Seung-Mo Hong

Bio: Seung-Mo Hong is an academic researcher from University of Ulsan. The author has contributed to research in topics: Pancreatic cancer & Pancreas. The author has an hindex of 53, co-authored 361 publications receiving 17907 citations. Previous affiliations of Seung-Mo Hong include University of Virginia Health System & Johns Hopkins University School of Medicine.

Core Signaling Pathways in Human Pancreatic Cancers Revealed by Global Genomic Analyses

Abstract: There are currently few therapeutic options for patients with pancreatic cancer, and new insights into the pathogenesis of this lethal disease are urgently needed. Toward this end, we performed a comprehensive genetic analysis of 24 pancreatic cancers. We first determined the sequences of 23,219 transcripts, representing 20,661 protein-coding genes, in these samples. Then, we searched for homozygous deletions and amplifications in the tumor DNA by using microarrays containing probes for approximately 10(6) single-nucleotide polymorphisms. We found that pancreatic cancers contain an average of 63 genetic alterations, the majority of which are point mutations. These alterations defined a core set of 12 cellular signaling pathways and processes that were each genetically altered in 67 to 100% of the tumors. Analysis of these tumors' transcriptomes with next-generation sequencing-by-synthesis technologies provided independent evidence for the importance of these pathways and processes. Our data indicate that genetically altered core pathways and regulatory processes only become evident once the coding regions of the genome are analyzed in depth. Dysregulation of these core pathways and processes through mutation can explain the major features of pancreatic tumorigenesis.

Detection of Circulating Tumor DNA in Early- and Late-Stage Human Malignancies

Abstract: The development of noninvasive methods to detect and monitor tumors continues to be a major challenge in oncology. We used digital polymerase chain reaction-based technologies to evaluate the ability of circulating tumor DNA (ctDNA) to detect tumors in 640 patients with various cancer types. We found that ctDNA was detectable in >75% of patients with advanced pancreatic, ovarian, colorectal, bladder, gastroesophageal, breast, melanoma, hepatocellular, and head and neck cancers, but in less than 50% of primary brain, renal, prostate, or thyroid cancers. In patients with localized tumors, ctDNA was detected in 73, 57, 48, and 50% of patients with colorectal cancer, gastroesophageal cancer, pancreatic cancer, and breast adenocarcinoma, respectively. ctDNA was often present in patients without detectable circulating tumor cells, suggesting that these two biomarkers are distinct entities. In a separate panel of 206 patients with metastatic colorectal cancers, we showed that the sensitivity of ctDNA for detection of clinically relevant KRAS gene mutations was 87.2% and its specificity was 99.2%. Finally, we assessed whether ctDNA could provide clues into the mechanisms underlying resistance to epidermal growth factor receptor blockade in 24 patients who objectively responded to therapy but subsequently relapsed. Twenty-three (96%) of these patients developed one or more mutations in genes involved in the mitogen-activated protein kinase pathway. Together, these data suggest that ctDNA is a broadly applicable, sensitive, and specific biomarker that can be used for a variety of clinical and research purposes in patients with multiple different types of cancer.

A Revised Classification System and Recommendations From the Baltimore Consensus Meeting for Neoplastic Precursor Lesions in the Pancreas.

Abstract: International experts met to discuss recent advances and to revise the 2004 recommendations for assessing and reporting precursor lesions to invasive carcinomas of the pancreas, including pancreatic intraepithelial neoplasia (PanIN), intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm, and other lesions. Consensus recommendations include the following: (1) To improve concordance and to align with practical consequences, a 2-tiered system (low vs. high grade) is proposed for all precursor lesions, with the provision that the current PanIN-2 and neoplasms with intermediate-grade dysplasia now be categorized as low grade. Thus, "high-grade dysplasia" is to be reserved for only the uppermost end of the spectrum ("carcinoma in situ"-type lesions). (2) Current data indicate that PanIN of any grade at a margin of a resected pancreas with invasive carcinoma does not have prognostic implications; the clinical significance of dysplasia at a margin in a resected pancreas with IPMN lacking invasive carcinoma remains to be determined. (3) Intraductal lesions 0.5 to 1 cm can be either large PanINs or small IPMNs. The term "incipient IPMN" should be reserved for lesions in this size with intestinal or oncocytic papillae or GNAS mutations. (4) Measurement of the distance between an IPMN and invasive carcinoma and sampling of intervening tissue are recommended to assess concomitant versus associated status. Conceptually, concomitant invasive carcinoma (in contrast with the "associated" group) ought to be genetically distinct from an IPMN elsewhere in the gland. (5) "Intraductal spread of invasive carcinoma" (aka, "colonization") is recommended to describe lesions of invasive carcinoma invading back into and extending along the ductal system, which may morphologically mimic high-grade PanIN or even IPMN. (6) "Simple mucinous cyst" is recommended to describe cysts >1 cm having gastric-type flat mucinous lining at most minimal atypia without ovarian-type stroma to distinguish them from IPMN. (7) Human lesions resembling the acinar to ductal metaplasia and atypical flat lesions of genetically engineered mouse models exist and may reflect an alternate pathway of carcinogenesis; however, their biological significance requires further study. These revised recommendations are expected to improve our management and understanding of precursor lesions in the pancreas.

