Single drug biomarker prediction for ER- breast cancer outcome from chemotherapy

Identification and Prognostic Value Exploration of Cyclophosphamide (Cytoxan)-Centered Chemotherapy Response-Associated Genes in Breast Cancer

In this study, we aimed to explore cyclophosphamide (Cytoxan) response-associated genes and constructed a model to predict the prognosis of breast cancer (BRCA) patients. Samples obtained from TCGA and GEO databases were subjected to Weighted Gene Coexpression Network Analysis (WGCNA) and univariate Cox and LASSO Cox regression analysis to identify and validate the Cytoxan response-related prognostic signature. Moreover, multivariate Cox regression analysis was performed to analyze the independence of factors, and the nomogram model was constructed by including all the independent factors. WGCNA revealed that 159 genes are significantly correlated with Cytoxan response in BRCA samples, and the samples with a different prognosis could be effectively distinguished based on the expression of those 159 genes. Ten genes were further selected to be related to the prognosis of BRCA patients, including PCDHB2, GRIK2, FRMD7, CCSER1, PCDHGA1, PCDHA1, LRRC37A6P, PCDHGA12, ZNF486, and PCDHGB5, based on the Risk Score model. Among them, PCDHA1 expression was validated in cells and patient samples. Multivariate Cox regression analysis confirmed that the Risk Score is an independent factor. Furthermore, the nomogram model showed that the predicted survival probability is closely related to the actual survival probability. In conclusion, we identified 159 genes potentially correlated with the Cytoxan response of BRCA patients, which had prognostic value in BRCA.

Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

MOTIVATION

Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor.

RESULTS

Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line-derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times (P = .048) and in patients with pancreatic cancer treated with gemcitabine (P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

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

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.

TNFSF13 upregulation confers chemotherapeutic resistance via triggering autophagy initiation in triple-negative breast cancer

Since chemotherapy is a main strategy to treat triple-negative breast cancer (TNBC) patients currently, identifying a biomarker to predict chemotherapeutic responses is urgently needed for patients to avoid suffering through unnecessary chemotherapeutic treatments. Here, we found that the endogenous expression of TNFSF13 in a panel of TNBC cell lines highly correlates with paclitaxel (PTX) and doxorubicin IC₅₀ concentrations. Whereas knocking down TNFSF13 enhances PTX effectiveness in PTX-insensitive MDA-MB231 cells, recombinant TNFSF13 (recTNFSF13) desensitizes PTX-sensitive HCC1806 cells to PTX treatment. Moreover, Kaplan-Meier analysis revealed that higher TNFSF13 mRNA expression significantly predicts an increased risk for cancer recurrence in estrogen receptor (ER)-negative breast cancer patients receiving an anthracycline-based treatment. Accordingly, immunohistochemistry experiments indicated that higher levels of TNFSF13 protein are detected in TNBC patients who do not respond to an anthracycline-based treatment. The in silico analysis and Western blotting demonstrated that TNFSF13 expression inversely associates with the activity of the Akt-mTOR pathway, which acts as a negative regulator of autophagy activity. Significantly, the pharmaceutical inhibition of autophagy activity restores the therapeutic effectiveness of PTX in TNFSF13-treated HCC1806 cells. These findings suggest that TNFSF13 can serve as a predictive biomarker for TNBC patients, who can use it to decide whether to receive chemotherapy. KEY MESSAGES: TNFSF13 upregulation correlates with a poor response to chemotherapy in TNBCs. TNFSF13 promotes autophagy initiation in chemotherapeutic resistant TNBCs. Therapeutic targeting of autophagy initiation overcomes the TNFSF13-related chemoresistance. TNFSF13 could be a predictive biomarker for TNBC patients receiving chemotherapy.

Preparation, Characterization, and Pharmacokinetic Study of a Novel Long-Acting Targeted Paclitaxel Liposome with Antitumor Activity

Background

Breast cancer is the leading cause of cancer death in women. Chemotherapy to inhibit the proliferation of cancer cells is considered to be the most important therapeutic strategy. The development of long-circulating PEG and targeting liposomes is a major advance in drug delivery. However, the techniques used in liposome preparation mainly involve conventional liposomes, which have a short half-life, high concentrations in the liver and spleen reticuloendothelial system, and no active targeting.

Methods

Four kinds of paclitaxel liposomes were prepared and characterized by various analytical techniques. The long-term targeting effect of liposomes was verified by fluorescence detection methods in vivo and in vitro. Pharmacokinetic and acute toxicity tests were conducted in ICR mice to evaluate the safety of different paclitaxel preparations. The antitumor activity of ES-SSL-PTX was investigated in detail using in vitro and in vivo human breast cancer MCF-7 cell models.

Results

ER-targeting liposomes had a particle size of 137.93±1.22 nm and an acceptable encapsulation efficiency of 88.07±1.25%. The liposome preparation is best stored at 4°C, and is stable for up to 48 hrs. Cytotoxicity test on MCF-7 cells demonstrated the stronger cytotoxic activity of liposomes in comparison to free paclitaxel. We used the near-infrared fluorescence imaging technique to confirm that ES-SSL-PTX was effectively targeted and could quickly and specifically identify the tumor site. Pharmacokinetics and acute toxicity in vivo experiments were carried out. The results showed that ES-SSL-PTX could significantly prolong the half-life of the drug, increase its circulation time in vivo, improve its bioavailability and reduce its toxicity and side effects. ES-SSL-PTX can significantly improve the pharmacokinetic properties of paclitaxel, avoid allergic reaction of the original solvent, increase antitumor efficacy and reduce drug toxicity and side effects.

Conclusion

ES-SSL-PTX has great potential for improving the treatment of breast cancer, thereby improving patient prognosis and quality of life.