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bioRxiv2018

Hidden Markov Models Lead to Higher Resolution Maps of Mutation Signature Activity in Cancer

Knowing the activity of the mutational processes shaping a cancer genome may provide insight into tumorigenesis and personalized therapy. It is thus important to uncover the characteristic signatures of active mutational processes in patients from their patterns of single base substitutions. However, mutational processes do not act uniformly on the genome and are biased by factors such as the genomes chromatin structure or replication origins. These factors may lead to statistical dependencies among neighboring mutations, calling for modeling approaches that can account for such dependencies to better estimate mutational process activities.\n\nHere we develop the first sequence-dependent models for mutation signatures. We apply these models to characterize genomic and other factors that influence the activity of previously validated mutation signatures in breast cancer. We find that our tool, SO_SCPLOWIGC_SCPLOWMO_SCPLOWAC_SCPLOW, can accurately assign genomic mutations to mutation signatures, yielding assignments that are of higher likelihood than those obtained with models that assume independence between signatures and align better with current biological knowledge. Our analysis resolves a controversy related to the dependency of APOBEC signatures on replication time and links Signatures 18 and 30 to oxidative damage.\n\nModeling the sequential dependencies of mutation signatures leads to improved estimates of mutation signature activity both at the tumor-level and within specific genomic regions, yielding higher resolution maps of mutation signature activity in cancer.

bioinformatics↗

bioRxiv2015

Systematic Functional Annotation and Visualization of Biological Networks

Large-scale biological networks map functional connections between most genes in the genome and can potentially uncover high level organizing principles governing cellular functions. These networks, however, are famously complex and often regarded as disordered masses of tangled interactions (\"hairballs\") that are nearly impenetrable to biologists. As a result, our current understanding of network functional organization is very limited. To address this problem, I developed a systematic quantitative approach for annotating biological networks and examining their functional structure. This method, named Spatial Analysis of Functional Enrichment (SAFE), detects network regions that are statistically overrepresented for a functional group or a quantitative phenotype of interest, and provides an intuitive visual representation of their relative positioning within the network. By successfully annotating the Saccharomyces cerevisiae genetic interaction network with Gene Ontology terms, SAFE proved to be sensitive to functional signals and robust to noise. In addition, SAFE annotated the network with chemical genomic data and uncovered a new potential mechanism of resistance to the anti-cancer drug bortezomib. Finally, SAFE showed that protein-protein interactions, despite their apparent complexity, also have a high level functional structure. These results demonstrate that SAFE is a powerful new tool for examining biological networks and advancing our understanding of the functional organization of the cell.

Systems Biology↗

bioRxiv2019

Cytoplasmic volume and limiting nucleoplasmin scale nuclear size during Xenopus laevis development

How nuclear size is regulated relative to cell size is a fundamental cell biological question. Reductions in both cell and nuclear sizes during Xenopus laevis embryogenesis provide a robust scaling system to study mechanisms of nuclear size regulation. To test if the volume of embryonic cytoplasm is limiting for nuclear growth, we encapsulated gastrula stage embryonic cytoplasm and nuclei in droplets of defined volume using microfluidics. Nuclei grew and reached new steady-state sizes as a function of cytoplasmic volume, supporting a limiting component mechanism of nuclear size control. Through biochemical fractionation, we identified the histone chaperone nucleoplasmin (Npm2) as a putative nuclear size-scaling factor. Cellular amounts of Npm2 decrease over development, and nuclear size was sensitive to Npm2 levels both in vitro and in vivo, affecting nuclear histone levels and chromatin organization. Thus, reductions in cell volume with concomitant decreases in Npm2 amounts represent a developmental mechanism of nuclear size-scaling that may also be relevant to cancers with increased nuclear size.

cell biology↗

bioRxiv2016

Pan-Cancer Analysis Reveals Technical Artifacts in The Cancer Genome Atlas (TCGA) Germline Variant Calls

