Target For Cancer Drug Discovery Research Articles

Discovery of LLC355 as an Autophagy-Tethering Compound for the Degradation of Discoidin Domain Receptor 1.

Discoidin domain receptor 1 (DDR1) is a potential target for cancer drug discovery. Although several DDR1 kinase inhibitors have been developed, recent studies have revealed the critical roles of the noncatalytic functions of DDR1 in tumor progression, metastasis, and immune exclusion. Degradation of DDR1 presents an opportunity to block its noncatalytic functions. Here, we report the discovery of the DDR1 degrader LLC355 by employing autophagosome-tethering compound technology. Compound LLC355 efficiently degraded DDR1 protein with a DC50 value of 150.8 nM in non-small cell lung cancer NCI-H23 cells. Mechanistic studies revealed compound LLC355 to induce DDR1 degradation via lysosome-mediated autophagy. Importantly, compound LLC355 potently suppressed cancer cell tumorigenicity, migration, and invasion and significantly outperformed the corresponding inhibitor 1. These results underline the therapeutic advantage of targeting the noncatalytic function of DDR1 over inhibition of its kinase activity.

Ribosome subunit attrition and activation of the p53–MDM4 axis dominate the response of MLL-rearranged cancer cells to WDR5 WIN site inhibition

The chromatin-associated protein WD Repeat Domain 5 (WDR5) is a promising target for cancer drug discovery, with most efforts blocking an arginine-binding cavity on the protein called the ‘WIN’ site that tethers WDR5 to chromatin. WIN site inhibitors (WINi) are active against multiple cancer cell types in vitro, the most notable of which are those derived from MLL-rearranged (MLLr) leukemias. Peptidomimetic WINi were originally proposed to inhibit MLLr cells via dysregulation of genes connected to hematopoietic stem cell expansion. Our discovery and interrogation of small-molecule WINi, however, revealed that they act in MLLr cell lines to suppress ribosome protein gene (RPG) transcription, induce nucleolar stress, and activate p53. Because there is no precedent for an anticancer strategy that specifically targets RPG expression, we took an integrated multi-omics approach to further interrogate the mechanism of action of WINi in human MLLr cancer cells. We show that WINi induce depletion of the stock of ribosomes, accompanied by a broad yet modest translational choke and changes in alternative mRNA splicing that inactivate the p53 antagonist MDM4. We also show that WINi are synergistic with agents including venetoclax and BET-bromodomain inhibitors. Together, these studies reinforce the concept that WINi are a novel type of ribosome-directed anticancer therapy and provide a resource to support their clinical implementation in MLLr leukemias and other malignancies.

Ribosome subunit attrition and activation of the p53-MDM4 axis dominate the response of MLL-rearranged cancer cells to WDR5 WIN site inhibition.

The chromatin-associated protein WD Repeat Domain 5 (WDR5) is a promising target for cancer drug discovery, with most efforts blocking an arginine-binding cavity on the protein called the 'WIN' site that tethers WDR5 to chromatin. WIN site inhibitors (WINi) are active against multiple cancer cell types in vitro, the most notable of which are those derived from MLL-rearranged (MLLr) leukemias. Peptidomimetic WINi were originally proposed to inhibit MLLr cells via dysregulation of genes connected to hematopoietic stem cell expansion. Our discovery and interrogation of small-molecule WINi, however, revealed that they act in MLLr cell lines to suppress ribosome protein gene (RPG) transcription, induce nucleolar stress, and activate p53. Because there is no precedent for an anticancer strategy that specifically targets RPG expression, we took an integrated multi-omics approach to further interrogate the mechanism of action of WINi in human MLLr cancer cells. We show that WINi induce depletion of the stock of ribosomes, accompanied by a broad yet modest translational choke and changes in alternative mRNA splicing that inactivate the p53 antagonist MDM4. We also show that WINi are synergistic with agents including venetoclax and BET-bromodomain inhibitors. Together, these studies reinforce the concept that WINi are a novel type of ribosome-directed anticancer therapy and provide a resource to support their clinical implementation in MLLr leukemias and other malignancies.

Inhibitors of NAD+ Production in Cancer Treatment: State of the Art and Perspectives.

The addiction of tumors to elevated nicotinamide adenine dinucleotide (NAD+) levels is a hallmark of cancer metabolism. Obstructing NAD+ biosynthesis in tumors is a new and promising antineoplastic strategy. Inhibitors developed against nicotinamide phosphoribosyltransferase (NAMPT), the main enzyme in NAD+ production from nicotinamide, elicited robust anticancer activity in preclinical models but not in patients, implying that other NAD+-biosynthetic pathways are also active in tumors and provide sufficient NAD+ amounts despite NAMPT obstruction. Recent studies show that NAD+ biosynthesis through the so-called "Preiss-Handler (PH) pathway", which utilizes nicotinate as a precursor, actively operates in many tumors and accounts for tumor resistance to NAMPT inhibitors. The PH pathway consists of three sequential enzymatic steps that are catalyzed by nicotinate phosphoribosyltransferase (NAPRT), nicotinamide mononucleotide adenylyltransferases (NMNATs), and NAD+ synthetase (NADSYN1). Here, we focus on these enzymes as emerging targets in cancer drug discovery, summarizing their reported inhibitors and describing their current or potential exploitation as anticancer agents. Finally, we also focus on additional NAD+-producing enzymes acting in alternative NAD+-producing routes that could also be relevant in tumors and thus become viable targets for drug discovery.

An 19F NMR fragment-based approach for the discovery and development of BRCA2-RAD51 inhibitors to pursuit synthetic lethality in combination with PARP inhibition in pancreatic cancer

The BRCA2-RAD51 interaction remains an intriguing target for cancer drug discovery due to its vital role in DNA damage repair mechanisms, which cancer cells become particularly reliant on. Moreover, RAD51 has many synthetically lethal partners, including PARP1-2, which can be exploited to induce synthetic lethality in cancer. In this study, we established a 19F-NMR-fragment based approach to identify RAD51 binders, leading to two initial hits. A subsequent SAR program identified 46 as a low micromolar inhibitor of the BRCA2-RAD51 interaction. 46 was tested in different pancreatic cancer cell lines, to evaluate its ability to inhibit the homologous recombination DNA repair pathway, mediated by BRCA2-RAD51 and trigger synthetic lethality in combination with the PARP inhibitor talazoparib, through the induction of apoptosis. Moreover, we further analyzed the 46/talazoparib combination in 3D pancreatic cancer models. Overall, 46 showed its potential as a tool to evaluate the RAD51/PARP1-2 synthetic lethality mechanism, along with providing a prospect for further inhibitors development.

KR4SL: knowledge graph reasoning for explainable prediction of synthetic lethality.

Synthetic lethality (SL) is a promising strategy for anticancer therapy, as inhibiting SL partners of genes with cancer-specific mutations can selectively kill the cancer cells without harming the normal cells. Wet-lab techniques for SL screening have issues like high cost and off-target effects. Computational methods can help address these issues. Previous machine learning methods leverage known SL pairs, and the use of knowledge graphs (KGs) can significantly enhance the prediction performance. However, the subgraph structures of KG have not been fully explored. Besides, most machine learning methods lack interpretability, which is an obstacle for wide applications of machine learning to SL identification. We present a model named KR4SL to predict SL partners for a given primary gene. It captures the structural semantics of a KG by efficiently constructing and learning from relational digraphs in the KG. To encode the semantic information of the relational digraphs, we fuse textual semantics of entities into propagated messages and enhance the sequential semantics of paths using a recurrent neural network. Moreover, we design an attentive aggregator to identify critical subgraph structures that contribute the most to the SL prediction as explanations. Extensive experiments under different settings show that KR4SL significantly outperforms all the baselines. The explanatory subgraphs for the predicted gene pairs can unveil prediction process and mechanisms underlying synthetic lethality. The improved predictive power and interpretability indicate that deep learning is practically useful for SL-based cancer drug target discovery. The source code is freely available at https://github.com/JieZheng-ShanghaiTech/KR4SL.

Repurposing of FDA Approved Drugs and Neuropep peptides as Anticancer Agents Against ErbB1 and ErbB2.

ErbB1 and ErbB2 are the most important biological targets in cancer drug discovery and development of dual inhibitors for the cancer therapy. FDA approved drugs and Neuropep peptides were used to fit into the ATP binding site of the tyrosine kinases; ErbB1 and ErbB2 proteins. Cytoscape, iGEMDOCK, HPEPDOCK and DataWarrior softwares were used to study the role of these agents as anticancer drugs. Eleven FDA approved drugs and eleven Neuropep peptides showed the strongest 2D interactions and significant binding energy with the proteins. Invitro MTT anticancer assay revealed that, the test compounds, peptide YSFGL and doxorubicin showed significant IC50 value (µM) of 26.417±0.660 and 7.675±0.278 respectively which are compared with the lapatinib standard IC50 value (µM) of 2.380±0.357 against A549 cells and IC50 value (µM) of 39.047±0.770 and 8.313±0.435 respectively which are compared with the lapatinib standard IC50 value (µM) of 3.026±0.180 against MDA-MB-231 cells.

Turning down PI3K/AKT/mTOR signalling pathway by natural products: an in silico multi-target approach

ABSTRACT The PI3K/AKT/mTOR pathway is a significant target for cancer drug discovery. Many efforts have focused on discovering new inhibitors against key kinase proteins involved in this pathway for cancer treatment. PI3K/mTOR dual inhibitors, such as PKI-179, have been reported to be more effective than agents that act only on a single protein target. The present computational study aimed to discover triple target inhibitors against PI3K, AKT, and mTOR proteins. Accordingly, the PI3K protein bound with the ligand was used as input for e-pharmacophore modelling to generate the pharmacophore hypothesis and then screened for a library of 270,540 natural products from the Zinc database resulting in 57,220 compounds that matched the hypothesis. These compounds were then docked into the active site of PI3K, resulting in 292 compounds with better docking scores than the co-crystallized ligand. These compounds were re-docked into AKT and mTOR proteins. Besides, MM-GBSA binding free energy calculations, MD simulations, and ADMET prediction were carried out, leading to 5 potential triple-target inhibitors namely, ZINC000014644152, ZINC000014760695, ZINC000014644839, ZINC000095099451, and ZINC000005998557. In conclusion, these inhibitors may be possible leads for inhibiting PI3K/AKT/mTOR pathway, and they may be further evaluated in vitro and clinically as anticancer agents.

Production of human embryonic kidney 293T cells stably expressing C-X-C chemokine receptor type 4 (CXCR4) as a screening tool for anticancer lead compound targeting CXCR4

AimThe C-X-C chemokine-receptor type 4 (CXCR4) is an emerging target for cancer drug discovery due to its high expression in cancer cells. The present study aimed to produce CXCR4 overexpressing HEK293T cells for a non-radioactive binding assay as a platform to identify drug candidates targeting CXCR4. Main methodsHEK293T cells stably expressing human CXCR4 were constructed by transfection of CXCR4 plasmids from the human CXCR4 gene. The CXCR4 overexpressing HEK293T cells were obtained by fluorescence-activated sorting and verified by conducting the competition binding assay of a known CXCR4 inhibitor, AMD3100 (plerixafor), to determine the IC50 value against monoclonal anti-human CD184 (hCD184) antibody tagged with fluorescence probe, phycoerythrin (PE). The non-radioactive binding assay using CXCR4 overexpressing HEK293T cells and PE-anti hCD184 was applied as a platform for identifying the target of natural compounds that exhibited cytotoxicity against cancer cell lines. Key findingsThe CXCR4 overexpressing HEK293T cells were produced with high expression (99.8%). The IC50 value of plerixafor determined by fluorescence tagged antibody-based competition assay using our developed cells agree with previously reported values using a radioligand binding assay. We observed no significant displacement of bound PE-anti-hCD184 by the test natural compounds which could be due to non-specific binding to other functional targets or organelles, low potency of the natural compounds, or binding to CXCR4 at deeper pockets. SignificanceThe verified non-radioactive binding assay can serve as an alternative screening tool for anticancer lead compounds targeting CXCR4 and an essential tool for proof of mechanism study in the drug discovery.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery.

CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

Targeting the Non-Catalytic Functions: a New Paradigm for Kinase Drug Discovery?

Protein kinases have been highly fruitful targets for cancer drug discovery in the past two decades, while most of these drugs bind to the "adenosine triphosphate (ATP)-site" and inhibit kinase catalytic activity. Recently, accumulated evidence suggests that kinases possess functions beyond catalysis through their scaffolds, and the scaffolding functions could play critical roles in multiple cellular signaling and cell fate controls. Small molecules modulating the noncatalytic functions of kinases are rarely reported but emerge as new promising therapeutic strategies for various diseases. Herein, we summarize the characterized noncatalytic functions of kinases, and highlight the recent progress on developing small-molecule modulators of the noncatalytic functions of kinases. Mechanisms and characteristics of different kinds of modulators are also discussed. It is also speculated that targeting the noncatalytic functions would represent a new direction for kinase-based drug discovery.

Acacetin Inhibits the Growth of STAT3-Activated DU145 Prostate Cancer Cells by Directly Binding to Signal Transducer and Activator of Transcription 3 (STAT3)

Signal transducer and activator of transcription 3 (STAT3) plays a critical role in the formation and growth of human cancer. Therefore, STAT3 is a therapeutic target for cancer drug discovery. Acacetin, a flavone present in various plants, inhibits constitutive and inducible STAT3 activation in STAT3-activated DU145 prostate cancer cells. Acacetin inhibits STAT3 activity by directly binding to STAT3, which we confirmed by a pull-down assay with a biotinylated compound and two level-free methods, namely, a drug affinity responsive target stability (DARTS) experiment and a cellular thermal shift assay (CETSA). Acacetin inhibits STAT3 phosphorylation at the tyrosine 705 residue and nuclear translocation in DU145 cells, which leads to the downregulation of STAT3 target genes. Acacetin then induces apoptosis in a time-dependent manner. Interestingly, acacetin induces the production of reactive oxygen species (ROS) that are not involved in the acacetin-induced inhibition of STAT3 activation because the suppressed p-STAT3 level is not rescued by treatment with GSH or NAC, which are general ROS inhibitors. We also found that acacetin inhibits tumor growth in xenografted nude mice. These results suggest that acacetin, as a STAT3 inhibitor, could be a possible drug candidate for targeting STAT3 for the treatment of cancer in humans.

Abstract 2872: Methods of assessment of polysialyltransferase inhibitors for treatment of tumor cell dissemination

Abstract Polysialic acid (polySia) decorates the surface of NCAM (neuronal cell adhesion molecule) on neuroendocrine tumors, notably neuroblastoma and small cell lung cancer, and is strongly associated with poor prognosis and aggressive disease in patients in the clinic [1]. PolySia modulates tumor cell-cell and cell-matrix adhesion, migration, invasion and metastasis. SiRNA knockdown of polysialyltransferase (polyST) ST8SiaII, the enzyme primarily responsible for polySia synthesis in tumors, abrogates tumor cell migration and invasion. PolyST is a selective and largely unexplored therapeutic target for neuroblastoma dissemination [1]. While assays are available to assess polyST enzyme activity, there is no methodology available specifically optimized for identification of novel polyST inhibitors. We describe the development of cell-free and cell-based assays that enable assessment of polysialyltransferase inhibition. Development of the HPLC-fluorescence-based enzyme assay includes a comprehensive optimization of assay conditions, including evaluation of metal ion composition, enzyme concentration, substrate and acceptor concentration, temperature, pH and tolerance to DMSO, followed by validation using known polyST inhibitors. Thorough analysis of each of the assay components provided a set of optimized conditions. Under these optimized conditions, the experimentally observed Ki value for CMP, a competitive polyST inhibitor, was strongly correlated with the predicted Ki value, based on the classical Cheng-Prusoff equation [average fold error (AFE) = 1.043]. These results indicate that this assay can provide medium-throughput analysis for enzyme inhibitors with high accuracy. We additionally report optimized HPLC-based and ELISA-based methodologies for assessment of polyST inhibition in neuroblastoma cells, using endoneuraminidase N as control, and assessment of ICT3176 (a polysialylation inhibitor) as a test agent. In conclusion, in vitro cell-free and cell-based assays for accurate measurement of polysialyltransferase (polyST) inhibition are described, specifically designed for routine identification of potential polyST inhibitors, generation of kinetics data and assessment of mode of inhibition, and assessment of effects on cellular polysialylation. Given the considerable and growing interest in the polySTs as important anti-metastatic targets in cancer drug discovery, these are vital tools to enable preclinical identification and evaluation of novel polyST inhibitors for neuroblastoma therapy. [1] Falconer, R.A. et al., Curr. Cancer Drug Targets, 2012, 12, 925-939. Citation Format: Xiaoxiao Guo, Jodie R. Malcolm, Anjana Patel, Marrwa M. Ali, Goreti Ribeiro Morais, Steven D. Shnyder, Paul M. Loadman, Laurence H. Patterson, Robert A. Falconer. Methods of assessment of polysialyltransferase inhibitors for treatment of tumor cell dissemination [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2872.

EGCG binds intrinsically disordered N-terminal domain of p53 and disrupts p53-MDM2 interaction

Epigallocatechin gallate (EGCG) from green tea can induce apoptosis in cancerous cells, but the underlying molecular mechanisms remain poorly understood. Using SPR and NMR, here we report a direct, μM interaction between EGCG and the tumor suppressor p53 (KD = 1.6 ± 1.4 μM), with the disordered N-terminal domain (NTD) identified as the major binding site (KD = 4 ± 2 μM). Large scale atomistic simulations (>100 μs), SAXS and AUC demonstrate that EGCG-NTD interaction is dynamic and EGCG causes the emergence of a subpopulation of compact bound conformations. The EGCG-p53 interaction disrupts p53 interaction with its regulatory E3 ligase MDM2 and inhibits ubiquitination of p53 by MDM2 in an in vitro ubiquitination assay, likely stabilizing p53 for anti-tumor activity. Our work provides insights into the mechanisms for EGCG’s anticancer activity and identifies p53 NTD as a target for cancer drug discovery through dynamic interactions with small molecules.

Mechanistic insights into the effect of phosphorylation on Ras conformational dynamics and its interactions with cell signaling proteins.

Ras undergoes interconversion between the active GTP-bound state and the inactive GDP-bound state. This GTPase cycle, which controls the activities of Ras, is accelerated by Ras GTPase-activating proteins (GAPs) and guanine nucleotide exchange factors (SOS). Oncogenic Ras mutations could affect the GTPase cycle and impair Ras functions. Additionally, Src-induced K-Ras Y32/64 dual phosphorylation has been reported to disrupt GTPase cycle and hinder Ras downstream signaling. However, the underlying mechanisms remain unclear. To address this, we performed molecular dynamics simulations (~30μs in total) on unphosphorylated and phosphorylated K-Ras4B in GTP- and GDP-bound states, and on their complexes with GTPase cycle regulators (GAP and SOS) and the effector protein Raf. We found that K-Ras4B dual phosphorylation mainly alters the conformation at the nucleotide binding site and creates disorder at the catalytic site, resulting in the enlargement of GDP binding pocket and the retard of Ras-GTP intrinsic hydrolysis. We observed phosphorylation-induced shift in the distribution of Ras-GTP inactive-active sub-states and recognized potential druggable pockets in the phosphorylated Ras-GTP. Moreover, decreased catalytic competence or signal delivery abilities due to reduced binding affinities and/or distorted catalytic conformations of GAP, SOS and Raf were observed. In addition, the allosteric pathway from Ras/Raf interface to the distal Raf L4 loop was compromised by Ras phosphorylation. These results reveal the mechanisms by which phosphorylation influences the intrinsic or GAP/SOS catalyzed transformations between GTP- and GDP-bound states of Ras and its signal transduction to Raf. Our findings project Ras phosphorylation as a target for cancer drug discovery.

Studying the Binding Modes of Novel 2-Aminopyridine Derivatives as Effective and Selective c-Met Kinase Type 1 Inhibitors Using Molecular Modeling Approaches

The mesenchymal epithelial cell transforming factor c-Met, encoded by c-Met proto-oncogene and known as a high-affinity receptor for Hepatocyte Growth Factor (HGF), is one of the receptor tyrosine kinases (RTKs) members. The HGF/c-Met signaling pathway has close correlation with tumor growth, invasion and metastasis. Thus, c-Met kinase has emerged as a prominent therapeutic target for cancer drug discovery. Recently a series of novel 2-aminopyridine derivatives targeting c-Met kinase with high biological activity were reported. In this study, 3D quantitative structure-activity relationship (QSAR), molecular docking and molecular dynamics simulations (MD) were employed to research the binding modes of these inhibitors.The results show that both the atom-based and docking-based CoMFA (Q2 = 0.596, R2 = 0.950 in atom-based model and Q2 = 0.563, R2 = 0.985 in docking-based model) and CoMSIA (Q2 = 0.646, R2 = 0.931 in atom-based model and Q2 = 0.568, R2 = 0.983 in docking-based model) models own satisfactory performance with good reliabilities and powerful external predictabilities. Molecular docking study suggests that Tyr1230 and Arg1208 might be the key residues, and electrostatic and hydrogen bond interactions were shown to be vital to the activity, concordance with QSAR analysis. Then MD simulation was performed to further explore the binding mode of the most potent inhibitor. The obtained results provide important references for further rational design of c-Met Kinase type I inhibitors.

Identification of Potential Chemical Substrates as Fuel for Hypoxic Tumors That May Be Linked to Invadopodium Formation in Hypoxia-Induced MDA-MB-231 Breast-Cancer Cell Line.

Hypoxia plays a significant role in solid tumors by the increased expression of hypoxia-inducible factor-1α (HIF-1α), which is known to promote cancer invasion and metastasis. Cancer-cell invasion dynamically begins with the degradation of the extracellular matrix (ECM) via invadopodia formation. The chemical substrates that are utilized by hypoxic cells as fuel to drive invadopodia formation are still not fully understood. Therefore, the aim of the study was to maintain MDA-MB-231 cells under hypoxia conditions to allow cells to form a large number of invadopodia as a model, followed by identifying their nutrient utilization. The results of the study revealed an increase in the number of cells forming invadopodia under hypoxia conditions. Moreover, Western blot analysis confirmed that essential proteins for hypoxia and invadopodia, including HIF-1α, vascular endothelial growth factor (VEGF), metallopeptidase-2 (MMP-2), and Rho guanine nucleotide exchange factor 7 (β-PIX), significantly increased under hypoxia. Interestingly, phenotype microarray showed that only 11 chemical substrates from 367 types of substrates were significantly metabolized in hypoxia compared to in normoxia. This is thought to be fuel for hypoxia to drive the invasion process. In conclusion, we found 11 chemical substrates that could have potential energy sources for hypoxia-induced invadopodia formation of these cells. This may in part be a target in the hypoxic tumor and invadopodia formation. Additionally, these findings can be used as potential carrier targets in cancer-drug discovery, such as the usage of dextrin.

Abstract 5165: Novel approach to colorectal cancer drug target discovery in human FFPE tissue using the Adaptive Dynamic Artificial Polyligand Targeting System

