Oncology Drug Discovery Research Articles

Perspective on Tumor Organoids in Drug Screening and Cancer Precision Medicine

The landscape of cancer research is swiftly evolving, presenting fresh optimism through innovative tactics that customize treatment for each patient. In 2016, Clevers unveiled a groundbreaking study in Cell, introducing organoids that emulate essential structural and functional traits of organs like the kidney, lung, gut, brain, and retina. This technology enables the replication of human organ development and various pathologies within a controlled laboratory environment. Mounting evidence underscores the potential of patient-derived organoids (PDOs) to reshape cancer treatment and revolutionize drug discovery in oncology. This commentary explores these pioneering research avenues and their profound implications for clinical oncology and pharmaceutical development.

Abstract 2945: Using pooled minimal genome-wide sgRNA libraries to drive oncology drug discovery

Abstract The emergence of CRISPR technologies has enabled rapid and precise gene editing opportunities. The Functional Genomics Centre (FGC) - a joint venture between Cancer Research UK’s (CRUK) drug discovery engine, Cancer Research Horizons, and AstraZeneca has been launched to better utilize and improve the CRISPR technology within cancer research. The FGC leverages the strengths of both CRUK’s and AstraZeneca’s world-class oncology research and drug discovery experience, with the potential to drive scientific innovation in how new drug targets are identified and validated, and uniquely democratize access to the latest cutting-edge research tools and expertise for many thousands of scientists and clinicians funded by CRUK. Here, we will showcase how the FGC is leading the development and refinement of functional genomics technologies. Through independent evaluation of publicly available CRISPR tools and leveraging this data we have designed optimized, minimal genome-wide CRISPR libraries. These libraries permit a reduction of the experimental scale of CRISPR screens by 50% and enable our Target Discovery platform to impact more projects, faster, at a lower cost and importantly - more sustainably. Citation Format: Sebastian Lukasiak, Alex Kalinka, Nikhil Gupta, Khalid Saeed, Angelos Papadopoulos, Ultan McDermott, Gregory J. Hannon, Douglas Ross-Thriepland, David Walter. Using pooled minimal genome-wide sgRNA libraries to drive oncology drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2945.

Statistical classification of treatment responses in mouse clinical trials for stratified medicine in oncology drug discovery

Translational oncology research strives to explore a new aspect: identifying subgroups that exhibit treatment response even during pre-clinical phases. In this study, we focus on PDX models and their implementation in mouse clinical trials (MCT). Our primary objective was to identify subgroups with different treatment responses using Latent Class Mixed Model (LCMM).We used a public dataset and focused on one treatment, encorafenib, and two indications, melanoma and colorectal cancer, for which efficacy depends on a specific mutation BRAF V600E. One LCMM per indication was implemented to classify treatment responses at the PDX level, analyzing the growth kinetics of treated tumors and matched controls within the PDX models. A simulation study was carried out to explore the performance of LCMM in this context. For both applications, LCMM identified classes for which the higher the proportion of mutated BRAF V600E PDX models the greater the treatment effect, which is aligned with encorafenib use recommendations. The simulation study showed that LCMM could identify classes with large differences in treatment effects. LCMM is a suitable tool for MCT to explore treatment response subgroups of PDX. Once these subgroups are defined, characterization of their phenotypes/genotypes could be performed to explore treatment response predictors.

Abstract C087: DHX9 inhibition as a novel therapeutic modality in microsatellite instable colorectal cancer

Abstract Background: DHX9 is a DEAH-box RNA helicase which has been reported to play important roles in replication, transcription, translation and RNA splicing/processing, all of which contribute to DHX9’s role in the maintenance of genomic stability. Functionally, DHX9 unwinds and/or resolves regions of double-stranded DNA and RNA helices but has a greater propensity for secondary structures such as DNA/RNA hybrids (R-loops), circular RNA (circRNA) and DNA/RNA G-quadraplexes. Overexpression of DHX9 is evident in multiple cancer types, including colorectal cancer (CRC) and lung cancer. In particular, microsatellite instable (MSI) tumors exhibiting defective mismatch repair (dMMR) show a strong dependence on DHX9, making this helicase an attractive target for oncology drug discovery. Here, we will demonstrate validation of DHX9 as a novel target for CRC-MSI, and the identification of potent and selective in vitro and in vivo small molecule inhibitors of DHX9. Materials and Methods: DHX9 targeted siRNA or DHX9 small molecule inhibitors were used to assess anti-proliferative activity in multiple different CRC-MSI and CRC-MSS cell lines through either CellTiter-Glo proliferation or colony formation assays. Downstream biological consequences of DHX9 knockdown or inhibition in vitro were determined using immuno-fluorescent imaging, western blot, flow cytometry to measure RNA/DNA secondary structure, cell cycle changes, apoptosis, and qPCR to measure circBRIP1 induction. CRC-MSI and CRC-MSS tumor xenografts were treated with DHX9 inhibitor ATX968 BID orally for 21-28 days. Tumor and plasma samples were collected for pharmacokinetics (PK) and pharmacodynamic (PD) (circBRIP1) measurement. Results: We demonstrate that DHX9 inhibition in CRC-MSI leads to an increase in RNA/DNA secondary structures such as R-loops, G-quadruplexes and Alu mediated circRNA such as circBRIP1, leads to subsequent DNA damage and increased replication stress. Cell lines that exhibit defective DNA repair pathways such as dMMR are unable to resolve this replication stress and demonstrate S-G2 phase cell cycle arrest prior to onset of apoptosis. We confirmed this selective dependency in a panel of cancer cell lines, where anti-proliferative effects mediated by DHX9 inhibition were associated with dMMR status. Furthermore, DHX9 tool compound ATX968 was well tolerated in vivo across a 28-day treatment period with robust and durable tumor regression observed in the MSI CRC tumor xenograft model. Following cessation of treatment, minimal tumor regrowth was observed in a 28-day post treatment window. Tumor and plasma concentrations of ATX968 and changes in PD markers of DHX9 inhibition were measured and resulting PK, PD and efficacy data were highly correlated. Conclusions: Together, these preclinical data validate DHX9 as a tractable new target with potential utility as a novel treatment for patients with CRC-MSI. Conflict of Interests: All authors are current or former employees and shareholders of Accent Therapeutics, Inc. Citation Format: Jennifer Castro, Matthew H Daniels, David Brennan, Brian T Johnston, Rishabh Bansal, Monique Laidlaw, Chuang Lu, Deepali Gotur, Young-Tae Lee, Kevin Knockenhauer, April Case, Jie Wu, Anugraha Raman, Jae Eun Cheong, Julie Liu, Shane M Buker, E. Allen Sickmier, Stephen J Blakemore, P. Ann Boriack-Sjodin, Kenneth W Duncan, Serena J Silver, Scott Ribich, Robert A Copeland. DHX9 inhibition as a novel therapeutic modality in microsatellite instable colorectal cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C087.

Comparison of Tumor Binding Across Tumor Types and Cell Lines to Support Free Drug Considerations for Oncology Drug Discovery

Tumor binding is an important parameter to derive unbound tumor concentration to explore pharmacokinetics (PK) and pharmacodynamics (PD) relationships for oncology disease targets. Tumor binding was evaluated using eleven matrices, including various commonly used ex vivo human and mouse xenograft and syngeneic tumors, tumor cell lines and liver as a surrogate tissue. The results showed that tumor binding is highly correlated among the different tumors and tumor cell lines except for the mouse melanoma (B16F10) tumor type. Liver fraction unbound (fu) has a good correlation with B16F10 tumor binding. Liver also demonstrates a two-fold equivalency, on average, with binding of other tumor types when a scaling factor is applied. Predictive models were developed for tumor binding, with correlations established with LogD (acids), predicted muscle fu (neutrals) and measured plasma protein binding (bases) to estimate tumor fu when experimental data are not available. Many approaches can be applied to obtain and estimate tumor binding values. One strategy proposed is to use a surrogate tumor tissue, such as mouse xenograft ovarian cancer (OVCAR3) tumor, as a surrogate for tumor binding (except for B16F10) to provide an early assessment of unbound tumor concentrations for development of PK/PD relationships.

Drug Repurposing for Cancers With Limited Survival: Protocol for a Retrospective Cohort Study.

Only 5% of the molecules tested in oncology phase 1 trials reach the market after an average of 7.5 years of waiting and at a cost of tens of millions of dollars. To reduce the cost and shorten the time of discovery of new treatments, "drug repurposing" (research with molecules already approved for another indication) and the use of secondary data (not collected for the purpose of research) have been proposed. Due to advances in informatics in clinical care, secondary data can, in some cases, be of equal quality to primary data generated through prospective studies. The objective of this study is to identify drugs currently marketed for other indications that may have an effect on the prognosis of patients with cancer. We plan to monitor a cohort of patients with high-lethality cancers treated in the public health system of Catalonia between 2006 and 2012, retrospectively, for survival for 5 years after diagnosis or until death. A control cohort, comprising people without cancer, will also be retrospectively monitored for 5 years. The following study variables will be extracted from different population databases: type of cancer (patients with cancer cohort), date and cause of death, pharmacological treatment, sex, age, and place of residence. During the first stage of statistical analysis of the patients with cancer cohort, the drugs consumed by the long-term survivors (alive at 5 years) will be compared with those consumed by nonsurvivors. In the second stage, the survival associated with the consumption of each relevant drug will be analyzed. For the analyses, groups will be matched for potentially confounding variables, and multivariate analyses will be performed to adjust for residual confounding variables if necessary. The control cohort will be used to verify whether the associations found are exclusive to patients with cancer or whether they also occur in patients without cancer. We anticipate discovering multiple significant associations between commonly used drugs and the survival outcomes of patients with cancer. We expect to publish the initial results in the first half of 2024. This retrospective study may identify several commonly used drugs as candidates for repurposing in the treatment of various cancers. All analyses are considered exploratory; therefore, the results will have to be confirmed in subsequent clinical trials. However, the results of this study may accelerate drug discovery in oncology. DERR1-10.2196/48925.

Translation of AI into oncology clinical practice.

Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We dissect the unique challenges and opportunities associated with each of these cases. Finally, we summarize the general requirements for successful AI/ML implementation in the clinic, highlighting specific examples and points of emphasis including the importance of multidisciplinary collaboration of stakeholders, role of domain experts in AI augmentation, transparency of AI/ML models, and the establishment of a comprehensive quality assurance program to mitigate risks of training bias and data drifts, all culminating toward safer and more beneficial AI/ML applications in oncology labs and clinics.

Nexo launches with oncology drug discovery program

Nexo launches with oncology drug discovery program

Abstract 1537: DRIVE-Biologics: All the steps from discovery to development of novel biological entities

Abstract Biologics first revolutionized cancer treatment in the late 1900s with the approval of rituximab and trastuzumab, two monoclonal antibodies targeting antigens expressed on tumor cells. Another milestone was achieved in the early 2010s with the approval of antibodies targeting immune checkpoints. Nowadays, the discovery and development of new biological entities and biological therapeutic products represent a rapidly growing market in various therapeutic areas, with about 10 to 15 biologics being approved each year. We have built a premium expert ecosystem services - DRIVE-Biologics - to support and accelerate biologics drug discovery and development in oncology, immuno-oncology, and inflammatory diseases. The DRIVE-Biologics consortium provides a unique integrated solution with specialist services from strategic partners, to access market analysis, establish the ability to design, optimize, and develop novel biological entities addressing the therapeutic target of interest. DRIVE-Biologics supplies the high-level, IND-focused discipline to rigorously manage the integrated programs integrating CMC, manufacturing, regulatory affairs and clinical trial. We will present the lead optimization and multiparameter preclinical evaluation process to select and assess biological candidates for downstream development and clinical studies: -Custom cellular model development for discovery and potency analysis; -In vitro screening, target engagement, and mechanism of action elucidation with cellular models ranging from tumor cell lines, immune cells or primary patient samples; -In vivo efficacy and safety studies using refined and highly characterized syngeneic, xenogeneic, patient-derived xenograft or humanized mouse models up to non-human primates; -DMPK capabilities to develop and validate bioanalytical methods (including GLP-compliancy) such as LBA and qPCR/RT-qPCR and also assess immunogenicity; -Biodistribution and tumor specificity analysis of bioconjugated and radiolabeled biologics. Herein, we provide DRIVE-BIO optimization and preclinical evaluation process to select promising biologics and list the key parameters to be checked. We will present our recent results which highlight the importance to optimize these parameters to improve the efficacy of biologics. Citation Format: Emily Fang, Campbell Bunce, Philip Payne, Rafael Teran, Olivier Duchamp, Caroline Mignard, Francis Bichat, Fabrice Viviani. DRIVE-Biologics: All the steps from discovery to development of novel biological entities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1537.

