Resistance to Neoadjuvant Treatment in Breast Cancer: Clinicopathological and Molecular Predictors

Phenotypes and cytokines of NK cells in triple-negative breast cancer resistant to checkpoint blockade immunotherapy

Neoadjuvant checkpoint blockade immunotherapy (NATI) significantly prolonged outcomes for triple-negative breast cancer (TNBC). Residual tumor cells that survive NATI represent high-risk cell populations with metastatic potential and usually evade immunosurveillance by NK cells. Using an 82-protein panel, we here profiled single-cell membrane proteomics of CD56+ (NCAM1+) NK cells from tumor, peri-cancerous tissue, as well as peripheral blood from 28 TNBC patients post-NATI of residual cancer burden II/III. Unsupervised clustering resulted in several distinct clusters: 2 tumor-infiltrating NK (TINK) clusters with divergent functions of immune activation (TNFRSF7+) and suppression (SELL+); 2 immuno-suppressive peri-cancerous clusters; and 1 periphery-specific cluster. Considering the contradiction of the 2 TINK clusters, we further tested cytokine functions of SELL + and TNFRSF7 + TINKs by single-cell secreting proteomics using a 32-cytokine panel. Consistently, SELL + TINK clusters were characterized by immuno-suppressive secretion patterns (IL10+). A low proportion of SELL + TINK cluster and low proportion of IL10 + secreting SELL + TINK cluster (single-cell secreting proteomics) were both associated with better progression-free survival time. These findings were validated in an independent cohort of 15 patients during 16-month follow-up. Overall, we identified a distinct immuno-suppressive TINK cell group, featuring IL10 + secreting and SELL expression with a strong relation to poor survival prognosis in TNBC patients post-NATI.

Personalized multifactorial risk assessment in neoadjuvant-treated breast carcinoma

PURPOSE

Due to biological heterogeneity of breast carcinoma, predicting the individual response to neoadjuvant treatment (NAT) is complex. Consequently, there are no comprehensive, generally accepted practices to guide post-treatment follow-up. We present clinical and histopathological criteria to advance the prediction of disease outcome in NA-treated breast cancer.

METHODS

A retrospective consecutive cohort of 257 NA-treated Finnish breast cancer patients with up to 13-year follow-up and the corresponding tissue samples of pre- and post-NAT breast and metastatic specimen were evaluated for prognostic impacts. All relevant clinical and biomarker characteristics potentially correlated with tumor response to NAT, course of disease, or outcome of breast cancer were included in the statistical analyses.

RESULTS

The results highlight the intensified characterization of distinguished prognostic factors and previously overlooked histological features, e.g., mitotic and apoptotic activity. Particularly, decreased PR indicated 3.8-fold (CI 1.9-7.4, p = 0.0001) mortality risk, and a > 10.5-year shorter survival for the majority, > 75% of patients (Q1). Clinically applicable prognostic factors both preceding and following NAT were identified and compiled into heat maps to quantify mortality and recurrence risks. Combinations of risk factors for aggressive disease were exemplified as an interactive tool (bcnatreccalc.utu.fi) to illustrate the spectrum of disease outcomes.

CONCLUSION

The results emphasize the value of comprehensive evaluation of conventional patient and biomarker characteristics, especially concerning re-assessment of biomarkers, risk-adapted surveillance, and personalized treatment strategies. Future personalized NA-treatment strategies might benefit from models combining risk-adapted surveillance data and post-NAT re-assessed biomarkers.

A predictive model for neoadjuvant therapy response in breast cancer

Neoadjuvant therapy is a standard treatment for breast cancer, but its effectiveness varies among patients. This highlights the importance of developing accurate predictive models. Our study uses metabolomics and machine learning to predict the response to neoadjuvant therapy in breast cancer patients.

OBJECTIVE

To develop and validate predictive models using machine learning and circulating metabolites for forecasting responses to neoadjuvant therapy among breast cancer patients, enhancing personalized treatment strategies.

