy of chromosome 1q is required for malignant growth in cancers harboring this alteration. Mechanistically, gaining chromosome 1q increases the expression of MDM4 and suppresses TP53 signaling, and we show that TP53 mutations are mutually-exclusive with 1q aneuploidy in human cancers. Thus, specific aneuploidies play essential roles in tumorigenesis, raising the possibility that targeting these "aneuploidy addictions" could represent a novel approach for cancer treatment.

Proteome Integral Solubility Alteration (PISA) for High-Throughput Ligand Target Deconvolution with Increased Statistical Significance and Reduced Sample Amount

Proteome Integral Solubility Alteration (PISA) is a recently developed mass spectrometry-based, deep proteomics method for unbiased, proteome-wide target deconvolution of ligands, requiring no chemical ligand modification. PISA can be applied to living cells for studying target engagement in vivo or alternatively to protein extracts to identify in vitro ligand-interacting proteins. Here we describe the PISA workflow optimized in our lab. PISA improves the target discovery throughput 10-100 folds compared to the previously used proteomics methods and provides higher statistical significance for target candidates by enabling several biological replicates. Sample multiplexing makes all-in-one analysis of multiple ligands simultaneously possible. PISA dramatically reduces analysis costs, allowing many research questions in need of target deconvolution to be addressed, and unlocks the potential of miniaturizing biological models, including primary cells.

The diverse landscape of dermatologic toxicities of non-immune checkpoint inhibitor monoclonal antibody-based cancer therapy

BACKGROUND

Since their first approval 25 years ago, monoclonal antibodies (mAbs) have become important targeted cancer therapeutics. However, dermatologic toxicities associated with non-immune checkpoint inhibitor (non-ICI) mAbs may complicate the course of cancer treatment. Data on the incidence and types of these reactions are limited.

METHODS

A comprehensive review was conducted on dermatologic toxicities associated with different classes of non-ICI mAbs approved for treatment of solid tumors and hematologic malignancies. The review included prospective Phase 1, 2, and 3 clinical trials; retrospective literature reviews; systematic reviews/meta-analyses; and case series/reports.

RESULTS

Dermatologic toxicities were associated with several types of non-ICI mAbs. Inflammatory reactions were the most common dermatologic toxicities, manifesting as maculopapular, urticarial, papulopustular/acneiform, and lichenoid/interface cutaneous adverse events (cAEs) with non-ICI mAbs. Immunobullous reactions were rare and a subset of non-ICI mAbs were associated with the development of vitiligo cAEs.

CONCLUSION

Dermatologic toxicities of non-ICI mAbs are diverse and mostly limited to inflammatory reactions. Awareness of the spectrum of the histopathologic patterns of cAE from non-ICI mAbs therapy is critical in the era of oncodermatology and oncodermatopathology.

The era of high-quality chemical probes

Small-molecule chemical probes are among the most important tools to study the function of proteins in cells and organisms. Regrettably, the use of weak and non-selective small molecules has generated an abundance of erroneous conclusions in the scientific literature. More recently, minimal criteria have been outlined for investigational compounds, encouraging the selection and use of high-quality chemical probes. Here, we briefly recall the milestones and key initiatives that have paved the way to this new era, illustrate examples of recent high-quality chemical probes and provide our perspective on future challenges and developments.

G-rich motifs within phosphorothioate-based antisense oligonucleotides (ASOs) drive activation of FXN expression through indirect effects

Friedreich's ataxia is an incurable disease caused by frataxin (FXN) protein deficiency, which is mostly induced by GAA repeat expansion in intron 1 of the FXN gene. Here, we identified antisense oligonucleotides (ASOs), complementary to two regions within the first intron of FXN pre-mRNA, which could increase FXN mRNA by ∼2-fold in patient fibroblasts. The increase in FXN mRNA was confirmed by the identification of multiple overlapping FXN-activating ASOs at each region, two independent RNA quantification assays, and normalization by multiple housekeeping genes. Experiments on cells with the ASO-binding sites deleted indicate that the ASO-induced FXN activation was driven by indirect effects. RNA sequencing analyses showed that the two ASOs induced similar transcriptome-wide changes, which did not resemble the transcriptome of wild-type cells. This RNA-seq analysis did not identify directly base-paired off-target genes shared across ASOs. Mismatch studies identified two guanosine-rich motifs (CCGG and G4) within the ASOs that were required for FXN activation. The phosphorodiamidate morpholino oligomer analogs of our ASOs did not activate FXN, pointing to a PS-backbone-mediated effect. Our study demonstrates the importance of multiple, detailed control experiments and target validation in oligonucleotide studies employing novel mechanisms such as gene activation.

