Computational and systems oncology最新文献

Misexpression and remodeling of the extracellular matrix is a canonical hallmark of cancer, although the extent of cancer-associated aberrations in the genes coding for extracellular matrix (ECM) proteins and the consequences thereof are not well understood. In this study, we examined the alterations in core matrisomal genes across a set of nine cancers. These genes, especially the ones encoding for ECM glycoproteins (GP), were observed to be more susceptible to mutations than copy number variations across cancers. We classified the glycoprotein genes based on the ubiquity of their mutations across the nine cancer groups and estimated their evolutionary age using phylostratigraphy. To our surprise, the ECM glycoprotein genes commonly mutated across all cancers were predominantly unicellular in origin, whereas those commonly showing mutations in specific cancers evolved mostly during and after the unicellular-multicellular transition. Pathway annotation for biological interactions revealed that the most pervasively mutated glycoprotein set regulated a larger set of inter-protein interactions and constituted more cohesive interaction networks relative to the cancer-specific mutated set. In addition, ontological prediction revealed the pervasively mutated set to be strongly enriched for basement membrane (BM) dynamics. Our results suggest that ancient unicellular-origin ECM GP were canalized into playing critical tissue morphogenetic roles, and when disrupted through matrisomal gene mutations, associated with neoplastic transformation of a wide set of human tissues.

Oxygenation of tumors and the effect of hypoxia on cancer cell metabolism is a widely studied subject. Hypoxia-inducible factor (HIF), the main actor in the cell response to hypoxia, represents a potential target in cancer therapy. HIF is involved in many biological processes such as cell proliferation, survival, apoptosis, angiogenesis, iron metabolism, and glucose metabolism. This protein regulates the expressions of lactate dehydrogenase (LDH) and pyruvate dehydrogenase (PDH), both essential for the conversion of pyruvate to be used in aerobic and anaerobic pathways. HIF upregulates LDH, increasing the conversion of pyruvate into lactate which leads to higher secretion of lactic acid by the cell and reduced pH in the microenvironment. HIF indirectly downregulates PDH, decreasing the conversion of pyruvate into acetyl coenzyme A, which leads to reduced usage of the tricarboxylic acid (TCA) cycle in aerobic pathways. Upregulation of HIF may promote the use of anaerobic pathways for energy production even in normal extracellular oxygen conditions. Higher use of glycolysis even in normal oxygen conditions is called the Warburg effect. In this paper, we focus on HIF variations during tumor growth and study, through a mathematical model, its impact on the two metabolic key genes PDH and LDH, to investigate its role in the emergence of the Warburg effect. Mathematical equations describing the enzyme regulation pathways were solved for each cell of the tumor represented in an agent-based model to best capture the spatio-temporal oxygen variations during tumor development caused by cell consumption and reduced diffusion inside the tumor. Simulation results show that reduced HIF degradation in normoxia can induce higher lactic acid production. The emergence of the Warburg effect appears after the first period of hypoxia before oxygen conditions return to a normal level. The results also show that targeting the upregulation of LDH and the downregulation of PDH could be relevant in therapy.

In the tumor microenvironment (TME), functional interactions among tumor, immune, and stromal cells and the extracellular matrix play key roles in tumor progression, invasion, immune modulation, and response to treatment. Intra-tumor heterogeneity is ubiquitous not only at the genetic and transcriptomic levels but also in the composition and characteristics of TME. However, quantitative inference on spatial heterogeneity in the TME is still limited. Here, we propose a framework to use network graph-based spatial statistical models on spatially annotated molecular data to gain insights into modularity and spatial heterogeneity in the TME. Applying the framework to spatial transcriptomics data from pancreatic ductal adenocarcinoma samples, we observed significant global and local spatially correlated patterns in the abundance score of tumor cells; in contrast, immune cell types showed dispersed patterns in the TME. Hypoxia, EMT, and inflammation signatures contributed to intra-tumor spatial variations. Spatial patterns in cell type abundance and pathway signatures in the TME potentially impact tumor growth dynamics and cancer hallmarks. Tumor biopsies are integral to the diagnosis and clinical management of cancer patients; our data suggest that owing to intra-tumor non-genetic spatial heterogeneity, individual biopsies may underappreciate the extent of clinically relevant, functional variations across geographic regions within tumors.

