scholarly journals Single-Cell Mutational Profiling Describes the Molecular Heterogeneity of Clonal Evolution in MDS during Therapy and Relapse

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 5503-5503
Author(s):  
Alexey Aleshin ◽  
Robert Durruthy-Durruthy ◽  
Bruno C. Medeiros ◽  
Dennis J. Eastburn ◽  
Peter L Greenberg
Keyword(s):  
Single Cell ◽  
Clonal Evolution ◽  
Next Generation Sequencing Data ◽  
Molecular Heterogeneity ◽  
Disease Relapse ◽  
Sequencing Data ◽  
Employment Equity ◽  
Hematopoietic Stem ◽  
Single Cell Sequencing ◽  
Equity Ownership

Abstract Background: Myelodysplastic syndromes (MDS) are a collection of clonal diseases of dysfunctional hematopoietic stem cells, characterized by ineffective hematopoiesis, cytopenias, and dysplasia. Increased understanding of the mutational landscape of MDS has led to initial improvements in prognostic models based on clinical and cytogenetic variables. However, bulk sequencing techniques are limited in their ability to delineate clonal complexity and identify rare drug resistant subclones. To better understand clonal heterogeneity and clonal evolution of MDS we applied a high-throughput single cell sequencing technique to both diagnostic and longitudinal MDS samples. Methods: Samples were examined for 5 patients with MDS at diagnosis and, when available, progression. Mutational bulk sequencing was performed by NGS panel sequencing and exon sequencing was available in select cases. Single cell processing was performed using the Tapestri (Mission Bio) platform. Briefly, individual cells were isolated using a microfluidic approach, followed by barcoding and genomic DNA amplification for individual cancer cells confined to droplets. Barcodes are then used to reassemble the genetic profiles of cells from next generation sequencing data. We applied this approach to individual MDS samples, genotyping the most clinically relevant loci across upwards of 10,000 individual cells. Results: Single-cell sequencing was able to be performed successfully on all samples tested and recapitulated bulk sequencing data. We observed high concordance between bulk variant allele frequencies (VAFs) and sample level VAFs derived from single cell sequencing data (r2 = 0.98). Additionally, single cell analysis allowed for resolution of subclonal architecture and tumor phylogenetic evolution beyond what was predicted from bulk sequencing alone. Single-cell SNVs were able to resolve host and donor cell populations after bone marrow transplant and accurately predict chimerism and disease relapse. Furthermore, we were able to resolve the co-occurance of molecular alterations within subclones and establish zygosity of individual mutations at a single cell level. Rare subclones associated with disease relapse, were able to be identified in initial diagnostic samples that were frequently under the limit of detection of bulk NGS. Conclusions: Our results suggest more molecular complexity in MDS tumor samples than implied from bulk sequencing methods alone and indicates utility of single-cell sequencing for identification of resistant clones and longitudinal therapy monitoring. Disclosures Aleshin: Mission Bio, Inc.: Consultancy; Natera, Inc.: Employment. Durruthy-Durruthy:Mission Bio, Inc.: Employment, Equity Ownership. Medeiros:Genentech: Employment; Celgene: Consultancy, Research Funding. Eastburn:Mission Bio, Inc.: Employment, Equity Ownership.

scholarly journals High-throughput single-cell DNA sequencing of AML tumors with droplet microfluidics

2017 ◽  
Author(s):  
Maurizio Pellegrino ◽  
Adam Sciambi ◽  
Sebastian Treusch ◽  
Robert Durruthy-Durruthy ◽  
Kaustubh Gokhale ◽  
...  
Keyword(s):  
Single Cell ◽  
Clonal Evolution ◽  
Droplet Microfluidics ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Selection For ◽  
Genetic Profiles ◽  
Generation Sequencing

