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1.
Nat Commun ; 11(1): 27, 2020 Jan 07.
Artigo em Inglês | MEDLINE | ID: mdl-31911640

RESUMO

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: rg = 0.098, p = 2.3 × 10-8) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: rg = 0.137, p = 2.0 × 10-12). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis.

2.
Int J Cancer ; 146(7): 1862-1878, 2020 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-31696517

RESUMO

We have recently completed the largest GWAS on lung cancer including 29,266 cases and 56,450 controls of European descent. The goal of our study has been to integrate the complete GWAS results with a large-scale expression quantitative trait loci (eQTL) mapping study in human lung tissues (n = 1,038) to identify candidate causal genes for lung cancer. We performed transcriptome-wide association study (TWAS) for lung cancer overall, by histology (adenocarcinoma, squamous cell carcinoma and small cell lung cancer) and smoking subgroups (never- and ever-smokers). We performed replication analysis using lung data from the Genotype-Tissue Expression (GTEx) project. DNA damage assays were performed in human lung fibroblasts for selected TWAS genes. As expected, the main TWAS signal for all histological subtypes and ever-smokers was on chromosome 15q25. The gene most strongly associated with lung cancer at this locus using the TWAS approach was IREB2 (pTWAS = 1.09E-99), where lower predicted expression increased lung cancer risk. A new lung adenocarcinoma susceptibility locus was revealed on 9p13.3 and associated with higher predicted expression of AQP3 (pTWAS = 3.72E-6). Among the 45 previously described lung cancer GWAS loci, we mapped candidate target gene for 17 of them. The association AQP3-adenocarcinoma on 9p13.3 was replicated using GTEx (pTWAS = 6.55E-5). Consistent with the effect of risk alleles on gene expression levels, IREB2 knockdown and AQP3 overproduction promote endogenous DNA damage. These findings indicate genes whose expression in lung tissue directly influences lung cancer risk.

3.
Cancer Res ; 79(24): 6227-6237, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31558563

RESUMO

Radiotherapy is integral to the care of a majority of patients with cancer. Despite differences in tumor responses to radiation (radioresponse), dose prescriptions are not currently tailored to individual patients. Recent large-scale cancer cell line databases hold the promise of unravelling the complex molecular arrangements underlying cellular response to radiation, which is critical for novel predictive biomarker discovery. Here, we present RadioGx, a computational platform for integrative analyses of radioresponse using radiogenomic databases. We fit the dose-response data within RadioGx to the linear-quadratic model. The imputed survival across a range of dose levels (AUC) was a robust radioresponse indicator that correlated with biological processes known to underpin the cellular response to radiation. Using AUC as a metric for further investigations, we found that radiation sensitivity was significantly associated with disruptive mutations in genes related to nonhomologous end joining. Next, by simulating the effects of different oxygen levels, we identified putative genes that may influence radioresponse specifically under hypoxic conditions. Furthermore, using transcriptomic data, we found evidence for tissue-specific determinants of radioresponse, suggesting that tumor type could influence the validity of putative predictive biomarkers of radioresponse. Finally, integrating radioresponse with drug response data, we found that drug classes impacting the cytoskeleton, DNA replication, and mitosis display similar therapeutic effects to ionizing radiation on cancer cell lines. In summary, RadioGx provides a unique computational toolbox for hypothesis generation to advance preclinical research for radiation oncology and precision medicine. SIGNIFICANCE: The RadioGx computational platform enables integrative analyses of cellular response to radiation with drug responses and genome-wide molecular data. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/79/24/6227/F1.large.jpg.See related commentary by Spratt and Speers, p. 6076.

