Back

Pre-existing stem cell heterogeneity dictates clonal responses to acquisition of cancer driver mutations

Singh, I.; Fernandez-Perez, D.; Sanchez-Sanchez, P.; Rodriguez-Fraticelli, A. E.

2024-05-17 cancer biology
10.1101/2024.05.14.594084 bioRxiv
Show abstract

Cancer cells display wide phenotypic variation even across patients with the same mutations. Differences in the cell of origin provide a potential explanation, but these assays have traditionally relied on surface markers, lacking the clonal resolution to distinguish heterogeneous subsets of stem and progenitor cells. To address this challenge, we developed STRACK, an unbiased framework to longitudinally trace clonal gene expression and expansion dynamics before and after acquisition of cancer mutations. We studied two different leukemia driver mutations, Dnmt3a-R882H and Npm1cA, and found that the response to both mutations was highly variable across different stem cell states. Specifically, a subset of differentiation-biased stem cells, which normally become outcompeted with time, can efficiently expand with both mutations. Npm1c mutations surprisingly reversed the intrinsic bias of the clone-of-origin, with stem-biased clones giving rise to more mature malignant states. We propose a clonal "reaction norm", in which pre-existing clonal states dictate different cancer phenotypic potential. Highlights- Single cell tracing of cancer initiation at the clonal level (STRACK). - Ex vivo expansion cultures sustain intrinsic and heritable HSC heterogeneity. - Premalignant mutations enhance the self-renewal of high-output stem cells, increasing their survival probability. - Transforming mutations reprogram low-output stem cell fates to more mature malignant states.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Cell Stem Cell
57 papers in training set
Top 0.1%
28.2%
2
Cell Reports
1338 papers in training set
Top 4%
8.4%
3
eLife
5422 papers in training set
Top 13%
6.4%
4
Developmental Cell
168 papers in training set
Top 4%
4.4%
5
Nature Communications
4913 papers in training set
Top 36%
4.0%
50% of probability mass above
6
Cancer Discovery
61 papers in training set
Top 0.5%
3.7%
7
Cell Systems
167 papers in training set
Top 4%
3.1%
8
EMBO Molecular Medicine
85 papers in training set
Top 0.9%
2.7%
9
Blood
67 papers in training set
Top 0.6%
2.1%
10
Nature Genetics
240 papers in training set
Top 3%
2.1%
11
Stem Cell Reports
118 papers in training set
Top 0.3%
2.1%
12
Cancer Cell
38 papers in training set
Top 0.9%
1.7%
13
Cell Reports Methods
141 papers in training set
Top 2%
1.7%
14
Cell Genomics
162 papers in training set
Top 4%
1.5%
15
Leukemia
39 papers in training set
Top 0.5%
1.5%
16
Science
429 papers in training set
Top 16%
1.2%
17
The EMBO Journal
267 papers in training set
Top 3%
1.1%
18
Nature Cell Biology
99 papers in training set
Top 4%
1.1%
19
Nature
575 papers in training set
Top 14%
0.9%
20
Genome Biology
555 papers in training set
Top 6%
0.9%
21
Nature Cancer
35 papers in training set
Top 1%
0.9%
22
Genome Medicine
154 papers in training set
Top 7%
0.8%
23
Cell Reports Medicine
140 papers in training set
Top 7%
0.8%
24
Molecular Cell
308 papers in training set
Top 9%
0.8%
25
Cell
370 papers in training set
Top 16%
0.8%
26
Nature Medicine
117 papers in training set
Top 5%
0.8%
27
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 44%
0.8%
28
iScience
1063 papers in training set
Top 31%
0.8%
29
Communications Biology
886 papers in training set
Top 25%
0.7%
30
Nature Immunology
71 papers in training set
Top 2%
0.7%