ABSTRACTTo enable the characterization of genetic heterogeneity in tumor cell populations, we developed a novel microfluidic approach that barcodes amplified genomic DNA from thousands of individual cancer cells confined to droplets. The barcodes are then used to reassemble the genetic profiles of cells from next generation sequencing data. Using this approach, we sequenced longitudinally collected AML tumor populations from two patients and genotyped up to 62 disease relevant loci across more than 16,000 individual cells. Targeted single-cell sequencing was able to sensitively identify tumor cells during complete remission and uncovered complex clonal evolution within AML tumors that was not observable with bulk sequencing. We anticipate that this approach will make feasible the routine analysis of heterogeneity in AML leading to improved stratification and therapy selection for the disease.

scholarly journals Single-Cell Mutational Profiling of Clonal Evolution in De Novo AML during Therapy and Relapse

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1469-1469
Author(s):  
Alexey Aleshin ◽  
Robert Durruthy-Durruthy ◽  
M. Ryan Corces ◽  
Melissa Stafford ◽  
Michaela Liedtke ◽  
...  
Keyword(s):  
Single Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
De Novo ◽  
Sequencing Data ◽  
Employment Equity ◽  
Advisory Committees ◽  
Single Cell Sequencing ◽  
Equity Ownership ◽  
De Novo Aml

Abstract Background: De novo acute myeloid leukemia (AML) is a molecularly heterogeneous disorder with clinically variable outcomes. Recent studies on the mutational landscape of AML have been informative in better stratifying risk of relapse. However, bulk sequencing techniques have been limited in their ability to delineate the true complexity of tumoral molecular heterogeneity and allow for efficient identification of drug resistant subclones. Here, we applied high-throughput single cell sequencing technique to identify patterns of clonal heterogeneity and evolution in longitudinal samples from patients with AML undergoing induction chemotherapy. Methods: Matched diagnosis, remission, and relapse samples were examined for 20 de novo AML cases including 15 relapsed and 5 non-relapsed controls. Mutational bulk sequencing was performed by NGS panel sequencing and exome sequencing was available in select cases. Single cell processing was performed using the Tapestri (Mission Bio) platform. Briefly, individual cells were isolated using a microfluidic approach, followed by barcoding and genomic DNA amplification for individual cancer cells confined to droplets. Barcodes were then used to reassemble the genetic profiles of cells from next generation sequencing data. We applied this approach to individual AML samples, genotyping the most clinically relevant loci across upwards of 10,000 individual cells. Results: Targeted single-cell sequencing was able to recapitulate bulk sequencing data from both peripheral blood and bone marrow aspirate samples. We observed high concordance between bulk VAFs and sample level VAFs derived from single cell sequencing data. Additionally, single cell analysis allowed for resolution of subclonal architecture and tumor phylogenetic evolution beyond what was predicted from bulk sequencing alone. Rare subclones associated with disease relapse, were identified in initial diagnostic samples that were frequently under the limit of detection of bulk NGS. Conclusions:Taken together, our results suggest a greater degree of heterogeneity in de novo AML samples than suggested with bulk sequencing methods alone and shows the utility of single-cell sequencing for longitudinal monitoring and identification of resistant clones prior to therapy initiation in select patients. We show here that this approach is a feasible and effective way to identify and track heterogeneous populations of cells in AML and may be valuable for MRD identification. Disclosures Aleshin: Mission Bio, Inc.: Consultancy; Natera, Inc.: Employment. Durruthy-Durruthy:Mission Bio, Inc.: Employment, Equity Ownership. Liedtke:Prothena: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech/Roche: Research Funding; Caelum: Membership on an entity's Board of Directors or advisory committees; Amgen/Onyx: Consultancy, Honoraria, Research Funding; BlueBirdBio: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; celgene: Research Funding. Medeiros:Celgene: Consultancy, Research Funding; Genentech: Employment. Eastburn:Mission Bio, Inc.: Employment, Equity Ownership.

scholarly journals Computational reconstruction of clonal hierarchies from bulk sequencing data of acute myeloid leukemia samples

2021 ◽  
Author(s):  
Thomas Stiehl ◽  
Anna Marciniak-Czochra
Keyword(s):  
Acute Myeloid Leukemia ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Clonal Evolution ◽  
Malignant Cell ◽  
Leukemic Cells ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Computational Reconstruction ◽  
Acute Myeloid