METHODS

Based on pathological analysis after neoadjuvant chemotherapy and surgery, this retrospective study analyzed 30 young women breast cancer patients from a single institution, categorized as responders or non-responders. Utilizing liquid chromatography-tandem mass spectrometry, we investigated the plasma metabolome, explicitly targeting 40 metabolites, to identify relevant biomarkers linked to therapy response, using machine learning to generate a predictive model and validate the results.

RESULTS

Eighteen significant biomarkers were identified, including specific acylcarnitines and amino acids. The most effective predictive model demonstrated a remarkable accuracy of 90.7% and an Area Under the Curve (AUC) of 0.999 at 95% confidence, illustrating its potential utility as a web-based application for future patient management. This model's reliability underscores the significant role of circulating metabolites in predicting therapy outcomes.

CONCLUSION

Our study's findings highlight the crucial role of metabolomics in advancing personalized medicine for breast cancer treatment by effectively identifying metabolite biomarkers correlated with neoadjuvant therapy response. This approach signifies a critical step towards tailoring treatment plans based on individual metabolic profiles, ultimately improving patient outcomes in breast cancer care.

Is the Neutrophil-to-Lymphocyte Ratio a Predictive Factor of Pathological Complete Response in Egyptian Breast Cancer Patients Treated with Neoadjuvant Chemotherapy?

Background and Objectives: The role of the neutrophil-to-lymphocyte ratio (NLR) as a predictor of response in breast cancers after neoadjuvant chemotherapy is controversial. This study aims to explore the relationship of NLR with pathological complete response (pCR) in a cohort of Egyptian breast cancer patients who received neoadjuvant chemotherapy. Materials and Methods: Forty-six breast cancer females received preoperative neoadjuvant chemotherapy and then underwent surgery. All resected tumors were evaluated to determine the pathologic effect of the neoadjuvant chemotherapy. A complete blood count was carried out at baseline before beginning the neoadjuvant chemotherapy. The absolute count of neutrophils was divided by the absolute count of lymphocytes to calculate the NLR. Results: Of the study patients, 18 (39.1%) were considered to have a low NLR (NLR < 1.76), and 28 (60.9%) were considered to have a high NLR (NLR ≥ 1.76). Patients with a low NLR had 18-fold higher rates of pCR when compared to patients with a high NLR (OR 18.1; 95% CI (1.058-310.757); p = 0.046). Conclusions: Our findings indicate that the pretreatment NLR is a pivotal predictor factor of the pathological complete response in Egyptian breast cancer patients treated with neoadjuvant chemotherapy.

Combined inhibition of CDK4/6 and AKT is highly effective against the luminal androgen receptor (LAR) subtype of triple negative breast cancer

Luminal Androgen Receptor (LAR) triple-negative breast cancers (TNBC) express androgen receptors (AR), exhibit high frequency of PIK3CA mutations and intact RB. Herein, we investigated combined blockade of the CDK4/6 and PI3K signaling with palbociclib, alpelisib, and capivasertib, which inhibit CDK4/6, PI3Kα, and AKT1-3, respectively. The combination of palbociclib/capivasertib, but not palbociclib/alpelisib, synergistically inhibited proliferation of MDA-MB-453 and MFM-223 LAR cells [synergy score 7.34 (p = 5.81x10-11) and 4.78 (p = 0.012), respectively]. The AR antagonist enzalutamide was inactive against MDA-MB-453, MFM-223, and CAL148 cells and did not enhance the efficacy of either combination. Palbociclib/capivasertib inhibited growth of LAR patient-derived xenografts more potently than palbociclib/alpelisib. Treatment of LAR cells with palbociclib suppressed phosphorylated-RB and resulted in adaptive phosphorylation/activation of S473 pAKT and AKT substrates GSK3β, PRAS40, and FoxO3a. Capivasertib blocked palbociclib-induced phosphorylation of AKT substrates more potently than alpelisib. Treatment with PI3Kβ inhibitors did not block phosphorylation of AKT substrates, suggesting that PI3Kβ did not mediate the adaptive response to CDK4/6 inhibition. Phosphokinase arrays of MDA-MB-453 cells treated with palbociclib showed time-dependent upregulation of PDGFRβ, GSK3β, STAT3, and STAT6. RNA silencing of PDGFRβ in palbociclib-treated MDA-MB-453 and MFM-223 cells blocked the upregulation of S473 pAKT, suggesting that the adaptive response to CDK4/6 blockade involves PDGFRβ signaling. Finally, treatment with palbociclib and the PDGFR inhibitor CP637451 arrested growth of MDA-MB-453 and MFM-223 cells to the same degree as palbociclib/capivasertib. These findings support testing the combination of CDK4/6 and AKT inhibitors in patients with LAR TNBC, and further investigation of PDGFR antagonists in this breast cancer subtype.

Markers of Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer: New in Molecular Oncology

It is known that complete pathomorphological response (pCR) after neoadjuvant therapy (NAC) in patients with breast cancer (BC) correlates with higher rates of recurrence-free and overall survival. In turn, the widespread use of neoadjuvant therapy for the treatment of breast cancer defines the clinical need for prognostic markers of response to ongoing therapy. Currently, some clinicopathological prognostic factors are used to assess the potential benefit of neoadjuvant systemic therapy for female patients, but they have limited applicability. In the era of precision medicine and personalised treatment, a search for new prognostic markers is needed to better tailor patient-specific therapy. To date, novel factors have been proposed to predict response to preoperative treatment in breast cancer patients, but they are either not yet used in routine clinical practice or have limited application. Thus, this review summarises data on both established and proven biomarkers and the latest prognostic factors for response to neoadjuvant treatment in breast cancer patients.

Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Probability Maps Derived from Quantitative Ultrasound Parametric Images

OBJECTIVE

Neoadjuvant chemotherapy (NAC) is widely used in the treatment of breast cancer. However, to date, there are no fully reliable, non-invasive methods for monitoring NAC. In this article, we propose a new method for classifying NAC-responsive and unresponsive tumors using quantitative ultrasound.

METHODS

The study used ultrasound data collected from breast tumors treated with NAC. The proposed method is based on the hypothesis that areas that characterize the effect of therapy particularly well can be found. For this purpose, parametric images of texture features calculated from tumor images were converted into NAC response probability maps, and areas with a probability above 0.5 were used for classification.

RESULTS

The results obtained after the third cycle of NAC show that the classification of tumors using the traditional method (area under the ROC curve AUC = 0.81-0.88) can be significantly improved thanks to the proposed new approach (AUC = 0.84-0.94). This improvement is achieved over a wide range of cutoff values (0.2-0.7), and the probability maps obtained from different quantitative parameters correlate well.

CONCLUSION

The results suggest that there are tumor areas that are particularly well suited to assessing response to NAC.

SIGNIFICANCE

The proposed approach to monitoring the effects of NAC not only leads to a better classification of responses, but also may contribute to a better understanding of the microstructure of neoplastic tumors observed in an ultrasound examination.

Successful Breast Conservation After Neoadjuvant Chemotherapy in Lobular Breast Cancer: The Role of Menopausal Status in Response to Treatment

BACKGROUND

While neoadjuvant chemotherapy (NAC) has been shown to increase rates of breast conservation surgery (BCS) for breast cancer, response rates in invasive lobular carcinoma (ILC) appear lower than other histologic subtypes. Some data suggest higher response rates to NAC in premenopausal versus postmenopausal patients, but this has not been studied in ILC. We evaluated the rates of successful BCS after NAC in patients with ILC stratified by menopausal status.

PATIENTS AND METHODS

We analyzed data from a single-institution cohort of 666 patients with stage I-III hormone receptor positive HER-2 negative ILC. We used t-tests, chi-squared tests, and multivariable logistic regression to investigate rates of NAC use, attempted BCS, and associations between NAC and successful BCS by menopausal status.

RESULTS

In 217 premenopausal and 449 postmenopausal patients, NAC was used more often in the premenopausal group (15.2% vs. 9.8%, respectively, p = 0.041). Among those who attempted breast conservation (51.3% of pre- and 64.8% of postmenopausal cohorts), NAC was not associated with successful BCS in either group. Interestingly, for postmenopausal patients, receipt of NAC was significantly associated with increased rates of completion mastectomy in those who had positive margins at the first attempt at BCS.

CONCLUSION

NAC was not associated with successful BCS in either premenopausal or postmenopausal patients with ILC. Although premenopausal patients were more likely to receive NAC, these data suggest that menopausal status may not be a good predictor of response to chemotherapy. Better predictors of response and more efficacious treatment for patients with ILC are needed.

Risk factors of breast cancer recurrence in pathologic complete response achieved by patients following neoadjuvant chemotherapy: a single-center retrospective study

Objective

Pathologic complete response (pCR) of breast cancer after neoadjuvant chemotherapy (NAC) is highly related to molecular subtypes. Patients who achieved tumor pCR after NAC have a better prognosis. However, despite of better prognosis, pCR patients have a potential for recurrence. There is little evidence of risk factors of recurrence in patients with pCR. We aim to analyze factors associated with tumor recurrence in patients who achieved pCR.

Methods

This study retrospectively reviewed the data of patients diagnosed with breast cancer who achieved pCR after receiving NAC between January 2009 and December 2018 in Samsung Medical Center. pCR was defined as no residual invasive cancer in the breast and axillary nodes even if there is residual ductal carcinoma in situ (ypT0 or ypTis with ypN0). Breast cancers are classified into 4 subtypes based on hormone receptors (HR) and human epithelial growth factor receptor 2 (HER2) status. Patients who had bilateral breast cancer, ipsilateral supraclavicular or internal mammary lymph node metastasis, inflammatory breast cancer, distant metastasis, unknown subtype, and histologically unique case were excluded from the study.

Results

In total 483 patients were included in this study except for patients who corresponded to the exclusion criteria. The median follow-up duration was 59.0 months (range, 0.5-153.3 months). Breast cancer recurred in 4.1% of patients (20 of 483). There was a significant difference in clinical T (P = 0.004) and clinical N (P = 0.034) stage in the Kaplan-Meier curve for disease-free survival. Molecular subtypes (P = 0.573), Ki67 (P = 1.000), and breast surgery type (P = 0.574) were not associated with tumor recurrence in patients who achieved pCR after NAC. In the clinical T stage and clinical N stage, there was a significant difference between recurrence and no-recurrence groups (clinical T stage; P = 0.045, clinical N stage; P = 0.002). Univariable Cox regression revealed statistical significance in the clinical T stage (P = 0.049) and clinical N stage (P = 0.010), while multivariable Cox regression demonstrated non-significance in the clinical T stage (P = 0.320) and clinical N stage (P = 0.073).

Conclusion

Results in this study showed that clinical T, clinical N stage, and molecular subtypes were not statistically significant predictors of recurrence in patients who achieved pCR after NAC. In spite of that, pCR after NAC may be more important than clinical staging and molecular subtype in early breast cancer. In addition, escalated treatments for patients with HER2 + or triple-negative tumors would be considered with a strict patient selection strategy to prevent over-treatment as well as achieve pCR.

Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features

BACKGROUND

This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response.

METHODS

We included 265 ER+HER2- LABC patients (2010-2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values.

RESULTS

The best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631).

CONCLUSIONS

We suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC.

Multigene profiles to guide the use of neoadjuvant chemotherapy for breast cancer: a Copenhagen Breast Cancer Genomics Study

Estrogen receptor (ER) and human epidermal growth factor 2 (HER2) expression guide the use of neoadjuvant chemotherapy (NACT) in patients with early breast cancer. We evaluate the independent predictive value of adding a multigene profile (CIT256 and PAM50) to immunohistochemical (IHC) profile regarding pathological complete response (pCR) and conversion of positive to negative axillary lymph node status. The cohort includes 458 patients who had genomic profiling performed as standard of care. Using logistic regression, higher pCR and node conversion rates among patients with Non-luminal subtypes are shown, and importantly the predictive value is independent of IHC profile. In patients with ER-positive and HER2-negative breast cancer an odds ratio of 9.78 (95% CI 2.60;36.8), P < 0.001 is found for pCR among CIT256 Non-luminal vs. Luminal subtypes. The results suggest a role for integrated use of up-front multigene subtyping for selection of a neoadjuvant approach in ER-positive HER2-negative breast cancer.

