Reporting Hormone Receptor Expression in Breast Carcinomas: Which Method has the Highest Prognostic Power and What Should be the Optimal Cut-off Value?

.: Hormone receptor (HR) expression is a critical marker that plays a role in the treatment and management of breast cancer. Even if patients receive hormone treatment with a hormone positivity rate of over 1%, it is controversial at what level of positivity they benefit from treatment and contribute positively to their prognosis. .: We retrospectively examined the estrogen receptor (ER) / progesterone receptor (PR) expression status, clinicopathological findings, and survival data of 386 patients who underwent surgery for breast cancer. ER/PR expressions of the patients were evaluated according to Allred, H-score and were also grouped according to staining percentages. Separate cut-off values were determined for each of these evaluation methods, and the prognostic power of these methods was investigated using receiver operating characteristic analysis. .: The prognostic power of all methods was found to be similar in terms of predicting survival. According to the staining percentage of the patients, survival was excellent if the ER value was >80% and the PR value was >1%. .: All recommended methods for reporting HRs have similar prognostic power. However, in patients with high percentage staining for ER using these methods, the prognosis is excellent. As a result, we predict that if the percentage of ER staining is low, changing the treatment management of patients may be considered clinically.

Training an automated circulating tumor cell classifier when the true classification is uncertain.

Circulating tumor cell (CTC) and tumor-derived extracellular vesicle (tdEV) loads are prognostic factors of survival in patients with carcinoma. The current method of CTC enumeration relies on operator review and, unfortunately, has moderate interoperator agreement (Fleiss' kappa 0.60) due to difficulties in classifying CTC-like events. We compared operator review, ACCEPT automated image processing, and refined the output of a deep-learning algorithm to identify CTC and tdEV for the prediction of survival in patients with metastatic and nonmetastatic cancers. Operator review is only defined for CTC. Refinement was performed using automatic contrast maximization CM-CTC of events detected in cancer and in benign samples (CM-CTC). We used 418 samples from benign diseases, 6,293 from nonmetastatic breast, 2,408 from metastatic breast, and 698 from metastatic prostate cancer to train, test, optimize, and evaluate CTC and tdEV enumeration. For CTC identification, the CM-CTC performed best on metastatic/nonmetastatic breast cancer, respectively, with a hazard ratio (HR) for overall survival of 2.6/2.1 vs. 2.4/1.4 for operator CTC and 1.2/0.8 for ACCEPT-CTC. For tdEV identification, CM-tdEV performed best with an HR of 1.6/2.9 vs. 1.5/1.0 with ACCEPT-tdEV. In conclusion, contrast maximization is effective even though it does not utilize domain knowledge.

Prognostic value of ß-Arrestins in combination with glucocorticoid receptor in epithelial ovarian cancer.

Hormones may be key factors driving cancer development, and epidemiological findings suggest that steroid hormones play a crucial role in ovarian tumorigenesis. We demonstrated that high glucocorticoid receptor (GR) expression is associated with a poor prognosis of epithelial ovarian cancer. Recent studies have shown that the GR affects ß-arrestin expression, and vice versa. Hence, we assessed the clinical significance of ß-arrestin expression in ovarian cancer and determined whether ß-arrestin and the GR synergistically have clinical significance and value as prognostic factors. We evaluated the expression of ß-arrestins 1 and 2 and the GR in 169 patients with primary epithelial ovarian cancer using immunohistochemistry. The staining intensity was graded on a scale of 0-4 and multiplied by the percentage of positive cells. We divided the samples into two categories based on the expression levels. ß-arrestin 1 and GR expression showed a moderate correlation, whereas ß-arrestin 2 and GR expression did not demonstrate any correlation. Patients with high ß-arrestin 1 and 2 expression exhibited improved survival rates, whereas patients with low GR expression showed a better survival rate. Patients with high ß-arrestin 1 and low GR levels had the best prognosis among all groups. ß-arrestin is highly expressed in ovarian cancer, suggesting its potential as a diagnostic and therapeutic biomarker. The combination of ß-arrestin and GR demonstrated greater predictive prognostic power than GR expression alone, implicating another possible role in prognostication.

Comparison of automated full-body bone metastases delineation methods and their corresponding prognostic power.

Objective.Manual disease delineation in full-body imaging of patients with multiple metastases is often impractical due to high disease burden. However, this is a clinically relevant task as quantitative image techniques assessing individual metastases, while limited, have been shown to be predictive of treatment outcome. The goal of this work was to evaluate the efficacy of deep learning-based methods for full-body delineation of skeletal metastases and to compare their performance to existing methods in terms of disease delineation accuracy and prognostic power.Approach.1833 suspicious lesions on 3718F-NaF PET/CT scans of patients with metastatic castration-resistant prostate cancer (mCRPC) were contoured and classified as malignant, equivocal, or benign by a nuclear medicine physician. Two convolutional neural network (CNN) architectures (DeepMedic and nnUNet)were trained to delineate malignant disease regions with and without three-model ensembling. Malignant disease contours using previously established methods were obtained. The performance of each method was assessed in terms of four different tasks: (1) detection, (2) segmentation, (3) PET SUV metric correlations with physician-based data, and (4) prognostic power of progression-free survival.Main Results.The nnUnet three-model ensemble achieved superior detection performance with a mean (+/- standard deviation) sensitivity of 82.9±ccc 0.1% at the selected operating point. The nnUnet single and three-model ensemble achieved comparable segmentation performance with a mean Dice coefficient of 0.80±0.12 and 0.79±0.12, respectively, both outperforming other methods. The nnUNet ensemble achieved comparable or superior SUV metric correlation performance to gold-standard data. Despite superior disease delineation performance, the nnUNet methods did not display superior prognostic power over other methods.Significance.This work showed that CNN-based (nnUNet) methods are superior to the non-CNN methods for mCRPC disease delineation in full-body18F-NaF PET/CT. The CNN-based methods, however, do not hold greater prognostic power for predicting clinical outcome. This merits more investigation on the optimal selection of delineation methods for specific clinical tasks.

Pathophysiological Role and Diagnostic Potential of R-Loops in Cancer and Beyond.

R-loops are DNA-RNA hybrids that play multifunctional roles in gene regulation, including replication, transcription, transcription-replication collision, epigenetics, and preserving the integrity of the genome. The aberrant formation and accumulation of unscheduled R-loops can disrupt gene expression and damage DNA, thereby causing genome instability. Recent links between unscheduled R-loop accumulation and the abundance of proteins that modulate R-loop biogenesis have been associated with numerous human diseases, including various cancers. Although R-loops are not necessarily causative for all disease entities described to date, they can perpetuate and even exacerbate the initially disease-eliciting pathophysiology, making them structures of interest for molecular diagnostics. In this review, we discuss the (patho) physiological role of R-loops in health and disease, their surprising diagnostic potential, and state-of-the-art techniques for their detection.

Identification of Neural Progenitor Cell-associated Chemoradiotherapy Resistance Gene Set (ARL4C, MSN, TNFAIP6) for Prognosis of Glioma.

