Evaluation of breast cancer PDX tumor heterogeneity at single cell resolution II
Breast cancer is the most commonly diagnosed cancer among women. PDXs (patient-derived xenografts) are similar to cancer cell lines but differ in that they are maintained in a physiological setting as soon as they are isolated from the patient and for subsequent passages. These models are valuable for preclinical trials because PDX models have been shown to closely match their patient counterparts, both in genomic profile and response to treatment. One challenge to treatment development is tumor heterogeneity. In this study, we profiled ER+ and triple negative breast cancer PDX models using single-cell RNA-sequencing. This data may help identify populations of cells which are susceptible to certain treatments in order to improve clinical outcomes for breast cancer patients. Overall design: ER+, HER2 amplified, and triple-negative mammary tumors were subjected to single-cell RNA sequencing with the goal of identifying and profiling sub-populations within models to improve outcomes of targeted treatments. This dataset contains a total of 117 samples that includes new and reanalyzed samples with and without raw data present in the SRA. This GEO record is organized as follows: the “Samples” section below contains 23 new samples with raw data submitted to the SRA; the “Relations” section contains links to the remaining 94 reanalyzed sample GSM records (see the “GSE276609_sample_file_association_for_reanalysis.txt.gz” file for mapping to reanalyzed data and sample names); of the 94 reanalyzed samples 25 are from GSE235168 that have raw data previously submitted to the SRA and are listed in the “TABLE OF REANALYZED/REUSED DATATABLE OF REANALYZED/REUSED DATA”; the remaining 69 samples are from GEO Series GSE161529 and do not have raw data archived in the SRA. These 69 samples from GSE161529 were re-analyzed by the original authors on our request by aligning the raw data using 10X Genomics CellRanger v6 software and then sharing the read count matrices with our team who incorporated them into the same pipeline as the rest of the data in this submission. 10X formatted read count matrices and additional process data for the 69 samples from GSE161529 can be found in the tar file below named “GSE276609_GSE161529_reanalysis.tar.gz”.  Processed data, including 10X formatted read count matrices, for the 25 reanalyzed samples from GSE235168 can be found in the tar file below named “GSE276609_GSE235168_reanalysis.tar.gz”. Likewise, the read counts and other processed data files for the 23 new samples that are part of this submission can be found in the “GSE276609_RAW.tar” tar file below. For all data, the processed files with “CancerCellsOnly” in the file name contains a list of the cell barcodes that we identified as cancer cells using the inferCNV tool as described in the manuscript.  In addition to read counts and cancer cell barcode lists, we provide 5 Loupe Cell Browser files that contain the merged dataset as presented in the paper (.cloupe files).  To view layouts consistent with the manuscript, you need to change the projection to the imported “UMAP_Seurat” or the imported “tSNE_Seurat” options as the defaults are not consistent with the publication figures.  Loupe file contents are as follows: MasterMerge_AllSamples.cloupe.gz contains all 117 samples with human cells only; CancerOnlyDataset.cloupe contains the subset of identified malignant cells from the Master Merge file with all normal samples removed (the CancerCellsOnly processed data files contain a list of barcodes for each sample that are in this dataset);   CancerOnly_TNBC-Samples.cloupe is a subset of the CancerOnlyDataset file contain only TNBC samples; CancerOnly_ERpositive-Samples.cloupe is a subset of the CancerOnlyDataset file that contains only ER+ samples; WHIM30_PDX_PDXO_SamplesOnly.cloupe contains only the WHIM30 PDX and organoid samples. ********************************************************************* The table below lists GEO accessions reused/reanalyzed for this study where fastq files are available at SRA. *********************************************************************