Abstract 1136: Targeting DHX9 inhibition as a novel therapeutic modality in microsatellite instable colorectal cancer

Abstract DHX9 is a multifunctional DEAH-box ATP-independent RNA helicase which has been reported to play important roles in replication, transcription, translation, RNA splicing and RNA processing which contribute to DHX9’s role in maintenance of genomic stability. Functionally, DHX9’s role involves binding to as well as unwinding and/or resolving double-stranded and single-stranded DNA/RNA, DNA/RNA hybrids (R-loops), circular RNA and DNA/RNA G quadraplexes. Overexpression of DHX9 is evident in multiple cancer types, including colorectal cancer (CRC) and lung cancer. In addition, microsatellite instable (MSI) tumors exhibiting defective mismatch repair (dMMR) show a strong dependence on DHX9, making this helicase an attractive target for oncology drug discovery. Here we describe data supporting targeting DHX9 in MSI CRC as a novel therapeutic, and the first identification of potent and selective in vitro and in vivo small molecule inhibitors of DHX9. We demonstrate that DHX9 inhibition in MSI CRC, delivered either through siRNA knockdown or compound treatment, leads to an increase in RNA/DNA secondary structures such as R-loops and circRNA (i.e. circBRIP1) inducing replication stress. Cell lines that are dMMR (i.e. MSI) are unable to resolve this replication stress, resulting in prevention of DNA replication in S phase and later onset of apoptosis. We were able to confirm this selective dependency in a panel of 20 CRC cell lines; anti-proliferative effects mediated by DHX9 inhibition were dependent on cell line dMMR status in a 10-day proliferation assay. Furthermore, compound 1, an orally bioavailable DHX9 inhibitor was used to investigate in vivo efficacy in MSI CRC (LS411N) and MSS CRC (SW480) xenograft models. Compound 1 was well tolerated across the 28-day treatment period with robust and durable tumor regression (TGI = 105 %) observed in the LS411N tumor xenograft model only. In addition, following cessation of treatment, minimal tumor regrowth was observed in a 28-day post treatment window. Tumor and plasma concentrations of compound 1 and changes in pharmacodynamic markers of DHX9 inhibition, such as circBRIP1 mRNA, were measured and resulting PK and PD data were highly correlated. Together, these preclinical data validate DHX9 as a tractable new target with potential utility as a novel treatment for patients with MSI CRC. Citation Format: Jennifer Castro, Matthew H. Daniels, Chuang Lu, David Brennan, Deepali Gotur, Young-Tae Lee, Kevin Knockenhauer, April Case, Jie Wu, Shane M. Buker, Julie Liu, Brian A. Sparling, E. Allen Sickmier, Stephen J. Blakemore, P. Ann Boriack-Sjodin, Kenneth W. Duncan, Scott Ribich, Robert A. Copeland. Targeting DHX9 inhibition as a novel therapeutic modality in microsatellite instable colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1136.

Biolayer Interferometry Assay for Cyclin-Dependent Kinase-Cyclin Association Reveals Diverse Effects of Cdk2 Inhibitors on Cyclin Binding Kinetics.

