Characterization Of Intratumoral Heterogeneity Research Articles

Inferring single-cell copy number profiles through cross-cell segmentation of read counts

BackgroundCopy number alteration (CNA) is one of the major genomic variations that frequently occur in cancers, and accurate inference of CNAs is essential for unmasking intra-tumor heterogeneity (ITH) and tumor evolutionary history. Single-cell DNA sequencing (scDNA-seq) makes it convenient to profile CNAs at single-cell resolution, and thus aids in better characterization of ITH. Despite that several computational methods have been proposed to decipher single-cell CNAs, their performance is limited in either breakpoint detection or copy number estimation due to the high dimensionality and noisy nature of read counts data.ResultsBy treating breakpoint detection as a process to segment high dimensional read count sequence, we develop a novel method called DeepCNA for cross-cell segmentation of read count sequence and per-cell inference of CNAs. To cope with the difficulty of segmentation, an autoencoder (AE) network is employed in DeepCNA to project the original data into a low-dimensional space, where the breakpoints can be efficiently detected along each latent dimension and further merged to obtain the final breakpoints. Unlike the existing methods that manually calculate certain statistics of read counts to find breakpoints, the AE model makes it convenient to automatically learn the representations. Based on the inferred breakpoints, we employ a mixture model to predict copy numbers of segments for each cell, and leverage expectation–maximization algorithm to efficiently estimate cell ploidy by exploring the most abundant copy number state. Benchmarking results on simulated and real data demonstrate our method is able to accurately infer breakpoints as well as absolute copy numbers and surpasses the existing methods under different test conditions. DeepCNA can be accessed at: https://github.com/zhyu-lab/deepcna.ConclusionsProfiling single-cell CNAs based on deep learning is becoming a new paradigm of scDNA-seq data analysis, and DeepCNA is an enhancement to the current arsenal of computational methods for investigating cancer genomics.

Abstract 6087: Evaluation of intra-tumor heterogeneity by single-cell copy number profiling of circulating multiple myeloma cells using Shannon index

Abstract Tumors develop through an evolutionary process in which genomic lesions accumulate in different coexisting subclones leading to intra-tumor heterogeneity (ITH). ITH allows the tumor to adapt to microenvironments and to resist therapies and has been shown to be a prognostic marker. In this work we show the characterization of ITH in Multiple Myeloma (MM) at single cell resolution from a pool of Circulating Multiple Myeloma Cells (CMMCs) and we study the relationship between number of sampled cells and diversity detected. Peripheral blood was collected from 3 patients affected by MM. Using CellSearch® system, blood was enriched for target CMMCs, with immuno-magnetic selection of CD138+, and immunofluorescently stained cells, positive for CD38-PE, DAPI and negative for CD45/CD19-APC, were enumerated. The samples were then analyzed and CMMCs were isolated with DEPArray platform. The DNA of each single cell (n=261) was amplified with Ampli1™ WGA kit and the WGA output was used for genome-wide single cell copy number alteration analysis with Ampli1 LowPass kit. Copy number profiling revealed from 2 to 3 coexisting subclones per patient, with minor clones size going from 6.5% to 33%. ITH was assessed by determining the Shannon index, which showed a range of values from 0.44 (low diversity) to 1.01 (high diversity) depending on source sample. We evaluated the effect of number of cells/profiles on Shannon index by bootstrapping (200 times) a number of copy number profiles ranging from 2 to the total number of profiles available per patient. Results showed that the mean Shannon index almost reaches a plateau at 15-20 cells/profiles in the case of lower diversity samples and at about 30 cells/profiles for the highest diversity sample. Moreover, as few as 10 cells allowed to differentiate low vs. high diversity samples. Cell enrichment and sorting by CellSearch and DEPArray platforms allow to quantify ITH at diversity level with high precision even from a limited number of initial single-cells and provide a promising method to objectively measure heterogeneity, as required in order to study its clinical implications. Citation Format: Alberto Ferrarini, Claudio Forcato, Valentina del Monaco, Mario Terracciano, Marianna Garonzi, Massimiliano Pellicano, Genny Buson, Francesca Fontana, Paola Tononi, Nicolo Manaresi. Evaluation of intra-tumor heterogeneity by single-cell copy number profiling of circulating multiple myeloma cells using Shannon index [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 6087.