Impact of SNPs, off-targets, and passive permeability on efficacy of BCL6 degrading drugs assigned by virtual screening and 3D-QSAR approach

B-cell lymphoma 6 (BCL6) regulates various genes and is reported to be overexpressed in lymphomas and other malignancies. Thus, BCL6 inhibition or its tagging for degradation would be an amenable therapeutic approach. A library of 2500 approved drugs was employed to find BCL6 inhibitory molecules via virtual screening. Moreover, the 3D core structure of 170 BCL6 inhibitors was used to build a 3D QSAR model and predict the biological activity. The SNP database was analyzed to study the impact on the destabilization of BCL6/drug interactions. Structural similarity search and molecular docking analyses were used to assess the interaction between possible off-targets and BCL6 inhibitors. The tendency of drugs for passive membrane permeability was also analyzed. Lifitegrast (DB11611) had favorable binding properties and biological activity compared to the BI-3802. Missense SNPs were located at the essential interaction sites of the BCL6. Structural similarity search resulted in five BTB-domain containing off-target proteins. BI-3802 and Lifitegrast had similar chemical behavior and binding properties against off-target candidates. More interestingly, the binding affinity of BI-3802 (against off-targets) was higher than Lifitegrast. Energetically, Lifitegrast was less favorable for passive membrane permeability. The interaction between BCL6 and BI-3802 is more prone to SNP-derived variations. On the other hand, higher nonspecific binding of BI-3802 to off-target proteins could bring about higher undesirable properties. It should also be noted that energetically less desirable passive membrane translocation of Lifitegrast would demand drug delivery vehicles. However, further empirical evaluation of Lifitegrast would unveil its true potential.

SCFFBXW7 regulates G2-M progression through control of CCNL1 ubiquitination

FBXW7, which encodes a substrate-specific receptor of an SCF E3 ligase complex, is a frequently mutated human tumor suppressor gene known to regulate the post-translational stability of various proteins involved in cellular proliferation. Here, using genome-wide CRISPR screens, we report a novel synthetic lethal genetic interaction between FBXW7 and CCNL1 and describe CCNL1 as a new substrate of the SCF-FBXW7 E3 ligase. Further analysis showed that the CCNL1-CDK11 complex is critical at the G2-M phase of the cell cycle since defective CCNL1 accumulation, resulting from FBXW7 mutation, leads to shorter mitotic time. Cells harboring FBXW7 loss-of-function mutations are hypersensitive to treatment with a CDK11 inhibitor, highlighting a genetic vulnerability that could be leveraged for cancer treatment.

Crystal structure of the CDK11 kinase domain bound to the small-molecule inhibitor OTS964

CDK11 is a cyclin-dependent kinase that controls proliferation by regulating transcription, RNA splicing, and the cell cycle. As its activity is increasingly associated with cancer, CDK11 is an attractive target for the development of small-molecule inhibitors. However, the development of CDK11 inhibitors with limited off-target effects against other CDKs poses a challenge based on the high conservation of sequence across family members. OTS964 is notable as it displays a measure of specificity for CDK11 in cells. To understand the basis for OTS964's specificity for CDK11, we solved a 2.6 Å crystal structure of the CDK11 kinase domain bound to OTS964. Despite the absence of cyclin, CDK11 adopts an active-like conformation when bound to OTS964. We identified amino acids likely to contribute to the specificity of OTS964 for CDK11 and assessed their contribution to OTS964 binding by isothermal titration calorimetry (ITC) in vitro and by resistance to OTS964 in cells.

A simplified and easy-to-use HIP HOP assay provides insights into chalcone antifungal mechanisms of action

Elucidating the mechanism of action of an antifungal or cytotoxic compound is a time-consuming process. Yeast chemogenomic profiling provides a compelling solution to the problem but is experimentally complex. Here, we demonstrate the use of a highly simplified yeast chemical genetic assay comprising just 89 yeast deletion strains, each diagnostic for a specific mechanism of action. We use the assay to investigate the mechanism of action of two antifungal chalcone compounds, trans-chalcone and 4'-hydroxychalcone, and narrow down the mechanism to transcriptional stress. Crucially, the assay eliminates mechanisms of action such as topoisomerase I inhibition and membrane disruption that have been suggested for related chalcone compounds. We propose this simplified assay as a useful tool to rapidly identify common off-target mechanisms.

DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs

The rational design of PROTACs is difficult due to their obscure structure-activity relationship. This study introduces a deep neural network model - DeepPROTACs to help design potent PROTACs molecules. It can predict the degradation capacity of a proposed PROTAC molecule based on structures of given target protein and E3 ligase. The experimental dataset is mainly collected from PROTAC-DB and appropriately labeled according to the DC₅₀ and Dmax values. In the model of DeepPROTACs, the ligands as well as the ligand binding pockets are generated and represented with graphs and fed into Graph Convolutional Networks for feature extraction. While SMILES representations of linkers are fed into a Bidirectional Long Short-Term Memory layer to generate the features. Experiments show that DeepPROTACs model achieves 77.95% average prediction accuracy and 0.8470 area under receiver operating characteristic curve on the test set. DeepPROTACs is available online at a web server ( https://bailab.siais.shanghaitech.edu.cn/services/deepprotacs/ ) and at github ( https://github.com/fenglei104/DeepPROTACs ).

Recent Advances in Nanomaterials for Asthma Treatment

Asthma is a chronic airway inflammatory disease with complex mechanisms, and these patients often encounter difficulties in their treatment course due to the heterogeneity of the disease. Currently, clinical treatments for asthma are mainly based on glucocorticoid-based combination drug therapy; however, glucocorticoid resistance and multiple side effects, as well as the occurrence of poor drug delivery, require the development of more promising treatments. Nanotechnology is an emerging technology that has been extensively researched in the medical field. Several studies have shown that drug delivery systems could significantly improve the targeting, reduce toxicity and improve the bioavailability of drugs. The use of multiple nanoparticle delivery strategies could improve the therapeutic efficacy of drugs compared to traditional delivery methods. Herein, the authors presented the mechanisms of asthma development and current therapeutic methods. Furthermore, the design and synthesis of different types of nanomaterials and micromaterials for asthma therapy are reviewed, including polymetric nanomaterials, solid lipid nanomaterials, cell membranes-based nanomaterials, and metal nanomaterials. Finally, the challenges and future perspectives of these nanomaterials are discussed to provide guidance for further research directions and hopefully promote the clinical application of nanotherapeutics in asthma treatment.

Proteome Integral Solubility Alteration (PISA) Assay in Mammalian Cells for Deep, High-Confidence, and High-Throughput Target Deconvolution

Chemical proteomics focuses on the drug-target-phenotype relationship for target deconvolution and elucidation of the mechanism of action-key and bottleneck in drug development and repurposing. Majorly due to the limits of using chemically modified ligands in affinity-based methods, new, unbiased, proteome-wide, and MS-based chemical proteomics approaches have been developed to perform drug target deconvolution, using full proteome profiling and no chemical modification of the studied ligand. Of note among them, thermal proteome profiling (TPP) aims to identify the target(s) by measuring the difference in melting temperatures between each identified protein in drug-treated versus vehicle-treated samples, with the thermodynamic interpretation of "protein melting" and curve fitting of all quantified proteins, at all temperatures, in each biological replicate. Including TPP, all the other chemical proteomics approaches often fail to provide target deconvolution with sufficient proteome depth, statistical power, throughput, and sustainability, which could hardly fulfill the final purpose of drug development. The proteome integral solubility alteration (PISA) assay provides no thermodynamic interpretation, but a throughput 10-100-fold compared to the other proteomics methods, high sustainability, much lower time of analysis and sample amount requirements, high confidence in results, maximal proteome coverage (~10,000 protein IDs), and up to five drugs / test molecules in one assay, with at least biological triplicates of each treatment. Each drug-treated or vehicle-treated sample is split into many fractions and exposed to a gradient of heat as solubility perturbing agent before being recomposed into one sample; each soluble fraction is isolated, then deep and quantitative proteomics is applied across all samples. The proteins interacting with the tested molecules (targets and off-targets), the activated mechanistic factors, or proteins modified during the treatment show reproducible changes in their soluble amount compared to vehicle-treated controls. As of today, the maximal multiplexing capability is 18 biological samples per PISA assay, which enables statistical robustness and flexible experimental design accommodation for fuller target deconvolution, including integration of orthogonal chemical proteomics methods in one PISA assay. Living cells for studying target engagement in vivo or, alternatively, protein extracts to identify in vitro ligand-interacting proteins can be studied, and the minimal need in sample amount unlocks target deconvolution using primary cells and their derived cultures. This protocol was validated in: J Biol Chem (2021), DOI: 10.1016/j.jbc.2021.10153 Graphical abstract.

