Using Breast Cancer Gene Expression Signatures in Clinical Practice: Unsolved Issues, Ongoing Trials and Future Perspectives

Varnier, Romain; Sajous, Christophe; Talhouet, S. de; Smentek, Colette; Péron, Julien; You, B.; Reverdy, Thibaut; Freyer, G.

doi:10.3390/cancers13194840

Cited by 19 publications

(15 citation statements)

References 137 publications

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“…The gene expression signature is measured using RT-qPCR in a FFPE viable tumor needle biopsy material, which simplifies the adoption of this approach by molecular pathology laboratories. Gene expression signatures measured directly in tumor tissue are recommended in international guidelines and routinely used to determine the prognosis and/ treatment decisions of different cancers, for example, breast 33 and prostate. 34 …”

Section: Discussionmentioning

confidence: 99%

“…31,32 Gene expression signatures have gained increasing impact in oncology as prognostic markers. For example multiple gene expression signatures are now routinely used and integrate international guidelines for patient stratification and therapy selection in breast cancer 33 and prostate cancer. 34 However, despite the enthusiasm driving the search for molecular biomarkers for HCC, [35][36][37][38][39][40][41][42][43][44][45] the lack of multicohort validation, relevant predictive power, together with the lack of focus on specific clinical decisions have precluded their widespread adoption.…”

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confidence: 99%

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A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation

Cardoso¹,

Silva

Neto³

et al. 2022

Annals of Surgery

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Objective: To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT). Background: Liver cancer is one of the most frequent causes of cancerrelated mortality. LT is the best treatment for hepatocellular carcinoma (HCC) patients but the scarcity of organs makes patient selection a critical step. In addition, clinical criteria widely applied in patient eligibility decisions miss potentially curable patients while selecting patients that relapse after transplantation. Methods: A literature systematic review singled out candidate biomarkers whose RNA levels were assessed by quantitative PCR in tumor tissue from 138 HCC patients submitted to LT ( > 5 years follow up, 32% beyond Milan criteria). The resulting 4 gene signature was combined with clinical variables to develop a decision algorithm using machine learning approaches. The method was named HepatoPredict. Results: HepatoPredict identifies 99% disease-free patients ( > 5 year) from a retrospective cohort, including many outside clinical criteria (16%-24%), thus reducing the false negative rate. This increased sensitivity is accompanied by an increased positive predictive value (88.5%-94.4%) without any loss of long-term overall survival or recurrence rates for patients deemed eligible by HepatoPredict; those deemed ineligible display marked reduction of survival and increased recurrence in the short and long term. Conclusions: HepatoPredict outperforms conventional clinical-pathologic selection criteria (Milan, UCSF), providing superior prognostic information. Accurately identifying which patients most likely benefit from LT enables an objective stratification of waiting lists and information-based allocation of optimal versus suboptimal organs.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation

Cardoso¹,

Silva

Neto³

et al. 2022

Annals of Surgery

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“…A follow-up time of 10 years is sufficient to capture both early and late relapse events [ 39 ]. In addition, the chosen clinical endpoints allow a direct comparison of the classifiers with the ROR-P categories of the MB cohort published by Xia et al [ 9 ].…”

Section: Methodsmentioning

confidence: 99%

Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer

Guttà

Morhard²,

Rehm

2023

PLoS Comput Biol

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Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein generative adversarial network (GAN) with gradient penalty and an embedded auxiliary classifier to obtain a trained GAN discriminator (T-GAN-D). Applied to 1244 patients of the METABRIC breast cancer cohort, this classifier outperformed established breast cancer biomarkers in separating low- from high-risk patients (disease specific death, progression or relapse within 10 years from initial diagnosis). Importantly, the T-GAN-D also performed across independent, merged transcriptome datasets (METABRIC and TCGA-BRCA cohorts), and merging data improved overall patient stratification. In conclusion, the reiterative GAN-based training process allowed generating a robust classifier capable of stratifying low- vs high-risk patients based on full transcriptome data and across independent and heterogeneous breast cancer cohorts.

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“…48 Most of the currently established prognostic signature scores have shown a similar ability to predict response to chemotherapy in the neoadjuvant setting with high scores, likely reflective of more proliferative tumors. 49 Further studies are underway to determine the clinical utility of multi-gene prognostic signature scores in the neoadjuvant setting however currently they are not used to guide neoadjuvant therapy. 14,50 Estrogen receptor (ER)-positive breast cancers are defined as having >1% staining for ER by IHC, however a small subgroup of patients have 1-9% ER-staining and are considered "ER low positive" and have a clinical phenotype similar to TNBC.…”

Section: Considerations For Neoadjuvant Chemotherapy Based On 21-gene...mentioning

confidence: 99%

“…Most of the currently established prognostic signature scores have shown a similar ability to predict response to chemotherapy in the neoadjuvant setting with high scores, likely reflective of more proliferative tumors. 49 Further studies are underway to determine the clinical utility of multi-gene prognostic signature scores in the neoadjuvant setting however currently they are not used to guide neoadjuvant therapy. 14 , 50 …”

Section: Introduction: the Clinical Rationale For Using Neoadjuvant C...mentioning

confidence: 99%

Optimal Choice of Neoadjuvant Chemotherapy for HER2-Negative Breast Cancer: Clinical Insights

Lucas¹,

Kelly²

2022

CMAR

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The neoadjuvant setting provides immense opportunities for translational research and drug development. The acceptance of pathological complete response (pCR) as a surrogate endpoint for clinical benefit has led to the widespread use of neoadjuvant treatment. Optimal neoadjuvant therapies are determined based on their ability to achieve the highest rates of pCR. Predicted rates of pCR for triple negative breast cancer (TNBC) treated with sequential taxane/anthracycline regimens range from 35% to 48%. With the addition of a platinum agent pCR rates of 55% are predicted. Further increases have been observed with the addition of immune checkpoint inhibitors to this standard chemotherapy backbone. In the pivotal KEYNOTE-522 clinical trial pCR rates of 65% and 69% were reported for chemotherapy plus pembrolizumab in the overall and PD-L1-positive subgroup respectively. The role of the neoadjuvant chemotherapy is less clear in hormone receptor (HR)-positive, human epidermal growth factor receptor-2 (HER2)-negative breast cancer. In general, HR–positive cancers have the least chance of achieving a pCR after neoadjuvant chemotherapy, especially if they are low-grade. If neoadjuvant chemotherapy is given for high-risk HR-positive, HER2-negative breast cancer, standard adjuvant anthracycline/taxane regimens are appropriate. Optimum endocrine therapy is the standard-of-care in the adjuvant setting regardless of pCR. There are several genomic signatures available to guide decisions regarding adjuvant chemotherapy use however these assays are not routinely used in the neoadjuvant setting. For high-risk patients meeting the criteria for the monarchE trial adjuvant abemaciclib in addition to endocrine therapy is associated with an improvement in disease free survival (DFS) at 3 years. Based on the OlympiA trial patients with germline BRCA mutations should be considered for adjuvant olaparib therapy. In this article we review neoadjuvant clinical trials that guide optimum treatment options for TNBC and HR-positive, HER2-negative breast cancer.

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Using Breast Cancer Gene Expression Signatures in Clinical Practice: Unsolved Issues, Ongoing Trials and Future Perspectives

Cited by 19 publications

References 137 publications

A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation

A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation

Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer

Optimal Choice of Neoadjuvant Chemotherapy for HER2-Negative Breast Cancer: Clinical Insights

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