Identification of relevant genetic alterations in cancer using topological data analysis

Assessment of Methylation in Selected ADAMTS Family Genes in Non-Small-Cell Lung Cancer

Alterations in the methylation of genetic material can influence carcinogenesis by the downregulation or overexpression of ADAMTS (a disintegrin-like and metalloprotease with thrombospondin motifs) protease genes. Through their proteolytic activity, these enzymes are also capable of promoting angiogenesis. Consequently, ADAMTS proteases can either facilitate or inhibit cancer progression. This study aimed to evaluate the methylation levels of the ADAMTS6, ADAMTS9, and ADAMTS12 genes in non-small-cell lung cancer (NSCLC) using data from bioinformatics databases. The focus was on differences between lung adenocarcinoma (LUAD) and lung squamous-cell carcinoma (LUSC) subtypes and their impact on patient overall survival (OS). ADAMTS6 gene expression is significantly reduced in LUSC, and analysis of ADAMTS9 gene expression showed a significantly reduced gene transcript level in LUAD and LUSC, while both NSCLC subtypes demonstrated ADAMTS12 upregulation. In LUSC, significantly elevated promoter methylation was found in all of the aforementioned genes, while in LUAD, higher promoter methylation was observed only for ADAMTS9 and ADAMTS12. The differential methylation region (DMR) pattern demonstrated by ADAMTS6, ADAMTS9, and ADAMTS12 is a useful tool for distinguishing normal from cancer cells. The areas under the curve (AUCs) ranged from 0.86 to 0.99 for both LUAD and LUSC subtypes. The methylation level of different CpG sites among selected ADAMTS members is related to patient survival, suggesting it may have value as a prognostic marker. The methylation degree of promoter regions in genes encoding ADAMTS family proteins could significantly influence LUSC and LUAD. Increased promoter methylation could also reduce certain gene expression, contributing to cancer progression. The expression levels and specific DMRs of ADAMTS genes may serve as prognostic markers correlating with patient OS. Assessing ADAMTS gene methylation could become a diagnostic tool for differentiating NSCLC subtypes and potentially guide therapeutic strategies. Further research is needed to fully understand the activity and mechanisms of ADAMTS family proteins.

Mutation impact on mRNA versus protein expression across human cancers

BACKGROUND

Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression-in addition to gene expression-has rarely been systematically investigated. This is significant as mRNA and protein levels frequently show only moderate correlation, driven by factors such as translation efficiency and protein degradation. Proteogenomic datasets from large tumor cohorts provide an opportunity to systematically analyze the effects of somatic mutations on mRNA and protein abundance and identify mutations with distinct impacts on these molecular levels.

RESULTS

We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across 6 cancer types. Protein-level impacts are validated for 47.2% of the somatic expression quantitative trait loci (seQTLs), including CDH1 and MSH3 truncations, as well as other mutations from likely "long-tail" driver genes. Devising a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs), we reveal several gene mutations, including NF1 and MAP2K4 truncations and TP53 missenses showing disproportional influence on protein abundance not readily explained by transcriptomics. Cross-validating with data from massively parallel assays of variant effects (MAVE), TP53 missenses associated with high tumor TP53 proteins are more likely to be experimentally confirmed as functional.

CONCLUSION

This study reveals that somatic mutations can exhibit distinct impacts on mRNA and protein levels, underscoring the necessity of integrating proteogenomic data to comprehensively identify functionally significant cancer mutations. These insights provide a framework for prioritizing mutations for further functional validation and therapeutic targeting.

Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology

Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able to obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.

p53 exerts anticancer effects by regulating enhancer formation and activity

The abnormality of the p53 tumor suppressor is crucial in lung cancer development, because p53 regulates target gene promoters to combat cancer. Recent studies have shown extensive p53 binding to enhancer elements. However, whether p53 exerts a tumor suppressor role by shaping the enhancer landscape remains poorly understood. In the current study, we employed several functional genomics approaches to assess the enhancer activity at p53 binding sites throughout the genome based on our established TP53 knockout (KO) human bronchial epithelial cells (BEAS-2B). A total of 943 active regular enhancers and 370 super-enhancers (SEs) disappeared upon the deletion of p53, indicating that p53 modulates the activity of hundreds of enhancer elements. We found that one p53-dependent SE, located on chromosome 9 and designated as KLF4-SE, regulated the expression of the Krüppel-like factor 4 ( KLF4) gene. Furthermore, the deletion of p53 significantly decreased the KLF4-SE enhancer activity and the KLF4 expression, but increased colony formation ability in the nitrosamines 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone-induced cell transformation model. Subsequently, in TP53 KO cells, the overexpression of KLF4 partially reversed the increased clonogenic capacity caused by p53 deficiency. Consistently, KLF4 expression also decreased in lung cancer tissues and cell lines. It appeared that overexpression of KLF4 significantly suppressed the proliferation and migration of lung cancer cells. Collectively, our results suggest that the regulation of enhancer formation and activity by p53 is an integral component of the p53 tumor suppressor function. Therefore, our findings offer some novel insights into the regulation mechanism of p53 in lung oncogenesis and introduce a new strategy for screening therapeutic targets.

