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Results for “Cancer Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 37 records · Page 2Linked to original sources

bioRxiv2017

The Activation of MEK1 by Enhanced Homodimerization Drives Tumorigenesis

Hyperactive RAS/RAF/MEK/ERK signaling has a well-defined role in cancer biology. Aberrant pathway activation occurs mostly upstream of MEK; however, MEK mutations are prevalent in some cancer subsets. Here we show that cancer-related MEK mutants can be classified as those activated by relieving the inhibitory role of helix A, and those with in-frame deletions of {beta}3-C loop, which exhibit differential resistance to MEK inhibitors in vitro and in vivo. The {beta}3-C loop deletions activate MEK1 through enhancing homodimerization that can drive intradimer cross-phosphorylation of activation loop. Further, we demonstrate that MEK1 dimerization is required both for its activation by RAF and for its catalytic activity towards ERK. Our study identifies a novel group of MEK mutants, illustrates some key steps in RAF/MEK/ERK activation, and has important implications for the design of therapies targeting hyperactive RAS/RAF/MEK/ERK signaling in cancers.

cancer biology↗

bioRxiv2018

REVA: a rank-based multi-dimensional measure of correlation

The neighbors principle implicit in any machine learning algorithm says that samples with similar labels should be close to one another in feature space as well. For example, while tumors are heterogeneous, tumors that have similar genomics profiles can also be expected to have similar responses to a specific therapy. Simple correlation coefficients provide an effective way to determine whether this principle holds when features and labels are both scalar, but not when either is multivariate. A new class of generalized correlation coefficients based on inter-point distances addresses this need and is called \"distance correlation\". There is only one rank-based distance correlation test available to date, and it is asymmetric in the samples, requiring that one sample be distinguished as a fixed point of reference. Therefore, we introduce a novel, nonparametric statistic, REVA, inspired by the Kendall rank correlation coefficient. We use U-statistic theory to derive the asymptotic distribution of the new correlation coefficient, developing additional large and finite sample properties along the way. To establish the admissibility of the REVA statistic, and explore the utility and limitations of our model, we compared it to the most widely used distance based correlation coefficient in a range of simulated conditions, demonstrating that REVA does not depend on an assumption of linearity, and is robust to high levels of noise, high dimensions, and the presence of outliers. We also present an application to real data, applying REVA to determine whether cancer cells with similar genetic profiles also respond similarly to a targeted therapeutic.\n\nAuthor summarySometimes a simple question arises: how does the distance between two samples in multivariate space compare to another scalar value associated with each sample. Here, we propose theory for a nonparametric test to statistically test this association. This test is independent of the scale of the scalar data, and thus generalizable to any comparison of samples with both high-dimensional data and a scalar. We apply the resulting statistic, REVA, to problems in cancer biology motivated by the model that cancer cells with more similar gene expression profiles to one another can be expected to have a more similar response to therapy.

bioinformatics↗

bioRxiv2018

Criticality in Tumor Evolution and Clinical Outcome

How mutation and selection determine the fitness landscape of tumors and hence clinical outcome is an open fundamental question in cancer biology, crucial for the assessment of therapeutic strategies and resistance to treatment. Here we explore the mutation-selection phase-diagram of 6721 primary tumors representing 23 cancer types, by quantifying the overall somatic point mutation load (ML) and selection (dN/dS) in the entire proteome of each tumor. We show that ML strongly correlates with patient survival, revealing two opposing regimes around a critical point. In low ML cancers, high number of mutations indicates poor prognosis, whereas high ML cancers show the opposite trend, due to mutational meltdown. Although the majority of cancers evolve near neutrality, deviations are observed at extreme MLs. Cancers with the highest ML evolve under purifying selection, whereas those with the lowest ML show signatures of positive selection, demonstrating how selection affects cancer fitness. Moreover, different cancers occupy specific positions on the ML-dN/dS plane, revealing a diversity of evolutionary trajectories. These results support and expand the theory of tumor evolution and its non-linear effects on survival.\n\nSignificance StatementIt remains an open fundamental question how mutation and selection co-determine the course of cancer evolution. We construct a selection-mutation phase diagram, using tumor mutation load and selection strength as key variables, and assess their association with clinical outcome. We demonstrate the existence of a biphasic evolutionary regime, whereby beyond a critical ML, the fitness of tumors decreases with the number of mutations, while the proteome evolves near neutrality. Deviations from neutrality in extreme ML elucidate how positive and purifying selections maintain tumor fitness. These results empirically corroborate the existence of a critical state in cancer evolution predicted by theory, and have fundamental and likely clinical implications.

