Tensor GSVD of patient- and platform-matched tumor and normal DNA copy-number profiles uncovers chromosome arm-wide patterns of tumor-exclusive platform-consistent alterations encoding for cell transformation and predicting ovarian cancer survival

Genomic analysis of an aggressive hepatic leiomyosarcoma case following treatment for hepatocellular carcinoma

A 70-year-old man undergoing treatment for immunoglobulin G4-related disease developed a liver mass on computed tomography during routine imaging examination. The tumor was located in the hepatic S1/4 region, was 38 mm in size, and showed arterial enhancement on dynamic contrast-enhanced computed tomography. We performed a liver biopsy and diagnosed moderately differentiated hepatocellular carcinoma. The patient underwent proton beam therapy. The tumor remained unchanged but enlarged after 4 years. The patient was diagnosed with hepatocellular carcinoma recurrence and received hepatic arterial chemoembolization. However, 1 year later, the patient developed jaundice, and the liver tumor grew in size. Unfortunately, the patient passed away. Autopsy revealed that the tumor consisted of spindle-shaped cells exhibiting nuclear atypia and a fission pattern and tested positive for α-smooth muscle actin and vimentin. No hepatocellular carcinoma components were observed, and the patient was pathologically diagnosed with hepatic leiomyosarcoma. Next-generation sequencing revealed somatic mutations in CACNA2D4, CTNNB1, DOCK5, IPO8, MTMR1, PABPC5, SEMA6D, and ZFP36L1. Based on the genetic mutation, sarcomatoid hepatocarcinoma was the most likely pathogenesis in this case. This mutation is indicative of the transition from sarcomatoid hepatocarcinoma to hepatic leiomyosarcoma.

[Research Advances of RAD51AP1 in Tumor Progression and Drug Resistance]

The genomic instability may lead to an initiation of cancer in many organisms. Homologous recombination repair (HRR) is vital in maintaining cellular genomic stability. RAD51 associated protein 1 (RAD51AP1), which plays a crucial role in HRR and primarily participates in forming D-loop, was reported as an essential protein for maintaining cellular genomic stability. However, recent studies showed that RAD51AP1 was significantly overexpressed in various cancer types and correlated with poor prognosis. These results suggested that RAD51AP1 may play a significant pro-cancer effect in multiple cancers. The underlying mechanism is still unclear. Cancer stemness-maintaining effects of RAD51AP1 might be considered as the most reliable mechanism. Meanwhile, RAD51AP1 also promoted resistance to radiation therapy and chemotherapy in many cancers. Thus, researches focused on RAD51AP1, and its regulatory molecules may provide new targets for overcoming cancer progression and treatment resistance. Here, we reviewed the latest research on RAD51AP1 in cancers and summarized its differential expression and prognostic implications. In this review, we also outlined the potential mechanisms of its pro-cancer and drug resistance-promoting effects to provide several potential directions for further research.
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Systematic pan-cancer analysis showed that RAD51AP1 was associated with immune microenvironment, tumor stemness, and prognosis

Although RAD51 associated protein 1 (RAD51AP1) is crucial in genome stability maintenance, it also promotes cancer development with an unclear mechanism. In this study, we collected intact expression data of RAD51AP1 from the public database, and verified it was significantly over-expressed in 33 cancer types and correlated with poor prognosis in 13 cancer types, including glioma, adrenocortical carcinoma, lung adenocarcinoma. We further authenticated that RAD51AP1 is up-regulated in several typical cancer cell lines and promotes cancer cell proliferation in vitro. Moreover, we also demonstrated that RAD51AP1 was significantly positively related to cancer stemness score mRNAsi in 27 cancer types and broadly correlated to tumor-infiltrating immune cells in various cancers in a diverse manner. It was also negatively associated with immunophenoscore (IPS) and Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) scores and positively correlated with mutant-allele tumor heterogeneity (MATH), tumor mutational burden (TMB), microsatellite instability (MSI), and PD-L1 expression in multiple cancers. The tumor stemness enhancing and tumor immune microenvironment affecting functions of RAD51AP1 might compose its carcinogenesis mechanism. Further investigations beyond the bioinformatics level should confirm these findings in each specific cancer.

