Anti-HER2 CD4+ T-Helper Type 1 Immune Response is Superior to Breast MRI for Assessing Response to Neoadjuvant Therapy in Patients with HER2-Positive Breast Cancer

Breast cancer vaccination: Latest advances with an analytical focus on clinical trials

Breast cancer (BC) is one of the main causes of cancer-related death worldwide. The heterogenicity of breast tumors and the presence of tumor resistance, metastasis, and disease recurrence make BC a challenging malignancy. A new age in cancer treatment is being ushered in by the enormous success of cancer immunotherapy, and therapeutic cancer vaccination is one such area of research. Nevertheless, it has been shown that the application of cancer vaccines in BC as monotherapy could not induce satisfying anti-tumor immunity. Indeed, the application of various vaccine platforms as well as combination therapies like immunotherapy could influence the clinical benefits of BC treatment. We analyzed the clinical trials of BC vaccination and revealed that the majority of trials were in phase I and II meaning that the BC vaccine studies lack favorable outcomes or they need more development. Furthermore, peptide- and cell-based vaccines are the major platforms utilized in clinical trials according to our analysis. Besides, some studies showed satisfying outcomes regarding carbohydrate-based vaccines in BC treatment. Recent advancements in therapeutic vaccines for breast cancer were promising strategies that could be accessible in the near future.

The Role of Nomogram Based on the Combination of Ultrasound Parameters and Clinical Indicators in the Degree of Pathological Remission of Breast Cancer

Background

The mortality rate of breast cancer (BC) ranks first among female tumors worldwide and presents a trend of younger age, which poses a great threat to women's health and life. Neoadjuvant chemotherapy (NAC) for breast cancer is defined as the first step of treatment for breast cancer patients without distant metastasis before planned surgical treatment or local treatment with surgery and radiotherapy. According to the current NCCN guidelines, patients with different molecular types of BC should receive neoadjuvant chemotherapy (NAC), which can not only achieve tumor downstaging, increase the chance of surgery, and improve the breast-conserving rate. In addition, it can identify new genetic pathways and drugs related to cancer, improve patient survival rate, and make new progress in breast cancer management.

Objective

To explore the role of the nomogram established by the combination of ultrasound parameters and clinical indicators in the degree of pathological remission of breast cancer.

Methods

A total of 147 breast cancer patients who received neoadjuvant chemotherapy and elective surgery in the Department of Ultrasound, Nantong Cancer Hospital, from May 2014 to August 2021 were retrospectively included. Postoperative pathological remission was divided into two groups according to Miller-Payne classification: no significant remission group (NMHR group, n = 93) and significant remission group (MHR group, n = 54). Clinical characteristics of patients were recorded and collected. The multivariate logistic regression model was used to screen the information features related to the MHR group, and then, a nomogram model was constructed; ROC curve area, consistency index (C-index, CI), calibration curve, and H-L test were used to evaluate the model. And the decision curve is used to compare the net income of the single model and composite model.

Results

Among 147 breast cancer patients, 54 (36.7%) had pathological remission. Multivariate logistic regression showed that ER, reduction/disappearance of strong echo halo, Adler classification after NAC, PR + CR, and morphological changes were independent risk factors for pathological remission (P < 0.05). Based on these factors, the nomogram was constructed and verified. The area under the curve (AUC) and CI were 0.966, the sensitivity and specificity were 96.15% and 92.31%, and the positive predictive value (PPV) and negative predictive value (NPV) were 87.72% and 97.15%, respectively. The mean absolute error of the agreement between the predicted value and the real value is 0.026, and the predicted risk is close to the actual risk. In the range of HRT of about 0.0∼0.9, the net benefit of the composite evaluation model is higher than that of the single model. H-L test results showed that χ ² = 8.430, P=0.393 > 0.05.

Conclusion

The nomogram model established by combining the changes of ultrasound parameters and clinical indicators is a practical and convenient prediction model, which has a certain value in predicting the degree of pathological remission after neoadjuvant chemotherapy.