BAY-9835: Discovery of the First Orally Bioavailable ADAMTS7 Inhibitor

The matrix metalloprotease ADAMTS7 has been identified by multiple genome-wide association studies as being involved in the development of coronary artery disease. Subsequent research revealed the proteolytic function of the enzyme to be relevant for atherogenesis and restenosis after vessel injury. Based on a publicly known dual ADAMTS4/ADAMTS5 inhibitor, we have in silico designed an ADAMTS7 inhibitor of the catalytic domain, which served as a starting point for an optimization campaign. Initially our inhibitors suffered from low selectivity vs MMP12. An X-ray cocrystal structure inspired us to exploit amino acid differences in the binding site of MMP12 and ADAMTS7 to improve selectivity. Further optimization composed of employing 5-membered heteroaromatic groups as hydantoin substituents to become more potent on ADAMTS7. Finally, fine-tuning of DMPK properties yielded BAY-9835, the first orally bioavailable ADAMTS7 inhibitor. Further optimization to improve selectivity vs ADAMTS12 seems possible, and a respective starting point could be identified.

Lung Inflammatory Phenotype in Mice Deficient in Fibulin-2 and ADAMTS-12

Interaction between extracellular matrix (ECM) components plays an important role in the regulation of cellular behavior and hence in tissue function. Consequently, characterization of new interactions within ECM opens the possibility of studying not only the functional but also the pathological consequences derived from those interactions. We have previously described the interaction between fibulin2 and ADAMTS-12 in vitro and the effects of that interaction using cellular models of cancer. Now, we generate a mouse deficient in both ECM components and evaluate functional consequences of their absence using different cancer and inflammation murine models. The main findings indicate that mice deficient in both fibulin2 and ADAMTS12 markedly increase the development of lung tumors following intraperitoneal urethane injections. Moreover, inflammatory phenotype is exacerbated in the lung after LPS treatment as can be inferred from the accumulation of active immune cells in lung parenchyma. Overall, our results suggest that protective effects in cancer or inflammation shown by fibulin2 and ADAMTS12 as interactive partners in vitro are also shown in a more realistic in vivo context.

Topological data analysis reveals core heteroblastic and ontogenetic programs embedded in leaves of grapevine (Vitaceae) and maracuyá (Passifloraceae)

Leaves are often described in language that evokes a single shape. However, embedded in that descriptor is a multitude of latent shapes arising from evolutionary, developmental, environmental, and other effects. These confounded effects manifest at distinct developmental time points and evolve at different tempos. Here, revisiting datasets comprised of thousands of leaves of vining grapevine (Vitaceae) and maracuyá (Passifloraceae) species, we apply a technique from the mathematical field of topological data analysis to comparatively visualize the structure of heteroblastic and ontogenetic effects on leaf shape in each group. Consistent with a morphologically closer relationship, members of the grapevine dataset possess strong core heteroblasty and ontogenetic programs with little deviation between species. Remarkably, we found that most members of the maracuyá family also share core heteroblasty and ontogenetic programs despite dramatic species-to-species leaf shape differences. This conservation was not initially detected using traditional analyses such as principal component analysis or linear discriminant analysis. We also identify two morphotypes of maracuyá that deviate from the core structure, suggesting the evolution of new developmental properties in this phylogenetically distinct sub-group. Our findings illustrate how topological data analysis can be used to disentangle previously confounded developmental and evolutionary effects to visualize latent shapes and hidden relationships, even ones embedded in complex, high-dimensional datasets.