The degree to which germline variation drives cancer development and shapes tumor phenotypes remains largely unexplored, possibly due to a lack of large scale publicly available germline data for a cancer cohort. Here we called germline variants on 9,618 cases from The Cancer Genome Atlas (TCGA) database representing 31 cancer types. We identified batch effects affecting loss of function (LOF) variant calls that can be traced back to differences in the way the sequence data were generated both within and across cancer types. Overall, LOF indel calls were more sensitive to technical artifacts than LOF Single Nucleotide Variant (SNV) calls. In particular, whole genome amplification of DNA prior to sequencing led to an artificially increased burden of LOF indel calls, which confounded association analyses relating germline variants to tumor type despite stringent indel filtering strategies. Due to the inherent noise we chose to remove all 614 amplified DNA samples, including all acute myeloid leukemia and virtually all ovarian cancer samples, from the final dataset. This study demonstrates how insufficient quality control can lead to false positive germlinetumor type associations and draws attention to the need to be sensitive to problems associated with a lack of uniformity in data generation in TCGA data.\n\nAuthor SummaryCancer research to date has largely focused on genetic aberrations specific to tumor tissue. In contrast, the degree to which germline, or inherited, variation contributes to tumorigenesis remains unclear, possibly due to a lack of accessible germline variant data. In this study we identify germline variants in 9,618 samples using raw germline exome data from The Cancer Genome Atlas (TCGA). There are substantial differences in the way exome sequence data was generated both across and within cancer types in TCGA. We observe that differences in sequence data generation introduced batch effects, or variation that is due to technical factors not true biological variation, in our variant data. Most notably, we observe that amplification of DNA prior to sequencing resulted in an excess of predicted damaging indel variants. We show how these batch effects can confound germline association analyses if not properly addressed. Our study highlights the difficulties of working with large public genomic datasets like TCGA where samples are collected over time and across data centers, and particularly cautions the use of amplified DNA samples for genetic association analyses.

genomics↗

bioRxiv2018

Multiparameter cell characterization using nanofluidic technology facilitates real-time phenotypic and genotypic elucidation of intratumor heterogeneity

Genetic and functional complexity of bulk tumor has become evident through rapid advances in sequencing technologies. As a unique integrated approach to characterizing tumor heterogeneity, we demonstrate the multifaceted capabilities of a novel nanofluidic platform to enable single-cell phenotypic and genetic profiling of ovarian cancer patient-derived tumor cells. This approach has enabled increased resolution of tumor cell phenotypic and genetic heterogeneity, providing a better understanding of underlying biological drivers of the disease. A range of CA-125 expression levels is observed within cells from individuals, demonstrating clonal diversity consistent with other phenotypic data. Further, TP53 mutation analysis demonstrates a sub-population of cells exhibiting high mutation frequency that likely drives downstream growth kinetics and protein expression. Finally, genomic data is orthogonally used to address clonal heterogeneity across ovarian tumors when compared to bulk sequencing, illustrating the potential for single-cell sequencing data integrated with cellular functional and growth data toward future therapeutic intervention.

genomics↗

bioRxiv2018

Systematic Elucidation and Validation of OncoProtein-Centric Molecular Interaction Maps

The largely incomplete and tissue-independent nature of cancer pathways represents a key limitation to the ability to elucidate mechanistic determinants of cancer phenotypes and to predict adaptive response to targeted therapy. To address these challenges, we propose replacing canonical cancer pathways with a more accurate, comprehensive, and context-specific architecture - dubbed a Protein-Centric molecular interaction Map (PC-Map) - representing modulators, effectors, and cognate binding-partners of any oncoprotein of interest. To reconstruct these complex molecular architectures de novo, we introduce a novel OncoSig algorithm. Validation of a lung adenocarcinoma specific (LUAD) KRAS-centric PC-Map recapitulated known KRAS biology and, more critically, identified a novel repertoire of proteins eliciting synthetic lethality in KRASG12D LUAD organoid cultures. Showing the generalizable nature of the algorithm, we elucidated PC-Maps for ten recurrently mutated oncoproteins, including KRAS, in distinct tumor contexts. This revealed a highly context-specific nature of cancers regulatory and signaling architectures to an unprecedented degree of resolution.

systems biology↗

bioRxiv2017

Simultaneous cell traction and growth measurements using light

Understanding cell mechanotransduction is important for discerning matrix structure-cell function relationships underlying health and disease. Despite the crucial role of mechanochemical signaling in phenomena such as cell migration, proliferation, and differentiation, measuring the cell-generated forces at the interface with the extracellular matrix during these biological processes remains challenging. An ideal method would provide continuous, non-destructive images of the force field applied by cells, over broad spatial and temporal scales, while simultaneously revealing the cell biological process under investigation. Toward this goal, we present the integration of a new real-time traction stress imaging modality, Hilbert phase dynamometry (HPD), with the technique of spatial light interference microscopy (SLIM) for label free monitoring of cell growth. HPD relies on extracting the displacement field in a deformable substrate, which is chemically patterned with a fluorescent grid. The displacements introduced by the cell are captured by the phase of the periodic signal associated with the grid, borrowing concepts from holography. The displacement field is uniquely converted into forces by solving an elasticity inverse problem. Because the measurement of displacement only uses the epi-fluorescence channel of an inverted microscope, we can simultaneously achieve measurements in transmission. We performed SLIM and extracted cell mass on the same field of view in addition to the measured displacement field. We used this technique to study mesenchymal stem cells and found that cells undergoing osteogenesis and adipogenesis exerted larger and more dynamic stresses than their precursor. Our results indicate that the MSCs develop the smallest forces and growth rates. We anticipate that simultaneous cell growth and traction measurements will improve our understanding of mechanotransduction, particularly during dynamic processes where the matrix properties provide context to guide cells towards a physiological or pathological outcome, e.g., tissue morphogenesis, or cancer metastasis.

cell biology↗

bioRxiv2017

Akt/PKB enhances non-canonical Wnt signals by compartmentalizing β-Catenin

Cellular proliferation is antagonistically regulated by canonical and non-canonical Wnt signals; their dysbalance triggers cancers. It is widely believed that the PI3-K[->] Akt pathway enhances canonical Wnt signals by affecting transcriptional activity and stability of {beta}-catenin. Here we demonstrate that the PI3-K[->]Akt pathway also enhances non-canonical Wnt signals by compartmentalizing {beta}-catenin. By phosphorylating the phosphoinositide(PI)-binding domain of a multimodular signal transducer, Daple, Akt abolishes Daples ability to bind PI3-P-enriched endosomes that engage dynein motor complex for long-distance trafficking of {beta}-catenin/E-cadherin complexes to pericentriolar recycling endosomes (PCREs). Phosphorylation compartmentalizes Daple/{beta}-catenin/E-cadherin complexes to cell-cell contact sites, enhances non-canonical Wnt signals, and thereby, suppresses colony growth. Dephosphorylation compartmentalizes {beta}-catenin on PCREs, a specialized compartment for prolonged unopposed canonical Wnt signaling, and enhances colony growth. Cancer-associated Daple mutants that are insensitive to Akt mimic a constitutively dephosphorylated state. This work not only identifies Daple as a platform for crosstalk between Akt and the non-canonical Wnt pathway, but also reveals the impact of such crosstalk during cancer initiation and progression.

cell biology↗

bioRxiv2017

Roles of the Brca2 and Wapl complexes with Pds5 in sister chromatid cohesion, cohesin localization, and gene expression

The cohesin complex topologically encircles chromosomes and mediates sister chromatid cohesion to ensure accurate chromosome segregation upon cell division. Cohesin also participates in DNA repair and gene transcription. The Nipped-B - Mau2 protein complex loads cohesin onto chromosomes and the Pds5 - Wapl complex removes cohesin. Pds5 is also essential for sister chromatid cohesion, indicating that it has functions beyond cohesin removal. The Brca2 DNA repair protein interacts with Pds5, but the roles of this complex beyond DNA repair are unknown. Here we show that Brca2 opposes Pds5 function in sister chromatid cohesion by assaying precocious sister chromatid separation in metaphase spreads of cultured cells depleted for these proteins. By genome-wide chromatin immunoprecipitation we find that Pds5 facilitates SA cohesin subunit association with DNA replication origins and that Brca2 inhibits SA binding, mirroring their effects on sister chromatid cohesion. Cohesin binding is maximal at replication origins and extends outward to occupy active genes and regulatory sequences. Pds5 and Wapl, but not Brca2, limit the distance that cohesin extends from origins, thereby determining which active genes, enhancers and silencers bind cohesin. Using RNA-seq we find that Brca2, Pds5 and Wapl influence the expression of most genes sensitive to Nipped-B and cohesin, largely in the same direction. These findings demonstrate that Brca2 regulates sister chromatid cohesion and gene expression in addition to its canonical role in DNA repair and expand the known functions of accessory proteins in cohesins diverse functions.\n\nAuthor summaryThe cohesin protein complex has multiple functions in eukaryotic cells. It ensures that when a cell divides, the two daughter cells receive the correct number of chromosomes. It does this by holding together the sister chromatids that are formed when chromosomes are duplicated by DNA replication. Cohesin also helps repair damaged DNA, and to regulate genes important for growth and development. Even minor deficiencies in some proteins that regulate cohesin cause significant human birth defects. Here we investigated in Drosophila cells how three proteins, Pds5, Wapl and Brca2, determine where cohesin binds to chromosomes, control cohesins ability to hold sister chromatids together, and participate in gene expression. We find that Pds5 and Wapl work together, likely during DNA replication, to determine which genes bind cohesin by controlling how far cohesin spreads out along chromosomes. Pds5 is required for cohesin to hold sister chromatids together, and Brca2 counteracts this function. In contrast to the opposing roles in sister chromatid cohesion, Pds5 and Brca2 work together to facilitate control of gene expression by cohesin. Brca2 plays a critical role in DNA repair, and these studies expand the known roles for Brca2 by showing that it also regulates sister chromatid cohesion and gene expression. BRCA2 mutations in humans increase susceptibility to breast and ovarian cancer, and these findings raise the possibility that changes in chromosome segregation or gene expression might contribute to the increased cancer risk associated with these mutations.

