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1.
Brief Bioinform ; 25(3)2024 Mar 27.
Artigo em Inglês | MEDLINE | ID: mdl-38754407

RESUMO

Predicting cancer drug response using both genomics and drug features has shown some success compared to using genomics features alone. However, there has been limited research done on how best to combine or fuse the two types of features. Using a visible neural network with two deep learning branches for genes and drug features as the base architecture, we experimented with different fusion functions and fusion points. Our experiments show that injecting multiplicative relationships between gene and drug latent features into the original concatenation-based architecture DrugCell significantly improved the overall predictive performance and outperformed other baseline models. We also show that different fusion methods respond differently to different fusion points, indicating that the relationship between drug features and different hierarchical biological level of gene features is optimally captured using different methods. Considering both predictive performance and runtime speed, tensor product partial is the best-performing fusion function to combine late-stage representations of drug and gene features to predict cancer drug response.


Assuntos
Antineoplásicos , Genótipo , Neoplasias , Redes Neurais de Computação , Humanos , Neoplasias/genética , Neoplasias/tratamento farmacológico , Antineoplásicos/uso terapêutico , Antineoplásicos/farmacologia , Aprendizado Profundo , Genômica/métodos , Biologia Computacional/métodos
2.
Phys Biol ; 21(2)2024 Feb 19.
Artigo em Inglês | MEDLINE | ID: mdl-38330444

RESUMO

Computational modeling of cancer can help unveil dynamics and interactions that are hard to replicate experimentally. Thanks to the advancement in cancer databases and data analysis technologies, these models have become more robust than ever. There are many mathematical models which investigate cancer through different approaches, from sub-cellular to tissue scale, and from treatment to diagnostic points of view. In this study, we lay out a step-by-step methodology for a data-driven mechanistic model of the tumor microenvironment. We discuss data acquisition strategies, data preparation, parameter estimation, and sensitivity analysis techniques. Furthermore, we propose a possible approach to extend mechanistic ordinary differential equation models to PDE models coupled with mechanical growth. The workflow discussed in this article can help understand the complex temporal and spatial interactions between cells and cytokines in the tumor microenvironment and their effect on tumor growth.


Assuntos
Neoplasias , Humanos , Fluxo de Trabalho , Neoplasias/patologia , Modelos Teóricos , Simulação por Computador , Modelos Biológicos , Microambiente Tumoral
3.
Brief Bioinform ; 22(4)2021 07 20.
Artigo em Inglês | MEDLINE | ID: mdl-33003193

RESUMO

Due to the high cost of flow and mass cytometry, there has been a recent surge in the development of computational methods for estimating the relative distributions of cell types from the gene expression profile of a bulk of cells. Here, we review the five common 'digital cytometry' methods: deconvolution of RNA-Seq, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), CIBERSORTx, single sample gene set enrichment analysis and single-sample scoring of molecular phenotypes deconvolution method. The results show that CIBERSORTx B-mode, which uses batch correction to adjust the gene expression profile of the bulk of cells ('mixture data') to eliminate possible cross-platform variations between the mixture data and the gene expression data of single cells ('signature matrix'), outperforms other methods, especially when signature matrix and mixture data come from different platforms. However, in our tests, CIBERSORTx S-mode, which uses batch correction for adjusting the signature matrix instead of mixture data, did not perform better than the original CIBERSORT method, which does not use any batch correction method. This result suggests the need for further investigations into how to utilize batch correction in deconvolution methods.


Assuntos
Citofotometria , RNA-Seq , Transcriptoma , Animais , Humanos
4.
PLoS Comput Biol ; 18(3): e1009953, 2022 03.
Artigo em Inglês | MEDLINE | ID: mdl-35294447

RESUMO

The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key cell types and cytokines involved in breast cancer development. We use time-course gene expression profiles of a mouse model to estimate the relative abundance of cells and cytokines. We then employ a least-squares optimization method to evaluate the model's parameters based on the mice data. The resulting dynamics of the cells and cytokines obtained from the optimal set of parameters exhibit a decent agreement between the data and predictions. We perform a sensitivity analysis to identify the crucial parameters of the model and then perform a local bifurcation on them. The results reveal a strong connection between adipocytes, IL6, and the cancer population, suggesting them as potential targets for therapies.


Assuntos
Neoplasias da Mama , Animais , Neoplasias da Mama/metabolismo , Linhagem Celular Tumoral , Citocinas , Modelos Animais de Doenças , Feminino , Humanos , Camundongos , Microambiente Tumoral
5.
Brief Bioinform ; 20(3): 985-994, 2019 05 21.
Artigo em Inglês | MEDLINE | ID: mdl-29112707

RESUMO

MOTIVATION: One of the main challenges in machine learning (ML) is choosing an appropriate normalization method. Here, we examine the effect of various normalization methods on analyzing FPKM upper quartile (FPKM-UQ) RNA sequencing data sets. We collect the HTSeq-FPKM-UQ files of patients with colon adenocarcinoma from TCGA-COAD project. We compare three most common normalization methods: scaling, standardizing using z-score and vector normalization by visualizing the normalized data set and evaluating the performance of 12 supervised learning algorithms on the normalized data set. Additionally, for each of these normalization methods, we use two different normalization strategies: normalizing samples (files) or normalizing features (genes). RESULTS: Regardless of normalization methods, a support vector machine (SVM) model with the radial basis function kernel had the maximum accuracy (78%) in predicting the vital status of the patients. However, the fitting time of SVM depended on the normalization methods, and it reached its minimum fitting time when files were normalized to the unit length. Furthermore, among all 12 learning algorithms and 6 different normalization techniques, the Bernoulli naive Bayes model after standardizing files had the best performance in terms of maximizing the accuracy as well as minimizing the fitting time. We also investigated the effect of dimensionality reduction methods on the performance of the supervised ML algorithms. Reducing the dimension of the data set did not increase the maximum accuracy of 78%. However, it leaded to discovery of the 7SK RNA gene expression as a predictor of survival in patients with colon adenocarcinoma with accuracy of 78%.


Assuntos
Adenocarcinoma/patologia , Algoritmos , Neoplasias do Colo/patologia , Aprendizado de Máquina , RNA/genética , Análise de Sequência de RNA/métodos , Adenocarcinoma/genética , Neoplasias do Colo/genética , Feminino , Humanos , Masculino , Análise de Sobrevida
6.
J Theor Biol ; 445: 33-50, 2018 05 14.
Artigo em Inglês | MEDLINE | ID: mdl-29470992

RESUMO

Multicellular tissues are continually turning over, and homeostasis is maintained through regulated proliferation and differentiation of stem cells and progenitors. Following tissue injury, a dramatic increase in cell proliferation is commonly observed, resulting in rapid restoration of tissue size. This regulation is thought to occur via multiple feedback loops acting on cell self-renewal or differentiation. Models of ordinary differential equations have been widely used to study the cell lineage system. Prior modeling studies have suggested that loss of homeostasis and initiation of tumorigenesis can be contributed to the loss of control of these processes, and the rate of symmetric versus asymmetric division of the stem cells may also be altered. While most of the previous works focused on analysis of stability, existence and uniqueness of steady states of multistage cell lineage models, in this work we attempt to understand the cell lineage model from a different perspective. We compare three variants of hierarchical stem cell lineage tissue models with different combinations of negative feedbacks and use sensitivity analysis to examine the possible strategies for the cells to achieve certain performance objectives. Our results suggest that multiple negative feedback loops must be present in the stem cell lineage to keep the fractions of stem cells to differentiated cells in the total population as robust as possible to variations in cell division parameters, and to minimize the time for tissue recovery in a non-oscillatory manner.


