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1.
Cancer Radiother ; 25(6-7): 570-575, 2021 Oct.
Artigo em Francês | MEDLINE | ID: mdl-34391650

RESUMO

Numerous clinical studies aim to integrate immunotherapy in radiotherapy oncology, either for generating abscopal responses in metastatic patients in combination with radiotherapy, or in the treatment of a locally advanced tumor. The search for biomarkers of response to treatment is a major axis in the development of these therapeutic combinations, to allow the early identification of patients who will benefit from the treatment, in the context of an increasingly personalized approach. We review some of the strategies that can be applied for personalization to combined radiotherapy and immunotherapy treatments.


Assuntos
Imunoterapia/métodos , Neoplasias/terapia , Medicina de Precisão/métodos , Radioterapia/métodos , Antígeno B7-H1/metabolismo , Terapia Combinada/métodos , Reparo de Erro de Pareamento de DNA , Eosinófilos , Genoma Humano , Humanos , Interferon Tipo I/metabolismo , Interferon Tipo I/efeitos da radiação , Linfócitos do Interstício Tumoral/imunologia , Mutação , Neoplasias/genética , Neoplasias/imunologia , Receptor de Morte Celular Programada 1/metabolismo , Genômica por Radiação
2.
Lancet Oncol ; 22(9): 1221-1229, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34363761

RESUMO

BACKGROUND: Despite advances in cancer genomics, radiotherapy is still prescribed on the basis of an empirical one-size-fits-all paradigm. Previously, we proposed a novel algorithm using the genomic-adjusted radiation dose (GARD) model to personalise prescription of radiation dose on the basis of the biological effect of a given physical dose of radiation, calculated using individual tumour genomics. We hypothesise that GARD will reveal interpatient heterogeneity associated with opportunities to improve outcomes compared with physical dose of radiotherapy alone. We aimed to test this hypothesis and investigate the GARD-based radiotherapy dosing paradigm. METHODS: We did a pooled, pan-cancer analysis of 11 previously published clinical cohorts of unique patients with seven different types of cancer, which are all available cohorts with the data required to calculate GARD, together with clinical outcome. The included cancers were breast cancer, head and neck cancer, non-small-cell lung cancer, pancreatic cancer, endometrial cancer, melanoma, and glioma. Our dataset comprised 1615 unique patients, of whom 1298 (982 with radiotherapy, 316 without radiotherapy) were assessed for time to first recurrence and 677 patients (424 with radiotherapy and 253 without radiotherapy) were assessed for overall survival. We analysed two clinical outcomes of interest: time to first recurrence and overall survival. We used Cox regression, stratified by cohort, to test the association between GARD and outcome with separate models using dose of radiation and sham-GARD (ie, patients treated without radiotherapy, but modelled as having a standard-of-care dose of radiotherapy) for comparison. We did interaction tests between GARD and treatment (with or without radiotherapy) using the Wald statistic. FINDINGS: Pooled analysis of all available data showed that GARD as a continuous variable is associated with time to first recurrence (hazard ratio [HR] 0·98 [95% CI 0·97-0·99]; p=0·0017) and overall survival (0·97 [0·95-0·99]; p=0·0007). The interaction test showed the effect of GARD on overall survival depends on whether or not that patient received radiotherapy (Wald statistic p=0·011). The interaction test for GARD and radiotherapy was not significant for time to first recurrence (Wald statistic p=0·22). The HR for physical dose of radiation was 0·99 (95% CI 0·97-1·01; p=0·53) for time to first recurrence and 1·00 (0·96-1·04; p=0·95) for overall survival. The HR for sham-GARD was 1·00 (0·97-1·03; p=1·00) for time to first recurrence and 1·00 (0·98-1·02; p=0·87) for overall survival. INTERPRETATION: The biological effect of radiotherapy, as quantified by GARD, is significantly associated with time to first recurrence and overall survival for patients with cancer treated with radiation. It is predictive of radiotherapy benefit, and physical dose of radiation is not. We propose integration of genomics into radiation dosing decisions, using a GARD-based framework, as the new paradigm for personalising radiotherapy prescription dose. FUNDING: None. VIDEO ABSTRACT.


Assuntos
Neoplasias/radioterapia , Genômica por Radiação/métodos , Dosagem Radioterapêutica , Bases de Dados Factuais , Humanos , Neoplasias/genética , Neoplasias/mortalidade , Medicina de Precisão , Recidiva , Taxa de Sobrevida
3.
Nat Rev Clin Oncol ; 18(4): 191-192, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33597721
4.
Curr Oncol Rep ; 23(1): 9, 2021 01 02.
Artigo em Inglês | MEDLINE | ID: mdl-33387095

RESUMO

PURPOSE OF REVIEW: Radiogenomics is a growing field that has garnered immense interest over the past decade, owing to its numerous applications in the field of oncology and its potential value in improving patient outcomes. Current applications have only begun to delve into the potential of radiogenomics, and particularly in interventional oncology, there is room for development and increased value of these applications. RECENT FINDINGS: The field of interventional oncology (IO) has seen valuable radiogenomic applications, from prediction of response to locoregional therapies in hepatocellular carcinoma to identification of genetic mutations in non-small cell lung cancer. Future directions that can increase the value of radiogenomics include applications that address tumor heterogeneity, predict immune responsiveness of tumors, and differentiate between oligoprogression and early widespread progression, among others. Radiogenomics, whether in terms of methodologies or applications, is still in the early stages of development and far from maturation. Future applications, particularly in the field of interventional oncology, will allow realization of its full potential.