4.
Br J Radiol ; 92(1103): 20190198, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31538514

RESUMO

OBJECTIVE: Radiation therapy is among the most effective and widely used modalities of cancer therapy in current clinical practice. In this era of personalized radiation medicine, high-throughput data now provide the means to investigate novel biomarkers of radiation response. Large-scale efforts have identified several radiation response signatures, which poses two challenges, namely, their analytical validity and redundancy of gene signatures. METHODS: To address these fundamental radiogenomics questions, we curated a database of gene expression signatures predictive of radiation response under oxic and hypoxic conditions. RadiationGeneSigDB has a collection of 11 oxic and 24 hypoxic signatures with the standardized gene list as a gene symbol, Entrez gene ID, and its function. We present the utility of this database by gaining an understanding of hypoxia-associated miRNA by applying a penalized multivariate model; by comparing breast cancer oxic signatures in cell line data vs patient data; and by comparing the similarity of head and neck cancer hypoxia signatures at the pathway level in clinical tumour data. RESULTS: We obtained a set of miRNA highly associated both positively and negatively to the hypoxia gene signatures, across pan-cancer. In addition, we identified moderate correlations between breast cancer oxic signatures in patient data, and significant differences across molecular subtypes. Moreover, we also found that different set of pathways to be enriched using the head and neck hypoxia signatures, although, they are found to be concordant when applied on the patient data. CONCLUSION: This valuable, curated repertoire of published gene expression signatures provides motivating case studies for how to search for similarities in radiation response for tumours arising from different tissues across model systems under oxic and hypoxic conditions, and how a well-curated set of gene signatures can be used to generate novel biological hypotheses about the functions of non-coding RNA. ADVANCES IN KNOWLEDGE: We envision that RadiationSigDB database will help accelerate preclinical radiotherapeutic discovery pipelines in terms of analytical validity of novel biomarkers of radiation response and the need for ensemble approaches to clinical genomic biomarkers.


Assuntos
Neoplasias da Mama/genética , Bases de Dados Factuais , Neoplasias de Cabeça e Pescoço/genética , MicroRNAs/genética , Transcriptoma/genética , Pesquisa Biomédica , Neoplasias da Mama/radioterapia , Marcadores Genéticos/genética , Neoplasias de Cabeça e Pescoço/radioterapia , Humanos , Hipóxia/genética , Oxigênio/fisiologia , Células Tumorais Cultivadas
5.
Sci Rep ; 9(1): 8770, 2019 Jun 19.
Artigo em Inglês | MEDLINE | ID: mdl-31217513

RESUMO

A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancer types. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research, and provide a foundation for future development of cancer-specific compendia.

6.
Bioinformatics ; 35(22): 4830-4833, 2019 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-31198954

RESUMO

MOTIVATION: High-throughput molecular profiles of human cells have been used in predictive computational approaches for stratification of healthy and malignant phenotypes and identification of their biological states. In this regard, pathway activities have been used as biological features in unsupervised and supervised learning schemes. RESULTS: We developed SIGN (Similarity Identification in Gene expressioN), a flexible open-source R package facilitating the use of pathway activities and their expression patterns to identify similarities between biological samples. We defined a new measure, the transcriptional similarity coefficient, which captures similarity of gene expression patterns, instead of quantifying overall activity, in biological pathways between the samples. To demonstrate the utility of SIGN in biomedical research, we establish that SIGN discriminates subtypes of breast tumors and patients with good or poor overall survival. SIGN outperforms the best models in DREAM challenge in predicting survival of breast cancer patients using the data from the Molecular Taxonomy of Breast Cancer International Consortium. In summary, SIGN can be used as a new tool for interrogating pathway activity and gene expression patterns in unsupervised and supervised learning schemes to improve prognostic risk estimation for cancer patients by the biomedical research community. AVAILABILITY AND IMPLEMENTATION: An open-source R package is available (https://cran.r-project.org/web/packages/SIGN/).

7.
J Immunother Cancer ; 7(1): 72, 2019 Mar 13.
Artigo em Inglês | MEDLINE | ID: mdl-30867072

RESUMO

BACKGROUND: Immune checkpoint inhibitors (ICIs) demonstrate unprecedented efficacy in multiple malignancies; however, the mechanisms of sensitivity and resistance are poorly understood and predictive biomarkers are scarce. INSPIRE is a phase 2 basket study to evaluate the genomic and immune landscapes of peripheral blood and tumors following pembrolizumab treatment. METHODS: Patients with incurable, locally advanced or metastatic solid tumors that have progressed on standard therapy, or for whom no standard therapy exists or standard therapy was not deemed appropriate, received 200 mg pembrolizumab intravenously every three weeks. Blood and tissue samples were collected at baseline, during treatment, and at progression. One core biopsy was used for immunohistochemistry and the remaining cores were pooled and divided for genomic and immune analyses. Univariable analysis of clinical, genomic, and immunophenotyping parameters was conducted to evaluate associations with treatment response in this exploratory analysis. RESULTS: Eighty patients were enrolled from March 21, 2016 to June 1, 2017, and 129 tumor and 382 blood samples were collected. Immune biomarkers were significantly different between the blood and tissue. T cell PD-1 was blocked (≥98%) in the blood of all patients by the third week of treatment. In the tumor, 5/11 (45%) and 11/14 (79%) patients had T cell surface PD-1 occupance at weeks six and nine, respectively. The proportion of genome copy number alterations and abundance of intratumoral 4-1BB+ PD-1+ CD8 T cells at baseline (P < 0.05), and fold-expansion of intratumoral CD8 T cells from baseline to cycle 2-3 (P < 0.05) were associated with treatment response. CONCLUSION: This study provides technical feasibility data for correlative studies. Tissue biopsies provide distinct data from the blood and may predict response to pembrolizumab.

