Neurodevelopmental disorders and cancer networks share pathways, but differ in mechanisms, signaling strength, and outcome

Epidemiological studies suggest that individuals with neurodevelopmental disorders (NDDs) are more prone to develop certain types of cancer. Notably, however, the case statistics can be impacted by late discovery of cancer in individuals afflicted with NDDs, such as intellectual disorders, autism, and schizophrenia, which may bias the numbers. As to NDD-associated mutations, in most cases, they are germline while cancer mutations are sporadic, emerging during life. However, somatic mosaicism can spur NDDs, and cancer-related mutations can be germline. NDDs and cancer share proteins, pathways, and mutations. Here we ask (i) exactly which features they share, and (ii) how, despite their commonalities, they differ in clinical outcomes. To tackle these questions, we employed a statistical framework followed by network analysis. Our thorough exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome levels and profiles of autism spectrum disorder (ASD) and cancers, point to signaling strength as the key factor: strong signaling promotes cell proliferation in cancer, and weaker (moderate) signaling impacts differentiation in ASD. Thus, we suggest that signaling strength, not activating mutations, can decide clinical outcome.

Neurodevelopmental disorders, like cancer, are connected to impaired chromatin remodelers, PI3K/mTOR, and PAK1-regulated MAPK

Neurodevelopmental disorders (NDDs) and cancer share proteins, pathways, and mutations. Their clinical symptoms are different. However, individuals with NDDs have higher probabilities of eventually developing cancer. Here, we review the literature and ask how the shared features can lead to different medical conditions and why having an NDD first can increase the chances of malignancy. To explore these vital questions, we focus on dysregulated PI3K/mTOR, a major brain cell growth pathway in differentiation, and MAPK, a critical pathway in proliferation, a hallmark of cancer. Differentiation is governed by chromatin organization, making aberrant chromatin remodelers highly likely agents in NDDs. Dysregulated chromatin organization and accessibility influence the lineage of specific cell brain types at specific embryonic development stages. PAK1, with pivotal roles in brain development and in cancer, also regulates MAPK. We review, clarify, and connect dysregulated pathways with dysregulated proliferation and differentiation in cancer and NDDs and highlight PAK1 role in brain development and MAPK regulation. Exactly how PAK1 activation controls brain development, and why specific chromatin remodeler components, e.g., BAF170 encoded by SMARCC2 in autism, await clarification.

Epithelial Wnt secretion drives the progression of inflammation-induced colon carcinoma in murine model

Colon cancer is initiated by stem cells that escape the strict control. This process is often driven through aberrant activation of Wnt signaling by mutations in components acting downstream of the receptor complex that unfetter tumor cells from the need for Wnts. Here we describe a class of colon cancer that does not depend on mutated core components of the Wnt pathway. Genetically blocking Wnt secretion from epithelial cells of such tumors results in apoptosis, reduced expression of colon cancer markers, followed by enhanced tumor differentiation. In contrast to the normal colonic epithelium, such tumor cells autosecrete Wnts to maintain their uncontrolled proliferative behavior. In humans, we determined certain cases of colon cancers in which the Wnt pathway is hyperactive, but not through mutations in its core components. Our findings illuminate the path in therapy to find further subtypes of Wnt-dependent colon cancer that might be responsive to Wnt secretion inhibitors.

•

Acquired expression of epithelial Wnts can drive colon cancer in murine AOM/DSS model

•

Blocking epithelial Wnt-secretion induces apoptosis of AOM/DSS cancer cells

•

The loss of epithelial Wnts promotes differentiation of Wnt-dependent colon tumors

•

Organoids derived from AOM/DSS cancer depend on self-autonomously secreted Wnts

Computational approaches leveraging integrated connections of multi-omic data toward clinical applications

In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing cellular activity. The complexity of the biological processes arises from the interactions between omic entities such as genes, proteins, and metabolites. Therefore, multi-omic data integration is crucial but challenging. The impact of the molecular alterations in multi-omic data is not local in the neighborhood of the altered gene or protein; rather, the impact diffuses in the network and changes the functionality of multiple signaling pathways and regulation of the gene expression. Additionally, multi-omic data is high-dimensional and has background noise. Several integrative approaches have been developed to accurately interpret the multi-omic datasets, including machine learning, network-based methods, and their combination. In this review, we overview the most recent integrative approaches and tools with a focus on network-based methods. We then discuss these approaches according to their specific applications, from disease-network and biomarker identification to patient stratification, drug discovery, and repurposing.