Survival in high-risk pediatric neuroblastoma has remained around 50% for the last 20 years, with immunotherapies and targeted therapies having had minimal impact. Here, we identify the small molecule CX-5461 as selectively cytotoxic to high-risk neuroblastoma and synergistic with low picomolar concentrations of topoisomerase I inhibitors in improving survival in vivo in orthotopic patient-derived xenograft neuroblastoma mouse models. CX-5461 recently progressed through phase I clinical trial as a first-in-human inhibitor of RNA-POL I. However, we also use a comprehensive panel of in vitro and in vivo assays to demonstrate that CX-5461 has been mischaracterized and that its primary target at pharmacologically relevant concentrations, is in fact topoisomerase II beta (TOP2B), not RNA-POL I. This is important because existing clinically approved chemotherapeutics have well-documented off-target interactions with TOP2B, which have previously been shown to cause both therapy-induced leukemia and cardiotoxicity—often-fatal adverse events, which can emerge several years after treatment. Thus, while we show that combination therapies involving CX-5461 have promising anti-tumor activity in vivo in neuroblastoma, our identification of TOP2B as the primary target of CX-5461 indicates unexpected safety concerns that should be examined in ongoing phase II clinical trials in adult patients before pursuing clinical studies in children.

The disorganization of cell types within tissues underlies many human diseases and has been studied for over a century using the conventional tools of pathology, including tissue-marking dyes such as the H&E stain. Recently, spatial transcriptomics technologies were developed that can measure spatially resolved gene expression directly in pathology-stained tissues sections, revealing cell types and their dysfunction in unprecedented detail. In parallel, artificial intelligence (AI) has approached pathologist-level performance in computationally annotating H&E images of tissue sections. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and AI-based pathology has performed less impressively outside their training datasets. Here, we describe a methodology that can computationally integrate AI-annotated pathology images with spatial transcriptomics data to markedly improve inferences of tissue cell type composition made over either class of data alone. We show that this methodology can identify regions of clinically relevant tumor immune cell infiltration, which is predictive of response to immunotherapy and was missed by an initial pathologist’s manual annotation. Thus, combining spatial transcriptomics and AI-based image annotation has the potential to exceed pathologist-level performance in clinical diagnostic applications and to improve the many applications of spatial transcriptomics that rely on accurate cell type annotations.

Survival in high-risk pediatric neuroblastoma has remained around 50% for the last 20 years, with immunotherapies and targeted therapies having had minimal impact. Here, we identify the small molecule CX-5461 as selectively cytotoxic to high-risk neuroblastoma and synergistic with low picomolar concentrations of topoisomerase I inhibitors improving survival in vivo in orthotopic patient-derived xenograft neuroblastoma mouse models. CX-5461 recently progressed through phase I clinical trial as a first-in-human inhibitor of RNA-POL I. However, we also use a comprehensive panel of in vitro and in vivo assays to demonstrate that CX-5461 has been mischaracterized and that its primary target at pharmacologically relevant concentrations, is in fact topoisomerase II beta (TOP2B), not RNA-POL I. These findings are important because existing clinically approved chemotherapeutics have well-documented off-target interactions with TOP2B, which have previously been shown to cause both therapy-induced leukemia and cardiotoxicity, often-fatal adverse events, which can emerge several years after treatment. Thus, while we show that combination therapies involving CX-5461 have promising anti-tumor activity in vivo in neuroblastoma, our identification of TOP2B as the primary target of CX-5461 indicates unexpected safety concerns that should be examined in ongoing phase II clinical trials in adult patients before pursuing clinical studies in children.