cancer biology↗

bioRxiv2017

Convergence of Wnt, Growth Factor and Trimeric G protein signals on Daple

Cellular proliferation, differentiation, and morphogenesis are shaped by multiple signaling cascades; their concurrent dysregulation plays an integral role in cancer progression and is a common feature of many malignancies. Three such cascades that contribute to the oncogenic potential are the Wnt/Frizzled(FZD), growth factor-receptor tyrosine kinases (RTKs), and G-proteins/GPCRs. Here we identify Daple, a modulator of trimeric G-proteins and a Dishevelled (Dvl)-binding protein as an unexpected point of convergence for all three cascades. Daple-dependent activation of Gi and enhancement of non-canonical Wnt signals is not just triggered by Wnt5a/FZD to suppress tumorigenesis, but also hijacked by growth factor-RTKs to stoke tumor progression. Phosphorylation of Daple by both RTKs and non-RTKs triggers Gi activation and potentiates non-canonical Wnt signals that trigger epithelial-mesenchymal transition. In patients with colorectal cancers, concurrent upregulation of Daple and the prototype RTK, EGFR, carried poor prognosis. Thus, this work defines a novel growth factor{leftrightarrow}G-protein{leftrightarrow}Wnt crosstalk paradigm in cancer biology.

cancer biology↗

bioRxiv2016

An RNA editing/binding-independent gene regulatory mechanism of ADARs and its clinical implication in cancer

Adenosine-to-inosine (A-to-I) editing, catalysed by Adenosine DeAminases acting on double-stranded RNA (dsRNA) (ADAR), occurs predominantly in the 3 untranslated regions (3UTRs). Here we uncover an unanticipated link between ADARs (ADAR1 and ADAR2) and the expression of target genes undergoing extensive 3UTR editing. Using METTL7A (Methyltransferase Like 7A), a novel tumor suppressor as an exemplary target gene, we demonstrate that its expression could be repressed by ADARs beyond their RNA editing and dsRNA binding functions. ADARs interact with Dicer to augment the processing of pre-miR-27a to mature miR-27a. Consequently, mature miR-27a targets the METTL7A 3UTR to repress its expression level. In sum, our study unveils that the extensive 3UTR editing is merely a footprint of ADAR binding, and is dispensable for the regulation of at least a subset of target genes. Instead, ADARs contribute to cancer progression by regulating cancer-related gene expression through their non-canonical functions independent of RNA editing and dsRNA binding. The functional significance of ADARs is much more diverse than previously appreciated and this gene regulatory function of ADARs is most likely to be of higher importance than the best-studied editing function. This novel non-editing side of ADARs opens another door to target cancer. This study is timely and represents a major break-through in the field of ADAR gene regulation and cancer biology.

Cancer Biology↗

bioRxiv2018

Integrative analysis of transcriptomic and clinical data uncovers the tumor suppressive activity of MITF in prostate cancer.

The dysregulation of gene expression is an enabling hallmark of cancer. Computational analysis of transcriptomics data from human cancer specimens, complemented with exhaustive clinical annotation, provides an opportunity to identify core regulators of the tumorigenic process. Here we exploit well-annotated clinical datasets of prostate cancer for the discovery of transcriptional regulators relevant to prostate cancer. Following this rationale, we identify Microphthalmia-associated transcription factor (MITF) as a prostate tumor suppressor among a subset of transcription factors. Importantly, we further interrogate transcriptomics and clinical data to refine MITF perturbation-based empirical assays and unveil Crystallin Alpha B (CRYAB) as an unprecedented direct target of the transcription factor that is, at least in part, responsible for its tumor suppressive activity in prostate cancer. This evidence was supported by the enhanced prognostic potential of a signature based on the concomitant alteration of MITF and CRYAB in prostate cancer patients. In sum, our study provides proof-of-concept evidence of the potential of the bioinformatics screen of publicly available cancer patient databases as discovery platforms, and demonstrates that the MITF-CRYAB axis controls prostate cancer biology.

