Heterogeneity of Myc expression in breast cancer exposes pharmacological vulnerabilities revealed through executable mechanistic modeling

Vitamin B5 supports MYC oncogenic metabolism and tumor progression in breast cancer

Tumors are intrinsically heterogeneous and it is well established that this directs their evolution, hinders their classification and frustrates therapy1-3. Consequently, spatially resolved omics-level analyses are gaining traction4-9. Despite considerable therapeutic interest, tumor metabolism has been lagging behind this development and there is a paucity of data regarding its spatial organization. To address this shortcoming, we set out to study the local metabolic effects of the oncogene c-MYC, a pleiotropic transcription factor that accumulates with tumor progression and influences metabolism10,11. Through correlative mass spectrometry imaging, we show that pantothenic acid (vitamin B5) associates with MYC-high areas within both human and murine mammary tumors, where its conversion to coenzyme A fuels Krebs cycle activity. Mechanistically, we show that this is accomplished by MYC-mediated upregulation of its multivitamin transporter SLC5A6. Notably, we show that SLC5A6 over-expression alone can induce increased cell growth and a shift toward biosynthesis, whereas conversely, dietary restriction of pantothenic acid leads to a reversal of many MYC-mediated metabolic changes and results in hampered tumor growth. Our work thus establishes the availability of vitamins and cofactors as a potential bottleneck in tumor progression, which can be exploited therapeutically. Overall, we show that a spatial understanding of local metabolism facilitates the identification of clinically relevant, tractable metabolic targets.

The effects of MYC on exosomes derived from cancer cells in the context of breast cancer

MYC amplification and overexpression in breast cancer occur 16% and 22%, respectively, and MYC has a linchpin role in breast carcinogenesis. Emerging evidence has started to shed light on central role of MYC in breast cancer progression. On the contrary, tumor-derived exosomes and their cargo molecules are required for the modulation of the tumor environment and to promote carcinogenesis. Still, how MYC regulates tumor-derived exosomes is still a matter of investigation in the context of breast cancer. Here, we investigated for the first time how MYC affects the biological functions of normal breast cells cocultured with exosomes derived from MYC-expression manipulated breast cancer cells. Accordingly, exosomes were isolated from MCF-7 and MDA-MB-231 cells that MYC expression was manipulated through siRNAs or lentiviral vectors by using exosome isolation reagent. Then, normal breast epithelial MCF-10A cells were treated with breast cancer cell-derived exosomes. The cellular activity of MCF-10A was investigated by cell growth assay, wound healing assay, and transwell assay. Our results suggested that MCF-10A cells treated with exosomes derived from MYC-overexpressing breast cancer cells demonstrated higher proliferation and migration capability compared with nontreated cells. Likewise, MCF-10A cells treated with exosomes derived from MYC-silenced cancer cells did not show high proliferation and invasive capacity. Overall, MYC can drive the functions of exosomes secreted from breast cancer cells. This may allow exploring a new mechanism how tumor cells regulate cancer progression and modulate tumor environment. The present study clears the way for further researches as in vivo studies and multi-omics that clarify exosomal content in an MYC-dependent manner.

Trap spaces of multi-valued networks: definition, computation, and applications

MOTIVATION

Boolean networks are simple but efficient mathematical formalism for modelling complex biological systems. However, having only two levels of activation is sometimes not enough to fully capture the dynamics of real-world biological systems. Hence, the need for multi-valued networks (MVNs), a generalization of Boolean networks. Despite the importance of MVNs for modelling biological systems, only limited progress has been made on developing theories, analysis methods, and tools that can support them. In particular, the recent use of trap spaces in Boolean networks made a great impact on the field of systems biology, but there has been no similar concept defined and studied for MVNs to date.

RESULTS

In this work, we generalize the concept of trap spaces in Boolean networks to that in MVNs. We then develop the theory and the analysis methods for trap spaces in MVNs. In particular, we implement all proposed methods in a Python package called trapmvn. Not only showing the applicability of our approach via a realistic case study, we also evaluate the time efficiency of the method on a large collection of real-world models. The experimental results confirm the time efficiency, which we believe enables more accurate analysis on larger and more complex multi-valued models.

AVAILABILITY AND IMPLEMENTATION

Source code and data are freely available at https://github.com/giang-trinh/trap-mvn.