8.
J Clin Invest ; 129(4): 1785-1800, 2019 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-30753167

RESUMO

Understanding the tumor immune microenvironment (TIME) promises to be key for optimal cancer therapy, especially in triple-negative breast cancer (TNBC). Integrating spatial resolution of immune cells with laser capture microdissection gene expression profiles, we defined distinct TIME stratification in TNBC, with implications for current therapies including immune checkpoint blockade. TNBCs with an immunoreactive microenvironment exhibited tumoral infiltration of granzyme B+CD8+ T cells (GzmB+CD8+ T cells), a type 1 IFN signature, and elevated expression of multiple immune inhibitory molecules including indoleamine 2,3-dioxygenase (IDO) and programmed cell death ligand 1 (PD-L1), and resulted in good outcomes. An "immune-cold" microenvironment with an absence of tumoral CD8+ T cells was defined by elevated expression of the immunosuppressive marker B7-H4, signatures of fibrotic stroma, and poor outcomes. A distinct poor-outcome immunomodulatory microenvironment, hitherto poorly characterized, exhibited stromal restriction of CD8+ T cells, stromal expression of PD-L1, and enrichment for signatures of cholesterol biosynthesis. Metasignatures defining these TIME subtypes allowed us to stratify TNBCs, predict outcomes, and identify potential therapeutic targets for TNBC.

9.
Cancer Res ; 78(8): 2140-2143, 2018 04 15.
Artigo em Inglês | MEDLINE | ID: mdl-29459407

RESUMO

Variations in physiological conditions can rewire molecular interactions between biological compartments, which can yield novel insights into gain or loss of interactions specific to perturbations of interest. Networks are a promising tool to elucidate intercellular interactions, yet exploration of these large-scale networks remains a challenge due to their high dimensionality. To retrieve and mine interactions, we developed CrosstalkNet, a user friendly, web-based network visualization tool that provides a statistical framework to infer condition-specific interactions coupled with a community detection algorithm for bipartite graphs to identify significantly dense subnetworks. As a case study, we used CrosstalkNet to mine a set of 54 and 22 gene-expression profiles from breast tumor and normal samples, respectively, with epithelial and stromal compartments extracted via laser microdissection. We show how CrosstalkNet can be used to explore large-scale co-expression networks and to obtain insights into the biological processes that govern cross-talk between different tumor compartments.Significance: This web application enables researchers to mine complex networks and to decipher novel biological processes in tumor epithelial-stroma cross-talk as well as in other studies of intercompartmental interactions. Cancer Res; 78(8); 2140-3. ©2018 AACR.


Assuntos
Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Algoritmos , Neoplasias da Mama/genética , Feminino , Humanos , Desenho de Programas de Computador
10.
J Am Med Inform Assoc ; 25(2): 158-166, 2018 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-29016819

RESUMO

Objectives: We sought to investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in drug clinical indications. Materials and Methods: We leveraged the curated cell line response data from PharmacoGx and applied an enrichment algorithm on drug sensitivity values' area under the drug dose-response curves (AUCs) with and without adjustment for general level of drug sensitivity. Results: We observed tissue specificity in 63% of tested drugs, with 8% of total interactions deemed significant (false discovery rate <0.05). By restricting the drug-tissue interactions to those with AUC > 0.2, we found that in 52% of interactions, the tissue was predictive of drug sensitivity (concordance index > 0.65). When compared with clinical indications, the observed overlap was weak (Matthew correlation coefficient, MCC = 0.0003, P > .10). Discussion: While drugs exhibit significant tissue specificity in vitro, there is little overlap with clinical indications. This can be attributed to factors such as underlying biological differences between in vitro models and patient tumors, or the inability of tissue-specific drugs to bring additional benefits beyond gold standard treatments during clinical trials. Conclusion: Our meta-analysis of pan-cancer drug screening datasets indicates that most tested drugs exhibit tissue-specific sensitivities in a large panel of cancer cell lines. However, the observed preclinical results do not translate to the clinical setting. Our results suggest that additional research into showing parallels between preclinical and clinical data is required to increase the translational potential of in vitro drug screening.


