Patient-Derived Models of Cancer in the NCI PDMC Consortium: Selection, Pitfalls, and Practical Recommendations.

For over a century, early researchers sought to study biological organisms in a laboratory setting, leading to the generation of both in vitro and in vivo model systems. Patient-derived models of cancer (PDMCs) have more recently come to the forefront of preclinical cancer models and are even finding their way into clinical practice as part of functional precision medicine programs. The PDMC Consortium, supported by the Division of Cancer Biology in the National Cancer Institute of the National Institutes of Health, seeks to understand the biological principles that govern the various PDMC behaviors, particularly in response to perturbagens, such as cancer therapeutics. Based on collective experience from the consortium groups, we provide insight regarding PDMCs established both in vitro and in vivo, with a focus on practical matters related to developing and maintaining key cancer models through a series of vignettes. Although every model has the potential to offer valuable insights, the choice of the right model should be guided by the research question. However, recognizing the inherent constraints in each model is crucial. Our objective here is to delineate the strengths and limitations of each model as established by individual vignettes. Further advances in PDMCs and the development of novel model systems will enable us to better understand human biology and improve the study of human pathology in the lab.

Single-cell analysis of endometriosis reveals a coordinated transcriptional programme driving immunotolerance and angiogenesis across eutopic and ectopic tissues.

Endometriosis is characterized by the growth of endometrial-like tissue outside the uterus. It affects many women during their reproductive age, causing years of pelvic pain and potential infertility. Its pathophysiology remains largely unknown, which limits early diagnosis and treatment. We characterized peritoneal and ovarian lesions at single-cell transcriptome resolution and compared them to matched eutopic endometrium, unaffected endometrium and organoids derived from these tissues, generating data on over 122,000 cells across 14 individuals. We spatially localized many of the cell types using imaging mass cytometry. We identify a perivascular mural cell specific to the peritoneal lesions, with dual roles in angiogenesis promotion and immune cell trafficking. We define an immunotolerant peritoneal niche, fundamental differences in eutopic endometrium and between lesion microenvironments and an unreported progenitor-like epithelial cell subpopulation. Altogether, this study provides a holistic view of the endometriosis microenvironment that represents a comprehensive cell atlas of the disease in individuals undergoing hormonal treatment, providing essential information for future therapeutics and diagnostics.

Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target.

A major rate-limiting step in developing more effective immunotherapies for GBM is our inadequate understanding of the cellular complexity and the molecular heterogeneity of immune infiltrates in gliomas. Here, we report an integrated analysis of 201,986 human glioma, immune, and other stromal cells at the single cell level. In doing so, we discover extensive spatial and molecular heterogeneity in immune infiltrates. We identify molecular signatures for nine distinct myeloid cell subtypes, of which five are independent prognostic indicators of glioma patient survival. Furthermore, we identify S100A4 as a regulator of immune suppressive T and myeloid cells in GBM and demonstrate that deleting S100a4 in non-cancer cells is sufficient to reprogram the immune landscape and significantly improve survival. This study provides insights into spatial, molecular, and functional heterogeneity of glioma and glioma-associated immune cells and demonstrates the utility of this dataset for discovering therapeutic targets for this poorly immunogenic cancer.

Targeting p21Cip1 highly expressing cells in adipose tissue alleviates insulin resistance in obesity.

Insulin resistance is a pathological state often associated with obesity, representing a major risk factor for type 2 diabetes. Limited mechanism-based strategies exist to alleviate insulin resistance. Here, using single-cell transcriptomics, we identify a small, critically important, but previously unexamined cell population, p21Cip1 highly expressing (p21high) cells, which accumulate in adipose tissue with obesity. By leveraging a p21-Cre mouse model, we demonstrate that intermittent clearance of p21high cells can both prevent and alleviate insulin resistance in obese mice. Exclusive inactivation of the NF-κB pathway within p21high cells, without killing them, attenuates insulin resistance. Moreover, fat transplantation experiments establish that p21high cells within fat are sufficient to cause insulin resistance in vivo. Importantly, a senolytic cocktail, dasatinib plus quercetin, eliminates p21high cells in human fat ex vivo and mitigates insulin resistance following xenotransplantation into immuno-deficient mice. Our findings lay the foundation for pursuing the targeting of p21high cells as a new therapy to alleviate insulin resistance.