Dynamic patterns of microRNA expression during acute myeloid leukemia state-transition

MicroRNAs (miRNAs) have been shown to hold prognostic value in acute myeloid leukemia (AML); however, the temporal dynamics of miRNA expression in AML are poorly understood. Using serial samples from a mouse model of AML to generate time-series miRNA sequencing data, we are the first to show that the miRNA transcriptome undergoes state-transition during AML initiation and progression. We modeled AML state-transition as a particle undergoing Brownian motion in a quasi-potential and validated the AML state-space and state-transition model to accurately predict time to AML in an independent cohort of mice. The critical points of the model provided a framework to align samples from mice that developed AML at different rates. Our mathematical approach allowed discovery of dynamic processes involved during AML development and, if translated to humans, has the potential to predict an individual's disease trajectory.

Roadmap on plasticity and epigenetics in cancer

The role of plasticity and epigenetics in shaping cancer evolution and response to therapy has taken center stage with recent technological advances including single cell sequencing. This roadmap article is focused on state-of-the-art mathematical and experimental approaches to interrogate plasticity in cancer, and addresses the following themes and questions: is there a formal overarching framework that encompasses both non-genetic plasticity and mutation-driven somatic evolution? How do we measure and model the role of the microenvironment in influencing/controlling non-genetic plasticity? How can we experimentally study non-genetic plasticity? Which mathematical techniques are required or best suited? What are the clinical and practical applications and implications of these concepts?

Scaling concepts in 'omics: Nuclear lamin-B scales with tumor growth and often predicts poor prognosis, unlike fibrosis

Physicochemical principles such as stoichiometry and fractal assembly can give rise to characteristic scaling between components that potentially include coexpressed transcripts. For key structural factors within the nucleus and extracellular matrix, we discover specific gene-gene scaling exponents across many of the 32 tumor types in The Cancer Genome Atlas, and we demonstrate utility in predicting patient survival as well as scaling-informed machine learning (SIML). All tumors with adjacent tissue data show cancer-elevated proliferation genes, with some genes scaling with the nuclear filament LMNB1, including the transcription factor FOXM1 that we show directly regulates LMNB1 SIML shows that such regulated cancers cluster together with longer overall survival than dysregulated cancers, but high LMNB1 and FOXM1 in half of regulated cancers surprisingly predict poor survival, including for liver cancer. COL1A1 is also studied because it too increases in tumors, and a pan-cancer set of fibrosis genes shows substoichiometric scaling with COL1A1 but predicts patient outcome only for liver cancer-unexpectedly being prosurvival. Single-cell RNA-seq data show nontrivial scaling consistent with power laws from bulk RNA and protein analyses, and SIML segregates synthetic from contractile cancer fibroblasts. Our scaling approach thus yields fundamentals-based power laws relatable to survival, gene function, and experiments.

A modular master regulator landscape controls cancer transcriptional identity

Despite considerable efforts, the mechanisms linking genomic alterations to the transcriptional identity of cancer cells remain elusive. Integrative genomic analysis, using a network-based approach, identified 407 master regulator (MR) proteins responsible for canalizing the genetics of individual samples from 20 cohorts in The Cancer Genome Atlas (TCGA) into 112 transcriptionally distinct tumor subtypes. MR proteins could be further organized into 24 pan-cancer, master regulator block modules (MRBs), each regulating key cancer hallmarks and predictive of patient outcome in multiple cohorts. Of all somatic alterations detected in each individual sample, >50% were predicted to induce aberrant MR activity, yielding insight into mechanisms linking tumor genetics and transcriptional identity and establishing non-oncogene dependencies. Genetic and pharmacological validation assays confirmed the predicted effect of upstream mutations and MR activity on downstream cellular identity and phenotype. Thus, co-analysis of mutational and gene expression profiles identified elusive subtypes and provided testable hypothesis for mechanisms mediating the effect of genetic alterations.