OS12.7.A Characterization of intra-tumoral heterogeneity and differential immune activation during malignant progression of meningiomas on single cell level

Abstract BACKGROUND As the most common intracranial tumor, meningiomas have caused increasing interest in the field of medical research. Based on their mutational profile, meningiomas can be separated into two main groups: NF2 altered meningiomas, which can occur at WHO grades 1 to 3, and non-NF2 mutant meningiomas with mutations in other genes, such as TRAF7, AKT1, KLF4, and SMO, which are usually of WHO grade 1. While this means that non-NF2 mutant meningiomas usually follow a benign course, risk stratification for NF2 mutant meningiomas remains difficult. As of now, the underlying mechanisms contributing to the malignant phenotype of some NF2 mutant meningiomas remained unknown, even though some molecular markers have been associated with an increased risk of recurrence. Here, we sought to characterize robust molecular subgroups for meningiomas and identify the critical steps in malignant progression of these tumors. MATERIAL AND METHODS We applied bulk RNA sequencing and proteomic analyses for 44 meningioma samples as well as single nuclei RNA sequencing analyses for an additional set of 26 meningiomas with a total of 46,002 nuclei. Both datasets comprised samples across the molecular landscape of meningiomas and WHO grades 1, 2, and 3. RESULTS The meningioma subgroups previously identified on epigenomic level were found consistently also on transcriptomic, proteomic, and phospho-proteomic levels. In addition, strong differences in numbers and types of infiltrating immune cells between subgroups became apparent. A decreased number of infiltrating macrophages and an activation to a more proinflammatory phenotype was observed for WHO grade 3 tumors. This observation correlated with lower expression levels of CSF1 in tumor cells of WHO grade 3 meningiomas, which was predicted to stimulate macrophages in WHO grade 1 and 2 tumors. Moreover, we identified several tumor cell subpopulations, each defined by a distinct phenotype, shared across samples. Their proportions in the tumor strongly depended on tumor grade. Especially a subpopulation characterized by an elevated stress response and TGFβ signaling activity was found specifically in WHO grade 3 cases. CONCLUSION Our findings establish molecular subgroups for meningiomas that are robust across multiple levels with characteristic differences in pathway activities and demonstrate a subtype-specific immune activation, both of which may be basis for novel treatment strategies.

Abstract A10: Phenotypic and genomic characterization of intratumoral heterogeneity in high-grade serous ovarian cancer