Cyclin-dependent kinases (CDKs) are key mediators of cell proliferation and have been a subject of oncology drug discovery efforts for over two decades. Several CDK and activator cyclin family members have been implicated in regulating the cell division cycle. While it is thought that there are canonical CDK-cyclin pairing preferences, the extent of selectivity is unclear, and increasing evidence suggests that the cell-cycle CDKs can be activated by a pool of available cyclins. The molecular details of CDK-cyclin specificity are not completely understood despite their importance for understanding cancer cell cycles and for pharmacological inhibition of cancer proliferation. We report here a biolayer interferometry assay that allows for facile quantification of CDK binding interactions with their cyclin activators. We applied this assay to measure the impact of Cdk2 inhibitors on Cyclin A (CycA) association and dissociation kinetics. We found that Type I inhibitors increase the affinity between Cdk2 and CycA by virtue of a slowed cyclin dissociation rate. In contrast, Type II inhibitors and other small-molecule Cdk2 binders have distinct effects on the CycA association and dissociation processes to decrease affinity. We propose that the differential impact of small molecules on the cyclin binding kinetics arises from the plasticity of the Cdk2 active site as the kinase transitions between active, intermediate, and inactive states.

Reinvestigation of Passerini and Ugi scaffolds as multistep apoptotic inducers via dual modulation of caspase 3/7 and P53-MDM2 signaling for halting breast cancer.

Selective induction of breast cancer apoptosis is viewed as the mainstay of various ongoing oncology drug discovery programs. Passerini scaffolds have been recently exploited as selective apoptosis inducers via a caspase 3/7 dependent pathway. Herein, the optimized Passerini caspase activators were manipulated to synergistically induce P53-dependent apoptosis via modulating the closely related P53-MDM2 signaling axis. The adopted design rationale and synthetic routes relied on mimicking the general thematic features of lead MDM2 inhibitors incorporating multiple aromatic rings. Accordingly, the cyclization of representative Passerini derivatives and related Ugi compounds into the corresponding diphenylimidazolidine and spiro derivative was performed, resembling the nutlin-based and spiro MDM-2 inhibitors, respectively. The study was also extended to explore the apoptotic induction capacity of the scaffold after simplification and modifications. MTT assay on MCF-7 and MDA-MB231 breast cancer cells compared to normal fibroblasts (WI-38) revealed their promising cytotoxic activities. The flexible Ugi derivatives 3 and 4, cyclic analog 8, Passerini adduct 12, and the thiosemicarbazide derivative 17 were identified as the study hits regarding cytotoxic potency and selectivity, being over 10-folds more potent (IC50 = 0.065-0.096 μM) and safer (SI = 4.4-18.7) than doxorubicin (IC50 = 0.478 μM, SI = 0.569) on MCF-7 cells. They promoted apoptosis induction via caspase 3/7 activation (3.1-4.1 folds) and P53 induction (up to 4 folds). Further apoptosis studies revealed that these compounds enhanced gene expression of BAX by 2 folds and suppressed Bcl-2 expression by 4.29-7.75 folds in the treated MCF-7 cells. Docking simulations displayed their plausible binding modes with the molecular targets and highlighted their structural determinants of activities for further optimization studies. Finally, in silico prediction of the entire library was computationally performed, showing that most of them could be envisioned as drug-like candidates.

Assessment of spatial transcriptomics for oncology discovery

Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.

Development of ASIC1a ligand-gated ion channel drug screening assays across multiple automated patch clamp platforms.

Human acid-sensing ion channels (ASIC) are ligand-gated ionotropic receptors expressed widely in peripheral tissues as well as sensory and central neurons and implicated in detection of inflammation, tissue injury, and hypoxia-induced acidosis. This makes ASIC channels promising targets for drug discovery in oncology, pain and ischemia, and several modulators have progressed into clinical trials. We describe the use of hASIC1a as a case study for the development and validation of low, medium and high throughput automated patch clamp (APC) assays suitable for the screening and mechanistic profiling of new ligands for this important class of ligand-gated ion channel. Initial efforts to expand on previous manual patch work describing an endogenous hASIC1a response in HEK cells were thwarted by low current expression and unusual pharmacology, so subsequent work utilized stable hASIC1a CHO cell lines. Ligand-gated application protocols and screening assays on the Patchliner, QPatch 48, and SyncroPatch 384 were optimized and validated based on pH activation and nM-μM potency of reference antagonists (e.g., Amiloride, Benzamil, Memantine, Mambalgin-3, A-317567, PcTx1). By optimizing single and stacked pipette tip applications available on each APC platform, stable pH-evoked currents during multiple ligand applications enabled cumulative EC50 and IC50 determinations with minimized receptor desensitization. Finally, we successfully demonstrated for the first time on an APC platform the ability to use current clamp to implement the historical technique of input resistance tracking to measure ligand-gated changes in membrane conductance on the Patchliner platform.