Assuntos
Algoritmos , Antineoplásicos/farmacologia , Ensaios de Seleção de Medicamentos Antitumorais , Neoplasias/tratamento farmacológico , Especificidade de Órgãos , Antineoplásicos/uso terapêutico , Área Sob a Curva , Linhagem Celular Tumoral/efeitos dos fármacos , Conjuntos de Dados como Assunto , Resistencia a Medicamentos Antineoplásicos , Humanos , Técnicas In Vitro
11.
Semin Cancer Biol ; 52(Pt 2): 135-150, 2018 10.
Artigo em Inglês | MEDLINE | ID: mdl-29278737

RESUMO

There has been a paradigm shift in translational oncology with the advent of novel molecular diagnostic tools in the clinic. However, several challenges are associated with the integration of these sophisticated tools into clinical oncology and daily practice. High-throughput profiling at the DNA, RNA and protein levels (omics) generate a massive amount of data. The analysis and interpretation of these is non-trivial but will allow a more thorough understanding of cancer. Linear modelling of the data as it is often used today is likely to limit our understanding of cancer as a complex disease, and at times under-performs to capture a phenotype of interest. Network science and systems biology-based approaches, using machine learning and network science principles, that integrate multiple data sources, can uncover complex changes in a biological system. This approach will integrate a large number of potential biomarkers in preclinical studies to better inform therapeutic decisions and ultimately make substantial progress towards precision medicine. It will however require development of a new generation of clinical trials. Beyond discussing the challenges of high-throughput technologies, this review will develop a framework on how to implement a network science approach in new clinical trial designs in order to advance cancer care.


Assuntos
Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo , Neoplasias/genética , Neoplasias/metabolismo , Medicina de Precisão/métodos , Animais , Ensaios Clínicos como Assunto , Humanos , Oncologia/métodos , Neoplasias/terapia
12.
Brief Bioinform ; 19(2): 263-276, 2018 03 01.
Artigo em Inglês | MEDLINE | ID: mdl-27881431

RESUMO

Drug combinations have been proposed as a promising therapeutic strategy to overcome drug resistance and improve efficacy of monotherapy regimens in cancer. This strategy aims at targeting multiple components of this complex disease. Despite the increasing number of drug combinations in use, many of them were empirically found in the clinic, and the molecular mechanisms underlying these drug combinations are often unclear. These challenges call for rational, systematic approaches for drug combination discovery. Although high-throughput screening of single-agent therapeutics has been successfully implemented, it is not feasible to test all possible drug combinations, even for a reduced subset of anticancer drugs. Hence, in vitro and in vivo screening of a large number of drug combinations are not practical. Therefore, devising computational methods to efficiently explore the space of drug combinations and to discover efficacious combinations has attracted a lot of attention from the scientific community in the past few years. Nevertheless, in the absence of consensus regarding the computational approaches used to predict efficacious drug combinations, a plethora of methods, techniques and hypotheses have been developed to date, while the research field lacks an elaborate categorization of the existing computational methods and the available data sources. In this manuscript, we review and categorize the state-of-the-art computational approaches for drug combination prediction, and elaborate on the limitations of these methods and the existing challenges. We also discuss about the recent pan-cancer drug combination data sets and their importance in revising the available methods or developing more performant approaches.


Assuntos
Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Biologia Computacional/métodos , Descoberta de Drogas , Neoplasias/tratamento farmacológico , Animais , Humanos
13.
Cancers (Basel) ; 9(9)2017 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-28878202

RESUMO

Several studies have shown that pediatric patients have an increased risk of developing a secondary malignancy several decades after treatment with radiotherapy and chemotherapy. In this work, we use a biologically motivated mathematical formalism to estimate the relative risks of breast, lung and thyroid cancers in childhood cancer survivors due to concurrent therapy regimen. This model specifically includes possible organ-specific interaction between radiotherapy and chemotherapy. The model predicts relative risks for developing secondary cancers after chemotherapy in breast, lung and thyroid tissues, and compared with the epidemiological data. For a concurrent therapy protocol, our model predicted relative risks of 3.2, 9.3, 4.5 as compared to the clinical data, i.e., 1.4, 8.0, 2.3 for secondary breast, lung and thyroid cancer risks, respectively. The extracted chemotherapy mutation induction rates for breast, lung and thyroid are 10 −9 , 0.5 × 10 −6 , 0.9 × 10 −7 respectively. We found that there exists no synergistic interaction between radiation and chemotherapy for neither mutation induction nor cell kill in lung tissue, but there is an interaction in cell kill for the breast and thyroid organs. These findings help understand the risks of current clinical protocols and might provide rational guidance to develop future multi-modality treatment protocols to minimize secondary cancer risks.