Abstract High-grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer, accounting for more than 70% of all epithelial ovarian cancers. It is characterized by high degrees of genomic instability and heterogeneity, with the majority of patients eventually acquiring resistance to platinum chemotherapy. The diversity in platinum-resistance mechanisms and limited effective predictive biomarkers mean delivering the best treatment options for patient tumor remains challenging. The purpose of this study is to understand the extent of intratumoral heterogeneity (ITH) in advanced-stage HGSOC and how this changes over time at relapse, to describe the molecular mechanisms behind peritoneal dissemination, and to delineate the link between ITH at the molecular and phenotypic levels. Patients undergoing radical upfront debulking for advanced HGS ovarian cancer underwent tumor mapping of their tumor dissemination patterns (n=50). Biopsies were collected from disseminated tumors (range 4-15, median=9), snap frozen, and placed in short-term cultures. Tumor cultures were treated with cisplatin, apoptosis/viability assayed and IC50 for cisplatin determined. DNA was extracted from frozen tumors (5 tumors per patient plus germline) and Illumina Human OmniExpress genotyping performed. Allele-specific copy number (CN) was quantified using ASCAT. Genomic heterogeneity was quantified as the estimated number of CN aberration events distinct between each pair of deposits. Clonal diversity within a patient’s deposits was calculated using the difference between within-patient and between-patient heterogeneity. When relapsed, patients had paired biopsies collected for genomic and phenotypic analysis. Broad heterogeneity was observed in response to platinum treatment in vitro across cases at the phenotypic level (n=50), with higher variances in apoptosis induction observed in patients with platinum-resistant disease. Genomic analysis of copy number data revealed widespread variations in patterns of evolution for different patients’ tumors, including the relationship between primary deposits and relapsed disease. Variations in CCNE1 CN were observed across multiple tumors in the same patients, and overall higher CCNE1 CN associated with poorer patient outcome (p=0.041). Extensive heterogeneity is observed at the phenotypic and genomic levels in HGSOC patients, which correlates with the subsequent development of platinum-resistant disease. CCNE1 copy number variations across multiple intra-abdominal samples within patients indicate that single-site biopsies do not truly represent overall disseminated HGSOC biology and may have implications for overinterpretation of studies relating to outcome and platinum resistance. Citation Format: Paula Cunnea, Ed Curry, Katherine Nixon, Ratri Wulandari, Kerstin Thol, Chun Hei Kwok, Jennifer Ploski, Iain McNeish, Elizabeth Christie, David Bowtell, Christina Fotopoulou. Phenotypic and genomic characterization of intratumoral heterogeneity in high-grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr A10.

Abstract A132: Characterization of intratumoral heterogeneity in drug sensitivity and non-cell autonomous mechanisms of drug resistance using subclonal cell populations derived from a single breast cancer cell line

Abstract Intratumoral heterogeneity impacts both tumor progression and drug sensitivity. While intratumoral genetic heterogeneity has been extensively described, there is still little understanding of the extent of heterogeneity in drug sensitivity within a single tumor, the intra tumor dynamics of different cell populations during anti-cancer compound treatment, and the role of non-cell autonomous mechanism in drug resistance. The objective of this study is to characterize heterogeneity in drug sensitivity within a model for human breast cancer, identify interactions between clonal population influencing drug sensitivity, and use this to model the dynamics of sub populations in a tumor during anti-cancer compound treatment. We have characterized 31 subclonal cell populations (SCPs) that were generated by single cell cloning of the triple-negative breast cancer cell line MDA-MB-468. These SCPs display considerable phenotypic and molecular heterogeneity with respect to morphology, proliferation rate, RNA expression and tumorigenicity in mice. To examine the extent of heterogeneity in drug sensitivity, we designed and screened a 200-small molecule compound library in 24 SCPs and the parental cell line. We observed considerable heterogeneity in drug sensitivity and selected 36 compounds for validation in the SCPs that expressed the greatest range of sensitivity (determined by IC50). To investigate whether interactions between clonal population can influence drug sensitivity heterogeneously responsive subclonal cell lines were transduced with lentiviruses encoding for the H2B-GFP or H2B-RFP fusion protein and then cocultured in the absence or presence of different compounds. We observed shifts in drug sensitivity for SCPs that were cocultured with differentially responsive clones. Examples include SCPs 19 and 27 two highly sensitive populations that were protected by the presence of less sensitive SCPs when under gemcitabine treatment, resulting in IC50-values that were 20-fold higher then when cultured alone. RNA-sequencing and RPPA analysis will be used to characterize this non-cell autonomous mechanism of resistance and the relationship with identified transcriptional programs or genes which expression correlates with drug sensitivity. We are also generating and will evaluate predictive models for drug combination using the SCP drug sensitivity profiles. The results of this study show that there is considerable heterogeneity in drug sensitivity among clonal subpopulations derived from a single cancer cell line. Ongoing experiments will provide insight into the effect of drug combinations and unique treatment schedules on distinct tumor cell subpopulations and drug sensitivity within heterogenous mixtures. Moreover, we will be able to test for and investigate non-cell autonomous modes of drug resistance, which may guide the development of new drug combination treatments or schedules. Citation Format: Chandler M Friend, Joan S Brugge, Hendrik J Kuiken, Steven M Corsello, Christopher C Mader. Characterization of intratumoral heterogeneity in drug sensitivity and non-cell autonomous mechanisms of drug resistance using subclonal cell populations derived from a single breast cancer cell line [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A132. doi:10.1158/1535-7163.TARG-19-A132

