Uncoupling of EGFR–RAS signaling and nuclear localization of YBX1 in colorectal cancer

The transcription factor YBX1 can act as a mediator of signals transmitted via the EGFR–RAS–MAPK axis. YBX1 expression has been associated with tumor progression and prognosis in multiple types of cancer. Immunohistochemical studies have revealed dependency between YBX1 expression and individual EGFR family members. We analyzed YBX1 and EGFR family proteins in a colorectal cancer (CRC) cohort and provide functional analyses of YBX1 in the context of EGFR–RAS–MAPK signaling. Immunohistochemistry for YBX1 and EGFR family receptors with two antibodies for YBX1 and EGFR were performed and related to clinicopathological data. We employed Caco2 cells expressing an inducible KRASV12 gene to determine effects on localization and levels of YBX1. Mouse xenografts of Caco2-KRASV12 cells were used to determine YBX1 dynamics in a tissue context. The two different antibodies against YBX1 showed discordant immunohistochemical stainings in cell culture and clinical specimens. Expression of YBX1 and EGFR family members were not correlated in CRC. Analysis of Caco2 xenografts displayed again heterogeneity of YBX1 staining with both antibodies. Our results suggest that YBX1 is controlled via complex regulatory mechanisms involving tumor stroma interaction and signal transduction processes. Our study highlights that YBX1 antibodies have different specificities, advocating their use in a combined manner

Block-Centric Visualization Of Histological Whole Slide Images With Application To Revealing Growth-Patterns Of Early Colorectal Adenomas And Aberrant Crypt Foci

Introduction/ Background Comfortable navigation through diagnostic images is a prospective challenge for the acceptance of virtual microscopy applications in routine pathology [1],[2]. Tracing different regions of interest through multiple sections on one or several slides is a typical task in diagnostic slide examination. This laborious and time-consuming co-localization is currently executed by pathologists. Retaining the relative positions of tissue structures while alternating between multiple slides is still not feasible in a satisfactory manner in conventional nor virtual microscopy. Aims To address this issue we present a more comfortable and intuitive method to read slides using computer-assisted navigation. Furthermore, we demonstrate the strengths of our method by applying it to large series of serial colorectal tissue sections, creating new kinds of visualizations of different adenomatous mucosal architectures in human tissue, while looking for human correlates of lesions recently described in mice [3]. Methods Histological images contain multiple distortions from different sources in the laboratory and digitalization process. An interconnection model was created to describe distortions by several layers, providing a normalized tissue representation. Layers were associated with specific distortions with each layer serving as a new level of abstraction. The first layers enabled a coarse alignment of tissue sections. Further alignment is achieved by piecewise, multi-resolution, SIFT-based [4] correspondence extraction and refinement. Inside the convex hull of all fiducial points local affine transformations were applied whereas a global affine transformation was used on the outside. Animated stacks were generated for regions of interest using local rigid transformations to preserve exact morphological coherences. For subsequent creation of 3D models, the relevant histological objects within these images were annotated by pathologists, partly using computer assisted segmentation based on active contours [5]. These annotations were used subsequently to create simplified 3D models by applying VTK [6].  Results The presented methods provide an efficient means to retrieve correspondences and additional spatial information from serial sections of histological slides. They also show good applicability for specimen from different origin. Alignment methods can be applied to generate block-centric visualizations such as parallel and transparent viewing of multiple stains. Moreover, the generated stack videos and 3D models demonstrate the very good accuracy of section alignment even in large series. The visualizations enable pathologists and researchers to grasp the 3D structural relationships in the tissue at a glance, providing an excellent tool to communicate more complex histomorphological findings. Interestingly, we see two kinds of tubular adenomas, which could imply multiple ways to tubular adenoma formation in FAP-patients, possibly akin to the recent observations in mice [3]

Efficacy of the combination of long-acting release octreotide and tamoxifen in patients with advanced hepatocellular carcinoma: a randomised multicentre phase III study

To assess the efficacy of the combination of long-acting release (LAR) octreotide and tamoxifen (TMX) for the treatment of advanced hepatocellular carcinoma (HCC). A total of 109 patients with advanced HCC were randomised to receive octreotide LAR combined with TMX (n=56) (experimental treatment group) or TMX alone (n=53; control group). The clinical, biological and tumoural parameters were recorded every 3 months until death. Primary end point was patient survival; secondary end points were the impact of therapy on tumour response, quality of life and variceal bleeding episodes. Univariate and multivariate analyses were performed for assessment of specific prognostic factors. The median survival was 3 months (95% CI 1.4–4.6) for the experimental treatment group and 6 months (CI 95% 2–10) for the control group (P=0.609). There was no difference in terms of α-foetoprotein (α-FP) decrease, tumour regression, improvement of quality of life and prevention of variceal bleeding between the two groups. Variables associated with a better survival in the multivariate analysis were: presence of cirrhosis, α-FP level <400 ng ml−1 and Okuda stage I. The combination of octreotide LAR and TMX does not influence survival, tumour progression or quality of life in patients with advanced HCC

Exome-wide somatic mutation characterization of small bowel adenocarcinoma

Small bowel adenocarcinoma (SBA) is an aggressive disease with limited treatment options. Despite previous studies, its molecular genetic background has remained somewhat elusive. To comprehensively characterize the mutational landscape of this tumor type, and to identify possible targets of treatment, we conducted the first large exome sequencing study on a population-based set of SBA samples from all three small bowel segments. Archival tissue from 106 primary tumors with appropriate clinical information were available for exome sequencing from a patient series consisting of a majority of confirmed SBA cases diagnosed in Finland between the years 2003-2011. Paired-end exome sequencing was performed using Illumina HiSeq 4000, and OncodriveFML was used to identify driver genes from the exome data. We also defined frequently affected cancer signalling pathways and performed the first extensive allelic imbalance (Al) analysis in SBA. Exome data analysis revealed significantly mutated genes previously linked to SBA (TP53, KRAS, APC, SMAD4, and BRAF), recently reported potential driver genes (SOX9, ATM, and ARID2), as well as novel candidate driver genes, such as ACVR2A, ACVR1B, BRCA2, and SMARCA4. We also identified clear mutation hotspot patterns in ERBB2 and BRAF. No BRAF V600E mutations were observed. Additionally, we present a comprehensive mutation signature analysis of SBA, highlighting established signatures 1A, 6, and 17, as well as U2 which is a previously unvalidated signature. Finally, comparison of the three small bowel segments revealed differences in tumor characteristics. This comprehensive work unveils the mutational landscape and most frequently affected genes and pathways in SBA, providing potential therapeutic targets, and novel and more thorough insights into the genetic background of this tumor type.Peer reviewe

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs