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Stroma reprogramming by FAK synergizes with MAPK inhibition and renders PDAC responsive to cytotoxic- and immuno-therapies II

GSE248058 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2024/09/04 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 18 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
18 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.
Paper
Stromal reprogramming overcomes resistance to RAS-MAPK inhibition to improve pancreas cancer responses to cytotoxic and immune therapyScience translational medicine · 2024
Summary (written by the submitters)
Pancreatic ductal adenocarcinoma(PDAC) is a highly lethal malignancy with poor response rate to therapy. An immune suppressive tumor microenvironment (TME) and oncogenic mutations in KRAS have been implicated as drivers of resistance to both conventional and immune therapies. As such, targeting RAS/MAPK signaling is an attractive strategy.
Read the full summary ↓
However, in practice, MAPK inhibition has not yet shown clinical efficacy, likely due to rapid acquisition resistance in PDAC cells. Tumor intrinsic mechanisms of resistance to RAS/MAPK have been studied, however, the unique PDAC TME may also be a key driver in resistance. Previous studies have shown that FAK inhibition can reprogram the PDAC TME and delay PDAC progression in animal models. Herein, we found that long-term FAK inhibitor treatment leads to hyperactivation of the MAPK pathway in both genetically engineered mouse models and in post-treatment PDAC tissues from FAK inhibitor clinical trials. Concomitant inhibition of both FAK (VS4718) and RAF/MEK (V6766) signaling dramatically suppressed tumor cell growth, leading to increased survival across multiple PDAC mouse models. The mechanisms of synergy include both changes in tumor-intrinsic signaling and modulation of tumor/stroma interactions that drive MEK resistance. In the TME, we found that cancer associated fibroblasts (CAFs) can impair the downregulation of cMyc by MEK inhibition in PDAC cells. This results in de-novo resistance to MEK inhibition in fibrotic conditions. By contrast, FAK inhibitors reprogram CAFs to suppress the production of key growth factors, including FGF1, that drives resistance to RAF/MEK inhibition. While combined FAK and RAF/MEK inhibition only leads to disease stasis, the addition of chemotherapy to the combination led to tumor regression and improved long-term survival in PDAC mouse models. Analysis of tumor immunity showed that the combination of FAK and MEK inhibition improved anti-tumor immunity and improved priming of T cell responses, which was further improved with the addition of chemotherapy. These findings led to testing of FAK (Defactinib) plus RAF/MEK (Avutometinib) inhibition in combination with gemcitabine and nab-paclitaxel in advanced pancreatic cancer patients (NCT05669482). Finally, we tested if the addition of immunotherapy could enhance the efficacy of the FAKi/MEKi/chemotherapy and found adding in either PD1 or CTLA4/PD1 blockade led to long term disease control in PDAC animal models. Together, these studies identified novel FAK inhibition as a route to overcome both tumor intrinsic and stromal-derived resistance to MAPK inhibition and showed that this combination can be exploited to increase the efficacy of cytotoxic and immunotherapy approaches.
Published in
Stromal reprogramming overcomes resistance to RAS-MAPK inhibition to improve pancreas cancer responses to cytotoxic and immune therapy
Liu X, Baer JM, Stone ML et al. · Science translational medicine 2024 · PMID 39441902 · doi:10.1126/scitranslmed.ado2402
This dataset
Open GSE248058 in finder → Find more mouse RNA-seq datasets →
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 GSE248058_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 PRJNA1041465 and SRA study SRP472444. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39441902) ↗
Study design
6 conditions, mostly in triplicate
KP2_organoid_Vehicle_24h ×3 KP2_organoid_FAKi_24h ×3 KP2_organoid_MEKi_24h ×3 KP2_organoid_FAKi+MEKi_24h ×3 KP2_organoid_Gemcitabine_24h ×3 KP2_organoid_Gemcitabine+FAKi+MEKi_24h ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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
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