Assuntos
Diferenciação Celular/fisiologia , Autorrenovação Celular/fisiologia , Modelos Biológicos , Regeneração/fisiologia , Células-Tronco/fisiologia , Animais , Humanos , Células-Tronco/citologia
7.
Bull Math Biol ; 80(9): 2273-2305, 2018 09.
Artigo em Inglês | MEDLINE | ID: mdl-29978308

RESUMO

Colon and intestinal crypts have been widely chosen to study cell dynamics because of their fairly simple structures. In the colon and intestinal crypts, stem cells (SCs) are located at very bottom of the crypt, fully differentiated cells (FDs) are located in the top of the crypt, and transit-amplifying cells (TAs) are in the middle of the crypt between FDs and SCs. Recently, it has been discovered that there are two types of stem cells in the intestinal crypts: central stem cells (CeSCs) and border stem cells. To investigate dynamics of mutants in colon and intestinal crypts, we develop a four-compartmental stochastic model, which includes two SC compartments, and TAs and FDs compartments. We calculate the probability of the progeny of marked or mutant cells located at each of these compartments taking over the entire crypt or being washed out from the crypt. We found that the progeny of CeSCs will take over the entire crypt with a probability close to one. Interestingly, the progeny of advantageous mutant TAs and FDs will be washed out faster than disadvantageous mutants. Saliently, the model predicts that the time that the progeny of wild-type central stem cells will take over the mouse intestinal crypt is around 60 days, which is in perfect agreement with an experimental observation.


Assuntos
Carcinogênese/patologia , Colo/citologia , Mucosa Intestinal/citologia , Modelos Biológicos , Células-Tronco/citologia , Algoritmos , Animais , Carcinogênese/genética , Movimento Celular , Neoplasias do Colo/etiologia , Neoplasias do Colo/genética , Neoplasias do Colo/patologia , Simulação por Computador , Feminino , Humanos , Masculino , Conceitos Matemáticos , Mutação , Probabilidade , Processos Estocásticos
8.
Phys Biol ; 12(5): 055001, 2015 Jul 31.
Artigo em Inglês | MEDLINE | ID: mdl-26228740

RESUMO

In recent years, by using modern imaging techniques, scientists have found evidence of collaboration between different types of stem cells (SCs), and proposed a bi-compartmental organization of the SC niche. Here we create a class of stochastic models to simulate the dynamics of such a heterogeneous SC niche. We consider two SC groups: the border compartment, S1, is in direct contact with transit-amplifying (TA) cells, and the central compartment, S2, is hierarchically upstream from S1. The S1 SCs differentiate or divide asymmetrically when the tissue needs TA cells. Both groups proliferate when the tissue requires SCs (thus maintaining homeostasis). There is an influx of S2 cells into the border compartment, either by migration, or by proliferation. We examine this model in the context of double-hit mutant generation, which is a rate-limiting step in the development of many cancers. We discover that this type of a cooperative pattern in the stem niche with two compartments leads to a significantly smaller rate of double-hit mutant production compared with a homogeneous, one-compartmental SC niche. Furthermore, the minimum probability of double-hit mutant generation corresponds to purely symmetric division of SCs, consistent with the literature. Finally, the optimal architecture (which minimizes the rate of double-hit mutant production) requires a large proliferation rate of S1 cells along with a small, but non-zero, proliferation rate of S2 cells. This result is remarkably similar to the niche structure described recently by several authors, where one of the two SC compartments was found more actively engaged in tissue homeostasis and turnover, while the other was characterized by higher levels of quiescence (but contributed strongly to injury recovery). Both numerical and analytical results are presented.


Assuntos
Neoplasias/genética , Neoplasias/patologia , Nicho de Células-Tronco , Diferenciação Celular , Proliferação de Células , Simulação por Computador , Homeostase , Humanos , Modelos Biológicos , Mutação , Células-Tronco Neoplásicas/citologia , Células-Tronco Neoplásicas/metabolismo , Células-Tronco Neoplásicas/patologia , Células-Tronco/citologia , Células-Tronco/metabolismo , Células-Tronco/patologia , Processos Estocásticos
9.
NPJ Digit Med ; 7(1): 77, 2024 Mar 22.
Artigo em Inglês | MEDLINE | ID: mdl-38519626

RESUMO

The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.

10.
Bioengineering (Basel) ; 10(11)2023 Nov 16.
Artigo em Inglês | MEDLINE | ID: mdl-38002445

RESUMO

This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients' gene expression and clinical data through a variety of techniques to predict patients' outcomes, mechanistic models focus on investigating cells' and molecules' interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors' microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.

