Sequential neoadjuvant chemotherapy using pegylated liposomal doxorubicin and cyclophosphamide followed by taxanes with complete trastuzumab and pertuzumab treatment for HER2-positive breast cancer: A phase II single-arm study.

Objective: Despite cardiotoxicity overlap, the trastuzumab/pertuzumab and anthracycline combination remains crucial due to significant benefits. Pegylated liposomal doxorubicin (PLD), a less cardiotoxic anthracycline, was evaluated for efficacy and cardiac safety when combined with cyclophosphamide and followed by taxanes with trastuzumab/pertuzumab in human epidermal growth factor receptor-2 (HER2)-positive early breast cancer (BC). Methods: In this multicenter, phase II study, patients with confirmed HER2-positive early BC received four cycles of PLD (30-35 mg/m2) and cyclophosphamide (600 mg/m2), followed by four cycles of taxanes (docetaxel, 90-100 mg/m2 or nab-paclitaxel, 260 mg/m2), concomitant with eight cycles of trastuzumab (8 mg/kg loading dose, then 6 mg/kg) and pertuzumab (840 mg loading dose, then 420 mg) every 3 weeks. The primary endpoint was total pathological complete response (tpCR, ypT0/is ypN0). Secondary endpoints included breast pCR (bpCR), objective response rate (ORR), disease control rate, rate of breast-conserving surgery (BCS), and safety (with a focus on cardiotoxicity). Results: Between May 27, 2020 and May 11, 2022, 78 patients were treated with surgery, 42 (53.8%) of whom had BCS. After neoadjuvant therapy, 47 [60.3%, 95% confidence interval (95% CI), 48.5%-71.2%] patients achieved tpCR, and 49 (62.8%) achieved bpCR. ORRs were 76.9% (95% CI, 66.0%-85.7%) and 93.6% (95% CI, 85.7%-97.9%) after 4-cycle and 8-cycle neoadjuvant therapy, respectively. Nine (11.5%) patients experienced asymptomatic left ventricular ejection fraction (LVEF) reductions of ≥10% from baseline, all with a minimum value of >55%. No treatment-related abnormal cardiac function changes were observed in mean N-terminal pro-BNP (NT-proBNP), troponin I, or high-sensitivity troponin. Conclusions: This dual HER2-blockade with sequential polychemotherapy showed promising activity with rapid tumor regression in HER2-positive BC. Importantly, this regimen showed an acceptable safety profile, especially a low risk of cardiac events, suggesting it as an attractive treatment approach with a favorable risk-benefit balance.

Safety and efficacy of microwave ablation for the treatment of low-risk papillary thyroid microcarcinoma: a prospective multicenter study.

OBJECTIVES: To assess the safety and efficacy of ultrasound-guided thermal ablation for low-risk papillary thyroid microcarcinoma (PTMC) via a prospective multicenter study. METHODS: From January 2017 through June 2021, low-risk PTMC patients were screened. The management details of active surveillance (AS), surgery, and thermal ablation were discussed. Among patients who accepted thermal ablation, microwave ablation (MWA) was performed. The main outcome was disease-free survival (DFS). The secondary outcomes were tumor size and volume changes, local tumor progression (LTP), lymph node metastasis (LNM), and complication rate. RESULTS: A total of 1278 patients were included in the study. The operation time of ablation was 30.21 ± 5.14 min with local anesthesia. The mean follow-up time was 34.57 ± 28.98 months. Six patients exhibited LTP at 36 months, of whom 5 patients underwent a second ablation, and 1 patient received surgery. The central LNM rate was 0.39% at 6 months, 0.63% at 12 months, and 0.78% at 36 months. Of the 10 patients with central LNM at 36 months, 5 patients chose ablation, 3 patients chose surgery and the other 2 patients chose AS. The overall complication rate was 1.41%, and 1.10% of patients developed hoarseness of the voice. All of the patients recovered within 6 months. CONCLUSIONS: Thermal ablation of low-risk PTMC was observed to be safe and efficacious with few minor complications. This technique may help to bridge the gap between surgery and AS as treatment options for patients wishing to have their PTMC managed in a minimally invasive manner. CLINICAL RELEVANCE STATEMENT: This study proved that microwave ablation is a safe and effective treatment method for papillary thyroid microcarcinoma. KEY POINTS: Percutaneous US-guided microwave ablation of papillary thyroid microcarcinoma is a very minimally invasive treatment under local anesthesia during a short time period. The local tumor progression and complication rate of microwave ablation in the treatment of papillary thyroid microcarcinoma are very low.

Tamoxifen resistance-related ceRNA network for breast cancer.

