Back

Single-cell based elucidation of molecularly-distinct glioblastoma states and drug sensitivity

Ding, H.; Burgenske, D. M.; Zhao, W.; Subramaniam, P. S.; Bakken, K. K.; He, L.; Alvarez, M. J.; Laise, P.; Paull, E. O.; Spinazzi, E. F.; Dovas, A.; Marie, T.; Upadhyayula, P.; Dela Cruz, F.; Diolaiti, D.; Kung, A.; Bruce, J. N.; Canoll, P.; Sims, P. A.; Sarkaria, J. N.; Califano, A.

2019-06-19 systems biology
10.1101/675439 bioRxiv
Show abstract

Glioblastoma heterogeneity and plasticity remain controversial, with proposed subtypes representing the average of highly heterogeneous admixtures of independent transcriptional states. Single-cell, protein-activity-based analysis allowed full quantification of >6,000 regulatory and signaling proteins, thus providing a previously unattainable single-cell characterization level. This helped identify four novel, molecularly distinct subtypes that successfully harmonize across multiple GBM datasets, including previously published bulk and single-cell profiles and single cell profiles from seven orthotopic PDX models, representative of prior subtype diversity. GBM is thus characterized by the plastic coexistence of single cells in two mutually-exclusive developmental lineages, with additional stratification provided by their proliferative potential. Consistently, all previous subtypes could be recapitulated by single-cell mixtures drawn from newly identified states. Critically, drug sensitivity was predicted and validated as highly state-dependent, both in single-cell assays from patient-derived explants and in PDX models, suggesting that successful treatment requires combinations of multiple drugs targeting these distinct tumor states.\n\nSignificanceWe propose a new, 4-subtype GBM classification, which harmonizes across bulk and single-cell datasets. Single-cell mixtures from these subtypes effectively recapitulate all prior classifications, suggesting that the latter are a byproduct of GBM heterogeneity. Finally, we predict single-cell level activity of three clinically-relevant drugs, and validate them in patient-derived explant.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Cancer Cell
42 papers in training set
Top 0.1%
31.0%
2
Cell Reports
1498 papers in training set
Top 3%
7.9%
3
iScience
1154 papers in training set
Top 0.8%
7.2%
4
eLife
5828 papers in training set
Top 19%
6.2%
50% of probability mass above
5
Neuro-Oncology
36 papers in training set
Top 0.2%
4.0%
6
Nature Communications
5641 papers in training set
Top 34%
3.4%
7
Cell
431 papers in training set
Top 4%
2.4%
8
Acta Neuropathologica Communications
89 papers in training set
Top 0.9%
2.4%
9
Communications Biology
993 papers in training set
Top 10%
2.1%
10
Genome Biology
637 papers in training set
Top 5%
2.1%
11
The EMBO Journal
309 papers in training set
Top 3%
2.1%
12
Cell Reports Methods
165 papers in training set
Top 2%
1.7%
13
Molecular Systems Biology
162 papers in training set
Top 2%
1.5%
14
Cancer Research
130 papers in training set
Top 2%
1.5%
15
npj Precision Oncology
53 papers in training set
Top 1%
1.4%
16
Cell Systems
201 papers in training set
Top 4%
1.1%
17
Molecular Cell
350 papers in training set
Top 4%
1.1%
18
Science Advances
1243 papers in training set
Top 27%
1.1%
19
Cell Reports Medicine
153 papers in training set
Top 4%
1.0%
20
Life Science Alliance
285 papers in training set
Top 6%
1.0%
21
Advanced Science
286 papers in training set
Top 9%
0.8%
22
OncoImmunology
24 papers in training set
Top 0.8%
0.6%
23
Journal of Experimental & Clinical Cancer Research
25 papers in training set
Top 0.9%
0.6%
24
EMBO Molecular Medicine
95 papers in training set
Top 3%
0.6%
25
Scientific Reports
3612 papers in training set
Top 78%
0.6%