Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 4 de 4
Filtrar
Más filtros

Bases de datos
Tipo del documento
Asunto de la revista
País de afiliación
Intervalo de año de publicación
1.
J Transl Med ; 22(1): 42, 2024 01 10.
Artículo en Inglés | MEDLINE | ID: mdl-38200511

RESUMEN

BACKGROUND: Immune checkpoint inhibitors (ICIs) have emerged as one of the most promising first-line therapeutics in the management of non-small cell lung cancer (NSCLC). However, only a subset of these patients responds to ICIs, highlighting the clinical need to develop better predictive and prognostic biomarkers. This study will leverage pre-treatment imaging profiles to develop survival risk models for NSCLC patients treated with first-line immunotherapy. METHODS: Advanced NSCLC patients (n = 149) were retrospectively identified from two institutions who were treated with first-line ICIs. Radiomics features extracted from pretreatment imaging scans were used to build the predictive models for progression-free survival (PFS) and overall survival (OS). A compendium of five feature selection methods and seven machine learning approaches were utilized to build the survival risk models. The concordance index (C-index) was used to evaluate model performance. RESULTS: From our results, we found several combinations of machine learning algorithms and feature selection methods to achieve similar performance. K-nearest neighbourhood (KNN) with ReliefF (RL) feature selection was the best-performing model to predict PFS (C-index = 0.61 and 0.604 in discovery and validation cohorts), while XGBoost with Mutual Information (MI) feature selection was the best-performing model for OS (C-index = 0.7 and 0.655 in discovery and validation cohorts). CONCLUSION: The results of this study highlight the importance of implementing an appropriate feature selection method coupled with a machine learning strategy to develop robust survival models. With further validation of these models on external cohorts when available, this can have the potential to improve clinical decisions by systematically analyzing routine medical images.


Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas , Neoplasias Pulmonares , Humanos , Carcinoma de Pulmón de Células no Pequeñas/diagnóstico por imagen , Carcinoma de Pulmón de Células no Pequeñas/terapia , Inmunoterapia , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/tratamiento farmacológico , Pronóstico , Radiómica , Estudios Retrospectivos
2.
BMC Cancer ; 24(1): 556, 2024 May 03.
Artículo en Inglés | MEDLINE | ID: mdl-38702617

RESUMEN

Radiotherapy is a mainstay of cancer treatment. The clinical response to radiotherapy is heterogeneous, from a complete response to early progression. Recent studies have explored the importance of patient characteristics in response to radiotherapy. In this editorial, we invite contributions for a BMC Cancer collection of articles titled 'Advances in personalized radiotherapy' towards the improvement of treatment response.


Asunto(s)
Neoplasias , Medicina de Precisión , Humanos , Medicina de Precisión/métodos , Neoplasias/radioterapia , Radioterapia/métodos , Radioterapia/tendencias , Resultado del Tratamiento
3.
Br J Radiol ; 92(1103): 20190198, 2019 Nov.
Artículo en Inglés | MEDLINE | ID: mdl-31538514

RESUMEN

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.


Asunto(s)
Neoplasias de la Mama/genética , Bases de Datos Factuales , Neoplasias de Cabeza y Cuello/genética , MicroARNs/genética , Transcriptoma/genética , Investigación Biomédica , Neoplasias de la Mama/radioterapia , Marcadores Genéticos/genética , Neoplasias de Cabeza y Cuello/radioterapia , Humanos , Hipoxia/genética , Oxígeno/fisiología , Células Tumorales Cultivadas
4.
Cancer Res ; 79(24): 6227-6237, 2019 12 15.
Artículo en Inglés | MEDLINE | ID: mdl-31558563

RESUMEN

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.


Asunto(s)
Biomarcadores de Tumor/genética , Biología Computacional/métodos , Modelos Biológicos , Neoplasias/radioterapia , Tolerancia a Radiación/genética , Línea Celular Tumoral , Reparación del ADN/efectos de la radiación , Bases de Datos Genéticas/estadística & datos numéricos , Conjuntos de Datos como Asunto , Relación Dosis-Respuesta en la Radiación , Perfilación de la Expresión Génica , Humanos , Mutación , Neoplasias/genética , Neoplasias/mortalidad , Medicina de Precisión/métodos , Resultado del Tratamiento
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA