Metastatic breast cancer

Metastatic breast cancer (also called stage IV) is breast cancer that has spread to another part of the body, most commonly the liver, brain, bones, or lungs. Cancer cells can break away from the original tumor in the breast and travel to other parts of the body through the bloodstream or the lymphatic system, which is a large network of nodes and vessels that works to remove bacteria, viruses, and cellular waste products. Breast cancer can come back in another part of the body months or years after the original diagnosis and treatment. Nearly 30% of women diagnosed with early-stage breast cancer will develop metastatic disease.

Data

Targeted Sequencing of 1365 metastatic Breast Cancer tumor/normal pairs via MSK-IMPACT to understand the role of INK4 on CDK4/6 resistance.

There are several different types of breast cancer. 1. Ductal Carcinoma in Situ (DCIS) also known as intra-ductal carcinoma - Non-invasive breast cancer - Abnormal cells have not spread through the ducts into surrounding breast tissue - Has not yet spread outside of breast - May become invasive breast cancer

1. Lobular Carcinoma in Situ (LCIS) also called lobular neoplasia
2. Abnormal cell growth starts in the lobules of the breast
3. Not considered a true cancer because it is not likely to spread to surrounding tissues
4. LCIS is an indicator that a woman may be more likely to develop invasive cancer in either breast

3.Invasive Ductal Carcinoma (IDC) also known as infiltrating ductal carcinoma - The most common type of breast cancer - Starts in the ducts of the breast then grows into fatty breast tissue - May also spread to other parts of the body through the lymph system and bloodstream - Approximately 8 out of 10 invasive breast cancers are invasive ductal carcinomas

1. Invasive Lobular Carcinoma (ILC) also known as infiltrating lobular carcinoma
2. Starts in the lobules of the breast
3. Can spread to other parts of the body
4. May be harder to detect with a mammogram
5. Approximately 1 in 10 invasive breast cancers are invasive lobular carcinomas

Acknowledgements

Data from cBioPortal. - https://www.cbioportal.org/study/summary?id=breast_ink4_msk_2021 - Whttps://www.breastcancer.org/symptoms/types/recur_metast - https://www.abcf.org/about-breast-cancer/types-of-breast-cancer/?gclid=CjwKCAiAxJSPBhAoEiwAeO_fP3SJXjyU4lO4iE2Umrpxe3n0WBadoG7_JK27fSh49eatMGGBUl3kcBoCuHkQAvD_BwE

Inspiration

Inspiration came from the research on the effect of genetic mutations on breast cancer and its progression.

The original data is downloaded from the cbioportal website: https://www.cbioportal.org/datasets, it is TCGA PanCancer Atlas data that generated by TCGA Research Network https://www.cancer.gov/tcga, and is processed for bioinformatics research. The folder contains CAN, MET, Methylation, and RPPA data for 8 cancer types.

The cBioPortal for Cancer Genomics provides visualization, analysis and download of large-scale cancer genomics data sets.

A portal that provides visualization, analysis and download of large-scale cancer genomics data sets.

1.Clinical DataTranscript data and Clinicopathological information was downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.Gdc.cancer.gov/), including 41 cases of para-tumor, 473 cases of CRC tumor and 452 clinical cases.

The survival and transcriptional data of 250 CRC cases were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). The transcript dataset GSE161158 uploaded in November 2020 by Moffitt Cancer Research Center, University of Miami was used (10). Lists of immune related genes were download from ImmPort (https://www.immport. org/home) and Innate DB (https://www.innatedb.ca/). KEGG (http://www.gsea-msigdb.org/gsea/index.jsp) gene sets and all Gene Ontology (GO) gene sets were used as Gene Symbols. Gene mutation information was downloaded from cBioPortal (http://www.cbioportal.org/).2.Murine Data2.1 IRGPI Genes Expression in CRC Murine ModelSPF Balb/c male mice, 6 - 8 weeks old, body mass (20 ± 5) g, purchased from Huaxing Experimental Animal Farm of Huiji District (Zhengzhou City, China), experimental animal license NO. is SCXK (Yu) 2019-0002. All animal experiments was approved by the Experimental Mouse Ethics Committee of Nanjing University of Traditional Chinese Medicine (NO. 202010A026).The above mice were randomly divided into: Control group (C), CRC Model group (M)，10 per group. Five Balb/c male mice were taken as tumor-bearing mice, and 1×107 CT26 cells were subcutaneously injected into the left axilla, and sacrificed one week later. The subcutaneous tumor was removed under sterile conditions, placed in sterile PBS, and disintegrated into several 1 mm3 masses. Under sterile conditions, the two groups of mice were dissected to expose the colon, the 1 mm3 tumor mass was fixed to the colon of the CRC Model group with tissue glue, while nothing was fixed in the Control group, and then the abdomen of the two groups of mice was sutured. After 3 days of postoperative recovery, mice were weighed, and Micro-CT scans were performed on the 26th day (under the condition of isoflurane respiratory anesthesia), and on the 27th day, the mice were sacrificed after anesthesia with 2 % sodium pentobarbital. Total RNA was extracted from the colon of the Control group and the tumor tissue of the CRC Model group by FastPure Cell/Tissue Total RNA Isolation Kit (Vazyme, China, Cat#RC101-01) , and after reverse transcribed into cDNA using HiScript® Ⅲ RT SuperMix for q PCR (Vazyme, China, Cat#R323-01), Real-Time PCR was used to detect the expression of IRCPI genes in each group using BlastaqTM Green 2× qPCR MasterMix (abm, Canada, Cat#G891). The primer sequences are shown in Supplementary Table S4.2.2 Immune Infiltration in CRC murine model The liver, colon, tumor, and mesentery of paraffin-embedded mice were sectioned, and then stained with hematoxylin-eosin (HE staining), and photographed with an upright white light photographic microscope (Nikon, Japan, Eclipse Ci-L).

TIME immune cells were detected by flow cytometry (FCM). PBMCs were extracted by RBC lysate (FcMACS, China, Cat#FMS-RBC500). At least 5×106 cell suspensions(100 μL) were incubated with FC blocker at 4 ℃ for 10 min, then Anti-Human/Mouse CD11b FITC Antibody (PeproTech, USA, Cat#03221-50)、PE-Cy™7 Rat Anti-Mouse CD86 Antibody (BD Pharmingen™, USA, Cat#560582) and Alexa Fluor® 488 Anti-Mouse CD206 Antibody (Biolegend, USA, Cat#141710) were used to marked Macrophages; Alexa Fluor® 488 anti-mouse CD19 Antibody (Invitrogen, USA, REF#11-0193-81) and PE/Cy7 anti-mouse/rat/human CD27 Antibody (Biolegend, USA, Cat#124216) were used to marked B cells; Anti-Mouse CD4 APC-Cyanine7 (PeproTech, USA, Cat#06122-87)、Anti-Mouse CD8a FITC Antibody (PeproTech, USA, Cat#10122-50)、Anti-Mouse CD25 APC Antibody (PeproTech, USA, Cat#07312-80) and Anti-Mouse/Rat FOXP3 PE Antibody (PeproTech, USA, Cat#83422-60) were used to marked T cells, and PBMCs monochromic tubes were made respectively. The cells were detected on the Amnis FlowSight flow cytometer (Merck Millipore, USA), and immunocyte subsets were analyzed using the IDEAS software (Merck Millipore, USA). Supplement Fig.S7 visualized the analysis strategies for IRGPI immunocyte subsets by Flow cytometry.

Possible correlation between AR- and UPR-associated gene expression was assessed in the global gene expression data available in the TCGA Prostate Adenocarcinoma cohort (n = 190) (http://www.cbioportal.org/public-portal/index.do). Tumors were stratified according to AR status into three groups, that is ARlow (n = 60), ARmedium (n = 70), and ARhigh (n = 60). The levels of UPR gene expression in the three groups were compared using Pearson's correlation analysis by the R software and presented as a heatmap. There were significant differences between the three groups (Supplementary Table S2).. List of tagged entities: Tumors, , real-time PCR (bao:BAO_0002084)

Summary

This metadata record provides details of the data supporting the claims of the related article: “Signaling of MK2 Sustains Robust AP1 Activity for Triple Negative Breast Cancer Tumorigenesis through Direct Phosphorylation of JAB1”.

The related study showed that p38MAPK signalling pathway regulation of activator protein 1 (AP1) activity involves both MAPKAPK2 (MK2) and JAB1, a known JUN binding protein.

Type of data: signalling pathway activity

Subject of data: antibodies; Eukaryotic cell lines (ATCC); Mus musculus (Foxn1nu, female, Catalog# 007850, The Jackson laboratory)

Data access

The following files underlying the figures in the related manuscript are openly available with this data record:

• Fig.1 data.xlsx (Fig.1a,1b of the related article)

• Fig.2 data.xlsx (Fig.2c,2d and 2e)

• Fig.3 data.xlsx (Fig.3e and 3f)

• Fig.4 data.xlsx (Fig.4f,4g and 4f)

• Fig.5 data.xlsx (Fig.5b,5c and 5d)

• Fig.6 data.xlsx

• Fig.7 data.xlsx (Fig. 7e and 7f)

• Fig.8 data.xlsx (Fig.8a and 8c)

All other data supporting the related study can be found in the supplementary information file of the related article, and the corresponding author can make any materials available upon request. Un-cropped gels and western blots for Fig. to Fig.5 were included in Supplementary Materials (Fig.S11).”

