Pipeline for characterizing alternative mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity.

The cells of colorectal cancer (CRC) in their microenvironment experience constant stress, leading to dysregulated activity in the tumor niche. As a result, cancer cells acquire alternative pathways in response to the changing microenvironment, posing significant challenges for the design of effective cancer treatment strategies. While computational studies on high-throughput omics data have advanced our understanding of CRC subtypes, characterizing the heterogeneity of this disease remains remarkably complex. Here, we present a novel computational Pipeline for Characterizing Alternative Mechanisms (PCAM) based on biclustering to gain a more detailed understanding of cancer heterogeneity. Our application of PCAM to large-scale CRC transcriptomics datasets suggests that PCAM can generate a wealth of information leading to new biological understanding and predictive markers of alternative mechanisms. Our key findings include: 1) A comprehensive collection of alternative pathways in CRC, associated with biological and clinical factors. 2) Full annotation of detected alternative mechanisms, including their enrichment in known pathways and associations with various clinical outcomes. 3) A mechanistic relationship between known clinical subtypes and outcomes on a consensus map, visualized by the presence of alternative mechanisms. 4) Several potential novel alternative drug resistance mechanisms for Oxaliplatin, 5-Fluorouracil, and FOLFOX, some of which were validated on independent datasets. We believe that gaining a deeper understanding of alternative mechanisms is a critical step towards characterizing the heterogeneity of CRC. The hypotheses generated by PCAM, along with the comprehensive collection of biologically and clinically associated alternative pathways in CRC, could provide valuable insights into the underlying mechanisms driving cancer progression and drug resistance, which could aid in the development of more effective cancer therapies and guide experimental design towards more targeted and personalized treatment strategies. The computational pipeline of PCAM is available in GitHub (https://github.com/changwn/BC-CRC).

Binary pan-cancer classes with distinct vulnerabilities defined by pro- or anti-cancer YAP/TEAD activity.

Cancer heterogeneity impacts therapeutic response, driving efforts to discover over-arching rules that supersede variability. Here, we define pan-cancer binary classes based on distinct expression of YAP and YAP-responsive adhesion regulators. Combining informatics with in vivo and in vitro gain- and loss-of-function studies across multiple murine and human tumor types, we show that opposite pro- or anti-cancer YAP activity functionally defines binary YAPon or YAPoff cancer classes that express or silence YAP, respectively. YAPoff solid cancers are neural/neuroendocrine and frequently RB1-/-, such as retinoblastoma, small cell lung cancer, and neuroendocrine prostate cancer. YAP silencing is intrinsic to the cell of origin, or acquired with lineage switching and drug resistance. The binary cancer groups exhibit distinct YAP-dependent adhesive behavior and pharmaceutical vulnerabilities, underscoring clinical relevance. Mechanistically, distinct YAP/TEAD enhancers in YAPoff or YAPon cancers deploy anti-cancer integrin or pro-cancer proliferative programs, respectively. YAP is thus pivotal across cancer, but in opposite ways, with therapeutic implications.

Deamidation Shunts RelA from Mediating Inflammation to Aerobic Glycolysis.

Cell proliferation and inflammation are two metabolically demanding biological processes. How these competing processes are selectively executed in the same cell remains unknown. Here, we report that the enzyme carbamoyl-phosphate synthetase, aspartyl transcarbamoylase, and dihydroorotase (CAD) deamidates the RelA subunit of NF-κB in cancer cells to promote aerobic glycolysis and fuel cell proliferation in tumorigenesis. This post-translational modification switches RelA function from mediating the expression of NF-κB-responsive genes to that of glycolytic enzymes, thus shunting the cell's inflammatory response to aerobic glycolysis. Further, we profiled diverse human cancer cell lines and found that high CAD expression and a subset of RELA mutations correlated with RelA deamidation. And by use of inhibitors of key glycolytic enzymes, we validated the pivotal role of RelA deamidation in tumorigenesis of cancer cell lines. This work illuminates a mechanism by which protein deamidation selectively specifies gene expression and consequent biological processes.

Assessment of the Utility of Gene Positioning Biomarkers in the Stratification of Prostate Cancers.

There is a pressing need for additional clinical biomarkers to predict the aggressiveness of individual cancers. Here, we examine the potential usefulness of spatial genome organization as a prognostic tool for prostate cancer. Using fluorescence in situ hybridization on formalin-fixed, paraffin embedded human prostate tissue specimens, we compared the nuclear positions of four genes between clinically relevant subgroups of prostate tissues. We find that directional repositioning of SP100 and TGFB3 gene loci stratifies prostate cancers of differing Gleason scores. A more peripheral position of SP100 and TGFB3 in the nucleus, compared to benign tissues, is associated with low Gleason score cancers, whereas more internal positioning correlates with higher Gleason scores. Conversely, LMNA is more internally positioned in many non-metastatic prostate cancers, while its position is indistinguishable from benign tissue in metastatic cancer. The false positive rates were relatively low, whereas, the false negative rates of single or combinations of genes were high, limiting the clinical utility of this assay in its current form. Nevertheless, our findings of subtype-specific gene positioning patterns in prostate cancer provides proof-of-concept for the potential usefulness of spatial gene positioning for prognostic applications, and encourage further exploration of spatial gene positioning patterns to identify novel clinically relevant molecular biomarkers, which may aid treatment decisions for cancer patients.