RAD51AP1 promotes progression of ovarian cancer via TGF-β/Smad signalling pathway

Ovarian cancer (OC) is one of the leading causes of female deaths. However, the molecular pathogenesis of OC has still remained elusive. This study aimed to explore the potential genes associated with the progression of OC. In the current study, 3 data sets of OC were downloaded from the GEO database to identify hub gene. Somatic mutation data obtained from TCGA were used to analyse the mutation. Immune cells were used to estimate effect of the hub gene to the tumour microenvironment. RNA‐seq and clinical data of OC patients retrieved from TCGA were used to investigate the diagnostic and prognostic values of hub gene. A series of in vitro assays were performed to indicate the function of hub gene and its possible mechanisms in OC. As a result, RAD51AP1 was found as a hub gene, which expression higher was mainly associated with poor survival in OC patients. Up‐regulation of RAD51AP1 was closely associated with mutations. RAD51AP1 up‐regulation accompanied by accumulated Th2 cells, but reduced CD4 + T cells and CD8 + T cells. Nomogram demonstrated RAD51AP1 increased the accuracy of the model. Down‐regulation of RAD51AP1 suppressed proliferation, migration and invasion capabilities of OC cells in vitro. Additionally, scatter plots showed that RAD51AP1 was positively correlated with genes in TGF‐β/Smad pathway. The above‐mentioned results were validated by RT‐qPCR and Western blotting. In conclusion, up‐regulation of RAD51AP1 was closely associated with mutations in OC. RAD51AP1 might represent an indicator for predicting OS of OC patients. Besides, RAD51AP1 might accelerate progression of OC by TGF‐β/Smad signalling pathway.

Retrospective clinical trial experimentally validates glioblastoma genome-wide pattern of DNA copy-number alterations predictor of survival

Modeling of genomic profiles from the Cancer Genome Atlas (TCGA) by using recently developed mathematical frameworks has associated a genome-wide pattern of DNA copy-number alterations with a shorter, roughly one-year, median survival time in glioblastoma (GBM) patients. Here, to experimentally test this relationship, we whole-genome sequenced DNA from tumor samples of patients. We show that the patients represent the U.S. adult GBM population in terms of most normal and disease phenotypes. Intratumor heterogeneity affects ≈ 11 % and profiling technology and reference human genome specifics affect <1% of the classifications of the tumors by the pattern, where experimental batch effects normally reduce the reproducibility, i.e., precision, of classifications based upon between one to a few hundred genomic loci by >30%. With a 2.25-year Kaplan-Meier median survival difference, a 3.5 univariate Cox hazard ratio, and a 0.78 concordance index, i.e., accuracy, the pattern predicts survival better than and independent of age at diagnosis, which has been the best indicator since 1950. The prognostic classification by the pattern may, therefore, help to manage GBM pseudoprogression. The diagnostic classification may help drugs progress to regulatory approval. The therapeutic predictions, of previously unrecognized targets that are correlated with survival, may lead to new drugs. Other methods missed this relationship in the roughly 3B-nucleotide genomes of the small, order of magnitude of 100, patient cohorts, e.g., from TCGA. Previous attempts to associate GBM genotypes with patient phenotypes were unsuccessful. This is a proof of principle that the frameworks are uniquely suitable for discovering clinically actionable genotype-phenotype relationships.

Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches

Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient-specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient-specific combined therapy should be designed. Large-scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.

Oncogenic Roles of RAD51AP1 in Tumor Tissues Related to Overall Survival and Disease-Free Survival in Hepatocellular Carcinoma

OBJECTIVE

This study aimed to investigate the associations between RAD51AP1 and the outcomes of hepatocellular carcinoma (HCC).

METHODS

RAD51AP1 expression levels were compared in Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets. The Liver Hepatocellular Carcinoma (TCGA, Provisional) and GSE36376 datasets were used for survival analysis. RAD51AP1 associations with clinicopathological features were determined with the GSE36376 dataset.

RESULTS

RAD51AP1 mRNA expression was significantly upregulated in advanced liver fibrosis samples (S3-4 vs. S0-2 and G3-4 vs. G0-2) from hepatitis B virus (HBV)-related liver fibrosis patients and in tumor tissues and peripheral blood mononuclear cells (PBMCs) from HCC patients (all P < 0.05). HCC patients with high RAD51AP1 expression had significantly worse overall survival (OS) and disease-free survival (DFS) than those with low RAD51AP1 expression (P = 0.0034 and P = 0.0012, respectively) in the TCGA dataset, and these findings were validated with the GSE36376 dataset (P = 0.0074 and P = 0.0003, respectively). A Cox regression model indicated that RAD51AP1 was a risk factor for OS and DFS in HCC patients in GSE36376 (HR = 1.54, 95% CI = 1.02-2.32, P = 0.04 and HR = 1.71, 95% CI = 1.22-2.39, P = 0.002, respectively). Moreover, RAD51AP1 mRNA expression increased gradually with increasing tumor stage, including stratification by American Joint Committee on Cancer (AJCC) stages, Barcelona Clinic Liver Cancer (BCLC) stages and Edmondson grades. In addition, RAD51AP1 was overexpressed in HCC patients with intrahepatic metastasis, major portal vein invasion, vascular invasion and/or an alpha-fetoprotein (AFP) level > 300 ng/ml.

