| ANG-2 and PIGF survival analysis on breast cancer patients | ANG-2 and PIGF survival analysis on breast cancer patients |
| Abstract | Firstly, we show that in breast cancer patients the pro-angiogenic activity of TEM increased drastically from blood to tumor, suggesting that the tumor micro-environment shapes the highly pro-angiogenic phenotype of TEM. |
| Abstract | Results showed the successful in vitro reversion of such an activity by perturbation of in silico predicted target genes in tumor derived TEM, and indicated that targeting tumor TEM plasticity may constitute a novel valid therapeutic strategy in breast cancer . |
| Author Summary | In breast cancer , monocytes are angiogenic, i.e. |
| Author Summary | The identification of the tumor signals inducing the angiogenic activity of monocyte is of paramount significance because it represents the rationale for anti-angiogenic therapies in breast cancer . |
| Combining computational and experimental approaches to delineate the pathways controlling TEM pro-angiogenic function | The availability of limited amounts of patient TEM was partially overcome by taking advantage of our recently developed model system of TEM differentiated in vitro by exposing CD34+ cord blood hematopoietic progenitors to breast cancer cell conditioned culture medium [38,40]. |
| Introduction | In the present study we describe the application of a Boolean modeling based approach to investigate the molecular mechanisms underlying the angiogenic function of tumor monocytes from breast cancer patients and the experimental validation of in silico predictions derived from this modeling. |
| Introduction | Histology activities drive immunosuppressive function of TEM in human breast Cancer [38], in this study, we investigated the contribution of these pathways along with TGFBR-l and TNF-Rl pathways to TEM pro-angiogenic activity. |
| Introduction | We observed that the pro-angiogenic activity of TEM increased drastically from blood to tumor in breast cancer patients. |
| TEM from peripheral blood and tumor tissue of breast cancer patients show distinct pro-angiogenic phenotypes | TEM from peripheral blood and tumor tissue of breast cancer patients show distinct pro-angiogenic phenotypes |
| TEM from peripheral blood and tumor tissue of breast cancer patients show distinct pro-angiogenic phenotypes | The angigoenic profile of TEM was investigated in a group of 40 newly diagnosed breast cancer patients (Table 1). |
| Abstract | By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. |
| Abstract | Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. |
| Introduction | We here compute signalling entropy for a total of 5360 tumour samples, focusing on two highly heterogeneous cancers, non-small cell lung cancer (NSCLC) and breast cancer , which constitute the two leading causes of cancer death worldwide [21]. |
| Introduction | In breast cancer, the power of gene eXpression based prognostic indicators, such as OncotypeDX and MammaPrint [24, 25], is highly subtype dependent [26, 27] and a clinical breast cancer prognostic signature, which is independent of estrogen receptor (ER) status is lacking. |
| Introduction | By examining gene eXpression profiles of over 3500 primary breast cancers and 1300 lung adenocarcinomas, we here demonstrate that signalling entropy is prognostic in breast cancer , regardless of ER status, and in lung adenocarcinomas, within the stage I stratum. |
| Signalling entropy is prognostic in the major subtypes of breast cancer | Signalling entropy is prognostic in the major subtypes of breast cancer |
| Signalling entropy is prognostic in the major subtypes of breast cancer | In order to assess the prognostic significance of signalling entropy in breast cancer, we first computed its value for each microarray sample of the Molecular Taxonomy of Breast Cancer International Research Consortium dataset (METABRIC) [30], a total of 1980 samples divided into a discovery and validation sets of equal proportion. |
| Signalling entropy is prognostic in the major subtypes of breast cancer | Using outcome first as a binary phenotype, we observed that patients who died of breast cancer had a higher signalling entropy than patients who were alive at last follow up, a result which was seen in both METABRIC subsets (p < 1e — 7). |
| Abstract | For example, we identified both known and new endometrial cancer hotspots in the tyrosine kinase domain of the FGFR2 protein, one of which is also a hotspot in breast cancer , and found new two hotspots in the Immunoglobulin I-set domain in colon cancer. |
