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bioRxiv2025

Ophiobolin A selectively alters mitochondria, metabolism and redox biology in breast cancer and mammary epithelial cells which have undergone epithelial to mesenchymal transition

Breast cancer progression is facilitated by the epithelial to mesenchymal transition (EMT), generating cancer cells with enhanced metastatic capacity and resistance to chemotherapeutics. EMT is known to impart changes in metabolic pathways and mitochondrial function. Here, we show a natural product possesses EMT-specific cytotoxic activity via alterations in metabolic and mitochondrial functions. The fungus-derived sesterterpenoid, ophiobolin A (OpA), possesses nanomolar cytotoxic activity and a high therapeutic index, although its target and mechanism of action remain unknown. Here, we utilized a model of mammary epithelial cells and breast cancer cell lines with and without EMT features to characterize the mechanism of selectivity towards EMT(+) cells by OpA and to identify novel targets for the treatment of EMT-enriched breast cancer. Proteins interacting with OpA in EMT(+) cells were identified through proteomic studies. We utilized trans-mitochondrial cybrids to determine that mitochondria mediate sensitivity to OpA. Furthermore, we report effects on glycolysis, oxidative metabolism, and disruption of metabolite abundance in the TCA cycle. Alterations to mitochondrial iron concentrations and glutathione production, mediated partly by OpA engagement with the metabolic proteins CISD3 and SLC25A40, were also detected in EMT(+) cells. Antioxidant mechanisms are activated by OpA in EMT(+) cells via the NRF2-ARE pathway, verified by decreased cytotoxicity in EMT(+) cells pretreated with the NRF2 activator CDDO. Collectively, we conclude that OpA selectivity toward EMT is mediated by the mitochondria, and at sub-cytotoxic levels, generates a metabolic shift leading to cell death countered by antioxidant mechanisms.

cancer biology↗

bioRxiv2025

Nutrient availability-driven changes in extracellular matrix biochemical and mechanical properties regulate pancreatic cancer cell biology

The extracellular matrix (ECM) provides key biochemical and biomechanical cues that govern fundamental cellular processes, including growth and migration. ECM dysregulation and altered cell-matrix interactions are a driver of cancer progression, exemplified by pancreatic ductal adenocarcinoma (PDAC), where an abnormally dense, collagen-rich, and stiff ECM correlates with poor patient outcomes. The PDAC microenvironment is poorly perfused, resulting in altered nutrient availability, yet how this metabolic stress shapes the ECM and its biological activity remains poorly understood. Herein, we demonstrate that glutamine, a key amino acid depleted in poorly perfused PDAC regions, regulates the biochemical composition, mechanical properties, and biological activity of fibroblast-derived ECM. As glutamine availability increases, fibroblasts shift from producing a basement membrane-like ECM toward an interstitial, mature ECM enriched in fibrillar collagens. The ECM generated under glutamine-rich conditions is stiffer, which limits PDAC cell growth, while simultaneously, the elevated collagen I content promotes migration in a 3D spheroid model. Mechanistically, glutamine-dependent collagen I engages integrin 2 (ITGA2) to activate focal adhesion kinase signaling, driving PDAC cell migration independent of growth. In PDAC patients, glutamine stress inversely correlates with collagen expression in CAFs, with collagen I-ITGA2 as the most enriched ECM receptor interaction pair compared to the normal pancreas. These findings establish nutrient availability as a key regulator of ECM biology and offer new avenues to therapeutically intervene with dysregulated cell-matrix interactions in PDAC.

cell biology↗

bioRxiv2024

Oriented Cell Dataset: efficient imagery analyses using angular representation

In this work, we propose a new public dataset for cell detection in bright-field microscopy images annotated with Oriented Bounding Boxes (OBBs), named Oriented Cell Dataset (OCD). We show that OBBs provide a more accurate shape representation compared to standard Horizontal Bounding Boxes (HBBs), with slight overhead of one extra click in the annotation process. Our dataset also contains a subset of images with five independent expert annotations, which allows inter-annotation analysis to determine if the results produced by algorithms are within the expected variability of human experts. We investigated how to automate cell biology microscopy images by training seven popular OBB detectors in the proposed dataset, and focused our analyses on two main problems in cancer biology: cell confluence and polarity determination, the latter not possible through HBB representation. All models achieved statistically similar results to the biological applications compared to human annotation, enabling the automation of cell biology and cancer cell biology microscopy image analysis. Our code and dataset are available at https://github.com/LucasKirsten/Deep-Cell-Tracking-EBB.

bioinformatics↗

bioRxiv2021

A breast cancer patient-derived xenograft and organoid platform for drug discovery and precision oncology

Model systems that recapitulate the complexity of human tumors and the reality of variable treatment responses are urgently needed to better understand cancer biology and to develop more effective cancer therapies. Here we report development and characterization of a large bank of patient-derived xenografts (PDX) and matched organoid cultures from tumors that represent some of the greatest unmet needs in breast cancer research and treatment. These include endocrine-resistant, treatment-refractory, and metastatic breast cancers and, in some cases, multiple tumor collections from the same patients. The models can be grown long-term with high fidelity to the original tumors. We show that development of matched PDX and PDX-derived organoid (PDxO) models facilitates high-throughput drug screening that is feasible and cost-effective, while also allowing in vivo validation of results. Our data reveal consistency between drug screening results in organoids and drug responses in breast cancer PDX. Moreover, we demonstrate the feasibility of using these patient-derived models for precision oncology in real time with patient care, using a case of a triple negative breast cancer with early metastatic recurrence as an example. Our results uncovered an FDA-approved drug with high efficacy against the models. Treatment with the PDxO-directed therapy resulted in a complete response for the patient and a progression-free survival period more than three times longer than her previous therapies. This work provides valuable new methods and resources for functional precision medicine and drug development for human breast cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=189 HEIGHT=200 SRC="FIGDIR/small/433268v2_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@134d5cdorg.highwire.dtl.DTLVardef@1c0a419org.highwire.dtl.DTLVardef@556a4forg.highwire.dtl.DTLVardef@1b68c9c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Individualized discovery of rare cancer drivers in global network context

Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become "hard currency" of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of gene interaction networks and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations co-occurrence in tumour genomes. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to ten largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. We demonstrate that the individualized discovery revealed driver events which were individually rare, not detectable by other computational approaches, and related to cancer biology domains poorly covered by previous analyses. Considering the novel driver candidates and their constellations in individual tumor genomes opens a novel avenue for personalized cancer medicine.

systems biology↗

bioRxiv2017

Ennet: exert enhaner-only somatic mutations to discover potential cancer-driving biological networks

Whole genome sequencing technology has facilitated the discovery of a large number of somatic mutations in enhancers (SMEs), whereas the utility of SMEs in tumorigenesis has not been fully explored. Here we present Ennet, a method to comprehensively investigate SMEs enriched networks (SME-networks) in cancer by integrating SMEs, enhancer-gene interactions and gene-gene interactions. Using Ennet, we performed a pan-cancer analysis in 2004 samples from 8 cancer types and found many well-known cancer drivers were involved in the SME-networks, including ESR1, SMAD3, MYC, EGFR, BCL2 and PAX5. Meanwhile, Ennet also identified many new networks with less characterization but have potentially important roles in cancer, including a large SME-network in medulloblastoma (MB), which contains genes enriched in the glutamate receptor and neural development pathways. Interestingly, SME-networks are specific across cancer types, and the vast majority of the genes identified by Ennet have few mutations in gene bodies. Collectively, our work suggests that using enhancer-only somatic mutations can be an effective way to discover potential cancer-driving networks. Ennet provides a new perspective to explore new mechanisms for tumor progression from SMEs.

bioinformatics↗

bioRxiv2018

Some lessons and perspectives for applications of stochastic models in biological and cancer research

Randomness is an unavoidable feature of inner cellular environment and its effects propagate to higher levels of living matter organization such as cells, tissues, and organisms. Approaching those systems experimentally to understand their dynamics is a complex task because of the plethora of compounds interacting in a web that combines intra and inter level elements such that a coordinate behavior come up. Such a characteristic points to the necessity of establishing principles that help on the description, categorization, classification, and the prediction of the behavior of biological systems. The theoretical machinery already available, or the ones to be discovered motivated by biological problems, can play an important role on that quest. Here we exemplify the applicability of theoretical tools by discussing some biological problems that we have approached mathematically: fluctuations in gene expression and cell proliferation in the context of loss of contact inhibition. We discuss the methods that we have employed aiming to provide the reader with a phenomenological, biologically motivated, perspective of the use of theoretical methods. Furthermore, we discuss some of our conclusions after employing our approach and some research perspectives that they motivate.

systems biology↗

bioRxiv2025

Functional Impact of Glycosylation and Splicing Variants of LRIG1 on EGFR Proteostasis in Cancer

LRIG1, a membrane glycoprotein, has emerged as a significant stem cell marker and negative regulator of receptor tyrosine kinases (RTKs), including EGFR. Glycosylation is a major post-translational modification, which plays a crucial role in protein function and stability. In cancer biology, abnormal glycosylation can serve as a biomarker and contribute to pathogenesis. We aimed to investigate the effects of glycosylation on LRIG1 functions. Through database analysis and experimental approaches, we focused on evolutionarily conserved glycosylation sites of LRIG1, particularly N74 in humans. We found that a mutation of the N74 glycosylation site (N74Q) enhances LRIG1s binding to EGFR and promotes EGFR degradation. Furthermore, we identified a naturally occurring splice variant of LRIG1 lacking exon 2, which includes the N74 site, that shows similar enhanced EGFR binding and degradation. Analysis of TCGA data revealed that exon 2 skipping in LRIG1 occurs in various cancers, with higher levels correlating with poorer survival in breast and ovarian cancers. Our findings suggest that the absence of glycosylation at N74 enhances LRIG1-EGFR binding, providing an example of glycosylation negatively regulating protein-protein interaction. This mechanism, achieved through alternative splicing, provides insights into the importance of glycosylation deficiency in cancer biology.

biochemistry↗

bioRxiv2025

Anti-resonance in developmental signaling regulates cell fate decisions

Cells process dynamic signaling inputs to regulate fate decisions during development. While oscillations or waves in key developmental pathways, such as Wnt, have been widely observed, the principles governing how cells decode these signals remain unclear. By leveraging optogenetic control of the Wnt signaling pathway in both HEK293T cells and H9 human embryonic stem cells, we systematically map the relationship between signal frequency and downstream pathway activation. We find that cells exhibit a minimal response to Wnt at certain frequencies, a behavior we term anti-resonance. We developed both detailed biochemical and simplified hidden variable models that explain how anti-resonance emerges from the interplay between fast and slow pathway dynamics. Remarkably, we find that frequency directly influences cell fate decisions involved in human gastrulation; signals delivered at anti-resonant frequencies result in dramatically reduced mesoderm differentiation. Our work reveals a previously unknown mechanism of how cells decode dynamic signals and how anti-resonance may filter against spurious activation. These findings establish new insights into how cells decode dynamic signals with implications for tissue engineering, regenerative medicine, and cancer biology. Significance StatementWnt signaling is responsible for driving stem cell differentiation. Our study explores a wide range of temporal patterns of Wnt activation using state-of-the-art optogenetics, advanced imaging and modeling. We identify anti-resonant frequencies that suppress mesoderm differentiation. We confirm this anti-resonant suppression for two human cell lines, HEK and embryonic stem cells, and expect that it also occurs in other pathways, as it arises from the interplay of different timescales along the pathway. Our work opens new avenues for systematically exploring signal spaces that natural systems can robustly respond to, and has broader implications for tissue engineering, regenerative medicine, and cancer biology.

cell biology↗

bioRxiv2021

iMUT-seq: high-resolution mapping of DSB mutational landscapes reveals new insights into the mutagenic mechanisms of DSB repair

DNA double-strand breaks (DSBs) are the most mutagenic form of DNA damage, and play a significant role in cancer biology, neurodegeneration and aging. However, studying DSB-induced mutagenesis is currently limited by the tools available for mapping these mutations. Here, we describe iMUT-seq, a technique that profiles DSB-induced mutations at high-sensitivity and single-nucleotide resolution around endogenous DSBs spread across the genome. By depleting 20 different DSB-repair factors we defined their mutational signatures in detail, revealing remarkable insights into the mechanisms of DSB-induced mutagenesis. We find that homologous-recombination (HR) is mutagenic in nature, displaying high levels of base substitutions and mononucleotide deletions due to DNA-polymerase errors, but simultaneously reduced translocation events, suggesting the primary role of HR is the specific suppression of genomic rearrangements. The results presented here offer new fundamental insights into DSB-induced mutagenesis and have significant implications for our understanding of cancer biology and the development of DDR-targeting chemotherapeutics.

molecular biology↗

bioRxiv2015

Hybrid Systems Modeling for (Cancer) Systems Biology

The advent of biological data of increasingly higher resolution in space and time has triggered the use of dynamic models to explain and predict the evolution of biological systems over space and time. Computer-aided system modeling and analysis in biology has led to many new discoveries and explanations that would otherwise be intractable to articulate without the available data and computing power. Nevertheless, the complexity in biology still challenges many labs in capturing studied phenomena in models that are tractable and simple enough to analyze. Moreover, the popular use of ordinary differential equation models have their limitations in that they solely capture continuous dynamics, while we observe many discrete dynamic phenomena in biology such as gene switching or mutations. Hybrid systems modeling provides a framework in which both continuous and discrete dynamics can be simulated and analyzed. Moreover, it provides techniques to develop approximations and abstractions of complex dynamics that are tractable to analyze.

Systems Biology↗

bioRxiv2018

Open source tools and standardized data in cancer systems biology

To reach the full potential of multicellular systems biology, mathematical and computational modelers must pool their efforts to share and curate biophysical measurements, create and combine mathematical models, analyze and visualize model predictions, and validate and refine against shared data. An ecosystem of open source software that reads standardized data is essential. We review the state-of-the-art in open source software and data standards in multicellular systems biology, and point out areas of needed growth to move beyond isolated models to community-driven frameworks that shed light on complex problems in multicellular systems biology.

systems biology↗

bioRxiv2018

Constructing predictive cancer systems biology models

Systems biology combines computational modeling with quantitative experimental measurements to study complex biological processes. Here, we outline an approach for parameterizing and validating a systems biology model to yield predictive tool that can generate testable hypotheses and expand biological understanding.

systems biology↗

bioRxiv2024

Tumor microenvironment noise-induced polarization: the main challenge in macrophages' immunotherapy for cancer.

Disturbance of epigenetic processes can lead to altered gene function and malignant cellular transformation. In particular, changes in the epigenetic landscape are a central topic in cancer biology. The initiation and progression of cancer are now recognized to involve both epigenetic and genetic alterations. In this paper, we study the epigenetic mechanism (related to the tumor microenvironment) responsible for increasing tumor-associated macrophages that promote the occurrence and metastasis of tumor cells, support tumor angiogenesis, inhibit T cell-mediated anti-tumor immune response, and lead to tumor progression. We show that the tumor benefits from the macrophages high degree of plasticity and larger epigenetic basins corresponding to phenotypes that favor cancer development through a process that we call noise-induced polarization. Moreover, we propose a mechanism to promote the appropriate epigenetic stability for immunotherapies involving macrophages, which includes p53 and APR-246 (eprenetapopt). Our results show that a combination therapy may be necessary to ensure the proper epigenetic stability of macrophages, which otherwise will contribute to cancer progression. On the other hand, we conclude that macrophages may remain in the anti-tumoral state in types of cancer that exhibit less TP53 mutation, like colorectal cancer; in these cases, macrophages immunotherapy may be more suitable. We finally mention the relevance of the epigenetic potential (Waddingtons landscape) as the backbone for our study, which encapsulates the biological information of the system.

cancer biology↗

bioRxiv2021

A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy.

Advances in cancer biology are increasingly dependent on integration of heterogeneous datasets. Large scale efforts have systematically mapped many aspects of cancer cell biology; however, it remains challenging for individual scientists to effectively integrate and understand this data. We have developed a new data retrieval and indexing framework that allows us to integrate publicly available data from different sources and to combine publicly available data with new or bespoke datasets. Beyond a database search, our approach empowered testable hypotheses of new synthetic lethal gene pairs, genes associated with sex disparity, and immunotherapy targets in cancer. Our approach is straightforward to implement, well documented and is continuously updated which should enable individual users to take full advantage of efforts to map cancer cell biology.

bioinformatics↗

bioRxiv2021

Identifying common transcriptome signatures of cancer by interpreting deep learning models