AbstractAcute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the clonal composition of the leukemic cells, however it is still not routinely available in clinical practice. In this work we develop a computational algorithm that allows identifying all clonal hierarchies that are compatible with bulk variant allele frequencies measured in a patient sample. The clonal hierarchies represent descendance relations between the different clones and reveal the order in which mutations have been acquired. The proposed computational approach is tested using single cell sequencing data that allow comparing the outcome of the algorithm with the true structure of the clonal hierarchy. We investigate which problems occur during reconstruction of clonal hierarchies from bulk sequencing data. Our results suggest that in many cases only a small number of possible hierarchies fits the bulk data. This implies that bulk sequencing data can be used to obtain insights in clonal evolution.

scholarly journals Longitudinal Monitoring of AML Tumors with High-Throughput Single-Cell DNA Sequencing Reveals Rare Clones Prognostic for Disease Progression and Therapy Response

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1476-1476
Author(s):  
Dennis J. Eastburn ◽  
Christine M. McMahon ◽  
Robert Durruthy-Durruthy ◽  
Martin Carroll ◽  
Catherine C. Smith ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Board Of Directors ◽  
High Throughput ◽  
Employment Equity ◽  
Advisory Committees ◽  
Single Cell Sequencing ◽  
Oncogenic Ras ◽  
Flt3 Inhibitor ◽  
Equity Ownership

Abstract AML (acute myeloid leukemia) is increasingly being treated with precision medicine. To better inform treatment, the mutational content of patient samples must be determined. However, current tumor sequencing paradigms are inadequate to fully characterize many instances of the disease. A major challenge has been the unambiguous identification of potentially rare and genetically heterogeneous neoplastic cell populations, capable of critically impacting tumor evolution and the acquisition of therapeutic resistance. Standard bulk population sequencing is unable to identify rare alleles and definitively determine whether mutations co-occur within the same cell. Single-cell sequencing has the potential to address these key issues and transform our ability to accurately characterize clonal heterogeneity in AML. Previous single-cell studies examining genetic variation in AML have relied upon laborious, expensive and low-throughput technologies that are not readily scalable for routine analysis of the disease. We applied a newly developed platform technology to perform targeted single-cell DNA sequencing on over 140,000 cells and generated high-resolution maps of clonal architecture from AML tumor samples. Marrow and/or peripheral blood samples were collected prior to, during treatment, and at clinical progression to the FLT3 inhibitor gilteritinib given on a clinical trial for relapsed/refractory AML with FLT3 mutation. Single-cell sequencing of multiple patient samples demonstrated that relapse clones acquired oncogenic RAS mutations. We utilized the high-throughput and sensitivity of our single-cell approach to more definitively assess where in the course of treatment these RAS mutated clones were acquired. Oncogenic RAS harboring clones, comprising between 0.4%, and 0.1% of tumor populations, were identified in patient samples either prior to or shortly after onset of treatment. Significantly, these RAS variant alleles were not detectable with targeted bulk sequencing. Throughout the course of treatment with the FLT3 inhibitor gilteritinib, the RAS mutant clones selectively expanded and were responsible for resistance to therapy and relapse. These findings point to the presence of underlying genetic heterogeneity in AML and demonstrate the utility of sensitively assaying clonal architecture to better inform patient stratification and therapy selection. Disclosures Eastburn: Mission Bio, Inc.: Employment, Equity Ownership. Durruthy-Durruthy:Mission Bio, Inc.: Employment, Equity Ownership. Smith:Astellas Pharma: Research Funding. Perl:Actinium Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; NewLink Genetics: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo: Consultancy; Arog: Consultancy; Novartis: Membership on an entity's Board of Directors or advisory committees; Astellas: Consultancy; AbbVie: Membership on an entity's Board of Directors or advisory committees.

scholarly journals Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples

2021 ◽  
Vol 12 ◽  
Author(s):  
Thomas Stiehl ◽  
Anna Marciniak-Czochra
Keyword(s):  
Acute Myeloid Leukemia ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Clonal Evolution ◽  
Malignant Cell ◽  
Leukemic Cells ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Computational Reconstruction ◽  
Acute Myeloid

Acute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the clonal composition of the leukemic cells, however it is still not routinely available in clinical practice. In this work we develop a computational algorithm that allows identifying all clonal hierarchies that are compatible with bulk variant allele frequencies measured in a patient sample. The clonal hierarchies represent descendance relations between the different clones and reveal the order in which mutations have been acquired. The proposed computational approach is tested using single cell sequencing data that allow comparing the outcome of the algorithm with the true structure of the clonal hierarchy. We investigate which problems occur during reconstruction of clonal hierarchies from bulk sequencing data. Our results suggest that in many cases only a small number of possible hierarchies fits the bulk data. This implies that bulk sequencing data can be used to obtain insights in clonal evolution.

scholarly journals RobustClone: a robust PCA method for tumor clone and evolution inference from single-cell sequencing data

2020 ◽  
Vol 36 (11) ◽  
pp. 3299-3306
Author(s):  
Ziwei Chen ◽  
Fuzhou Gong ◽  
Lin Wan ◽  
Liang Ma
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Clonal Evolution ◽  
Low Rank ◽  
Supplementary Information ◽  
Breast Cancer Dataset ◽  
Sequencing Data ◽  
Cancer Dataset ◽  
Single Cell Sequencing ◽  
Model Free

Abstract Motivation Single-cell sequencing (SCS) data provide unprecedented insights into intratumoral heterogeneity. With SCS, we can better characterize clonal genotypes and reconstruct phylogenetic relationships of tumor cells/clones. However, SCS data are often error-prone, making their computational analysis challenging. Results To infer the clonal evolution in tumor from the error-prone SCS data, we developed an efficient computational framework, termed RobustClone. It recovers the true genotypes of subclones based on the extended robust principal component analysis, a low-rank matrix decomposition method, and reconstructs the subclonal evolutionary tree. RobustClone is a model-free method, which can be applied to both single-cell single nucleotide variation (scSNV) and single-cell copy-number variation (scCNV) data. It is efficient and scalable to large-scale datasets. We conducted a set of systematic evaluations on simulated datasets and demonstrated that RobustClone outperforms state-of-the-art methods in large-scale data both in accuracy and efficiency. We further validated RobustClone on two scSNV and two scCNV datasets and demonstrated that RobustClone could recover genotype matrix and infer the subclonal evolution tree accurately under various scenarios. In particular, RobustClone revealed the spatial progression patterns of subclonal evolution on the large-scale 10X Genomics scCNV breast cancer dataset. Availability and implementation RobustClone software is available at https://github.com/ucasdp/RobustClone. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Analysis of Mechanisms Underlying Clonal Evolution of AML By a New Single-Cell Sequencing Platform

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 542-542
Author(s):  
Ryosaku Inagaki ◽  
Masahiro Marshall Nakagawa ◽  
Yasuhito Nannya ◽  
Qi Xingxing ◽  
Lanying Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Single Cell ◽  
Research Funding ◽  
Clonal Evolution ◽  
Clonal Expansion ◽  
Ras Pathway ◽  
Sequencing Platform ◽  
Single Cell Sequencing ◽  
Equity Ownership