A Narrative Review of Ultrasound Technologies for the Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer

The incidence and mortality rate of breast cancer (BC) in women currently ranks first worldwide, and neoadjuvant chemotherapy (NAC) is widely used in patients with BC. A variety of imaging assessment methods have been used to predict and evaluate the response to NAC. Ultrasound (US) has many advantages, such as being inexpensive and offering a convenient modality for follow-up detection without radiation emission. Although conventional grayscale US is typically used to predict the response to NAC, this approach is limited in its ability to distinguish viable tumor tissue from fibrotic scar tissue. Contrast-enhanced ultrasound (CEUS) combined with a time-intensity curve (TIC) not only provides information on blood perfusion but also reveals a variety of quantitative parameters; elastography has the potential capacity to predict NAC efficiency by evaluating tissue stiffness. Both CEUS and elastography can greatly improve the accuracy of predicting NAC responses. Other US techniques, including three-dimensional (3D) techniques, quantitative ultrasound (QUS) and US-guided near-infrared (NIR) diffuse optical tomography (DOT) systems, also have advantages in assessing NAC response. This paper reviews the different US technologies used for predicting NAC response in BC patients based on the previous literature.

The predictive value of DKI in breast cancer: Does tumour subtype affect pathological response evaluations?

PURPOSE

To explore the differences in quantitative parameters based on diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) between different immunohistochemical indicator statuses and their predictive value for neoadjuvant chemotherapy (NAC) among different phenotypes of breast cancer.

METHODS

Eighty-one breast cancer patients who underwent NAC were enrolled in this retrospective study. Correlations between diffusion parameters and immunohistochemical indicators were determined using Spearman's test, and receiver operating characteristic (ROC) curves were constructed to assess the apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) in predicting the pathologic complete response (PCR).

RESULTS

Correlations were observed between MK values and hormone receptor (HR) expression (oestrogen receptor (ER): r = 0.315 and progesterone receptor (PR): r = 0.268). The parameters ADC_(0,1000), MK, and MD all showed correlations with Ki67 expression (r = 0.276, 0.316 and - 0.224, respectively). ER and Ki67 expression and the parameters MD and MK were significantly different between the PCR and non-PCR groups (AUC = 0.783, 0.688, 0.649 and 0.684, respectively). After splitting patients into subgroups, no significant differences were observed between the PCR and non-PCR groups with human epidermal growth factor receptor 2 (HER2) + and triple-negative (TN) breast cancer. However, we were surprised to find that ADC_{(0, 1000)}, MD, and MK were significantly different between different remission groups with HR+/HER2+ subtypes, and the AUCs of each parameter reached 0.794, 0.825, and 0.712, respectively.

CONCLUSION

MK was correlated with HR expression. ADC_{(0, 1000)} and DKI were correlated with Ki67 expression. ADC_{(0, 1000)} and the non-Gaussian diffusion model are suitable for predicting PCR in patients with HR+/HER2+ breast cancer before NAC.

Temporary blockade of interferon-γ ameliorates doxorubicin-induced cardiotoxicity without influencing the anti-tumor effect

Doxorubicin (DOX) is commonly used as an anti-cancer agent. However, its severe cardiotoxicity often makes it life threatening even long after DOX therapy during childhood. We recently reported interferon-γ (IFN-γ) necessary for DOX-induced acute cardiotoxicity in a p38 dependent way and, asked here for the potential of IFN-γ blockade to prevent DOX-induced chronic cardiotoxicity during tumor therapy. In our model system, mice without or with growing tumors repeatedly received DOX treatment. Simultaneous injection of anti-IFN-γ antibody R46-A2 with DOX to block IFN-γ signal efficiently protected the cardiac function of DOX treated recipients. Importantly, a single late injection of R46-A2 after DOX exposure also ameliorated DOX induced cardiac dysfunction in tumor-bearing mice. The anti-IFN-γ treatment did not affect the DOX-mediated tumor suppression effect and it left the main cellular immune response intact. Therefore, temporary blockade of IFN-γ may represent a novel strategy to ameliorate established DOX induced cardiotoxicity (DIC) or prevent its development in tumor therapy.

HER2-LAMP vaccines effectively traffic to endolysosomal compartments and generate enhanced polyfunctional T cell responses that induce complete tumor regression

BACKGROUND

The advent of immune checkpoint blockade antibodies has demonstrated that effective mobilization of T cell responses can cause tumor regression of metastatic cancers, although these responses are heterogeneous and restricted to certain histologic types of cancer. To enhance these responses, there has been renewed emphasis in developing effective cancer-specific vaccines to stimulate and direct T cell immunity to important oncologic targets, such as the oncogene human epidermal growth factor receptor 2 (HER2), expressed in ~20% of breast cancers (BCs).

