An N-terminal splice variant of human Stat5a that interacts with different transcription factors is the dominant form expressed in invasive ductal carcinoma

Novel insights linking BRCA1-IRIS role in mammary gland development to formation of aggressive PABCs: the case for longer breastfeeding

Pregnancy-associated breast cancer (PABC) is diagnosed during or shortly after pregnancy. Although rare, PABC is a serious occurrence often of the triple negative (TNBC) subtype. Here we show progesterone, prolactin, and RANKL upregulate BRCA1-IRIS (IRIS) in separate and overlapping subpopulations of human mammary epithelial cell lines, which exacerbates the proliferation, survival, and the TNBC-like phenotype in them. Conversely, vitamin D₃ reduces IRIS expression in TNBC cell lines, which attenuates growth, survival, and the TNBC-like phenotype in them. In the mouse, Brca1-Iris (Iris, mouse IRIS homolog) is expressed at low-level in nulliparous mice, increases ~10-fold in pregnant/lactating mice, to completely disappear in involuting mice, and reappears at low-level in regressed glands. Mice underwent 3 constitutive pregnancies followed by a forced involution (after 5 days of lactation) contained ~10-fold higher Iris in their mammary glands compared to those underwent physiological involution (after 21 days of lactation). While protein extracts from lactating glands promote proliferation in IRIS^low and IRIS overexpressing (IRISOE) cells, extracts from involuting glands promote apoptosis in IRIS^low, and aneuploidy in IRISOE cells. In a cohort of breast cancer patients, lack of breastfeeding was associated with formation of chemotherapy resistant, metastatic IRISOE breast cancers. We propose that terminal differentiation triggered by long-term breastfeeding reduces IRIS expression in mammary cells allowing their elimination by the inflammatory microenvironment during physiological involution. No/short-term breastfeeding retains in the mammary gland IRISOE cells that thrive in the inflammatory microenvironment during forced involution to become precursors for aggressive breast cancers shortly after pregnancy.

Development and verification of a personalized immune prognostic feature in breast cancer

IMPACT STATEMENT

Breast cancer is among the highest prevalent malignant tumors worldwide with a low survival ratio. Immune-related genes have great potential as prognostic indicator in many types of tumors. Therefore, we have attempted to develop immune-related gene markers to enhance the prognosis of breast cancer. 17-IRGPs signature was constructed as a newly developed prognostic indicator to predict the survival of BC patients.

Prognostic alternative mRNA splicing signature in non-small cell lung cancer

Alternative splicing provides a major mechanism to generate protein diversity. Increasing evidence suggests a link of dysregulation of splicing associated with cancer. Genome-wide alternative splicing profiling in lung cancer remains largely unstudied. We generated alternative splicing profiles in 491 lung adenocarcinoma (LUAD) and 471 lung squamous cell carcinoma (LUSC) patients in TCGA using RNA-seq data, prognostic models and splicing networks were built by integrated bioinformatics analysis. A total of 3691 and 2403 alternative splicing events were significantly associated with patient survival in LUAD and LUSC, respectively, including EGFR, CD44, PIK3C3, RRAS2, MAPKAP1 and FGFR2. The area under the curve of the receiver-operator characteristic curve for prognostic predictor in NSCLC was 0.817 at 2000 days of overall survival which were also over 0.8 in LUAD and LUSC, separately. Interestingly, splicing correlation networks uncovered opposite roles of splicing factors in LUAD and LUSC. We created prognostic predictors based on alternative splicing events with high performances for risk stratification in NSCLC patients and uncovered interesting splicing networks in LUAD and LUSC which could be underlying mechanisms.

Biomarker discovery to improve prediction of breast cancer survival: using gene expression profiling, meta-analysis, and tissue validation

Purpose

Breast cancer is the leading cause of cancer death worldwide in women. The molecular mechanism for human breast cancer is unknown. Gene microarray has been widely used in breast cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis survival. So far, the valuable multigene signatures in clinical practice are unclear, and the biological importance of individual genes is difficult to detect, as the described signatures virtually do not overlap. Early prognosis of this disease, breast invasive ductal carcinoma (IDC) and breast ductal carcinoma in situ (DCIS), is vital in breast surgery.

Methods

Thus, this study reports gene expression profiling in large breast cancer cohorts from Gene Expression Omnibus, including GSE29044 (N=138) and GSE10780 (N=185) test series and four independent validation series GSE21653 (N=266), GSE20685 (N=327), GSE26971 (N=276), and GSE12776 (N=204). Significantly differentially expressed genes in human breast IDC and breast DCIS were detected by transcriptome microarray analysis.

Results

We created a set of three genes (MAMDC2, TSHZ2, and CLDN11) that were significantly correlated with disease-free survival of breast cancer patients using a univariate Cox regression model (significance level P<0.01) in a meta-analysis. Based on the risk score of the three genes, the test series patients could be separated into low-risk and high-risk groups with significantly different survival times. This signature was validated in the other three cohorts. The prognostic value of this three-gene signature was confirmed in the internal validation series and another four independent breast cancer data sets. The prognostic impact of one of the three genes, CLDN11, was confirmed by immunohistochemistry. CLDN11 was significantly overexpressed in human breast IDC as compared with normal breast tissues and breast DCIS.

Conclusion

Using novel gene expression profiling together with a meta-analysis validation approach, we have identified a three-gene signature with independent prognostic impact. Furthermore, CLDN11 may offer a biomarker to predict prognosis as well as a new target for prognostic and therapeutic intervention for human breast IDC.

SURVIV for survival analysis of mRNA isoform variation

The rapid accumulation of clinical RNA-seq data sets has provided the opportunity to associate mRNA isoform variations to clinical outcomes. Here we report a statistical method SURVIV (Survival analysis of mRNA Isoform Variation), designed for identifying mRNA isoform variation associated with patient survival time. A unique feature and major strength of SURVIV is that it models the measurement uncertainty of mRNA isoform ratio in RNA-seq data. Simulation studies suggest that SURVIV outperforms the conventional Cox regression survival analysis, especially for data sets with modest sequencing depth. We applied SURVIV to TCGA RNA-seq data of invasive ductal carcinoma as well as five additional cancer types. Alternative splicing-based survival predictors consistently outperform gene expression-based survival predictors, and the integration of clinical, gene expression and alternative splicing profiles leads to the best survival prediction. We anticipate that SURVIV will have broad utilities for analysing diverse types of mRNA isoform variation in large-scale clinical RNA-seq projects.