Background Leukemic cell populations are highly heterogeneous in terms of both gene mutations and gene expression, which is shaped by acquisition of multiple mutations and expansion of adapted clone. This evolutional process is clinically important because it is observed in the contexts of treatment resistance and relapse as well as leukemic transformation, and molecular mechanisms involved in clonal selection can be exploited as a therapeutic target. Nevertheless, direct analysis of such mechanisms in patients' cells is hampered by technical difficulties to characterize both clonal structure and gene expression at a single-cell resolution. On this issue, we have recently developed a new method which enables simultaneously detection of mutations and whole transcriptome information at single-cell level by extensively modifying an existing single cell RNA-seq (Nakagawa et al. ASH abstract 2018). The aim of this study is to understand heterogeneity of clones and to clarify mechanisms behind clonal expansion in AML by longitudinal analysis using our novel single-cell sequencing platform. Results In order to estimate clone frequencies and select samples to be analyzed by single-cell sequencing, we first sequenced bulk bone marrow cells from patients with AML. Of interest, we found that AML samples frequently harbored multiple clones having different Ras pathway mutations, most frequently involving NRAS, which exhibited dynamic change in their clone size during the course of AML. These are interesting targets of the analysis of mechanism of clonal evolution of AML. Thus, three patients having multiple (n=3-5) Ras pathway mutations were investigated by sequencing their bone marrow Lin-, CD34+ cells using the newly established single-cell method, which successfully separated distinct clones having distinct mutations, where all of detected Ras pathway mutations were present in independent clones as expected. In order to examine these independent clones with Ras pathway mutations might show equal or heterogenous cellular phenotypes, proliferation or differentiation statuses as determined from transcriptome data was analyzed for all detected NRAS mutated clones. Among the NRAS mutated clones, some showed significant increase in proliferation-associated gene expression signature (calculated as proliferation score) compared with NRAS wild type clones, and no NRAS mutated clones showed decrease of the score, which is consistent with pro-proliferative function of Ras pathway. Interestingly, such increase in proliferation showed considerable heterogeneity among clones, where some NRAS mutated clones showed greatly increased proliferation scores compared to other NRAS mutated clones. Differentiation statuses of NRAS clones also showed heterogeneity among clones. In order to examine whether this inter-clone proliferation difference correlates with clone dynamics, we then analyzed longitudinal bone marrow samples for a patient who showed different proliferation between clones. The NRAS mutated clone with highly increased proliferation compared with wild type clone (NRAS p.G12S) had undergone rapid expansion in 3 months (cell frequency 0.08 to 0.74) in spite of continuous azacitidine treatment, while the NRAS mutated clone with less increase in proliferation (NRAS p.G12D) had showed regression (cell frequency 0.72 to 0.14). To investigate the mechanism of this therapy-resistant clonal expansion, we compared transcriptome data of these clones. Unlike the regressed clone, the expanded clone uniquely exhibited increase in expression of genes in PI3K/AKT pathway and unfolded protein response (UPR) pathway, one of cellular stress response pathway. UPR is recently reported to responsible for the promoted survival and competitive advantage in mouse hematopoietic stem cells with Nras mutations (Liu et al. Nat. Cell Biol. 2019). Our data suggest that the enhanced UPR pathway contributes to clonal expansion also in human AML with Ras pathway mutations. Conclusions Using a newly developed single-cell sequencing platform, we have successfully characterized gene expression profiles associated with clonal evolution of AML with Ras pathway mutations. Simultaneous measurement of both mutations and transcriptomes at a single-cell level will help understand the mechanism of clonal evolution of AML. Disclosures Inagaki: Sumitomo Dainippon Pharma Co., Ltd.: Employment. Nakagawa:Sumitomo Dainippon Pharma Co., Ltd.: Research Funding. Yoda:Chordia Therapeutics Inc.: Research Funding. Ogawa:RegCell Corporation: Equity Ownership; Asahi Genomics: Equity Ownership; Qiagen Corporation: Patents & Royalties; Dainippon-Sumitomo Pharmaceutical, Inc.: Research Funding; ChordiaTherapeutics, Inc.: Consultancy, Equity Ownership; Kan Research Laboratory, Inc.: Consultancy.

