Ovarian cancer is a histologically, clinically, and molecularly diverse disease with a five-year survival rate of less than 30%. It has been estimated that approximately 21,980 new cases of epithelial ovarian cancer will be diagnosed and 14,270 deaths will occur in the United States in 2015, making it the most lethal gynecologic malignancy. Ovarian tumor tissue is composed of cancer cells and a collection of different stromal cells. There is increasing evidence that demonstrates that stromal involvement is important in ovarian cancer pathogenesis. Therefore, stroma-specific signaling pathways, stroma-derived factors, and genetic changes in the tumor stroma present unique opportunities for improving the diagnosis and treatment of ovarian cancer. Cancer-associated fibroblasts (CAFs) are one of the major components of the tumor stroma that have demonstrated supportive roles in tumor progression. In this review, we highlight various types of signaling crosstalk between ovarian cancer cells and stromal cells, particularly with CAFs. In addition to evaluating the importance of signaling crosstalk in ovarian cancer progression, we discuss approaches that can be used to target tumor-promoting signaling crosstalk and how these approaches can be translated into potential ovarian cancer treatment.
The SSc-LIA has good agreement with conventional techniques for selected antibodies and has good diagnostic utility.
The role of epithelial to mesenchymal transition (EMT) in metastasis is a longstanding source of controversy, largely due to an inability to monitor transient and reversible EMT phenotypes in vivo. We have established a novel and unique EMT lineage tracing system in spontaneous breast to lung metastasis models. In these models, mesenchymal-specific Cre-mediated recombination initiates permanent switch of fluorescent markers in tumor cells undergoing EMT. This allows us to track EMT tumor cells in the primary tumor, circulation and distant organs following the trail of breast to lung metastasis in vivo. We confirmed that within a predominantly epithelial primary tumor, a small portion of tumor cells undergo EMT. Strikingly, lung metastases were mainly comprised of non-EMT tumor cells maintaining their epithelial phenotype. Inhibiting EMT by overexpressing miR-200 did not impact lung metastasis development. However, EMT cells significantly contribute to recurrent lung metastasis formation after chemotherapy. These cells survived cyclophosphamide treatment due to reduced proliferation, apoptotic tolerance, and elevated expression of chemoresistance-related genes. This study suggests the potential of an EMT-targeting strategy, in conjunction with conventional chemotherapies, in the treatment of breast cancer patients. Citation Format: Kari R. Fischer, Anna Durrans, Sharrell Lee, Jianting Sheng, Hyejin Choi, Fuhai Li, Stephen Wong, Nasser K. Altorki, Vivek Mittal, Dingcheng Gao. Epithelial to mesenchymal transition is not required for breast to lung metastasis but contributes to chemoresistance. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4721. doi:10.1158/1538-7445.AM2015-4721
Accurate estimation of mortality in patients with cancer is important when discussing prognosis and selecting treatment. Survival estimation for many cancers is based on Tumor-Node-Metastasis (TNM) staging systems that involve three factors: tumor extent, lymph node involvement, and metastasis. The most recent clinical staging of melanoma uses TNM staging but does not include a growing number of other prognostic features. The Ensemble Algorithm of Clustering of Cancer Data (EACCD) by Chen et al. is a machine learning algorithm that regroups patients with different prognostic factors according to the survival dissimilarity. This algorithm has the potential to integrate emerging prognostic factors to more accurately stage melanoma. In this study, we use EACCD to examine the current AJCC staging of melanoma by analyzing a melanoma dataset from the National Cancer Centers Surveillance, Epidemiology, and End Rresults (SEER) database. Our results demonstrates that the EACCD algorithm generates results in-line with AJCC staging and may provide a mechanism to incorporate other prognostic factors to produce a more nuanced estimation of prognosis and survival.