| Cancer-type-specific positioning of mutations within a given gene | Other interesting examples included that of the histone-lysine N-methyltransferase MLL3 protein, for which the PHD finger domain is mutated in breast cancer and prostate cancer, and for which the SET domain is mutated in glioblastoma and medulloblastoma. |
| Cancer-type-specific positioning of mutations within a given gene | Domain-associated mutational biases have been reported in several studies focusing on single well-known cancer genes such as the PI3KCA gene in colon and breast cancer [32], and the NOTCH1 gene in leukemia, breast and ovarian cancer [53]. |
| Discussion | By comparing the domain-level mutational landscapes of different cancers generated by our study to previously reported gene-level mutation landscapes in small cell lung cancer, melanoma, colon cancer, and breast cancer [14, 70—73] , we noticed at least ten cancer-type-spe-cif1c SMDs that do not correspond to any previously reported highly mutated cancer-associat-ed genes. |
| Discussion | Of the 12 SMDs we identified for breast cancer , only three correspond to a certain highly mutated domain type reported in the study by Nehrt et al (the P13K_p85B domain and PI3Ka domain encoded by PI3KCA, and the P53 DNA binding domain encoded by TP53). |
| Introduction | examined 100 colon cancer and 522 breast cancer samples to identify specific domain types with heightened mutation rates, succeeding even within genes that have generally lower mutation rates in colon or breast cancer [32, 33]. |
| Introduction | To better distinguish this study from previous related studies, such as the domain landscape in colon and breast cancer by Nehrt et al, we note that we are systematically analyzing multiple (twenty-one) cancer types. |
| Mutational trends of oncoproteins and tumor suppressor proteins | For example, the fibroblast growth factor receptor 2 (FGFR2) is generally regarded as an oncoprotein in breast cancer [15]. |
| Mutational trends of oncoproteins and tumor suppressor proteins | Consistent with this view, we found a single hotspot (p. N549) for FGFR2 in breast cancer in the kinase domain, which had not been reported as a hot-spot for breast cancer . |
| Mutational trends of oncoproteins and tumor suppressor proteins | The p.R248 and p.R273 hotspots were within the DNA binding site, and have each been reported as sites of potentially oncogenic mutations in many cancer types, including breast cancer [55]. |
| Oncogenic mutational hotspots appearing in multiple cancer types | We found both C420 and R88 to be positions of mutational hotspots in endometrium, colon and breast cancer . |
| Abstract | Using transcriptome sequencing data from chronic lymphocytic leukemia, breast cancer and uveal melanoma tumor samples, we show that hundreds of cryptic 3’ splice sites (3’SSs) are used in cancers with SF3B1 mutations. |
| Introduction | Recurrent mutations in the highly conserved HEAT 5—9 repeats of splicing factor 3B subunit 1 (SF3BI) have been reported in myelodysplastic syndrome, chronic lymphocytic leukemia (CLL), breast cancer (BRCA), uveal melanoma (UM), and pancreatic cancer [2—7]. |
| Sample selection | We excluded any cancer types with less than four SF3BI mutants or for which paired-end RNA-sequencing data was not available leaving breast cancer (BRCA), lung adenocarcinoma (LUAD), and lung squamous cell carcinoma (LUSC). |
| Supporting Information | We also obtained data from breast cancer (BRCA; 14 mutant, 18 Wild-type), lung squamous cell carcinoma (LUSC; four mutant, five Wild-type) and lung adenocarcinoma (LUAD; seven mutant, nine Wild-type) samples from the TCGA and uveal melanoma (UM; four mutant, four Wild-type) samples from Harbour et al. |
| Supporting Information | Breast cancer proximal cryptic 3’SS coverage. |
| Introduction | A recent study reported that circulating tumor cells are detected in 13 out of 36 breast cancer survivors 7—22 years after receiving mastectomy [43]. |
| Introduction | Most recently, in an in vitro experiment with metastatic breast cancer cells [54], it was shown that cell motility and drug gradient of chemotherapy together can lead to fast emerging resistant cells in areas of high concentrations that would otherwise completely inhibit cell growth. |
| Introduction | Metastases of solid tumors (such as breast cancer [42] and melanoma [10]) tend to have well-defined spatial compartments because of low dissemination rate, whereas the compartment structure of liquid cancer (e.g. |