BACKGROUND: Glioma is the most common malignant intracranial tumor with high lethality. Despite surgery combined with chemoradiotherapy, the prognosis for patients with glioma remains poor. This is primarily due to acquired chemoradiotherapy resistance. Therefore, to improve the prognosis of glioma, further study into the mechanism of chemoradiotherapy resistance is needed. OBJECTIVE: This study aimed to (1) evaluate the prognosis of patients with glioma by using a prognostic risk score model constructed by chemoradiotherapy resistance genes, (2) provide new targets and directions for precise treatment of glioma, and (3) discuss the tumor heterogeneity of tumor cells. METHODS: According to therapy class and overall survival (OS), we identified 53 genes associated with glioma chemoradiotherapy resistance in The Cancer Genome Atlas Glioblastoma (TCGA GBM) database. Considering the important role of chemoradiotherapy resistance-related genes in the prognosis of glioma, we preliminarily screened and identified vital prognostic factors among these genes by using the Cox regression model of absolute contraction and selection operators in the TCGA GBM lower-grade glioma (TCGA GBMLGG) dataset. Next, the heterogeneity of the chemoradiotherapy resistance-associated genes in different glioma cells was revealed by single-cell sequencing in the GSE117891 cohort. RESULTS: A prognostic risk score model consisting of three genes (ARL4C, MSN, TNFAIP6) was constructed. The expression of this model was high in glioma neural progenitor cells (NPCs) and low in glioma oligodendrocytes. The OS rates were significantly lower in the high- vs. low-risk group. CONCLUSION: Our 3 gene risk score complements the current glioma diagnosis and provides a novel insight into chemoradiotherapy resistance mechanisms for the prognosis of patients with glioma.

Examination of Independent Prognostic Power of Gene Expressions and Histopathological Imaging Features in Cancer.

Cancer prognosis is of essential interest, and extensive research has been conducted searching for biomarkers with prognostic power. Recent studies have shown that both omics profiles and histopathological imaging features have prognostic power. There are also studies exploring integrating the two types of measurements for prognosis modeling. However, there is a lack of study rigorously examining whether omics measurements have independent prognostic power conditional on histopathological imaging features, and vice versa. In this article, we adopt a rigorous statistical testing framework and test whether an individual gene expression measurement can improve prognosis modeling conditional on high-dimensional imaging features, and a parallel analysis is conducted reversing the roles of gene expressions and imaging features. In the analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma and liver hepatocellular carcinoma data, it is found that multiple individual genes, conditional on imaging features, can lead to significant improvement in prognosis modeling; however, individual imaging features, conditional on gene expressions, only offer limited prognostic power. Being among the first to examine the independent prognostic power, this study may assist better understanding the "connectedness" between omics profiles and histopathological imaging features and provide important insights for data integration in cancer modeling.

Comprehensive Molecular Characterization of the Hippo Signaling Pathway in Cancer.

Hippo signaling has been recognized as a key tumor suppressor pathway. Here, we perform a comprehensive molecular characterization of 19 Hippo core genes in 9,125 tumor samples across 33 cancer types using multidimensional "omic" data from The Cancer Genome Atlas. We identify somatic drivers among Hippo genes and the related microRNA (miRNA) regulators, and using functional genomic approaches, we experimentally characterize YAP and TAZ mutation effects and miR-590 and miR-200a regulation for TAZ. Hippo pathway activity is best characterized by a YAP/TAZ transcriptional target signature of 22 genes, which shows robust prognostic power across cancer types. Our elastic-net integrated modeling further reveals cancer-type-specific pathway regulators and associated cancer drivers. Our results highlight the importance of Hippo signaling in squamous cell cancers, characterized by frequent amplification of YAP/TAZ, high expression heterogeneity, and significant prognostic patterns. This study represents a systems-biology approach to characterizing key cancer signaling pathways in the post-genomic era.

Gene expression profiling in breast cancer.

In recent years, molecular research has translated into remarkable changes of breast cancer diagnostics and therapeutics. Molecular tests such as the 21 gene expression test (Oncotype DX(TM)) and 70 gene microarray test (MammaPrint(®)) have revolutionized the predictive and prognostic tools in the clinic. By stratifying the risk of recurrence for patients, the tests are able to provide clinicians with more information on the treatment outcomes of using chemotherapy, HER2 targeted therapy or endocrine therapy or the combination of the therapies for patients with particular genetic expressions. However, it is still questionable for clinical applications as some areas remain unclear and that the true benefit still needs prospective evaluation. Such studies are under way and are anxiously awaited. In this paper, the limitation of the molecular tests are discussed. As we are moving towards personalized medicine, molecular profiling will not only result in better outcomes but in a certain proportion of patients, likely will spare unnecessary use of cytotoxic compounds and reduce the cost to the health care systems.