Interaction Between SNP Genotype and Efficacy of Anastrozole and Exemestane in Early-Stage Breast Cancer

Lessons learned from a candidate gene study investigating aromatase inhibitor treatment outcome in breast cancer

The role of germline genetics in adjuvant aromatase inhibitor (AI) treatment efficacy in ER-positive breast cancer is poorly understood. We employed a two-stage candidate gene approach to examine associations between survival endpoints and common germline variants in 753 endocrine resistance-related genes. For a discovery cohort, we screened the Breast Cancer Association Consortium database (n ≥ 90,000 cases) and retrieved 2789 AI-treated patients. Cox model-based analysis revealed 125 variants associated with overall, distant relapse-free, and relapse-free survival (p-value ≤ 1E-04). In validation analysis using five independent cohorts (n = 8857), none of the six selected candidates representing major linkage blocks at CELA2B/CASP9, NR1I2/GSK3B, LRP1B, and MIR143HG (CARMN) were validated. We discuss potential reasons for the failed validation and replication of published findings, including study/treatment heterogeneity and other limitations inherent to genomic treatment outcome studies. For the future, we envision prospective longitudinal studies with sufficiently long follow-up and endpoints that reflect the dynamic nature of endocrine resistance.

Nitidine chloride regulates cell function of bladder cancer in vitro through downregulating Lymphocyte antigen 75

Nitidine chloride (NC) is effective on cancer in many tumors, but its effect on bladder cancer (BC) is unknown. We conducted cell function experiments to verify the antineoplastic effect of NC on BC cell lines (5637, T24, and UM-UC-3) in vitro. Then, mRNAs of NC-treated and NC-untreated BC cells were extracted for mRNA sequencing. Differentially expressed genes (DEGs), expression analysis, and drug molecular docking were conducted to discover the target gene of NC. Finally, functional enrichment was analyzed to explore the underlying mechanisms. NC dramatically inhibited proliferation, migration, and invasion, and it induced apoptosis and arrested the S and G2/M phases of BC cell lines. Lymphocyte antigen 75 (LY75) appeared to be the target of NC. LY75 was highly expressed and had the ability to distinguish BC tissue from non-cancerous tissue. Then, drug molecular docking confirmed the targeting relationship between NC and LY75. Gene enrichment analysis showed that the downregulated genes, after being treated with NC, were mainly enriched in pathways relevant to cell pathophysiological processes. NC inhibits BC cell proliferation, migration, and invasion, induces apoptosis, and arrests cell cycles by downregulating the expression of LY75. This study provides molecular and theoretical bases for NC treatment of BC.

Novel inflammatory biomarkers associated with stroke severity: results from a cross-sectional stroke cohort study

BACKGROUND

Stroke is a leading cause of mortality and disability worldwide and its occurrence is expected to increase in the future. Blood biomarkers have proven their usefulness in identification and monitoring of the disease. Stroke severity is a major factor for estimation of prognosis and risk of recurrent events, but knowledge on respective blood biomarkers is still scarce. Stroke pathophysiology comprises a multitude of ischemia-induced inflammatory and immune mediated responses. Therefore, the assessment of an immune-related panel in correlation with stroke severity seems promising.

METHODS

In the present cross-sectional evaluation, a set of 92 blood biomarkers of a standardized immune panel were gathered (median 4.6 days after admission) and related to stroke severity measures, assessed at hospital admission of acute stroke patients. Multivariable logistic regression models were used to determine associations between biomarkers and modified Rankin Scale (mRS), linear regression models were used for associations with National Institute of Health Stroke Scale.

RESULTS

415 patients (mean age 69 years; 41% female) were included for biomarker analysis. C-type lectin domain family 4 member G (CLEC4G; OR = 2.89, 95% CI [1.49; 5.59], p_adj = 0.026, Cytoskeleton-associated protein 4 (CKAP4; OR = 2.38, 95% CI [1.43; 3.98], p_adj = 0.019), and Interleukin-6 (IL-6) (IL6; OR = 1.97, 95% CI [1.49; 2.62], p_adj < 0.001) were positively associated with stroke severity measured by mRS, while Lymphocyte antigen 75 (LY75; OR = 0.37, 95% CI [0.19; 0.73], p_adj = 0.049) and Integrin alpha-11 (ITGA11 OR = 0.24, 95% CI [0.14, 0.40] p_adj < 0.001) were inversely associated. When investigating the relationships with the NIHSS, IL-6 (β = 0.23, 95% CI [0.12, 0.33] p_adj = 0.001) and ITGA11 (β =  - 0.60, 95% CI [- 0.83, - 0.37] p_adj < 0.001) were significantly associated.

CONCLUSIONS

Higher relative concentrations of plasma CLEC4G, CKAP4, and IL-6 were associated with higher stroke severity, whereas LY75 and ITGA11 showed an inverse association. Future research might show a possible use as therapeutic targets and application in individual risk assessments.

Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL

Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.

lncRNA and breast cancer: Progress from identifying mechanisms to challenges and opportunities of clinical treatment

Breast cancer is a malignant tumor that has a high mortality rate and mostly occurs in women. Although significant progress has been made in the implementation of personalized treatment strategies for molecular subtypes in breast cancer, the therapeutic response is often not satisfactory. Studies have reported that long non-coding RNAs (lncRNAs) are abnormally expressed in breast cancer and closely related to the occurrence and development of breast cancer. In addition, the high tissue and cell-type specificity makes lncRNAs particularly attractive as diagnostic biomarkers, prognostic factors, and specific therapeutic targets. Therefore, an in-depth understanding of the regulatory mechanisms of lncRNAs in breast cancer is essential for developing new treatment strategies. In this review, we systematically elucidate the general characteristics, potential mechanisms, and targeted therapy of lncRNAs and discuss the emerging functions of lncRNAs in breast cancer. Additionally, we also highlight the advantages and challenges of using lncRNAs as biomarkers for diagnosis or therapeutic targets for drug resistance in breast cancer and present future perspectives in clinical practice.