Bayesian Inference for Single-cell Clustering and Imputing

Single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is noise-prone due to experimental errors and cell type-specific biases. Current computational approaches for analyzing single-cell data involve a global normalization step which introduces incorrect biases and spurious noise and does not resolve missing data (dropouts). This can lead to misleading conclusions in downstream analyses. Moreover, a single normalization removes important cell type-specific information. We propose a data-driven model, BISCUIT, that iteratively normalizes and clusters cells, thereby separating noise from interesting biological signals. BISCUIT is a Bayesian probabilistic model that learns cell-specific parameters to intelligently drive normalization. This approach displays superior performance to global normalization followed by clustering in both synthetic and real single-cell data compared with previous methods, and allows easy interpretation and recovery of the underlying structure and cell types.

Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance

Large-scale genomic studies have identified multiple somatic aberrations in breast cancer, including copy number alterations and point mutations. Still, identifying causal variants and emergent vulnerabilities that arise as a consequence of genetic alterations remain major challenges. We performed whole-genome small hairpin RNA (shRNA) "dropout screens" on 77 breast cancer cell lines. Using a hierarchical linear regression algorithm to score our screen results and integrate them with accompanying detailed genetic and proteomic information, we identify vulnerabilities in breast cancer, including candidate "drivers," and reveal general functional genomic properties of cancer cells. Comparisons of gene essentiality with drug sensitivity data suggest potential resistance mechanisms, effects of existing anti-cancer drugs, and opportunities for combination therapy. Finally, we demonstrate the utility of this large dataset by identifying BRD4 as a potential target in luminal breast cancer and PIK3CA mutations as a resistance determinant for BET-inhibitors.

Wishbone identifies bifurcating developmental trajectories from single-cell data

Recent single-cell analysis technologies offer an unprecedented opportunity to elucidate developmental pathways. Here we present Wishbone, an algorithm for positioning single cells along bifurcating developmental trajectories with high resolution. Wishbone uses multi-dimensional single-cell data, such as mass cytometry or RNA-Seq data, as input and orders cells according to their developmental progression, and it pinpoints bifurcation points by labeling each cell as pre-bifurcation or as one of two post-bifurcation cell fates. Using 30-channel mass cytometry data, we show that Wishbone accurately recovers the known stages of T-cell development in the mouse thymus, including the bifurcation point. We also apply the algorithm to mouse myeloid differentiation and demonstrate its generalization to additional lineages. A comparison of Wishbone to diffusion maps, SCUBA and Monocle shows that it outperforms these methods both in the accuracy of ordering cells and in the correct identification of branch points.

Wishbone identifies bifurcating developmental trajectories from single-cell data

Recent single-cell analysis technologies offer an unprecedented opportunity to elucidate developmental pathways. Here we present Wishbone, an algorithm for positioning single cells along bifurcating developmental trajectories with high resolution. Wishbone uses multi-dimensional single-cell data, such as mass cytometry or RNA-seq data, as input and orders cells according to their developmental progression by pinpointing bifurcation points and labeling each cell as pre-bifurcation or as one of two post-bifurcation cell fates. Using 30-channel mass cytometry data, we show that Wishbone accurately recovers the known stages of T cell development in the mouse thymus, including the bifurcation point. We also apply the algorithm to mouse myeloid differentiation and demonstrate its generalization to additional lineages. A comparison of Wishbone to diffusion maps, SCUBA and Monocle shows that it outperforms these methods both in the accuracy of ordering cells and in the correct identification of branch points.

Abstract P2-06-02: BIN3 is an 8p21 tumor suppressor regulating the epithelial attachment checkpoint

An important characteristic of multicellular organisms is the control that tissue architecture exerts on the growth and differentiation of individual cells. Epithelial cells sense their location through interactions with the extracellular matrix (ECM) as well as neighboring cells. These interactions generate input signals, including survival, that are critical to maintain tissue and cellular homeostasis. When attachment is compromised, epithelial cells undergo an intrinsically programmed cell death (apoptosis) that is termed anoikis (from the Greek "loss of home"). Importantly, failure to execute the anoikis program could result in adherent epithelial cells surviving in suspension or being able to proliiferate at sites different from their original environment. Thus, anoikis is a line of defense that must be circumvented by cancerous epithelial cells for them to leave their home niche, thrive in inappropriate ECM environments, and establish long distance metastases. Thus, elucidating how epithelial cancer cells escape anoikis is critical to understanding cancer progression.