Abstract Identification of novel biomarkers and drug targets for colorectal cancer remains a critically unmet medical need. Target identification has historically been driven by preclinical models which poorly recapitulate the complex biology of human cancers. We describe a unique approach to drug target identification and comprehensive profiling directly in clinical specimens. Using the Adaptive Dynamic Artificial Polyligand Targeting (ADAPT) system, we aimed to identify such disease specific drug targets using a highly complex library of single-stranded oligodeoxynucleotides (ODNs). To generate the library, the ODNs were enriched using FFPE tissue specimens from pathologically determined colorectal cancer (CRC) and counter-selected against normal adjacent tissue (NAT) regions. Once the specificity of the library was verified via differential binding on independent CRC cases, it was used to affinity purify cognate biding partners from 393 CRC FFPE lysates with appropriate representation of cases with either KRAS, NRAS or BRAF mutations. Greater than 700 proteins were identified by mass spectrometry exclusively in the CRC FFPE tissue lysates and not in the NAT FFPE tissue lysates for all 393 CRC cases. Classification of these proteins into the druggable protein classes revealed G-protein coupled receptors, transporters, voltage-gated ion channels, proteases, and protein kinases. In addition, subsequent verification studies confirmed novel candidate cell surface protein targets appropriate for targeting by antibody-drug conjugates (ADC) or other biologic modalities. mRNA expression corresponding to the 700 potential targets was analyzed in over 1600 CRC FFPE tumor samples and >9000 other cancer types as well as in a broad panel of normal tissues by Whole Transcriptome Sequencing (WTS) and immunohistochemistry (IHC). We conclude that the unbiased ADAPT platform provides a robust and novel approach to drug target discovery with highly selective expression profiles directly from FFPE clinical specimens. Citation Format: Stephanie Williams, Chao Sima, Tassilo Hornung, Matthew Rosenow, Teresa Tinder, Varun Maher, Michelle Kassner, Gerri Ortiz, Perrin Mok, Nicholas Helle, Stephen Logie, Heather ONeill, Mark Miglarese, David Spetzler. Novel approach to colorectal cancer drug target discovery in human FFPE tissue using the Adaptive Dynamic Artificial Polyligand Targeting System [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5165.

High-performance CRISPR-Cas12a genome editing for combinatorial genetic screening

CRISPR-based genetic screening has revolutionized cancer drug target discovery, yet reliable, multiplex gene editing to reveal synergies between gene targets remains a major challenge. Here, we present a simple and robust CRISPR-Cas12a-based approach for combinatorial genetic screening in cancer cells. By engineering the CRISPR-AsCas12a system with key modifications to the Cas protein and its CRISPR RNA (crRNA), we can achieve high efficiency combinatorial genetic screening. We demonstrate the performance of our optimized AsCas12a (opAsCas12a) through double knockout screening against epigenetic regulators. This screen reveals synthetic sick interactions between Brd9&Jmjd6, Kat6a&Jmjd6, and Brpf1&Jmjd6 in leukemia cells.

Abstract IA11: Tissue- and cell-specific functions of Hras, Kras4A, and Kras4B in skin and lung cancer

Abstract Mouse models of cancer have been instrumental in the development of our understanding of the processes that drive multistage carcinogenesis from the initiation stage to metastasis. Tumors induced in mice by exposure to carcinogenic chemicals replicate many of the biologic and genetic features of multistage cancer in human populations. Skin and lung tumors initiated by chemical mutagens generally have carcinogen-specific mutations in Hras or Kras, respectively, but the reasons for this selectivity are unknown. We have used mouse models of skin and lung cancer to address some of the unanswered questions in cancer biology related to the specific functions of Hras, Kras4A, and Kras4B in cancer development in vivo. The KRAS gene, encoding two splice isoforms (KRAS4A and KRAS4B), is mutated in many lethal human tumors and has become a major target for cancer drug discovery. We show that germline deletion of either the major Kras4B or the minor Kras4A isoform in mice confers complete resistance to development of Kras mutant lung cancers. Double heterozygotes that are able to express both isoforms, but in which splicing has been uncoupled, are also tumor resistant, suggesting that coordinated expression of both splice variants is necessary for cancer initiation in vivo. KRAS4A is enriched in human tumor cells with stem cell properties, and the two splice variants show different responses to inducers of hypoxia or ER stress. Our data help to rationalize previous conflicting information on metabolic changes in KRAS mutant tumors and suggest that KRAS4A and 4B may require separate targeting in cancer stem and progenitor cells. Citation Format: Allan Balmain. Tissue- and cell-specific functions of Hras, Kras4A, and Kras4B in skin and lung cancer [abstract]. In: Proceedings of the AACR Special Conference on Targeting RAS-Driven Cancers; 2018 Dec 9-12; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(5_Suppl):Abstract nr IA11.