11.
iScience ; 26(5): 106596, 2023 May 19.
Artigo em Inglês | MEDLINE | ID: mdl-37168560

RESUMO

The intricate network of interactions between cells and molecules in the tumor microenvironment creates a heterogeneous ecosystem. The proximity of the cells and molecules to their activators and inhibitors is essential in the progression of tumors. Here, we develop a system of partial differential equations coupled with linear elasticity to investigate the effects of spatial interactions on the tumor microenvironment. We observe interesting cell and cytokine distribution patterns, which are heavily affected by macrophages. We also see that cytotoxic T cells get recruited and suppressed at the site of macrophages. Moreover, we observe that anti-tumor macrophages reorganize the patterns in favor of a more spatially restricted cancer and necrotic core. Furthermore, the adjoint-based sensitivity analysis indicates that the most sensitive model's parameters are directly related to macrophages. The results emphasize the widely acknowledged effect of macrophages in controlling cancer cells population and spatially arranging cells in the tumor microenvironment.

12.
Cancers (Basel) ; 14(24)2022 Dec 13.
Artigo em Inglês | MEDLINE | ID: mdl-36551627

RESUMO

Osteosarcoma is the most common malignant bone tumor in children and adolescents with a poor prognosis. To describe the progression of osteosarcoma, we expanded a system of data-driven ODE from a previous study into a system of Reaction-Diffusion-Advection (RDA) equations and coupled it with Biot equations of poroelasticity to form a bio-mechanical model. The RDA system includes the spatio-temporal information of the key components of the tumor microenvironment. The Biot equations are comprised of an equation for the solid phase, which governs the movement of the solid tumor, and an equation for the fluid phase, which relates to the motion of cells. The model predicts the total number of cells and cytokines of the tumor microenvironment and simulates the tumor's size growth. We simulated different scenarios using this model to investigate the impact of several biomedical settings on tumors' growth. The results indicate the importance of macrophages in tumors' growth. Particularly, we have observed a high co-localization of macrophages and cancer cells, and the concentration of tumor cells increases as the number of macrophages increases.

13.
J Pers Med ; 12(10)2022 Oct 09.
Artigo em Inglês | MEDLINE | ID: mdl-36294824

RESUMO

The interactions between cells and molecules in the tumor microenvironment can give insight into the initiation and progression of tumors and their optimal treatment options. In this paper, we developed an ordinary differential equation (ODE) mathematical model of the interaction network of key players in the clear cell renal cell carcinoma (ccRCC) microenvironment. We then performed a global gradient-based sensitivity analysis to investigate the effects of the most sensitive parameters of the model on the number of cancer cells. The results indicate that parameters related to IL-6 have high a impact on cancer cell growth, such that decreasing the level of IL-6 can remarkably slow the tumor's growth.

14.
SoftwareX ; 182022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35782394

RESUMO

There are many experimental methods for characterizing immune profiles of tumors, such as flow and mass cytometry. However, these approaches are time and resource intensive. Thus, several "digital cytometry" methods have been developed to extract cell frequencies from RNA-seq data. Here, we introduce TumorDecon, named for its potential to deconvolve the distribution of cells from the gene expression levels of a bulk of cells, such as a tumor. The Python package provides an accessible way of applying these methods. It includes four deconvolution methods as well as several gene sets, signature matrices, and functions for generating custom signature matrices.

15.
J Pers Med ; 12(5)2022 May 17.
Artigo em Inglês | MEDLINE | ID: mdl-35629230

RESUMO

The evolution of breast tumors greatly depends on the interaction network among different cell types, including immune cells and cancer cells in the tumor. This study takes advantage of newly collected rich spatio-temporal mouse data to develop a data-driven mathematical model of breast tumors that considers cells' location and key interactions in the tumor. The results show that cancer cells have a minor presence in the area with the most overall immune cells, and the number of activated immune cells in the tumor is depleted over time when there is no influx of immune cells. Interestingly, in the case of the influx of immune cells, the highest concentrations of both T cells and cancer cells are in the boundary of the tumor, as we use the Robin boundary condition to model the influx of immune cells. In other words, the influx of immune cells causes a dominant outward advection for cancer cells. We also investigate the effect of cells' diffusion and immune cells' influx rates in the dynamics of cells in the tumor micro-environment. Sensitivity analyses indicate that cancer cells and adipocytes' diffusion rates are the most sensitive parameters, followed by influx and diffusion rates of cytotoxic T cells, implying that targeting them is a possible treatment strategy for breast cancer.

16.
Front Digit Health ; 4: 1007784, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36274654

RESUMO

We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community.

17.
Math Biosci Eng ; 18(2): 1879-1897, 2021 02 22.
Artigo em Inglês | MEDLINE | ID: mdl-33757216

RESUMO

Tumor immune microenvironment has been shown to be important in predicting the tumor progression and the outcome of treatments. This work aims to identify different immune patterns in osteosarcoma and their clinical characteristics. We use the latest and best performing deconvolution method, CIBERSORTx, to obtain the relative abundance of 22 immune cells. Then we cluster patients based on their estimated immune abundance and study the characteristics of these clusters, along with the relationship between immune infiltration and outcome of patients. We find that abundance of CD8 T cells, NK cells and M1 Macrophages have a positive association with prognosis, while abundance of γδ T cells, Mast cells, M0 Macrophages and Dendritic cells have a negative association with prognosis. Accordingly, the cluster with the lowest proportion of CD8 T cells, M1 Macrophages and highest proportion of M0 Macrophages has the worst outcome among clusters. By grouping patients with similar immune patterns, we are also able to suggest treatments that are specific to the tumor microenvironment.


Assuntos
Neoplasias Ósseas , Osteossarcoma , Linfócitos T CD8-Positivos , Humanos , Macrófagos , Microambiente Tumoral
18.
Cancers (Basel) ; 13(11)2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34071939

RESUMO

Many colon cancer patients show resistance to their treatments. Therefore, it is important to consider unique characteristic of each tumor to find the best treatment options for each patient. In this study, we develop a data driven mathematical model for interaction between the tumor microenvironment and FOLFIRI drug agents in colon cancer. Patients are divided into five distinct clusters based on their estimated immune cell fractions obtained from their primary tumors' gene expression data. We then analyze the effects of drugs on cancer cells and immune cells in each group, and we observe different responses to the FOLFIRI drugs between patients in different immune groups. For instance, patients in cluster 3 with the highest T-reg/T-helper ratio respond better to the FOLFIRI treatment, while patients in cluster 2 with the lowest T-reg/T-helper ratio resist the treatment. Moreover, we use ROC curve to validate the model using the tumor status of the patients at their follow up, and the model predicts well for the earlier follow up days.

19.
Cells ; 10(8)2021 08 06.
Artigo em Inglês | MEDLINE | ID: mdl-34440778

RESUMO

Since all tumors are unique, they may respond differently to the same treatments. Therefore, it is necessary to study their characteristics individually to find their best treatment options. We built a mathematical model for the interactions between the most common chemotherapy drugs and the osteosarcoma microenvironments of three clusters of tumors with unique immune profiles. We then investigated the effects of chemotherapy with different treatment regimens and various treatment start times on the behaviors of immune and cancer cells in each cluster. Saliently, we suggest the optimal drug dosages for the tumors in each cluster. The results show that abundances of dendritic cells and HMGB1 increase when drugs are given and decrease when drugs are absent. Populations of helper T cells, cytotoxic cells, and IFN-γ grow, and populations of cancer cells and other immune cells shrink during treatment. According to the model, the MAP regimen does a good job at killing cancer, and is more effective than doxorubicin and cisplatin combined or methotrexate alone. The results also indicate that it is important to consider the tumor's unique growth rate when deciding the treatment details, as fast growing tumors need early treatment start times and high dosages.


Assuntos
Protocolos de Quimioterapia Combinada Antineoplásica/administração & dosagem , Neoplasias Ósseas/tratamento farmacológico , Tomada de Decisão Clínica , Técnicas de Apoio para a Decisão , Modelos Teóricos , Osteossarcoma/tratamento farmacológico , Protocolos de Quimioterapia Combinada Antineoplásica/efeitos adversos , Neoplasias Ósseas/imunologia , Neoplasias Ósseas/metabolismo , Neoplasias Ósseas/patologia , Cisplatino/administração & dosagem , Citotoxicidade Imunológica/efeitos dos fármacos , Células Dendríticas/efeitos dos fármacos , Células Dendríticas/imunologia , Células Dendríticas/metabolismo , Doxorrubicina/administração & dosagem , Esquema de Medicação , Proteína HMGB1/metabolismo , Humanos , Interferon gama/metabolismo , Linfócitos do Interstício Tumoral/efeitos dos fármacos , Linfócitos do Interstício Tumoral/imunologia , Linfócitos do Interstício Tumoral/metabolismo , Metotrexato/administração & dosagem , Osteossarcoma/imunologia , Osteossarcoma/metabolismo , Osteossarcoma/patologia , Seleção de Pacientes , Medicina de Precisão , Linfócitos T Auxiliares-Indutores/efeitos dos fármacos , Linfócitos T Auxiliares-Indutores/imunologia , Linfócitos T Auxiliares-Indutores/metabolismo , Fatores de Tempo , Microambiente Tumoral
20.
Sci Rep ; 11(1): 4338, 2021 02 22.
Artigo em Inglês | MEDLINE | ID: mdl-33619294

RESUMO

Since the outcome of treatments, particularly immunotherapeutic interventions, depends on the tumor immune micro-environment (TIM), several experimental and computational tools such as flow cytometry, immunohistochemistry, and digital cytometry have been developed and utilized to classify TIM variations. In this project, we identify immune pattern of clear cell renal cell carcinomas (ccRCC) by estimating the percentage of each immune cell type in 526 renal tumors using the new powerful technique of digital cytometry. The results, which are in agreement with the results of a large-scale mass cytometry analysis, show that the most frequent immune cell types in ccRCC tumors are CD8+ T-cells, macrophages, and CD4+ T-cells. Saliently, unsupervised clustering of ccRCC primary tumors based on their relative number of immune cells indicates the existence of four distinct groups of ccRCC tumors. Tumors in the first group consist of approximately the same numbers of macrophages and CD8+ T-cells and and a slightly smaller number of CD4+ T cells than CD8+ T cells, while tumors in the second group have a significantly high number of macrophages compared to any other immune cell type (P-value [Formula: see text]). The third group of ccRCC tumors have a significantly higher number of CD8+ T-cells than any other immune cell type (P-value [Formula: see text]), while tumors in the group 4 have approximately the same numbers of macrophages and CD4+ T-cells and a significantly smaller number of CD8+ T-cells than CD4+ T-cells (P-value [Formula: see text]). Moreover, there is a high positive correlation between the expression levels of IFNG and PDCD1 and the percentage of CD8+ T-cells, and higher stage and grade of tumors have a substantially higher percentage of CD8+ T-cells. Furthermore, the primary tumors of patients, who are tumor free at the last time of follow up, have a significantly higher percentage of mast cells (P-value [Formula: see text]) compared to the patients with tumors for all groups of tumors except group 3.


Assuntos
Biomarcadores Tumorais , Carcinoma de Células Renais/diagnóstico , Carcinoma de Células Renais/etiologia , Suscetibilidade a Doenças , Neoplasias Renais/diagnóstico , Neoplasias Renais/etiologia , Carcinoma de Células Renais/metabolismo , Suscetibilidade a Doenças/imunologia , Feminino , Regulação Neoplásica da Expressão Gênica , Humanos , Imuno-Histoquímica , Imunofenotipagem , Neoplasias Renais/metabolismo , Linfócitos do Interstício Tumoral/imunologia , Linfócitos do Interstício Tumoral/metabolismo , Linfócitos do Interstício Tumoral/patologia , Masculino , Gradação de Tumores , Prognóstico , Microambiente Tumoral/genética , Microambiente Tumoral/imunologia , Macrófagos Associados a Tumor/imunologia , Macrófagos Associados a Tumor/metabolismo , Macrófagos Associados a Tumor/patologia
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