CONCLUSIONS

Contributing to an advanced tumor stage, intrahepatic metastasis, vascular invasion and AFP level elevation, RAD51AP1 upregulation was significantly associated with OS and DFS in HCC patients.

GSVD- and tensor GSVD-uncovered patterns of DNA copy-number alterations predict adenocarcinomas survival in general and in response to platinum

More than a quarter of lung, uterine, and ovarian adenocarcinoma (LUAD, USEC, and OV) tumors are resistant to platinum drugs. Only recently and only in OV, patterns of copy-number alterations that predict survival in response to platinum were discovered, and only by using the tensor GSVD to compare Agilent microarray platform-matched profiles of patient-matched normal and primary tumor DNA. Here, we use the GSVD to compare whole-genome sequencing (WGS) and Affymetrix microarray profiles of patient-matched normal and primary LUAD, USEC, and OV tumor DNA. First, the GSVD uncovers patterns similar to one Agilent OV pattern, where a loss of most of the chromosome arm 6p combined with a gain of 12p encode for transformation. Like the Agilent OV pattern, the WGS LUAD and Affymetrix LUAD, USEC, and OV patterns are correlated with shorter survival, in general and in response to platinum. Like the tensor GSVD, the GSVD separates these tumor-exclusive genotypes from experimental inconsistencies. Second, by identifying the shorter survival phenotypes among the WGS- and Affymetrix-profiled tumors, the Agilent pattern proves to be a technology-independent predictor of survival, independent also of the best other indicator at diagnosis, i.e., stage. Third, like no other indicator, the pattern predicts the overall survival of OV patients experiencing progression-free survival, in general and in response to platinum. We conclude that comparative spectral decompositions, such as the GSVD and tensor GSVD, underlie a mathematically universal description of the relationships between a primary tumor's genotype and a patient's overall survival phenotype, which other methods miss.

Tensor clustering with algebraic constraints gives interpretable groups of crosstalk mechanisms in breast cancer

We introduce a tensor-based clustering method to extract sparse, low-dimensional structure from high-dimensional, multi-indexed datasets. This framework is designed to enable detection of clusters of data in the presence of structural requirements which we encode as algebraic constraints in a linear program. Our clustering method is general and can be tailored to a variety of applications in science and industry. We illustrate our method on a collection of experiments measuring the response of genetically diverse breast cancer cell lines to an array of ligands. Each experiment consists of a cell line–ligand combination, and contains time-course measurements of the early signalling kinases MAPK and AKT at two different ligand dose levels. By imposing appropriate structural constraints and respecting the multi-indexed structure of the data, the analysis of clusters can be optimized for biological interpretation and therapeutic understanding. We then perform a systematic, large-scale exploration of mechanistic models of MAPK–AKT crosstalk for each cluster. This analysis allows us to quantify the heterogeneity of breast cancer cell subtypes, and leads to hypotheses about the signalling mechanisms that mediate the response of the cell lines to ligands.

Mathematically universal and biologically consistent astrocytoma genotype encodes for transformation and predicts survival phenotype