Morphology and gene expression profiling provide complementary information for mapping cell state

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb human A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOAs) and gene targets, we find that the two assays not only provide a partially shared but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.

Recent advances of bioresponsive polymeric nanomedicine for cancer therapy

A bioresponsive polymeric nanocarrier for drug delivery is able to alter its physical and physicochemical properties in response to a variety of biological signals and pathological changes, and can exert its therapeutic efficacy within a confined space. These nanosystems can optimize the biodistribution and subcellular location of therapeutics by exploiting the differences in biochemical properties between tumors and normal tissues. Moreover, bioresponsive polymer-based nanosystems could be rationally designed as precision therapeutic platforms by optimizing the combination of responsive elements and therapeutic components according to the patient-specific disease type and stage. In this review, recent advances in smart bioresponsive polymeric nanosystems for cancer chemotherapy and immunotherapy will be summarized. We mainly discuss three categories, including acidity-sensitive, redox-responsive, and enzyme-triggered polymeric nanosystems. The important issues regarding clinical translation such as reproducibility, manufacture, and probable toxicity, are also commented.

Kinase perturbations redirect mitochondrial function in cancer

Protein kinases take the center stage in numerous signaling pathways by phosphorylating compartmentalized protein substrates for controlling cell proliferation, cell cycle and metabolism. Kinase dysfunctions have been linked to numerous human diseases such as cancer. This has led to the development of kinase inhibitors which aim to target oncogenic kinase activities. The specificity of the cancer blockers depends on the range of targeted kinases. Therefore, the question arises of how cell-type-specific off-target effects impair the specificities of cancer drugs. Blockade of kinase activities has been shown to converge on the energetic organelle, the mitochondria. In this review, we highlight examples of selected major kinases that impact mitochondrial signaling. Further, we discuss pharmacological strategies to target kinase activities linked to cancer progression and redirecting mitochondrial function. Finally, we propose that cell-based recordings of mitochondrial bioenergetic states might predict off-target or identify specific on-target effects of kinase inhibitors.

CRISPR-Induced Loss of Connexin 43 Expression Sensitizes KRAS Mutant Cells to Cisplatin

Gap Junction intercellular communication (GJIC) is often dysregulated in cancers, and this dysregulation has been shown to have pro-tumorigenic effects. Connexins (Cxs) are transmembrane proteins that make up gap junctions. Previous studies have indicated that RNA interference (RNAi)-based suppression of Cx43 increases cellular resistance to the chemotherapeutic agent cisplatin. Interestingly, we found that the loss of Cx43 expression induced by the CRISPR-Cas9 technology sensitizes cells to cisplatin in a KRAS mutant-dependent manner.

Small-Molecule Inhibition of the Acyl-Lysine Reader ENL as a Strategy against Acute Myeloid Leukemia

The chromatin reader eleven-nineteen leukemia (ENL) has been identified as a critical dependency in acute myeloid leukemia (AML), but its therapeutic potential remains unclear. We describe a potent and orally bioavailable small-molecule inhibitor of ENL, TDI-11055, which displaces ENL from chromatin by blocking its YEATS domain interaction with acylated histones. Cell lines and primary patient samples carrying MLL rearrangements or NPM1 mutations are responsive to TDI-11055. A CRISPR-Cas9-mediated mutagenesis screen uncovers an ENL mutation that confers resistance to TDI-11055, validating the compound's on-target activity. TDI-11055 treatment rapidly decreases chromatin occupancy of ENL-associated complexes and impairs transcription elongation, leading to suppression of key oncogenic gene expression programs and induction of differentiation. In vivo treatment with TDI-11055 blocks disease progression in cell line- and patient-derived xenograft models of MLL-rearranged and NPM1-mutated AML. Our results establish ENL displacement from chromatin as a promising epigenetic therapy for molecularly defined AML subsets and support the clinical translation of this approach.

