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Enhancing Immunotherapy Outcomes: Spatial Multi-Omics Predictive Models for Non-Small Cell Lung Cancer

GSE271689 Homo sapiens (human) RNA-seq (gene expression) 586 samples Submitted 2024/08/22 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 586 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
Homo sapiens (human)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
586 samples: 134 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.
Summary (written by the submitters)
Non–small cell lung cancer (NSCLC) has an increasing number of targeted and systemic therapies allowing subsets of patients to achieve long–term durable benefit. Fundamental to understanding responses to a given therapy is comprehensive molecular characterization of the underlying tumor immune microenvironment (TIME). The TIME may inform models to predict immunotherapy outcomes and features to delineate therapeutic responses and clinical endpoints.
Read the full summary ↓
We hypothesize that an integrated multi–omics approach will uncover interactions within the NSCLC TIME and identify novel biomarkers that are predictive of immunotherapy responses, thus aiding precision oncology. To develop a spatially resolved TIME model for NSCLC immunotherapy, we utilized a multi–omics approach, combining spatial mapping of protein expression at the single–cell resolution by Phenocycler Fusion (PCF) and multi–cellular readout whole transcriptome profiling at cellular compartment resolution by Digital Spatial Profiling (DSP–GeoMx–WTA). This approach facilitated a detailed examination of the TIME in NSCLC samples from patients undergoing first–line immunotherapy. We studied two independent cohorts of advanced NSCLC tissue samples, treated with PD–1–based immunotherapies. We derived gene signatures from cell type signatures to predict treatment outcomes using a multistage Least Absolute Shrinkage and Selection Operator (LASSO) approach. Our spatial proteomic analysis identified three distinct cell types, proliferating tumor cells, granulocytes, and vessels, associated with resistance to immunotherapy. A high proportion of these cell types demonstrated a hazard ratio (HR) of 3.8 (p = 0.004) in the Yale training cohort and 1.8 (p = 0.05) in the UQ validation cohort. In the response cell–type model, higher levels of M1 macrophages, M2 macrophages, and CD4 T cells had an HR of 0.4 (p = 0.019) in the training cohort and 0.49 (p = 0.036) in the validation. In the transcriptomic analysis, gene signatures derived from these cell types predicted outcomes with high accuracy. The resistance gene model, which included 8 genes associated with epithelial–mesenchymal transition (EMT) and cell migration, showed an HR of 5.3 (p < 0.001) in the Yale training cohort, 2.2 (p = 0.036) in the UQ validation cohort, and 1.7 (p = 0.042) in the Greece validation cohort. Conversely, the response gene model, consisting of 8 genes associated with immunomodulation, had an HR of 0.22 (p = 0.005) in the Yale training cohort, 0.38 (p = 0.034) in the UQ validation cohort, and 0.56 (p = 0.041) in the Greece validation cohort. Multivariable analysis, adjusting for age, sex, disease stage, prior chemotherapy, type of immunotherapy, line of immunotherapy, histology, and smoking status, confirmed that the resistance signature remained significantly associated with worse outcomes, while the response signature remained associated with improved outcomes in both the UQ and Greece cohorts. This research highlights the potential of a multi–omics cell typing and gene expression profiling approach in advancing NSCLC treatment toward precision oncology. By offering insights into the TIME and unveiling novel biomarkers, our model seeks to define resistance and to improve prediction of response to treatment.
This dataset
Open GSE271689 in finder → Find more human RNA-seq datasets → Lung cancer landscape →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE271689_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA1132922 and SRA study SRP518492. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
134 conditions, mostly in triplicate
100_DSP-1001660003044-C-A ×3 101_DSP-1001660003044-C-A ×3 111_DSP-1001660003044-C-B ×3 116_DSP-1001660003044-C-B ×3 117_DSP-1001660003044-C-B ×3 119_DSP-1001660003044-C-C ×3 17_DSP-1001660003044-C-C ×3 2_DSP-1001660003044-C-D ×3 +173 more

Supports a between-group comparison across 353 samples.

How we read this: 134 replicated groups read from the first 400 of 586 sample titles; they account for 353 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.

Samples in this study

+ 546 more — browse all 586 samples with per-sample file links →

More Lung cancer datasets

Search all human RNA-seq datasets in GEO →

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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.