Hormone Receptor and HER2 Status Switch in Non-pCR Breast Cancer Specimens after Neoadjuvant Therapy

Introduction

This project aimed to identify the frequency of a switch of hormone receptor (HR) and/or HER2 status after neoadjuvant chemotherapy (NAC) for early breast cancer.

Methods

Tumor samples from patients without pathological complete response (non-pCR) were evaluated. Pathological complete response (pCR) was defined as no invasive tumor in breast and lymph nodes (ypT0/is ypN0). HR and HER2 status determined before NAC was compared with the corresponding receptor status determined in the surgical specimen after NAC.

Results

245 consecutive patients with primary invasive breast cancer, treated with NAC with/without targeted therapy between January 1, 2016 and December 31, 2019, at the LMU Breast Center, Munich, Germany, were identified. In 128 patients (52%), surgery revealed non-pCR after completed NAC. In 35 cases (27%), a switch of either HR and/or HER2 status between the initial biopsy and the surgical specimen was detected. Twenty cases had a switch in HR status, while 15 cases had a switch in HER2 status.

Conclusion

In a substantial number (27%) of non-pCR cases, a switch in biomarker status after completed neoadjuvant treatment was detected. These results are consistent with prior evidence. Yet, routine reevaluation of HR and HER2 status is not recommended in guidelines so far. Future research needs to address the impact of HR and HER2 status switch on therapy adaptation and on subsequent patient outcome. Particularly, in view of the recent therapy advances, it will be critical to evaluate whether individualization of treatment concepts based on the biology of the non-pCR specimens is preferable to the initial therapy concept based on the pathology at primary diagnosis.

Prediction of response to neoadjuvant chemotherapy in breast cancer with recurrent neural networks and raw ultrasound signals

Objective. Prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer is important for patient outcomes. In this work, we propose a deep learning based approach to NAC response prediction in ultrasound (US) imaging. Approach. We develop recurrent neural networks that can process serial US imaging data to predict chemotherapy outcomes. We present models that can process either raw radio-frequency (RF) US data or regular US images. The proposed approach is evaluated based on 204 sequences of US data from 51 breast cancers. Each sequence included US data collected before the chemotherapy and after each subsequent dose, up to the 4th course. We investigate three pre-trained convolutional neural networks (CNNs) as back-bone feature extractors for the recurrent network. The CNNs were pre-trained using raw US RF data, US b-mode images and RGB images from the ImageNet dataset. The first two networks were developed using US data collected from malignant and benign breast masses. Main results. For the pre-treatment data, the better performing network, with back-bone CNN pre-trained on US images, achieved area under the receiver operating curve (AUC) of 0.81 (±0.04). Performance of the recurrent networks improved with each course of the chemotherapy. For the 4th course, the better performing model, based on the CNN pre-trained with RGB images, achieved AUC value of 0.93 (±0.03). Statistical analysis based on the DeLong test presented that there were no significant differences in AUC values between the pre-trained networks at each stage of the chemotherapy (p-values > 0.05). Significance. Our study demonstrates the feasibility of using recurrent neural networks for the NAC response prediction in breast cancer US.