14.
Int J Radiat Biol ; 91(3): 209-17, 2015 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-25356906

RESUMO

UNLABELLED: Abstract Purpose: Numerous studies have implicated elevated second cancer risks as a result of radiation therapy. Our aim in this paper was to contribute to an understanding of the effects of radiation quality on second cancer risks. In particular, we developed a biologically motivated model to study the effects of linear energy transfer (LET) of charged particles (including protons, alpha particles and heavy ions Carbon and Neon) on the risk of second cancer. MATERIALS AND METHODS: A widely used approach to estimate the risk uses the so-called initiation-inactivation-repopulation model. Based on the available experimental data for the LET dependence of radiobiological parameters and mutation rate, we generalized this formulation to include the effects of radiation quality. We evaluated the secondary cancer risks for protons in the clinical range of LET, i.e., around 4-10 (KeV/µm), which lies in the plateau region of the Bragg peak. RESULTS: For protons, at a fixed radiation dose, we showed that the increase in second cancer risks correlated directly with increasing values of LET to a certain point, and then decreased. Interestingly, we obtained a higher risk for proton LET of 10 KeV/µm compared to the lower LET of 4 KeV/µm in the low dose region. In the case of heavy ions, the risk was higher for Carbon ions than Neon ions (even though they have almost the same LET). We also compared protons and alpha particles with the same LET, and it was interesting to note that the second cancer risks were higher for protons compared to alpha particles in the low-dose region. CONCLUSION: Overall, this study demonstrated the importance of including LET dependence in the estimation of second cancer risk. Our theoretical risk predictions were noticeably high; however, the biological end points should be tested experimentally for multiple treatment fields and to improve theoretical predictions.


Assuntos
Transferência Linear de Energia , Modelos Biológicos , Neoplasias Induzidas por Radiação/etiologia , Segunda Neoplasia Primária/etiologia , Radioterapia/efeitos adversos , Partículas alfa/efeitos adversos , Partículas alfa/uso terapêutico , Neoplasias da Mama/etiologia , Morte Celular/efeitos da radiação , Proliferação de Células/efeitos da radiação , Feminino , Radioterapia com Íons Pesados , Íons Pesados/efeitos adversos , Doença de Hodgkin/radioterapia , Humanos , Taxa de Mutação , Fótons/efeitos adversos , Fótons/uso terapêutico , Terapia com Prótons , Prótons/efeitos adversos , Radiobiologia/estatística & dados numéricos , Radioterapia/métodos , Dosagem Radioterapêutica , Fatores de Risco
15.
Radiat Environ Biophys ; 54(1): 25-36, 2015 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-25404281

RESUMO

Although the survival rate of cancer patients has significantly increased due to advances in anti-cancer therapeutics, one of the major side effects of these therapies, particularly radiotherapy, is the potential manifestation of radiation-induced secondary malignancies. In this work, a novel evolutionary stochastic model is introduced that couples short-term formalism (during radiotherapy) and long-term formalism (post-treatment). This framework is used to estimate the risks of second cancer as a function of spontaneous background and radiation-induced mutation rates of normal and pre-malignant cells. By fitting the model to available clinical data for spontaneous background risk together with data of Hodgkin's lymphoma survivors (for various organs), the second cancer mutation rate is estimated. The model predicts a significant increase in mutation rate for some cancer types, which may be a sign of genomic instability. Finally, it is shown that the model results are in agreement with the measured results for excess relative risk (ERR) as a function of exposure age and that the model predicts a negative correlation of ERR with increase in attained age. This novel approach can be used to analyze several radiotherapy protocols in current clinical practice and to forecast the second cancer risks over time for individual patients.


Assuntos
Modelos Biológicos , Taxa de Mutação , Neoplasias Induzidas por Radiação/genética , Segunda Neoplasia Primária/genética , Fatores Etários , Evolução Molecular , Humanos , Neoplasias Induzidas por Radiação/epidemiologia , Segunda Neoplasia Primária/epidemiologia , Risco
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