Localized immune surveillance of primary melanoma in the skin deciphered through executable modeling

While skin is a site of active immune surveillance, primary melanomas often escape detection. Here, we have developed an in silico model to determine the local cross-talk between melanomas and Langerhans cells (LCs), the primary antigen-presenting cells at the site of melanoma development. The model predicts that melanomas fail to activate LC migration to lymph nodes until tumors reach a critical size, which is determined by a positive TNF-α feedback loop within melanomas, in line with our observations of murine tumors. In silico drug screening, supported by subsequent experimental testing, shows that treatment of primary tumors with MAPK pathway inhibitors may further prevent LC migration. In addition, our in silico model predicts treatment combinations that bypass LC dysfunction. In conclusion, our combined approach of in silico and in vivo studies suggests a molecular mechanism that explains how early melanomas develop under the radar of immune surveillance by LC.

Clonal interactions in cancer: integrating quantitative models with experimental and clinical data

Tumors consist of different genotypically distinct subpopulations-or subclones-of cells. These subclones can influence neighboring clones in a process called "clonal interaction." Conventionally, research on driver mutations in cancer has focused on their cell-autonomous effects that lead to an increase in fitness of the cells containing the driver. Recently, with the advent of improved experimental and computational technologies for investigating tumor heterogeneity and clonal dynamics, new studies have shown the importance of clonal interactions in cancer initiation, progression, and metastasis. In this review we provide an overview of clonal interactions in cancer, discussing key discoveries from a diverse range of approaches to cancer biology research. We discuss common types of clonal interactions, such as cooperation and competition, its mechanisms, and the overall effect on tumorigenesis, with important implications for tumor heterogeneity, resistance to treatment, and tumor suppression. Quantitative models-in coordination with cell culture and animal model experiments-have played a vital role in investigating the nature of clonal interactions and the complex clonal dynamics they generate. We present mathematical and computational models that can be used to represent clonal interactions and provide examples of the roles they have played in identifying and quantifying the strength of clonal interactions in experimental systems. Clonal interactions have proved difficult to observe in clinical data; however, several very recent quantitative approaches enable their detection. We conclude by discussing ways in which researchers can further integrate quantitative methods with experimental and clinical data to elucidate the critical-and often surprising-roles of clonal interactions in human cancers.

A Tale of Two Cancers: A Current Concise Overview of Breast and Prostate Cancer

Simple Summary

Breast and prostate cancers are serious public health issues that create considerable burden to both people and healthcare systems worldwide. Cancer is a heterogeneous disease influenced by numerous components, and its diverse intricate pathology challenges disease prevention, diagnosis, treatment, and survival. Although recent statistics suggest improvements in cancer diagnosis and treatment, many challenges remain before cancers are curable. This review presents relevant summarized information related to breast and prostate cancer.

Abstract

Cancer is a global issue, and it is expected to have a major impact on our continuing global health crisis. As populations age, we see an increased incidence in cancer rates, but considerable variation is observed in survival rates across different geographical regions and cancer types. Both breast and prostate cancer are leading causes of morbidity and mortality worldwide. Although cancer statistics indicate improvements in some areas of breast and prostate cancer prevention, diagnosis, and treatment, such statistics clearly convey the need for improvements in our understanding of the disease, risk factors, and interventions to improve life span and quality of life for all patients, and hopefully to effect a cure for people living in developed and developing countries. This concise review compiles the current information on statistics, pathophysiology, risk factors, and treatments associated with breast and prostate cancer.

Executable network of SARS-CoV-2-host interaction predicts drug combination treatments

The COVID-19 pandemic has pushed healthcare systems globally to a breaking point. The urgent need for effective and affordable COVID-19 treatments calls for repurposing combinations of approved drugs. The challenge is to identify which combinations are likely to be most effective and at what stages of the disease. Here, we present the first disease-stage executable signalling network model of SARS-CoV-2-host interactions used to predict effective repurposed drug combinations for treating early- and late stage severe disease. Using our executable model, we performed in silico screening of 9870 pairs of 140 potential targets and have identified nine new drug combinations. Camostat and Apilimod were predicted to be the most promising combination in effectively supressing viral replication in the early stages of severe disease and were validated experimentally in human Caco-2 cells. Our study further demonstrates the power of executable mechanistic modelling to enable rapid pre-clinical evaluation of combination therapies tailored to disease progression. It also presents a novel resource and expandable model system that can respond to further needs in the pandemic.

