Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in ER (+) and/or PR (+) and HER2 (-) Breast Cancer

Background: Although intrinsic molecular subtype has been widely used, there remains great clinical heterogeneity of prognosis in the estrogen receptor (ER)- and/or progesterone receptor (PR)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer (BC).

Methods: The transcriptome expression data of messenger RNA (mRNA) were downloaded from The Cancer Genome Atlas (TCGA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and the Gene Expression Omnibus (GEO) databases. Immune-related genes were acquired from the ImmPort database and additional literature search. Univariate Cox, LASSO regression, and multivariate Cox regression were used to screen prognostic immune-related genes and establish the risk signature. The correlation between the risk signature and clinical characteristics, the abundances of immune cells within the tumor microenvironment, and cancer phenotypes were further assessed.

Results: Of note, 102 immune-related prognostic genes were identified in the METABRIC dataset by univariate Cox analysis. Consecutively, 7 immune-related genes (SHMT2, AGA, COL17A1, FLT3, SLC7A2, ATP6AP1, and CCL19) were selected to establish the risk signature by LASSO regression and multivariate Cox analysis. Its performance was further verified in TCGA and GSE21653 datasets. Multivariate Cox analysis showed that the risk signature was an independent prognostic factor. The 7-gene signature showed a significant correlation with intrinsic molecular subtypes and 70-gene signature. Furthermore, the CD4⁺ memory T cells were significantly higher in the low-risk group while a significantly higher proportion of M0-type macrophages was found in the high-risk group in both METABRIC and TCGA cohorts, which may have an influence on the prognosis. Furthermore, we found that the low-risk group may be associated with the immune-related pathway and the high-risk group was with the cell cycle-related pathway, which also showed an impact on the prognosis.

Conclusion: These seven immune-related gene risk signatures provided an effective method for prognostic stratification in ER (+) and/or PR (+) and HER2 (−) BC.

Digital image analysis using video microscopy of human-derived prostate cancer vs normal prostate organoids to assess migratory behavior on extracellular matrix proteins

The advent of perpetuating living organoids derived from patient tissue is a promising avenue for cancer research but is limited by difficulties with precise characterization. In this brief communication, we demonstrate via time-lapse imaging distinct phenotypes of prostate organoids derived from patient material- without confirmation of cellular identity. We show that organoids derived from histologically normal tissue more readily spread on a physiologic extracellular matrix (ECM) than on pathologic ECM (p<0.0001), while tumor-derived organoids spread equally on either substrate (p=0.2406). This study is an important proof-of-concept to defer precise characterization of organoids and still glean information into disease pathology.

A genetically defined disease model reveals that urothelial cells can initiate divergent bladder cancer phenotypes

Small cell carcinoma of the bladder (SCCB) is a lethal variant of bladder cancer with no effective treatment. A lack of available preclinical models and clinical cohorts impedes our understanding of its molecular pathogenesis. In this study, we provided a tumor model as functional evidence showing that SCCB and other bladder cancer phenotypes can be derived from normal human urothelial cells. We further demonstrated that SCCB has a distinct transcriptome and identified SCCB-associated cell surface proteins (CSPs) that can be further evaluated as potential therapeutic targets. We show that our model shares CSP profile with clinical SCCB samples. Our findings create a foundation to understand the molecular underpinnings of SCCB and provide tools for developing therapeutic strategies.

Small cell carcinoma of the bladder (SCCB) is a rare and lethal phenotype of bladder cancer. The pathogenesis and molecular features are unknown. Here, we established a genetically engineered SCCB model and a cohort of patient SCCB and urothelial carcinoma samples to characterize molecular similarities and differences between bladder cancer phenotypes. We demonstrate that SCCB shares a urothelial origin with other bladder cancer phenotypes by showing that urothelial cells driven by a set of defined oncogenic factors give rise to a mixture of tumor phenotypes, including small cell carcinoma, urothelial carcinoma, and squamous cell carcinoma. Tumor-derived single-cell clones also give rise to both SCCB and urothelial carcinoma in xenografts. Despite this shared urothelial origin, clinical SCCB samples have a distinct transcriptional profile and a unique transcriptional regulatory network. Using the transcriptional profile from our cohort, we identified cell surface proteins (CSPs) associated with the SCCB phenotype. We found that the majority of SCCB samples have PD-L1 expression in both tumor cells and tumor-infiltrating lymphocytes, suggesting that immune checkpoint inhibitors could be a treatment option for SCCB. We further demonstrate that our genetically engineered tumor model is a representative tool for investigating CSPs in SCCB by showing that it shares a similar a CSP profile with clinical samples and expresses SCCB–up-regulated CSPs at both the mRNA and protein levels. Our findings reveal distinct molecular features of SCCB and provide a transcriptional dataset and a preclinical model for further investigating SCCB biology.

Cancer Phenotype Development: A Literature Review

EHR-based, computable phenotypes can be leveraged by healthcare organizations and researchers to improve the cohort identification process. The ability to identify patient cohorts using aspects of care and outcomes based on clinical characteristics or diagnostic conditions and/or risk factors presents opportunities to researchers targeting specific populations for drug development and disease interventions. The objective of this review was to summarize the literature describing the development and use of phenotypes for cohort identification of cancer patients. A survey of the literature indexed in PubMed was performed to identify studies using EHR-based phenotypes for use in cancer studies. Specific search criteria were formulated by leveraging a phenotype identification guideline developed by the Phenotypes, Data Standards, and Data Quality Core of the NIH Health Care Systems Research Collaboratory. The final set of articles was examined further to identify 1) the cancer of interest and 2) the different approaches used for phenotype development, validation and implementation. The articles reviewed were specific to breast cancer, colorectal cancer, ovarian cancer, and lung cancer. The approaches taken for phenotype development and validation varied slightly among the relevant publications. Four studies relied on chart review, three utilized machine learning techniques, one took an ontological approach, and one utilized natural language processing (NLP).

Leptin receptor signaling via Janus kinase 2/Signal transducer and activator of transcription 3 impacts on ovarian cancer cell phenotypes

Ovarian cancer is a leading cause of cancer mortality in women world-wide. Considerable progress has been made to characterize the different subtypes of ovarian cancer, but specific therapies remain limited and prognosis poor. Cytokine signaling via the interleukin-6 receptor (IL-6R) family and related receptors has been implicated in a number of cancers, including those with an ovarian origin. The leptin receptor (LEPR) is structurally related to these receptors and utilizes similar downstream pathways. LEPR has diverse roles in metabolism, appetite and bone formation with obesity linked to both elevated levels of leptin and increased cancer incidence. This study investigated a potential role for LEPR signaling in ovarian cancer. Leptin stimulation led to increased proliferation, survival and migration of LEPR-expressing ovarian cancer cell lines, with the effects shown to be mediated by the downstream Janus kinase 2/Signal transducer and activator of transcription 3 (JAK2/STAT3) pathway. A significant correlation was identified between high co-expression of leptin and LEPR and decreased patient survival. This study collectively suggests that leptin/LEPR signaling via JAK2/STAT3 has the potential to significantly impact on pathogenesis in a subset of ovarian cancer patients who may benefit from strategies that dampen this pathway.