JAB1 expression in different breast cancer subtypes were downloaded from https://tcga.xenahubs.net/download/TCGA.BRCA.sampleMap/HiSeqV2.gz, and https://tcga.xenahubs.net/download/TCGA.BRCA.sampleMap/BRCA_clinicalMatrix.gz. For analysis of p38MAPK activity in breast cancer, Reverse Phase Protein Array (RPPA) z score and corresponding clinical data from TCGA Breast Cancer Invasive Carcinoma, PanCancer Atlas were first downloaded through cBioportal (https://www.cbioportal.org/).

Corresponding author(s) for this study

Shuang Huang, Department of Anatomy and Cell Biology, University of Florida College of Medicine, Gainesville, FL 32610. E-mail: shuanghuang@ufl.edu

Study approval

University of Florida Institutional Animal Care and Use Committee.

Title: Hypoxia-dependent spatial transcriptomics predicted the prognosis and efficacy of immunotherapy in claudin-low breast cancer The raw data in this study includes four parts. Original data list Part 1 Source data (raw data, original data, individual data points) for the information presented in your tables and figures. This should be in a generally readable format and Excel files are preferred. This study peformed Spatial Transcriptomics (ST) to demonstrate their spatial distribution in human claudin-low breast cancer MDA-MB-231 engraft. 10x genomics official software Space Ranger 1.0.0 was used for data preprocessing, gene expression quantitative and point identification. The original Space Ranger files for 4 samples were also submitted as additional files in Jianguoyun website (https://www.jianguoyun.com/c/sd/15eabb7/5bdc92d86263a0b7). Software Seurat 4.0 was used to analyze and cluster the four samples. UMAP algorithm were used to reduce the dimension of data and visualize data. The differentially expressed genes and cluster type in 12 clusters listed in Supplementary Table 3. Part 2 If your raw data were obtained from publicly available datasets, please provide the working sheets used to analyse these data. RNA-Sequence data and associated clinical data for 1904 breast cancers (METABRIC) were downloaded from the cBioPortal website (http://www.cbioportal.org/datasets). We used these data to validate the relationship between the breast cancer hypoxia-dependent spatial clusters score and immune cell infiltrition, immune funtion and breast cancer subtype. Supplementary table 4 listed the clinicopathological factors and ssGSEA score of each sample. Supplementary table 5 showed the gene sets used in this study. Part 3 If statistical programs (such as R, SAS, SPSS, MATLAB) were used for the data analysis or figure creation in your manuscript, please provide all the relevant code/script files (such as .R or .SPS) and data sheets to replicate your analyses or figures. The breast cancer hypoxia-dependent spatial clusters score and immune fuction-related score was calculated based on the ssGSEA R launguage for 1904 human breast cancers. Kaplan–Meier (K-M) analysis were carried out using R language. The R script files for ssGSEA and K-M survival were submitted as additional files in Jianguoyun website（https://www.jianguoyun.com/c/sd/15eabb7/5bdc92d86263a0b7）. SPSS 20.0 software was used to perform Cox proportional hazards regression, ANOVA test, Chi-squared test, and Pearson correlation. The correlation heatmaps were provided by HIPLOT website (https://hiplot.com.cn/basic/cor-heatmap). Part 4 This part of data are the original images in Figure 2, Figure 3 and Figure S1. There are immunofluorescence staining pictures and immunohistochemical staining pictures.

This data record describes the data files supporting the related publication "FGFR4 overexpression and hotspot mutations in metastatic ER+ breast cancer are enriched in the lobular subtype" and contains clinicopathologic, FGFR4 expression and FGFR4 hotspot mutationallele frequency data files.The related study investigates acquired resistance to endocrine therapy in Invasive lobular carcinoma (ILC); a common histological subtype in which most tumours are ER+ and so treated with endocrine therapy. Study design summaryThe related study is a subset analysis of existing RNA sequencing focusing only on ER+ patients treated with endocrine therapy prior to recurrence, and reporting additional FGFR4 expression data from paired gastrointestinal (GI) and ovarian metastases.Sample size26 patients. Treatment-naïve primary tumors and 29 endocrine-treated metastases, consisting of 7 bone, 7 brain, 5 GI, and 10 ovarian metastases were collected.Data access:For all data requests please contact:Prof Steffi OesterreichWomen’s Cancer Research CenterMagee Women’s Research InstituteCraft Avenue, Room B705Pittsburgh, PA 15213, USATel: 412-641-8555oesterreichs@upmc.eduRaw RNA-Seq data for the paired primary and metastatic samples will be made available upon request and under regulatory compliance via DUAData files and formatsData supporting figure 1A: files or repository identifiers: mm134_qpcr.xlsx, sum44_qpcr.xlsformat: MS Excellocation of data file(s): Institutional file storagedata access: Contact Prof Oesterreich with data requestsdescription: FGFR4 expression in endocrine-resistant cell linessee also: LTED and Tamoxifen resistant cell line expression data belowData supporting figure 1B: files or repository identifiers: Supplementary Table S1.xlsxformat: MS Excellocation of data file(s): figshare (this data record)data access: publicly accessibledescription: FGFR4 Expression in Paired ER+Endocrine−Treated Breast Metastasessee also: section on RNA-Seq expressionData supporting figure 2A-D: files or repository identifiers: FGFR4_allVUS_3_13_2018.csvformat: CSV text filelocation of data file(s): institutional file storagedata access: contact Prof Oesterreich with data requestssee also: FGFR4 mutations data belowdescription: FGFR4 mutations in breast metastases, FGFR4 hotspot mutations (N535, V550) in breast metastases vs non-breast metastases, FGFR4 hotspot mutations in metastatic IDC vs metastatic ILC and FGFR4 hotspot mutations in endocrine-treated metastasesData supporting supplementary material: files or repository identifiers: IHC_image_ranks.xlsxformat: MS Excellocation of data file(s): institutional file storagedata access: contact Prof Oesterreich with data requestssee also: FGFR4 expression in RATHER data belowdescription: FGFR4 antibody validation in engineered Sum44PE cells, FGFR4 RNA and protein correlation based on querying of the RATHER consortium microarray and RPPA data of ILC primary tumors.LTED and Tamoxifen resistant cell line expression: files or repository identifiers: GSE12708, GSE75971, GSE116744format: CEL, TXT text file, CSV text filelocation of data file(s): NCBI Gene Expression Omnibusdata access: publicly shared at the GEO repository. GSE75971,GSE116744 also available in shiny app format at https://leeoesterreich.org/resourcesdescription: RNA-Sequencing data on long-term estrogen deprived (LTED) cell lines (GSE116744, GSE75971) and microarray analysis data on tamoxifen resistant cell lines (GSE75971)RNA-Seq expressionfiles or repository identifiers: metPairs_endoTreated_log2CPM.Rda, 55 raw transcript count files (BO_1M.ts.count.0.8.2, etc)format: R data frame, TXT text fileslocation of data file(s): https://github.com/leeoesterreich/npjBreast_2019 data access: publicly accessibledescription: log2 TMM-normalized CPM for all endocrine-treated metastases, and transcript countsFGFR4 expression in RATHER: files or repository identifiers: GSE68057format: TXT text filelocation of data file(s): NCBI Gene Expression Omnibusdata access: publicly accessibledescription: Microarray based gene expression for invasive lobular cancer samples; part of the RATHER (RAtional THERapy for breast cancer: individualized treatment for difficult-to-treat breast cancer subtypes) consortium.FGFR4 expression in paired samples: files or repository identifiers: Supplementary Table S1.xlsxformat: MS Excellocation of data file(s): figshare (this data record)data access: publicly accessibledescription: Clinicopathologic data and FGFR4 expression for matched primary: metastatic tumorsFGFR4 hotspot mutations in MSK-IMPACT: files or repository identifiers: Supplementary Table S2.xlsxformat: MS Excellocation of data file(s): figshare (this data record)data access: publicly accessibledescription: Clinicopathologic data and FGFR4 hotspot mutation allele frequencies from MSK-IMPACTFGFR4 mutationsfiles or repository identifiers: brca_igr_2015 (Lefebvre et al.), breast_msk_2018 (MSK-IMPACT), MCTP (MET500)format: websiteslocation of data file(s): http://www.cbioportal.org/study?id=brca_igr_2015, http://www.cbioportal.org/study?id=breast_msk_2018, & https://met500.path.med.umich.edudata access: publicly accessibledescription: FGFR4 hotspots (N535 and V550) queried in Lefebvre et al. and MSK-IMPACT using the cBio portal, and MET500 using the MET500 portal.The data in FGFR4_allVUS_3_13_2018.csv was accessible to the authors through a DUA with Foundation Medicine- (as approved by Western Institutional Review Board). This data will not be made publicly available in order to protect patient privacy. Collection and analysis of tumor specimens was approved under the University of Pittsburgh (distant metastases) and Charite Universitaetsmedizin Berlin IRB (paired local recurrence) guidelines. Additional patient details will not be made publicly available to protect patient privacy.

In silico genomics research provides students with opportunities to conduct authentic, course-embedded, biomedical research. However, tools for conducting this type of research can be challenging to learn for both students and faculty. This project curriculum guides students through the analysis of human cancer patient genomic data from The Cancer Genome Atlas (TCGA) using the analytical tool, cBioPortal. The project has a “plug and play” style guide where students “plug” variables into template research questions and follow steps to collect and analyze appropriate data (“play”) in cBioPortal. The project is designed to take place over a semester, with five checkpoints to maintain student progress and provide feedback: Checkpoint 1. Selection of Research Question and cBioPortal Activity, Checkpoint 2. Annotated Bibliography and Abstract, Checkpoint 3. Draft of Scientific Poster, Checkpoint 4. Peer Review of Scientific Posters, and Checkpoint 5. Poster Presentations. The project culminates with a formal poster presentation, allowing students to share their work and gain experience in scientific communication. Here, the project curriculum, student guidelines, and assessments are presented.

Primary Image: This image was created using cBioPortal. The image shows partial OncoPrints for the five most commonly mutated genes in gliomas (top), pancreatic cancers (middle), and breast cancers (bottom). The OncoPrints were generated using data from the Pan-cancer analysis of whole genomes (ICGC/TCGA, Nature 2020).

The related study sought to define the repertoire of somatic genetic alterations in Partner and Localizer of BRCA2 (PALB2)-associated breast cancers and to determine whether PALB2-associated breast cancers display bi-allelic inactivation of PALB2 and/or genomic features of Homologous recombination deficiency (HRD). Additionally, the genomic landscape of breast cancers from pathogenic PALB2 germline mutation carriers was compared to that of breast cancers arising in BRCA1 or BRCA2 germline mutation carriers, and to that of non-BRCA1/2/PALB2-associated breast cancers. Participant consent:This study was approved by Memorial Sloan Kettering Cancer Center’s Institutional Review Board (IRB) and by the local ethics committees/ IRBs of the authors’ institutions. Written informed consents were obtained as required by the protocols approved by the IRBs/local ethics committees of the respective authors’ institutions. This study is in compliance with the Declaration of Helsinki.Study description and methodology: The aim of the study was to characterize the repertoire of somatic genetic alterations in PALB2-associated breast cancers and to determine whether bi-allelic inactivation of PALB2 and/or genomic features of HRD are present in these tumors, with the aim to identifying features that may aid in the selection of patients likely to benefit from HRD-directed therapies. This was achieved by carrying out a number of assays including massively parallel sequencing. This study included 24 invasive breast cancers (invasive ductal carcinomas) from women with pathogenic PALB2 germline mutations. All tumor samples included in this study were derived from formalin-fixed paraffin-embedded (FFPE) material. Immunohistochemistry was used to assess estrogen receptor (ER) and HER2 status of the tumor samples. HER2 amplification was assessed in selected cases by fluorescence in situ hybridization (FISH). Genomic DNA was extracted from tumor and matched normal blood or saliva samples. Fourteen cases were subjected to whole-exome sequencing (WES) and WES sequencing data from two cases were retrieved from The Cancer Genome Atlas (TCGA). In addition, 8 cases were analysed by targeted capture massively parallel sequencing using the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) sequencing assay. Comparisons of mutation burden, mutation frequencies, copy number alterations (CNAs) and genomic features indicative of HRD were conducted between the PALB2-associated breast cancers, non-BRCA1/2/PALB2 associated breast cancers with matched ER and HER2 status (n=683), and BRCA1- (n=17) and BRCA2-associated (n=16) breast cancers with bi-allelic inactivation from TCGA. For information on how massively parallel sequencing, bioinformatics analysis and statistical analysis were performed, please refer to the related manuscript and associated supplementary methods.Data files:Data file format: Data files supporting the figures, tables, supplementary figures and supplementary tables are all in Txt file format. Data files supporting supplementary table 4 are in Excel and Txt file formats.Data file access: WES sequencing data (supporting figures 1-5, table 1, supplementary figures 2-6 and supplementary tables 1-5) can be accessed from: https://identifiers.org/cbioportal:brca_msk_li_2019.MSK-IMPACT sequencing data (supporting figures 1, 2, 4 and 5, table 1, supplementary figures 2, 4 and 5 and supplementary tables 1-5) can be accessed from: https://identifiers.org/cbioportal:brca_msk_li_2019.TCGA Breast Cancer sequencing data (supporting figures 4 and 5, supplementary figures 4-6 and supplementary table 5) can be accessed from: https://identifiers.org/cbioportal:brca_tcga_pan_can_atlas_2018 or from the related publication https://doi.org/10.1016/j.cell.2018.02.060.Additional data supporting supplementary table 4 can be accessed from table 2 and supplementary table 1 of the related publication: https://doi.org/10.1002/path.5055.Data file description:Data supporting figure 1: WES and MSK-IMPACT sequencing data showing non-synonymous somatic mutations in 24 PALB2-associated breast cancers. Data supporting figure 2: WES and MSK-IMPACT sequencing data showing copy number alterations in the 24 PALB2-associated breast cancers analysed by WES or MSK-IMPACT.Data files supporting figure 3: WES sequencing data analyzed for the assessment of HRD genomic features including mutational signatures, large-scale state transition scores, average deletion length and number of genes affected by copy number alterations in 16 PALB2-associated breast cancers with and without bi-allelic PALB2 inactivation. Data files supporting figure 4: WES, MSK-IMPACT and TCGA breast cancer sequencing data showing a comparison between all 24 PALB2-associated breast cancers and the 683 ER+/HER2-, ER+/HER2+ and ER-/HER2- non- BRCA1/2/PALB2-associated breast cancers from TCGA. These data files also show a comparison between the ER+/HER2- PALB2-asso...

Figure S1. Colony formation in shCon or shXLF cells treated with DMSO, cisplatin, cisplatin + L189 (LIG4 inhibitor), oxaliplatin, or oxaliplatin + L189, respectively. Figure S2. Genomic alteration frequency of core factors of NHEJ pathway (XLF, XRCC4, XRCC5, XRCC6, LIG4, PRKDC, TP53BP1, and DCLRE1C) was generated using cBioPortal [ http://www.cbioportal.org ] from database of TCGA (193 patients) and AMC (231 patients). Tumor types are indicated at the bottom and ordered by the frequency of samples harboring mutation, deletion, amplification and multiple alterations. Overall alteration frequency in liver cancer was highlighted. Figure S3. RNA-sequencing data organization of NHEJ pathway genes (XLF, XRCC4, XRCC5, XRCC6, LIG4, PRKDC, TP53BP1, and DCLRE1C) by TCGA to show frequency of NHEJ pathway gene amplification, gene expression, missense or truncating mutations. (ZIP 1521Â kb)

This record provides details of the data supporting the claims of the related article: “Multiscale -omic assessment of EWSR1-NFATc2 fusion positive sarcomas identifies the mTOR pathway as a potential therapeutic target”.The related study aimed to generate genomic profiles of EWSR1 fusion positive sarcomas to support the use of precision medicine in the development of therapeutics for the Ewing family of tumors.Type of data: Genomic profiling data; Gene expression data; Drug sensitivity data; TCGA datasets, copy number data; case report data Subject of data: EWSR1 fusion positive sarcoma patientsSample size: 1,024 retrospectively analysed samples, 1 case reportData accessThe genomic profiling of pathogenic variants in EWSR1-NFATc2 fusion positive sarcomas are openly available as part of this figshare metadata record in the file ‘Variants observed in EWSR1-NFATc2 sarcoma.xlsx’. The gene expression data used in this study are openly available from the Gene Expression Omnibus repository via the following accessions: https://identifiers.org/geo:GSE60740 and https://identifiers.org/geo:GSE34620. The drug sensitivity data files used in this study were Cell_Lines_Details.xlsx; GDSC1_fitted_dose_response_17Jul19.xlsx; GDSC2_fitted_dose_response_17Jul19.xlsx; mutations_latest.csv; gene_cnv_2019-04-15_1109.csv; rnaseq_latest.csv. All of these files are openly accessible from the Genomics of Drug Sensitivity in Cancer Database (https://www.cancerrxgene.org/).The TCGA dataset files used in this study were the Clinical merged and illuminahiseq_rnaseqv2-RSEM_genes_normalized files for each of the 33 cancer types. All of these files are openly accessible from firebrowse.org (http://gdac.broadinstitute.org/runs/stddata_2016_01_28/data/).The copy number data files for each of the 33 cancer types were downloaded from cBioPortal (https://www.cbioportal.org/).The case report data cannot be openly shared in order to protect patient confidentiality De-identified information is available in the text and supplemental figures of the related article. Clinical and genomic data for this case can be made available on request. Corresponding author for this studyJames L. Chen (James.Chen@osumc.edu)Study approval Western Institutional Review Board (protocol No. 20152817)

Needs .env with following keys with FIXME edited.

AZURE_OPENAI_ENDPOINT="https://FIXME.openai.azure.com"
AZURE_OPENAI_API_KEY="FIXME"
AZURE_OPENAI_API_VERSION="2023-05-15"

Contains vector databases for cBioPortal documentation, Google Groups, and study articles; data collected around July 2024.

Install

uv venv
source .venv/bin/activate
uv pip install -r pyproject.toml

Run

python chroma_vectorstore_example.py

Additional file 2. Table S1. Databases and accession numbers. The original RNA-seq data were downloaded from the recount2 website ( http://idies.jhu.edu/recount/data/fc_rc/rse_fc_TCGA_prostate.Rdata ) and the database of Genomic and Phenotypes (dbGaP) under accession number phs000178.v11.p8.c1, phs000915.v2.p2.c1. The original clinical information of both cohorts is deposited in the CbioPortal ( https://www.cbioportal.org ) and GDCPortal ( https://portal.gdc.cancer.gov )

Epidermal growth factor receptor (EGFR) signaling and components of the fibrinolytic system, including urokinase-type plasminogen activator (uPA) and thrombomodulin (TM), have been implicated in tumor progression. In the present study, we employed cBioPortal platform (http://www.cbioportal.org/), cancer cell lines, and an in vivo model of immunocompromised mice to evaluate a possible cooperation between EGFR signaling, uPA, and TM expression/function in the context of cervical cancer. cBioPortal analysis revealed that EGFR, uPA, and TM are positively correlated in tumor samples of cervical cancer patients, showing a negative prognostic impact. Aggressive human cervical cancer cells (CASKI) presented higher gene expression levels of EGFR, uPA, and TM compared to its less aggressive counterpart (C-33A cells). EGFR induces uPA expression in CASKI cells through both PI3K-Akt and MEK1/2-ERK1/2 downstream effectors, whereas TM expression induced by EGFR was dependent on PI3K/Akt signaling alone. uPA induced cell-morphology modifications and cell migration in an EGFR-dependent and -independent manner, respectively. Finally, treatment with cetuximab reduced in vivo CASKI xenografted-tumor growth in nude mice, and decreased intratumoral uPA expression, while TM expression was unaltered. In conclusion, we showed that EGFR signaling regulated expression of the fibrinolytic system component uPA in both in vitro and in vivo settings, while uPA also participated in cell-morphology modifications and migration in a human cervical cancer model.

In breast cancer (BC), detecting low volumes of axillary lymph node (ALN) metastasis pre-operatively is difficult and novel biomarkers are needed. In this study, the authors compared reactive (tumour-free; n = 5) and macrometastatic (containing tumour deposits >2mm; n = 4) ALNs by combining whole section multiplex immunofluorescence with Tandem Mass Tag (TMT)-labelled Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) of the circulating perfusate.

Data access: The mass-spectrometry based proteomics data generated during the study, are publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722. The multiplex immunofluorescence data generated during this study, are available in the figshare repository, as part of this data record. The TCGA data analysed during the study, are available in the cBioPortal for Cancer Genomics: https://identifiers.org/cbioportal:brca_tcga. All other data supporting the findings of this study, are available as part of the supplementary files that accompany the article.

Study approval and patient consent: Patients consented for use of their tissue in the current study. The study protocol was approved by King's Health Partners Biobank (Research Ethics Committee No: 18/EE/0025).

Study aims and methodology: Lymph seems to contain higher concentrations of circulating biomarkers, particularly in the early stages of metastasis. Proteomic studies have shown that lymph reflects the pathophysiology of the tissue from which it derives, and has recently been shown to be relevant to melanoma biomarker discovery and stage prediction. To date, no such studies have been performed on lymphatic exudate from BC patients undergoing an axillary lymph node dissection (ALND) however. In this study, the authors characterised the proteome of the circulating fluid collected from perfused ALNs (perfusate) using Tandem Mass Tag (TMT) labelled mass spectrometry (MS)-based shotgun proteomics.

ALNs were harvested from 10 BC patients and perfused ex vivo at 37˚C as described previously (Research Ethics Committee No: 18/EE/0025). The following are described in more detail in the related article: patient cohort and ALN harvest, perfusate collection, multiplex immunofluorescence (MIF), proteomic analysis, neutrophil quantification, The Cancer Genome Atlas (TCGA) BC proteomics analysis and statistical analysis.

Data supporting the figures, tables, supplementary figures and supplementary tables in the related article:

Data supporting figure 1 and supplementary figure 1: REPLICANT Metastatic Lymph Node Multiplex IF.xlsx and REPLICANT Reactive Lymph Nodes Multiplex IF.xlsx, both in .xlsx file format. The files are part of this figshare data record. The data files contain cell density counts from REPLICANT reactive (tumour-free) lymph nodes and metastatic lymph nodes, generated algorithmically from multiplex immunofluorescence (Vectra).

Data supporting figures 2 and 3; and table 1: Proteomics data publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722.

Data supporting figure 4 and supplementary figure 2: TCGA data publicly available at cBioPortal for Cancer Genomics: https://identifiers.org/cbioportal:brca_tcga.

Data supporting figure 5: Supplementary Tables 4 and 5.xlxs. The data are available as part of the supplementary files that accompany the article.

Data supporting figure 6: Supplementary Table 6.xlxs. The data are available as part of the supplementary files that accompany the article.

Data supporting supplementary table 1: Proteomics data publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722.

Data supporting supplementary table 2: TCGA data publicly available at cBioPortal for Cancer Genomics: https://identifiers.org/cbioportal:brca_tcga.

Data supporting supplementary table 3: Proteomics data publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722 and supplementary files of article https://doi.org/10.1002/pmic.200800303.

Data supporting supplementary table 4: Proteomics data publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722 and supplementary files of article https://doi.org/10.1002/path.3959.

Data supporting supplementary table 5: Proteomics data publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722 and supplementary files of article https://doi.org/10.1016/j.neo.2017.10.009.

Data supporting supplementary table 6: Proteomics data publicly available in the PRIDE repository: https://identifiers.org/pride.project:PXD022722, and Supplementary Tables 4 and 5.xlxs.

The cBioPortal for Cancer Genomics was utilized as the primary database to collect patient data and analyze survival outcomes. A total of 16 studies were organized, focusing on clinical data related to patient survival: Brain Lower Grade Glioma (TCGA, Firehose Legacy), Brain Lower Grade Glioma (TCGA, PanCancer Atlas), Diffuse Glioma (GLASS Consortium), Diffuse Glioma (GLASS Consortium, Nature 2019), Diffuse Glioma (MSK, Clin Cancer Res 2024), Glioma (MSK, Clin Cancer Res 2019), Glioma (MSK, Nature 2019), Low-Grade Gliomas (UCSF, Science 2014), Merged Cohort of LGG and GBM (TCGA, Cell 2016), Brain Tumor PDXs (Mayo Clinic, Clin Cancer Res 2020), Glioblastoma (CPTAC, Cell 2021), Glioblastoma (Columbia, Nat Med. 2019), Glioblastoma (TCGA, Cell 2013), Glioblastoma (TCGA, Nature 2008), Glioblastoma Multiforme (TCGA, Firehose Legacy), Glioblastoma Multiforme (TCGA, PanCancer Atlas). The clinical tab was selected for analysis, and survival graphs were generated for patient groups based on survival status (live and deceased groups). The p-values associated with the survival curves were carefully evaluated to ensure the best accuracy. This study's survival data encompassed 2,444 patients in the living group and 3,226 patients in the deceased group.

Triple-negative breast cancer (TNBC) is a heterogeneous disease that lacks both effective patient stratification strategies and therapeutic targets. Whilst elevated levels of the MET receptor tyrosine kinase are associated with TNBCs and predict poor clinical outcome, the functional role of MET in TNBC is still poorly understood. In this study, the authors utilized an established Met-dependent transgenic mouse model of TNBC, human cell lines, and patient-derived xenografts to investigate the role of MET in TNBC tumourigenesis.

Data access: Processed RNA sequencing datasets generated during the study, are available in Gene expression Omnibus: https://identifiers.org/geo:GSE162272. The raw RNA sequencing data are available in Sequence Read Archive: https://identifiers.org/ncbi/insdc.sra:SRP294504. All other datasets generated and analysed during the study (including tumoursphere formation assays, tumoursphere proliferation assays, immunohistochemistry data, quantitative RT-PCR, in vivo inhibitor treatments (including tumour volume calculations), flow cytometry data and immunofluorescence data) are publicly available in the figshare repository as part of this data record. The publicly available TCGA data analysed during the study are available in cBioPortal for Cancer Genomics: https://identifiers.org/cbioportal:brca_tcga_pub. Microarray data from the MMTV-Metmt;Trp53fl/+;Cre tumours analysed during the study, are available in Gene Expression Omnibus: https://identifiers.org/geo:GSE41601. RNA sequencing data from breast cancer pairs of primary tumors and PDXs, analysed during the study, are also available in Gene Expression Omnibus: https://identifiers.org/geo:GSE142767. Uncropped Western blots are part of the supplementary files that accompany the article.

Study approval and patient consent: All human participants provided informed consent for this study and tissue was collected at McGill University Health Center in accordance with the protocols approved by the research ethics board (SUR-99-780). All animal studies linked to this protocol were approved by the McGill University Animal Care Committee (2014-7514). The Biobank protocol (05-006) and the protocol to generate PDX from biobank tissues (14-168) were both approved by Jewish General Hospital ethics committee.

Study aims and methodology: In the present study, the authors assayed tumour-initiating cells (TIC) properties to directly investigate the role of Met in tumour initiation and identify FGFR1 signaling as a key convergent pathway with Met for the maintenance of TICs.

Primary mouse cell lines were established by dissociation of MMTV-Metmt, Trp53fl/+;Cre, and MMTV-Metmt;Trp53fl/+;Cre mammary tumours as previously described. Additionally, the following cell lines were used during the study: BT-20, HCC70, HCC1937, HCC1954, HCC1395, MDA-MB-468, MDA-MB-436, MDA-MB-157, MDA-MB-231, BT-549, and Hs578T.

The following are described in more detail in the published article: cell culture, patient-derived xenografts, antibodies and reagents, lentiviral infection, tumoursphere formation assays, tumoursphere proliferation assays, Western blot analysis, quantitative RT-PCR, immunohistochemistry, RNA sequencing, in vivo limiting dilution assay, in vivo inhibitor treatments, flow cytometry, immunofluorescence, tumour dissociation, analysis of gene expression data, and statistical analysis.

Data supporting the figures, supplementary figures and supplementary tables in the article:

This data record consists of a total of 38 data files in the following file formats: .xlsx, .pdf, .csv, .txt, .png and tiff.

A list of all the datasets generated during the study, are included in the file Sung, V. et al.xlsx.

Table S1 shows IC50 of auranofin in 129 lung cancer cell lines. Table S2 shows correlations of auranofin activity and gene expression levels (based 76 cell lines whose mRNA levels were available). Table S3 shows co-expressions of GSR with other genes (data obtained from cBioportal.org)