Clinical tumor stage is the most important predictor of pathological complete response rate after neoadjuvant chemotherapy in breast cancer patients

Goorts, B.; Nijnatten, Thiemo J A van; Munck, Linda de; Moossdorff, M.; Heuts, E. M.; Boer, Maaike de; Lobbes, Marc; Smidt, Marjolein L.

doi:10.1007/s10549-017-4155-2

Cited by 86 publications

(81 citation statements)

References 23 publications

(25 reference statements)

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“…Although the TNBC-RPS showed good prediction power in TNBC patients, we were concerned that tumor stage or grade might have confounded these findings; it has been reported that TNBC patients with a more advanced tumor stage or grade tend to have better response to NCT. 59 To evaluate this, we first examined the predictive ability of the TNBC-RPS in the validation metadata for each tumor stage. By calculating the TNBC-RPS for each individual stage in both pCR and RD patients (Table S9), we found that pCR patients had significantly higher RPS than RD patients (P = .007, Stage I; P = 9e-6, Stage II; P = .004, Stage III; P = 1e-6, Stage IV; Table S9).…”

Section: The Tnbc-rps Predicts Nct Response In Each Clinical Stagementioning

confidence: 99%

Gene signature‐based prediction of triple‐negative breast cancer patient response to Neoadjuvant chemotherapy

2020

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Neoadjuvant chemotherapy is the current standard of care for large, advanced, and/or inoperable tumors, including triple‐negative breast cancer. Although the clinical benefits of neoadjuvant chemotherapy have been illustrated through numerous clinical trials, more than half of the patients do not experience therapeutic benefit and needlessly suffer from side effects. Currently, no clinically applicable biomarkers are available for predicting neoadjuvant chemotherapy response in triple‐negative breast cancer; the discovery of such a predictive biomarker or marker profile is an unmet need. In this study, we introduce a generic computational framework to calculate a response‐probability score (RPS), based on patient transcriptomic profiles, to predict their response to neoadjuvant chemotherapy. We first validated this framework in ER‐positive breast cancer patients and showed that it predicted neoadjuvant chemotherapy response with equal performance to several clinically used gene signatures, including Oncotype DX and MammaPrint. Then, we applied this framework to triple‐negative breast cancer data and, for each patient, we calculated a response probability score (TNBC‐RPS). Our results indicate that the TNBC‐RPS achieved the highest accuracy for predicting neoadjuvant chemotherapy response compared to previously proposed 143 gene signatures. When combined with additional clinical factors, the TNBC‐RPS achieved a high prediction accuracy for triple‐negative breast cancer patients, which was comparable to the prediction accuracy of Oncotype DX and MammaPrint in ER‐positive patients. In conclusion, the TNBC‐RPS accurately predicts neoadjuvant chemotherapy response in triple‐negative breast cancer patients and has the potential to be clinically used to aid physicians in stratifying patients for more effective neoadjuvant chemotherapy.

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Section: The Tnbc-rps Predicts Nct Response In Each Clinical Stagementioning

confidence: 99%

Gene signature‐based prediction of triple‐negative breast cancer patient response to Neoadjuvant chemotherapy

2020

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“…Among other means of predicting response to NAT, such as using tumor stage and receptor status 19 , features extracted from pre-treatment MRI have shown promise for this purpose 8,18,20–28 . Using MRI could provide a fast and noninvasive way to further stratify patients at no additional cost to patients who already require MRI as part of a standard preoperative workup for breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set

Cain

Saha

Harowicz

et al. 2018

Breast Cancer Res Treat

142

116

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Purpose To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients. Methods Institutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI. A comprehensive set of 529 radiomic features was extracted from each patient’s pretreatment MRI. The patients were divided into equal groups to form a training set and an independent test set. Two multivariate machine learning models (logistic regression and a support vector machine) based on imaging features were trained to predict pCR in (a) all patients with NAT, (b) patients with neoadjuvant chemotherapy (NACT), and (c) triple negative or human epidermal growth factor receptor 2-positive (TN/HER2+) patients who had NAT. The multivariate models were tested using the independent test set, and the area under the receiver operating characteristics (ROC) curve (AUC) was calculated. Results Out of the 288 patients, 64 achieved pCR. The AUC values for predicting pCR in TN/HER+ patients who received NAT were significant (.707, 95%CI: 0.582–0.833, p < 0.002). Conclusions The multivariate models based on pre-treatment MRI features were able to predict pCR in TN/HER2+ patients.

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“…Individual responses to NAC vary widely, depending on molecular subtype ( i.e. estrogen receptor (ER) negative and human epidermal growth factor receptor 2 (HER2) positive tumors respond better) [ 2 ], tumor size [ 3 ] and treatment regimen [ 4 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer

et al. 2018

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BackgroundThe main purpose was to investigate the correlation between magnetic resonance imaging (MRI)-based response patterns halfway through neoadjuvant chemotherapy and immunotherapy (NAC) and pathological tumor response in patients with breast cancer. Secondary purposes were to compare the predictive value of MRI-based response patterns measured halfway through NAC and after NAC and to measure interobserver variability.MethodsAll consecutive patients treated with NAC for primary invasive breast cancer from 2012 to 2015 and who underwent breast MRI before, halfway through (and after) NAC were included. All breast tumors were reassessed on MRI by two experienced breast radiologists and classified into six patterns: type 0 (complete radiologic response); type 1 (concentric shrinkage); type 2 (crumbling); type 3 (diffuse enhancement); type 4 (stable disease); type 5 (progressive disease). Percentages of tumors showing pathological complete response (pCR), > 50% tumor reduction and > 50% tumor diameter reduction per MRI-based response pattern were calculated. Correlation between MRI-based response patterns and pathological tumor reduction was studied with Pearson’s correlation coefficient, and interobserver agreement was tested with Cohen’s Kappa.ResultsPatients (n = 76; mean age 53, range 29–72 years) with 80 tumors (4 bilateral) were included. There was significant correlation between these MRI-based response patterns halfway through NAC and tumor reduction on pathology assessment (reader 1 r = 0.33; p = 0.003 and reader 2 r = 0.45; p < 0.001). Type-0, type-1 or type-2 patterns halfway through NAC showed highest tumor reduction rates on pathology assessment, with > 50% tumor reduction in 90%, 78% and 65% of cases, respectively. In 83% of tumors with type 0 halfway through NAC, pathology assessment showed pCR. There was no significant correlation between MRI-based response patterns after NAC and tumor reduction rates on pathology assessment (reader 1 r = − 0.17; p = 0.145 and reader 2 r = − 0.17; p = 0.146). In 41% of tumors with type 0 after NAC, pathology assessment showed pCR.ConclusionMRI-based response patterns halfway through NAC can predict pathologic response more accurately than MRI-based response patterns after NAC. Complete radiological response halfway NAC is associated with 83% pCR, while complete radiological response after NAC seems to be correct in only 41% of cases.Electronic supplementary materialThe online version of this article (10.1186/s13058-018-0950-x) contains supplementary material, which is available to authorized users.

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Clinical tumor stage is the most important predictor of pathological complete response rate after neoadjuvant chemotherapy in breast cancer patients

Cited by 86 publications

References 23 publications

Gene signature‐based prediction of triple‐negative breast cancer patient response to Neoadjuvant chemotherapy

Gene signature‐based prediction of triple‐negative breast cancer patient response to Neoadjuvant chemotherapy

Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set

MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer

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