Predicting therapy resistance and toxicity in breast cancer patients

A.G.J. van Rossum

Research output: ThesisDoctoral thesis 2 (Research NOT UU / Graduation UU)

Abstract

Breast cancer is one of the most common types of cancer worldwide. Despite advances in systemic treatment leading to increased survival rates, a substantial number of patients still dies of the disease. A personalized treatment strategy is needed to further improve breast cancer survival. Both prognostic and predictive biomarkers are indispensable for an individualized treatment plan. Although several prognostic markers are currently used in the clinic, predictive markers are scarce. This thesis describes the identification of predictive biomarkers for survival benefit and toxicity of systemic treatment for early or metastatic breast cancer patients in four different studies. The primary aim of the MATADOR trial was to identify a gene expression profile that predicts survival benefit of adjuvant dose dense scheduled doxorubicin and cyclophosphamide (ddAC) or conventionally scheduled docetaxel, doxorubicin and cyclophosphamide (TAC). Using RNA-sequencing data, we identified a gene expression profile with prognostic value, but limited predictive capacity. However, enrichment in immune-related gene expression appeared to be associated with favorable outcome after TAC, but not after ddAC in patients with a basal tumor. Assessing the clinical applicability of this association using tumor infiltrating lymphocytes, we found that triple negative breast cancer (TNBC) patients with high TILs (≥20%) had a numerically longer RFS when treated with TAC than treated with ddAC, while patients with low TILs (<20%) derived more benefit from ddAC, with a significant interaction. Also, we assessed previously described potential biomarkers for toxicity of the two regimens. Whereas genetic variants in FGFR4 were associated with the occurrence of febrile neutropenia and genetic variants in TECTA and GSTP1 with peripheral neuropathy, most associations could not be replicated in our cohort. The GAIN-2 study showed that intensified, sequential epirubicin, paclitaxel and cyclophosphamide (ETC) resulted in similar disease free survival (DFS) and OS as concurrently given epirubicin and cyclophosphamide followed by paclitaxel and capecitabine (EC-TX). We assessed the BRCA1-like profile as predictor of superior survival of intensified chemotherapy with ETC. However, DFS and OS were not significantly different between the BRCA1-like subgroups, nor between the treatments when split by BRCA1-like subgroup. In phase 1b of the POSEIDON study we aimed to find the recommended phase 2 dose (RP2D) of PI3K inhibitor taselisib combined with tamoxifen. In metastatic ER-positive breast cancer patient, the RP2D of taselisib was 4mg QD. Responses were more abundant in patients with PIK3CA mutant disease. Phase 2 of the POSEIDON study will indicate whether taselisib and tamoxifen prolong PFS compared with placebo and tamoxifen and whether PIK3CA mutation status can be used as predictive biomarker for survival benefit of taselisib. In the Triple-B study, we aimed to evaluate two biomarkers for survival benefit: baseline plasma VEGF receptor 2 (pVEGFR-2) levels for the addition of bevacizumab and the BRCA1-like profile for alkylating or platinum-based chemotherapy. Interim analysis showed that PFS was significantly longer in bevacizumab-treated patients compared with patients who were treated with chemotherapy only. In this small cohort, pVEGFR-2 level could not be validated as predictive biomarker for survival benefit of bevacizumab.
Original languageEnglish
Awarding Institution
  • University Medical Center (UMC) Utrecht
Supervisors/Advisors
  • Linn, Sabine, Primary supervisor
  • Kok, M., Co-supervisor
Award date18 Jun 2019
Place of Publication[Utrecht]
Publisher
Print ISBNs978-94-6375-393-7
Publication statusPublished - 18 Jun 2019

Keywords

  • breast cancer
  • predictive biomarkers
  • systemic treatment
  • gene expression profile
  • tumour-infiltrating lymphocytes
  • single nucleotide polymorphisms
  • BRCA1-like profile
  • VEGFR-2
  • PIK3CA mutation

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