Biology subjects

Gomez Tejeda Zanudo, J.

Publications and source records attributed to Gomez Tejeda Zanudo, J..

4 recordsLinked to original sources

bioRxiv2020

Cell-line-specific network models of ER+ breast cancer identify PI3Kα inhibitor sensitivity factors and drug combinations

Durable control of invasive solid tumors necessitates identifying therapeutic resistance mechanisms and effective drug combinations. A promising approach to tackle the cancer drug resistance problem is to build mechanistic mathematical models of the signaling network of cancer cells, and explicitly model the dynamics of information flow through this network under distinct genetic conditions and in response to perturbations. In this work, we used a network-based mathematical model to identify sensitivity factors and drug combinations for the PI3K inhibitor alpelisib, which was recently approved for ER+ PIK3CA mutant breast cancer. We experimentally validated the model-predicted efficacious combination of alpelisib and BH3 mimetics (e.g. MCL1 inhibitors) in ER+ breast cancer cell lines. We also experimentally validated the reduced sensitivity to alpelisib caused by FOXO3 knockdown, which is a novel potential resistance mechanism. Our experimental results showed cell line-specific sensitivity to the combination of alpelisib and BH3 mimetics, which was driven by the choice of BH3 mimetics. We find that cell lines were sensitive to the addition of either MCL1 inhibitor s63845 alone or in combination with BCL-XL/BCL-2 inhibitor navitoclax, and that the need for the combination of both BH3 mimetics was predicted by the expression of BCL-XL. Based on these results, we developed cell line-specific network models that are able to recapitulate the observed differential response to alpelisib and BH3 mimetics, and also incorporate the most recent knowledge on resistance and response to PI3K inhibitors. Overall, we present an approach for the development, experimental testing, and refining of mathematical models, which we apply to the context of PI3K inhibitor drug resistance in breast cancer. Our approach predicted and validated PI3K inhibitor sensitivity factors (FOXO3 knockdown) and drug combinations (BH3 mimetics), and illustrates that network-based mathematical models can contribute to overcoming the challenge of cancer drug resistance.

cancer biology↗

bioRxiv2019

The Angiosarcoma Project: enabling genomic and clinical discoveries in a rare cancer through patient-partnered research

Despite collectively accounting for 25% of tumors in U.S. adults, rare cancers have significant unmet clinical needs as they are difficult to study due to low incidence and geographically dispersed patient populations. We sought to assess whether a patient-partnered research approach using online engagement can overcome these challenges and accelerate scientific discovery in rare cancers, focusing on angiosarcoma (AS), an exceedingly rare sarcoma with a dismal prognosis and an annual U.S. incidence of 300 cases. Here, we describe the development of the Angiosarcoma Project (ASCproject), an initiative enabling patients across the U.S. and Canada to remotely share their clinical information and biospecimens for research. The project generates and publicly releases clinically annotated genomic data on tumor and germline specimens on an ongoing basis. Over 18 months, 338 AS patients registered for the ASCproject, comprising a significant fraction of all patients. Whole exome sequencing of 47 AS tumors revealed several recurrently mutated genes, including KDR, TP53, and PIK3CA. Activating mutations in PIK3CA were observed nearly exclusively in primary breast AS, suggesting a therapeutic rationale in these patients. AS of the head, neck, face, and scalp (HNFS) was associated with high tumor mutation burden and a dominant mutational signature of UV light exposure, suggesting that UV damage may be a causative factor in HNFS AS and that this AS subset might be amenable to immune checkpoint inhibitor therapy. Medical record review revealed two patients with HNFS AS received off-label treatment with anti-PD-1 therapy and experienced exceptional responses, highlighting immune checkpoint inhibition as a therapeutic avenue for HNFS AS. This patient-partnered approach has catalyzed an opportunity to discover the etiology and potential therapies for AS patients. Collectively, this proof of concept study demonstrates that empowering patients to directly participate in research can overcome barriers in rare diseases and enable biological and clinical discoveries.

cancer biology↗

bioRxiv2018

Target Control in Logical Models Using the Domain of Influence of Nodes

Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control, and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small subset of nodes is most relevant to the systems state and the goal is to drive the target nodes into their desired states. An example of target control would be driving a cell to commit to apoptosis (programmed cell death). From the experimental perspective, gene knockout, pharmacological inhibition of proteins and providing sustained external signals are among practical intervention techniques. We identify methodologies to use the stabilizing effect of sustained interventions for target control in logical models of biomolecular networks. Specifically, we define the domain of influence of a node (in a certain state) to be the nodes (and their corresponding states) that will be ultimately stabilized by the sustained state of this node regardless of the initial state of the system. We also define the related concept of the logical domain of influence of a node, and develop an algorithm for its identification using an auxiliary network that incorporates the regulatory logic. This way a solution to the target control problem is a set of nodes whose domain of influence can cover the desired target node states. We perform greedy randomized adaptive search in state space to find such solutions. We apply our strategy to several biological networks to demonstrate its effectiveness.

systems biology↗

bioRxiv2017

A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer

BackgroundMechanistic models of within-cell signal transduction networks can explain how these networks integrate internal and external inputs to give rise to the appropriate cellular response. These models can be fruitfully used in cancer cells, whose aberrant decision-making regarding their survival or death, proliferation or quiescence can be connected to errors in the state of nodes or edges of the signal transduction network.\n\nResultsHere we present a comprehensive network, and discrete dynamic model, of signal transduction in breast cancer based on the literature of ER+, HER2+, and PIK3CA-mutant breast cancers. The network model recapitulates known resistance mechanisms to PI3K inhibitors and suggests other possibilities for resistance. The model also reveals known and novel combinatorial interventions that are more effective than PI3K inhibition alone.\n\nConclusionsThe use of a logic-based, discrete dynamic model enables the identification of results that are mainly due to the organization of the signaling network, and those that also depend on the kinetics of individual events. Network-based models such as this will play an increasing role in the rational design of high-order therapeutic combinations.

cancer biology↗