Background: Tamoxifen (TMX) is one of the most widely used drugs to treat breast cancer (BC). However, acquired drug resistance is still a major obstacle to its application, rendering it crucial to explore the mechanisms of TMX resistance in BC. This aims of this study were to identify the mechanisms of TMX resistance and construct ceRNA regulatory networks in breast cancer. Methods: GEO2R was used to screen for differentially expressed mRNAs (DEmRNAs) leading to drug resistance in BC cells. MiRTarbase and miRNet were used to predict miRNAs and lncRNAs upstream, and the competing endogenous RNA (ceRNA) regulatory network of BC cell resistance was constructed by starBase. We used the Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis (GEPIA) to analyze the expression and prognostic differences of genes in the ceRNA network with core axis, and qRT-PCR was used to further verify the above conclusions. Results: We found that 21 DEmRNAs were upregulated and 43 DEmRNA downregulated in drug-resistant BC cells. DEmRNAs were noticeably enriched in pathways relevant to cancer. We then constructed a protein-protein interaction (PPI) network based on the STRING database and defined 10 top-ranked hub genes among the upregulated and downregulated DEmRNAs. The 20 DEmRNAs were predicted to obtain 113 upstream miRNAs and 501 lncRNAs. Among them, 7 mRNAs, 22 lncRNAs, and 11 miRNAs were used to structure the ceRNA regulatory network of drug resistance in BC cells. 4 mRNAs, 4 lncRNAs, and 3 miRNAs were detected by GEPIA and the Kaplan-Meier plotter to be significantly associated with BC expression and prognosis. The differential expression of the genes in BC cells was confirmed by qRT-PCR. Conclusion: The ceRNA regulatory network of TMX-resistant BC was successfully constructed and confirmed. This will provide an important resource for finding therapeutic targets for TMX resistance, where the discovery of candidate conventional mechanisms can aid clinical decision-making. In addition, this resource will help discover the mechanisms behind this type of resistance.

MYCBP2 expression correlated with inflammatory cell infiltration and prognosis immunotherapy in thyroid cancer patients.

Introduction: Immune checkpoint inhibitors (ICIs) have shown promising results for the treatment of multiple cancers. ICIs and related therapies may also be useful for the treatment of thyroid cancer (TC). In TC, Myc binding protein 2 (MYCBP2) is correlated with inflammatory cell infiltration and cancer prognosis. However, the relationship between MYCBP2 expression and ICI efficacy in TC patients is unclear. Methods: We downloaded data from two TC cohorts, including transcriptomic data and clinical prognosis data. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict the efficacy of ICIs in TC patients. MCPcounter, xCell, and quanTIseq were used to calculate immune cell infiltration scores. Gene set enrichment analysis (GSEA) and single sample GSEA (ssGSEA) were used to evaluate signaling pathway scores. Immunohistochemical (IHC) analysis and clinical follow up was used to identify the MYCBP2 protein expression status in patients and associated with clinical outcome. Results: A higher proportion of MYCBP2-high TC patients were predicted ICI responders than MYCBP2-low patients. MYCBP2-high patients also had significantly increased infiltration of CD8+ T cells, cytotoxic lymphocytes (CTLs), B cells, natural killer (NK) cells and dendritic cells (DC)s. Compared with MYCBP2-low patients, MYCBP2-high patients had higher expression of genes associated with B cells, CD8+ T cells, macrophages, plasmacytoid dendritic cells (pDCs), antigen processing and presentation, inflammatory stimulation, and interferon (IFN) responses. GSEA and ssGSEA also showed that MYCBP2-high patients had significantly increased activity of inflammatory factors and signaling pathways associated with immune responses.In addiation, Patients in our local cohort with high MYCBP2 expression always had a better prognosis and greater sensitivity to therapy while compared to patients with low MYCBP2 expression after six months clinic follow up. Conclusions: In this study, we found that MYCBP2 may be a predictive biomarker for ICI efficacy in TC patients. High MYCBP2 expression was associated with significantly enriched immune cell infiltration. MYCBP2 may also be involved in the regulation of signaling pathways associated with anti-tumor immune responses or the production of inflammatory factors.

COL11A1 as an novel biomarker for breast cancer with machine learning and immunohistochemistry validation.

Machine learning (ML) algorithms were used to identify a novel biological target for breast cancer and explored its relationship with the tumor microenvironment (TME) and patient prognosis. The edgR package identified hub genes associated with overall survival (OS) and prognosis, which were validated using public datasets. Of 149 up-regulated genes identified in tumor tissues, three ML algorithms identified COL11A1 as a hub gene. COL11A1was highly expressed in breast cancer samples and associated with a poor prognosis, and positively correlated with a stromal score (r=0.49, p<0.001) and the ESTIMATE score (r=0.29, p<0.001) in the TME. Furthermore, COL11A1 negatively correlated with B cells, CD4 and CD8 cells, but positively associated with cancer-associated fibroblasts. Forty-three related immune-regulation genes associated with COL11A1 were identified, and a five-gene immune regulation signature was built. Compared with clinical factors, this gene signature was an independent risk factor for prognosis (HR=2.591, 95%CI 1.831-3.668, p=7.7e-08). A nomogram combining the gene signature with clinical variables, showed better predictive performance (C-index=0.776). The model correction prediction curve showed little bias from the ideal curve. COL11A1 is a potential therapeutic target in breast cancer and may be involved in the tumor immune infiltration; its high expression is strongly associated with poor prognosis.

Identification of Cancer-Associated Fibroblast Subtype of Triple-Negative Breast Cancer.

Background: There is limited knowledge about the role of cancer-associated fibroblasts (CAF) in the tumor microenvironment of triple-negative breast cancer (TNBC). Methods: Three hundred and thirty-five TNBC samples from four datasets were retrieved and analyzed. In order to determine the CAF subtype by combining gene expression profiles, an unsupervised clustering analysis was adopted. The prognosis, enriched pathways, immune cells, immune scores, and tumor purity were compared between CAF subtypes. The genes with the highest importance were selected by bioinformatics analysis. The machine learning model was built to predict the TNBC CAF subtype by these selected genes. Results: TNBC samples were classified into two CAF subtypes (CAF+ and CAF-). The CAF- subtype of TNBC was linked to the longer overall survival and more immune cells than the CAF+ subtype. CAF- and CAF+ were enriched in immune-related pathways and extracellular matrix pathways, respectively. Bioinformatics analysis identified 9 CAF subtype-related markers (ADAMTS12, AEBP1, COL10A1, COL11A1, CXCL11, CXCR6, EDNRA, EPPK1, and WNT7B). We constructed a robust random forest model using these 9 genes, and the area under the curve (AUC) value of the model was 0.921. Conclusion: The current study identified CAF subtypes based on gene expression profiles and found that CAF subtypes have significantly different overall survival, immune cells, and immunotherapy response rates.

Construction of ceRNA Networks Associated With CD8 T Cells in Breast Cancer.

Background: The infiltration of CD8 T cells is usually linked to a favorable prognosis and may predict the therapeutic response of breast cancer patients to immunotherapy. The purpose of this research is to investigate the competing endogenous RNA (ceRNA) network correlated with the infiltration of CD8 T cells. Methods: Based on expression profiles, CD8 T cell abundances for each breast cancer (BC) patient were inferred using the bioinformatic method by immune markers and expression profiles. We were able to extract the differentially expressed RNAs (DEmRNAs, DEmiRNAs, and DElncRNAs) between low and high CD8 T-cell samples. The ceRNA network was constructed using Cytoscape. Machine learning models were built by lncRNAs to predict CD8 T-cell abundances. The lncRNAs were used to develop a prognostic model that could predict the survival rates of BC patients. The expression of selected lncRNA (XIST) was validated by quantitative real-time PCR (qRT-PCR). Results: A total of 1,599 DElncRNAs, 89 DEmiRNAs, and 1,794 DEmRNAs between high and low CD8 T-cell groups were obtained. Two ceRNA networks that have positive or negative correlations with CD8 T cells were built. Among the two ceRNA networks, nine lncRNAs (MIR29B2CHG, NEAT1, MALAT1, LINC00943, LINC01146, AC092718.4, AC005332.4, NORAD, and XIST) were selected for model construction. Among six prevalent machine learning models, artificial neural networks performed best, with an area under the curve (AUC) of 0.855. Patients from the high-risk category with BC had a lower survival rate compared to those from the low-risk group. The qRT-PCR results revealed significantly reduced XIST expression in normal breast samples, which was consistent with our integrated analysis. Conclusion: These results potentially provide insights into the ceRNA networks linked with T-cell infiltration and provide accurate models for T-cell prediction.

A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype.

Background: Immune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB. Methods: Open datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB. Results: A total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/. Conclusion: TNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed.

Diagnostic segregation of human breast tumours using Fourier-transform infrared spectroscopy coupled with multivariate analysis: Classifying cancer subtypes.

The present study aimed to investigate whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with multivariate analysis could be applied to discriminate and classify among breast tumour molecular subtypes based on the unique spectral "fingerprints" of their biochemical composition. The different breast cancer tissues and normal breast tissues were collected and identified by pathology and ATR-FTIR spectroscopy respectively. The study indicates that the levels of the lipid-to-protein, nucleic acid-to-lipid, phosphate-to-carbohydrate and their secondary structure ratio, including RNA-to-DNA, Amide I-to-Amide II, and RNA-to-lipid ratios were significantly altered among the molecular subtype of breast tumour compared with normal breast tissues, which helps explain the changes in the biochemical structure of different molecular phenotypes of breast cancer. Tentatively-assigned characteristic peak ratios of infrared (IR) spectra reflect the changes of the macromolecule structure in different issues to a great extent and can be used as a potential biomarker to predict the molecular subtype of breast tumour. The present study acts as the first case study to show the successful application of IR spectroscopy in classifying subtypes of breast cancer with biochemical alterations. Therefore, the present study is likely to help to provide a new diagnostic approach for the accurate diagnosis of breast tumours and differential molecular subtypes and has the potential to be used for further intraoperative management.