Assuntos
Neoplasias , Genômica por Radiação , Radioterapia (Especialidade) , Inteligência Artificial , Humanos , Neoplasias/genética , Neoplasias/radioterapia
5.
Biomed Pharmacother ; 133: 111013, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33227705

RESUMO

OBJECTIVE: Early detection of platinum resistance for ovarian cancer treatment remains challenging. This study aims to develop a machine learning model incorporating genomic data such as Single-Nucleotide Polymorphisms (SNPs) of Human Sulfatase 1 (SULF1) with a CT radiomic model based on pre-treatment CT images, to predict platinum resistance for ovarian cancer (OC) treatment. METHODS: A cohort of 102 patients with pathologically confirmed OC was retrospectively enrolled into this study from January 2006 to February 2018. All patients had platinum-based chemotherapy after maximal cyto-reductive surgery. This cohort was separated into two groups according to treatment response, i.e., the group with platinum-resistant disease (PR group) and the group with platinum-sensitive disease (PS group). We genotyped 12 SNPs of SULF1 for all OC patients using Mass Array Method. Radiomic features, SNP data and clinicopathological data of the 102 patients were used to build the differentiation models. The study participants were divided into two cohorts: the training cohort (n = 71) and the validation cohort (n = 31). Feature selection and predictive modeling were performed using least absolute shrinkage and selection operator (LASSO), Random Forest Classifier and Support Vector Machine methods. Model performance for predicting platinum resistance was assessed with respect to its calibration, discrimination, and clinical application. RESULTS: For prediction of platinum resistance, the approach combining the radiomics, clinicopathological data and SNP data demonstrated higher classification efficiency, with an AUC value of 0.993 (95 % CI: 0.83 to 0.98) in the training cohort and 0.967 (95 % CI: 0.83 to 0.98) in validation cohort, than the performance with only the SNPs of SULF1 model (AUC: training, 0.843 [95 %CI: 0.738-0.948]; validation, 0.815 [0.601-1.000]), or with only the radiomic model (AUC: training, 0.874 [95 %CI: 0.789-0.960]; validation, 0.832 [95 %CI: 0.687-0.976]). This integrated approach also showed good calibration and favorable clinical utility. CONCLUSIONS: A predictive model combining pretreatment CT radiomics with genomic data such as SNPs of SULF1 could potentially help to predict platinum resistance in ovarian cancer treatment.


Assuntos
Antineoplásicos/uso terapêutico , Resistencia a Medicamentos Antineoplásicos/genética , Tomografia Computadorizada Multidetectores , Neoplasias Ovarianas/tratamento farmacológico , Testes Farmacogenômicos , Variantes Farmacogenômicos , Compostos de Platina/uso terapêutico , Polimorfismo de Nucleotídeo Único , Genômica por Radiação , Sulfotransferases/genética , Quimioterapia Adjuvante , Procedimentos Cirúrgicos de Citorredução , Feminino , Humanos , Aprendizado de Máquina , Pessoa de Meia-Idade , Variações Dependentes do Observador , Neoplasias Ovarianas/diagnóstico por imagem , Neoplasias Ovarianas/genética , Valor Preditivo dos Testes , Reprodutibilidade dos Testes , Estudos Retrospectivos
6.
Clin Cancer Res ; 27(17): 4794-4806, 2021 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-34210685

RESUMO

PURPOSE: Intratumoral heterogeneity (ITH) challenges the molecular characterization of clear cell renal cell carcinoma (ccRCC) and is a confounding factor for therapy selection. Most approaches to evaluate ITH are limited by two-dimensional ex vivo tissue analyses. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can noninvasively assess the spatial landscape of entire tumors in their natural milieu. To assess the potential of DCE-MRI, we developed a vertically integrated radiogenomics colocalization approach for multi-region tissue acquisition and analyses. We investigated the potential of spatial imaging features to predict molecular subtypes using histopathologic and transcriptome correlatives. EXPERIMENTAL DESIGN: We report the results of a prospective study of 49 patients with ccRCC who underwent DCE-MRI prior to nephrectomy. Surgical specimens were sectioned to match the MRI acquisition plane. RNA sequencing data from multi-region tumor sampling (80 samples) were correlated with percent enhancement on DCE-MRI in spatially colocalized regions of the tumor. Independently, we evaluated clinical applicability of our findings in 19 patients with metastatic RCC (39 metastases) treated with first-line antiangiogenic drugs or checkpoint inhibitors. RESULTS: DCE-MRI identified tumor features associated with angiogenesis and inflammation, which differed within and across tumors, and likely contribute to the efficacy of antiangiogenic drugs and immunotherapies. Our vertically integrated analyses show that angiogenesis and inflammation frequently coexist and spatially anti-correlate in the same tumor. Furthermore, MRI contrast enhancement identifies phenotypes with better response to antiangiogenic therapy among patients with metastatic RCC. CONCLUSIONS: These findings have important implications for decision models based on biopsy samples and highlight the potential of more comprehensive imaging-based approaches.


Assuntos
Carcinoma de Células Renais/diagnóstico por imagem , Carcinoma de Células Renais/genética , Carcinoma de Células Renais/patologia , Neoplasias Renais/diagnóstico por imagem , Neoplasias Renais/genética , Neoplasias Renais/patologia , Imageamento por Ressonância Magnética/métodos , Genômica por Radiação , Microambiente Tumoral , Adulto , Idoso , Idoso de 80 Anos ou mais , Inibidores da Angiogênese/uso terapêutico , Carcinoma de Células Renais/tratamento farmacológico , Feminino , Humanos , Neoplasias Renais/tratamento farmacológico , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos
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