cancer biology↗

bioRxiv2018

Opioids trigger breast cancer metastasis through E-Cadherin downregulation and STAT3 activation promoting epithelial mesenchymal transition

The opioid crisis of pain medication bears risks from addiction to cancer progression, but little experimental facts exist. Expression of {delta}-opioid receptors (DORs) correlates with poor prognosis for breast cancer (BCa) patients, but mechanism and genetic/pharmacologic proof of key changes in opioid-triggered cancer biology are lacking. We show that oncogenic STAT3 signaling and E-Cadherin downregulation are triggered by opioid-ligated DORs, promoting metastasis. Human and murine transplanted BCa cells (MDA-MB-231, 4T1) displayed enhanced metastasis upon opioid-induced DOR stimulation, and DOR-antagonist blocked metastasis. Opioid-exposed BCa cells showed enhanced migration, STAT3 activation, down-regulation of E-Cadherin and expression of epithelial-mesenchymal transition (EMT) markers. STAT3 knockdown or upstream inhibition through the JAK1/2 kinase inhibitor ruxolitinib prevented opioid-induced BCa cell metastasis and migration. We conclude that opioids trigger metastasis through oncogenic JAK1/2-STAT3 signaling.

cancer biology↗

bioRxiv2018

KRASG12D and TP53R167H Cooperate to Induce Pancreatic Ductal Adenocarcinoma in Sus Scrofa Pigs

Although survival has improved in recent years, the prognosis of patients with advanced pancreatic ductal adenocarcinoma (PDAC) remains poor. Despite substantial differences in anatomy, physiology, genetics, and metabolism, the overwhelming majority of preclinical testing relies on transgenic mice. Hence, while mice have allowed for tremendous advances in cancer biology, they have been a poor predictor of drug performance/toxicity in the clinic. Given the greater similarity of sus scrofa pigs to humans, we engineered transgenic sus scrofa expressing a LSL-KRASG12D-TP53R167H cassette. By applying Adeno-Cre to pancreatic duct cells in vitro, cells self-immortalized and established tumors in immunocompromised mice. When Adeno-Cre was administered to the main pancreaticduct in vivo, pigs developed extensive PDAC at the injection site hallmarked by excessive proliferation and desmoplastic stroma. This serves as the first large animal model of pancreatic carcinogenesis, and may allow for insight into new avenues of translational research not before possible in rodents.

cancer biology↗

bioRxiv2017

Clingen Cancer Somatic Working Group: standardizing and democratizing access to cancer molecular diagnostic data to drive translational research

A growing number of academic and community clinics are conducting genomic testing to inform treatment decisions for cancer patients (1). In the last 3-5 years, there has been a rapid increase in clinical use of next generation sequencing (NGS) based cancer molecular diagnostic (MolDx) testing (2). The increasing availability and decreasing cost of tumor genomic profiling means that physicians can now make treatment decisions armed with patient-specific genetic information. Accumulating research in the cancer biology field indicates that there is significant potential to improve cancer patient outcomes by effectively leveraging this rich source of genomic data in treatment planning (3). To achieve truly personalized medicine in oncology, it is critical to catalog cancer sequence variants from MolDx testing for their clinical relevance along with treatment information and patient outcomes, and to do so in a way that supports large-scale data aggregation and new hypothesis generation. One critical challenge to encoding variant data is adopting a standard of annotation of those variants that are clinically actionable. Through the NIH-funded Clinical Genome Resource (ClinGen) (4), in collaboration with NLMs ClinVar database and >50 academic and industry based cancer research organizations, we developed the Minimal Variant Level Data (MVLD) framework to standardize reporting and interpretation of drug associated alterations (5). We are currently involved in collaborative efforts to align the MVLD framework with parallel, complementary sequence variants interpretation clinical guidelines from the Association of Molecular Pathologists (AMP) for clinical labs (6). In order to truly democratize access to MolDx data for care and research needs, these standards must be harmonized to support sharing of clinical cancer variants. Here we describe the processes and methods developed within the ClinGens Somatic WG in collaboration with over 60 cancer care and research organizations as well as CLIA-certified, CAP-accredited clinical testing labs to develop standards for cancer variant interpretation and sharing.

bioinformatics↗

bioRxiv2017

polyCluster: Defining Communities of Reconciled Cancer Subtypes with Biological and Prognostic Significance

To stratify cancer patients for most beneficial therapies, it is a priority to define robust molecular subtypes using clustering methods and \"big data\". If each of these methods produces different numbers of clusters for the same data, it is difficult to achieve an optimal solution. Here, we introduce \"polyCluster\", a tool that reconciles clusters identified by different methods into context-specific subtype \"communities\" using a hypergeometric test or a measure of relative proportion of common samples. The polycluster was tested using a breast cancer dataset, and latter using uveal melanoma datasets to identify novel subtype communities with significant metastasis-free prognostic differences. Available at: https://github.com/syspremed/polyClustR

genomics↗

bioRxiv2017

Classification Of Gene Signatures For Their Information Value And Functional Redundancy

Large collections of gene signatures play a pivotal role in interpreting results of omics data analysis but suffer from compositional (large overlap) and functional (redundant read-outs) redundancy, and many gene signatures rarely pop-up in statistical tests. Based on pan-cancer data analysis, here we define a restricted set of 962 so called informative signatures and demonstrate that they have more chances to appear highly enriched in cancer biology studies. We show that the majority of informative signatures conserve their weights for the composing genes (eigengenes) from one cancer type to another. We construct InfoSigMap, an interactive online map showing the structure of compositional and functional redundancies between informative signatures and charting the territories of biological functions accessible through transcriptomic studies. InfoSigMap can be used to visualize in one insightful picture the results of comparative omics data analyses and suggests reconsidering existing annotations of certain reference gene set groups.

systems biology↗

bioRxiv2018

Comprehensive transcriptomic analysis of cell lines as models of primary tumor samples across 22 tumor types

Cancer cell lines are commonly used as models for cancer biology. While they are limited in their ability to capture complex interactions between tumors and their surrounding environment, they are a cornerstone of cancer research and many important findings have been discovered utilizing cell line models. Not all cell lines are appropriate models of primary tumors, however, which may contribute to the difficulty in translating in vitro findings to patients. Previous studies have leveraged public datasets to evaluate cell lines as models of primary tumors, but they have been limited in scope to specific tumor types and typically ignore the presence of tumor infiltrating cells in the primary tumor samples. We present here a comprehensive pan-cancer analysis utilizing approximately 9,000 transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 different tumor types. After adjusting for tumor purity in the primary tumor samples, we performed correlation analysis and differential gene expression analysis between the primary tumor samples and cell lines. We found that cell-cycle pathways are consistently upregulated in cell lines, while no pathways are consistently upregulated across the primary tumor samples. In a case study, we compared colorectal cancer cell lines with primary tumor samples across the colorectal subtypes and identified three colorectal cell lines that were derived from fibroblasts rather than tumor epithelial cells. Lastly, we propose a new set of cell lines panel, the TCGA-110, which contains the most representative cell lines from 22 different tumor types as a more comprehensive and informative alternative to the NCI-60 panel. Our analysis of the other tumor types are available in our web app (http://comphealth.ucsf.edu/TCGA110) as a resource to the cancer research community, and we hope it will allow researchers to select more appropriate cell line models and increase the translatability of in vitro findings.

bioinformatics↗

bioRxiv2017

Depth Dependent Nanomechanical Analysis of Extracellular Matrix in Multicell Spheroids.

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC=\"FIGDIR/small/193516_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (44K):\norg.highwire.dtl.DTLVardef@4ba6b3org.highwire.dtl.DTLVardef@ad3c2dorg.highwire.dtl.DTLVardef@149e92corg.highwire.dtl.DTLVardef@16b6e51_HPS_FORMAT_FIGEXP M_FIG C_FIG Nanomechanical investigation with Atomic Force Microscope has revealed new details regarding various nanomechanical heterogeneities for cells that are embedded in extracellular matrix in Multicellular spheroidal culture. This investigation sheds new insight into dynamic relationship of cells with their surrounding environment in tumors and 3D multicellular cultures.\n\nNanotechnology has revolutionized the field of cancer biology and has opened new avenues towards understanding nanomechanical variations in rapidly growing tumors. Over the last decade Atomic Force Microscope (AFM) has played an important role in understanding nanomechanical properties of various cancer cell lines. This study is focused on Lewis Lung Carcinoma Cell (LLC) tumors as 3D multicellular spheroid (MS). Such multicellular structures have enabled investigation of various components of tumors in-vitro. To better comprehend mechanical properties of cells and its surrounding extracellular matrix (ECM), depth dependent indentation measurements were conducted with Atomic Force Microscope (AFM). Force-vs-indentation curves were used to create stiffness profiles as function of depth. Here studies were focused on outer most layer i.e. proliferation zone of the spheroid. AFM investigations of sample a MS revealed three nanomechanical topographies, Type A- high modulus due to collagen stress fibers, Type B- high stiffness at cell membrane & ECM interface and Type C - increased modulus due to cell lying deep inside matrix at the a depth of 1.35 microns. Various nanomechanical heterogeneities revealed in this investigation can shed new light in developing correct dosage regime for various tumor dissolving drugs and designing more controlled artificial extracellular matrix systems for replicating tissue growth in-vitro.\n\nShort Statistical SummaryThis article describes nanomechanical characteristics of the cells embedded in extracellular matrix in a multicellular spheroid. The paper contains 6350 words including title page and references. Graphical Content contains 46 words. This article contains 6 Figures and zero tables.

bioengineering↗

bioRxiv2018

Evaluating cancer cell lines as models for metastatic breast cancer

Metastasis is the most common cause of cancer-related death and, as such, there is an urgent need to discover new therapies to treat metastasized cancers. Cancer cell lines are widely-used models to study cancer biology and test drug candidates. However, it is still unknown to what extent they adequately resemble the disease in patients. The recent accumulation of large-scale genomic data in cell lines, mouse models, and patient tissue samples provides an unprecedented opportunity to evaluate the suitability of cell lines for metastatic cancer research. In this work, we used breast cancer as a case study. The comprehensive comparison of the genetic profiles of 57 breast cancer cell lines with those of metastatic breast cancer samples revealed substantial genetic differences. In addition, we identified cell lines that more closely resemble different subtypes of metastatic breast cancer. Surprisingly, a combined analysis of mutation, copy number variation and gene expression data suggested that MDA-MB-231, the most commonly used triple negative cell line for metastatic breast cancer research, had little genomic similarity with Basal-like metastatic breast cancer samples. We further compared cell lines with organoids, a new type of preclinical model which are becoming more popular in recent years. We found that organoids outperformed cell lines in resembling the transcriptome of metastatic breast cancer samples. However, additional differential expression analysis suggested that both types of models could not mimic the effects of tumor microenvironment and meanwhile had their own bias towards modeling specific biological processes. Our work provides a guide of cell line selection in metastasis-related study and sheds light on the potential of organoids in translational research.

bioinformatics↗

bioRxiv2018

Model-based analysis of positive selection significantly expands the list of cancer driver genes, including RNA methyltransferases

Identifying driver genes is a central problem in cancer biology, and many methods have been developed to identify driver genes from somatic mutation data. However, existing methods either lack explicit statistical models, or rely on very simple models that do not capture complex features in somatic mutations of driver genes. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a more comprehensive model-based approach to driver gene identification. This new method explicitly models, at the single-base level, the effects of positive selection in cancer driver genes as well as highly heterogeneous background mutational process. Its selection model captures elevated mutation rates in functionally important sites using multiple external annotations, as well as spatial clustering of mutations. Its background mutation model accounts for both known covariates and unexplained local variation. Simulations under realistic evolutionary models demonstrate that driverMAPS greatly improves the power of driver gene detection over state-of-the-art approaches. Applying driverMAPS to TCGA data across 20 tumor types identified 159 new potential driver genes. Cross-referencing this list with data from external sources strongly supports these findings. The novel genes include the mRNA methytransferases METTL3-METTL14, and we experimentally validated METTL3 as a potential tumor suppressor gene in bladder cancer. Our results thus provide strong support to the emerging hypothesis that mRNA modification is an important biological process underlying tumorigenesis.