Cancer is a set of diseases characterized by unchecked cell proliferation and invasion of surrounding tissues. The many genes that have been genetically associated with cancer or shown to directly contribute to oncogenesis vary widely between tumor types, but common gene signatures that relate to core cancer pathways have also been identified, signifying that cancer cases display common hallmark molecular features. It is not clear however whether there exist additional sets of genes or transcriptomic features that are less well known in cancer biology but that are also commonly deregulated across several cancer types. Here, in order to agnostically identify transcriptomic features that are commonly shared between cancer types, we used RNA-Seq datasets encompassing thousands of samples from 19 healthy tissue types and 18 solid tumor types to train three feed-forward neural networks, based either on protein-coding gene expression, lncRNA expression or splice junction use, to distinguish between healthy and tumor samples. All three models achieve high precision, recall and accuracy on test sets derived from 13 datasets used during training and on an independent test dataset, indicating that our models recognize transcriptome signatures that are consistent across tumors. Analysis of attribution values extracted from our models reveals that genes that are commonly altered in cancer by expression or splicing variations are under strong evolutionary and selective constraints, suggesting that they have important cellular functions. Importantly, we found that genes composing our cancer transcriptome signatures are not frequently affected by mutations or genomic alterations and that their functions differ widely from the genes genetically associated with cancer. Finally, our results also highlighted that deregulation of RNA-processing genes and aberrant splicing are pervasive features across a large array of solid tumor types. The transcriptomic features that we highlight here define cancer signatures that may reflect causal variations or consequences of disease state, or a combination of both.

cancer biology↗

bioRxiv2024

DNMT3AR882H Is Not Required for Disease Maintenance in Primary Human AML, but Is Associated With Increased Leukemia Stem Cell Frequency

Genetic mutations are being thoroughly mapped in human cancers, yet a fundamental question in cancer biology is whether such mutations are functionally required for cancer initiation, maintenance of established cancer, or both. Here, we study this question in the context of human acute myeloid leukemia (AML), where DNMT3AR882 missense mutations often arise early, in pre-leukemic clonal hematopoiesis, and corrupt the DNA methylation landscape to initiate leukemia. We developed CRISPR-based methods to directly correct DNMT3AR882 mutations in leukemic cells obtained from patients. Surprisingly, DNMT3AR882 mutations were largely dispensable for disease maintenance. Replacing DNMT3AR882 mutants with wild-type DNMT3A did not impair the ability of AML cells to engraft in vivo, and minimally altered DNA methylation. Taken together, DNMT3AR882 mutations are initially necessary for AML initiation, but are largely dispensable for disease maintenance. The notion that initiating oncogenes differ from those that maintain cancer has important implications for cancer evolution and therapy. STATEMENT OF SIGNIFICANCEUnderstanding which driver mutations are required for cancer initiation, maintenance, or both phases remains poorly understood. Here, we uncover that highly prevalent pre-leukemic DNMT3A mutations are only required during disease initiation, but become dispensable after leukemic transformation, uncovering the context-specific role of this driver mutation with important therapeutic implications.

cancer biology↗

bioRxiv2019

A workflow for visualizing human cancer biopsies using large-format electron microscopy

Recent developments in large format electron microscopy have enabled generation of images that provide detailed ultrastructural information on normal and diseased cells and tissues. Analyses of these images increase our understanding of cellular organization and interactions and disease-related changes therein. In this manuscript, we describe a workflow for two-dimensional (2D) and three-dimensional (3D) imaging, including both optical and scanning electron microscopy (SEM) methods, that allow pathologists and cancer biology researchers to identify areas of interest from human cancer biopsies. The protocols and mounting strategies described in this workflow are compatible with 2D large format EM mapping, 3D focused ion beam-SEM and serial block face-SEM. The flexibility to use diverse imaging technologies available at most academic institutions makes this workflow useful and applicable for most life science samples. Volumetric analysis of the biopsies studied here revealed morphological, organizational and ultrastructural aspects of the tumor cells and surrounding environment that cannot be revealed by conventional 2D EM imaging. Our results indicate that although 2D EM is still an important tool in many areas of diagnostic pathology, 3D images of ultrastructural relationships between both normal and cancerous cells, in combination with their extracellular matrix, enables cancer researchers and pathologists to better understand the progression of the disease and identify potential therapeutic targets.

cancer biology↗