DNA alterations have been observed in astrocytoma for decades. A copy-number genotype predictive of a survival phenotype was only discovered by using the generalized singular value decomposition (GSVD) formulated as a comparative spectral decomposition. Here, we use the GSVD to compare whole-genome sequencing (WGS) profiles of patient-matched astrocytoma and normal DNA. First, the GSVD uncovers a genome-wide pattern of copy-number alterations, which is bounded by patterns recently uncovered by the GSVDs of microarray-profiled patient-matched glioblastoma (GBM) and, separately, lower-grade astrocytoma and normal genomes. Like the microarray patterns, the WGS pattern is correlated with an approximately one-year median survival time. By filling in gaps in the microarray patterns, the WGS pattern reveals that this biologically consistent genotype encodes for transformation via the Notch together with the Ras and Shh pathways. Second, like the GSVDs of the microarray profiles, the GSVD of the WGS profiles separates the tumor-exclusive pattern from normal copy-number variations and experimental inconsistencies. These include the WGS technology-specific effects of guanine-cytosine content variations across the genomes that are correlated with experimental batches. Third, by identifying the biologically consistent phenotype among the WGS-profiled tumors, the GBM pattern proves to be a technology-independent predictor of survival and response to chemotherapy and radiation, statistically better than the patient's age and tumor's grade, the best other indicators, and MGMT promoter methylation and IDH1 mutation. We conclude that by using the complex structure of the data, comparative spectral decompositions underlie a mathematically universal description of the genotype-phenotype relations in cancer that other methods miss.

Personalized disease signatures through information-theoretic compaction of big cancer data

Every individual cancer develops and grows in its own specific way, giving rise to a recognized need for the development of personalized cancer diagnostics. This suggested that the identification of patient-specific oncogene markers would be an effective diagnostics approach. However, tumors that are classified as similar according to the expression levels of certain oncogenes can eventually demonstrate divergent responses to treatment. This implies that the information gained from the identification of tumor-specific biomarkers is still not sufficient. We present a method to quantitatively transform heterogeneous big cancer data to patient-specific transcription networks. These networks characterize the unbalanced molecular processes that deviate the tissue from the normal state. We study a number of datasets spanning five different cancer types, aiming to capture the extensive interpatient heterogeneity that exists within a specific cancer type as well as between cancers of different origins. We show that a relatively small number of altered molecular processes suffices to accurately characterize over 500 tumors, showing extreme compaction of the data. Every patient is characterized by a small specific subset of unbalanced processes. We validate the result by verifying that the processes identified characterize other cancer patients as well. We show that different patients may display similar oncogene expression levels, albeit carrying biologically distinct tumors that harbor different sets of unbalanced molecular processes. Thus, tumors may be inaccurately classified and addressed as similar. These findings highlight the need to expand the notion of tumor-specific oncogenic biomarkers to patient-specific, comprehensive transcriptional networks for improved patient-tailored diagnostics.

Role of RAD51AP1 in homologous recombination DNA repair and carcinogenesis

Homologous recombination (HR) serves to repair DNA double-strand breaks and damaged replication forks and is essential for maintaining genome stability and tumor suppression. HR capacity also determines the efficacy of anticancer therapy. Hence, there is an urgent need to better understand all HR proteins and sub-pathways. An emerging protein that is critical for RAD51-mediated HR is RAD51-associated protein 1 (RAD51AP1). Although much has been learned about its biochemical attributes, the precise molecular role of RAD51AP1 in the HR reaction is not yet fully understood. The available literature also suggests that RAD51AP1 expression may be relevant for cancer development and progression. Here, we review the efforts that led to the discovery of RAD51AP1 and elaborate on our current understanding of its biochemical profile and biological function. We also discuss how RAD51AP1 may help to promote cancer development and why it could potentially represent a promising new target for therapeutic intervention.

Tensor factorization toward precision medicine

Precision medicine initiatives come amid the rapid growth in quantity and variety of biomedical data, which exceeds the capacity of matrix-oriented data representations and many current analysis algorithms. Tensor factorizations extend the matrix view to multiple modalities and support dimensionality reduction methods that identify latent groups of data for meaningful summarization of both features and instances. In this opinion article, we analyze the modest literature on applying tensor factorization to various biomedical fields including genotyping and phenotyping. Based on the cited work including work of our own, we suggest that tensor applications could serve as an effective tool to enable frequent updating of medical knowledge based on the continually growing scientific and clinical evidence. We encourage extensive experimental studies to tackle challenges including design choice of factorizations, integrating temporality and algorithm scalability.

Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm

Integrative analyses of high-throughput ‘omic data, such as DNA methylation, DNA copy number alteration, mRNA and protein expression levels, have created unprecedented opportunities to understand the molecular basis of human disease. In particular, integrative analyses have been the cornerstone in the study of cancer to determine molecular subtypes within a given cancer. As malignant tumors with similar morphological characteristics have been shown to exhibit entirely different molecular profiles, there has been significant interest in using multiple ‘omic data for the identification of novel molecular subtypes of disease, which could impact treatment decisions. Therefore, we have developed intNMF, an integrative approach for disease subtype classification based on non-negative matrix factorization. The proposed approach carries out integrative clustering of multiple high dimensional molecular data in a single comprehensive analysis utilizing the information across multiple biological levels assessed on the same individual. As intNMF does not assume any distributional form for the data, it has obvious advantages over other model based clustering methods which require specific distributional assumptions. Application of intNMF is illustrated using both simulated and real data from The Cancer Genome Atlas (TCGA).

Recurrent patterns of DNA copy number alterations in tumors reflect metabolic selection pressures

Copy number alteration (CNA) profiling of human tumors has revealed recurrent patterns of DNA amplifications and deletions across diverse cancer types. These patterns are suggestive of conserved selection pressures during tumor evolution but cannot be fully explained by known oncogenes and tumor suppressor genes. Using a pan-cancer analysis of CNA data from patient tumors and experimental systems, here we show that principal component analysis-defined CNA signatures are predictive of glycolytic phenotypes, including 18F-fluorodeoxy-glucose (FDG) avidity of patient tumors, and increased proliferation. The primary CNA signature is enriched for p53 mutations and is associated with glycolysis through coordinate amplification of glycolytic genes and other cancer-linked metabolic enzymes. A pan-cancer and cross-species comparison of CNAs highlighted 26 consistently altered DNA regions, containing 11 enzymes in the glycolysis pathway in addition to known cancer-driving genes. Furthermore, exogenous expression of hexokinase and enolase enzymes in an experimental immortalization system altered the subsequent copy number status of the corresponding endogenous loci, supporting the hypothesis that these metabolic genes act as drivers within the conserved CNA amplification regions. Taken together, these results demonstrate that metabolic stress acts as a selective pressure underlying the recurrent CNAs observed in human tumors, and further cast genomic instability as an enabling event in tumorigenesis and metabolic evolution.

DNA Damage Follows Repair Factor Depletion and Portends Genome Variation in Cancer Cells after Pore Migration

Migration through micron-size constrictions has been seen to rupture the nucleus, release nuclear-localized GFP, and cause localized accumulations of ectopic 53BP1-a DNA repair protein. Here, constricted migration of two human cancer cell types and primary mesenchymal stem cells (MSCs) increases DNA breaks throughout the nucleoplasm as assessed by endogenous damage markers and by electrophoretic "comet" measurements. Migration also causes multiple DNA repair proteins to segregate away from DNA, with cytoplasmic mis-localization sustained for many hours as is relevant to delayed repair. Partial knockdown of repair factors that also regulate chromosome copy numbers is seen to increase DNA breaks in U2OS osteosarcoma cells without affecting migration and with nucleoplasmic patterns of damage similar to constricted migration. Such depletion also causes aberrant levels of DNA. Migration-induced nuclear damage is nonetheless reversible for wild-type and sub-cloned U2OS cells, except for lasting genomic differences between stable clones as revealed by DNA arrays and sequencing. Gains and losses of hundreds of megabases in many chromosomes are typical of the changes and heterogeneity in bone cancer. Phenotypic differences that arise from constricted migration of U2OS clones are further illustrated by a clone with a highly elongated and stable MSC-like shape that depends on microtubule assembly downstream of the transcription factor GATA4. Such changes are consistent with reversion to a more stem-like state upstream of cancerous osteoblastic cells. Migration-induced genomic instability can thus associate with heritable changes.

Platform-Independent Genome-Wide Pattern of DNA Copy-Number Alterations Predicting Astrocytoma Survival and Response to Treatment Revealed by the GSVD Formulated as a Comparative Spectral Decomposition

