Targeting NTRK1 Enhances Immune Checkpoint Inhibitor Efficacy in NTRK1 Wild-type Non-Small Cell Lung Cancer
RNA-seq (gene expression) · mouse · 19 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Mus musculus (mouse)
- Samples
- 19 samples: 6 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Read the full summary ↓
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE274960_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1148643 and SRA study SRP526683. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 19 samples.
How we read this: 6 replicated groups read from 19 sample titles; they account for 19 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM8462838 LL2_IgG_1 LL2_IgG
- GSM8462839 LL2_IgG_2 LL2_IgG
- GSM8462840 LL2_IgG_3 LL2_IgG
- GSM8462841 LL2_E_1 LL2_E
- GSM8462842 LL2_E_2 LL2_E
- GSM8462843 LL2_E_3 LL2_E
- GSM8462844 LL2_PD_1 LL2_PD
- GSM8462845 LL2_PD_2 LL2_PD
- GSM8462846 LL2_PD_3 LL2_PD
- GSM8462847 LL2_E_PD_1 LL2_E_PD
- GSM8462848 LL2_E_PD_2 LL2_E_PD
- GSM8462849 LL2_E_PD_3 LL2_E_PD
- GSM8462850 LL2_E_PD_4 LL2_E_PD
- GSM8462851 LL2_CNTRL_1 LL2_CNTRL
- GSM8462852 LL2_CNTRL_2 LL2_CNTRL
- GSM8462853 LL2_CNTRL_3 LL2_CNTRL
- GSM8462854 LL2_shNTRK_1 LL2_shNTRK
- GSM8462855 LL2_shNTRK_2 LL2_shNTRK
- GSM8462856 LL2_shNTRK_3 LL2_shNTRK
- GSE300288 Single-cell and spatial transcriptomics analysis of tobacco-associated lung adenocarcinoma development, and in response to targeting inflammation alone or in combination with immune checkpoint blockade in precancerous phase [scRNA-seq] 31 samples shares checkpoint, targeting
- GSE293295 Discovery of BMS-986408, a First-In-Class Dual DGK⍺ and DGK Inhibitor That Unleashes PD-1 Checkpoint and CAR-T Cell Immunotherapies 20 samples shares checkpoint, inhibitor
- GSE308848 Targeting AEBP1 to mitigate pro-tumor activity of cancer-associated fibroblasts and increase therapeutic efficacy to anti-PD-1 [scRNA-Seq] 12 samples shares targeting, efficacy
- GSE337208 scRNA seq. of PDAC models treated with Kras inhibitors in combination with checkpoint immunotherapy 9 samples shares checkpoint, inhibitor
- GSE250056 Targeting chemokine crosstalk between T-cells and macrophages to treat immune checkpoint inhibito-mediated myocarditis 68 samples shares checkpoint, targeting
- GSE291447 Targeting CD11b+ regulatory B cells via RAS/RAF/PI3K pathway inhibition augments response to immune checkpoint blockade or CD40 agonism in metastatic melanoma 60 samples shares checkpoint, targeting
- GSE284500 Transcriptional profiling of hippocampus from a zQ175 KI mouse model of Huntington's disease treated with either HTT-10150 siRNA targeting full-length HTT or combination of HTT-634 and HTT-486 siRNAs targeting HTT1a 192 samples shares targeting
- GSE299686 Longitudinal liquid biopsy identifies an early predictive biomarker of immune checkpoint blockade response in head and neck squamous cell carcinoma [bulk RNA-seq] 178 samples shares checkpoint
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.