Large Cell Adenocarcinoma Small Cell Squamous

Large Cell Adenocarcinoma Squamous Adenocarcinoma Adenocarcinoma Adenocarcinoma Squamous Squamous Squamous

Repeating the same test using lung cancer signatures of the same type—10 adenocarcinoma signatures—did not lead to much improvement.

For adenocarcinoma , the median number of drugs identified by two signatures was 26 (Fig.

But there were two outliers: an adenocarcinoma signature [16] that shares zero drugs with any other signature, and a signature of carcinoid tumours [17] that shares a median of only three drugs with other signatures.

For example, in NCI-H23 lung adenocarcinoma cells, the median pGI5O for our predicted lung cancer drugs is 10'5'1 M, while for other CMap drugs it is 10'4'0 M (P < 10'4); values of 10'4 M are considered inactive in NCI-60.

For example, daunorubi-cin and the chemically related doxorubicin are topoisomerase inhibitors and commonly-used chemotherapeutic agents; sirolimus (rapamycin) is currently in clinical trials for several cancers, and was recently shown to increase NSCLC tumour cell sensitivity to erlotinib [19]; vori-nostat, a histone deacetylase inhibitor, enhanced the response to carboplatin or paclitaxel in patients with advanced NSCLC [20]; MS-275, also a histone deacetylase inhibitor, enhanced the response to erlotinib in an erlotinib-resistant lung adenocarcinoma cell line [21].

[8] combined two microarray data sets to create a single transcriptional signature of lung adenocarcinoma and screened it against CMap.

They tested one of their drug hits (17-AAG) in vitro and found that it inhibited growth in two lung adenocarcinoma cell lines.

Grey: 10 gene signatures of the same lung cancertype ( adenocarcinoma ) were used to retrieve 10 lists of drugs with the CMap online tool; top drugs from all pairs of signatures were compared.

drugs tested in CMap Build I linked 72 of them to adenocarcinoma of the lung, and 67 to squamous cell carcinoma of the lung [12].

A signalling entropy derived prognostic score outperforms microarray based prognostic indicators in lung adenocarcinoma

We next investigated whether a similar SE score could be computed for lung adenocarcinoma .

Signalling entropy is correlated with, yet prognostically independent of tumour stage in lung adenocarcinoma , we therefore aimed to derive a score that represented the prognostic power of our measure independently of tumour stage.

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.

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 .

Signalling entropy is prognostic in stage I lung adenocarcinoma

We next investigated the prognostic power of our measure in lung adenocarcinoma .

To evaluate the clinical associations of our measure we first computed signalling entropy for each mi-croarray sample in The Director’s Challenge dataset profiling 398 tumours [42], and for the 455 lung adenocarcinoma RNA-seq tumour samples downloaded from The Cancer Genome Atlas (TCGA) database (http://cancergenome.nih.gov/).

We observed cryptic 3’SS selection in a TCGA lung adenocarcinoma sample With a hotspot mutation but not in lung cancer samples With SF3BI mutations outside of the five hot-spots (S4 Fig.

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).

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.