Prevalence of the alternative lengthening of telomeres telomere maintenance mechanism in human cancer subtypes.

Abstract: Approximately 10% to 15% of human cancers lack detectable telomerase activity, and a subset of these maintain telomere lengths by the telomerase-independent telomere maintenance mechanism termed alternative lengthening of telomeres (ALT). The ALT phenotype, relatively common in subtypes of sarcomas and astrocytomas, has rarely been reported in epithelial malignancies. However, the prevalence of ALT has not been thoroughly assessed across all cancer types. We therefore comprehensively surveyed the ALT phenotype in a broad range of human cancers. In total, two independent sets comprising 6110 primary tumors from 94 different cancer subtypes, 541 benign neoplasms, and 264 normal tissue samples were assessed by combined telomere-specific fluorescence in situ hybridization and immunofluorescence labeling for PML protein. Overall, ALT was observed in 3.73% (228/6110) of all tumor specimens, but was not observed in benign neoplasms or normal tissues. This is the first report of ALT in carcinomas arising from the bladder, cervix, endometrium, esophagus, gallbladder, kidney, liver, and lung. Additionally, this is the first report of ALT in medulloblastomas, oligodendrogliomas, meningiomas, schwannomas, and pediatric glioblastoma multiformes. Previous studies have shown associations between ALT status and prognosis in some tumor types; thus, further studies are warranted to assess the potential prognostic significance and unique biology of ALT-positive tumors. These findings may have therapeutic consequences, because ALT-positive cancers are predicted to be resistant to anti-telomerase therapies.

Classification and regression trees

Abstract: Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14-23 DOI: 10.1002/widm.8 This article is categorized under: Technologies > Classification Technologies > Machine Learning Technologies > Prediction Technologies > Statistical Fundamentals

PD-1 Blockade in Tumors with Mismatch-Repair Deficiency

Abstract: BackgroundSomatic mutations have the potential to encode “non-self” immunogenic antigens. We hypothesized that tumors with a large number of somatic mutations due to mismatch-repair defects may be susceptible to immune checkpoint blockade. MethodsWe conducted a phase 2 study to evaluate the clinical activity of pembrolizumab, an anti–programmed death 1 immune checkpoint inhibitor, in 41 patients with progressive metastatic carcinoma with or without mismatch-repair deficiency. Pembrolizumab was administered intravenously at a dose of 10 mg per kilogram of body weight every 14 days in patients with mismatch repair–deficient colorectal cancers, patients with mismatch repair–proficient colorectal cancers, and patients with mismatch repair–deficient cancers that were not colorectal. The coprimary end points were the immune-related objective response rate and the 20-week immune-related progression-free survival rate. ResultsThe immune-related objective response rate and immune-related progression-free survival ...

Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.

Abstract: Background Intratumor heterogeneity may foster tumor evolution and adaptation and hinder personalized-medicine strategies that depend on results from single tumor-biopsy samples. Methods To examine intratumor heterogeneity, we performed exome sequencing, chromosome aberration analysis, and ploidy profiling on multiple spatially separated samples obtained from primary renal carcinomas and associated metastatic sites. We characterized the consequences of intratumor heterogeneity using immunohistochemical analysis, mutation functional analysis, and profiling of messenger RNA expression. Results Phylogenetic reconstruction revealed branched evolutionary tumor growth, with 63 to 69% of all somatic mutations not detectable across every tumor region. Intratumor heterogeneity was observed for a mutation within an autoinhibitory domain of the mammalian target of rapamycin (mTOR) kinase, correlating with S6 and 4EBP phosphorylation in vivo and constitutive activation of mTOR kinase activity in vitro. Mutational intratumor heterogeneity was seen for multiple tumor-suppressor genes converging on loss of function; SETD2, PTEN, and KDM5C underwent multiple distinct and spatially separated inactivating mutations within a single tumor, suggesting convergent phenotypic evolution. Gene-expression signatures of good and poor prognosis were detected in different regions of the same tumor. Allelic composition and ploidy profiling analysis revealed extensive intratumor heterogeneity, with 26 of 30 tumor samples from four tumors harboring divergent allelic-imbalance profiles and with ploidy heterogeneity in two of four tumors. Conclusions Intratumor heterogeneity can lead to underestimation of the tumor genomics landscape portrayed from single tumor-biopsy samples and may present major challenges to personalized-medicine and biomarker development. Intratumor heterogeneity, associated with heterogeneous protein function, may foster tumor adaptation and therapeutic failure through Darwinian selection. (Funded by the Medical Research Council and others.)

Cancer Genome Landscapes

Abstract: Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of “mountains” (genes altered in a high percentage of tumors) and a much larger number of “hills” (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or “drive” tumorigenesis. A typical tumor contains two to eight of these “driver gene” mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.