Genetic Alterations in the PI3K/AKT Pathway and Baseline AKT Activity Define AKT Inhibitor Sensitivity in Breast Cancer Patient-derived Xenografts

Abstract

Purpose: AZD5363/capivasertib is a pan-AKT catalytic inhibitor with promising activity in combination with paclitaxel in triple-negative metastatic breast cancer harboring PI3K/AKT-pathway alterations and in estrogen receptor-positive breast cancer in combination with fulvestrant. Here, we aimed to identify response biomarkers and uncover mechanisms of resistance to AZD5363 and its combination with paclitaxel.

Experimental design: Genetic and proteomic markers were analyzed in 28 HER2-negative patient-derived xenografts (PDXs) and in patient samples, and correlated to AZD5363 sensitivity as single agent and in combination with paclitaxel.

Results: Four PDX were derived from patients receiving AZD5363 in the clinic which exhibited concordant treatment response. Mutations in PIK3CA/AKT1 and absence of mTOR complex 1 (mTORC1)-activating alterations, for example, in MTOR or TSC1, were associated with sensitivity to AZD5363 monotherapy. Interestingly, excluding PTEN from the composite biomarker increased its accuracy from 64% to 89%. Moreover, resistant PDXs exhibited low baseline pAKT S473 and residual pS6 S235 upon treatment, suggesting that parallel pathways bypass AKT/S6K1 signaling in these models. We identified two mechanisms of acquired resistance to AZD5363: cyclin D1 overexpression and loss of AKT1 p.E17K.

Conclusions: This study provides insight into putative predictive biomarkers of response and acquired resistance to AZD5363 in HER2-negative metastatic breast cancer.

Figure 1.. Therapeutic efficacy of AZD5363 in PDX correlates with patient’s response and AKT phosphorylation by IHC

(A) On the top, waterfall plot representing the tumor growth of 28 patient-derived xenografts (PDX) treated with 100mg/kg AZD5363 twice daily at intermittent dose. The percentage change from initial tumor volume is shown at the time-point of best response. +20%, −30% and −95% are marked by dotted lines to indicate the range of progressive disease (PD), stable disease (SD), partial response (PR) and complete response (CR). Below, the percentage change in tumor growth inhibition of AZD5363-treated vs. the control-treated arm is represented. Striped bars indicate PDX models derived from patients who were treated with an AZD5363-containing regimen. The box underneath summarizes the subtype classification, based on IHC or PAM50, as well as the relevant genomic alterations in the PI3K/AKT/MTOR pathway. PTEN status by IHC is also provided, based on a H-Score≤10 cut-off. Regarding copy number alterations, partially colored box indicates heterozygosity. Error bars indicate SD from at least 2 tumors. (B) Representation of PDXs vs. patient response for the four models derived from BEECH and D3610C0001 part E. The size of the circles represents the number of subjects in each response category. (C) Immunohistochemistry scoring of pAKT S473 in FFPE samples from untreated PDXs in relationship to AZD5363-treatment response. Mean of the PDXs in each group and SEM is indicated. PTEN+, H-Score>10; PTEN−, H-Score≤10; p value, unpaired t-test with Welch’s correction. The optimum cut-off point was established by the Youden’s index, which maximizes the sum of the sensitivity and specificity of the biomarker analyzing the ROC curve. Sens: sensitivity, Spec: specificity.

Figure 2.. PI3K/AKT pathway inhibition is independent of AZD5363 therapeutic efficacy

(A) Analysis of pAKT T308 and S473 levels assessed by Western blot in AZD5363-resistant (PD) versus -sensitive (SD/PR/CR) PDX in the absence (dots) and presence (squares) of AZD5363 treatment. For illustration purposes, only the mean value of each PDX was plotted, however, for the statistical analysis all available data was used. A linear mixed model was used to compare baseline levels and on-AZD5363 treatment levels between sensitive and resistant groups using Benjamini and Hochberg to adjust for multiple testing (see more details in Statistical analysis). n.s., not significant. (B) Analysis of AKT/mTORC1 pathway modulation by Western blot as in panel (A).

Figure 3.. Proliferation arrest is induced by AZD5363 in sensitive tumors

(A) Analysis of Ki67-positive cells by IHC in FFPE samples from untreated (dots) and AZD5363-treated (squares) PDXs in relationship to AZD5363-treatment response. For illustration purposes, only the mean value of each PDX was plotted, however, for the statistical analysis all available data was used. A linear mixed model was used to compare baseline levels and on-AZD5363 treatment levels between sensitive and resistant groups using Benjamini and Hochberg to adjust for multiple testing (see more details in Statistical analysis). (B) Analysis of cyclin D1 expression by IHC in the same samples as in panel (A). A linear mixed model was used to calculate the statistics comparing untreated and treated samples in each group (Benjamini and Hochberg adjustment method for multiple testing, see more details in Statistical analysis). The Youden’s index, which maximizes the sum of the test sensitivity and specificity, was used to select the optimum cut-point.

Figure 4.. Acquired resistance to AZD5363 is associated with cyclin D1 amplification in PDX225 or by loss of AKT1p.E17Kin patient 433

(A) Tumor growth inhibition of the AZD5363-sensitive PDX225, acquisition of drug resistance and confirmation of lack of response to AZD5363 after serial transplantation. (B) Analysis of the copy-number alterations (CNA) detected in AZD5363 sensitive (X axis) and resistant (Y axis) PDX225. Acquired CNA are highlighted in red. (C) Immunohistochemistry staining of Cyclin D1 and Ki67, in a representative FFPE tumor from AZD5363 sensitive (top) and resistant (bottom) PDX225 tumor. Scale bars: 100 μm. (D) Analysis of AKT1p.E17K allele frequency and levels of tumor markers in serum before treatment initiation, during and at progression in the same patient as the PDX data in panel (E). (E) Analysis of the variant allele frequency (VAF) in the AZD5363-sensitive PDX433.2 (derived from Pt433, on-treatment) vs. PDX433.3 (derived from Pt433, post-AZD5363). Acquired changes in VAFs are highlighted in red.

Figure 5.. Therapeutic efficacy of AZD5363 in combination with paclitaxel is observed in TP53wt PDX

(A) Waterfall plot representing the tumor growth of 15 PDXs treated with paclitaxel (top) or with paclitaxel in combination with AZD5363 (bottom). Percentage change from initial tumor volume at the time point of best response is shown. +20%, −30% and −95% are marked by dotted lines to indicate the range of PD, SD, PR and CR. Striped bars indicate PDX models derived from patients who were treated with an AZD5363-containing regimen. The box underneath summarizes the molecular subtype, TP53 status and whether the patient had received taxanes before PDX establishment. Error bars indicate SD from at least two tumors. (B) Quantification of Ki67 in two combination-sensitive models (PDX093 and PDX292). One-way ANOVA with Tukey’s multiple comparison test was used to calculate the statistics. (C) RNA expression of Coutant et al. mutant TP53-related gene signature in paired samples from pre-, on- and post-AZD5363 biopsies (labeled as 1, 2 and 3, respectively) of patients 279, 292 and 320 from the BEECH trial.