molecular biology↗

bioRxiv2018

An F-actin shell ruptures the nuclear envelope by sorting pore-dense and pore-free membranes in meiosis of starfish oocytes

The nucleus of oocytes, traditionally referred to as the germinal vesicle, is unusually large and its nuclear envelope (NE) is densely packed with nuclear pore complexes (NPCs) stockpiled for embryonic development. We have shown that breakdown of this specialized NE during meiosis of starfish oocytes is mediated by an Arp2/3-nucleated F-actin shell, in contrast to microtubule-driven tearing in somatic cells. The detailed mechanism of how the cytoskeletal forces disrupt the NE remains poorly understood in any system. Here, we address the mechanism of F-actin-driven NE rupture by using live-cell and correlated super-resolution light and electron microscopy. We show that actin is nucleated within the lamina and sprouts filopodia-like spikes towards the nuclear membranes forcing lamina and nuclear membranes apart. These F-actin spikes protrude pore-free nuclear membranes, whereas the adjoining membrane stretches accumulate packed NPCs associated with the still-intact lamin network. NPC conglomerates sort into a distinct tubular-vesicular membrane network, while breaks appear in pore-free, ER-like regions. Together, our work reveals a novel function for Arp2/3-mediated membrane shaping in NE rupture that is likely to have broad relevance in regulating NE dynamics in diverse other contexts such as nuclear rupture frequently observed in cancer cells.

cell biology↗

bioRxiv2018

Topconfects: a package for confident effect sizes in differential expression analysis provides improved usability ranking genes of interest

BackgroundA differential gene expression analysis may produce a set of significantly differentially expressed genes that is too large to easily investigate, so that a means of ranking genes by their biological interest level is desirable. The life-sciences have grappled with the abuse of p-values to rank genes for this purpose. As an alternative, a lower confidence bound on the magnitude of Log Fold Change (LFC) could be used to rank genes, but it has been unclear how to reconcile this with the need to perform False Discovery Rate (FDR) correction. The TREAT test of McCarthy and Smyth is a step in this direction, finding genes significantly exceeding a specified LFC threshold. Here we describe the use of test inversion on TREAT to present genes ranked by a confidence bound on the LFC, while still controlling FDR.\n\nResultsTesting the Topconfects R package with simulated gene expression data shows the method outperforming current statistical approaches across a wide range of experiment sizes in the identification of genes with largest LFCs. Applying the method to a TCGA breast cancer data-set shows the method ranks some genes with large LFC higher than would traditional ranking by p-value. Importantly these two ranking methods lead to a different biological emphasis, in terms both of specific highly ranked genes and gene-set enrichment.\n\nConclusionsThe choice of ranking method in differential expression analysis can affect the biological interpretation. The common default of ranking by p-value is implicitly by an effect size in which each gene is standardized to its own variability, rather than comparing genes on a common scale, which may not be appropriate. The Topconfects approach of presenting genes ranked by confident LFC effect size is a variation on the TREAT method with improved usability, removing the need to fine-tune a threshold parameter and removing the temptation to abuse p-values as a de-facto effect size.

bioinformatics↗

bioRxiv2017

Similarity identification in gene expression patterns as a new approach in phenotype classification

Stratifying healthy and malignant phenotypes and identifying their biological states using high-throughput molecular data has been the focus of many computational approaches during the last decade. Using multivariate changes in expression of genes within biological pathways, as fingerprints of complex phenotypes, we developed a new methodology for Similarity Identification in Gene expressioN (SIGN). In this approach, we use centroid classifier to identify phenotype of each biological sample. To obtain similarity of a given biological sample with classes of phenotypes, we defined a new distance measure, transcriptional similarity coefficient (TSC) which captures similarity of gene expression patterns between a biological pathway in two samples or populations. We showed that TSC, as an interpretable and stable distance measure in SIGN, captures all oncogenic hallmarks for breast cancer even with low sample size, by comparing healthy and patient tumor samples in the largest breast cancer dataset. In this study, we demonstrate that SIGN is a flexible, yet robust approach for classification based on transcriptomics data. Comparing early and late relapses within each molecular subtypes of breast cancer, our method enabled subtype-specific stratification of breast cancer patients into groups with significantly different survival. Moreover, we used SIGN to classify with more than 99% specificity the site of extraction of healthy and tumor samples from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets. We showed that SIGN also enables robust identification of hematopoietic stem cell and progenitors within the hematopoietic hierarchy. We further explored chemical perturbation data in the Connectivity Map (CMAP) database and showed that SIGN was able to classify seven classes of drugs based on their mechanism of action. In conclusion, we showed that SIGN can be used to achieve interpretable and robust transcriptomic-based classification of healthy and malignant samples, as well as drugs based on their known mechanism of action, supporting the generalizability and relevance of the method for the analysis of gene expression profiles.

bioinformatics↗

bioRxiv2018

A prize-collecting Steiner tree application for signature selection to stratify diffuse large B-cell lymphoma subtypes

BackgroundWith the explosion of high-throughput data available in biology, the bottleneck is shifted to effective data interpretation. By taking advantage of the available data, it is possible to identify the biomarkers and signatures to distinguish subtypes of a specific cancer in the context of clinical trials. This requires sophisticated methods to retrieve the information out of the data, and various algorithms have been recently devised.\n\nResultsHere, we applied the prize-collecting Steiner tree (PCST) approach to obtain a gene expression signature for the classification of diffuse large B-cell lymphoma (DLBCL). The PCST is a network-based approach to capture new insights about genomic data by incorporating an interaction network landscape. Moreover, we adopted the ElasticNet incorporating PCA as a classification method. We used seven public gene expression profiling datasets (three for training, and four for testing) available in the literature, and obtained 10 genes as signature. We tested these genes by employing ElasticNet, and compared the performance with the DAC algorithm as current golden standard. The performance of the PCST signature with ElasticNet outperformed the DAC in distinguishing the subtypes. In addition, the gene expression signature was able to accurately stratify DLBCL patients on survival data.\n\nConclusionsWe developed a network-based optimization technique that performs unbiased signature selection by integrating genomic data with biological networks. Our classifier trained with the obtained signature outperformed the state-of-the-art method in subtype distinction and survival data stratification in DLBCL. The proposed method is a general approach that can be applied on other classification problems.

bioinformatics↗

bioRxiv2016

Stochasticity in the Genotype-Phenotype Map: Implications for the Robustness and Persistence of Bet-Hedging