In order to uncover genes that modulate the anoikis response and are altered in human cancers, we performed a functional genomics study. We couple genome wide RNAi to identify gene functions that, when silenced, induce resistance to anoikis with a novel computational method, ISAR-DEL, specifically aimed at pinpointing candidate tumor suppressor genes based on recurrent loss of copy number. Our studies identified Bridging Integrator 3 (BIN3) as a novel tumor suppressor located on the chromosomal region 8p21.3, one of the most frequently lost regions in epithelial cancers.

Mechanistically, we link BIN3 tumor suppression function to its ability to sense changes in the curvature of the cell membrane and relocate to the cell membrane after cell detachment to induce a proapoptotic cascade. Once BIN3 has translocated to the cell membrane it modulates the relocation and function of CDC42. In these conditions, CDC42 transmits the signal that leads to the activation of the stress protein P38-α and programmed cell death mediated by accumulation of the apoptotic facilitator BimEL. Overall, our results explain how changes in cell geometry are integrated in the cellular signaling network and present, for the first time, BIN3 as a novel breast cancer tumors suppressor.

Citation Format: Silva J, Marshall N, Sanchez-Garcia F, Parsons R, Pe'er D. BIN3 is an 8p21 tumor suppressor regulating the epithelial attachment checkpoint. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P2-06-02.

Abstract S1-06: The miR-424/503 cluster is a breast cancer tumor suppressor with a role in chemoresistance

Recently, we have identified the miR-424(322)/503 cluster as an important regulator of mammary epithelial homeostasis. The miR-424(322)/503 cluster was identified as one of the few miRNAs significantly upregulated during involution after pregnancy. By generating a knock-out mouse model, we found that regression of the mammary epithelium after pregnancy was compromised in the absence of miR-424(322)/503. Mechanistically, our studies unveiled that miR-424(322)/503 is induced by the canonical TGF-β-SMAD pathway, and that it orchestrates changes in the mammary epithelium by downregulating the expression of key components of signal transduction (IGF1R) and apoptosis (BCL2) Llobet et al. Genes&Development 2014).

Remarkably, our new studies have revealed that miR-424(322)/503-/- female mice develop hyperplasia and mammary tumors that are promoted by pregnancy. Thus, we investigated the status of this cluster in human breast cancers. For this we analyzed the METABRIC dataset. These studies revealed that the miR-424(322)503 cluster was heterozygously deleted in ∼16% of breast cancers and that its deletion correlates with lower expression levels of the mature miRNA forms. Importantly, miR-424(322)/503 is located on the X-chromosome and we have confirmed that is monoallelically expressed due to X-chromosome inactivation. Thus, the mutation of the active allele strongly impacts the expression of the miR-cluster. Deletions of the miR-424(322)/503 locus were more frequent in molecular subtypes with aggressive behavior (Luminal B, HER2+ and Basal) and both deletions and low expression of the cluster were associated with poor prognosis and reduced survival.

Some of the miR-424(322)/503 targets that we have previously validated (BCL2 and IGF1R) are involved in resistance to chemotherapy. Thus, we investigated in vivo, utilizing our knock-out mouse model, if loss of miR-424(322)/503 induces resistance to chemotherapeutic drugs. For this we crossed our miR-424(322)/503-/- animals with the HER2+ model FVB/N-Tg(MMTVneu)202Mul/J. Tumors emerging in HER2+/miR-424(322)/503-/- animals presented higher levels of BCL2 and hyperactivation of the IGF1R-AKT signaling compared to HER2+/miR-424(322)/503+/+ counterparts. Furthermore, these tumors were resistant to standard chemotherapy. Importantly, inhibition of BCL2 with ABT-199 and IGF1R with BMS-754807, two compounds currently in clinical trial, completely reverted chemotherapy resistance.

Overall, our data present evidence supporting a tumor suppressor role for miR-424(322)/503 cluster and its implication in resistance to chemotherapy.