Neuroblastoma is a highly heterogeneous disease not only in the clinical presentation of individual patients, but also in the cellular composition of any given tumor. Insights into this diversity have only recently been enabled due to advancements in single cell technologies, which have facilitated investigation of this disease at unprecedented resolution and detail. Coinciding with the growing number of scRNA-seq technologies, so too are the number of single cell datasets encompassing neuroblastoma patients across several institutions. However, due to the rarity of the affliction and sample access, the cohort pool in each aforementioned scRNA-seq study is limited to a reduced representation of the spectrum of disease classifications, which limits the ability of any single study to draw conclusions about neuroblastoma as a whole. Moreover, inconsistencies in data acquisition and analytical approaches across these studies have led to diverging interpretations. As such, we decided to amass the entirety of publicly available neuroblastoma scRNA-seq studies, representing a more comprehensive cross-section of patient presentations, towards the goal of conducting an exhaustive meta-analysis of the underlying data. To this end, we have implemented a generalizable non-negative matrix factorization (NMF)-based framework targeted at discovering conserved gene expression programs in malignant neuroblastoma as well as the supporting microenvironment. Using graph-based network analyses for classification of gene expression programs, we have identified conserved signatures of malignant and non-tumor cell types in neuroblastoma. In addition to defining the landscape of expression programs in human neuroblastoma patients, we have also utilized the NMF analysis to assess the alignment of several preclinical models to human signatures. We have identified gene expression programs that align to malignant human expression programs as well as signatures more closely related to non-tumor cell types. These include previously characterized divergent mesenchymal and adrenergic programs, as well as undescribed liver/metabolic, neuronal, and glial signatures. When considering the affinity of neuroblastoma models to malignant human profiles we observed specific agreement between certain preclinical signatures and subtype classifications found in patient samples. Careful consideration of these results will allow researchers to guide preclinical studies by cross-referencing neuroblastoma models of interest with patient profiles. Overall, we characterize the most updated view of the landscape of neuroblastoma by documenting the full repertoire of gene expression programs across patient and preclinical models. Citation Format: Richard Chapple, Charlie Wright, Min Pan, Paul Geeleher. Meta-analysis of neuroblastoma single cell RNA-seq datasets identifies conserved and divergent gene expression programs across human and preclinical models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4075.

Neuroblastoma is a common pediatric cancer, where preclinical studies have suggested chemotherapy resistance is driven by a mesenchymal-like gene expression program. However, the poor clinical outcomes imply we need a better understanding of the relationship between patient tumors and preclinical models. Here, we generated single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We developed an unsupervised machine learning approach to compare the gene expression programs found in preclinical models to a large cohort of human neuroblastoma tumors. The dominant adrenergic programs were well preserved in preclinical models, but contrary to previous reports do not unambiguously map to an obvious cell of origin. The mesenchymal-like program was less clearly preserved, and primarily restricted to cancer-associated fibroblasts and Schwann-like cellsin vivo. Surprisingly however, we identified a subtle, weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some high-risk tumors. This program appears distinct from mesenchymal cell lines but was maintained in PDX and a similar program could be chemotherapy-induced in our GEMM after only 24 hours, suggesting an uncharacterized therapy-escape mechanism. Collectively, our findings advance the understanding of neuroblastoma heterogeneity and can inform the development of new treatments.