For the last few decades modern biology has focused on quantifying, understanding and mapping the genetic characteristics of cells. This genotype-driven perspective has led to significant advances in our understanding and treatment of diseases such as cancer e.g. the discovery of driver mutations and the development of molecularly-targeted therapeutics. However, this perspective has largely ignored the functional outcome of genetic changes: the cellular phenotype. In part, this is simply because phenotypes are neither easy to define or measure as they critically depend on both genotype and context. Heterogeneity at the gene scale has been known for sometime, and there has been significant effort invested in trying to find patterns within it, but much less is understood about how this heterogeneity manifests itself in phenotypic change, i.e. the genotype-phenotype map (GP-map). This mapping is not one-to-one but many-to-many and is fundamentally the junction at which both genes and environment meet to produce phenotypes. Many genotypes produce similar phenotypes, and multiple phenotypes can emerge from a single genotype. To further complicate matters, genetically identical cells in uniform environments still exhibit phenotypic heterogeneity. Therefore a central open question in biology today is how can we connect the abundance of genomic data with cell phenotypic behaviour, this is especially pertinent to the issue of treatment resistance as many therapies act on cellular phenotypes.\n\nOur focus here is to tackle the GP-map question through the use of the simplest functional mapping we can define that also captures phenotypic heterogeneity: a molecular switch. Molecular switches are ubiquitous in biology, observed in many organisms and naturally map molecular components to decisions (i.e. phenotypes). Often stochastic in nature, such switches can be the difference between life or death in environments that fluctuate unpredictably, since they will ensure that at least some offspring are adapted to future environments. For convenience we use Chemical Reaction Networks (CRNs) to define the map of gene products to phenotypes, allowing us to investigate the impact of distinct mappings (CRNs) and perturbations to them. We observe that key biological properties naturally emerge, including both robustness and persistence. Robustness may explain why such bet hedging strategies are common in biology, and not readily destroyed through mutation. Whereas persistence may explain the apparent paradox of bet-hedging - why does phenotypic hedging exist in environments beneficial to only one of the phenotypes, when selection necessarily acts against it? The structure of the molecular switch, itself subject to selection, can slow the loss of hedging to ensure a survival mechanism even against environmental catastrophes which are very rare. Critically, these properties when taken together have profound and significant implications for the emergence of treatment resistance, since the timescale of extinction depends heavily on the underlying GP-map.

Evolutionary Biology↗

bioRxiv2016

A scored human protein-protein interaction network to catalyze genomic interpretation

Human protein-protein interaction networks are critical to understanding cell biology and interpreting genetic and genomic data, but are challenging to produce in individual large-scale experiments. We describe a general computational framework that through data integration and quality control provides a scored human protein-protein interaction network (InWeb_IM). Juxtaposed with five comparable resources, InWeb_IM has 2.8 times more interactions (~585K) and a superior functional signal showing that the added interactions reflect real cellular biology. InWeb_IM is a versatile resource for accurate and cost-efficient functional interpretation of massive genomic datasets illustrated by annotating candidate genes from >4,700 cancer genomes and genes involved in neuropsychiatric diseases.

Genomics↗

bioRxiv2016

Soft sweeps are the dominant mode of adaptation in the human genome

The degree to which adaptation in recent human evolution shapes genetic variation remains controversial. This is in part due to the limited evidence in humans for classic \"hard selective sweeps,\" wherein a novel beneficial mutation rapidly sweeps through a population to fixation. However, positive selection may often proceed via \"soft sweeps\" acting on mutations already present within a population. Here we examine recent positive selection across six human populations using a powerful machine learning approach that is sensitive to both hard and soft sweeps. We found evidence that soft sweeps are widespread and account for the vast majority of recent human adaptation. Surprisingly, our results also suggest that linked positive selection affects patterns of variation across much of the genome, and may increase the frequencies of deleterious mutations. Our results also reveal insights into the role of sexual selection, cancer risk, and central nervous system development in recent human evolution.

evolutionary biology↗

bioRxiv2018

TP53I11 Suppresses Extracellular Matrix-independent Survival and Mesenchymal Transition in Mammary Epithelial Cells

Extracellular matrix (ECM)-independent survival is an essential prerequisite for tumor metastasis and a hallmark of epithelial cancer stem cells and epithelial-mesenchymal transition (EMT). We found that, in MCF10A and MDA-MB-231 cells, loss of TP53I11 (Tumor Protein P53 Inducible Protein 11) enhanced the ECM-independent survival and suppressed glucose starvation induced cell death by increasing the activation of AMPK that confer cells metabolic flexibility to survive under stress conditions. We show here that, TP53I11 enhanced glycolysis and promoted proliferation of MCF10A and MDA-MB-231 cells in normal culture, but exerted negative effect on EMT, cell migration and invasion, and its overexpression suppressed tumor progression and metastasis of MDA-MB-231 cells in vivo. Considering cancer cells also are confronted with the hostile environment such as nutrient scarcity during tumorigenesis and metastasis, our findings suggested that the disruption of metabolic flexibility by TP53I11 through inhibiting AMPK activation resulted in the suppression of tumorigenesis and metastasis of breast cancer.

molecular biology↗

bioRxiv2018

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available, that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

systems biology↗