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

Abstract

Alpelisib is a selective inhibitor of phosphoinositide 3-kinase (PI3K)α, shown to improve outcomes for PIK3CA-mutant, hormone receptor–positive metastatic breast cancers when combined with antiestrogen therapy. To uncover mechanisms of resistance, we conducted a detailed, longitudinal analysis of tumor and plasma circulating tumor DNA (ctDNA) among such patients from a phase I/II trial combining alpelisib with an aromatase inhibitor (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.

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Fig. 1: Study design.
Fig. 2: Safety and AEs for the study.
Fig. 3: Treatment outcome and response for 44 evaluable patients.
Fig. 4: ctDNA analyses of pre- and post-progression samples.
Fig. 5: Change in ∆VAF of known pathogenic mutations in post- versus pre-treatment ctDNA specimens.

Data availability

The assembled prospective somatic mutational data from ctDNA and tumors for the entire cohort have been deposited for visualization and download in the cBioPortal for Cancer Genomics (http://cbioportal.org/) under the following URL: https://www.cbioportal.org/study/summary?id=breast_alpelisib_2020.

Source data for Figs. 15 and for Extended Data Figs. 1 and 35 are available online.

All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The custom codes written to analyze the cfDNA data are available at https://github.com/ndbrown6/MSK-BYL-NC

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Acknowledgements

This work was supported by Novartis Pharmaceuticals and National Institutes of Health awards P30 CA008748, R01 CA190642 (M.S.), R01 CA234361 (D.B.S. and S.C.), R01CA204999 (S.C.), the Breast Cancer Alliance Young Investigator Award (P.R.), Conquer Cancer Foundation Young Investigator Award (P.D.S.), the Breast Cancer Research Foundation (M.S., J.S.R.-F. and S.C.), Damon Runyon Cancer Research Foundation (S.C.), Stand Up to Cancer (M.S., Cancer Drug Combination Convergence Team), the V Foundation (M.S.), the National Science Foundation (M.S.), the Geoffrey Beene Cancer Research Center and a kind gift from B. Smith.

Author information

Affiliations

Authors

Contributions

Authors contributed as follows: study conception: P.R., M.N.D., P.D.S., M.E.M., M.S. and S.C.; data acquisition: P.R., M.N.D., P.D.S., W.T., B.T.L., N.V., S.M., K.J., B.A.C., P.S., S.Z., A.C., E.C., S.M.S., A.C., M.F.B., R.J.N., J.I.O., R.B.L., D.B.S., M.E.L., E.B. and M.E.M.; data analysis and interpretation: P.R., D.N.B., H.H.W., R.S., S.P., A.S., R.J.N., J.I.O. and J.S.R.-F.; bioinformatics and genomic analysis: P.R., D.N.B., H.H.W., R.S., P.S., A.S. and J.S.R.-F.; manuscript first draft: P.R., M.N.D., P.D.S. and M.E.M. S.C. wrote the manuscript with input from all authors. All authors were responsible for manuscript review and approval.

Corresponding authors

Correspondence to Pedram Razavi or Sarat Chandarlapaty.

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Competing interests

P.R. reports consulting or advisory role for Novartis, AstraZeneca and 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 Tmunity 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 and AstraZeneca; N.V. reports a consulting or advisory role for Novartis; K.J. reports a consulting or advisory role for ADC Therapeutics, AstraZeneca, Jounce Therapeutics, Novartis, Pfizer, Spectrum and Taiho and research funding from ADC Therapeutics (Inst), Clovis Oncology (Inst), Debio (Inst), Genentech (Inst), Novartis (Inst), Novita (Inst), Pfizer (Inst) and other relationships with Jounce Therapeutics, Novartis, Pfizer and Taiho; A.C. reports being an advisory board member for Accurate Medical and a Stockholder for Amgen; L.N. reports honoraria from Advanced Breast Cancer 4 International Consensus Conference, Bluprint Oncology Concepts, Celgene, Context Therapeutics and MCI Breast Cancer Symposium, consulting or advisory roles for Advanced Breast Cancer International Consensus Conference, Bluprint Oncology Concepts, Celgene, Context Therapeutics and MCI Breast Cancer Symposium and travel and accommodations expenses from Advanced Breast Cancer International Consensus Conference, Celgene and 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 roles for AstraZeneca, McKesson, Merck and Pfizer, research funding from Abbvie (Inst), AstraZeneca (Inst), InVitae (Inst), Medivation (Inst), Myriad Genetics (Inst) and Tesaro (Inst) and travel and 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 on the Advisory Board of the 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 and Genentech and ad hoc consulting for Novartis, Context Therapeutics, Sermonix, Eli Lilly, BMS and Revolution Medicine.

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Extended data

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. Source data

Extended Data Fig. 2 Normalized ∆VAF and ∆CCF model.

Toy model showing the calculation of a, normalized ∆VAF and b, ∆CCF 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 that is 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 (∆VAF) 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 ∆VAF. 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 while the other one expanded in the post-treatment sample (positive ∆VAF). PR: partial response, SD: stable disease, PD: progressive disease, NE: not evaluable for response. Source data

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

a, Comparison of normalized change in ctDNA mutations (∆VAF) with relative change in mutations cancer cell fractions (∆CCF). 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 approach54,55. The analysis includes 379 mutations. Pearson’s correlation coefficients (R) and two-sided p values are provided. Source data

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). Source data

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

Reporting Summary

Supplementary Table 3

Sequences of oligonucleotides used for making Y537S knock in MCF7 cell line

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Razavi, P., Dickler, M.N., Shah, P.D. et al. Alterations in PTEN and ESR1 promote clinical resistance to alpelisib plus aromatase inhibitors. Nat Cancer 1, 382–393 (2020). https://doi.org/10.1038/s43018-020-0047-1

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