Neurokinin B-Expressing Neurons of the Central Extended Amygdala Mediate Inhibitory Synaptic Input onto Melanin-Concentrating Hormone Neuron Subpopulations.

The lateral hypothalamic area (LHA) is a highly conserved brain region critical for maintaining physiological homeostasis and goal-directed behavior. LHA neurons that express melanin-concentrating hormone (MCH) are key regulators of arousal, energy balance, and motivated behavior. However, cellular and functional diversity among LHAMCH neurons is not well understood. Previous anatomic and molecular data suggest that LHAMCH neurons may be parsed into at least two distinct subpopulations, one of which is enriched in neurokinin-3 receptor (NK3R), the receptor for neurokinin B (NKB), encoded by the Tac2 gene. This tachykininergic ligand-receptor system has been implicated in reproduction, fear memory, and stress in other brain regions, but NKB interactions with LHAMCH neurons are poorly understood. We first identified how LHAMCH subpopulations may be distinguished anatomically and electrophysiologically. To dissect functional connectivity between NKB-expressing neurons and LHAMCH neurons, we used Cre-dependent retrograde and anterograde viral tracing in male Tac2-Cre mice and identified Tac2/EYFP+ neurons in the bed nucleus of the stria terminalis and central nucleus of the amygdala, the central extended amygdala, as major sources of NKB input onto LHAMCH neurons. In addition to innervating the LHA, these limbic forebrain NKB neurons also project to midbrain and brainstem targets. Finally, using a dual-virus approach, we found that optogenetic activation of these inputs in slices evokes GABA release onto a subset of LHAMCH neurons but lacked specificity for the NK3R+ subpopulation. Overall, these data define parallel tachykininergic/GABAergic limbic forebrain projections that are positioned to modulate multiple nodes of homeostatic and behavioral control.SIGNIFICANCE STATEMENT The LHA orchestrates fundamental behavioral states in the mammalian hypothalamus, including arousal, energy balance, memory, stress, and motivated behavior. The neuropeptide MCH defines one prominent population of LHA neurons, with multiple roles in the regulation of homeostatic behavior. Outstanding questions remain concerning the upstream inputs that control MCH neurons. We sought to define neurochemically distinct pathways in the mouse brain that may communicate with specific MCH neuron subpopulations using viral-based retrograde and anterograde neural pathway tracing and optogenetics in brain slices. Here, we identify a specific neuropeptide-defined forebrain circuit that makes functional synaptic connections with MCH neuron subpopulations. This work lays the foundation for further manipulating molecularly distinct neural circuits that modulate innate behavioral states.

Somatostatin-expressing parafacial neurons are CO2/H+ sensitive and regulate baseline breathing.

Glutamatergic neurons in the retrotrapezoid nucleus (RTN) function as respiratory chemoreceptors by regulating breathing in response to tissue CO2/H+. The RTN and greater parafacial region may also function as a chemosensing network composed of CO2/H+-sensitive excitatory and inhibitory synaptic interactions. In the context of disease, we showed that loss of inhibitory neural activity in a mouse model of Dravet syndrome disinhibited RTN chemoreceptors and destabilized breathing (Kuo et al., 2019). Despite this, contributions of parafacial inhibitory neurons to control of breathing are unknown, and synaptic properties of RTN neurons have not been characterized. Here, we show the parafacial region contains a limited diversity of inhibitory neurons including somatostatin (Sst)-, parvalbumin (Pvalb)-, and cholecystokinin (Cck)-expressing neurons. Of these, Sst-expressing interneurons appear uniquely inhibited by CO2/H+. We also show RTN chemoreceptors receive inhibitory input that is withdrawn in a CO2/H+-dependent manner, and chemogenetic suppression of Sst+ parafacial neurons, but not Pvalb+ or Cck+ neurons, increases baseline breathing. These results suggest Sst-expressing parafacial neurons contribute to RTN chemoreception and respiratory activity.

Cellular taxonomy and spatial organization of the murine ventral posterior hypothalamus.