15 (PB001) - Impact of orthotopic versus subcutaneous implantation on patient-derived xenograft transcriptomic profile

Background: Patient-derived xenografts (PDXs) have become the leading model system in oncology drug discovery. Depending on their site of implantation, PDX models from solid tumors can be divided into two categories: subcutaneous (s.c.) and orthotopic (ortho). The advantage of s.c. implantation is that tumor size can be easily monitored, allowing fast validation of drug efficacy. However, since the s.c. microenvironment is likely different from the tissue of origin for most tumor types, there is concern that s.c.

Advancing genome edited allogeneic cell therapies into the clinic

Steve Kanner is the CSO at Caribou Biosciences responsible for the company’s therapeutic discovery, research, and development activities. Before joining Caribou in 2017, Steve was Vice President, Head of Biology, at Arrowhead Pharmaceuticals, leading a department in discovery of RNAi therapeutics for oncology, genetic diseases, and other indications. Prior to Arrowhead Pharmaceuticals, he served in various positions of increasing responsibility in both oncology and inflammation drug discovery at Bristol-Myers Squibb, Agensys/Astellas, and Astex Pharmaceuticals. Steve has authored over 85 publications in both peer-reviewed journals and books, and he is an inventor on numerous U.S. and foreign patents and patent applications. Steve received his undergraduate degree in Genetics from the University of California, Berkeley and his PhD in Immunology and Microbiology from the University of Miami’s Miller School of Medicine. His post-doctoral fellowship was funded by the National Institutes of Health and was conducted at the University of Virginia.

Translational and Clinical Relevance of PDX‐Derived Organoid Models in Oncology Drug Discovery and Development

Patient-derived cancer disease models conserve many key features of the original human cancers, potentially allowing higher predictive power than traditional cell line models. Accordingly, in vivo patient-derived xenografts (PDX) are frequently utilized in preclinical and translational oncology studies as patient surrogates for population-based screens ("mouse clinical trials"), for which large PDX biobanks have been generated over the last decade from various cancer types. In vitro patient-derived organoids (PDO) have recently emerged as a disruptive technology, enabling early "patient in a dish" clinical trials. Like PDX, PDOs retain the histology/genomics of the original tumor and are highly predictive of the clinical response. Organoids derived from adult stem cells (ASC) in patient tissue can function as mini-organs. They have greater advantages over other 3D in vitro systems, making them highly predictive, reliable, and consistent in vitro models. Large biobanks enable the adoption of organoids in early drug screening and patient selection. PDX biobanks, as a source of human material, have been used to create 3D in vitro screens, but with limitations. However, creating organoids from the ASCs residing in PDXs has been successfully used as a rapid and cost-effective way to enable higher throughput in vitro screens and generate matched in vitro/in vivo model pairs that retain genomic, histopathological, and pharmacology profiles. This overview summarizes the generation of matched in vitro/in vivo models from patient material, the advantages over other systems, and the applications to drug discovery. © 2022 Wiley Periodicals LLC.

Abstract MS1-1: CRISPR screens to identify novel DNA repair defects synthetic lethal therapies

Abstract The orchestration of DNA repair is of fundamental importance to the maintenance of genomic integrity and tumor suppression. DNA damage must be detected in the context of the varied chromatin landscape, its presence must be communicated throughout the cell to alter many ongoing processes, and the machinery that will mend the lesion must be recruited to the damage site. In my presentation, I will discuss our recent efforts in mapping genome maintenance pathways using genome-scale CRISPR/Cas9 screens in human cells. I will highlight how these screens can also identify potentially actionable synthetic lethal genetic interactions that could form the basis of new oncology drug discovery efforts. I will focus particularly on our efforts to uncover and characterize the synthetic lethal interaction network surrounding BRCA1 and BRCA2 deficiency, as well as our work that led to the discovery that inhibition of the PKMYT1 kinase is lethal in cells with amplification in the cyclin E-coding gene, CCNE1. Citation Format: D Durocher. CRISPR screens to identify novel DNA repair defects synthetic lethal therapies [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr MS1-1.