Citation Format: Silva J, Llobet-Navas D, Rodriguez-Barrueco R, Sanchez-Garcia F, Pe'er D. The miR-424/503 cluster is a breast cancer tumor suppressor with a role in chemoresistance. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr S1-06.

Abstract A25: Cytometric atlas of the human breast: Comprehensive characterization reveals 12 distinct cell populations

Since formulation of the cellular theory in the early 19th century—relatively not so long ago—it has become universally recognized that tissues and organisms are formed and shaped by cells of many different types, all operating in beautiful harmony. Many mechanisms of this orchestration remain largely a mystery to this day, yet are important for us to understand if we are to fully comprehend fundamental processes in disease, especially one as formidable and perplexing as cancer. In the current era of progressive technological advances, many areas of biology are converging, and we now recognize and expect differences between cells, even among those of the same type, but whose origins rest in different tissues. Yet, many core tenets are being universally applied. The detailed cellular composition of tissues is clouded with uncertainty for many tissue types, and this includes the breast. To clarify the cell types in the breast and build a solid foundation for future work, we have meticulously examined a large collection of normal breast tissues, microscopically and by flow cytometry. We developed a complex antibody panel and rigorous gating strategy capable of objectively resolving and sorting each major cell type. We have sequenced mRNAs expressed by even the rarest of cell subpopulations and refined or developed methods for culturing each. This includes: several different luminal and epithelial fractions, myoepithelial cells, adipocytes, leukocytes, pericytes, erythrocytes, adipose-derived mesenchymal stem cells, and both lymphatic and vascular endothelial cells –12 different types in all. Future studies are aimed at developing heterotypic culture models using these cells, but also to use our new knowledge and experience to similarly explore heterogeneity of ductal carcinoma in situ (DCIS) and invasive breast cancers. We will present the development of this method and characterization to date of the sorted cell populations.

Citation Format: William Curtis Hines, Ambrose Carr, Kate Thi, Zhenmao Wan, Dana Pe'er, Mina J. Bissell. Cytometric atlas of the human breast: Comprehensive characterization reveals 12 distinct cell populations. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr A25.

PD-1 Blockade Expands Intratumoral Memory T Cells

Tumor responses to programmed cell death protein 1 (PD-1) blockade therapy are mediated by T cells, which we characterized in 102 tumor biopsies obtained from 53 patients treated with pembrolizumab, an antibody to PD-1. Biopsies were dissociated, and single-cell infiltrates were analyzed by multicolor flow cytometry using two computational approaches to resolve the leukocyte phenotypes at the single-cell level. There was a statistically significant increase in the frequency of T cells in patients who responded to therapy. The frequency of intratumoral B cells and monocytic myeloid-derived suppressor cells significantly increased in patients' biopsies taken on treatment. The percentage of cells with a regulatory T-cell phenotype, monocytes, and natural killer cells did not change while on PD-1 blockade therapy. CD8(+) memory T cells were the most prominent phenotype that expanded intratumorally on therapy. However, the frequency of CD4(+) effector memory T cells significantly decreased on treatment, whereas CD4(+) effector T cells significantly increased in nonresponding tumors on therapy. In peripheral blood, an unusual population of blood cells expressing CD56 was detected in two patients with regressing melanoma. In conclusion, PD-1 blockade increases the frequency of T cells, B cells, and myeloid-derived suppressor cells in tumors, with the CD8(+) effector memory T-cell subset being the major T-cell phenotype expanded in patients with a response to therapy.

PD-1 Blockade Expands Intratumoral Memory T Cells

Tumor responses to programmed cell death protein 1 (PD-1) blockade therapy are mediated by T cells, which we characterized in 102 tumor biopsies obtained from 53 patients treated with pembrolizumab, an antibody to PD-1. Biopsies were dissociated, and single-cell infiltrates were analyzed by multicolor flow cytometry using two computational approaches to resolve the leukocyte phenotypes at the single-cell level. There was a statistically significant increase in the frequency of T cells in patients who responded to therapy. The frequency of intratumoral B cells and monocytic myeloid-derived suppressor cells significantly increased in patients' biopsies taken on treatment. The percentage of cells with a regulatory T-cell phenotype, monocytes, and natural killer cells did not change while on PD-1 blockade therapy. CD8(+) memory T cells were the most prominent phenotype that expanded intratumorally on therapy. However, the frequency of CD4(+) effector memory T cells significantly decreased on treatment, whereas CD4(+) effector T cells significantly increased in nonresponding tumors on therapy. In peripheral blood, an unusual population of blood cells expressing CD56 was detected in two patients with regressing melanoma. In conclusion, PD-1 blockade increases the frequency of T cells, B cells, and myeloid-derived suppressor cells in tumors, with the CD8(+) effector memory T-cell subset being the major T-cell phenotype expanded in patients with a response to therapy.

Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data

We introduce an iterative normalization and clustering method for single-cell gene expression data. The emerging technology of single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is confounded by technical variation emanating from experimental errors and cell type-specific biases. Current approaches perform a global normalization prior to analyzing biological signals, which does not resolve missing data or variation dependent on latent cell types. Our model is formulated as a hierarchical Bayesian mixture model with cell-specific scalings that aid the iterative normalization and clustering of cells, teasing apart technical variation from biological signals. We demonstrate that this approach is superior to global normalization followed by clustering. We show identifiability and weak convergence guarantees of our method and present a scalable Gibbs inference algorithm. This method improves cluster inference in both synthetic and real single-cell data compared with previous methods, and allows easy interpretation and recovery of the underlying structure and cell types.

Abstract IA24: Computational dissection of phenotypic and functional heterogeneity in cancer

Cells within a single tumor are known to display extensive phenotypic and functional heterogeneity. Many life-threatening features of cancer, including drug resistance, metastasis and relapse, are facets of intratumor heterogeneity. With emerging single-cell measurement technologies, the field is poised to make important strides in understanding and controlling this heterogeneity. However, these technologies require advances in analytical methods to interpret the complex data they produce. Using mass cytometry, which measures single cells in ~31 simultaneous proteomic features, we developed novel methods for analyzing phenotypic heterogeneity in cancer. We use AML as an example to demonstrate the power of our approach. The heart of our approach is Phenograph, a graph-based representation of single-cells which represents the phenotypic structure of the sample and can be partitioned into subsets of densely interconnected nodes, called communities. Using Phenograph, we deconstructed several AML samples into discrete phenotypes. Analyzing the resulting subpopulations provided insights into functional heterogeneity of AML. Phenograph can be applied to characterize heterogeneity and primitive subpopulations in additional cancers.

Citation Format: Dana Pe'er. Computational dissection of phenotypic and functional heterogeneity in cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr IA24.

Abstract A32: Computational dissection of phenotypic and functional heterogeneity in acute myeloid leukemia

Cells within a single tumor are known to display extensive phenotypic and functional heterogeneity. Many life-threatening features of cancer, including drug resistance, metastasis and relapse, are facets of intratumor heterogeneity. With emerging single-cell measurement technologies, the field is poised to make important strides in understanding and controlling this heterogeneity. However, these technologies require coordinated advances in analytical methods to interpret the complex data they produce.

Acute myeloid leukemia (AML) is an aggressive bone marrow malignancy in which the importance of cellular heterogeneity has been well characterized. However, previous studies have only scraped the surface of the heterogeneity in this disease. Using mass cytometry, which measures single cells in ~40 simultaneous proteomic features, we developed novel methods for analyzing phenotypic heterogeneity in cancer. Our approach provides an extensive compendium of surface-marker and signaling phenotypes in AML that extends current boundaries of knowledge.

The heart of our approach is a graph-based representation of the single-cell samples. In this representation, each cell is modeled by a node connected to its neighbors—the cells most phenotypically similar to it. Constructed by local rules connecting cells, the graph as a whole represents the phenotypic structure of the sample. The graph can be partitioned into subsets of densely interconnected nodes, called communities, which represent distinct phenotypic subpopulations. Unlike parametric methods such as mixture models, this method makes no assumption about the size, distribution, or number of subpopulations.

Using our graph-based approach, we deconstructed several AML samples into discrete phenotypes. Comparing phenotypes across patients, we found a striking degree of order. Every phenotype identifiable phenotype was discoverable in multiple (but not all) patients, implying a constraint on the space of allowable AML phenotypes. For each phenotype we also identified cognate healthy cell types at different stages of bone marrow maturation, indicating a constraint that is linked to normal developmental programs.

Our data contain measurements of under various environmental perturbations and we designed a method to statistically quantify evoked signaling responses, producing high-dimensional signaling phenotypes for each subpopulation, which we regard as a representation of cellular functional potential. We found a tight coupling between surface and signaling phenotypes in healthy cells that is disrupted in AML. We identified a primitive signaling phenotype, derived from healthy stem and progenitor cells, which was not correlated with the primitive surface marker profile typically used to define primitive cells in AML. Using single-cell frequencies to deconvolve existing bulk gene expression data, we identified genes associated with this primitive signaling phenotype. These genes are enriched for primitive hematopoietic annotations and produce a clinically predictive signature that is more powerful than genes associated with the primitive surface profile, validating the utility of our approach and indicating novel regulators of the primitive hematopoietic cell state.

Citation Format: Jacob H. Levine, Erin F. Simonds, Sean C. Bendall, Garry P. Nolan, Dana Pe'er. Computational dissection of phenotypic and functional heterogeneity in acute myeloid leukemia. [abstract]. In: Proceedings of the AACR Special Conference on Hematologic Malignancies: Translating Discoveries to Novel Therapies; Sep 20-23, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(17 Suppl):Abstract nr A32.

Trajectories of cell-cycle progression from fixed cell populations

An accurate dissection of sources of cell-to-cell variability is crucial for quantitative biology at the single-cell level but has been challenging for the cell cycle. We present Cycler, a robust method that constructs a continuous trajectory of cell-cycle progression from images of fixed cells. Cycler handles heterogeneous microenvironments and does not require perturbations or genetic markers, making it generally applicable to quantifying multiple sources of cell-to-cell variability in mammalian cells.

Toward understanding and exploiting tumor heterogeneity

The extent of tumor heterogeneity is an emerging theme that researchers are only beginning to understand. How genetic and epigenetic heterogeneity affects tumor evolution and clinical progression is unknown. The precise nature of the environmental factors that influence this heterogeneity is also yet to be characterized. Nature Medicine, Nature Biotechnology and the Volkswagen Foundation organized a meeting focused on identifying the obstacles that need to be overcome to advance translational research in and tumor heterogeneity. Once these key questions were established, the attendees devised potential solutions. Their ideas are presented here.

Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis

Acute myeloid leukemia (AML) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic, and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed PhenoGraph, which algorithmically defines phenotypes in high-dimensional single-cell data. PhenoGraph revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into AML pathophysiology.

Scalable microfluidics for single-cell RNA printing and sequencing

Many important biological questions demand single-cell transcriptomics on a large scale. Hence, new tools are urgently needed for efficient, inexpensive manipulation of RNA from individual cells. We report a simple platform for trapping single-cell lysates in sealed, picoliter microwells capable of printing RNA on glass or capturing RNA on beads. We then develop a scalable technology for genome-wide, single-cell RNA-Seq. Our device generates pooled libraries from hundreds of individual cells with consumable costs of $0.10–$0.20 per cell and includes five lanes for simultaneous experiments. We anticipate that this system will serve as a general platform for single-cell imaging and sequencing.

Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm

Mass-tag cell barcoding (MCB) labels individual cell samples with unique combinatorial barcodes, after which they are pooled for processing and measurement as a single multiplexed sample. The MCB method eliminates variability between samples in antibody staining and instrument sensitivity, reduces antibody consumption and shortens instrument measurement time. Here we present an optimized MCB protocol. The use of palladium-based labeling reagents expands the number of measurement channels available for mass cytometry and reduces interference with lanthanide-based antibody measurement. An error-detecting combinatorial barcoding scheme allows cell doublets to be identified and removed from the analysis. A debarcoding algorithm that is single cell-based rather than population-based improves the accuracy and efficiency of sample deconvolution. This debarcoding algorithm has been packaged into software that allows rapid and unbiased sample deconvolution. The MCB procedure takes 3-4 h, not including sample acquisition time of ∼1 h per million cells.

