RET,a targetable driver of pancreatic adenocarcinoma

Pancreatic ductal adenocarcinoma (PDA) remains a deadly disease, affecting about 40,000 individuals in the United States annually. We aimed to characterize the role of RET as a co-driver of pancreas tumorigenesis. To assess the role of RET as a co-driver of PDA, we generated a novel triple mutant transgenic mouse based on the cre-activated p53^R172H gene and a constitutively active RET ^M919T mutant (PRC). Survival analysis was performed using Kaplan–Meier analysis. Study of human PDA specimens and Pdx-1-Cre/Kras^G12D /p53^R172H (KPC) mice revealed that RET is upregulated during pancreas tumorigenesis, from inception through precursor lesions, to invasive cancer. We demonstrated that activation of RET is capable of inducing invasive pancreatic carcinomas in the background of the P53 inactivation mutation. Compared to KPC mice, PRC animals had distinct phenotypes, including longer latency to tumor progression, longer survival, and the presence of multiple macrometastases. Enhanced activation of the MAPK pathway was observed as early as the PanIN 2 stage. Sequencing of the exonic regions of KRAS in PRC-derived PDA cells revealed no evidence of KRAS mutations. RET can be an essential co-driver of pancreatic tumorigenesis in conjugation with KRAS activity. These data suggest that RET may be a potential target in the treatment of PDA.     

Oxyntic gland polyp/adenoma

Gastric oxyntic gland polyp/adenomas, OGA are uncommon polypoid lesions that arise from oxyntic mucosa located in the body and fundus of the stomach. A case of oxyntic gland polyp/adenoma with typical histological and immunohistochemical features is presented and controversy over nomenclature and biological behavior is discussed.     

Factor analysis for survival time prediction with informative censoring and diverse covariates

Fulfilling the promise of precision medicine requires accurately and precisely classifying disease states. For cancer, this includes prediction of survival time from a surfeit of covariates. Such data presents an opportunity for improved prediction, but also a challenge due to high dimensionality. Furthermore, disease populations can be heterogeneous. Integrative modeling is sensible, as the underlying hypothesis is that joint analysis of multiple covariates provides greater explanatory power than separate analyses. We propose an integrative latent variable model that combines factor analysis for various data types and an exponential proportional hazards (EPH) model for continuous survival time with informative censoring. The factor and EPH models are connected through low-dimensional latent variables that can be interpreted and visualized to identify subpopulations. We use this model to predict survival time. We demonstrate this model's utility in simulation and on four Cancer Genome Atlas datasets: diffuse lower-grade glioma, glioblastoma multiforme, lung adenocarcinoma, and lung squamous cell carcinoma. These datasets have small sample sizes, high-dimensional diverse covariates, and high censorship rates. We compare the predictions from our model to three alternative models. Our model outperforms in simulation and is competitive on real datasets. Furthermore, the low-dimensional visualization for diffuse lower-grade glioma displays known subpopulations.     

Spatial and Temporal Heterogeneity of Panel-Based Tumor Mutational Burden in Pulmonary Adenocarcinoma: Separating Biology From Technical Artifacts

BackgroundTumor mutational burden (TMB) is an emerging biomarker used to identify patients who are more likely to benefit from immuno-oncology therapy. Aside from various unsettled technical aspects, biological variables such as tumor cell content and intratumor heterogeneity may play an important role in determining TMB.MethodsTMB estimates were determined applying the TruSight Oncology 500 targeted sequencing panel. Spatial and temporal heterogeneity was analyzed by multiregion sequencing (two to six samples) of 24 pulmonary adenocarcinomas and by sequencing a set of matched primary tumors, locoregional lymph node metastases, and distant metastases in five patients.ResultsOn average, a coding region of 1.28 Mbp was covered with a mean read depth of 609x. Manual validation of the mutation-calls confirmed a good performance, but revealed noticeable misclassification during germline filtering. Different regions within a tumor showed considerable spatial TMB variance in 30% (7 of 24) of the cases (maximum difference, 14.13 mut/Mbp). Lymph node–derived TMB was significantly lower (p = 0.016). In 13 cases, distinct mutational profiles were exclusive to different regions of a tumor, leading to higher values for simulated aggregated TMB. Combined, intratumor heterogeneity and the aggregated TMB could result in divergent TMB designation in 17% of the analyzed patients. TMB variation between primary tumor and distant metastases existed but was not profound.ConclusionsOur data show that, in addition to technical aspects such as germline filtering, the tumor content and spatially divergent mutational profiles within a tumor are relevant factors influencing TMB estimation, revealing limitations of single-sample–based TMB estimations in a clinical context.