&lt;p&gt;Predictors of Neoadjuvant Chemotherapy Response in Breast Cancer: A Review&lt;/p&gt;

Xu, Weilin; Chen, Xiu; Deng, Fei; Zhang, Jian; Zhang, Wei; Tang, Jinhai

doi:10.2147/ott.s253056

Cited by 18 publications

(11 citation statements)

References 115 publications

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“…However, not all BC patients benefit from the neoadjuvant chemotherapy (NACT) setting and, therefore, it is critical to differentiate between the subjects that will respond positively and those who will not, in order to choose alternative and more effective therapies. Regarding NACT efficacy, recent studies tackle the relationship between BC phenotypes and treatment outcomes [7][8][9], revealing pathological complete response (pCR) as a surrogate biomarker of response and survival [10,11]. Nevertheless, this procedure is invasive and timeconsuming.…”

Section: Introductionmentioning

confidence: 99%

Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach

et al. 2022

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Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography−high‐resolution mass spectrometry (LC‐HRMS)‐based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA–simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple‐negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted‐based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow‐up in the clinical practice.

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Section: Introductionmentioning

confidence: 99%

Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach

et al. 2022

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“…6 Furthermore, in many cancers, including breast (BRCA) and non-small cell lung cancer (NSCLC), there is a current lack of validated predictive and prognostic biomarkers capable of definitively guiding first-line chemotherapeutic interventions. [7][8][9] Tumor angiogenesis has long been shown to be crucial in cancer progression. Through influence over the body's machinery for synthesizing vasculature, a tumor will initiate the rapid formation of new blood vessels from preexisting vessels in its surrounding peritumoral environment.…”

Section: Introductionmentioning

confidence: 99%

Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

Braman

Prasanna

Bera

et al. 2022

Clinical Cancer Research

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Purpose: Tumor-associated vasculature is distinguished its convolutedness, leakiness, and chaotic architecture, which facilitates the creation of a treatment resistant tumor microenvironment. Measurable differences in these attributes might help stratify patients by potential benefit of systemic therapy. In this work, we present a new category of radiomic quantitative tumor-associated vasculature (QuanTAV) biomarkers and investigate their ability to predict outcomes across multiple cancers, imaging modalities, and treatment regimens. Experimental Design: We isolated tumor vasculature and extracted mathematical measurements of twistedness and organization using routine pre-treatment radiology (computed tomography or contrast-enhanced MRI) from 558 patients total, who received one of four therapeutic intervention strategies for breast (n=371) or non-small cell lung cancer (NSCLC, n=187). QuanTAV response scores and risk scores/groups were derived and then assessed for response and survival prediction for each therapy. Results:Classifiers of QuanTAV measurements significantly (p<.05) predicted response in held out testing cohorts alone (AUCs=0.63-0.71). Similarly, QuanTAV risk scores were prognostic of recurrence-free survival in treatment cohorts for breast cancer chemotherapy-only (p=0.0022, HR=1.25, 95% CI 1.08-1.44, C-index=.66) and NSCLC chemoradiation+surgery (p=0.039, HR=1.28, 95% CI 1.01-1.62, C-index=0.66) cohorts. QuanTAV high/low risk groups were independently prognostic among all treatments, including chemotherapy-only NSCLC patients (p=0.034, HR=2.29, 95% CI 1.07-4.94, C-index=0.62). Conclusions: Across domains, we observed an association of vascular morphology on CT and MRI – as captured by metrics of vessel curvature, torsion, and organizational heterogeneity – and treatment outcome. Our findings suggest the potential of shape and structure of the tumor-associated vasculature as biomarkers for multiple cancers and treatments.

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“…No specific methods have been established to predict the treatment response of patients receiving NAC; therefore, many predictors have been proposed to predict the prognosis of BC patients and select appropriate treatment strategies ( Xu et al, 2020 ). In addition, multi-omics analysis has become a burgeoning approach to identify solid tumors for different molecular characteristics and clinical outcomes in recent years ( Su et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Correlation Between Immune-Related Genes and Tumor-Infiltrating Immune Cells With the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer

et al. 2022

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Background: In the absence of targeted therapy or clear clinically relevant biomarkers, neoadjuvant chemotherapy (NAC) is still the standard neoadjuvant systemic therapy for breast cancer. Among the many biomarkers predicting the efficacy of NAC, immune-related biomarkers, such as immune-related genes and tumor-infiltrating lymphocytes (TILs), play a key role.Methods: We analyzed gene expression from several datasets in the Gene Expression Omnibus (GEO) database and evaluated the relative proportion of immune cells using the CIBERSORT method. In addition, mIHC/IF detection was performed on clinical surgical specimens of triple-negative breast cancer patients after NAC.Results: We obtained seven immune-related genes, namely, CXCL1, CXCL9, CXCL10, CXCL11, IDO1, IFNG, and ORM1 with higher expression in the pathological complete response (pCR) group than in the non-pCR group. In the pCR group, the levels of M1 and γδT macrophages were higher, while those of the M2 macrophages and mast cells were lower. After NAC, the proportions of M1, γδT cells, and resting CD4 memory T cells were increased, while the proportions of natural killer cells and dendritic cells were decreased with downregulated immune-related genes. The results of mIHC/IF detection and the prognostic information of corresponding clinical surgical specimens showed the correlation of proportions of natural killer cells, CD8-positive T cells, and macrophages with different disease-free survival outcomes.Conclusion: The immune-related genes and immune cells of different subtypes in the tumor microenvironment are correlated with the response to NAC in breast cancer, and the interaction between TILs and NAC highlights the significance of combining NAC with immunotherapy to achieve better clinical benefits.

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<p>Predictors of Neoadjuvant Chemotherapy Response in Breast Cancer: A Review</p>

Cited by 18 publications

References 115 publications

Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach

Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach

Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

Correlation Between Immune-Related Genes and Tumor-Infiltrating Immune Cells With the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer

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