Systems biology. Conditional density-based analysis of T cell signaling in single-cell data

Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. High-dimensional single-cell technologies, such as mass cytometry, offer an opportunity to characterize signaling circuit-wide. However, the challenge of developing and applying computational approaches to interpret such complex data remains. Here, we developed computational methods, based on established statistical concepts, to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. In comparing signaling between naïve and antigen-exposed CD4(+) T lymphocytes, we find that although these two cell subtypes had similarly wired networks, naïve cells transmitted more information along a key signaling cascade than did antigen-exposed cells. We validated our characterization on mice lacking the extracellular-regulated mitogen-activated protein kinase (MAPK) ERK2, which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naïve cells as compared with antigen-exposed cells, as predicted. We demonstrate that by using cell-to-cell variation inherent in single-cell data, we can derive response functions underlying molecular circuits and drive the understanding of how cells process signals.

Abstract 432: BIN3 is a novel 8p21 tumor suppressor gene that regulates the attachment checkpoint in epithelial cells

An important characteristic of multicellular organisms is the control that the tissue architecture exerts on the fate of individual cells. Epithelial cells sense their location through interactions with the extracellular matrix (ECM) and remove themselves by programmed cell death (anoikis) when those interactions are disturbed. Importantly, anoikis is a line of defense that has to be circumvented by cancerous epithelial cells to be able to leave their home environment and establish long distance metastases. Here, by combining a genome-wide RNAi screen and a novel algorithm to study copy number alterations (ISAR-DEL), we identify the Bridging Integrator-3 (BIN3) as a novel 8p21 tumor suppressor gene whose inactivation promotes escape from anoikis in epithelial cancers. Mechanistically, we link the tumor suppression function of BIN3 to its ability to relocate to the cell membrane after cell detachment and to induce a proapoptotic cascade. This death signaling is mediated by CDC42 activation of the P38-α stress pathway and the consequent accumulation of the apoptotic facilitator BimEL. Our results identify BIN3 as a novel epithelial tumor suppressor gene, provide novel insights on the mechanisms of attachment tumor suppressor checkpoint and highlight the importance of anoikis escape in driving cell transformation and metastasis in human cancer.

Note: This abstract was not presented at the meeting.

Citation Format: Netonia Marshall, Felix Sanchez, David Llobet, Ruth Rodriguez Barrueco, Veronica Castro, Dylan Kotlia, Maira Pires, Patricia Villagrasa, Preeti Putcha, Ramon Parsons, Dana Pe'er, Jose Silva. BIN3 is a novel 8p21 tumor suppressor gene that regulates the attachment checkpoint in epithelial cells. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 432. doi:10.1158/1538-7445.AM2014-432

Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development

Tissue regeneration is an orchestrated progression of cells from an immature state to a mature one, conventionally represented as distinctive cell subsets. A continuum of transitional cell states exists between these discrete stages. We combine the depth of single-cell mass cytometry and an algorithm developed to leverage this continuum by aligning single cells of a given lineage onto a unified trajectory that accurately predicts the developmental path de novo. Applied to human B cell lymphopoiesis, the algorithm (termed Wanderlust) constructed trajectories spanning from hematopoietic stem cells through to naive B cells. This trajectory revealed nascent fractions of B cell progenitors and aligned them with developmentally cued regulatory signaling including IL-7/STAT5 and cellular events such as immunoglobulin rearrangement, highlighting checkpoints across which regulatory signals are rewired paralleling changes in cellular state. This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and "corrupted" developmental processes including cancer.

The onset of p53 loss of heterozygosity is differentially induced in various stem cell types and may involve the loss of either allele

p53 loss of heterozygosity (p53LOH) is frequently observed in Li-Fraumeni syndrome (LFS) patients who carry a mutant (Mut) p53 germ-line mutation. Here, we focused on elucidating the link between p53LOH and tumor development in stem cells (SCs). Although adult mesenchymal stem cells (MSCs) robustly underwent p53LOH, p53LOH in induced embryonic pluripotent stem cells (iPSCs) was significantly attenuated. Only SCs that underwent p53LOH induced malignant tumors in mice. These results may explain why LFS patients develop normally, yet acquire tumors in adulthood. Surprisingly, an analysis of single-cell sub-clones of iPSCs, MSCs and ex vivo bone marrow (BM) progenitors revealed that p53LOH is a bi-directional process, which may result in either the loss of wild-type (WT) or Mut p53 allele. Interestingly, most BM progenitors underwent Mutp53LOH. Our results suggest that the bi-directional p53LOH process may function as a cell-fate checkpoint. The loss of Mutp53 may be regarded as a DNA repair event leading to genome stability. Indeed, gene expression analysis of the p53LOH process revealed upregulation of a specific chromatin remodeler and a burst of DNA repair genes. However, in the case of loss of WTp53, cells are endowed with uncontrolled growth that promotes cancer.

Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development

Tissue regeneration is an orchestrated progression of cells from an immature state to a mature one, conventionally represented as distinctive cell subsets. A continuum of transitional cell states exists between these discrete stages. We combine the depth of single-cell mass cytometry and an algorithm developed to leverage this continuum by aligning single cells of a given lineage onto a unified trajectory that accurately predicts the developmental path de novo. Applied to human B cell lymphopoiesis, the algorithm (termed Wanderlust) constructed trajectories spanning from hematopoietic stem cells through to naive B cells. This trajectory revealed nascent fractions of B cell progenitors and aligned them with developmentally cued regulatory signaling including IL-7/STAT5 and cellular events such as immunoglobulin rearrangement, highlighting checkpoints across which regulatory signals are rewired paralleling changes in cellular state. This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and "corrupted" developmental processes including cancer.

The miR-424(322)/503 cluster orchestrates remodeling of the epithelium in the involuting mammary gland

The mammary gland undergoes continuous remodeling. Llobet-Navas et al. identify the microRNA cluster miR-424(322)/503 as an important regulator of epithelial involution after pregnancy. TGF-β regulates the expression of this miR cluster, which in turn targets BCL-2 and IGF1R. This work suggests a model in which activation of the TGF-β pathway after weaning induces the transcription of the miR-424(322)/503 cluster to down-regulate the expression of key genes.

The mammary gland is a very dynamic organ that undergoes continuous remodeling. The critical regulators of this process are not fully understood. Here we identify the microRNA cluster miR-424(322)/503 as an important regulator of epithelial involution after pregnancy. Through the generation of a knockout mouse model, we found that regression of the secretory acini of the mammary gland was compromised in the absence of miR-424(322)/503. Mechanistically, we show that miR-424(322)/503 orchestrates cell life and death decisions by targeting BCL-2 and IGF1R (insulin growth factor-1 receptor). Furthermore, we demonstrate that the expression of this microRNA cluster is regulated by TGF-β, a well-characterized regulator of mammary involution. Overall, our data suggest a model in which activation of the TGF-β pathway after weaning induces the transcription of miR-424(322)/503, which in turn down-regulates the expression of key genes. Here, we unveil a previously unknown, multilayered regulation of epithelial tissue remodeling coordinated by the microRNA cluster miR-424(322)/503.

Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype

Unraveling the molecular processes that lead from genotype to phenotype is crucial for the understanding and effective treatment of genetic diseases. Knowledge of the causative genetic defect most often does not enable treatment; therefore, causal intermediates between genotype and phenotype constitute valuable candidates for molecular intervention points that can be therapeutically targeted. Mapping genetic determinants of gene expression levels (also known as expression quantitative trait loci or eQTL studies) is frequently used for this purpose, yet distinguishing causation from correlation remains a significant challenge. Here, we address this challenge using extensive, multi-environment gene expression and fitness profiling of hundreds of genetically diverse yeast strains, in order to identify truly causal intermediate genes that condition fitness in a given environment. Using functional genomics assays, we show that the predictive power of eQTL studies for inferring causal intermediate genes is poor unless performed across multiple environments. Surprisingly, although the effects of genotype on fitness depended strongly on environment, causal intermediates could be most reliably predicted from genetic effects on expression present in all environments. Our results indicate a mechanism explaining this apparent paradox, whereby immediate molecular consequences of genetic variation are shared across environments, and environment-dependent phenotypic effects result from downstream integration of environmental signals. We developed a statistical model to predict causal intermediates that leverages this insight, yielding over 400 transcripts, for the majority of which we experimentally validated their role in conditioning fitness. Our findings have implications for the design and analysis of clinical omics studies aimed at discovering personalized targets for molecular intervention, suggesting that inferring causation in a single cellular context can benefit from molecular profiling in multiple contexts.