Drug-induced expression of the RNA-binding protein HuR attenuates the adaptive response to BRAF inhibition in melanoma

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Drug-induced expression of the RNA-binding protein HuR attenuates the adaptive response to BRAF inhibition in melanoma

MERAT, Rastine, et al.

Abstract

Strategies that aim to limit the adaptive response to pathway inhibition in BRAF-mutated melanoma face the inherent limit of signaling redundancy and multiplicity of possible bypass mechanisms. Drug-induced expression of selected RNA-binding proteins, like the ubiquitously expressed HuR, has the potential to differentially stabilize the expression of many genes involved in the compensatory mechanisms of adaptive response. Here, we detect in BRAF-mutated melanoma cell lines having a higher propensity for adaptive response and in non-responding melanoma tumors, a larger proportion of HuRLow cells in the expression distribution of HuR. Using knockdown experiments, we demonstrate, through expression profiling and phenotypic assays, that increasing the proportion of HuRLow cells favors the adaptive response to BRAF inhibition, provided that the HuRLow state stays reversible. The MAPK dependency of melanoma cells appears to be diminished as the proportion of HuRLow cells increases. In single-cell assays, we demonstrate that the HuRLow cells display plasticity in their growth expression profile. Importantly, the adaptive [...]

MERAT, Rastine, et al . Drug-induced expression of the RNA-binding protein HuR attenuates the adaptive response to BRAF inhibition in melanoma. Biochemical and Biophysical Research Communications , 2019, vol. 517, no. 2, p. 181-187

PMID : 31279529

DOI : 10.1016/j.bbrc.2019.06.154

Available at:

http://archive-ouverte.unige.ch/unige:126980

Disclaimer: layout of this document may differ from the published version.

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Relative expression

CASP3 p53 p21 EGFR MET

PDGFRB pERK1/2 pS6 _235/6 pAKT p4EBP1

10

10 100 0.1 1 100 10 100 1000 100 100 1000

0.1 1 10000 0.1 1 10 0.1 1 10

10

10 100 0.1 1 100 10 100 1000 1000 100 1000

0.1 1 0.1 1 10 100 0.1 1 10

c ell cou n t

TP1 TP2

TP2 Li ₂ CO ₃

25

0 25 0 10 25 0 25 0 25 0 25

0 15

15 0 0 15 0 15 0 1 5 0

15 0 1 5 0

15 0 20 0 25 0

15 0 15 0

15 0

15 0 12 0 12 0

12 0 15 0

0 15

0 14 0 14 0 14 0 14 0 14 0 14

15 0 0 14

15 0 0 60 0 8 0 0 5 0 0 5 0 0 4 0

0 60 0 4 0 0 50 0 60 0 5 0

0 35 0 30 0 25 0 30 0 3 0 0 30

0 4 0 0 4 0 0 4 0 0 4 0 0 3 0

0 4 0 0 35 0 35 0 40 0 40

Fig. S1. Mass cytometry. Expression distribution of the indicated markers in the six defined clusters (see Fig.4 legend). The TP1, TP2 and TP2Li ₂ CO ₃ cells are superimposed for comparison. The vertical axis represents the absolute number of cells.

R. Merat et al. / Supplemental Figures and Table (2019)

12 0 0 25 0 8

0 5 0 3 0 8 0

4 0

c el l c ou nt 14

12 0 8 0 0 15 0 0 8 12 0 2 0

20 0

Relative expression

0.01 0.1 1 10 100

10 100 1000

1

10 100 1000

1

10

0.01 0.1 1 100 0.1 1 10 100

10 100 1000

0.1 1 0.1 1 10 100 1000

p4EBP1

EGFR MET

pS6 _235/6

pERK1/2 pAKT

HuR Ki67 pRb

HuR

7 0

10 100 1000

1

HuR Ki67 pRb

10

0.01 0.1 1 100 0.1 1 10 100 1000

10 100 1000

0.1 1

p21

10 100

0.1 1

p53

10 100

0.1 1

10 100 1000

0.1 1

p21

10 100 1000

0.1 1

p53

10 100

0.1 1

TP1 TP2

TP2 Li ₂ CO ₃

EGFR

10 100 1000

1

0 6

MET

10 100

0.1 1

0 5

pERK1/2

0 3 0 3

pAKT

0.1 1 10 100

0.01 0.1 1 10 100

pS6 _235/6

0 4

10 100 1000

0.1 1

p4EBP1

0 6

10 100 1000

0.1 1

Table S1.

Patients / metastatic tumors characteristics and HuR expression distribution analysis .

R. Merat et al. / Supplemental Figures and Table (2019)

Fig. S2. Regardless of the SPADE-defined clustering (still represented), two visually identified suppressed cell subpopulations in TP2Li ₂ CO ₃ cells,

delineated in Fig.4 within the viSNE maps, were analysed separately for the indicated markers (see Fig.4 legend).

Fig. S3. Clustering optimization using the knee approach among a multitude of possible parametrization of the viSNE and SPADE algorithms. The parameters used in the optimal clustering are indicated. 12 000 combinations of t-SNE embeddings and a subsequent SPADE-defined number of clusters are plotted as a function of their relative error sum of squares (SSE) to fit an optimization curve and define the curve function 𝑓 𝑥 . The optimal clustering (red arrow) correponds to the maximum value of the curvature function:

𝐾𝑓 𝑥 = ^{𝑓 ′′ (𝑥)}

(1+𝑓 ^′ (𝑥) ² ) ^1.5

R. Merat et al. / Supplemental Figures and Table (2019)

t-SNE: Perplexity: 85 Iteration: 5000 Ƞ: 10 Ɵ: 0 SPADE: 6 clusters

Number clusters

R ela tiv e SSE (% )

0 100 200 300 400 500

3 0 40 5 0 60 7 0 80 9 0 100