Topological data analysis reveals a core gene expression backbone that defines form and function across flowering plants

Since they emerged approximately 125 million years ago, flowering plants have evolved to dominate the terrestrial landscape and survive in the most inhospitable environments on earth. At their core, these adaptations have been shaped by changes in numerous, interconnected pathways and genes that collectively give rise to emergent biological phenomena. Linking gene expression to morphological outcomes remains a grand challenge in biology, and new approaches are needed to begin to address this gap. Here, we implemented topological data analysis (TDA) to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, we created a topological representation of the shape of gene expression across plant evolution, development, and environment for the phylogenetically diverse flowering plants. The TDA-based Mapper graphs form a well-defined gradient of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function. This suggests that there are distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses. Genes that correlate with the tissue lens function are enriched in central processes such as photosynthetic, growth and development, housekeeping, or stress responses. Together, our results highlight the power of TDA for analyzing complex biological data and reveal a core expression backbone that defines plant form and function.

Mutation Impact on mRNA Versus Protein Expression across Human Cancers

Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression has rarely been systematically investigated. We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across six cancer types. Protein-level impacts are validated for 47.2% of the somatic expression quantitative trait loci (seQTLs), including mutations from likely "long-tail" driver genes. Devising a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs), we reveal several gene mutations, including NF1 and MAP2K4 truncations and TP53 missenses showing disproportional influence on protein abundance not readily explained by transcriptomics. Cross-validating with data from massively parallel assays of variant effects (MAVE), TP53 missenses associated with high tumor TP53 proteins were experimentally confirmed as functional. Our study demonstrates the importance of considering protein-level expression to validate mutation impacts and identify functional genes and mutations.

ADAMTS12 is a stromal modulator in chronic liver disease

Adamalysins, a family of metalloproteinases containing a disintegrin and metalloproteinases (ADAMs) and ADAM with thrombospondin motifs (ADAMTSs), belong to the matrisome and play important roles in various biological and pathological processes, such as development, immunity and cancer. Using a liver cancer dataset from the International Cancer Genome Consortium, we developed an extensive in silico screening that identified a cluster of adamalysins co-expressed in livers from patients with hepatocellular carcinoma (HCC). Within this cluster, ADAMTS12 expression was highly associated with recurrence risk and poorly differentiated HCC signatures. We showed that ADAMTS12 was expressed in the stromal cells of the tumor and adjacent fibrotic tissues of HCC patients, and more specifically in activated stellate cells. Using a mouse model of carbon tetrachloride-induced liver injury, we showed that Adamts12 was strongly and transiently expressed after a 24 h acute treatment, and that fibrosis was exacerbated in Adamts12-null mice submitted to carbon tetrachloride-induced chronic liver injury. Using the HSC-derived LX-2 cell line, we showed that silencing of ADAMTS12 resulted in profound changes of the gene expression program. In particular, genes previously reported to be induced upon HSC activation, such as PAI-1, were mostly down-regulated following ADAMTS12 knock-down. The phenotype of these cells was changed to a less differentiated state, showing an altered actin network and decreased nuclear spreading. These phenotypic changes, together with the down-regulation of PAI-1, were offset by TGF-β treatment. The present study thus identifies ADAMTS12 as a modulator of HSC differentiation, and a new player in chronic liver disease.

What cannot be seen correctly in 2D visualizations of single-cell 'omics data?

A common strategy for exploring single-cell 'omics data is visualizing 2D nonlinear projections that aim to preserve high-dimensional data properties such as neighborhoods. Alternatively, mathematical theory and other computational tools can directly describe data geometry, while also showing that neighborhoods and other properties cannot be well-preserved in any 2D projection.

The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia

Although children and adolescents with acute lymphoblastic leukaemia (ALL) have high survival rates, approximately 15-20% of patients relapse. Risk of relapse is routinely estimated at diagnosis by biological factors, including flow cytometry data. This high-dimensional data is typically manually assessed by projecting it onto a subset of biomarkers. Cell density and "empty spaces" in 2D projections of the data, i.e. regions devoid of cells, are then used for qualitative assessment. Here, we use topological data analysis (TDA), which quantifies shapes, including empty spaces, in data, to analyse pre-treatment ALL datasets with known patient outcomes. We combine these fully unsupervised analyses with Machine Learning (ML) to identify significant shape characteristics and demonstrate that they accurately predict risk of relapse, particularly for patients previously classified as 'low risk'. We independently confirm the predictive power of CD10, CD20, CD38, and CD45 as biomarkers for ALL diagnosis. Based on our analyses, we propose three increasingly detailed prognostic pipelines for analysing flow cytometry data from ALL patients depending on technical and technological availability: 1. Visual inspection of specific biological features in biparametric projections of the data; 2. Computation of quantitative topological descriptors of such projections; 3. A combined analysis, using TDA and ML, in the four-parameter space defined by CD10, CD20, CD38 and CD45. Our analyses readily extend to other haematological malignancies.

