Cell-of-Origin and Genetic, Epigenetic, and Microenvironmental Factors Contribute to the Intra-Tumoral Heterogeneity of Pediatric Intracranial Ependymoma

Intra-tumoral heterogeneity (ITH) is a complex multifaceted phenomenon that posits major challenges for the clinical management of cancer patients. Genetic, epigenetic, and microenvironmental factors are concurrent drivers of diversity among the distinct populations of cancer cells. ITH may also be installed by cancer stem cells (CSCs), that foster unidirectional hierarchy of cellular phenotypes or, alternatively, shift dynamically between distinct cellular states. Ependymoma (EPN), a molecularly heterogeneous group of tumors, shows a specific spatiotemporal distribution that suggests a link between ependymomagenesis and alterations of the biological processes involved in embryonic brain development. In children, EPN most often arises intra-cranially and is associated with an adverse outcome. Emerging evidence shows that EPN displays large intra-patient heterogeneity. In this review, after touching on EPN inter-tumoral heterogeneity, we focus on the sources of ITH in pediatric intra-cranial EPN in the framework of the CSC paradigm. We also examine how single-cell technology has shed new light on the complexity and developmental origins of EPN and the potential impact that this understanding may have on the therapeutic strategies against this deadly pediatric malignancy.

Drivers of Transcriptional Variance in Human Intestinal Epithelial Organoids

Human intestinal epithelial organoids (enteroids and colonoids) are tissue cultures used for understanding the physiology of the intestinal epithelium. Here, we explored the effect on the transcriptome of common variations in culture methods, including extracellular matrix substrate, format, tissue segment, differentiation status, and patient heterogeneity. RNA-sequencing datasets from 276 experiments performed on 37 human enteroid and colonoid lines from several groups in the Texas Medical Center. DESeq2 and Gene Set Enrichment Analysis (GSEA) was used to identify differentially expressed genes and enriched of pathways. PERMANOVA, Pearson's correlation, and dendrogram analysis of the data originally indicated three tiers of influence of culture methods on transcriptomic variation: substrate (collagen vs. Matrigel) and format (3D, transwell, and monolayer) had the largest effect; segment of origin (duodenum, jejunum, ileum, colon) and differentiation status had a moderate effect, and patient heterogeneity and specific experimental manipulations (e.g., pathogen infection) had the smallest effect. GSEA identified hundreds of pathways that varied between culture methods, such as IL1 cytokine signaling enriched in transwell vs. monolayer cultures, and E2F target genes enriched in collagen vs. Matrigel cultures. The transcriptional influence of the format was furthermore validated in a synchronized experiment performed with various format-substrate combinations. Additionally, experimental manipulations such as infection had modest effects. These results show that common variations in culture conditions can have large effects on intestinal organoids and should be accounted for when designing experiments and comparing results between laboratories. Our data constitute the largest RNA-seq dataset interrogating human intestinal organoids.

The Diverse Applications of Pancreatic Ductal Adenocarcinoma Organoids

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal solid malignancies. While immortalized cancer cell lines and genetically engineered murine models have increased our understanding of PDAC tumorigenesis, they do not recapitulate inter- and intra-patient heterogeneity. PDAC patient derived organoid (PDO) biobanks have overcome this hurdle, and provide an opportunity for the high throughput screening of potential new therapies. This review provides a summary of the PDAC PDO biobanks established to date, and discusses how they have advanced our understanding of PDAC biology. Looking forward, the development of coculturing techniques for specific immune or stromal cell populations will enable a better understanding of the crosstalk that occurs within the tumor microenvironment, and the impact of this crosstalk on treatment response.

Two-Stage Approaches to Accounting for Patient Heterogeneity in Machine Learning Risk Prediction Models in Oncology

PURPOSE Machine learning models developed from electronic health records data have been increasingly used to predict risk of mortality for general oncology patients. But these models may have suboptimal performance because of patient heterogeneity. The objective of this work is to develop a new modeling approach to predicting short-term mortality that accounts for heterogeneity across multiple subgroups in the presence of a large number of electronic health record predictors. METHODS We proposed a two-stage approach to addressing heterogeneity among oncology patients of different cancer types for predicting their risk of mortality. Structured data were extracted from the University of Pennsylvania Health System for 20,723 patients of 11 cancer types, where 1,340 (6.5%) patients were deceased. We first modeled the overall risk for all patients without differentiating cancer types, as is done in the current practice. We then developed cancer type–specific models using the overall risk score as a predictor along with preselected type-specific predictors. The overall and type-specific models were compared with respect to discrimination using the area under the precision-recall curve (AUPRC) and calibration using the calibration slope. We also proposed metrics that characterize the degree of risk heterogeneity by comparing risk predictors in the overall and type-specific models. RESULTS The two-stage modeling resulted in improved calibration and discrimination across all 11 cancer types. The improvement in AUPRC was significant for hematologic malignancies including leukemia, lymphoma, and myeloma. For instance, the AUPRC increased from 0.358 to 0.519 (∆ = 0.161; 95% CI, 0.102 to 0.224) and from 0.299 to 0.354 (∆ = 0.055; 95% CI, 0.009 to 0.107) for leukemia and lymphoma, respectively. For all 11 cancer types, the two-stage approach generated well-calibrated risks. A high degree of heterogeneity between type-specific and overall risk predictors was observed for most cancer types. CONCLUSION Our two-stage modeling approach that accounts for cancer type–specific risk heterogeneity has improved calibration and discrimination than a model agnostic to cancer types.

Circulating tumor cell heterogeneity in neuroendocrine prostate cancer by single cell copy number analysis

Neuroendocrine prostate cancer is an aggressive variant of prostate cancer that may arise de novo or develop from pre-existing prostate adenocarcinoma as a mechanism of treatment resistance. The combined loss of tumor suppressors RB1, TP53, and PTEN are frequent in NEPC but also present in a subset of prostate adenocarcinomas. Most clinical and preclinical studies support a trans-differentiation process, whereby NEPC arises clonally from a prostate adenocarcinoma precursor during the course of treatment resistance. Here we highlight a case of NEPC with significant intra-patient heterogeneity observed across metastases. We further demonstrate how single-cell genomic analysis of circulating tumor cells combined with a phenotypic evaluation of cellular diversity can be considered as a window into tumor heterogeneity in patients with advanced prostate cancer.

Effects of the Autophagy-Inhibiting Agent Chloroquine on Acute Myeloid Leukemia Cells; Characterization of Patient Heterogeneity

Autophagy is a highly conserved cellular degradation process that prevents cell damage and promotes cell survival, and clinical efforts have exploited autophagy inhibition as a therapeutic strategy in cancer. Chloroquine is a well-known antimalarial agent that inhibits late-stage autophagy. We evaluated the effects of chloroquine on cell viability and proliferation of acute myeloid leukemia acute myeloid leukemia (AML) cells derived from 81 AML patients. Our results show that chloroquine decreased AML cell viability and proliferation for the majority of patients. Furthermore, a subgroup of AML patients showed a greater susceptibility to chloroquine, and using hierarchical cluster analysis, we identified 99 genes upregulated in this patient subgroup, including several genes related to leukemogenesis. The combination of chloroquine with low-dose cytarabine had an additive inhibitory effect on AML cell proliferation. Finally, a minority of patients showed increased extracellular constitutive mediator release in the presence of chloroquine, which was associated with strong antiproliferative effects of chloroquine as well as cytarabine. We conclude that chloroquine has antileukemic activity and should be further explored as a therapeutic drug against AML in combination with other cytotoxic or metabolic drugs; however, due to the patient heterogeneity, chloroquine therapy will probably be effective only for selected patients.