Abstract 3775: Characterization of intratumoral heterogeneity in drug sensitivity and modeling of drug combination effects using subclonal cell populations derived from a single breast cancer cell line

Abstract Intratumoral heterogeneity can have profound effects on tumor progression and drug resistance. While tumor cell heterogeneity has been described at many levels, there is a poor understanding of the extent of heterogeneity in drug sensitivity within a single tumor, the dynamics of tumor cell subpopulations during treatment with anti-cancer agents and non-cell autonomous mechanisms of drug resistance. The overall objective of this study is to characterize heterogeneity in drug sensitivity within a model for human breast cancer and to model and evaluate the effect of drug combinations on the dynamics of subpopulations within heterogeneous mixtures. We have characterized 31 subclonal cell populations (SCPs) that were generated through single cell cloning of the triple-negative breast cancer cell line MDA-MB-468. These SCPs display considerable heterogeneity with respect to morphology, proliferation rate, colony formation in soft agar and tumorigenicity in mice. To examine the extent of heterogeneity in drug sensitivity, we designed and screened a 200-small molecule compound library in 24 SCPs and the parental cell line. We observed considerable heterogeneity in drug sensitivity and selected 36 compounds for validation, including GPX4 inhibitor ML-210 and gemcitabine, to which the SCPs displayed 12-fold and 150-fold differences in the EC50, respectively. The results of these validation experiments were used to identify transcriptional programs and individual genes which expression correlates with drug sensitivity. We have generated and will evaluate predictive models for drug combination using the SCP drug sensitivity profiles. To test the effect of individual drugs and combinations, as well as different treatment schedules on heterogeneous populations, we will examine the dynamics of individual SCPs within mixed cultures. To this end, we have transduced 22 SCPs with CloneTracer lentiviruses, each encoding for a unique DNA barcode. The distribution of these unique barcodes can be determined by next generation sequencing. Previously, we have found that propagation of a 22 SCP mixture results in reproducible changes in the composition of mixed monolayer cultures with approximately half of the SCPs having a relative abundance of 1% each in the mixed population following propagation for four months. The results of this study show that there is considerable heterogeneity in drug sensitivity among clonal subpopulations derived from a single cancer cell line. Ongoing experiments will provide insight into the effect of drug combinations and unique treatment schedules on distinct tumor cell subpopulations within heterogenous mixtures. Moreover, using this panel of SCPs, we will be able to test for and investigate non-cell autonomous modes of drug resistance, which may guide the development of new drug combinations. Citation Format: Hendrik J. Kuiken, Chandler M. Friend, Steven M. Corsello, Christopher C. Mader, Joan S. Brugge. Characterization of intratumoral heterogeneity in drug sensitivity and modeling of drug combination effects using subclonal cell populations derived from a single breast cancer cell line [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3775.

