Prognostic and functional role of subtype-specific tumor-stroma interaction in breast cancer

CircZFR promotes colorectal cancer progression via stabilizing BCLAF1 and regulating the miR-3127-5p/RTKN2 axis

Aberrant expression of circular RNAs (circRNAs) is frequently linked to colorectal cancer (CRC). Here, we identified circZFR as a promising biomarker for CRC diagnosis and prognosis. CircZFR was upregulated in CRC tissues and serum exosomes and its level was linked to cancer incidence, advanced-stages, and metastasis. In both in vitro and in vivo settings, circZFR promoted the growth and spread while suppressing apoptosis of CRC. Exosomes with circZFR overexpression promoted the proliferation and migration of cocultured CRC cells. Mechanistically, epithelial splicing regulatory protein 1 (ESRP1) in CRC cells may enhance the production of circZFR. BCL2-associated transcription factor 1 (BCLAF1) bound to circZFR, which prevented its ubiquitinated degradation. Additionally, circZFR sponged miR-3127-5p to boost rhotekin 2 (RTKN2) expression. Our TCP1-CD-QDs nanocarrier was able to carry and deliver circZFR siRNA (si-circZFR) to the vasculature of CRC tissues and cells, which inhibited the growth of tumors in patient-derived xenograft (PDX) models. Taken together, our results show that circZFR is an oncogenic circRNA, which promotes the development and spread of CRC in a BCLAF1 and miR-3127-5p-dependent manner. CircZFR is a possible serum biopsy marker for the diagnosis and a desirable target for further treatment of CRC.

A pre-operative prognostic model predicting all cause and cause specific mortality for women presenting with invasive breast cancer

PURPOSE

The aim of this study is to develop a pre-operative prognostic model based on known pre-operative factors.

METHODS

A database of ultrasound (US) lesions undergoing biopsy documented US lesion size, stiffness, and patient source prospectively. Women with invasive cancer presenting between 2010 and 2015 were the study group. Breast and axillary core results and ER, PR and HER receptor status were collected prospectively. Assessment of US skin thickening, US distal enhancement and presence of chronic kidney disease (CKD) was performed retrospectively. Patient survival and cause of death were ascertained from computer records. Predictive models for (i) all-cause mortality (ACM) and (ii) breast cancer death (BCD) were built and then validated using bootstrap k-fold cross-validation. A comparison of predictive performance was made between a full cause-specific Cox model, a sub cause-specific Cox model, and a full Fine-Gray sub-distribution hazard model.

RESULTS

1136 patients were included in the study. The median follow-up time was 6.2 years. 125 (11%) women died from breast cancer and 155 (14%) died from other causes. For the prediction of BCD, the cause-specific Cox sub-model performed the best. The time dependent AUC begins above 0.91 in year one to 3 reducing to 0.83 in year 6. The factors included in the Cox sub model were tumour size, skin thickening, source of detection, tumour grade, ER status, pre-operative nodal metastasis and CKD.

CONCLUSION

We have shown that a model based on preoperative factors can predict BCD. Such prediction if externally validated and incorporating treatment data could be useful for treatment planning and patient counselling.

Dissecting Time- from Tumor-Related Gene Expression Variability in Bilateral Breast Cancer

Metachronous (MBC) and synchronous bilateral breast tumors (SBC) are mostly distinct primaries, whereas paired primaries and their local recurrences (LRC) share a common origin. Intra-pair gene expression variability in MBC, SBC, and LRC derives from time/tumor microenvironment-related and tumor genetic background-related factors and pairs represents an ideal model for trying to dissect tumor-related from microenvironment-related variability. Pairs of tumors derived from women with SBC (n = 18), MBC (n = 11), and LRC (n = 10) undergoing local-regional treatment were profiled for gene expression; similarity between pairs was measured using an intraclass correlation coefficient (ICC) computed for each gene and compared using analysis of variance (ANOVA). When considering biologically unselected genes, the highest correlations were found for primaries and paired LRC, and the lowest for MBC pairs. By instead limiting the analysis to the breast cancer intrinsic genes, correlations between primaries and paired LRC were enhanced, while lower similarities were observed for SBC and MBC. Focusing on stromal-related genes, the ICC values decreased for MBC and were significantly different from SBC. These findings indicate that it is possible to dissect intra-pair gene expression variability into components that are associated with genetic origin or with time and microenvironment by using specific gene subsets.