For over 100 years, the traditional tools of pathology, such as tissue-marking dyes (e.g. the H&E stain) have been used to study the disorganization and dysfunction of cells within tissues. This has represented a principal diagnostic and prognostic tool in cancer. However, in the last 5 years, new technologies have promised to revolutionize histopathology, with Spatial Transcriptomics technologies allowing us to measure gene expression directly in pathology-stained tissue sections. In parallel with these developments, Artificial Intelligence (AI) applied to histopathology tissue images now approaches pathologist level performance in cell type identification. However, these new technologies still have severe limitations, with Spatial Transcriptomics suffering difficulties distinguishing transcriptionally similar cell types, and AI-based pathology tools often performing poorly on real world out-of-batch test datasets. Thus, century-old techniques still represent standard-of-care in most areas of clinical cancer diagnostics and prognostics. Here, we present a new frontier in digital pathology: describing a conceptually novel computational methodology, based on Bayesian probabilistic modelling, that allows Spatial Transcriptomics data to be leveraged together with the output of deep learning-based AI used to computationally annotate H&E-stained sections of the same tumor. By leveraging cell-type annotations from multiple independent pathologists, we show that this integrated methodology achieves better performance than any given pathologist’s manual tissue annotation in the task of identifying regions of immune cell infiltration in breast cancer, and easily outperforms either technology alone. We also show that on a subset of histopathology slides examined, the methodology can identify regions of clinically relevant immune cell infiltration that were missed entirely by an initial pathologist’s manual annotation. While this use case has clear diagnostic and prognostic value in cancer (e.g. predicting response to immunotherapy), our methodology is generalizable to any type of pathology images and also has broad applications in spatial transcriptomics data analytics, where most applications (such as identifying cell-cell interactions) rely on correct cell type annotations having been established a priori. We anticipate that this work will spur many follow-up studies, including new computational innovations building on the approach. The work sets the stage for better-than-pathologist performance in other cell-type annotation tasks, with relevant applications in diagnostics and prognostics across almost all cancers. Citation Format: Asif Zubair, Rich Chapple, Sivaraman Natarajan, William C. Wright, Min Pan, Hyeong-Min Lee, Heather Tillman, John Easton, Paul Geeleher. Jointly leveraging spatial transcriptomics and deep learning models for image annotation achieves better-than-pathologist performance in cell type identification in tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 456.

Drug combinations are the basis of treatment for modern diseases but arriving at successful combination therapies is fraught with challenges. Decades ago, the limited number of drugs represented a tractable candidate list from which to design combination experiments. However, the current pool of single-agent drugs to potentially combine is far too large to brute-force screen, and purely computational predictions have performed poorly. Suitable screening methods are needed, but the design of experimental approaches has proven to be highly complex; researchers need to carefully balance many variables such as appropriate drug concentration ranges, number of doses, inclusion of replicates, and throughput. Perhaps the most significant obstacle facing these studies is the approach to data analysis, where conflicting definitions of synergy and unintuitive metrics serve to confuse researchers and render largely uninterpretable results. Collectively, these challenges hamper the progress of drug combination research and ultimately translational impact. To overcome these limitations, we have developed a fully self-contained framework to handle both the experimental design and analysis of drug combination experiments. Our method, called Combocat, provides a straightforward way to test and analyze any number of drug combinations and samples, and is suitable for high-throughput. Combocat provides a high-resolution of concentration combinations compared to most current approaches. This is automated by common instruments and uses scripts included within our protocol. Through careful template design, we were able to include 3 replicates of each 10x10 matrix, single-agent drugs, and controls - all within a single 384-well plate. We found our method to work robustly with varying sample types (Human cancer, bacteria, fungi), and readouts. After data generation, files can simply be dragged into our Combocat analysis tool directly. We provide a free, web-based software suite to fully automate the analysis after data collection. The Combocat web tool is intuitive and facilitates interactive exploration of synergy. It also provides a rich array of information such as dose-response curves, IC50 values, synergy matrices, ranked hit plots, and more. Data normalization, synergy algorithms, scoring functions, and other complex calculations are run swiftly and automatically in the background with no need for user input. Notably, we employ statistical testing by taking advantage of experimental replicates, which is a feature we found lacking in most methods. We use a well-documented synergy metric but also decided to formulate our own Combocat score which considers statistical measurements and assay quality. The Combocat score provides an easy interpretation of results and facilitates quick identification of top hits. Collectively, our platform will be used to enhance and expedite the selection of effective drug combinations. Citation Format: William C. Wright, Min Pan, Hyeong-Min Lee, Gregory A. Phelps, Jonathan Low, Duane Currier, Richard E. Lee, Taosheng Chen, Paul Geeleher. Combocat: A high-throughput framework for drug combination studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1907.