Abstract 3395: A scalable microfluidic platform for determining cellular heterogeneity by copy number detection

Abstract The ability to sequence entire genomes at the level of single cells (SCs) is an essential tool for mapping heterogeneity in cancer. Knowledge of this variability provides insight into cancer dynamics and the efficacy of therapeutics. Copy number variant (CNV) analysis requires only sparse coverage creating an affordable means for measuring cellular differences genome-wide. However, current approaches to SC DNA sequencing suffer from low throughput, cumbersome workflows and remain accessible to only a few select laboratories. We have developed a microfluidic, partitioning-based solution that allows sensitive CNV detection of thousands of SCs concomitantly. Isolated cells or nuclei are treated to gain complete accessibility to genomic DNA then co-partitioned along with gel beads supporting oligonucleotide barcodes. Barcoded fragments, representing genomic abundance, are generated and converted into Illumina-compatible libraries. The workflow supports various sample types including primary cells, cultured cells, tissue-dissociated cells and nuclei extracted from flash-frozen tissues. Accompanying this platform we have also developed a scalable bioinformatics pipeline for genome alignment, normalization, copy-number detection, and clustering of SC profiles. Using clusters for CNV analysis we can reliably detect 100 kb scale events and, in large clones, identify CNV events down to tens of kilobases with high confidence. Analysis of our SC whole-genome sequencing data shows excellent coverage uniformity, comparable to leading plate-based SC amplification techniques. To validate the accuracy of CNV detection, calls are measured against orthogonal data from well-characterized cell lines including RPMI8226, HCC1954, and HCC1143 and high concordance is observed. Furthermore, we are able to observe multiple clones including CNVs in normal cell lines GM12878 and BJ suggesting absolute cell line validation requires whole-genome SC sequencing. Evaluating nuclei extracted from stage 1 breast carcinoma and stage 1 renal cell carcinoma we find clones exhibiting common somatic mutations. Furthermore, many cells exhibit extensive genomic heterogeneity, effects we attribute to DNA replication, with the frequency of such events elevated in fast growing samples such as tumors or fresh cell cultures. In conclusion, we demonstrate a novel droplet-based system that permits scalable CNV calling at the SC level enabling high resolution characterization of intra-tumor heterogeneity. It is our intention that a reliable and cost-effective system will encourage widespread adoption of SC analysis as a means of characterizing the progression of cancer, lead to more effective treatments and unveil genomic diversity in other genetically heterogenous systems such as neuronal cells, high-passage cell lines and samples from aging populations. Citation Format: Andrew Price, Jon Sorenson, Kamila Belhocine, Claudia Catalanotti, Zeljko Dzakula, Susana Jett, Viajy Kumar, Bill Lin, Tony Makarewicz, Alaina Puleo, Mohammad Rahimi, Sanjam Sawhney, Joe Shuga, Maengseok Song, Katie Sullivan-Bibee, Tobias Wheeler, Yifeng Yin, Michael Schnall-Levin, Rajiv Bharadwaj. A scalable microfluidic platform for determining cellular heterogeneity by copy number detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3395.

Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data.

Characterization of intratumoral heterogeneity is critical to cancer therapy, as the presence of phenotypically diverse cell populations commonly fuels relapse and resistance to treatment. Although genetic variation is a well-studied source of intratumoral heterogeneity, the functional impact of most genetic alterations remains unclear. Even less understood is the relative importance of other factors influencing heterogeneity, such as epigenetic state or tumor microenvironment. To investigate the relationship between genetic and transcriptional heterogeneity in a context of cancer progression, we devised a computational approach called HoneyBADGER to identify copy number variation and loss of heterozygosity in individual cells from single-cell RNA-sequencing data. By integrating allele and normalized expression information, HoneyBADGER is able to identify and infer the presence of subclone-specific alterations in individual cells and reconstruct the underlying subclonal architecture. By examining several tumor types, we show that HoneyBADGER is effective at identifying deletions, amplifications, and copy-neutral loss-of-heterozygosity events and is capable of robustly identifying subclonal focal alterations as small as 10 megabases. We further apply HoneyBADGER to analyze single cells from a progressive multiple myeloma patient to identify major genetic subclones that exhibit distinct transcriptional signatures relevant to cancer progression. Other prominent transcriptional subpopulations within these tumors did not line up with the genetic subclonal structure and were likely driven by alternative, nonclonal mechanisms. These results highlight the need for integrative analysis to understand the molecular and phenotypic heterogeneity in cancer.

New Approaches for Chemosensitivity Testing in Malignant Diseases

Due to the irreplaceable role of chemotherapy in cancer treatment, research has focused on improving the efficacies of individual drugs and minimizing, or completely suppressing, their negative side effects. Based on long-term experience and the results of clinical trials, the selection of appropriate treatment is currently based on classical clinical diagnostic criteria, such as tumor size, grade, and the presence or absence of standard markers. However, complications arise due to variability between patients and tumor heterogeneity. Characterization of intratumoral heterogeneity and acquisition of more reliable drug performance indicators should improve personalized therapy. Development and selection of suitable models are therefore important issues in cancer research focused on predicting sensitivity to therapy. This work provides an overview of various chemosensitivity tests that have been previously employed and those that are currently used. Great emphasis is placed on comparing 2D and 3D cell culture models, since their importance and popularity are increasing. Particular attention is paid to in vivo systems, which have significantly improved recently and are tested in clinical trials to predict responses to therapy. This work provides a brief overview of chemosensitivity tests, focusing on the importance of individual tests and their application in decision-making and patient stratification to improve the clinical responses of patients and the development of targeted personalized therapy.Key words: cell culture techniques - personalized medicine - drug screening - biological models - tumor cell lines - carcinoma - cytotoxicity assays This work was supported by the project MEYSNPS I-LO1413 and GACR 17-05838S. The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study. The Editorial Board declares that the manuscript met the ICMJE recommendation for biomedical papers.Submitted: 21. 9. 2017Accepted: 20. 12. 2017.

Abstract 2588: GenePattern Notebook: an environment for reproducible cancer research

Abstract As the availability of genetic and genomic data and analysis tools from large-scale cancer initiatives continues to increase, the need has become more urgent for a software environment that supports the entire “idea to dissemination” cycle of an integrative cancer genomics analysis. Such a system would need to provide access to a large number of analysis tools without the need for programming, be sufficiently flexible to accommodate the practices of non-programming biologists as well as experienced bioinformaticians, and would provide a way for researchers to encapsulate their work into a single “executable document” including not only the analytical workflow but also the associated descriptive text, graphics, and supporting research. To address these needs, we have developed GenePattern Notebook, based on the GenePattern environment for integrative genomics and the Jupyter Notebook system. GenePattern Notebook unites the phases of in silico research – experiment design, collaborative analysis, and publication – into a single interface. GenePattern Notebook presents a familiar lab-notebook format that allows researchers to build a record of their work by creating “cells” containing text, graphics, or executable analyses. Researchers add, delete, and modify cells as the research evolves, supporting the initial research phases of prototyping and collaborative analysis. When an analysis is ready for publication, the same document that was used in the design and analysis phases becomes a research narrative that interleaves text, graphics, data, and executable analyses, serving as the complete, reproducible, in silico methods section for a publication. GenePattern Notebook features are designed to make it easy for nonprogramming users to create and adapt notebooks. We have developed new cell types that allow users to choose analyses, specify input parameters and datasets, navigate results, send result files to new analyses, and create richly formatted text, all without the need for programming. We have released a freely available online GenePattern Notebook workspace, http://notebook.genepattern.org, where researchers can develop and publish notebook documents. We have provided a collection of template notebooks that walk users through various machine learning analyses, and are collaborating with cancer research laboratories to create integrative cancer genomics notebooks as well. Notebook topics in development include characterization of intratumoral heterogeneity from single cell RNA-Seq data, effective clinical interpretation of comprehensive genomic profiling from whole exome sequencing of a patient’s tumor and germ line samples, and identification of master regulators/transcription factors associated with the downstream transcriptional effects associated with the activation of an oncogene. Citation Format: Michael M. Reich, Thorin T. Tabor, Ted Liefeld, Barbara Hill, Helga Thorvaldsdottir, Jill P. Mesirov. GenePattern Notebook: an environment for reproducible cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2588. doi:10.1158/1538-7445.AM2017-2588

