Modelling bladder cancer in mice: opportunities and challenges

Kobayashi, Takashi; Owczarek, Tomasz B.; McKiernan, James M.; Abate‐Shen, Cory

doi:10.1038/nrc3858

Cited by 129 publications

(126 citation statements)

References 155 publications

(174 reference statements)

Supporting

Mentioning

125

Contrasting

Unclassified

Order By: Relevance

“…For patients with nonmuscle‐invasive BCa (NMIBC), the recurrence rate could reach up to 61% during the first 2 years (TFTY) after transurethral resection of bladder tumor (TURBT) . Previous studies support the idea that an early radical cystectomy (RC) should be performed sooner rather than later for tumor relapse after TURBT operation to potentially increase the survival interval . Besides, for the muscle‐invasive BCa (MIBC) patients, although RC with bilateral lymph nodes dissection and ileal conduit could eliminate most lesions, 5–50% patients would develop local or metastatic recurrences in the next 24 months .…”

mentioning

confidence: 99%

A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

Wang

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Background Preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients. Purpose To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk. Study Type Retrospective. Population Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21). Field Strength/Sequence 3.0T MRI/T2‐weighted (T2W), multi‐b‐value diffusion‐weighted (DW), and dynamic contrast‐enhanced (DCE) sequences. Assessment Radiomics features were extracted from the T2W, DW, apparent diffusion coefficient, and DCE images. A Rad_Score model was constructed using the support vector machine‐based recursive feature elimination approach and a logistic regression model. Combined with the important clinical factors, including age, gender, grade, and muscle‐invasive status (MIS) of the archived lesion, tumor size and number, surgery, and image signs like stalk and submucosal linear enhancement, a radiomics‐clinical nomogram was developed, and its performance was evaluated in the training and the validation cohorts. The potential clinical usefulness was analyzed by the decision curve. Statistical Tests Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction. Results Of the 1872 features, the 32 with the highest area under the curve (AUC) of receiver operating characteristic were selected for the Rad_Score calculation. The nomogram developed by two independent predictors, MIS and Rad_Score, showed good performance in the training (accuracy 88%, AUC 0.915, P << 0.01) and validation cohorts (accuracy 80.95%, AUC 0.838, P = 0.009). The decision curve exhibited when the risk threshold was larger than 0.3, more benefit was observed by using the radiomics‐clinical nomogram than using the radiomics or clinical model alone. Data Conclusion The proposed radiomics‐clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1893–1904.

show abstract

mentioning

confidence: 99%

A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

Wang

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…The PDX model established in the present study preserved histological characteristics of original tumor as well as strong IGF‐II expression, which helped better understanding of this rare pathogenic entity. PDX models recently attract considerable attention particularly for the researches on certain cancers that have been hampered by the shortage of available animal models …”

Section: Discussionmentioning

confidence: 99%

Bladder urothelial carcinoma producing insulin‐like growth factor II: A case report

et al. 2018

Self Cite

View full text Add to dashboard Cite

Introduction Non‐islet cell tumor hypoglycemia is a rare paraneoplastic syndrome associated with tumors. Although it mainly occurs in solid tumors of mesenchymal and epithelial origin, but rarely also in hematopoietic and neuroendocrine origin. Case presentation We describe a 65‐year‐old man with a muscle‐invasive bladder urothelial carcinoma, which rapidly progressed against systemic chemotherapy consisting of gemcitabine and cisplatin. Notably, the patient developed hypoglycemia at the terminal stage of the disease. Pathological diagnosis was giant cell urothelial carcinoma, which was strongly positive for insulin‐like growth factor‐II in immunohistochemistry. We established patient‐derived xenograft from insulin‐like growth factor‐II producing bladder urothelial carcinoma that caused non‐islet cell tumor hypoglycemia. Although we evaluated the efficacy of the neutralizing antibody, there was no statistically significant inhibitory effect on tumor growth. Conclusion To the best of our knowledge, this is the first report of insulin‐like growth factor‐II‐producing urothelial carcinoma that have been recapitulated in a patient‐derived xenograft model.

show abstract

“…For example, many GEM models of prostate cancer are based on loss-of-function of Pten 24 , which is prevalent in the human disease. Similarly, treatment of mice with tobacco-related carcinogens leads to bladder cancer, reminiscent of human bladder cancer for which a major risk factor is smoking 25 . Fourth, the histological and molecular properties of mouse tumors should resemble their human counterparts as closely as possible.…”

Section: What Constitutes An Informative Mouse Model Of Cancer Prevenmentioning

confidence: 99%

Optimizing mouse models for precision cancer prevention

2016

Self Cite

View full text Add to dashboard Cite

As cancer has become increasingly more prevalent in our society, cancer prevention research has evolved toward placing a greater emphasis on reducing cancer deaths and minimizing the adverse consequences of having cancer. “Precision cancer prevention” takes into account the collaboration of intrinsic and extrinsic factors for influencing cancer incidence and aggressiveness in the context of the individual, as well as the recognition that such knowledge can improve early detection and more accurate discrimination of cancerous lesions. The premise of this review is that analyses of mouse models can greatly augment precision cancer prevention. However, as of now, mouse models, and particularly genetically-engineered mouse (GEM) models, have yet to be fully integrated into prevention research. Herein we discuss opportunities and challenges for “precision mouse modeling”, including their essential criteria of mouse models for prevention research, representative success stories, and opportunities for the more refined analyses in future studies.

show abstract

Modelling bladder cancer in mice: opportunities and challenges

Cited by 129 publications

References 155 publications

A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

Bladder urothelial carcinoma producing insulin‐like growth factor II: A case report

Optimizing mouse models for precision cancer prevention

Contact Info

Product

Resources

About