Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 5 de 5
Filtrar
1.
Nat Methods ; 18(1): 92-99, 2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-33408405

RESUMEN

Many experimental and bioinformatics approaches have been developed to characterize the human T cell receptor (TCR) repertoire. However, the unknown functional relevance of TCR profiling hinders unbiased interpretation of the biology of T cells. To address this inadequacy, we developed tessa, a tool to integrate TCRs with gene expression of T cells to estimate the effect that TCRs confer on the phenotypes of T cells. Tessa leveraged techniques combining single-cell RNA-sequencing with TCR sequencing. We validated tessa and showed its superiority over existing approaches that investigate only the TCR sequences. With tessa, we demonstrated that TCR similarity constrains the phenotypes of T cells to be similar and dictates a gradient in antigen targeting efficiency of T cell clonotypes with convergent TCRs. We showed this constraint could predict a functional dichotomization of T cells postimmunotherapy treatment and is weakened in tumor contexts.


Asunto(s)
Linfocitos Infiltrantes de Tumor/inmunología , Neoplasias/inmunología , Receptores de Antígenos de Linfocitos T/genética , Receptores de Antígenos de Linfocitos T/inmunología , Análisis de la Célula Individual/métodos , Linfocitos T/inmunología , Transcriptoma , Biología Computacional/métodos , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Neoplasias/genética , Neoplasias/patología
2.
BMC Bioinformatics ; 23(1): 469, 2022 Nov 08.
Artículo en Inglés | MEDLINE | ID: mdl-36348271

RESUMEN

Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology. However, the one-to-many correspondence between a patient and multiple TCR sequences hinders researchers from simply adopting classical statistical/machine learning methods. There were recent attempts to model this type of data in the context of multiple instance learning (MIL). Despite the novel application of MIL to cancer detection using TCR sequences and the demonstrated adequate performance in several tumor types, there is still room for improvement, especially for certain cancer types. Furthermore, explainable neural network models are not fully investigated for this application. In this article, we propose multiple instance neural networks based on sparse attention (MINN-SA) to enhance the performance in cancer detection and explainability. The sparse attention structure drops out uninformative instances in each bag, achieving both interpretability and better predictive performance in combination with the skip connection. Our experiments show that MINN-SA yields the highest area under the ROC curve scores on average measured across 10 different types of cancers, compared to existing MIL approaches. Moreover, we observe from the estimated attentions that MINN-SA can identify the TCRs that are specific for tumor antigens in the same T cell repertoire.


Asunto(s)
Neoplasias , Redes Neurales de la Computación , Humanos , Aprendizaje Automático , Curva ROC , Receptores de Antígenos de Linfocitos T , Atención , Neoplasias/diagnóstico
3.
Breast Cancer Res Treat ; 187(3): 853-865, 2021 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-33620590

RESUMEN

PURPOSE: Many women diagnosed with breast cancer have survived previous cancer; yet little is known about the impact of previous cancer on overall and cancer-specific survival. METHODS: This population-based cohort study using SEER-Medicare data included women (age ≥ 66 years) diagnosed with breast cancer between 2005 and 2015. Separately by breast cancer stage, we estimated effect of previous cancer on overall survival using Cox regression and on cause-specific survival using competing risk regression; all survival analyses adjusted for covariates. RESULTS: Of 138,576 women diagnosed with breast cancer, 8% had a previous cancer of another organ site, most commonly colorectal or uterine cancer or melanoma. Many of these women (46.3%) were diagnosed within 5 years of breast cancer. For all breast cancer stages except IV wherein there was no difference, women with vs. without previous cancer had worse overall survival. This survival disadvantage was driven by deaths due to the previous cancer and other causes. In contrast, women with previous cancer generally had favorable breast-cancer-specific survival, although this varied by stage. Overall survival varied by previous cancer type, timing, and stage; previous lung cancer, cancer diagnosed within 1 year of incident breast cancer, and previous cancer at a distant stage were associated with the worst survival. In contrast, women with a previous melanoma had equivalent overall survival to women without previous cancer. CONCLUSION: We observed variable impact of previous cancer on overall and breast-cancer-specific survival depending on breast cancer stage at diagnosis and the type, timing, and stage of previous cancer.


