No abstract

In this paper we introduce a clustering algorithm capable of simultaneously factorizing two distinct gene expression datasets with the aim of uncovering gene regulatory programs that are common to the two phenotypes. The siNMF algorithm simultaneously searches for two factorizations that share the same gene expression profiles. The two key ingredients of this algorithm are the nonnegativity constraint and the offset variables, which together ensure the sparseness of the factorizations. While cancer is a very heterogeneous disease, there is overwhelming recent evidence that the differences between cancer subtypes implicate entire pathways and biological processes involving large numbers of genes, rather than changes in single genes. We have applied our simultaneous factorization algorithm looking for gene expression profiles that are common between the more homogeneous pancreatic ductal adenocarcinoma (PDAC) and the more heterogeneous colon adenocarcinoma. The fact that the PDAC signature is active in a large fraction of colon adeocarcinoma suggests that the oncogenic mechanisms involved may be similar to those in PDAC, at least in this subset of colon samples. There are many approaches to uncovering common mechanisms involved in different phenotypes, but most are based on comparing gene lists. The approach presented in this paper additionally takes gene expression data into account and can thus be more sensitive.

Since anatomic MRI is presently not able to directly discern neuronal loss in Parkinson’s Disease (PD), studying the associated functional connectivity (FC) changes seems a promising approach toward developing non-invasive and non-radioactive neuroimaging markers for this disease. While several groups have reported such FC changes in PD, there are also significant discrepancies between studies. Investigating the reproducibility of PD-related FC changes on independent datasets is therefore of crucial importance. We acquired resting-state fMRI scans for 43 subjects (27 patients and 16 normal controls, with 2 replicate scans per subject) and compared the observed FC changes with those obtained in two independent datasets, one made available by the PPMI consortium (91 patients, 18 controls) and a second one by the group of Tao Wu (20 patients, 20 controls). Unfortunately, PD-related functional connectivity changes turned out to be non-reproducible across datasets. This could be due to disease heterogeneity, but also to technical differences. To distinguish between the two, we devised a method to directly check for disease heterogeneity using random splits of a single dataset. Since we still observe non-reproducibility in a large fraction of random splits of the same dataset, we conclude that functional heterogeneity may be a dominating factor behind the lack of reproducibility of FC alterations in different rs-fMRI studies of PD. While global PD-related functional connectivity changes were non-reproducible across datasets, we identified a few individual brain region pairs with marginally consistent FC changes across all three datasets. However, training classifiers on each one of the three datasets to discriminate PD scans from controls produced only low accuracies on the remaining two test datasets. Moreover, classifiers trained and tested on random splits of the same dataset (which are technically homogeneous) also had low test accuracies, directly substantiating disease heterogeneity.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies. It is typically detected at an advanced stage, at which the therapeutic options are very limited. One remarkable feature of PDAC that contributes to its resilience to treatment is the extreme stromal activation seen in these tumors. Often, the vast majority of tumor bulk consists of non-tumor cells that together provide a tumor-promoting environment. One of the signals that maintains and activates the stroma is the developmental protein Sonic Hedgehog (SHH). As the disease progresses, tumor cells produce increasing amounts of SHH, which activates the surrounding stroma to aid in tumor progression. To better understand this response and identify targets for inhibition, we aimed to elucidate the proteins that mediate the SHH-driven stromal response in PDAC. For this a novel mixed-species coculture model was set up in which the cancer cells are human, and the stroma is modeled by mouse fibroblasts. In conjunction with next-generation sequencing we were able to use the sequence difference between these species to genetically distinguish between the epithelial and stromal responses to SHH. The stromal SHH-dependent genes from this analysis were validated and their relevance for human disease was subsequently determined in two independent patient cohorts. In non-microdissected tissue from PDAC patients, in which a large amount of stroma is present, the targets were confirmed to associate with tumor stroma versus normal pancreatic tissue. Patient survival analysis and immunohistochemistry identified CDA, EDIL3, ITGB4, PLAUR and SPOCK1 as SHH-dependent stromal factors that are associated with poor prognosis in PDAC patients. Summarizing, the presented data provide insight into the role of the activated stroma in PDAC, and how SHH acts to mediate this response. In addition, the study has yielded several candidates that are interesting therapeutic targets for a disease for which treatment options are still inadequate.

Our results show higher connectivity between sensorimotor and spatial attention areas in patients that may be related to the reduced movement automaticity in PD.