A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer

Non-small cell lung cancer (NSCLC), the primary histological form of lung cancer, accounts for about 25%-the highest-of all cancer deaths. As NSCLC is often undetected until symptoms appear in the late stages, it is imperative to discover more effective tumor-associated biomarkers for early diagnosis. Topological data analysis is one of the most powerful methodologies applicable to biological networks. However, current studies fail to consider the biological significance of their quantitative methods and utilize popular scoring metrics without verification, leading to low performance. To extract meaningful insights from genomic data, it is essential to understand the relationship between geometric correlations and biological function mechanisms. Through bioinformatics and network analyses, we propose a novel composite selection index, the C-Index, that best captures significant pathways and interactions in gene networks to identify biomarkers with the highest efficiency and accuracy. Furthermore, we establish a 4-gene biomarker signature that serves as a promising therapeutic target for NSCLC and personalized medicine. The C-Index and biomarkers discovered were validated with robust machine learning models. The methodology proposed for finding top metrics can be applied to effectively select biomarkers and early diagnose many diseases, revolutionizing the approach to topological network research for all cancers.

Mathematics of neural stem cells: Linking data and processes

Adult stem cells are described as a discrete population of cells that stand at the top of a hierarchy of progressively differentiating cells. Through their unique ability to self-renew and differentiate, they regulate the number of end-differentiated cells that contribute to tissue physiology. The question of how discrete, continuous, or reversible the transitions through these hierarchies are and the precise parameters that determine the ultimate performance of stem cells in adulthood are the subject of intense research. In this review, we explain how mathematical modelling has improved the mechanistic understanding of stem cell dynamics in the adult brain. We also discuss how single-cell sequencing has influenced the understanding of cell states or cell types. Finally, we discuss how the combination of single-cell sequencing technologies and mathematical modelling provides a unique opportunity to answer some burning questions in the field of stem cell biology.

Raman spectroscopy and topological machine learning for cancer grading

In the last decade, Raman Spectroscopy is establishing itself as a highly promising technique for the classification of tumour tissues as it allows to obtain the biochemical maps of the tissues under investigation, making it possible to observe changes among different tissues in terms of biochemical constituents (proteins, lipid structures, DNA, vitamins, and so on). In this paper, we aim to show that techniques emerging from the cross-fertilization of persistent homology and machine learning can support the classification of Raman spectra extracted from cancerous tissues for tumour grading. In more detail, topological features of Raman spectra and machine learning classifiers are trained in combination as an automatic classification pipeline in order to select the best-performing pair. The case study is the grading of chondrosarcoma in four classes: cross and leave-one-patient-out validations have been used to assess the classification accuracy of the method. The binary classification achieves a validation accuracy of 81% and a test accuracy of 90%. Moreover, the test dataset has been collected at a different time and with different equipment. Such results are achieved by a support vector classifier trained with the Betti Curve representation of the topological features extracted from the Raman spectra, and are excellent compared with the existing literature. The added value of such results is that the model for the prediction of the chondrosarcoma grading could easily be implemented in clinical practice, possibly integrated into the acquisition system.

Immunomodulatory role of metalloproteases in cancers: Current progress and future trends

Metalloproteinases (MPs) is a large family of proteinases with metal ions in their active centers. According to the different domains metalloproteinases can be divided into a variety of subtypes mainly including Matrix Metalloproteinases (MMPs), A Disintegrin and Metalloproteases (ADAMs) and ADAMs with Thrombospondin Motifs (ADAMTS). They have various functions such as protein hydrolysis, cell adhesion and remodeling of extracellular matrix. Metalloproteinases expressed in multiple types of cancers and participate in many pathological processes involving tumor genesis and development, invasion and metastasis by regulating signal transduction and tumor microenvironment. In this review, based on the current research progress, we summarized the structure of MPs, their expression and especially immunomodulatory role and mechanisms in cancers. Additionally, a relevant and timely update of recent advances and future directions were provided for the diagnosis and immunotherapy targeting MPs in cancers.

The role of microfibrillar‐associated protein 2 in cancer

Microfibrillar-associated protein 2 (MFAP2), a component of the extracellular matrix, is important in controlling growth factor signal transduction. Recent studies have shown that MFAP2, an effective prognostic molecule for various tumors, is associated with tumor occurrence and development and may be involved in remodeling the extracellular matrix and regulating proliferation, apoptosis, invasion, tumor cell metastasis, and tumor angiogenesis. However, MFAP2’s specific mechanism in these tumor processes remains unclear. This article reviewed the possible mechanism of MFAP2 in tumorigenesis and progression and provided a reference for the clinical prognosis of patients with cancer and new therapeutic target discovery.

Multiscale Methods for Signal Selection in Single-Cell Data

Analysis of single-cell transcriptomics often relies on clustering cells and then performing differential gene expression (DGE) to identify genes that vary between these clusters. These discrete analyses successfully determine cell types and markers; however, continuous variation within and between cell types may not be detected. We propose three topologically motivated mathematical methods for unsupervised feature selection that consider discrete and continuous transcriptional patterns on an equal footing across multiple scales simultaneously. Eigenscores (eigi) rank signals or genes based on their correspondence to low-frequency intrinsic patterning in the data using the spectral decomposition of the Laplacian graph. The multiscale Laplacian score (MLS) is an unsupervised method for locating relevant scales in data and selecting the genes that are coherently expressed at these respective scales. The persistent Rayleigh quotient (PRQ) takes data equipped with a filtration, allowing the separation of genes with different roles in a bifurcation process (e.g., pseudo-time). We demonstrate the utility of these techniques by applying them to published single-cell transcriptomics data sets. The methods validate previously identified genes and detect additional biologically meaningful genes with coherent expression patterns. By studying the interaction between gene signals and the geometry of the underlying space, the three methods give multidimensional rankings of the genes and visualisation of relationships between them.

TAaCGH Suite for Detecting Cancer-Specific Copy Number Changes Using Topological Signatures

Copy number changes play an important role in the development of cancer and are commonly associated with changes in gene expression. Persistence curves, such as Betti curves, have been used to detect copy number changes; however, it is known these curves are unstable with respect to small perturbations in the data. We address the stability of lifespan and Betti curves by providing bounds on the distance between persistence curves of Vietoris–Rips filtrations built on data and slightly perturbed data in terms of the bottleneck distance. Next, we perform simulations to compare the predictive ability of Betti curves, lifespan curves (conditionally stable) and stable persistent landscapes to detect copy number aberrations. We use these methods to identify significant chromosome regions associated with the four major molecular subtypes of breast cancer: Luminal A, Luminal B, Basal and HER2 positive. Identified segments are then used as predictor variables to build machine learning models which classify patients as one of the four subtypes. We find that no single persistence curve outperforms the others and instead suggest a complementary approach using a suite of persistence curves. In this study, we identified new cytobands associated with three of the subtypes: 1q21.1-q25.2, 2p23.2-p16.3, 23q26.2-q28 with the Basal subtype, 8p22-p11.1 with Luminal B and 2q12.1-q21.1 and 5p14.3-p12 with Luminal A. These segments are validated by the TCGA BRCA cohort dataset except for those found for Luminal A.

ADAMTS8 inhibited lung cancer progression through suppressing VEGFA

BACKGROUND

ADAMTS8 expression has been identified to be low in many cancers including lung cancer. However, the specific functions and regulatory system of ADAMTS8 remain to be unveiled.

PURPOSE

To study the potential modulatory mechanism of ADAMTS8 in lung cancer in cell and xenograft mice models.

METHODS

Differential expression of ADAMTS8 in lung cancer was analyzed on online tools. So was the overall survival curve in association with ADAMTS8/VEGFA expression in lung cancer patients. RT-qPCR was applied to validate the ADAMTS8 expression in lung cancer cell lines H460 and A549, with the normal lung epithelial cell Beas-2b as a control. Thereafter, overexpressed and knockdown plasmids were constructed for transfection. Colony and flow cytometry methods were used for cell proliferation and apoptosis. RT-qPCR and Western blot methods validated the changes in VEGFA after ADAMTS8 regulation in cells. Tube formation and Transwell methods were applied to observe the changes in tube formation and migration in HUVECs induced by tumor conditioned medium (TCM). Stable-transfected cells were injected subcutaneously into nude mice. H&E and Immunohistochemistry were applied to analyze the pathological differences and protein changes of ADAMTS8, VEGFA and CD31.

RESULTS

High ADAMTS8 was correlated with high overall survival rate in lung cancer patients. ADAMTS8 was also abnormally downregulated in NSCLC cells. Upregulation of ADAMTS8 suppressed cell proliferation and enhanced apoptosis while downregulation of ADAMTS8 promoted cell proliferation and decreased apoptosis. VEGFA was negatively correlated with ADAMTS8 in lung cancer tissues. Upregulation of ADAMTS8 inhibited VEGFA in mRNA and protein levels. Further, knockdown of ADAMTS8 induced tube formation and migration of HUVECs and upregulation of ADAMTS8 inhibited this. In addition, upregulation of ADAMTS8 in nude mice inhibited tumor growth and also suppressed VEGFA and CD31 in tumors.

CONCLUSION

ADAMTS8 inhibited lung cancer progression through suppressing VEGFA in lung cancer.

An Integrated Pan-Cancer Analysis of ADAMTS12 and Its Potential Implications in Pancreatic Adenocarcinoma

Background

A Disintegrin and Metallopeptidase with Thrombospondin Type 1 Motif 12 (ADAMTS12), a member of the ADAMTS family of multidomain extracellular protease enzymes, is involved in the progression of many tumors. However, a pan-cancer analysis of this gene has not yet been performed. Its role in pancreatic adenocarcinoma (PAAD) also remains unclear.

Methods

The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression data (GTEx) databases were used to analyze ADAMTS12 expression in pan-cancer. We assessed the expression, clinical characteristics, prognostic significance, copy number alteration, methylation, and mutation of ADAMTS12 and its correlation with the tumor immune microenvironment. qRT-PCR and immunohistochemistry assays were also performed to validate the expression of ADAMTS12 in PAAD.

Results

Through bioinformatics analysis and preliminary experimental verification, ADAMTS12 was found to be substantially overexpressed in PAAD. High expression level of ADAMTS12 was correlated with worse survival rates in patients with PAAD and high infiltration levels of tumor-associated macrophages, cancer-associated fibroblasts, immune checkpoint proteins, and immunosuppressive genes.

Conclusion

Our findings suggest ADAMTS12 as a potential prognostic biomarker in PAAD. Elevated ADAMTS12 expression may also indicate an immunosuppressive microenvironment.

Exploration of the Mechanism of Lianhua Qingwen in Treating Influenza Virus Pneumonia and New Coronavirus Pneumonia with the Concept of "Different Diseases with the Same Treatment" Based on Network Pharmacology

The 31 main components of Lianhua Qingwen (LHQW) were obtained through a literature and database search; the components included glycyrrhizic acid, emodin, chlorogenic acid, isophoroside A, forsythia, menthol, luteolin, quercetin, and rutin. Sixty-eight common targets for the treatment of novel coronavirus pneumonia (NCP) and influenza virus pneumonia (IVP) were also obtained. A "component-target-disease" network was constructed with Cytoscape 3.2.1 software, and 20 key targets, such as cyclooxygenase2 (COX2), interleukin-6 (IL-6), mitogen-activated protein kinase14 (Mapk14), and tumor necrosis factor (TNF), were screened from the network. The David database was used to perform a Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment analysis and gene ontology (GO) biological process enrichment. Results showed that the key targets of LHQW in the treatment of NCP and IVP mainly involved biological processes, such as immune system process intervention, cell proliferation, apoptosis and invasion, toxic metabolism, cytokine activity, and regulation of the synthesis process. KEGG enrichment analysis revealed that 115 signalling pathways were related to the treatment of LHQW. Amongst them, IL-17, T cell receptor, Th17 cell differentiation, TNF, toll-like receptor, MAPK, apoptosis, and seven other signalling pathways were closely related to the occurrence and development of NCP and IVP. Molecular docking showed that each component had different degrees of binding with six targets, namely, 3C-like protease (3CL), angiotensin-converting enzyme 2 (ACE2), COX2, hemagglutinin (HA), IL-6, and neuraminidase (NA). Rutin, isoforsythiaside A, hesperidin and isochlorogenic acid B were the best components for docking with the six core targets. The first five components with the best docking results were isoforsythiaside, hesperidin, isochlorogenic acid B, forsythin E, and quercetin. In conclusion, LHQW has many components, targets, and pathways. The findings of this work can provide an important theoretical basis for determining the mechanism of LHQW in treating NCP and IVP.

Quantification of the Selective Advantage of Driver Mutations Is Dependent on the Underlying Model and Stage of Tumor Evolution

Measuring the selective fitness advantages provided by driver mutations has the potential to facilitate a precise quantitative understanding of cancer evolution. However, accurately measuring the selective advantage of driver mutations has remained a challenge in the field. Early studies reported small selective advantages of drivers, on the order of 1%, whereas newer studies report much larger selective advantages, as high as 1,200%. In this article, we argue that the calculated selective advantages of cancer drivers are dependent on the underlying mathematical model and stage of cancer evolution and that comparisons of numerical values of selective advantage without regard for the underlying model and stage can lead to spurious conclusions.

Indigenous Ancestry and Admixture in the Uruguayan Population

The Amerindian group known as the Charrúas inhabited Uruguay at the timing of European colonial contact. Even though they were extinguished as an ethnic group as a result of a genocide, Charrúan heritage is part of the Uruguayan identity both culturally and genetically. While mitochondrial DNA studies have shown evidence of Amerindian ancestry in living Uruguayans, here we undertake whole-genome sequencing of 10 Uruguayan individuals with self-declared Charruan heritage. We detect chromosomal segments of Amerindian ancestry supporting the presence of indigenous genetic ancestry in living descendants. Specific haplotypes were found to be enriched in “Charrúas” and rare in the rest of the Amerindian groups studied. Some of these we interpret as the result of positive selection, as we identified selection signatures and they were located mostly within genes related to the infectivity of specific viruses. Historical records describe contacts of the Charrúas with other Amerindians, such as Guaraní, and patterns of genomic similarity observed here concur with genomic similarity between these groups. Less expected, we found a high genomic similarity of the Charrúas to Diaguita from Argentinian and Chile, which could be explained by geographically proximity. Finally, by fitting admixture models of Amerindian and European ancestry for the Uruguayan population, we were able to estimate the timing of the first pulse of admixture between European and Uruguayan indigenous peoples in approximately 1658 and the second migration pulse in 1683. Both dates roughly concurring with the Franciscan missions in 1662 and the foundation of the city of Colonia in 1680 by the Spanish.

Significance of CD8+ T cell infiltration-related biomarkers and the corresponding prediction model for the prognosis of kidney renal clear cell carcinoma

Cytotoxic T cells expressing cell surface CD8 played a key role in anti-cancer immunotherapy, including kidney renal clear cell carcinoma (KIRC). Here we set out to comprehensively analyze and evaluate the significance of CD8⁺ T cell-related markers for patients with KIRC. We checked immune cell response in KIRC and identified cell type-specific markers and related pathways in the tumor-infiltrating CD8⁺ T (TIL-CD8T) cells. We used these markers to explore their prognostic signatures in TIL-CD8⁺ T by evaluating their prognostic efficacy and group differences at various levels. Through pan-cancer analysis, 12 of 63 up-regulated and 162 of 396 down-regulated genes in CD8+ T cells were found to be significantly correlated with the survival prognosis. Based on our highly integrated multi-platform analyses across multiple datasets, we constructed a 6-gene risk scoring model specific to TIL-CD8T. In this model, high TIL-CD8 sig score was corresponding to a higher incidence frequency of copy number variation and drug sensitivity to sorafenib. Moreover, the prognosis of patients with the same or similar immune checkpoint gene levels could be distinguished from each other by TIL-CD8 sig score.

The topology of data: Opportunities for cancer research

Motivation

Topological methods have recently emerged as a reliable and interpretable framework for extracting information from high-dimensional data, leading to the creation of a branch of applied mathematics called Topological Data Analysis (TDA). Since then, TDA has been progressively adopted in biomedical research. Biological data collection can result in enormous datasets, comprising thousands of features and spanning diverse datatypes. This presents a barrier to initial data analysis as the fundamental structure of the dataset becomes hidden, obstructing the discovery of important features and patterns. TDA provides a solution to obtain the underlying shape of datasets over continuous resolutions, corresponding to key topological features independent of noise. TDA has the potential to support future developments in healthcare as biomedical datasets rise in complexity and dimensionality. Previous applications extend across the fields of neuroscience, oncology, immunology and medical image analysis. TDA has been used to reveal hidden subgroups of cancer patients, construct organizational maps of brain activity and classify abnormal patterns in medical images. The utility of TDA is broad and to understand where current achievements lie, we have evaluated the present state of TDA in cancer data analysis.

Results

This article aims to provide an overview of TDA in Cancer Research. A brief introduction to the main concepts of TDA is provided to ensure that the article is accessible to readers who are not familiar with this field. Following this, a focussed literature review on the field is presented, discussing how TDA has been applied across heterogeneous datatypes for cancer research.

