Alterations in PTEN and ESR1 promote clinical resistance to alpelisib plus aromatase inhibitors

Abstract

Alpelisib is a selective inhibitor of PI3Kα, shown to improve outcomes for PIK3CA mutant, hormone receptor positive (HR+) metastatic breast cancers (MBC) when combined with antiestrogen therapy. To uncover mechanisms of resistance, we conducted a detailed, longitudinal analysis of tumor and plasma circulating tumor DNA among such patients from a phase I/II trial combining alpelisib with an aromatase inhibitor (AI) (NCT01870505). The trial's primary objective was to establish safety with maculopapular rash emerging as the most common grade 3 adverse event (33%). Among 44 evaluable patients, the observed clinical benefit rate was 52%. Correlating genetic alterations with outcome, we identified loss-of-function PTEN mutations in 25% of patients with resistance. ESR1 activating mutations also expanded in number and allele fraction during treatment and were associated with resistance. These data indicate that genomic alterations that mediate resistance to alpelisib or antiestrogen may promote disease progression and highlight PTEN loss as a recurrent mechanism of resistance to PI3Kα inhibition.

Conflict of interest statement

Competing Interests P.R. reports consulting or advisory role for Novartis, AstraZeneca, Foundation Medicine, and institutional research support from Illumina and GRAIL; M.N.D. is an employee of Eli Lilly, P.D.S. reports consulting with Tmnity and research funding from AstraZeneca; B.T.L. reports consulting/advisory board for Genentech, ThermoFisher Scientific, Guardant Health, Hengrui Therapeutics, Mersana Therapeutics, Biosceptre Australia and institutional research support from Illumina, GRAIL, Genentech, AstraZeneca; N.V. reports consulting or advisory role for Novartis; K.J. reports consulting or advisory role for ADC Therapeutics; AstraZeneca; Jounce therapeutics; Novartis; Pfizer; Spectrum; Taiho, research funding from ADC Therapeutics (Inst); Clovis Oncology (Inst); Debio (Inst); Genentech (Inst); Novartis (Inst); Novita (Inst); Pfizer (Inst) and other relationship with Jounce Therapeutics; Novartis; Pfizer; Taiho; A.C. reports being an advisory board member for Accurate Medical a Stockholder for Amgen; L.N. reports honoraria from Advanced Breast Cancer 4 International Consensus Conference; Bluprint Oncology Concepts; Celgene; Context Therapeutics; MCI Breast Cancer Symposium, consulting or advisory role for Advanced Breast Cancer International Consensus Conference; Bluprint Oncology Concepts; Celgene; Context Therapeutics; MCI Breast Cancer Symposium and travel, accommodations, expenses from Advanced Breast Cancer International Consensus Conference; Celgene; MCI Breast Cancer Symposium; A.S., R.J.N., J.I.O., and R.B.L. are employees and stockholders of Guardant Health; M.E.R. reports honoraria from AstraZeneca, consulting or advisory role for AstraZeneca; McKesson; Merck; Pfizer, research funding from Abbvie (Inst); AstraZeneca (Inst); InVitae (Inst); Medivation (Inst); Myriad Genetics (Inst); Tesaro (Inst), and travel, accommodations, expenses from AstraZeneca; M.E.L. reports serving as a consultant or speaking for Legacy Healthcare Services, Adgero Bio, Amryt, Celldex, Debiopharm, Galderma, Johnson & Johnson, Novocure, Lindi, Merck, Sharp and Dohme Corp, Helsinn Healthcare SA, Janssen Research & Development LLC, Menlo Therapeutics, Novartis, Roche, AbbVie, Boehringer Ingelheim, Allergan, Amgen, E. R. Squibb & Sons, LLC, EMD Serono, AstraZeneca, Genentech, Leo Pharma, Seattle Genetics, Bayer, Manner, SAS, Lutris, Pierre Fabre, Paxman Coolers, Adjucare, Dignitana, Biotechspert, Teva Mexico, Parexel, OnQuality, Novartis, Our Brain Bank, and Takeda Millenium; and receiving research funding from Veloce, US Biotest, Berg, BMS, Lutris, Paxman, and Novocure; J.S.R-F reports personal/consultancy fees from VolitionRx, Page.AI, Goldman Sachs, Grail, Ventana Medical Systems, Roche, Genentech and Invicro; D.B.S. received honoraria and consulted for Pfizer, Loxo, Vivideon, Illumina and Lilly Oncology; M.S. is in the Advisory Board of Bioscience Institute and Menarini Ricerche, received research funds from Puma Biotechnology, Daiichi Sankyo, Targimmune, Immunomedics and Menarini Ricerche, is a co-founder of Medendi Medical Travel and received honoraria from Menarini Ricerche and ADC; S.C. reports institutional research funding from Novartis, Eli Lilly, Sanofi, Daiichi Sankyo, Genentech and Ad hoc consulting for Novartis, Context Therapeutics, Sermonix, Eli Lilly, BMS, and Revolution Medicine. The other coauthors report no competing interests.

Figures

Extended Data Fig. 1. Characterization of ctDNA variants.

a) Comparison of variant allele fraction (VAF) of PIK3CA mutations measured using the targeted ctDNA assay (y-axis) and ddPCR (x-axis). cfDNA samples extracted from 65 samples (35 patients) with canonical hotspot PIK3CA mutations were subjected to ddPCR. An aliquot of the same cfDNA isolate was used for targeted DNA assay (G360 assay, Guardant Health, CA). b) ctDNA VAFs of all the somatic variants detected in ctDNA restricted to 38 patients with available tumor next generation sequencing results. Colors indicate the altered gene and black borders indicate whether the alteration was detected in the tumor tissue. c) Comparison of VAF of mutations detected in the pre-treatment and post-treatment ctDNA samples of six patients with evaluable paired ctDNA specimens and PTEN loss-of-function mutations in either sample. The colors of the circles indicate mutated gene.

Extended Data Fig. 2. Normalized ΔVAF and ΔCCF model.

Toy model showing the calculation of a) normalized ΔVAF and b) ΔCCF for a fictitious pair of pre- and post-treatment ctDNA samples. Three mutations are shown; PIK3CA, ESR1 and NF1. The canonical PIK3CA mutation is expected to be clonal in the pre- and post-treatment ctDNA samples. The difference in VAF of PIK3CA between the pair is a surrogate of difference in purity and tumor burden. To quantify the change in VAF or CCF of other mutations in addition to what is expected from the difference in purity or tumor burden, we calculate the Log10 difference between the post-treatment cfDNA and the value obtained from the regression i.e. in the examples above, observed post-treatment NF1 VAF – expected post-treatment NF1 VAF. In both cases, the regression has zero y-intercept.

Extended Data Fig. 3. Change in variant allele fraction (ΔVAF) of all mutations in post- vs pre-treatment ctDNA specimens.

Heatmap of change in VAF comparing the post-treatment with pre-treatment ctDNA of 32 evaluable patients (n=64 samples) normalized according to the change in ctDNA fraction as a proxy of change in disease burden. The size of the boxes represents the relative change and the color gradient of the boxes represent increase or decrease in ΔVAF. The top section shows the time to treatment failure (weeks), reason off study, and the best response on therapy. Multiple mutations in a same gene are indicated, for example patient #47 had two different PIK3CA mutations with one mutation having no change whilst the other one expanded in the post-treatment sample (positive ΔVAF). PR: partial response, SD: stable disease, PD: progressive disease, NE: not evaluable for response.

Extended Data Fig. 4. Comparison of normalized change in ctDNA mutations.

a) Comparison of normalized change in ctDNA mutations (ΔVAF) with relative change in mutations cancer cell fractions (ΔCCF). The analysis includes 256 mutations. b) Comparison of estimated CCF based on combined tumor and ctDNA approach (Methods) with CCF estimated based on ctDNA-only approach,. The analysis includes 379 mutations. Pearson’s correlation coefficients (R) and two-sided p values are provided.

Extended Data Fig. 5. Mutational signatures in two hypermutated cases with acquired PTEN mutations.

96 base substitution profiles of pre-treatment ctDNA samples from the two hypermutated cases that eventually developed PTEN mutations under therapy showing dominant APOBEC signatures (Signatures 2 and 13).

