| Literature DB >> 23301057 |
Sherene Loi1, Stefan Michiels, Jose Baselga, John M S Bartlett, Sandeep K Singhal, Vicky S Sabine, Andrew H Sims, Tarek Sahmoud, J Michael Dixon, Martine J Piccart, Christos Sotiriou.
Abstract
The phosphatidylinositol 3' kinase (PI3K) pathway is commonly activated in breast cancer and aberrations such as PI3K mutations are common. Recent exciting clinical trial results in advanced estrogen receptor-positive (ER) breast cancer support mTOR activation is a major means of estrogen-independent tumor growth. Hence the means to identify a responsive breast cancer population that would most benefit from these compounds in the adjuvant or earlier stage setting is of high interest. Here we study PIK3CA genotype as well as a previously reported PI3K/mTOR-pathway gene signature (PIK3CA-GS) and their ability to estimate the level of PI3K pathway activation in two clinical trials of newly diagnosed ER-positive breast cancer patients- a total of 81 patients- one of which was randomized between letrozole and placebo vs letrozole and everolimus. The main objectives were to correlate the baseline PIK3CA genotype and GS with the relative change from baseline to day 15 in Ki67 (which has been shown to be prognostic in breast cancer) and phosphorylated S6 (S240) immunohistochemistry (a substrate of mTOR). In the randomized dataset, the PIK3CA-GS could identify those patients with the largest relative decreases in Ki67 to letrozole/everolimus (R = -0.43, p = 0.008) compared with letrozole/placebo (R = 0.07, p = 0.58; interaction test p = 0.02). In a second dataset of pre-surgical everolimus alone, the PIK3CA-GS was not significantly correlated with relative change in Ki67 (R = -0.11, p = 0.37) but with relative change in phosphorlyated S6 (S240) (R = -0.46, p = 0.028). PIK3CA genotype was not significantly associated with any endpoint in either datasets. Our results suggest that the PIK3CA-GS has potential to identify those ER-positive BCs who may benefit from the addition of everolimus to letrozole. Further evaluation of the PIK3CA-GS as a predictive biomarker is warranted as it may facilitate better selection of responsive patient populations for mTOR inhibition in combination with letrozole.Entities:
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Year: 2013 PMID: 23301057 PMCID: PMC3534682 DOI: 10.1371/journal.pone.0053292
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Baseline characteristics of the two datasets used in this analysis.
| Variable | Dataset A (included in this analysis) | Dataset A Original phase II population# | Dataset B |
|
|
| 67.45 yrs |
|
| SD |
| 8.99 |
|
|
| |||
| I |
| 18 (6.7%) |
|
| II |
| 105 (39%) |
|
| III |
| 52 (19.2%) |
|
| unknown |
| 95 (35%) |
|
|
| |||
| <2.0 cm | – | – |
|
| >2.0 cm |
| 270 (100%) |
|
|
| Randomized Letrozole (2.5 mg/daily)+placebo OR; Letrozole+Everolimus (10 mg/day) | Single arm everolimus (5 mg/day) | |
|
|
| 120 |
|
|
|
| 270 |
|
|
|
| 90.7% (letrozole/everolimus) |
|
|
| 74.8% (letrozole/placebo) | ||
|
|
| 52 (57% letrozole/everolimus) |
|
|
| 25 (30% letrozole/placebo) | ||
|
|
| 35.8% (76/212) |
|
|
| Affymetrix GeneChips U133A As per standard protocols (Affymetrix, Santa Clara, CA). | Illumina HumanRef -8 v2 Expression Beadchip (Illumina, Cambridge, UK) | |
The second column compares the dataset A with the original previously published global trial population.
# as previously published in Baselga et al, JCO 2009 [11] (original cohort of which the Dataset A is a subset); Dataset B published by Sabine et al, BCR 2010 [13].
As previously defined in Dowsett et al (CCR 2007) [9];
Figure 1Receiver Operating Characteristic (ROC) curves showing the association of the PIK3CA-GS with PIK3CA mutation status. A.
Dataset A, n = 22/58 (38.5%) AUC = 0.67, 95%CI: 0.5–0.8, p = 0.04), B. Dataset B n = 6/23 (26%) AUC 0.77, 95%CI : 0.6–0.96, p = = 0.059). C and D show the individual samples for each dataset and distinguish between sequenced kinase (exon 20- squares) and helical (exon 9-circles) mutations and wild-type (WT-black) PIK3CA.
Figure 2Relative change in %Ki67 from baseline to day 15 by treatment arm.
According PIK3CA genotype in A dataset A; B dataset B According to PIK3CA-GS scores by treatment arm: regression lines for relative change in %Ki67 from baseline to day 15 C dataset A; D dataset B E Relative change in %Ki67 from baseline to day 15 by treatment arm and according to increasing tertiles of the PIK3CA-GS in dataset A; F Number of Absolute D15 responders by treatment arm and according to increasing tertiles of the PIK3CA-GS in dataset A.
Figure 3Regression lines for relative change in %Ki67 from baseline to day 15 according to PIK3CA-GS scores by treatment arm by PIK3CA genotype in dataset A. A
PIK3CA WT; B PIK3CA mutant Relative change in %Ki67 from baseline to day 15 by treatment arm and according to increasing tertiles of the PIK3CA-GS by PIK3CA genotype in dataset A C PIK3CA WT; D PIK3CA mutant.
Figure 4Associations between PIK3CA-GS and relative change in phosphorylated S6. A
Dataset B: relative percentage change in pS6 (S240) as measured by IHC from baseline to day 15 according PIK3CA genotype B Regression line for relative change in pS6 from baseline to day 15 according to PIK3CA-GS in dataset B C Dataset A: Relative percentage change in phosphorylated S6 (S240) as measured by IHC from baseline to day 15 according PIK3CA genotype and treatment arm.