Efficacy of pH-Sensitive Nanomedicines in Tumors with Different c-MYC Expression Depends on the Intratumoral Activation Profile

Effective inhibition of the protein derived from cellular myelocytomatosis oncogene (c-Myc) is one of the most sought-after goals in cancer therapy. While several c-Myc inhibitors have demonstrated therapeutic potential, inhibiting c-Myc has proven challenging, since c-Myc is essential for normal tissues and tumors may present heterogeneous c-Myc levels demanding contrasting therapeutic strategies. Herein, we developed tumor-targeted nanomedicines capable of treating both tumors with high and low c-Myc levels by adjusting their ability to spatiotemporally control drug action. These nanomedicines loaded homologues of the bromodomain and extraterminal (BET) motif inhibitor JQ1 as epigenetic c-Myc inhibitors through pH-cleavable bonds engineered for fast or slow drug release at intratumoral pH. In tumors with high c-Myc expression, the fast-releasing (FR) nanomedicines suppressed tumor growth more effectively than the slow-releasing (SR) ones, whereas, in the low c-Myc tumors, the efficacy of the nanomedicines was the opposite. By studying the tumor distribution and intratumoral activation of the nanomedicines, we found that, despite SR nanomedicines achieved higher accumulation than the FR counterparts in both c-Myc high and low tumors, the antitumor activity profiles corresponded with the availability of activated drugs inside the tumors. These results indicate the potential of engineered nanomedicines for c-Myc inhibition and spur the idea of precision pH-sensitive nanomedicine based on cancer biomarker levels.

Capitalizing on Synthetic Lethality of MYC to Treat Cancer in the Digital Age

Deregulation of MYC is among the most frequent oncogenic drivers of cancer. Developing targeted therapies against MYC is, therefore, one of the most critical unmet needs of cancer therapy. Unfortunately, MYC has been labelled as undruggable due to the lack of success in developing clinically relevant MYC-targeted therapies. Synthetic lethality is a promising approach that targets MYC-dependent vulnerabilities in cancer. However, translating the synthetic lethality targets to the clinics is still challenging due to the complex nature of cancers. This review highlights the most promising mechanisms of MYC synthetic lethality and how these discoveries are currently translated into the clinic. Finally, we discuss how in silico computational platforms can improve clinical success of synthetic lethality-based therapy.

Methods for Determining Myc-Induced Apoptosis

Although many oncoproteins promote cell growth and proliferation, some also possess the potential to induce cell cycle arrest or cell death by apoptosis. Elevated and deregulated expression of the Myc protein promotes apoptosis in both cultured cells and in some tissues in vivo. Here we describe techniques to detect Myc-induced apoptosis in vitro using flow cytometry, microscopy, and immunoblotting, and in vivo using immunohistochemical staining, immunoblotting, and analysis of RNA expression.

Executable cancer models: successes and challenges

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field.

Differential impacts of charcoal-stripped fetal bovine serum on c-Myc among distinct subtypes of breast cancer cell lines

Charcoal-stripped fetal bovine serum (CS-FBS) is frequently used in studies on hormone-responsive cancers to provide hormone-free cell culture conditions. CS-FBS may influence the growth of cancer cells; however, the underlying mechanisms remain unclear. In this study, we aimed to clarify the effects of CS-FBS on distinct subtypes of breast cancer cells. We found that the crucial oncoprotein c-Myc was significantly inhibited in estrogen receptor alpha (ER-α)-positive breast cancer cells when cultured in CS-FBS-supplemented medium, but it was not suppressed in ER-α-negative cells. The addition of 17β-estradiol (E2) to CS-FBS-supplemented medium rescued the CS-FBS-induced inhibition of c-Myc, while treatment with 5α-dihydrotestosterone (DHT) suppressed c-Myc expression. Our data demonstrated that CS-FBS may impede the growth of ER-α-positive breast cancer cells via c-Myc inhibition, and this was possibly due to the removal of estrogen. These results highlighted that the core drivers of c-Myc expression were subtype-specific depending on the distinct cell context and special caution should be exercised when using CS-FBS in studies of hormone-responsive cancer cells.