Asunto(s)
Neoplasias de la Mama , Anciano , Neoplasias de la Mama/diagnóstico , Neoplasias de la Mama/epidemiología , Neoplasias de la Mama/patología , Estudios de Cohortes , Femenino , Humanos , Medicare , Estadificación de Neoplasias , Programa de VERF , Estados Unidos/epidemiología
4.
Comput Struct Biotechnol J ; 19: 3255-3268, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34141144

RESUMEN

As a branch of machine learning, multiple instance learning (MIL) learns from a collection of labeled bags, each containing a set of instances. The learning process is weakly supervised due to ambiguous instance labels. Since its emergence, MIL has been applied to solve various problems including content-based image retrieval, object tracking/detection, and computer-aided diagnosis. In biomedical research, the use of MIL has been focused on medical image analysis and molecule activity prediction. We review and apply 16 methods to investigate the applicability of MIL to a novel biomedical application, cancer detection using T-cell receptor (TCR) sequences. This important application can be a viable approach for large-scale cancer screening, as TCRs can be easily profiled from a subject's peripheral blood. We consider two feasible data-generating mechanisms, and for the purpose of performance evaluation, we simulate data under each mechanism, where we vary potentially important factors to mimic realistic situations. We also apply the methods to sequencing data of ten cancer types from The Cancer Genome Atlas, as an early proof of concept for distinguishing tumor patients from healthy individuals via TCR sequencing of peripheral blood. We find that given an appropriate MIL method is used, satisfactory performance with Area Under the Receiver Operating Characteristic Curve above 80% can be achieved for five in the ten cancers. Based on our numerical results, we make suggestions about selection of a proper method and avoidance of any method with poor performance. We further point out directions of future research as well as identify a pressing need of new MIL methodologies for improved performance (for some cancer types) and more explainable outcomes.

5.
Cancer Med ; 10(14): 4752-4767, 2021 07.
Artículo en Inglés | MEDLINE | ID: mdl-34190429

RESUMEN

Patients with previous cancer are often excluded from clinical trials despite limited evidence about their prognosis. We examined the effect of previous cancer on overall and colorectal cancer (CRC)-specific survival of patients newly diagnosed with CRC. This population-based cohort study from the U.S.A. included patients aged ≥66 years and diagnosed with CRC between 2005 and 2015 in linked Surveillance, Epidemiology, and End Results-Medicare data. We estimated the stage-specific effects of a previous cancer on overall survival using Cox regression and on CRC-specific survival using competing risk regression. We also examined the effect of previous cancer type, timing, and stage on overall survival. Of 112,769 patients, 14.1% were previously diagnosed with another cancer--commonly prostate (32.9%) or breast (19.4%) cancer, with many (47.1%) diagnosed <5 years of CRC. For all CRC stages except IV, in which there was no difference, patients with previous cancer (vs. without) had worse overall survival. However, patients with previous cancer had improved CRC-specific survival. Overall survival for those with stage 0-III CRC varied by previous cancer type, timing, and stage; for example, patients with previous melanoma had overall survival equivalent to those with no previous cancer. Our results indicate that, in general, CRC patients with previous cancer have worse overall survival but superior CRC-specific survival. Given their equivalent survival to those without previous cancer, patients with previous melanoma and those with stage IV CRC with any type of previous cancer should be eligible to participate in clinical trials.


Asunto(s)
Supervivientes de Cáncer , Neoplasias Colorrectales/mortalidad , Neoplasias Primarias Secundarias/mortalidad , Anciano , Anciano de 80 o más Años , Neoplasias de la Mama/mortalidad , Neoplasias de la Mama/patología , Supervivientes de Cáncer/estadística & datos numéricos , Causas de Muerte , Estudios de Cohortes , Neoplasias Colorrectales/diagnóstico , Neoplasias Colorrectales/patología , Femenino , Humanos , Neoplasias Pulmonares/mortalidad , Neoplasias Pulmonares/patología , Masculino , Melanoma/mortalidad , Melanoma/patología , Estadificación de Neoplasias/mortalidad , Neoplasias Primarias Secundarias/patología , Modelos de Riesgos Proporcionales , Neoplasias de la Próstata/mortalidad , Neoplasias de la Próstata/patología , Programa de VERF , Neoplasias Cutáneas/mortalidad , Neoplasias Cutáneas/patología , Estados Unidos/epidemiología , Neoplasias de la Vejiga Urinaria/mortalidad , Neoplasias de la Vejiga Urinaria/patología
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA