Back

Metabolic plasticity of sphingolipids governs cancer cell fitness in acidic tumor ecosystems

Chalar, R.; Khatri, N.; Obeid, J.; Downey, E.; Song, J.-H.; Xiao, Y.; Chen, A.; Resnick, A. E.; Karbalaei, K.; Allopenna, J.; Mao, C.; Clarke, C. J.; Velazquez, F.; Canals, D.; Hannun, Y. A.; Damaghi, M.

2026-01-15 cancer biology
10.64898/2026.01.14.699600 bioRxiv
Show abstract

Cancer cells must adapt to harsh tumor microenvironments, including acidic stress, to survive and thrive. Understanding how cancer cells achieve this adaptation can uncover new biomarkers and therapeutic strategies. In this study, we investigated the spatial metabolic phenotypic heterogeneity of breast cancer cells in acidic habitats using spatial multiomics approaches on 3D spheroids. We found that cancer cells dynamically regulate sphingolipid metabolism to finetune their cell state to cope with acidic selection pressures. Cancer cells evolve mechanisms to deal with initially accumulating toxic ceramides but later adapt to it by rerouting SL metabolic pathways to eliminate them. Using advanced MALDI image analysis, and SL inhibitors on patient derived organoids, we demonstrated that cancer cells can switch between metabolic routes when key pathways are blocked, showcasing remarkable cell state plasticity. These insights highlight the potential to target metabolic plasticity as a novel therapeutic strategy to disrupt cancer adaptation and evolution, offering new avenues for cancer treatment.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 18%
9.7%
Similar papers in this journal
2
Cancer Discovery
● 66 papers in training set
Top 0.3%
7.8%
Similar papers in this journal
3
Science Advances
● 1243 papers in training set
Top 4%
6.2%
Similar papers in this journal
4
Cancer Research
● 130 papers in training set
Top 0.5%
6.2%
Similar papers in this journal
5
Nature Metabolism
○ 69 papers in training set
Top 0.4%
4.8%
Similar papers in this journal
6
Cell Metabolism
○ 57 papers in training set
Top 0.3%
4.3%
7
Nature
○ 645 papers in training set
Top 4%
4.3%
8
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 12%
4.3%
9
Cell Reports
○ 1498 papers in training set
Top 9%
4.3%
50% of probability mass above
10
eLife
● 5828 papers in training set
Top 31%
4.0%
11
Nature Chemical Biology
○ 119 papers in training set
Top 1%
2.4%
12
Nature Methods
○ 385 papers in training set
Top 3%
2.4%
13
Cell
○ 431 papers in training set
Top 4%
2.4%
14
The EMBO Journal
○ 309 papers in training set
Top 3%
1.9%
15
Science Signaling
● 65 papers in training set
Top 0.5%
1.9%
16
Nature Cell Biology
○ 118 papers in training set
Top 2%
1.7%
17
Advanced Science
○ 286 papers in training set
Top 5%
1.5%
18
Communications Biology
○ 993 papers in training set
Top 19%
1.3%
19
Molecular Cell
○ 350 papers in training set
Top 4%
1.3%
20
Nature Biotechnology
○ 172 papers in training set
Top 3%
1.1%
21
Journal of Clinical Investigation
● 179 papers in training set
Top 4%
1.1%
22
Cancer Cell
○ 42 papers in training set
Top 1%
1.1%
23
iScience
○ 1154 papers in training set
Top 31%
1.0%
24
Cancer Research Communications
● 51 papers in training set
Top 2%
0.9%
25
Journal of Cell Biology
● 392 papers in training set
Top 4%
0.8%
26
Cell Reports Medicine
○ 153 papers in training set
Top 5%
0.8%
27
Science
● 477 papers in training set
Top 9%
0.8%
28
Cell Systems
○ 201 papers in training set
Top 5%
0.8%
29
Cell Stem Cell
○ 62 papers in training set
Top 2%
0.8%
30
Nature Cancer
○ 39 papers in training set
Top 2%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.