We use the generalized singular value decomposition (GSVD), formulated as a comparative spectral decomposition, to model patient-matched grades III and II, i.e., lower-grade astrocytoma (LGA) brain tumor and normal DNA copy-number profiles. A genome-wide tumor-exclusive pattern of DNA copy-number alterations (CNAs) is revealed, encompassed in that previously uncovered in glioblastoma (GBM), i.e., grade IV astrocytoma, where GBM-specific CNAs encode for enhanced opportunities for transformation and proliferation via growth and developmental signaling pathways in GBM relative to LGA. The GSVD separates the LGA pattern from other sources of biological and experimental variation, common to both, or exclusive to one of the tumor and normal datasets. We find, first, and computationally validate, that the LGA pattern is correlated with a patient's survival and response to treatment. Second, the GBM pattern identifies among the LGA patients a subtype, statistically indistinguishable from that among the GBM patients, where the CNA genotype is correlated with an approximately one-year survival phenotype. Third, cross-platform classification of the Affymetrix-measured LGA and GBM profiles by using the Agilent-derived GBM pattern shows that the GBM pattern is a platform-independent predictor of astrocytoma outcome. Statistically, the pattern is a better predictor (corresponding to greater median survival time difference, proportional hazard ratio, and concordance index) than the patient's age and the tumor's grade, which are the best indicators of astrocytoma currently in clinical use, and laboratory tests. The pattern is also statistically independent of these indicators, and, combined with either one, is an even better predictor of astrocytoma outcome. Recurring DNA CNAs have been observed in astrocytoma tumors' genomes for decades, however, copy-number subtypes that are predictive of patients' outcomes were not identified before. This is despite the growing number of datasets recording different aspects of the disease, and due to an existing fundamental need for mathematical frameworks that can simultaneously find similarities and dissimilarities across the datasets. This illustrates the ability of comparative spectral decompositions to find what other methods miss.

Mutant allele specific imbalance in oncogenes with copy number alterations: Occurrence, mechanisms, and potential clinical implications

Mutant allele specific imbalance (MASI) was initially coined to describe copy number alterations associated with the mutant allele of an oncogene. The copy number gain (CNG) specific to the mutant allele can be readily observed in electropherograms. With the development of genome-wide analyses at base-pair resolution with copy number counts, we can now further differentiate MASI into those with CNG, with copy neutral alteration (also termed acquired uniparental disomy; UPD), or with loss of heterozygosity (LOH) due to the loss of the wild-type (WT) allele. Here we summarize the occurrence of MASI with CNG, aUPD, or MASI with LOH in some major oncogenes (such as EGFR, KRAS, PIK3CA, and BRAF). We also discuss how these various classifications of MASI have been demonstrated to impact tumorigenesis, progression, metastasis, prognosis, and potentially therapeutic responses in cancer, notably in lung, colorectal, and pancreatic cancers.

TNF-insulin crosstalk at the transcription factor GATA6 is revealed by a model that links signaling and transcriptomic data tensors

Signal transduction networks coordinate transcriptional programs activated by diverse extracellular stimuli, such as growth factors and cytokines. Cells receive multiple stimuli simultaneously, and mapping how activation of the integrated signaling network affects gene expression is a challenge. We stimulated colon adenocarcinoma cells with various combinations of the cytokine tumor necrosis factor (TNF) and the growth factors insulin and epidermal growth factor (EGF) to investigate signal integration and transcriptional crosstalk. We quantitatively linked the proteomic and transcriptomic data sets by implementing a structured computational approach called tensor partial least squares regression. This statistical model accurately predicted transcriptional signatures from signaling arising from single and combined stimuli and also predicted time-dependent contributions of signaling events. Specifically, the model predicted that an early-phase, AKT-associated signal downstream of insulin repressed a set of transcripts induced by TNF. Through bioinformatics and cell-based experiments, we identified the AKT-repressed signal as glycogen synthase kinase 3 (GSK3)-catalyzed phosphorylation of Ser(37) on the long form of the transcription factor GATA6. Phosphorylation of GATA6 on Ser(37) promoted its degradation, thereby preventing GATA6 from repressing transcripts that are induced by TNF and attenuated by insulin. Our analysis showed that predictive tensor modeling of proteomic and transcriptomic data sets can uncover pathway crosstalk that produces specific patterns of gene expression in cells receiving multiple stimuli.

Theoretical and Experimental Analyses of Tensor-Based Regression and Classification

We theoretically and experimentally investigate tensor-based regression and classification. Our focus is regularization with various tensor norms, including the overlapped trace norm, the latent trace norm, and the scaled latent trace norm. We first give dual optimization methods using the alternating direction method of multipliers, which is computationally efficient when the number of training samples is moderate. We then theoretically derive an excess risk bound for each tensor norm and clarify their behavior. Finally, we perform extensive experiments using simulated and real data and demonstrate the superiority of tensor-based learning methods over vector- and matrix-based learning methods.