Novel LAMC2 fusion protein has tumor-promoting properties in ovarian carcinoma.

Laminins are heterotrimeric ECM proteins composed of α, ß, and γ chains. The γ2 chain (Lm-γ2) is a frequently expressed monomer and its expression is closely associated with cancer progression. Laminin-γ2 contains an epidermal growth factor (EGF)-like domain in its domain III (DIII or LEb). Matrix metalloproteinases can cleave off the DIII region of Lm-γ2 that retains the ligand activity for EGF receptor (EGFR). Herein, we show that a novel short form of Lm-γ2 (Lm-γ2F) containing DIII is generated without requiring MMPs and chromosomal translocation between LAMC2 on chromosome 1 and NR6A1 gene locus on chromosome 9 in human ovarian cancer SKOV3 cells. Laminin-γ2F is expressed as a truncated form lacking domains I and II, which are essential for its association with Lm-α3 and -ß3 chains of Lm-332. Secreted Lm-γ2F can act as an EGFR ligand activating the EGFR/AKT pathways more effectively than does the Lm-γ2 chain, which in turn promotes proliferation, survival, and motility of ovarian cancer cells. LAMC2-NR6A1 translocation was detected using in situ hybridization, and fusion transcripts were expressed in ovarian cancer cell tissues. Overexpression and suppression of fusion transcripts significantly increased and decreased the tumorigenic growth of cells in mouse models, respectively. To the best of our knowledge, this is the first report regarding a fusion gene of ECM showing that translocation of LAMC2 plays a crucial role in the malignant growth and progression of ovarian cancer cells and that the consequent product is a promising therapeutic target against ovarian cancers.

Genomic characteristics and prognostic significance of co-mutated ASXL1/SRSF2 acute myeloid leukemia.

The ASXL1 and SRSF2 mutations in AML are frequently found in patients with preexisting myeloid malignancies and are individually associated with poor outcomes. In this multi-institutional retrospective analysis, we assessed the genetic features and clinical outcomes of 43 patients with ASXL1mut SRSF2mut AML and compared outcomes to patients with either ASXL1 (n = 57) or SRSF2 (n = 70) mutations. Twenty-six (60%) had secondary-AML (s-AML). Variant allele fractions suggested that SRSF2 mutations preceded ASXL1 mutational events. Median overall survival (OS) was 7.0 months (95% CI:3.8,15.3) and was significantly longer in patients with de novo vs s-AML (15.3 vs 6.4 months, respectively; P = .04 on adjusted analysis). Compared to ASXL1mut SRSF2wt and ASXL1wt SRSF2mut , co-mutated patients had a 1.4 and 1.6 times increase in the probability of death, respectively (P = .049), with a trend towards inferior OS (median OS = 7.0 vs 11.5 vs 10.9 months, respectively; P = .10). Multivariable analysis suggests this difference in OS is attributable to the high proportion of s-AML patients in the co-mutated cohort (60% vs 32% and 23%, respectively). Although this study is limited by the retrospective data collection and the relatively small sample size, these data suggest that ASXL1mut SRSF2mut AML is a distinct subgroup of AML frequently associated with s-AML and differs from ASXL1mut SRSF2wt /ASXL1wt SRSF2mut with respect to etiology and leukemogenesis.

Hepatitis delta virus interacts with splicing factor SF3B155 and alters pre-mRNA splicing of cell cycle control genes.

Hepatitis delta virus (HDV) is the agent responsible for the most severe form of human viral hepatitis. The HDV genome consists of a single-stranded circular RNA molecule that encodes for one single protein, the delta antigen. Given its simplicity, HDV must make use of several host cellular proteins to accomplish its life cycle processes, including transcription, replication, post-transcriptional, and post-translational modifications. Consequently, identification of the interactions established between HDV components and host proteins assumes a pivotal interest in the search of novel therapeutic targets. Here, we used the yeast three-hybrid system to screen a human liver cDNA library to identify host proteins that interact with the HDV genomic RNA. One of the identified proteins corresponded to the splicing factor SF3B155, a component of the U2snRNP complex that is essential for the early recognition of 3' splice sites in the pre-mRNAs of human genes. We show that the interaction between the HDV genomic RNA and SF3B155 occurs in vivo and that the expression of HDV promotes changes in splicing of human genes whose alternative splicing is SF3B155-dependent. We further show that expression of HDV triggers alterations in several constitutive and alternative splicing events in the tumor suppressor RBM5 transcript, with consequent reduction of its protein levels. This is the first description that HDV expression promotes changes in the splicing of human genes, and we suggest that the HDV-induced alternative splicing changes, through SF3B155 sequester, may contribute for the early progression to hepatocellular carcinoma characteristic of HDV-infected patients.

Between-region genetic divergence reflects the mode and tempo of tumor evolution.

Given the implications of tumor dynamics for precision medicine, there is a need to systematically characterize the mode of evolution across diverse solid tumor types. In particular, methods to infer the role of natural selection within established human tumors are lacking. By simulating spatial tumor growth under different evolutionary modes and examining patterns of between-region subclonal genetic divergence from multiregion sequencing (MRS) data, we demonstrate that it is feasible to distinguish tumors driven by strong positive subclonal selection from those evolving neutrally or under weak selection, as the latter fail to dramatically alter subclonal composition. We developed a classifier based on measures of between-region subclonal genetic divergence and projected patient data into model space, finding different modes of evolution both within and between solid tumor types. Our findings have broad implications for how human tumors progress, how they accumulate intratumoral heterogeneity, and ultimately how they may be more effectively treated.

Parameter estimation for multistage clonal expansion models from cancer incidence data: A practical identifiability analysis.

Many cancers are understood to be the product of multiple somatic mutations or other rate-limiting events. Multistage clonal expansion (MSCE) models are a class of continuous-time Markov chain models that capture the multi-hit initiation-promotion-malignant-conversion hypothesis of carcinogenesis. These models have been used broadly to investigate the epidemiology of many cancers, assess the impact of carcinogen exposures on cancer risk, and evaluate the potential impact of cancer prevention and control strategies on cancer rates. Structural identifiability (the analysis of the maximum parametric information available for a model given perfectly measured data) of certain MSCE models has been previously investigated. However, structural identifiability is a theoretical property and does not address the limitations of real data. In this study, we use pancreatic cancer as a case study to examine the practical identifiability of the two-, three-, and four-stage clonal expansion models given age-specific cancer incidence data using a numerical profile-likelihood approach. We demonstrate that, in the case of the three- and four-stage models, several parameters that are theoretically structurally identifiable, are, in practice, unidentifiable. This result means that key parameters such as the intermediate cell mutation rates are not individually identifiable from the data and that estimation of those parameters, even if structurally identifiable, will not be stable. We also show that products of these practically unidentifiable parameters are practically identifiable, and, based on this, we propose new reparameterizations of the model hazards that resolve the parameter estimation problems. Our results highlight the importance of identifiability to the interpretation of model parameter estimates.

Spatial Measures of Genetic Heterogeneity During Carcinogenesis.

In this work we explore the temporal dynamics of spatial heterogeneity during the process of tumorigenesis from healthy tissue. We utilize a spatial stochastic model of mutation accumulation and clonal expansion in a structured tissue to describe this process. Under a two-step tumorigenesis model, we first derive estimates of a non-spatial measure of diversity: Simpson's Index, which is the probability that two individuals sampled at random from the population are identical, in the premalignant population. We next analyze two new measures of spatial population heterogeneity. In particular we study the typical length scale of genetic heterogeneity during the carcinogenesis process and estimate the extent of a surrounding premalignant clone given a clinical observation of a premalignant point biopsy. This evolutionary framework contributes to a growing literature focused on developing a better understanding of the spatial population dynamics of cancer initiation and progression.

Animal Models--Decoding the Molecular Biology of Oral Cancer.

Animal models have long been used to understand the initiation and progression of carcinogenesis, including that of oral mucosa.(1) One of the earliest models used was the chemical-induced oral cancer model, among which the Syrian Hamster check pouch was preferred for its ideal anatomical location and physiological features.(2) Salley et al(3) demonstrated that the cheek pouch mucosa underwent gradual changes from hyperplasia, carcinoma in situ to squamous cell carcinoma when exposed to polycyclic hydrocarbon 9, 10 dimethyl-1,2, benzanthracene (DMBA). Morris(4) standardized the dosage of carcinogen to 0.5% solution of DMBA in acetone and established that 5-week old animals were ideal to induce tumor with minimum time lag and maximum yield. Lin et al(5) demonstrated the synergistic effect of arecaidine with DMBA.

Combined effects of asbestos and cigarette smoke on the development of lung adenocarcinoma: different carcinogens may cause different genomic changes.

The carcinogens in cigarette smoke are distinct from asbestos. However, an understanding of their differential effects on lung adenocarcinoma development remains elusive. We investigated loss of heterozygosity (LOH) and the p53 mutation in 132 lung adenocarcinomas, for which asbestos body burden (AB; in numbers per gram of dry lung) was measured using adjacent normal lung. All cases were classified into 9 groups based on a matrix of cumulative smoking (CS in pack­years; CS=0, 00 groups, LOH frequency increased as AB and/or CS was elevated and was significantly higher in the ≥1,000 AB, ≥25 CS group (p=0.032). p53 mutation frequency was the lowest in the AB=0, CS=0 group, increased as AB and/or CS rose, and was significantly higher in the ≥1,000 AB, ≥25 CS group (p=0.039). p53 mutations characteristic of smoking were frequently observed in the CS>0 groups contrary to non-specific mutations in the CS=0, AB>0 groups. Combined effects of asbestos and smoking were suggested by LOH and p53 analyses. Sole exposure to asbestos did not increase LOH frequency but increased non­specific p53 mutations. These findings indicate that the major carcinogenic mechanism of asbestos may be tumor promotion, acting in an additive or synergistic manner, contributing to the genotoxic effect of smoking. Since this study was based on a general cancer center's experience, the limited sample size did not permit the consideration that the result was conclusive. Further investigation with a large sample size is needed to establish the mechanism of asbestos-induced lung carcinogenesis.