Beyond Synthetic Lethality: Charting the Landscape of Pairwise Gene Expression States Associated with Survival in Cancer.

The phenotypic effect of perturbing a gene's activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional relationship between two genes-synthetic lethality (SL). Here, we define the more general concept of "survival-associated pairwise gene expression states" (SPAGEs) as gene pairs whose joint expression levels are associated with survival. We describe a data-driven approach called SPAGE-finder that when applied to The Cancer Genome Atlas (TCGA) data identified 71,946 SPAGEs spanning 12 distinct types, only a minority of which are SLs. The detected SPAGEs explain cancer driver genes' tissue specificity and differences in patients' response to drugs and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer SPAGEs and lay a conceptual basis for future studies of SPAGEs and their translational applications.

Computational Methods for Subtyping of Tumors and Their Applications for Deciphering Tumor Heterogeneity.

With the rapid development of deep sequencing technologies, many programs are generating multi-platform genomic profiles (e.g., somatic mutation, DNA methylation, and gene expression) for a large number of tumors. This activity has provided unique opportunities and challenges to stratify tumors and decipher tumor heterogeneity. In this chapter, we summarize several computational methods to address the challenge of tumor stratification with different types of genomic data. We further introduce their applications in emerging large-scale genomic data to show their effectiveness in deciphering tumor heterogeneity and clinical relevance.

Kallikreins Stepwise Scoring Reveals Three Subtypes of Papillary Thyroid Cancer with Prognostic Implications.

BACKGROUND: Papillary thyroid cancer (PTC) is the most common type of thyroid cancer. Unlike most cancers, its incidence has dramatically increased in the last decades mainly due to increased diagnosis of indolent PTCs. Adequate risk stratification is crucial to avoid the over-treatment of low-risk patients, as well as the under-treatment of high-risk patients, but the currently available markers are still insufficient. Kallikreins (KLKs) are emergent biomarkers in cancer, but their involvement in PTC is unknown. METHODS: This study analyzed DNA methylation (HumanMethylation arrays) and gene expression (RNA-Seq) of KLKs, BRAF and RAS mutations, and clinical data from four published thyroid cancer data sets including normal and tumor tissues (n = 73, n = 475, n = 20, and n = 82) as discovery, training, and validation series. The C4.5 classification algorithm was used to generate a decision tree. Disease-free survival was estimated using Kaplan-Meier and Cox approaches. Specific analyses were performed using real-time polymerase chain reaction and immunohistochemistry. RESULTS: The entire KLK family was deregulated in PTC, displaying a specific epigenetic and transcriptional profile strongly associated with BRAFV600E or RAS mutations. Thus, a decision-tree algorithm was developed based on three KLKs with >80% sensitivity and >95% specificity, identifying BRAF- and RAS-mutated tumors. Notably, tumors lacking these mutations were classified as BRAF- or RAS-like. Most importantly, the KLK algorithm uncovered a novel PTC subtype showing favorable prognostic features. CONCLUSIONS: The KLK algorithm could lead to a new clinically applicable strategy with important implications for the risk stratification of PTC and the management of patients.

PhyloOncology: Understanding cancer through phylogenetic analysis.

Despite decades of research and an enormity of resultant data, cancer remains a significant public health problem. New tools and fresh perspectives are needed to obtain fundamental insights, to develop better prognostic and predictive tools, and to identify improved therapeutic interventions. With increasingly common genome-scale data, one suite of algorithms and concepts with potential to shed light on cancer biology is phylogenetics, a scientific discipline used in diverse fields. From grouping subsets of cancer samples to tracing subclonal evolution during cancer progression and metastasis, the use of phylogenetics is a powerful systems biology approach. Well-developed phylogenetic applications provide fast, robust approaches to analyze high-dimensional, heterogeneous cancer data sets. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Individualizing breast cancer treatment-The dawn of personalized medicine.

Identification of breast cancer not being a single disease but backed by multiple heterogeneous oncogenic subpopulations is of growing interest in developing personalized therapies to provide optimal outcomes. Through this review, we bring attention to evolution of tumor and microenvironment heterogeneity as a predominant challenge in stratifying therapies. Establishment of a 'precancer niche' serves as a prerequisite for genetically initiated cells to survive and promote neoplastic evolution towards clinically established cancer through development of tumor and its microenvironment. Additionally, continuous evolutionary interplay between tumor and recruited stromal cells along with many other components in the tumor microenvironment adds up to further complexity in developing targeted therapies. However, through continued excellence in developing high throughput technologies including the advent of single-nucleus sequencing, which makes it possible to sequence individual tumor cells, leads to improved abilities in decoding the heterogenic perturbations through reconstruction of tumor evolutionary lineages. Furthermore, simple liquid-biopsies in form of enumeration/characterization of circulating tumor cells and tumor microvesicles found in peripheral circulation, shed from distinct tumor lesions, show great promise as prospective biomarkers towards better prognosis in tailoring individualized therapies to breast cancer patients. Lastly, by means of network medicinal approaches, it is seemingly possible to develop a map of the cell's intricate wiring network, helping to identify appropriate interconnected protein networks through which the disease spreads, offering a more patient-specific outcome. Although these therapeutic interventions through designing personalized oncology-based trials are promising, owing to continuous tumor evolution, targeting genome instability survival pathways might become an economically viable alternative.