Comparison of progressed and unresponsive patients with responsive patients at ınterim assessment during breast cancer neoadjuvant chemotherapy

Aim: It was aimed to compare the breast cancer patients who were progressed or unresponsive to neoadjuvant chemotherapy with the patients clinically responsive to the treatment at interim radiological assessment. Materials and Methods: Female patients operated in our hospital for breast cancer after neoadjuvant chemotherapy were retrospectively screened. Patients having interim radiological assessment were included in the study. Patients were divided into three groups as responsive, unresponsive (stable) and progressive according to the imaging results. Unresponsive and progressive patients were compared to responsive patients in terms of patient and tumor characteristics. Results: A total of 96 patients were included in the study. According to the interim imaging results, 90.6% of patients (87 patients) had a radiological response to the treatment. Four patients (4.2%) with radiological unresponsiveness and five patients (5.2%) with radiological progression (9 patients in total, 9.4%) were referred to operation. The mean age of the unresponsive patients was found to be statistically higher than the responding patients (60 vs. 49, p=0.035). The tumor grade and Ki-67 index of unresponsive patients were lower than the responsive patients (respectively; 1.5±0.6 vs. 2.4±0.5, p=0.007 and 10±4 vs. 37±22, p=0.003). Although the tumor grade and Ki-67 index were higher in patients who progressed than the responders, they weren’t statistically significant. Unresponsive patients were mostly luminal A (3/4 patients), and progressive patients were mostly triple negative (3/5 patients) molecular subtype. Conclusion: Luminal breast cancers with low proliferation index and grade tend to be insensitive to neoadjuvant chemotherapy. On the other hand, hormone receptor negative tumors with high proliferation index and grade may respond well to neoadjuvant chemotherapy and may also pose a risk for progression. Further clinical studies are needed.

Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine

Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.

Dynamic changes in intrinsic subtype, immunity status, and risk score before and after neoadjuvant chemo- and HER2-targeted therapy without pCR in HER2-positive breast cancers: A cross-sectional analysis

Very few studies have been done in HER2 positive patients without complete pathological response (pCR) after combined neoadjuvant chemo- and HER2-target therapy to investigate changes in intrinsic subtype, risk of recurrence (ROR) score, and immunity status before and after treatment. Patients with nonmetastatic HER2-positive breast cancer failed to achieve pCR after neoadjuvant chemotherapy plus trastuzumab were included in current study. We examined the distribution of PAM50 subtypes, ROR score and immunity score in 25 paired baseline and surgical samples. The Miller-Payne grading system was used to evaluate the efficacy of the neoadjuvant therapy. It was observed that the distribution of intrinsic subtype, ROR category and immunity subgroup varied according to hormone receptor (HR) status. HER2-enriched and basal-like subtypes, median-high ROR categories and immunity-weak subgroup were dominant in baseline tumors. Compared to baseline samples, conversion of intrinsic subtype, ROR categories and immunity subgroups were found in 15 (60.0%), 13(52.0%), and 11(44.0%) surgical samples, respectively. The PAM50 subtype, ROR category, and immunity subgroup were concordant between baseline and surgical samples where nonluminal subtypes, median-high ROR categories and i-weak subgroup were still common. In conclusion, the HER2-positive breast cancer is highly heterogeneous with a distribution of 72-gene expression varying according to HR co-expression. The dynamics of the 72-gene expression pre- and posttreatment may become novel biomarker for guiding adjuvant therapy and hence warrant further investigation.

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

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.

Identification of immune-related biomarkers for predicting neoadjuvant chemotherapy sensitivity in HER2 negative breast cancer via bioinformatics analysis

BACKGROUND

Neoadjuvant chemotherapy (NAC) is an important treatment for breast cancer (BC) patients. However, due to the lack of specific therapeutic targets, only 1/3 of human epidermal growth factor receptor 2 (HER2)-negative patients reach pathological complete response (pCR). Therefore, there is an urgent need to identify novel biomarkers to distinguish and predict NAC sensitive in BC patients.

METHODS

The GSE163882 dataset, containing 159 BC patients treated with NAC, was downloaded from the Gene Expression Omnibus (GEO) database. Patients with pathological complete response (pCR) and those with residual disease (RD) were compared to obtain the differentially expressed genes (DEGs). Functional enrichment analyses were conducted on these DEGs. Then, we intersect the DEGs and immune-related genes to obtain the hub immune biomarkers, and then use the linear fitting model ("glm" package) to construct a prediction model composed of 9 immune biomarkers. Finally, the single sample gene set enrichment analysis (ssGSEA) algorithm was used to analyze immune cell invasion in BC patients, and the correlation between immune cell content and immune gene expression levels was analyzed.

