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Correction: A distinct p53 target gene set predicts for response to the selective p53-HDM2 inhibitor NVP-CGM097

Correction
Cite this article as: eLife 2016;5:e19317 doi: 10.7554/eLife.19317
4 figures and 1 table

Figures

TP53 wild-type status is necessary but not sufficient to predict sensitivity to NVP-CGM097 and NVP-CFC218.

(A) Chemical structure of NVP-CGM097 and NVP-CFC218. (B) In vitro activity of NVP-CFC218 and NVP-CGM097 in TR-FRET binding assay (a) and cellular proliferation assay in human cancer cell lines (b). Data are expressed as concentration causing 50% inhibition and shown as mean ± SD from multiple (n≥8) independent experiments. (c) Selectivity is determined by the ratio of GI50 obtained using the HCT-116 p53-null and the HCT-116 p53WT isogenic pair of cell lines. (d) Selectivity is determined by the ratio of GI50 obtained using SAOS-2 (p53-null) and SJSA-1 (p53WT and HDM2-amplified) osteosarcoma pair of cell lines. (C and D) Scatter plot showing IC50 values expressed in μM of NVP-CFC218 in cell viability assays of p53 wild-type cell lines (C) and p53 mutated cell lines (D), colored by their response to NVP-CFC218. The data used to generate these plots, as well as cell line identity are available in Figure 1—source data 1. (E) Contingency table indicating the total number of sensitive and insensitive cell lines to NVP-CFC218. The p-value of 5.1 × 10–23 shows a significant association between sensitivity to NVP-CFC218 and TP53 wild-type status. (F) Main enriched compound target p-values from the Global Compound Selectivity Analysis. p-values are minus log10 transformed. The red color refers to compounds that are more selective in the wild-type p53 (p53WT) strata than in the mutated p53 (p53MUT) strata. The blue color indicates the reverse profile. Brighter colors indicate which target classes pass the 0.25 FDR cut-off. The length of each red/blue segment corresponds to the proportion of p53WT selective/p53MUT selective compounds in each target class.

Figure 1—source data 1

List of cell lines tested for their sensitivity to NVP-CFC218 (n=356).

https://cdn.elifesciences.org/articles/19317/elife-19317-fig1-data1-v1.docx
Figure 2 with 1 supplement
Predictive modeling of NVP-CFC218 chemical sensitivity from CCLE expression data.

(A) Prediction accuracy estimation by bootstrapping analysis upon increasing feature set size. Estimates are averaged over 20 bootstrapping repeats. The maximum accuracy is observed for 17 features (red dot). Sensitivity and specificity are shown in Figure 2—figure supplement 1. The averaged data used to generate the accuracy, sensitivity and specificity plots are available in Figure 2—source data 1. (B) Feature occurrence frequencies of 13 feature selections in 100 bootstrapping repeats. Fifty five features were selected at list once. The 13 most frequently occurring features (red dots) were selected more than 30 times. (C) ROC curves for the three predictive models under comparison, i.e. the p53 mutation status, the 215-feature and the 13-gene signature models. (D) Precision-Recall plot for the 13-gene signature. Five curves are typically shown since cross-validation was repeated 5 times. (E) Performance estimates of the three compared predictive models: AUC (Area Under the Curve, from the ROC curve shown in C), Sensitivity (fraction of correctly predicted sensitive cell lines), Specificity (fraction of correctly predicted insensitive cell lines), PPV (positive predicted value, fraction of sensitive cell lines predicted as such) and NPV (negative predictive value, fraction of insensitive cell lines predicted as such). Measures are averaged over the 5 iterations of 5-fold cross-validations.

Figure 2—figure supplement 2
Prediction performance of NVP-CFC218 in p53WT CCLE cell lines.

The measures of prediction accuracy are calculated restricted to the wild-type TP53 subset of CCLE cell lines (n=68). (A) ROC curves for the 215-feature set and the 13-gene signature predictive models. (B) Precision-Recall plot for the 13-gene signature. Five curves are typically shown since cross-validation was repeated 5 times. (C) Performance estimates of the 13-gene signature and TP53 mutation status as predictive models.

Figure 3—figure supplement 1
Validation of the predictive 13-gene signature in p53WT cell lines.