Fig. 1.. Study design.

a) Treatment schema and dosing cohorts of the study arms. b) CONSORT diagram of patients included in the TTF, CBR and ORR evaluation. c) Distribution of PIK3CA mutations for the study cohort positioned by their amino acid coordinates across the protein domain based on the pre-treatment tumor sequencing results. Exp: expansion cohort, TFF: time to treatment failure, CBR: clinical benefit rate, ORR: objective response rate, NGS: next generation sequencing.

Fig. 2.. Safety and adverse events for the study.

a) Treatment associated adverse events for 51 patients included in the study. b) Presentation of maculopapular rash in the trunk. c) Skin histology demonstrating a superficial lympho-histiocytic infiltrate with eosinophils.

Fig. 3.. Treatment outcome and response for 44 evaluable patients.

a) The top section shows the PIK3CA mutation status of the baseline tumors. The top barplot represents the percent best change from baseline in the target lesions assessed by the RECIST version 1.1 criteria. The bars are color coded based on the tumor tissue baseline PTEN or ESR1 mutation status. The middle section shows best overall response. The bottom barplot shows progression free survival color coded by the study arms. The lower section indicates the dose of alpelisib. b) Baseline, on- treatment and post-progression CT scan of the patient with a sustained complete response in the paravertebral mass. c) Cancer cell fractions (CCF) with binomial exact 95% confidence intervals calculated around the point estimate of CCF of PIK3CA mutations in 32 patients with tumor sequencing data sufficient for clonality analysis. Patients are ordered by increasing clinical benefit and weeks on therapy. P value is calculated based on two-sided Fisher’s exact test of clonality (clonal vs subclonal) and clinical benefit. d) Pre-treatment tumor tissue genomic alterations and clinical benefit in 38 evaluable patients with available next-generation sequencing results (one patient with evaluable genomic data was clinically unevaluable). PTEN loss of function alterations and ESR1 activating mutations were observed exclusively in baseline tumor tissue samples of patients with no clinical benefit. P values are based on two-sided Fisher’s exact test; *p= 0.0967, **p= 0.0067, the rest not significant.

Fig. 4.. ctDNA analyses of pre- and post-progression samples.

a) Comparison of variant allele fraction (VAF) of mutations detected in the pre-treatment and post-treatment ctDNA samples of 32 patients with evaluable paired ctDNA specimens (n=64). The colors of the circles indicate mutated gene, PIK3CA, AKT1, PTEN, ESR1, and others. b) Change in VAF of PTEN mutations (Y-axis) and time to treatment failure (X-axis). The colors of the lines indicate each individual patient. The PTEN mutations are labelled next to the circles and the previously identified loss-of-function mutations are indicated (shown in crimson). c) The median of pairwise change between the pre- and the post-treatment VAFs of mutations in each gene (including all the genes with a minimum frequency of 5 mutations in the cohort and excluding PIK3CA) for 32 patients with evaluable paired ctDNA specimens (n=64). The q values are calculated based on Benjamini and Hochberg method correction of two-sided Wilcoxon signed rank test p values. ESR1 and PTEN were the two significant genes (both q=0.0278) d) Change in pre- and post-treatment VAF of individual ESR1 mutations identified in ctDNA. The p value is calculated based on two-sided Wilcoxon signed rank test. The analysis includes 26 ESR1 mutations identified in 12 patients e) Proliferation of ESR1 Y537S mutant and parental MCF7 tumors treated with vehicle or 25mg/kg alpelisib with or without estradiol pellet. The error bars represent standard errors and the center represent the mean. Each experiment includes 10 animals. P values are two-sided and are based on a 2-way ANOVA test with Šidák correction for multiple comparisons.

Fig. 5.. Change in variant allele fraction (ΔVAF) of known pathogenic mutations in post- vs pre-treatment ctDNA specimens.

Heatmap of change in VAF comparing the post-treatment with pre-treatment ctDNA of 32 evaluable patients (n=64 samples) normalized according to the change in ctDNA fraction as a proxy of change in disease burden. The size of the boxes represents the relative change and the color gradient of the boxes represent increase or decrease in ΔVAF. The top section shows the time to treatment failure (weeks), reason off study, and the best response on therapy. Multiple mutations in a same gene are indicated, for example patient #47 had two different PIK3CA mutations with one mutation having no change whilst the other one expanded in the post-treatment sample (positive ΔVAF). PR: partial response, SD: stable disease, PD: progressive disease, NE: not evaluable for response.