The ventral posterior hypothalamus (VPH) is an anatomically complex brain region implicated in arousal, reproduction, energy balance, and memory processing. However, neuronal cell type diversity within the VPH is poorly understood, an impediment to deconstructing the roles of distinct VPH circuits in physiology and behavior. To address this question, we employed a droplet-based single-cell RNA sequencing (scRNA-seq) approach to systematically classify molecularly distinct cell populations in the mouse VPH. Analysis of >16,000 single cells revealed 20 neuronal and 18 non-neuronal cell populations, defined by suites of discriminatory markers. We validated differentially expressed genes in selected neuronal populations through fluorescence in situ hybridization (FISH). Focusing on the mammillary bodies (MB), we discovered transcriptionally-distinct clusters that exhibit neuroanatomical parcellation within MB subdivisions and topographic projections to the thalamus. This single-cell transcriptomic atlas of VPH cell types provides a resource for interrogating the circuit-level mechanisms underlying the diverse functions of VPH circuits.

CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.

BACKGROUND: Cancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site of origin cannot be found despite a standard diagnostic workup. Because knowledge of a patient's primary cancer remains fundamental to their treatment, CUP patients are significantly disadvantaged and most have a poor survival outcome. Developing robust and accessible diagnostic methods for resolving cancer tissue of origin, therefore, has significant value for CUP patients. METHODS: We developed an RNA-based classifier called CUP-AI-Dx that utilizes a 1D Inception convolutional neural network (1D-Inception) model to infer a tumor's primary tissue of origin. CUP-AI-Dx was trained using the transcriptional profiles of 18,217 primary tumours representing 32 cancer types from The Cancer Genome Atlas project (TCGA) and International Cancer Genome Consortium (ICGC). Gene expression data was ordered by gene chromosomal coordinates as input to the 1D-CNN model, and the model utilizes multiple convolutional kernels with different configurations simultaneously to improve generality. The model was optimized through extensive hyperparameter tuning, including different max-pooling layers and dropout settings. For 11 tumour types, we also developed a random forest model that can classify the tumour's molecular subtype according to prior TCGA studies. The optimised CUP-AI-Dx tissue of origin classifier was tested on 394 metastatic samples from 11 tumour types from TCGA and 92 formalin-fixed paraffin-embedded (FFPE) samples representing 18 cancer types from two clinical laboratories. The CUP-AI-Dx molecular subtype was also independently tested on independent ovarian and breast cancer microarray datasets FINDINGS: CUP-AI-Dx identifies the primary site with an overall top-1-accuracy of 98.54% in cross-validation and 96.70% on a test dataset. When applied to two independent clinical-grade RNA-seq datasets generated from two different institutes from the US and Australia, our model predicted the primary site with a top-1-accuracy of 86.96% and 72.46% respectively. INTERPRETATION: The CUP-AI-Dx predicts tumour primary site and molecular subtype with high accuracy and therefore can be used to assist the diagnostic work-up of cancers of unknown primary or uncertain origin using a common and accessible genomics platform. FUNDING: NIH R35 GM133562, NCI P30 CA034196, Victorian Cancer Agency Australia.

Mapping systemic lupus erythematosus heterogeneity at the single-cell level.

Patients with systemic lupus erythematosus (SLE) display a complex blood transcriptome whose cellular origin is poorly resolved. Using single-cell RNA sequencing, we profiled ~276,000 peripheral blood mononuclear cells from 33 children with SLE with different degrees of disease activity and 11 matched controls. Increased expression of interferon-stimulated genes (ISGs) distinguished cells from children with SLE from healthy control cells. The high ISG expression signature (ISGhi) derived from a small number of transcriptionally defined subpopulations within major cell types, including monocytes, CD4+ and CD8+ T cells, natural killer cells, conventional and plasmacytoid dendritic cells, B cells and especially plasma cells. Expansion of unique subpopulations enriched in ISGs and/or in monogenic lupus-associated genes classified patients with the highest disease activity. Profiling of ~82,000 single peripheral blood mononuclear cells from adults with SLE confirmed the expansion of similar subpopulations in patients with the highest disease activity. This study lays the groundwork for resolving the origin of the SLE transcriptional signatures and the disease heterogeneity towards precision medicine applications.

Identification of a T follicular helper cell subset that drives anaphylactic IgE.

Cross-linking of high-affinity immunoglobulin E (IgE) results in the life-threatening allergic reaction anaphylaxis. Yet the cellular mechanisms that induce B cells to produce IgE in response to allergens remain poorly understood. T follicular helper (TFH) cells direct the affinity and isotype of antibodies produced by B cells. Although TFH cell-derived interleukin-4 (IL-4) is necessary for IgE production, it is not sufficient. We report a rare population of IL-13-producing TFH cells present in mice and humans with IgE to allergens, but not when allergen-specific IgE was absent or only low-affinity. These "TFH13" cells have an unusual cytokine profile (IL-13hiIL-4hiIL-5hiIL-21lo) and coexpress the transcription factors BCL6 and GATA3. TFH13 cells are required for production of high- but not low-affinity IgE and subsequent allergen-induced anaphylaxis. Blocking TFH13 cells may represent an alternative therapeutic target to ameliorate anaphylaxis.

Cross-Species Single-Cell Analysis of Pancreatic Ductal Adenocarcinoma Reveals Antigen-Presenting Cancer-Associated Fibroblasts.

Cancer-associated fibroblasts (CAF) are major players in the progression and drug resistance of pancreatic ductal adenocarcinoma (PDAC). CAFs constitute a diverse cell population consisting of several recently described subtypes, although the extent of CAF heterogeneity has remained undefined. Here we use single-cell RNA sequencing to thoroughly characterize the neoplastic and tumor microenvironment content of human and mouse PDAC tumors. We corroborate the presence of myofibroblastic CAFs and inflammatory CAFs and define their unique gene signatures in vivo. Moreover, we describe a new population of CAFs that express MHC class II and CD74, but do not express classic costimulatory molecules. We term this cell population "antigen-presenting CAFs" and find that they activate CD4+ T cells in an antigen-specific fashion in a model system, confirming their putative immune-modulatory capacity. Our cross-species analysis paves the way for investigating distinct functions of CAF subtypes in PDAC immunity and progression. SIGNIFICANCE: Appreciating the full spectrum of fibroblast heterogeneity in pancreatic ductal adenocarcinoma is crucial to developing therapies that specifically target tumor-promoting CAFs. This work identifies MHC class II-expressing CAFs with a capacity to present antigens to CD4+ T cells, and potentially to modulate the immune response in pancreatic tumors.See related commentary by Belle and DeNardo, p. 1001.This article is highlighted in the In This Issue feature, p. 983.

Identification of EOMES-expressing spermatogonial stem cells and their regulation by PLZF.

Long-term maintenance of spermatogenesis in mammals is supported by GDNF, an essential growth factor required for spermatogonial stem cell (SSC) self-renewal. Exploiting a transgenic GDNF overexpression model, which expands and normalizes the pool of undifferentiated spermatogonia between Plzf +/+ and Plzf lu/lu mice, we used RNAseq to identify a rare subpopulation of cells that express EOMES, a T-box transcription factor. Lineage tracing and busulfan challenge show that these are SSCs that contribute to steady state spermatogenesis as well as regeneration following chemical injury. EOMES+ SSCs have a lower proliferation index in wild-type than in Plzf lu/lu mice, suggesting that PLZF regulates their proliferative activity and that EOMES+ SSCs are lost through proliferative exhaustion in Plzf lu/lu mice. Single cell RNA sequencing of EOMES+ cells from Plzf +/+ and Plzf lu/lu mice support the conclusion that SSCs are hierarchical yet heterogeneous.

Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs.

The protein kinase catalytic domain is one of the most abundant domains across all branches of life. Although kinases share a common core function of phosphoryl-transfer, they also have wide functional diversity and play varied roles in cell signaling networks, and for this reason are implicated in a number of human diseases. This functional diversity is primarily achieved through sequence variation, and uncovering the sequence-function relationships for the kinase family is a major challenge. In this study we use a statistical inference technique inspired by statistical physics, which builds a coevolutionary "Potts" Hamiltonian model of sequence variation in a protein family. We show how this model has sufficient power to predict the probability of specific subsequences in the highly diverged kinase family, which we verify by comparing the model's predictions with experimental observations in the Uniprot database. We show that the pairwise (residue-residue) interaction terms of the statistical model are necessary and sufficient to capture higher-than-pairwise mutation patterns of natural kinase sequences. We observe that previously identified functional sets of residues have much stronger correlated interaction scores than are typical.

Inference of Epistatic Effects Leading to Entrenchment and Drug Resistance in HIV-1 Protease.