Adipose-derived cells: building blocks of three-dimensional microphysiological systems.

ABSTRACTMicrophysiological systems (MPS) created with human-derived cells and biomaterial scaffolds offer a potential in vitro alternative to in vivo animal models. The adoption of three-dimensional MPS models has economic, ethical, regulatory, and scientific implications for the fields of regenerative medicine, metabolism/obesity, oncology, and pharmaceutical drug discovery. Key opinion leaders acknowledge that MPS tools are uniquely positioned to aid in the objective to reduce, refine, and eventually replace animal experimentation while improving the accuracy of the finding’s clinical translation. Adipose tissue has proven to be an accessible and available source of human-derived stromal vascular fraction (SVF) cells, a heterogeneous population available at point of care, and adipose-derived stromal/stem cells, a relatively homogeneous population requiring plastic adherence and culture expansion of the SVF cells. The adipose-derived stromal/stem cells or SVF cells, in combination with human tissue or synthetic biomaterial scaffolds, can be maintained for extended culture periods as three-dimensional MPS models under angiogenic, stromal, adipogenic, or osteogenic conditions. This review highlights recent literature relating to the versatile use of adipose-derived cells as fundamental components of three-dimensional MPS models for discovery research and development. In this context, it compares the merits and limitations of the adipose-derived stromal/stem cells relative to SVF cell models and considers the likely directions that this emerging field of scientific discovery will take in the near future.

Abstract P119: Differential sensitivity to poly(ADP-ribose) polymerase inhibitors in patient-derived cell models of breast cancer

Abstract Female breast cancer (BC) became the most commonly diagnosed cancer globally in 2020. One of the hallmarks of BC is both intratumor and intertumor heterogeneity. Efforts have been made to categorize this heterogeneity based on the presence or absence of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2/ERBB2). Receptor expression patterns are used to stratify BC and the subsequent treatments. Much of the current knowledge of breast cancer comes from in vitro and in vivo studies using well established cell lines, some of which have been cultured since the 1950s. Given that serial passaging over many years has been reported to alter the mutational profile of cell lines and therefore their relevance to clinical decisions on the primary tumour, new near-tumor cell models need to be generated. In this study, tumour biopsies or ascitic fluids were obtained from a local cancer research biobank, as well as directly from metastatic BC patients across the globe. Using mechanical dissociation and cell separation techniques we isolated tumor cells and grew them in proprietary media. The resultant models were subjected to whole exome and RNA sequencing. In addition, the models were tested in a 3-day cell death assay against a library of more than 50 FDA-approved drugs. The models and four BC cell lines (HCC1937, ZR-75-1, MDA-MB-436 and HCC1428) were also tested in a longer term 7-day assay against a panel of four poly(ADP-ribose) polymerase (PARP) inhibitors. A total of 10 patient-derived cell (PDC) models were successfully established from biopsies or ascitic fluids taken from patients with primary (1/10) or metastatic (9/10) BC. Models were established from patients with ER+ BC, HER2+ BC and triple negative BC (TNBC) within an average of 23 days from sample arrival. All models displayed a greater sensitivity to the non-targeted drugs in the library compared with the targeted drugs. Mutations in breast cancer associated 1 (BRCA1) and BRCA2 genes that were not present in BC cell lines were identified in a subset of the PDCs. All models showed some sensitivity to PARP inhibitors, and most were exquisitely sensitive to talazoparib with IC50s of less than 100 nM. Determining cell death responses in near-tumour PDCs should thus be adopted to enable more informed clinical decisions for treatment pathways and in parallel will be promising tools for oncology drug discovery. Citation Format: Immaculate Nalubowa, Subir Singh, Yuen Ngan Fan, Rachel Howard-Jones, Albert Bezman, Dominic I. James, Geoff Muckle, Gareth J. Griffiths. Differential sensitivity to poly(ADP-ribose) polymerase inhibitors in patient-derived cell models of breast cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P119.