K

Kari Fischer

SINTEF

Publishes on Cancer Cells and Metastasis, Cancer, Hypoxia, and Metabolism, Cancer Genomics and Diagnostics. 24 papers and 1.9k citations.

24Publications
1.9kTotal Citations

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Top publicationsby citations

<i>In Vivo</i> Visualization and Characterization of Epithelial–Mesenchymal Transition in Breast Tumors
Zhen Zhao, Xiaoping Zhu, Kemi Cui et al.|Cancer Research|2016
Cited by 72Open Access

The activation of the epithelial-to-mesenchymal transition (EMT) program is a critical step in cancer progression and metastasis, but visualization of this process at the single-cell level, especially in vivo, remains challenging. We established an in vivo approach to track the fate of tumor cells based on a novel EMT-driven fluorescent color switching breast cancer mouse model and intravital two-photon laser scanning microscopy. Specifically, the MMTV-PyMT, Rosa26-RFP-GFP, and Fsp1-Cre triple transgenic mouse model was used to monitor the conversion of RFP-positive epithelial cells to GFP-positive mesenchymal cells in mammary tumors under the control of the Fsp1 (ATL1) promoter, a gate-keeper of EMT initiation. RFP-positive cells were isolated from the tumors, sorted, and transplanted into mammary fat pads of SCID mice to monitor EMT during breast tumor formation. We found that the conversion from RFP- to GFP-positive and spindle-shaped cells was a gradual process, and that GFP-positive cells preferentially localized close to blood vessels, independent of tumor size. Furthermore, cells undergoing EMT expressed high levels of the HGF receptor, c-Met, and treatment of RFP-positive cells with the c-Met inhibitor, cabozantinib, suppressed the RFP-to-GFP conversion in vitro Moreover, administration of cabozantinib to mice with palpable RFP-positive tumors resulted in a silent EMT phenotype whereby GFP-positive cells exhibited reduced motility, leading to suppressed tumor growth. In conclusion, our imaging technique provides a novel opportunity for visualizing tumor EMT at the single-cell level and may help to reveal the intricacies underlying tumor dynamics and treatment responses. Cancer Res; 76(8); 2094-104. ©2016 AACR.

Identification of Reprogrammed Myeloid Cell Transcriptomes in NSCLC
Anna Durrans, Dingcheng Gao, Ravi Gupta et al.|PLoS ONE|2015
Cited by 26Open Access

Lung cancer is the leading cause of cancer related mortality worldwide, with non-small cell lung cancer (NSCLC) as the most prevalent form. Despite advances in treatment options including minimally invasive surgery, CT-guided radiation, novel chemotherapeutic regimens, and targeted therapeutics, prognosis remains dismal. Therefore, further molecular analysis of NSCLC is necessary to identify novel molecular targets that impact prognosis and the design of new-targeted therapies. In recent years, tumor "activated/reprogrammed" stromal cells that promote carcinogenesis have emerged as potential therapeutic targets. However, the contribution of stromal cells to NSCLC is poorly understood. Here, we show increased numbers of bone marrow (BM)-derived hematopoietic cells in the tumor parenchyma of NSCLC patients compared with matched adjacent non-neoplastic lung tissue. By sorting specific cellular fractions from lung cancer patients, we compared the transcriptomes of intratumoral myeloid compartments within the tumor bed with their counterparts within adjacent non-neoplastic tissue from NSCLC patients. The RNA sequencing of specific myeloid compartments (immature monocytic myeloid cells and polymorphonuclear neutrophils) identified differentially regulated genes and mRNA isoforms, which were inconspicuous in whole tumor analysis. Genes encoding secreted factors, including osteopontin (OPN), chemokine (C-C motif) ligand 7 (CCL7) and thrombospondin 1 (TSP1) were identified, which enhanced tumorigenic properties of lung cancer cells indicative of their potential as targets for therapy. This study demonstrates that analysis of homogeneous stromal populations isolated directly from fresh clinical specimens can detect important stromal genes of therapeutic value.