Lung cancer is a leading cause of deaths from cancer among men and women worldwide with an estimated 1.5 million deaths each year. Available molecularly targeted therapies benefit a limited number of patients and even in these patients acquired resistance is a major impediment to effective therapeutic response. To address the need for additional therapeutic targets, we used a unique approach to identify and target tumor-stroma paracrine crosstalk pathways in non-small cell lung cancer (NSCLC). To explore paracrine crosstalk in NSCLC, we analyzed transcriptomes of specific stromal and epithelial compartments that were sorted from Kras driven mouse model of NSCLC. Transcriptome analysis and computational modeling of multi-cellular network identified potential tumor-stroma crosstalk signaling pathways. We have selected the hepatocyte growth factor (HGF)-MET tyrosine kinase receptor signaling pathway because both HGF and MET are associated with poor survival in many cancer types including lung cancer. Notably, we identified reprogrammed intratumoral macrophages as unique source of HGF, while the expression of MET receptor was confined to tumor epithelial compartment. Importantly, we have observed elevated HGF in chemoresistant tumors both in mouse and human. Previous studies have implicated MET receptor amplification or mutations as the main mechanism of acquired resistance to EGFR TKIs, but little is known about the contribution of HGF ligand-dependent activation of MET signaling in NSCLC growth and resistance to chemotherapeutic regimens that are widely used. We have used a combination of culture experiments and in vivo genetic approaches to show that macrophage-derived HGF interacts with the MET receptor on tumor epithelial cells in a paracrine fashion, to activate downstream signaling pathways that promote tumor progression and mediate resistance to standard chemotherapies. Our investigations on this relatively unexplored HGF-MET paracrine interaction as a key mechanism of resistance to standard-of-care chemotherapeutic regimens-and not just EGFR targeted therapies-have the potential to benefit the NSCLC population as whole rather than a small subset of patients. Citation Format: Hyejin Choi, Ding Cheng Gao, Sharrell B. Lee, Anna Durrans, Seongho Ryu, Olivier Elemento, Stephen Wong, Nasser K. Altorki, Vivek Mittal. A novel HGF-MET paracrine signaling pathway promotes growth and resistance to chemotherapy in lung cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1159. doi:10.1158/1538-7445.AM2014-1159
Understanding the signaling mechanisms of breast tumor initiating cells (TIC) is important to design efficient therapeutic and management strategies of breast cancer. We present a network-based signature and a comprehensive signaling map for identification of candidates of drug repositioning and combinations for breast TIC. The network-based signature is based on an extended concept of network motifs, known as cancer-signaling bridges (CSBs), which can be used to expand the cancer drug-targets of known signaling pathways. We use the profiles of TIC derived from CD44+/CD24-/low breast cancer cells and mammospheres (MS) cells to establish network-based signatures. Facilitated by the signaling pathways that are highly connected with CSBs, e.g., MAPK, NOTCH, ECM-receptor, Jak-STAT, and Wnt, we first identify the high-confidence signaling paths automatically chosen out of CSBs by two scoring systems, namely, Differential Expression Score (DES) and Signaling Pathway Score (SPS). The high-confidence signaling paths are used to build the network-based signature that characterizes the breast TIC. The network-based signature for CD44+/CD24-/low cells is composed by 140 proteins and 132 protein-protein interactions and that for mammospheres contains 153 proteins and 119 protein-protein interactions. The FDA-approved drugs whose targets are included in the network-based signatures are repositioned to breast TIC as the candidates for the repositioning. Furthermore, we curate a comprehensive signaling map for breast TIC by using the available signaling transductions in BioCarta, KEGG, and IPA and include seven signaling pathways, PI3K/AKT, JAK/STAT, Notch, HH, Wnt, P53, and ECM. The signaling map enables us to further refine the repositioning candidates and eventually propose combination candidates by using the cross-talks of signaling pathways in the map. Using this mapping approach, the drugs targeting on TNF, KDR, and IKBKB, such as sunitinib, Arsenic trioxide, and Atorvastatin, are repositioned for treating breast TIC, while the crosstalks between pathways such as Notch+hedgehog and Hedgehog+wnt+PI3K are used to identify drug combination candidates from the target combinations, TNF+KDR, and TNF+IKBKB. Acknowledgements: This research is partially funded by NIH ICBP U54CA149169. The authors would also appreciate the discussion and advice of Jeff Rosen and David Tweardy from Baylor College of Medicine during the generation of comprehensive signaling map for breast TICs. Guangxu Jin and Hong Zhao contribute to this work equally. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4370. doi:10.1158/1538-7445.AM2011-4370
Although stromal and immune cells in the tumor microenvironment have been shown to directly affect tumor growth and chemoresistance, how the interactions among stromal and immune cells and their spatially resolved cell heterogeneity would influence ovarian patients’ survival remains largely unknown. To fill this gap, we developed a new imageomics method that (i) incorporates an artificial intelligence–based analytics pipeline for imaging mass cytometry (IMC) to increase the accuracy of cell segmentation and spatial information extraction in order to identify immune biomarkers and their interactions that can predict overall survival rates in patients with treatment-naïve ovarian cancer, and (ii) integrates quantitated spatial IMC image data with microdissected tumor and stromal transcriptomic data from the same patients as well as single-cell RNA sequencing data to detect genes correlated with the prognostic features and postulate novel mechanisms by which these genes contribute to the prognostic features.
Seventeen cases of primary and one case of metastatic breast cancer which expressed greater than 900 fmol oestrogen receptor sites per mg soluble protein were examined. All these patients were post-menopausal at the time of their presentation. These were a heterogeneous group of well-differentiated cellular breast carcinomas, comprising cases of invasive duct carcinoma with extensive tubular differentiation or with focal argyrophilia, tubulolobular carcinoma, lobular carcinoma of mixed type containing abundant intracytoplasmic lumina, papillary carcinoma and type B colloid carcinoma. There was very little tumour necrosis. Nodal Keywords: cancer, breast, histology, oestrogen receptors metastasis, tumour size and host inflammatory response did not appear to show any relationship with oestrogen receptor status. The patients, apart from two who died from other causes, remain alive (Fisher's exact test, P
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