genomics↗

bioRxiv2018

Population assignment from cancer genome profiling data

For a variety of human malignancies, incidence, treatment efficacy and overall prognosis show considerable variation between different populations and ethnic groups. Disentangling the effects related to particular population backgrounds can help in both understanding cancer biology and in tailoring therapeutic interventions. Because self-reported or inferred patient data can be incomplete or misleading due to migration and genomic admixture, a data-driven ancestry estimation should be preferred. While algorithms to analyze ancestry structure from healthy individuals have been developed, an easy-to-use tool to assign population groups based on genotyping data from SNP profiles is still missing and benchmarking for the validity of population assignment strategy for aberrant cancer genomes was not tested.\n\nWe benchmarked the consistency and accuracy of cross-platform population assignment. We also demonstrated its high accuracy to process unaltered as well as cancer genomes. Despite widespread and extensive somatic mutations of cancer profiling data, population assignment consistency between germline and highly mutated samples from cancer patients reached of 97% and 92% for assignment into 5 and 26 populations re-spectively. Comparison of our benchmarked results with self-reported meta-data estimated a matching rate between 88% to 92%. Despite a relatively high matching rate, the ethnicity labels indicated in meta-data are vague compared to the standardized output from our tool.\n\nWe have developed a bioinformatics tool to assign the populations from genome profiling data and validated its performance in healthy as well as aberrant cancer genomes. It is ready-to-use for genotyping data from nine commercial SNP array platforms or sequencing data. This tool is effective to scrutinize the population structure in cancer genomes and provides better measure to integrate genotyping data from various platforms instead of self-reported information. It will facilitate research on interplay between ethnicity related genetic background and molecular patterns in cancer entities and disentangling possible hereditary contributions.\n\nThe docker image of the tool is provided in DockerHub as \"baudisgroup/snp2pop\".

bioinformatics↗

bioRxiv2017

Cell proliferation depends on the direct binding between PKM2 and AKAP-Lbc

The M2 form of the glycolytic enzyme pyruvate kinase (PKM2) has generated much interest recently due to its important role in tumor metabolism. A yeast two-hybrid screen carried out by the Alliance for Cell Signaling suggests that PKM2 interacts with A-Kinase Anchoring Protein (AKAP)-Lbc.\n\nAKAP-Lbc (also known as AKAP13) is a scaffold protein that integrates signaling through multiple enzymes including protein kinases A and D and the small G protein Rho. AKAP-Lbc was originally identified in leukemic blast cells, and multiple reports implicate AKAP-Lbc in breast, prostate and thyroid cancers, however the role of AKAP-Lbc in cancer biology is not understood.\n\nCo-immunoprecipitation, pulldown and Bimolecular Fluorescence Complementation (BiFC) data indicate that PKM2 interacts with AKAP-Lbc. Mapping experiments indicate that PKM2 directly interacts with amino acid residues 1923-2817 of AKAP-Lbc. By disrupting the interaction between the two proteins with the expression of the AKAP-Lbc fragments, our data suggest that the binding between PKM2 and PKA plays a critical role in cell proliferation. The work indicates that the binding between AKAP-Lbc and PKM2 may be an important target to treat some cancers by reducing the cell proliferation.

biochemistry↗

bioRxiv2016

Personalized characterization of diseases using sample-specific networks

A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e., a sample-specific network method, which allows us to construct individual-specific networks based on molecular expression of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such sample-specific networks can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various cancers. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e., we even identified those drug resistance genes that actually have no clearly differential expression between samples with and without the resistance, due to the additional network information.

Systems Biology↗