Regulation of ADAMTS Proteases

A disintegrin and metalloprotease with thrombospondin type I motifs (ADAMTS) proteases are secreted metalloproteinases that play key roles in the formation, homeostasis and remodeling of the extracellular matrix (ECM). The substrate spectrum of ADAMTS proteases can range from individual ECM proteins to entire families of ECM proteins, such as the hyalectans. ADAMTS-mediated substrate cleavage is required for the formation, remodeling and physiological adaptation of the ECM to the needs of individual tissues and organ systems. However, ADAMTS proteases can also be involved in the destruction of tissues, resulting in pathologies such as arthritis. Specifically, ADAMTS4 and ADAMTS5 contribute to irreparable cartilage erosion by degrading aggrecan, which is a major constituent of cartilage. Arthritic joint damage is a major contributor to musculoskeletal morbidity and the most frequent clinical indication for total joint arthroplasty. Due to the high sequence homology of ADAMTS proteases in their catalytically active site, it remains a formidable challenge to design ADAMTS isotype-specific inhibitors that selectively inhibit ADAMTS proteases responsible for tissue destruction without affecting the beneficial functions of other ADAMTS proteases. In vivo, proteolytic activity of ADAMTS proteases is regulated on the transcriptional and posttranslational level. Here, we review the current knowledge of mechanisms that regulate ADAMTS protease activity in tissues including factors that induce ADAMTS gene expression, consequences of posttranslational modifications such as furin processing, the role of endogenous inhibitors and pharmacological approaches to limit ADAMTS protease activity in tissues, which almost exclusively focus on inhibiting the aggrecanase activity of ADAMTS4 and ADAMTS5.

ADAMTS-12: Functions and Challenges for a Complex Metalloprotease

Nineteen members of the ADAMTS family of secreted zinc metalloproteinases are present in the human degradome. A wide range of different functions are being attributed to these enzymes and the number of their known substrates is considerably increasing in recent years. ADAMTSs can participate in processes such as fertility, inflammation, arthritis, neuronal and behavioral disorders, as well as cancer. Since its first annotation in 2001, ADAMTS-12 has been described to participate in different processes displayed by members of this family of proteinases. In this sense, ADAMTS-12 performs essential roles in modulation and recovery from inflammatory processes such as colitis, endotoxic sepsis and pancreatitis. ADAMTS-12 has also been involved in cancer development acting either as a tumor suppressor or as a pro-tumoral agent. Furthermore, participation of ADAMTS-12 in arthritis or in neuronal disorders has also been suggested through degradation of components of the extracellular matrix. In addition, ADAMTS-12 proteinase activity can also be modified by interaction with other proteins and thus, can be an alternative way of modulating ADAMTS-12 functions. In this review we revised the most relevant findings about ADAMTS-12 function on the 20th anniversary of its identification.

Applications of Topological Data Analysis in Oncology

The emergence of the information age in the last few decades brought with it an explosion of biomedical data. But with great power comes great responsibility: there is now a pressing need for new data analysis algorithms to be developed to make sense of the data and transform this information into knowledge which can be directly translated into the clinic. Topological data analysis (TDA) provides a promising path forward: using tools from the mathematical field of algebraic topology, TDA provides a framework to extract insights into the often high-dimensional, incomplete, and noisy nature of biomedical data. Nowhere is this more evident than in the field of oncology, where patient-specific data is routinely presented to clinicians in a variety of forms, from imaging to single cell genomic sequencing. In this review, we focus on applications involving persistent homology, one of the main tools of TDA. We describe some recent successes of TDA in oncology, specifically in predicting treatment responses and prognosis, tumor segmentation and computer-aided diagnosis, disease classification, and cellular architecture determination. We also provide suggestions on avenues for future research including utilizing TDA to analyze cancer time-series data such as gene expression changes during pathogenesis, investigation of the relation between angiogenic vessel structure and treatment efficacy from imaging data, and experimental confirmation that geometric and topological connectivity implies functional connectivity in the context of cancer.

Topological Data Analysis: A New Method to Identify Genetic Alterations in Cancer

Cancer is the largest health problem worldwide. A number of targeted therapies are currently employed for the treatment of different cancers. Determining the molecular mechanisms that are necessary for cancer development and progression is the most critical step in targeted therapies. Currently, many studies have identified a large number of frequently mutated cancer-associated genes using recurrence-based methods. However, only the cancer-associated mutations with a mutation frequency >15% can be identified by these methods. In other words, they cannot be used to identify driver genes that have low mutation frequency but play a major role in tumorigenesis and development. Thus, there is an urgent need for a method for identifying cancer-associated genes that are not based on recurrence. In a study, recently published in Nature Communications, research team led by Prof. Raúl Rabadán from the Columbia University successfully devised a novel topological data analysis approach to identify low-prevalence cancer-associated gene mutations using expression data from multiple cancers.