Purpose: To evaluate ability of radiomic (computer-extracted imaging) features to distinguish non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT. Materials and Methods: For this retrospective study, screening or standard diagnostic noncontrast CT images were collected for 290 patients (mean age, 68 years; range, 18–92 years; 125 men [mean age, 67 years; range, 18–90 years] and 165 women [mean age, 68 years; range, 33–92 years]) from two institutions between 2007 and 2013. Histopathologic analysis was available for one nodule per patient. Corresponding nodule of interest was identified on CT axial images by a radiologist with manually annotation. Nodule shape, wavelet (Gabor), and texture-based (Haralick and Laws energy) features were extracted from intra- and perinodular regions. Features were pruned to train machine learning classifiers with 145 patients. In a test set of 145 patients, classifier results were compared against a convolutional neural network (CNN) and diagnostic readings of two radiologists. Results: Support vector machine classifier with intranodular radiomic features achieved an area under the receiver operating characteristic curve (AUC) of 0.75 on the test set. Combining radiomics of intranodular with perinodular regions improved the AUC to 0.80. On the same test set, CNN resulted in an AUC of 0.76. Radiologist readers achieved AUCs of 0.61 and 0.60, respectively. Conclusion: Radiomic features from intranodular and perinodular regions of nodules can distinguish non-small cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT. Summary Perinodular and intranodular radiomic features corresponding to texture and shape (radiomics) were evaluated to distinguish nonsmall cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT.

Variability of measurements was reduced with the computer-assisted perimeter method compared with the diameter method, which suggests that changes in volume can be detected more accurately with the perimeter method. The differences between these techniques seem large enough to have an impact on grading the response to therapy.

A set of features related to density and spatial architecture of TILs was found to be associated with a likelihood of recurrence of early-stage NSCLC. This information could potentially be used for helping in treatment planning and management of early-stage NSCLC.

In a search for novel transcriptional intermediary factors for the estrogen receptor (ER), we used the ligandbinding domain and hinge region of ER as bait in a yeast two-hybrid screen of a cDNA library derived from tamoxifen-resistant MCF-7 human breast tumors from an in vivo athymic nude mouse model. Here we report the isolation and characterization of the forkhead homologue in rhabdomyosarcoma (FKHR), a recently described member of the hepatocyte nuclear factor 3/forkhead homeotic gene family, as a nuclear hormone receptor (NR) intermediary protein. FKHR interacts with both steroid and nonsteroid NRs, although the effect of ligand on this interaction varies by receptor type. The interaction of FKHR with ER is enhanced by estrogen, whereas its interaction with thyroid hormone receptor and retinoic acid receptor is ligand-independent. In addition, FKHR differentially regulates the transactivation mediated by different NRs. Transient transfection of FKHR into mammalian cells dramatically represses transcription mediated by the ER, glucocorticoid receptor, and progesterone receptor. In contrast, FKHR stimulates rather than represses retinoic acid receptor-and thyroid hormone receptor-mediated transactivation. Most intriguingly, overexpression of FKHR dramatically inhibits the proliferation of ER-dependent MCF-7 breast cancer cells. Therefore, FKHR represents a bifunctional NR intermediary protein that can act as either a coactivator or corepressor, depending on the receptor type.The nuclear hormone receptors (NRs) 1 play an important role in a variety of physiological functions such as cell growth, development, differentiation, and homeostasis (1, 2). The NR superfamily is often divided into steroid and nonsteroid receptor subfamilies, which show different features in DNA binding and dimerization and a different effect on the basal transcriptional activity of the target (2, 3). The estrogen receptor (ER), a member of the steroid receptor family, is critical for the development and progression of breast cancer, and it is a useful diagnostic and therapeutic target (4 -8). Like other NRs, ER contains two distinct transactivation function domains (AFs): the ligand-independent (AF-1) and ligand-dependent (AF-2) activation domains (4,8). A large number of ER-interacting proteins have been identified that modify ER activity. Several coactivators have been characterized recently including SRC-1, Grip1/TIF2, RIP140, Trip1, CBP/P300, SPA/L7, and AIB1/ ACTR/RAC3/p/CIP (9 -12). In addition, several corepressors have also been identified including N-CoR and SMRT (13). The relative expression and/or activity of coactivators and corepressors in a particular environment may modulate the agonistic/ antagonistic activities of the partial ER antagonist, tamoxifen (Tam) (14 -17). Most recently, two bifunctional NR intermediary proteins, TIF1 and NSD1, have been described that can regulate transcription either positively or negatively, depending on both the promoter context and the cell type (18,19).To identify novel transcriptional...

The majority of small cell lung cancer (SCLC) patients demonstrate initial chemo-sensitivity, whereas a distinct subgroup of SCLC patients, termed chemo-refractory, do not respond to treatment. There is little understanding of how to distinguish these patients prior to disease treatment. Here we used gene expression profiling to stratify SCLC into subgroups and characterized a molecular phenotype that may identify, in part, chemo-refractive SCLC patients. Two subgroups of SCLC were identified in both cell lines and tumors by the reciprocal expression of two genes; INSM1, a neuroendocrine transcription factor, and YAP1, a key mediator of the Hippo pathway. The great majority of tumors expressed INSM1, which was prognostic for increased progression-free survival and associated with chemo-sensitivity in cell lines. YAP1 is expressed in a minority of SCLC tumors and was shown in cell lines to be downstream of the retinoblastoma protein (RB1) and associated with decreased drug sensitivity. RB1 expression in SCLC cell lines sensitizes them to CDK4/6 inhibitors. Wild-type RB1 mutation status, used as a surrogate marker of YAP1 expression, was prognostic for decreased patient survival and increased chemo-refractory tumor response. Thus, the reciprocal expression of INSM1 and YAP1 appears to stratify SCLC into distinct subgroups and may be useful, along with RB1 mutation status, to identify chemo-refractory SCLC patients.