| Literature DB >> 35203458 |
Gyöngyi Munkácsy1,2, Libero Santarpia3, Balázs Győrffy1,4.
Abstract
Patients with early-stage hormone receptor-positive, human epidermal growth factor receptor 2-negative (HER2-) breast cancer (BC) are typically treated with surgery, followed by adjuvant systemic endocrine therapy with or without adjuvant chemotherapy and radiation therapy. Current guidelines regarding the use of adjuvant systemic therapy depend on clinical and pathological factors, such as the morphological assessment of tumor subtype; histological grade; tumor size; lymphovascular invasion; and lymph node status combined with estrogen receptor, progesterone receptor, and HER2 biomarker profiles assessed using immunohistochemistry and in situ hybridization. Additionally, the prognostic and predictive value of tumor-infiltrating lymphocytes and their composition is emerging as a key marker in triple negative (TNBC) and HER2-enriched molecular breast tumor subtypes. However, all these factors do not necessarily reflect the molecular heterogeneity and complexity of breast cancer. In the last two decades, gene expression signatures or profiling (GEP) tests have been developed to predict the risk of disease recurrence and estimate the potential benefit of receiving adjuvant systemic chemotherapy in patients with luminal breast cancer. GEPs have been utilized to help physicians to refine decision-making process, complementing clinicopathological parameters, and can now be used to classify the risk of recurrence and tailoring personalized treatments. Several clinical trials using GEPs validate the increasing value of such assays in different clinical settings, addressing relevant clinical endpoints. Finally, the recent approval of immune checkpoint inhibitors in TNBC and the increasing use of immunotherapy in different molecular BC populations highlight the opportunity to refine current GEPs by including a variety of immune-related genes that may help to improve predicting drug response and finetune prognosis.Entities:
Keywords: adjuvant chemotherapy; classification; early breast cancer; gene expression profiling; immune-related genes; predictive; prognosis
Year: 2022 PMID: 35203458 PMCID: PMC8869155 DOI: 10.3390/biomedicines10020248
Source DB: PubMed Journal: Biomedicines ISSN: 2227-9059
Ongoing clinical studies applying gene expression profiling in early breast cancer.
| Clinical Trials (NCT#) and Study Phase | GEP Assay | Breast Cancer Population | Clinical Endpoints | Study Description |
|---|---|---|---|---|
| OPTIMA (ISRCTN | Prosigna/PAM50 | HR+/HER2−, pN1–2 or pN1mi with pT ≥ 20 mm or pN0 with pT ≥ 30 mm | iDFS | A de-escalation clinical trial to tailor adjuvant therapy for HR+/HER2− high clinical risk breast cancer |
| RxPONDER ( | Oncotype DX | HR+/HER2−, 1–3 N+, recurrence score ≤ 25 | iDFS | Role of recurrent score in predicting benefit of adjuvant chemotherapy in women with LN+ disease |
| GERICO 11/ASTER 70s ( | Genomic Grade test | HR+/HER2−, N0 or N+, age ≥ 70 years, PS ≤ 2 | OS | Adjuvant systemic treatment for ER+/HER2− breast cancers in patients over 70 years of age according to genomic grade index: chemotherapy + endocrine therapy vs. endocrine therapy |
| EXET ( | EndoPredict | HR+/HER2−, stage I–III, 0–3 N+, potential adjuvant chemotherapy, no neoadjuvant chemotherapy | DRFS | Extended Endocrine Trial: A prospective registry study testing the impact of EndoPredict on extended endocrine therapy decisions and patients’ outcomes |
| RESCUE ( | EndoPredict | HR+/HER2−, stage I–III, 0–3 N+ | DRFS | Prospective assessment of disease progression in breast cancer patients undergoing EndoPredict test—a care research study |
| DxCARTES ( | Oncotype DX | HR+/HER2−, Ki67 ≥ 20, stage II-IIIb T2–T4, N0–N2, age ≥ 18, PS ≤ 1 | Molecular differences on recurrence score between pre- and post-treatment | Neoadjuvant letrozole + palbociclib in HR+/HER2− patients with stage II–IIIb breast cancer, and pretreatment recurrence scores: 18–25 or 26–100 by Oncotype DX testing. Analysis of recurrent score and pathological feature changes at time of surgery |
| PLATO ( | MammaPrint | HR+/HER2−, stage I–IIIA, ineligible for BCS, age ≥ 19, PS ≤ 2 | Conversion rate | Multicenter study to increase BCS rate with personalized neoadjuvant strategy in HR+/HER2− breast cancer for whom BCS is not feasible. Neoadjuvant chemotherapy and endocrine therapy are conducted on genomic high and low risk patients, respectively |
| NSABP FB-13 ( | Oncotype DX | HR+/HER2−, T2–T4, candidate for neoadjuvant HT, premenopausal status, PS ≤ 1, recurrence score < 26 | Complete cell cycle arrest (percentage of patients with a Ki67 < 2.7%) | Neoadjuvant treatment for premenopausal HR+/HER2− breast cancer patients and evaluation of the clinical and biological effects of palbociclib with ovarian suppression and letrozole |
| POETIC-A ( | AIR-CIS | HR+/HER2−, postmenopausal status, ≥1.5 cm, grade 3 and/or Ki67 level ≥ 20% | Time to tumor recurrence (local or distant disease) | Preoperative endocrine therapy for individualized therapy with abemaciclib |
Aromatase Inhibitor Resistant-CDK4/6 Inhibitor Sensitive (AIR-CIS) test; Breast Conservative Surgery (BCS; DRFS, Distant Recurrence-free Survival; GEP, Gene Expression Profiling; Hormonal Therapy (HT); iDFS, invasive Disease-Free Survival; OS, Overall Survival.
Figure 1The complex interaction between cancer and the immune tumor microenvironment (TME) influences the outcome of immunotherapy and of several other anti-cancer therapies. A combined model and algorithm that captures both cancer and TME cells (including the immunosuppressive and immunostimulating cells) features through gene expression profiling may result in better prognosticators for measuring outcome and response to therapies.
Comparative approaches for gene expression profile analyses: gene arrays vs. RNA-seq.
| Gene Arrays | RNA-seq | |
|---|---|---|
| Number of genes | Whole transcriptome | Whole transcriptome |
| Section of genes | Targeted gene sequences | Entire genes |
| Bioinformatics complexity | Established tools | Multiple pipelines available |
| Alternative splicing | Can be included | Feasible but large read counts needed |
| Strength | Low cost, established pipelines, quality control straightforward | Higher specificity |
| Weakness | Gene sequence must be set upfront | Complex processing, potential GC content bias (mapping ambiguity for paralogous sequences), longer handling time, currently higher cost |
| Opportunity | High throughput, can be transformed to PCR | Can be used to identify novel transcribed regions, gene variations (e.g., mutations), assess allele-specific expression |
| Commercially available assays for breast cancer | MammaPrint, Oncotype Dx, Prosigna, etc. | None available yet |