Research Techniques Made Simple: Mass Cytometry Analysis Tools for Decrypting the Complexity of Biological Systems

Mass cytometry by time-of-flight experiments allow analysis of over 40 functional and phenotypic cellular markers simultaneously at the single-cell level. The data dimensionality escalation accentuates limitations, inherent to manual analysis, as being subjective, labor-intensive, slow, and often incapable of showing the detailed features of each unique cell within populations. The subsequent challenge of examining, visualizing, and presenting mass cytometry data has motivated continuous development of dimensionality reduction methods. As a result, an increasing recognition of the inherent diversity and complexity of cellular networks is emerging, with the discovery of unexpected cell subpopulations, hierarchies, and developmental pathways, such as those existing within the immune system. Here, we briefly review some frequently used and accessible mass cytometry data analysis tools, including principal component analysis (PCA); spanning-tree progression analysis of density-normalized events (SPADE); t-distributed stochastic neighbor embedding (t-SNE)-based visualization (viSNE); automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE); and cluster identification, characterization, and regression (CITRUS). Mass cytometry, used together with these innovative analytic tools, has the power to lead to key discoveries in investigative dermatology, including but not limited to identifying signaling phenotypes with predictive value for early diagnosis, prognosis, or relapse and a thorough characterization of intratumor heterogeneity and disease-resistant cell populations, that may ultimately unveil novel therapeutic approaches.

Abstract 5273: GenePattern Notebooks: an integrative analytical environment for cancer research

Abstract As the availability of genetic and genomic data and analysis tools from large-scale cancer initiatives continues to increase, the need has become more urgent for a software environment that supports the entire “idea to dissemination” cycle of an integrative cancer genomics analysis. Such a system would need to provide access to a large number of analysis tools without the need for programming, be sufficiently flexible to accommodate the practices of non-programming biologists as well as experienced bioinformaticians, and would provide a way for researchers to encapsulate their work into a single “executable document” that included not only the analytical workflow but also the associated descriptive text, graphics, and supporting research. To address these needs, we have developed GenePattern Notebook, based on the GenePattern environment for integrative genomics and the Jupyter (formerly IPython) Notebook system. GenePattern Notebook unites the phases of in silico research - experiment design, collaborative analysis, and publication - into a single interface. GenePattern Notebook presents a familiar lab-notebook format that allows researchers to build a record of their work by creating “cells” containing text, graphics, or executable analyses. Researchers add, delete, and modify cells as the research evolves, supporting the initial research phases of prototyping and collaborative analysis. When an analysis is ready for publication, the same document that was used in the design and analysis phases becomes a research narrative that interleaves text, graphics, data, and executable analyses, serving as the complete, reproducible, in silico methods section for a publication. GenePattern Notebook also supports programmers and bioinformaticians, providing seamless interoperation between code blocks and GenePattern analyses within a notebook document. We are collaborating with cancer research laboratories to create GenePattern Notebook documents to serve as examples of integrative genomics research that can be enabled through this system. Potential notebook topics include characterization of intratumoral heterogeneity from single cell RNA-Seq data, effective clinical interpretation of comprehensive genomic profiling from whole exome sequencing of a patient's tumor and germ line samples, and identification of master regulators/transcription factors associated with the downstream transcriptional effects associated with the activation of an oncogene. We will make these and other notebooks available in an online GenePattern Notebooks repository, where other researchers may also contribute their notebooks. Citation Format: Michael M. Reich, Thorin Tabor, John T. Liefeld, Peter Carr, Barbara Hill, Marc-Danie Nazaire, David Eby, Helga Thorvaldsdottir, Pablo Tamayo, Jill P. Mesirov. GenePattern Notebooks: an integrative analytical environment for cancer research. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5273.