Prediction of fulvestrant efficacy in patients with advanced breast cancer: retrospective-prospective evaluation of the predictive potential of a multigene expression assay

Christensen, Trine; Buhl, Anna Sofie Kappel; Christensen, Ib Jarle; Buhl, Ida Kappel; Balslev, Eva; Knoop, Ann; Danø, Hella; Glavicic, Vesna; Luczak, Adam Andrzej; Langkjer, Sven Tyge; Linnet, Søren; Jakobsen, Erik; Bogovic, Jurij; Ejlertsen, Bent; Rasmussen, Annie; Hansen, Anker; Knudsen, Steen; Jensen, Peter Buhl; Nielsen, Dorte

doi:10.1007/s12282-019-01017-7

Cited by 3 publications

(4 citation statements)

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“…Identification of pathological and molecular features predicting benefit from treatment in tumor samples of patients treated with fulvestrant has been attempted previously 21 , 22 . The Trans CONFIRM, a translational analysis within the CONFIRM trial aimed to identify clinical-pathological features and molecular signatures in the primary tumors predicting response to fulvestrant, showed in the 112 samples analyzed that only PgR and HER2 expression and a signature of 37 genes were independently associated with PFS 21 .…”

Section: Discussionmentioning

confidence: 99%

“…Of note, no correlation with ESR1 mutations on primary tumors was found 21 . Christensen et al investigated in mRNA extracted from 226 tumor samples of patients treated with fulvestrant the predictive value of a mathematical algorithm based on the expression of multiple genes (DRP) 22 . The DRP was associated although not significantly with outcome in patients treated in earlier lines and unexposed to previous adjuvant endocrine therapies 22 .…”

Section: Discussionmentioning

confidence: 99%

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Predictors of fulvestrant long-term benefit in hormone receptor-positive/HER2 negative advanced breast cancer

Torrisi

Vaira

Giordano

et al. 2022

Sci Rep

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We retrospectively investigated in women treated with fulvestrant for HR+/HER2 negative advanced breast cancer clinical, pathological and molecular features associated with long-term benefit from treatment defined as being progression-free at 18 months. Specifically, we analyzed on formalin-fixed paraffin-embedded tumor samples ESR1 and PI3KCA mutations and miRNAs profiles. 59 patients were evaluable (median age of 67 years, range 32–92). 18-month PFS rate was 27%; the lack of visceral metastases significantly predicted the likelihood of being progression-free at 18 months, while PI3KCA mutations, found in 36% of patients, were not associated with 18-month PFS. As of miRNAs, miR-549a, miR-644a, miR-16-5p were negatively while let-7c-5p was positively associated with 18-month PFS. In addition, miR-520d-3p and miR-548g-3p values were significantly lower while miR-603, miR-181a-5p and miR-199a-miR-199b-3p values were significantly higher in patients achieving 18-month PFS. In silico analysis of targets modulated by these two latter groups of miRNAs show that in patients achieving 18-month PFS the Hippo and Wnt signaling pathways were predicted to be upregulated while endocrine resistance was potentially repressed by miR-603, miR-181a-5p and miR-199a-miR-199b-3p. Our results provide additional clues on the molecular mechanisms involved in fulvestrant activity and resistance. Underlying pathways should be further elucidated and confirmed in larger cohorts.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predictors of fulvestrant long-term benefit in hormone receptor-positive/HER2 negative advanced breast cancer

Torrisi

Vaira

Giordano

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Identi cation of pathological and molecular features predicting bene t from treatment in tumor samples of patients treated with fulvestrant has been attempted previously (21,22).The TransCONFIRM, a translational analysis within the CONFIRM trial aimed to identify clinical-pathological features and molecular signatures in the primary tumors predicting response to fulvestrant, showed in the 112 samples analyzed that only PgR and HER2 expression and a signature of 37 genes were independently associated with PFS, (21). Of note, no correlation with ESR1 mutations on primary tumors was found (22). Christensen et al investigated in mRNA extracted from 226 tumor samples of patients treated with fulvestrant the predictive value of a mathematical algorithm based on the expression of multiple genes (DRP) (22).…”

Section: Discussionmentioning

confidence: 99%

“…Of note, no correlation with ESR1 mutations on primary tumors was found (22). Christensen et al investigated in mRNA extracted from 226 tumor samples of patients treated with fulvestrant the predictive value of a mathematical algorithm based on the expression of multiple genes (DRP) (22). The DRP was associated although not signi cantly with outcome in patients treated in earlier lines and unexposed to previous adjuvant endocrine therapies (23).…”

Section: Discussionmentioning

confidence: 99%

Clinical, pathological and molecular predictors of Fulvestrant long-term benefit in Hormone Receptor-positive / HER2 negative advanced breast cancer

Torrisi

Vaira

Giordano

et al. 2022

Preprint

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Purpose: The identification of factors predicting the benefit of treatments is a major goal for medical oncologists. Methods We retrospectively investigated in metastatic tumor tissues of women treated with fulvestrant for HR+/HER2 negative advanced breast cancer clinical, pathological and molecular features associated with long-term benefit from treatment defined as being progression-free at 18 months. Specifically, we analyzed ESR1 and PI3KCA mutations and miRNA profiles.Results: Fifty-nine patients were evaluable (median age 67 yrs, range 32-92). Overall mPFS was 10.7 months (range 2.2-14) while 18-month PFS rate was 27%; only the lack of visceral metastases significantly predicted the likelihood of being progression-free at 18 months (OR=0.25 95%CI 0.07-0.9 p=.035). PI3KCA mutations were found in 36% of patients and were associated with a numerically shorter mPFS but not with a lower likelihood of being progression-free at 18 months. As of miRNAs, miR-549a, miR-644a, miR-16-5p were negatively associated with 18-month PFS while let-7c-5p was positively associated with outcome. In addition, miR-520d-3p and miR-548g-3p values were significantly lower while miR-603, miR-181a and miR-199a-miR-199b-3p values were significantly higher in patients achieving 18-month PFS. In silico analysis of predicted targets suggested that miR-520d-3p and miR-548g-3p decrease might induce Hippo and Wnt signaling, while high levels of miR-603, mir-181a-5p,miR-199a-miR-199b-3p might repress endocrine resistance thus potentially promoting the response to treatment. Conclusions: Our clinical findings are consistent with literature data, while the mutation and miRNA results provide some clues on the molecular mechanisms involved in fulvestrant activity and resistance to be confirmed in larger cohorts of breast cancer patients.

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Hybrid deep model for predicting anti-cancer drug efficacy in colorectal cancer patients

Karthikeyan,

Jothilakshmi,

Suthir

2024

WEB

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Cancers are genetically diversified, so anticancer treatments have different levels of efficacy on people due to genetic differences. The main objective of this work is to predict the anticancer drug efficiency for colorectal cancer patients to reduce the mortality rates and provides immune energy for the patients. This paper proposes a novel anti-cancer drug efficacy system in colorectal cancer patients. The input data gene is normalized with the Min–Max normalization technique that normalizes the data in distinct scales. Subsequently, proposes an improved entropy-based feature to evaluate the uncertainty distribution of data, in which it induces weight to overcome the issue of computational complexity. Along with this feature, a correlation-based feature and statistical features are also retrieved. Subsequently, proposes a Recursive Feature Elimination with Hybrid Machine Learning (RFEHML) mechanism for selecting the appropriate feature set by eliminating the recursive features with the aid of hybrid Machine Learning strategies that combine decision tree and logistic regression. Also, the Gini impurity is employed for ranking the feature and selecting the maximum importance score by eliminating the least acquired importance score. Further, proposes a hybrid model for predicting the drug efficiency with the trained feature set. The hybrid model comprises of Long Short-Term Memory (LSTM) and Updated Rectified Linear Unit-Deep Convolutional Neural Network (UReLU-DCNN) model, in which DCNN is modified by updating the activation function at the fully connected layer. Consequently, the learned feature predicts the drug efficacy of anti-cancer in colorectal cancer patients by determining whether the patient is a responder or non-responder of the drug. Finally, the performance of the proposed RFEHML model is compared with other traditional approaches. It is found that the developed method has higher accuracy for each learning percentage, with values of 60LP = 92.48%, 70LP = 94.28%, 80LP = 95.24%, and 90LP = 96.86%, respectively.

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Prediction of fulvestrant efficacy in patients with advanced breast cancer: retrospective-prospective evaluation of the predictive potential of a multigene expression assay

Cited by 3 publications

References 31 publications

Predictors of fulvestrant long-term benefit in hormone receptor-positive/HER2 negative advanced breast cancer

Predictors of fulvestrant long-term benefit in hormone receptor-positive/HER2 negative advanced breast cancer

Clinical, pathological and molecular predictors of Fulvestrant long-term benefit in Hormone Receptor-positive / HER2 negative advanced breast cancer

Hybrid deep model for predicting anti-cancer drug efficacy in colorectal cancer patients

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