The same visualization as in Figure 3 is shown restricted to the wild-type p53 cell lines. A total of 42 or 30 p53WT cell lines were tested against NVP-CGM097 (A) or NVP-CFC218 (B), respectively, in a 3-day proliferation assay. The data used to generate the waterfall plots and cell line details are available in Figure 3—source data 1. The performance values of the 13-gene signature were derived from this experiment, and are shown in (C).

Figure 3—source data 1

Sensitivity prediction and sensitivity to NVP-CFC218 and NVP-CGM097 of an external set of cell lines (n=52).

https://cdn.elifesciences.org/articles/19317/elife-19317-fig3-data1-v1.docx
Validation of the predictive 13-gene signature in patient-derived tumor xenografts (PDX).

PDX models were established by subcutaneous implantation in nude mice of surgical tumor tissues from treatment-naive cancer patients. 55 PDX models of multiple cancer types were used for the study: melanoma (n = 19), colorectal cancer (n = 17), liposarcoma (n = 3), renal cell carcinoma (n = 3), hepatocellular carcinoma (n = 7), breast cancer (n = 1), pancreatic cancer (n = 2) and lung cancer (n = 3). Tumor-bearing mice (n = 4/dosing group/model) received vehicle or 100 mg/kg of NVP-CGM097 daily for 4 weeks. The response is reported as percentage change in tumor volume at a given day of treatment relative to day 0 (start of treatment). The cut-off used for sensitivity to NVP-CGM097 was based on RECIST adapted for full tumor volume measurement. The sensitivity call was made at the maximum effect time-point during the treatment period. Sensitivity predictions for each PDX model were generated using the 13-gene signature (Figure 6—source data 2). (A) Waterfall plot showing the tumor response to NVP-CGM097 for all the PDX models in the study (n = 55), color-coded by the prediction output. (B) The same visualization as in (A) is shown restricted to the wild-type p53 PDX models (n = 33). The data used to generate the waterfall plots and PDX model details are available in Figure 6—source data 1. The performance values of the 13-gene signature were derived from this experiment, and are shown in (C).

Figure 6—source data 1

Sensitivity prediction and sensitivity to NVP-CGM097 of a set of in vivo PDX models (n=55).

https://cdn.elifesciences.org/articles/19317/elife-19317-fig6-data1-v1.docx

Tables

Table 1

Summary of reannotated p53 mut cell lines by Sonkin in the ‘discovery’ and ‘validation sets, based on 'inactivating p53 alterations'.

#Cell line
name
TP53 inactivating mutation(s)Alternative reads/reference readsTP53 mRNA
(MAS5-150 201746_at)
TP53 CN
ratio
Discovery set
1HPB-ALLp.C124*183/21280.55
2NCI-H1373p.E339*8/0160.77
3SW403p.E51*5/0530.68
4U-937p.G187_splice75/0150.84
5HLFp.G244A69/02521.13
6HEL 92.1.7p.M133K22/01790.76
7BICR 31p.173_174VR>G267/15370.9
8SCaBERp.R110L3/02150.75
9SU-DHL-8p.R249G, p.Y234N26/31, 31/2718040.94
10KMM-1p.S241F43/68460.73
11HCC-78p.S241F76/02010.52
12ES-2p.S241F79/01540.97
13JHOS-2p.S261_splice47/511980.65
14KMS-1120.02
15COR-L237950.03*
16MDA-MB-45360.07
17MPP 8910.1
18NCI-H35870.1
19SK-N-AS10.5
20MG-6310.7
21COV64410.74
22NCI-H245210.9
23PE/CA-PJ4911.01
24KP431.01
25NCI-H2347200.54
26NCI-H2172240.93
27L3.3270.72
28VMRC-RCZ290.52
29RERF-LC-AI311.31
Validation set
1KASUMI-1p. R248Q52/02650.54
2COLO-818p. C135R34/02571.14
3IGR-37p. C229fs110/1190.59
4HCC202p. T284fs35/4140.8
5EFM-192Ap. F270fs7/1100.74
6NCI-H1568p. H179R89/12020.82
7COLO-783p. P27L38/03041.05
8GA-10p. I232N, p. P152L94/50, 52/764930.81
9VMRC-RCWp. I332_splice192/68631.65
10JHH-5p. PPQH190del107/412721.03
11HDLM-210.94
12RERF-LC-KJ251.3
  1. *deletion of exons 2,3,4; in red: cells that are selected as p53 mut in our corrected version that were initially considered as p53 wt in our original manuscript.

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