RESULTS

Nine immune-related biomarkers were obtained in the intersection of DEGs and immune-related genes. Compared with RD patients, CXCL9, CXCL10, CXCL11, CXCL13, GZMB, IDO1, and LYZ were highly expressed in pCR patients, while CXCL14 and ESR1 were lowly expressed in pCR patients. After linear fitting of the multi-gene expression model, the area under the curve (AUC) value of the ROC curve diagnosis of pCR patients was 0.844. Immunoinfiltration analysis showed that compared with RD patients, 15 of the 28 immune cell types examined showed high-infiltration in pCR patients, including activated CD8 T cells, effector memory CD8 T cells, and activated CD4 T cells.

CONCLUSIONS

This investigation ultimately identified 9 immune-related biomarkers as potential tools for assessing the sensitivity of NAC in HER2-negative BC patients. These biomarkers have great potential for predicting pCR BC patients.

Gene signatures in patients with early breast cancer and relapse despite pathologic complete response

A substantial minority of early breast cancer (EBC) patients relapse despite their tumors achieving pathologic complete response (pCR) after neoadjuvant therapy. We compared gene expression (BC360; nCounter^® platform; NanoString) between primary tumors of patients with post-pCR relapse (N = 14) with: (i) matched recurrent tumors from same patient (intraindividual analysis); and (ii) primary tumors from matched controls with pCR and no relapse (N = 41; interindividual analysis). Intraindividual analysis showed lower estrogen receptor signaling signature expression in recurrent tumors versus primaries (logFC = −0.595; P = 0.022). Recurrent tumors in patients with distant metastases also exhibited reduced expression of immune-related expression parameters. In interindividual analyses, primary tumor major histocompatibility complex class II expression was lower versus controls in patients with any relapse (logFC = −0.819; P = 0.030) or distant relapse (logFC = −1.151; P = 0.013). Primaries with later distant relapse also had greater homologous recombination deficiency than controls (logFC = 0.649; P = 0.026). Although no associations remained statistically significant following adjustment for false discovery rate, our results show that transcriptomic analyses have potential for prognostic value and may help in selecting optimal treatment regimens for EBC at risk of relapse and warrant further investigation.

Molecular Biomarkers Predict Pathological Complete Response of Neoadjuvant Chemotherapy in Breast Cancer Patients: Review

Simple Summary

Breast cancer is the most common cancer in women worldwide. Although many studies have aimed to understand the genetic basis of breast cancer, leading to increasingly accurate diagnoses, only a few molecular biomarkers are used in clinical practice to predict response to therapy. Current studies aim to develop more personalized therapies to decrease the adverse effects of chemotherapy. Personalized medicine not only requires clinical, but also molecular characterization of tumors, which allows the use of more effective drugs for each patient. The aim of this study was to identify potential molecular biomarkers that can predict the response to therapy after neoadjuvant chemotherapy in patients with breast cancer. In this review, we summarize genomic, transcriptomic, and proteomic biomarkers that can help predict the response to therapy.

Abstract

Neoadjuvant chemotherapy (NAC) is often used to treat locally advanced disease for tumor downstaging, thus improving the chances of breast-conserving surgery. From the NAC response, it is possible to obtain prognostic information as patients may reach a pathological complete response (pCR). Those who do might have significant advantages in terms of survival rates. Breast cancer (BC) is a heterogeneous disease that requires personalized treatment strategies. The development of targeted therapies depends on identifying biomarkers that can be used to assess treatment efficacy as well as the discovery of new and more accurate therapeutic agents. With the development of new “OMICS” technologies, i.e., genomics, transcriptomics, and proteomics, among others, the discovery of new biomarkers is increasingly being used in the context of clinical practice, bringing us closer to personalized management of BC treatment. The aim of this review is to compile the main biomarkers that predict pCR in BC after NAC.