In vitro migration assays are a cornerstone of cell biology and have found extensive utility in research. Over the past decade, several variations of the two-dimensional (2D) migration assay have improved our understanding of this fundamental process. However, the ability of these approaches to capture the functional heterogeneity during migration and their accessibility to inexperienced users has been limited. We downloaded published time-lapse 2D cell migration data sets and subjected them to feature extraction with the Fiji software. We used the “Analyze Particles” tool to extract 10 cell geometry features (CGFs), which were grouped into “shape,” “size,” and “position” descriptors. Next, we defined the migratory status of cells using the “MTrack2” plugin. All data obtained from Fiji were further subjected to rigorous statistical analysis with R version 4.0.2. We observed consistent associative trends between size and shape descriptors and validated our observations across four independent data sets. We used these descriptors to identify and characterize “nonmigrator (NM)” and “migrator (M)” subsets. Statistical analysis allowed us to identify considerable heterogeneity in the NM subset. Interestingly, differences in 2D-packing appeared to affect CGF trends and heterogeneity within the migratory subsets. We developed an analytical pipeline using open source tools, to identify and morphologically characterize functional migratory subsets from label-free, time-lapse imaging data. Our quantitative approach identified heterogeneity between nonmigratory cells and predicted the influence of 2D-packing on migration.

Ovarian cancer is commonly diagnosed in its late stages, and new treatment modalities are needed to improve patient outcomes and survival. We have recently established the synergistic effects of combination tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and procaspase activating compound (PAC-1) therapies in granulosa cell tumors (GCT) of the ovary, a rare form of ovarian cancer, using a mathematical model of the effects of both drugs in a GCT cell line. Here, to understand the mechanisms of combined TRAIL and PAC-1 therapy, study the viability of this treatment strategy, and accelerate preclinical translation, we leveraged our mathematical model in combination with population pharmacokinetics (PKs) models of both TRAIL and PAC-1 to expand a realistic heterogeneous cohort of virtual patients and optimize treatment schedules. Using this approach, we investigated treatment responses in this virtual cohort and determined optimal therapeutic schedules based on patient-specific PK characteristics. Our results showed that schedules with high initial doses of PAC-1 were required for therapeutic efficacy. Further analysis of individualized regimens revealed two distinct groups of virtual patients within our cohort: one with high PAC-1 elimination and one with normal PAC-1 elimination. In the high elimination group, high weekly doses of both PAC-1 and TRAIL were necessary for therapeutic efficacy; however, virtual patients in this group were predicted to have a worse prognosis when compared to those in the normal elimination group. Thus, PAC-1 PK characteristics, particularly clearance, can be used to identify patients most likely to respond to combined PAC-1 and TRAIL therapy. This work underlines the importance of quantitative approaches in preclinical oncology.

Intratumor heterogeneity (ITH) is an omnipresent property of cancers and predicts poor survival in most types of cancer. The propensity to metastasize and the regrowth of tumors after therapy are both associated with ITH. Quantification of the level of ITH in a malignancy is hence of great interest, and accurate inference of ITH could guide clinical decision making. However, ITH is an emergent property of billions of cells and requires mathematical modeling for inference from a limited number of measurements. Over the last decade, numerous mathematical and computational models have been introduced to infer ITH from variant-allele frequencies, copy number variations, or from data of experimental model systems. These quantitative modeling efforts have advanced the understanding of tumor evolution, underlined poor prognosis associated with ITH, and elucidated the importance of functional heterogeneity, that is, cancer stem cells. Yet, a comprehensive overview of the different mathematical models, their underlying assumptions, their limitations, and their strengths is missing. In this Perspective, we highlight the achievements of mathematical modeling and present a framework which allows better understanding of the mathematical models themselves.

Cancer progression, including the development of intratumor heterogeneity, is inherently a spatial process. Mathematical models of tumor evolution may be a useful starting point for understanding the patterns of heterogeneity that can emerge in the presence of spatial growth. A commonly studied spatial growth model assumes that tumor cells occupy sites on a lattice and replicate into neighboring sites. Our R package SITH provides a convenient interface for exploring this model. Our efficient simulation algorithm allows for users to generate 3D tumors with millions of cells in under a minute. For the distribution of mutations throughout the tumor, SITH provides interactive graphics and summary plots. Additionally, SITH can produce synthetic bulk and single-cell DNA-seq datasets by sampling from the simulated tumor. A streamlined application programming interface (API) makes SITH a useful tool for investigating the relationship between spatial growth and intratumor heterogeneity. SITH is a part of CRAN and can be installed by running install.packages(“SITH”) from the R console. See https://CRAN.R-project.org/package=SITH for the user manual and package vignette.

In order to promote continued growth, a tumor must recruit new blood vessels, a process known as tumor angiogenesis. Many therapies have been tested that aim to inhibit tumor angiogenesis, with the goal of starving the tumor of nutrients and preventing tumor growth. However, many of these therapies have been unsuccessful and can paradoxically further tumor development by leading to increased local tumor invasion and metastasis. In this study, we use agent-based modeling to examine how hypoxic and acidic conditions following anti-angiogenic therapy can influence tumor development. Under these conditions, we find that cancer cells experience a phenotypic shift to a state of higher survival and invasive capability, spreading further away from the tumor into the surrounding tissue. Although anti-angiogenic therapy alone promotes tumor cell adaptation and invasiveness, we find that augmenting chemotherapy with anti-angiogenic therapy improves chemotherapeutic response and delays the time it takes for the tumor to regrow. Overall, we use computational modeling to explain the behavior of tumor cells in response to anti-angiogenic treatment in the dynamic tumor microenvironment.

Epithelial-mesenchymal transition (EMT) is the transdifferentiation of epithelial cells to a mesenchymal phenotype, in which cells lose epithelial-like cell–cell adhesions and gain mesenchymal-like enhanced contractility and mobility. EMT is crucial for tissue regeneration and is also implicated in pathological conditions, such as cancer metastasis. Prior work has shown that transforming growth factor-$�\beta$1 (TGF-$�\beta$1) is a potent inducer of this biological process. In this study, we develop a computational model coupling mechanical and biochemical signaling in a multicellular tissue undergoing EMT. Specifically, we utilize a recently developed formulation that integrates a multicellular cellular Potts model (CPM), a lattice-based stochastic model governing cell movement; a first moment of area model, governing cellular traction and junctional forces; a finite element model, which defines extracellular matrix (ECM) substrate strains; an intracellular signaling TGF-$�\beta$1-mediated EMT model that governs cellular phenotype; and an extracellular signaling component governing ECM and TGF-$�\beta$1 signaling. In this study, we modeled the spatial cellular patterns that occur in tissue and the ECM during EMT. Our model predicts that EMT often initially occurs at a tissue boundary due to mechanochemical coupling, which results in transdifferentiation to progress inwards toward the center. Variation in model parameters demonstrated conditions enhancing and suppressing EMT, especially to drive EMT in the absence of TGF-$�\beta$1 and inhibit EMT in the presence of TGF-$�\beta$1. Specifically, enhancing the mechanochemical feedback typically promoted EMT, whereas greater assembled ECM degradation suppressed EMT. Simulated scratch test experiments illustrate that ECM composition can impact closure directly through EMT signaling. In conclusion, we integrated mechanical, biochemical, and extracellular signaling networks in a novel hybrid computational model to reproduce tissue formation dynamics of EMT.

Long-term treatment with interferon-alfa (IFN) can reduce the disease burden of patients diagnosed with myeloproliferative neoplasms (MPNs). Determining individual patient responses to IFN therapy may allow for efficient personalized treatment, reducing both drop-out and disease burden. A mathematical model describing hematopoietic stem cells and the immune system is suggested. Considering the bone marrow and the blood allows for modeling disease dynamics both in the absence and presence of IFN treatment. Through comprehensive modeling of the effects of IFN, the model was related to individualized patient-data consisting of longitudinal hematologic and molecular measurements. Treatment responses were modeled on a population level, allowing for personalized predictions from a single pretreatment data point. Personalized fits were found to agree well with data for individual patients. This allowed for a quantitative description of the treatment response, yielding a mechanistic interpretation of differences from patient to patient. The treatment responses of individual patients were combined and a formulation of treatment responses on the population level was described and simulated. Based on pretreatment data and the actual treatment scheduling, the population-level response was found to predict the treatment response of particular patients accurately over a five-year period. Mechanism-based modeling of treatment effects demonstrates that hematologic and molecular observable quantities can be predicted on the level of individual patients. Personalized patient-fits suggest that the effect of IFN treatment can be quantified and interpreted through mathematical modeling, despite variation in hematologic and molecular responses between patients. Mathematical modeling suggests that in general both hematologic and molecular markers must be considered to avoid early relapse. Furthermore, personalized model-fits provide quantitative measures of the hematologic and molecular responses, determining when treatment-cessation is appropriate. Proof-of-concept population-level modeling of treatment responses from pretreatment data successfully predicted clinical measures for a 5-year period. We believe that this approach could have direct clinical relevance, offering expert guidance for clinical decisions about IFN treatment of MPN patients.