Single Cell Network Profiling as a Tool to Identify AML Chemotherapy Resistant Cell Phenotypes Under In Vivo Therapeutic Pressure.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 397-397
Author(s):  
Alessandra Cesano ◽  
John Woronicz ◽  
Aileen Cleary Cohen ◽  
Todd Covey ◽  
Santosh Putta ◽  
...  
Keyword(s):  
Single Cell ◽  
Induction Chemotherapy ◽  
Intracellular Signaling ◽  
Cell Subpopulation ◽  
Disease Relapse ◽  
Employment Equity ◽  
Cell Network ◽  
Diagnostic Samples ◽  
Equity Ownership

Abstract Abstract 397 Background: Although most patients with acute myeloid leukemia (AML) have disease that responds initially to standard cytarabine based induction chemotherapy, approximately 2/3 experience leukemia relapse and succumb to the disease. A reliable and reproducible methodology which could identify individual patients at high risk of disease relapse could be used to selectively triage those patients to more intensive post-remission therapies and thereby improve their outcome while minimizing overall toxicity. Objectives: his study evaluated whether single cell network profiling (SCNP), in which cells are perturbed with a modulator and their response ascertained by multiparametric flow cytometry, could be used to characterize specific signaling network profiles associated with in vivo AML blast chemotherapy resistance. The initial hypothesis was to determine whether: a) intracellular signaling profiles dominant at relapse could be identified in subpopulations of cells present at diagnosis and b) whether the presence of blasts with these intracellular signaling profiles at diagnosis could predict for disease relapse. Methods: Modulated SCNP was evaluated after sample incubation with 14 cytokines (e.g. interleukins and G-CSF), growth factors (e.g. FLT3L, SCF), chemotherapeutic agents (e.g. cytarabine, etoposide), and other modulators. The use of fluorochrome-conjugated antibodies that recognized leukemic blasts and intracellular phospho-epitopes allowed signaling to be measured in specific cell types at the single cell level. In addition, drug transporters and surface receptor levels were measured. Results: Four pairs of AML bone marrow or peripheral blood samples were studied. Intra-patient comparison of intracellular signaling profiles were made between diagnostic samples and with either a primary refractory sample after induction chemotherapy or with a sample obtained at first relapse. High morphologic and functional heterogeneity of myeloblasts was observed in all of the samples both at diagnosis and after induction therapy. Notably, a subpopulation of leukemic cells characterized by simultaneous SCF-mediated increases in the levels of phosphorylated (p-) Akt and p-S6, herein defined as “SCF functional signature”, was identified. This functionally defined leukemic cell subpopulation, although dominant in the post induction samples, was present and detectable at a much lower frequency in the diagnostic samples. Cell surface markers, including c-kit expression, were unable to capture these functionally defined cellular subgroups in single defined subpopulations. The value of the newly identified SCF functional signature in predicting early relapse was then assessed in a larger, independent AML sample set of clinically annotated diagnostic samples. This set included 52 AML samples collected from patients whose leukemia achieved a complete remission after induction chemotherapy, 29 of whom experienced disease relapse within 2 years. In seven of the 52 diagnostic AML samples a subpopulation of blasts displaying the SCF functional signature identified above was detected. Six of these patients experienced disease relapse (five patients within ∼1 year and one patient within 2 years from initial CR). The seventh patient of the group, whose AML blasts carried the t(8;21) translocation (a marker of good prognosis), remained in complete continuous remission at 2 years. Noteworthy, the prognostic value of the SCF functional signature was independent of FLT3 mutational status: of the five patients who relapsed in only one case the leukemic blasts carried the FLT3 ITD mutation. Conclusions SCNP offers a novel approach to identify subpopulations of cells present at diagnosis with characteristics predictive of higher rates of relapse and this information can be used for monitoring or to guide therapy. Disclosures: Cesano: Nodality, Inc.: Employment, Equity Ownership. Woronicz:Nodality, Inc.: Employment, Equity Ownership. Cleary Cohen:Nodality Inc.: Employment, Equity Ownership. Covey:Nodality, Inc.: Employment, Equity Ownership. Putta:Nodality, Inc.: Employment, Equity Ownership. Gayko:Nodality, Inc.: Employment, Equity Ownership. Fantl:Nodality, Inc.: Employment, Equity Ownership. Kornblau:Nodality, Inc.: Consultancy.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

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