METHODS

In our study, we explored the use of alternative antigen trafficking through use of a lysosome-associated membrane protein 1 (LAMP) domain to enhance vaccine efficacy against HER2 and other model antigens in both in vitro and in vivo studies.

RESULTS

We found that inclusion of this domain in plasmid vaccines effectively trafficked antigens to endolysosomal compartments, resulting in enhanced major histocompatibility complex (MHC) class I and II presentation. Additionally, this augmented the expansion/activation of antigen-specific CD4+ and CD8+ T cells and also led to elevated levels of antigen-specific polyfunctional CD8+ T cells. Significantly, vaccination with HER2-LAMP produced tumor regression in ~30% of vaccinated mice with established tumors in an endogenous model of metastatic HER2+ BC, compared with 0% of HER2-WT vaccinated mice. This therapeutic benefit is associated with enhanced tumor infiltration of activated CD4+ and CD8+ T cells.

CONCLUSIONS

These data demonstrate the potential of using LAMP-based endolysosomal trafficking as a means to augment the generation of polyfunctional, antigen-specific T cells in order to improve antitumor therapeutic responses using cancer antigen vaccines.

Exploration of the immune-related signature and immune infiltration analysis for breast ductal and lobular carcinoma

Background

In this study, we aimed to explore the tumour associated immune signature of breast cancer (BC) and conduct integrative analyses with immune infiltrates in BC.

Methods

We downloaded the transcriptome profiling and clinical data of BC from The Cancer Genome Atlas (TCGA) database. The list of immune-related signatures was from the Innate database. The limma package was utilized to conduct the normalization, and we screened the differential immune signatures in BC. A univariate Cox regression model and the LASSO method were used to find the hub prognostic immune genes. The TAIG risk model was calculated based on the multivariate Cox regression results, and a receiver operating characteristic (ROC) curve was generated to assess the predictive power of TAIG. Moreover, we also conducted a correlation analysis between TAIG and the clinical characteristics. Additionally, we utilized the METABRIC cohort as the validation data set. The TIMER database is a comprehensive resource for performing systematic analyses of immune infiltrates across various malignancies. We evaluated the associations of immune signatures with several immune cells based on TIMER. Furthermore, we used the CIBERSORT algorithm to determine the fractions of immune cells in each sample and compared the differential distributions of immune infiltrates between two TAIG groups using the Wilcoxon rank-sum test.

Results

A total of 1,178 samples were obtained from the TCGA-BRCA database, but only 1,045 breast tumour samples were matched with complete transcriptome expression data. Meanwhile, we collected a total of 1,094 BC patients from the METABRIC cohort. We found a list of 1,399 differential immune signatures associated with survival, and functional analysis revealed that these genes participated in cytokine-cytokine receptor interactions, Th1 and Th2 cell differentiation and the JAK-STAT signalling pathway. The TAIG risk model was established from the multivariate Cox analysis, and we observed that high TAIG levels correlated with poor survival outcomes based on Kaplan-Meier analysis. The Kruskal-Wallis test suggested that high TAIG levels correlated with high AJCC-TNM stages and advanced pathological stages (P<0.01). We validated the well robustness of TAIG in METABRIC cohort and 5-year AUC reached up to 0.829. Moreover, we further uncovered the associations of hub immune signatures with immune cells and calculated the immune cell fractions in specific tumour samples based on gene signature expression. Last, we used the Wilcoxon rank-sum test to compare the differential immune density in the two groups and found that several immune cells had a significantly lower infiltrating density in the high TAIG groups, including CD8⁺ T cells (P=0.031), memory resting CD4⁺ T cells (P=0.026), M0 macrophages (P=0.023), and M2 macrophages (P=0.048).

Conclusions

In summary, we explored the immune signature of BC and constructed a TAIG risk model to predict prognosis. Moreover, we integrated the identified immune signature with tumour-infiltrating immune cells and found adverse associations between the TAIG levels and immune cell infiltrating density.