Back

Multi-omics, organoid-based modeling reveals an SRC/mTOR-dependent fetal-like stem cell trajectory in colorectal cancer

Mulholland, T.; Aybey, B.; Li, Z.; Schwarzmüller, L.; Rindtorff, N.; Tondo, L.; Sui, P.; Karabati, E.; Albrecht, P.; Riedesser, J. E.; Petersen, Y.; Miersch, T.; Valentini, E.; Burgermeister, E.; Zhan, T.; Dreikhausen, L.; Schulte, N.; Belle, S.; Wiemann, S.; Boutros, M.; Ebert, M. P.; Betge, J.

2026-07-09 cancer biology
10.64898/2026.07.03.735750 bioRxiv
Show abstract

BackgroundSingle-cell atlases have described diverse stem cell states in colorectal cancer (CRC), however, the overarching trajectories of those states and the underlying functional mechanisms, including their relevance for drug sensitivity, need better understanding. MethodsWe established 64 patient-derived organoids from microsatellite-stable colorectal cancers, characterized their transcriptomes and genomes, and performed drug screening with 62-140 clinically approved substances. We analyzed additional published transcriptome data from patient-derived organoids (72 patients from three independent datasets), TCGA-CRC data (466 patients), and single-cell transcriptomes of tumor biopsies (123,000 cells from six independent cohorts) to establish a functional and molecular landscape of CRC stem cells. We performed mechanistic follow-up analyses by mass-spectrometry-based proteomics, large-scale kinase inhibition assays and immunofluorescence analyses. ResultsWe find a continuous landscape of CRC stem cells that is characterized by distinct developmental programs: adult stem cell-to fetal-like regenerative states and transition between differentiation programs. By large-scale drug perturbations and multi-omics modeling, we identify a regenerative/fetal-like stem cell trajectory characterized by PI3K/mTOR dependency. We find the identified developmental axes conserved in organoid, clinical, as well as single-cell data, and the fetal-like PI3K/mTOR-dependent state to be associated with poor clinical prognosis. Mechanistically, PI3K/mTOR vulnerability is linked to a lack of adaptive capability due to suppressed mRNA translation and associated with an upregulated SRC signaling network. ConclusionsOur work moves beyond a molecular CRC landscape by combined functional perturbation analyses in organoids. This enables mechanistic modeling of stem cell state regulation and identifies an SRC/mTOR-dependent regenerative state in CRC, which might allow improved therapeutic targeting in the future.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Nature Communications
5641 papers in training set
Top 8%
18.7%
2
Cancer Cell
42 papers in training set
Top 0.1%
9.8%
3
Cell Reports
1498 papers in training set
Top 6%
5.6%
4
Cancer Research
130 papers in training set
Top 0.6%
5.2%
5
Genome Medicine
183 papers in training set
Top 0.8%
4.4%
6
Science Advances
1243 papers in training set
Top 7%
4.1%
7
Molecular Systems Biology
162 papers in training set
Top 0.6%
3.3%
50% of probability mass above
8
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 21%
2.4%
9
Cancer Discovery
66 papers in training set
Top 0.9%
2.4%
10
Cell Reports Medicine
153 papers in training set
Top 2%
2.1%
11
npj Precision Oncology
53 papers in training set
Top 0.7%
1.9%
12
Molecular Cancer
16 papers in training set
Top 0.1%
1.7%
13
Nature
645 papers in training set
Top 6%
1.7%
14
EMBO Molecular Medicine
95 papers in training set
Top 0.8%
1.7%
15
Cell Stem Cell
62 papers in training set
Top 0.9%
1.7%
16
Cell
431 papers in training set
Top 6%
1.7%
17
eLife
5828 papers in training set
Top 49%
1.7%
18
Nature Cancer
39 papers in training set
Top 1.0%
1.3%
19
Gut
40 papers in training set
Top 0.7%
1.1%
20
JCI Insight
277 papers in training set
Top 6%
1.1%
21
Nature Medicine
125 papers in training set
Top 2%
1.1%
22
Scientific Reports
3612 papers in training set
Top 64%
1.1%
23
Communications Medicine
113 papers in training set
Top 4%
1.0%
24
Communications Biology
993 papers in training set
Top 25%
1.0%
25
iScience
1154 papers in training set
Top 34%
0.9%
26
Molecular Cell
350 papers in training set
Top 5%
0.9%
27
Cell Communication and Signaling
51 papers in training set
Top 1%
0.9%
28
Cell Genomics
172 papers in training set
Top 4%
0.9%
29
EMBO Reports
263 papers in training set
Top 7%
0.9%
30
Genome Biology
637 papers in training set
Top 9%
0.6%