The Signal Transducer IL6ST (gp130) as a Predictive and Prognostic Biomarker in Breast Cancer

Novel biomarkers are needed to continue to improve breast cancer clinical management and outcome. IL6-like cytokines, whose pleiotropic functions include roles in many hallmarks of malignancy, rely on the signal transducer IL6ST (gp130) for all their signalling. To date, 10 separate independent studies based on the analysis of clinical breast cancer samples have identified IL6ST as a predictor. Consistent findings suggest that IL6ST is a positive prognostic factor and is associated with ER status. Interestingly, these studies include 4 multigene signatures (EndoPredict, EER4, IRSN-23 and 42GC) that incorporate IL6ST to predict risk of recurrence or outcome from endocrine or chemotherapy. Here we review the existing evidence on the promising predictive and prognostic value of IL6ST. We also discuss how this potential could be further translated into clinical practice beyond the EndoPredict tool, which is already available in the clinic. The most promising route to further exploit IL6ST’s promising predicting power will likely be through additional hybrid multifactor signatures that allow for more robust stratification of ER+ breast tumours into discrete groups with distinct outcomes, thus enabling greater refinement of the treatment-selection process.

Download Full-text

Efficient gene expression signature for a breast cancer immuno-subtype

PLoS ONE ◽

10.1371/journal.pone.0245215 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0245215

Author(s):

Ben Galili ◽

Xavier Tekpli ◽

Vessela N. Kristensen ◽

Zohar Yakhini

Keyword(s):

Breast Cancer ◽

Selection Process ◽

Expression Profiles ◽

Gene Expression Signature ◽

Classification Rule ◽

P Value ◽

Cancer Pathogenesis ◽

Efficient Gene ◽

Clinical Breast ◽

Dual Performance

Motivation and background The patient’s immune system plays an important role in cancer pathogenesis, prognosis and susceptibility to treatment. Recent work introduced an immune related breast cancer. This subtyping is based on the expression profiles of the tumor samples. Specifically, one study showed that analyzing 658 genes can lead to a signature for subtyping tumors. Furthermore, this classification is independent of other known molecular and clinical breast cancer subtyping. Finally, that study shows that the suggested subtyping has significant prognostic implications. Results In this work we develop an efficient signature associated with survival in breast cancer. We begin by developing a more efficient signature for the above-mentioned breast cancer immune-based subtyping. This signature represents better performance with a set of 579 genes that obtains an improved Area Under Curve (AUC). We then determine a set of 193 genes and an associated classification rule that yield subtypes with a much stronger statistically significant (log rank p-value < 2 × 10−4 in a test cohort) difference in survival. To obtain these improved results we develop a feature selection process that matches the high-dimensionality character of the data and the dual performance objectives, driven by survival and anchored by the literature subtyping.

Download Full-text

Abstract P1-06-01: Putting multigene signatures to the test: Prognostic assessment in population-based contemporary clinical breast cancer

10.1158/1538-7445.sabcs17-p1-06-01 ◽

2018 ◽

Author(s):

J Staaf ◽

J Vallon-Christersson ◽

J Häkkinen ◽

LH Saal ◽

C Hegardt ◽

...

Keyword(s):

Breast Cancer ◽

Population Based ◽

Prognostic Assessment ◽

Clinical Breast ◽

Multigene Signatures

Download Full-text

Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

Scientific Reports ◽

10.1038/s41598-021-84995-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chi-Ming Chu ◽

Huan-Ming Hsu ◽

Chi-Wen Chang ◽

Yuan-Kuei Li ◽

Yu-Jia Chang ◽

...

Keyword(s):

Breast Cancer ◽

Prediction Models ◽

Reference Model ◽

External Validation ◽

Risk Scores ◽

Consistent Finding ◽

Hazard Ratios ◽

Genetic Predictors ◽

Novel Biomarkers ◽

Cox Proportional Hazard Regression

AbstractGenetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0–81.4% and 74.6–78% respectively (rfm, ACC 63.2–65.5%, AUC 61.9–74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10–8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.

Download Full-text

DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

Cancer Control ◽

10.1177/1073274820988519 ◽

2021 ◽

Vol 28 ◽

pp. 107327482098851

Author(s):

Zeng-Hong Wu ◽

Yun Tang ◽

Yan Zhou

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Molecular Subtypes ◽

Methylation Status ◽

The Cancer Genome Atlas ◽

Training Dataset ◽

Consensus Clustering ◽

Breast Cancer Patients ◽

Cancer Subtypes ◽

Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

Download Full-text

Recent Discoveries of Macromolecule- and Cell-Based Biomarkers and Therapeutic Implications in Breast Cancer

International Journal of Molecular Sciences ◽

10.3390/ijms22020636 ◽

2021 ◽

Vol 22 (2) ◽

pp. 636

Author(s):

Hsing-Ju Wu ◽

Pei-Yi Chu

Keyword(s):

Breast Cancer ◽

Normal Breast ◽

New Drugs ◽

Cancer Type ◽

Luminal A ◽

Luminal B ◽

Therapeutic Implications ◽

Whole Cells ◽

Diagnosis And Prognosis ◽

Novel Biomarkers

Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality in women worldwide. Breast cancer is fairly heterogeneous and reveals six molecular subtypes: luminal A, luminal B, HER2+, basal-like subtype (ER−, PR−, and HER2−), normal breast-like, and claudin-low. Breast cancer screening and early diagnosis play critical roles in improving therapeutic outcomes and prognosis. Mammography is currently the main commercially available detection method for breast cancer; however, it has numerous limitations. Therefore, reliable noninvasive diagnostic and prognostic biomarkers are required. Biomarkers used in cancer range from macromolecules, such as DNA, RNA, and proteins, to whole cells. Biomarkers for cancer risk, diagnosis, proliferation, metastasis, drug resistance, and prognosis have been identified in breast cancer. In addition, there is currently a greater demand for personalized or precise treatments; moreover, the identification of novel biomarkers to further the development of new drugs is urgently needed. In this review, we summarize and focus on the recent discoveries of promising macromolecules and cell-based biomarkers for the diagnosis and prognosis of breast cancer and provide implications for therapeutic strategies.

Download Full-text

The Multifaced Role of STAT3 in Cancer and Its Implication for Anticancer Therapy

International Journal of Molecular Sciences ◽

10.3390/ijms22020603 ◽

2021 ◽

Vol 22 (2) ◽

pp. 603

Author(s):

Manlio Tolomeo ◽

Antonio Cascio

Keyword(s):

Tumor Promoter ◽

Tumor Type ◽

Signal Transducer ◽

Suppressor Factor ◽

Specific Tumor ◽

Cytokine Stimulation ◽

Pleiotropic Functions ◽

Stat3 Mutations ◽

Potent Tumor

Signal transducer and activator of transcription (STAT) 3 is one of the most complex regulators of transcription. Constitutive activation of STAT3 has been reported in many types of tumors and depends on mechanisms such as hyperactivation of receptors for pro-oncogenic cytokines and growth factors, loss of negative regulation, and excessive cytokine stimulation. In contrast, somatic STAT3 mutations are less frequent in cancer. Several oncogenic targets of STAT3 have been recently identified such as c-myc, c-Jun, PLK-1, Pim1/2, Bcl-2, VEGF, bFGF, and Cten, and inhibitors of STAT3 have been developed for cancer prevention and treatment. However, despite the oncogenic role of STAT3 having been widely demonstrated, an increasing amount of data indicate that STAT3 functions are multifaced and not easy to classify. In fact, the specific cellular role of STAT3 seems to be determined by the integration of multiple signals, by the oncogenic environment, and by the alternative splicing into two distinct isoforms, STAT3α and STAT3β. On the basis of these different conditions, STAT3 can act both as a potent tumor promoter or tumor suppressor factor. This implies that the therapies based on STAT3 modulators should be performed considering the pleiotropic functions of this transcription factor and tailored to the specific tumor type.

Download Full-text

Financial Anxiety is Associated With Cancer Screening Adherence in Women at High Risk of Breast Cancer

Annals of Behavioral Medicine ◽

10.1093/abm/kaab010 ◽

2021 ◽

Author(s):

Salene M W Jones ◽

Tammy A Schuler ◽

Tasleem J Padamsee ◽

M Robyn Andersen

Keyword(s):

Breast Cancer ◽

Cancer Screening ◽

High Risk ◽

Pap Smear ◽

Clinical Breast Examination ◽

Lower Odds ◽

Breast Examination ◽

Clinical Breast ◽

Financial Anxiety ◽

Screening Adherence

Abstract Background Previous studies have examined the impact of material financial hardship on cancer screening but without focusing on the psychological aspects of financial hardship. Purpose This study examined the effects of different types of financial anxiety on adherence to breast cancer screening in women at high risk of breast cancer. Adherence to cervical cancer screening was also examined to determine whether associations between financial anxiety and screening adherence were unique to breast cancer screening or more general. Methods Women (n = 324) aged 30–50 and at high risk for inherited breast cancer completed a survey on general financial anxiety, worry about affording healthcare, financial stigma due to cancer risk, and adherence to cancer screening. Multivariate analyses controlled for poverty, age, and race. Results More financial anxiety was associated with lower odds of mammogram adherence (odds ratio [OR] = 0.97, confidence interval [CI] = 0.94, 0.99), Pap smear adherence (OR = 0.98, CI = 0.96, 0.996), and clinical breast examination adherence (OR = 0.98, CI = 0.96, 0.995). More worry about affording healthcare was associated with lower odds of clinical breast examination adherence (OR = 0.95, CI = 0.91, 0.9992) but not mammogram or Pap smear adherence (p > .05). Financial stigma due to cancer risk was associated with lower odds of Pap smear adherence (OR = 0.87, CI = 0.77, 0.97) but no other cancer screenings (p > .07). Conclusions Financial anxiety may impede cancer screening, even for high-risk women aware of their risk status. Clinical interventions focused on social determinants of health may also need to address financial anxiety for women at high risk of breast cancer.

Download Full-text