Understanding the complex mutation patterns that give rise to drug resistant viral strains provides a foundation for developing more effective treatment strategies for HIV/AIDS. Multiple sequence alignments of drug-experienced HIV-1 protease sequences contain networks of many pair correlations which can be used to build a (Potts) Hamiltonian model of these mutation patterns. Using this Hamiltonian model, we translate HIV-1 protease sequence covariation data into quantitative predictions for the probability of observing specific mutation patterns which are in agreement with the observed sequence statistics. We find that the statistical energies of the Potts model are correlated with the fitness of individual proteins containing therapy-associated mutations as estimated by in vitro measurements of protein stability and viral infectivity. We show that the penalty for acquiring primary resistance mutations depends on the epistatic interactions with the sequence background. Primary mutations which lead to drug resistance can become highly advantageous (or entrenched) by the complex mutation patterns which arise in response to drug therapy despite being destabilizing in the wildtype background. Anticipating epistatic effects is important for the design of future protease inhibitor therapies.

Potts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness.

Potts Hamiltonian models of protein sequence co-variation are statistical models constructed from the pair correlations observed in a multiple sequence alignment (MSA) of a protein family. These models are powerful because they capture higher order correlations induced by mutations evolving under constraints and help quantify the connections between protein sequence, structure, and function maintained through evolution. We review recent work with Potts models to predict protein structure and sequence-dependent conformational free energy landscapes, to survey protein fitness landscapes and to explore the effects of epistasis on fitness. We also comment on the numerical methods used to infer these models for each application.

Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015.

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein-ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.

Structural propensities of kinase family proteins from a Potts model of residue co-variation.

Understanding the conformational propensities of proteins is key to solving many problems in structural biology and biophysics. The co-variation of pairs of mutations contained in multiple sequence alignments of protein families can be used to build a Potts Hamiltonian model of the sequence patterns which accurately predicts structural contacts. This observation paves the way to develop deeper connections between evolutionary fitness landscapes of entire protein families and the corresponding free energy landscapes which determine the conformational propensities of individual proteins. Using statistical energies determined from the Potts model and an alignment of 2896 PDB structures, we predict the propensity for particular kinase family proteins to assume a "DFG-out" conformation implicated in the susceptibility of some kinases to type-II inhibitors, and validate the predictions by comparison with the observed structural propensities of the corresponding proteins and experimental binding affinity data. We decompose the statistical energies to investigate which interactions contribute the most to the conformational preference for particular sequences and the corresponding proteins. We find that interactions involving the activation loop and the C-helix and HRD motif are primarily responsible for stabilizing the DFG-in state. This work illustrates how structural free energy landscapes and fitness landscapes of proteins can be used in an integrated way, and in the context of kinase family proteins, can potentially impact therapeutic design strategies.

Large-scale asynchronous and distributed multidimensional replica exchange molecular simulations and efficiency analysis.

We describe methods to perform replica exchange molecular dynamics (REMD) simulations asynchronously (ASyncRE). The methods are designed to facilitate large scale REMD simulations on grid computing networks consisting of heterogeneous and distributed computing environments as well as on homogeneous high-performance clusters. We have implemented these methods on NSF (National Science Foundation) XSEDE (Extreme Science and Engineering Discovery Environment) clusters and BOINC (Berkeley Open Infrastructure for Network Computing) distributed computing networks at Temple University and Brooklyn College at CUNY (the City University of New York). They are also being implemented on the IBM World Community Grid. To illustrate the methods, we have performed extensive (more than 60 ms in aggregate) simulations for the beta-cyclodextrin-heptanoate host-guest system in the context of one- and two-dimensional ASyncRE, and we used the results to estimate absolute binding free energies using the binding energy distribution analysis method. We propose ways to improve the efficiency of REMD simulations: these include increasing the number of exchanges attempted after a specified molecular dynamics (MD) period up to the fast exchange limit and/or adjusting the MD period to allow sufficient internal relaxation within each thermodynamic state. Although ASyncRE simulations generally require long MD periods (>picoseconds) per replica exchange cycle to minimize the overhead imposed by heterogeneous computing networks, we found that it is possible to reach an efficiency similar to conventional synchronous REMD, by optimizing the combination of the MD period and the number of exchanges attempted per cycle.