Adult tissue-derived mesenchymal stem cells (MSCs) have demonstrated therapeutic efficacy in treating diseases or repairing damaged tissues through mechanisms thought to be mediated by either cell replacement or secretion of paracrine factors. Characterized, self-renewing human ESCs could potentially be an invariable source of consistently uniform MSCs for therapeutic applications. Here we describe a clinically relevant and reproducible manner of generating identical batches of hESC-derived MSC (hESC-MSC) cultures that circumvents exposure to virus, mouse cells, or serum. Trypsinization and propagation of HuES9 or H1 hESCs in feeder- and serum-free selection media generated three polyclonal, karyotypically stable, and phenotypically MSC-like cultures that do not express pluripotency-associated markers but displayed MSC-like surface antigens and gene expression profile. They differentiate into adipocytes, osteocytes, and chondrocytes in vitro. Gene expression and fluorescence-activated cell sorter analysis identified CD105 and CD24 as highly expressed antigens on hESC-MSCs and hESCs, respectively. CD105+, CD24- monoclonal isolates have a typical MSC gene expression profiles and were identical to each other with a highly correlated gene expression profile (r(2) > .90). We have developed a protocol to reproducibly generate clinically compliant and identical hESC-MSC cultures.

BACKGROUND & AIMS Gastric cancer (GC) is a heterogeneous disease comprising multiple subtypes that each have distinct biological properties and effects in patients. We sought to identify new, intrinsic subtypes of GC by gene expression analysis of a large panel of GC cell lines. We tested if these subtypes might be associated with differences patient survival times and responses to various standard-of-care cytotoxic drugs. METHODS We analyzed gene expression profiles for 37 GC cell lines to identify intrinsic GC subtypes. These subtypes were validated in primary tumors from 521 patients in 4 independent cohorts, where the subtypes were determined by either expression profiling or subtype-specific immunohistochemical markers (LGALS4, CDH17). In vitro sensitivity to 3 chemotherapy drugs (5-FU, cisplatin, oxaliplatin) was also assessed. RESULTS Unsupervised cell line analysis identified 2 major intrinsic genomic subtypes (G-INT and G-DIF), that had distinct patterns of gene expression. The intrinsic subtypes, but not subtypes based on Lauren’s histopathologic classification, were prognostic of survival, based on univariate and multivariate analysis in multiple patient cohorts. The G-INT cell lines were significantly more sensitive to 5-FU and oxaliplatin, but more resistant to cisplatin, than the G-DIF cell lines. In patients, intrinsic subtypes were associated with survival time following adjuvant, 5-FU based therapy. CONCLUSIONS Intrinsic subtypes of GC, based on distinct patterns of expression, are associated with patient survival and response to chemotherapy. Classification of GC based on intrinsic subtypes might be used to determine prognosis and customize therapy.

Due to a mistake made during manuscript preparation, the Supplemental Data for this article listed incorrect sequences for the primer sets for methylation-specific PCR (MSP). The correct sequences are given below. These corrections do not affect the findings or conclusions of the study.Primer sets used for detection of methylated DNA were 5 0 -ataaagagaaattaggcgc-3 0 and 5 0 -ataaccctcgaaaaacgcg-3 0 (M3), 5 0 -gatgttt gtttaggtcgtagcggtc-3 0 and 5 0 -ccaaactcgaaattcgccgta-3 0 (M2), and 5 0 -tgcgattggttgcgtttcgc-3 0 and 5 0 -cgaaaatacgcataccgcg-3 0 (M1). Primer sets used for detection of unmethylated DNA were 5 0 -ataaagagaaattaggtgt-3 0 and 5 0 -ataaccctcaaaaaacaca-3 0 (M3), 5 0 -tgtttg tttaggttgtagtggttgt-3 0 and 5 0 -cccccaaactcaaaattcaccata-3 0 (M2), and 5 0 -tgtgattggttgtgttttgt-3 0 and 5 0 -caaaaatacacataccaca-3 0 (M1).

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Next Generation Sequencing (NGS) has the potential of becoming an important tool in clinical diagnosis and therapeutic decision-making in oncology owing to its enhanced sensitivity in DNA mutation detection, fast-turnaround of samples in comparison to current gold standard methods and the potential to sequence a large number of cancer-driving genes at the one time. We aim to test the diagnostic accuracy of current NGS technology in the analysis of mutations that represent current standard-of-care, and its reliability to generate concomitant information on other key genes in human oncogenesis. Thirteen clinical samples (8 lung adenocarcinomas, 3 colon carcinomas and 2 malignant melanomas) already genotyped for EGFR, KRAS and BRAF mutations by current standard-of-care methods (Sanger Sequencing and q-PCR), were analysed for detection of mutations in the same three genes using two NGS platforms and an additional 43 genes with one of these platforms. The results were analysed using closed platform-specific proprietary bioinformatics software as well as open third party applications. Our results indicate that the existing format of the NGS technology performed well in detecting the clinically relevant mutations stated above but may not be reliable for a broader unsupervised analysis of the wider genome in its current design. Our study represents a diagnostically lead validation of the major strengths and weaknesses of this technology before consideration for diagnostic use.

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Digital pathology and image analysis potentially provide greater accuracy, reproducibility and standardisation of pathology‐based trial entry criteria and endpoints, alongside extracting new insights from both existing and novel features. Image analysis has great potential to identify, extract and quantify features in greater detail in comparison to pathologist assessment, which may produce improved prediction models or perform tasks beyond manual capability. In this article, we provide an overview of the utility of such technologies in clinical trials and provide a discussion of the potential applications, current challenges, limitations and remaining unanswered questions that require addressing prior to routine adoption in such studies. We reiterate the value of central review of pathology in clinical trials, and discuss inherent logistical, cost and performance advantages of using a digital approach. The current and emerging regulatory landscape is outlined. The role of digital platforms and remote learning to improve the training and performance of clinical trial pathologists is discussed. The impact of image analysis on quantitative tissue morphometrics in key areas such as standardisation of immunohistochemical stain interpretation, assessment of tumour cellularity prior to molecular analytical applications and the assessment of novel histological features is described. The standardisation of digital image production, establishment of criteria for digital pathology use in pre‐clinical and clinical studies, establishment of performance criteria for image analysis algorithms and liaison with regulatory bodies to facilitate incorporation of image analysis applications into clinical practice are key issues to be addressed to improve digital pathology incorporation into clinical trials.

Small bowel accounts for only 0.5% of cancer cases in the US but incidence rates have been rising at 2.4% per year over the past decade. One-third of these are adenocarcinomas but little is known about their molecular pathology and no molecular markers are available for clinical use.Using a retrospective 28 patient matched normal-tumor cohort, next-generation sequencing, gene expression arrays and CpG methylation arrays were used for molecular profiling.Next-generation sequencing identified novel mutations in IDH1, CDH1, KIT, FGFR2, FLT3, NPM1, PTEN, MET, AKT1, RET, NOTCH1 and ERBB4. Array data revealed 17% of CpGs and 5% of RNA transcripts assayed to be differentially methylated and expressed respectively (p < 0.01). Merging gene expression and DNA methylation data revealed CHN2 as consistently hypermethylated and downregulated in this disease (Spearman −0.71, p < 0.001). Mutations in TP53 which were found in more than half of the cohort (15/28) and Kazald1 hypomethylation were both were indicative of poor survival (p = 0.03, HR = 3.2 and p = 0.01, HR = 4.9 respectively).By integrating high-throughput mutational, gene expression and DNA methylation data, this study reveals for the first time the distinct molecular profile of small bowel adenocarcinoma and highlights potential clinically exploitable markers.