SIGNIFICANCE

AML is a poor-prognosis disease for which new therapeutic approaches are desperately needed. We developed an orally bioavailable inhibitor of ENL, demonstrated its potent efficacy in MLL-rearranged and NPM1-mutated AML, and determined its mechanisms of action. These biological and chemical insights will facilitate both basic research and clinical translation. This article is highlighted in the In This Issue feature, p. 2483.

Pharmacogenomic Profiling of Pediatric Acute Myeloid Leukemia to Identify Therapeutic Vulnerabilities and Inform Functional Precision Medicine

Despite the expanding portfolio of targeted therapies for adults with acute myeloid leukemia (AML), direct implementation in children is challenging due to inherent differences in underlying genetics. Here we established the pharmacologic profile of pediatric AML by screening myeloblast sensitivity to approved and investigational agents, revealing candidates of immediate clinical relevance. Drug responses ex vivo correlated with patient characteristics, exhibited age-specific alterations, and concorded with activities in xenograft models. Integration with genomic data uncovered new gene-drug associations, suggesting actionable therapeutic vulnerabilities. Transcriptome profiling further identified gene-expression signatures associated with on- and off-target drug responses. We also demonstrated the feasibility of drug screening-guided treatment for children with high-risk AML, with two evaluable cases achieving remission. Collectively, this study offers a high-dimensional gene-drug clinical data set that could be leveraged to research the unique biology of pediatric AML and sets the stage for realizing functional precision medicine for the clinical management of the disease.

SIGNIFICANCE

We conducted integrated drug and genomic profiling of patient biopsies to build the functional genomic landscape of pediatric AML. Age-specific differences in drug response and new gene-drug interactions were identified. The feasibility of functional precision medicine-guided management of children with high-risk AML was successfully demonstrated in two evaluable clinical cases. This article is highlighted in the In This Issue feature, p. 476.

Small Interfering RNA Delivery Into Primary Human Natural Killer Cells for Functional Gene Analyses

Studying gene functions in human natural killer (NK) cells is key for advancing the understanding of NK cell biology and holds promise to pave the way for improving NK cell therapies against cancer. However, NK cells are challenging to manipulate, and investigation of gene functions in NK cells is hampered by variable delivery efficiencies and impaired viability upon electroporation, lipofection, or viral transduction. Here, we report a simple workflow for delivery of commercially available small interfering RNA molecules into primary human NK cells to enable functional gene analyses. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Enrichment of natural killer cells from human peripheral blood mononuclear cells Basic Protocol 2: Preparation of small interfering RNA Basic Protocol 3: Delivery of small interfering RNA into natural killer cells Support Protocol 1: Isolation of human peripheral blood mononuclear cells from buffy coats Support Protocol 2: Thawing and recovery of cryopreserved peripheral blood mononuclear cells Support Protocol 3: Evaluation of natural killer cell purity following magnetic enrichment Support Protocol 4: Exemplary assessment of knockdown efficiency.

Synergistic antitumor activity of sorafenib and MG149 in hepatocellular carcinoma cells

Advanced hepatocellular carcinoma (HCC) is among the most challenging cancers to overcome, and there is a need for better therapeutic strategies. Among the different cancer drugs that have been used in clinics, sorafenib is considered the standard first-line drug for advanced HCC. Here, to identify a chemical compound displaying a synergistic effect with sorafenib in HCC, we screened a focused chemical library and found that MG149, a histone acetyltransferase inhibitor targeting the MYST family, exhibited the most synergistic anticancer effect with sorafenib on HCC cells. The combination of sorafenib and MG149 exerted a synergistic anti-proliferation effect on HCC cells by inducing apoptotic cell death. We revealed that cotreatment with sorafenib and MG149 aggravated endoplasmic reticulum (ER) stress to promote the death of HCC cells rather than adaptive cell survival. In addition, combined treatment with sorafenib and MG149 significantly increased the intracellular levels of unfolded proteins and reactive oxygen species, which upregulated ER stress. Collectively, these results suggest that MG149 has the potential to improve the efficacy of sorafenib in advanced HCC via the upregulation of cytotoxic ER stress. [BMB Reports 2022; 55(10): 506-511].

What Are the Reasons for Continuing Failures in Cancer Therapy? Are Misleading/Inappropriate Preclinical Assays to Be Blamed? Might Some Modern Therapies Cause More Harm than Benefit?

Over 50 years of cancer research has resulted in the generation of massive amounts of information, but relatively little progress has been made in the treatment of patients with solid tumors, except for extending their survival for a few months at best. Here, we will briefly discuss some of the reasons for this failure, focusing on the limitations and sometimes misunderstanding of the clinical relevance of preclinical assays that are widely used to identify novel anticancer drugs and treatment strategies (e.g., "synthetic lethality"). These include colony formation, apoptosis (e.g., caspase-3 activation), immunoblotting, and high-content multiwell plate cell-based assays, as well as tumor growth studies in animal models. A major limitation is that such assays are rarely designed to recapitulate the tumor repopulating properties associated with therapy-induced cancer cell dormancy (durable proliferation arrest) reflecting, for example, premature senescence, polyploidy and/or multinucleation. Furthermore, pro-survival properties of apoptotic cancer cells through phoenix rising, failed apoptosis, and/or anastasis (return from the brink of death), as well as cancer immunoediting and the impact of therapeutic agents on interactions between cancer and immune cells are often overlooked in preclinical studies. A brief review of the history of cancer research makes one wonder if modern strategies for treating patients with solid tumors may sometimes cause more harm than benefit.

EGFL7 drives the evolution of resistance to EGFR inhibitors in lung cancer by activating NOTCH signaling

Accumulating evidence supports evolutionary trait of drug resistance. Like resilience in other systems, most tumor cells experience drug-tolerant state before full resistance acquired. However, the underlying mechanism is still poorly understood. Here, we identify that EGF like domain multiple 7 (EGFL7) is a responsive gene to epidermal growth factor receptor (EGFR) kinase inhibition during a period when tumors are decimated. Moreover, our data reveal that the adaptive increase of EGFL7 during this process is controlled by the depression of nonsense-mediated mRNA decay (NMD) pathway. Upregulation of EGFL7 activates NOTCH signaling in lung cancer cells, which slows down the decrease of c-Myc caused by EGFR inhibition, thereby helping the survival of cancer cells. Our data, taken together, demonstrate that EGFL7 is a driver gene for resistance to EGFR kinase inhibition, and suggest that targeting EGFL7/NOTCH signaling may improve the clinical benefits of EGFR inhibitors in patients with EGFR mutant tumors.

Overlapping gene dependencies for PARP inhibitors and carboplatin response identified by functional CRISPR-Cas9 screening in ovarian cancer

PARP inhibitors (PARPi) have revolutionized the therapeutic landscape of epithelial ovarian cancer (EOC) treatment with outstanding benefits in regard to progression-free survival, especially in patients either carrying BRCA1/2 mutations or harboring defects in the homologous recombination repair system. Yet, it remains uncertain which PARPi to apply and how to predict responders when platinum sensitivity is unknown. To shed light on the predictive power of genes previously suggested to be associated with PARPi response, we systematically reviewed the literature and identified 79 publications investigating a total of 93 genes. The top candidate genes were further tested using a comprehensive CRISPR-Cas9 mutagenesis screening in combination with olaparib treatment. Therefore, we generated six constitutive Cas9⁺ EOC cell lines and profiled 33 genes in a CRISPR-Cas9 cell competition assay using non-essential (AAVS1) and essential (RPA3 and PCNA) genes for cell fitness as negative and positive controls, respectively. We identified only ATM, MUS81, NBN, BRCA2, and RAD51B as predictive markers for olaparib response. As the major survival benefit of PARPi treatment was reported in platinum-sensitive tumors, we next assessed nine top candidate genes in combination with three PARPi and carboplatin. Interestingly, we observed similar dropout rates in a gene and compound independent manner, supporting the strong correlation of cancer cell response to compounds that rely on DNA repair for their effectiveness. In addition, we report on CDK12 as a common vulnerability for EOC cell survival and proliferation without altering the olaparib response, highlighting its potential as a therapeutic target in EOC.