scholarly journals Scale-Up Evaluation of a Composite Tumor Marker Assay for the Early Detection of Renal Cell Carcinoma

Diagnostics ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 750
Author(s):  
Dong Su Kim ◽  
Won Sik Ham ◽  
Won Sik Jang ◽  
Kang Su Cho ◽  
Young Deuk Choi ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Early Detection ◽  
Tumor Marker ◽  
Cell Carcinoma ◽  
Tumor Markers ◽  
Renal Cell ◽  
Scale Up ◽  
Composite Tumor ◽  
Single Marker ◽  
Marker Assay

The early detection of renal cell carcinoma (RCC) using tumor markers remains an attractive prospect for the potential to downstage the disease. To validate the scale-up clinical performance of potential tumor markers for RCC (as a single marker and as a composite tumor marker composed of nicotinamide N-methyltransferase (NNMT), L-Plastin (LCP1), and non-metastatic cells 1 protein (NM23A)), the scale-up assay was performed. Patients with RCC from multiple domestic institutes were included in the clinical evaluation for reassessment and improvement of the established triple markers of our product. For the diagnostic performance of the composite markers, the best-split cutoff points of each marker (147 pg/mL for NNMT, 1780 pg/mL for LCP1, and 520 pg/mL for NM23A) were installed. Serum levels of NNMT, LCP1, and NM23A were greatly increased in subjects with RCC (p < 0.0001). In 1042 blind sample tests with control individuals (n = 500) and patients with RCC (n = 542), the diagnostic sensitivity and specificity of the composite three-marker assay were 0.871 and 0.894, respectively, and the resulting AUC (Area under Curve) of ROC (Receiver Operating Characteristic) was 0.917. As a single marker, the diagnostic accuracies of NNMT, LCP1, and NM23A, as estimated by ROC, were 0.833, 0.844, and 0.601, respectively. The composite three-marker assay with NNMT, LCP1, and NM23A is a more improved novel serum marker assay for the early detection of RCC in cases of renal mass or unknown condition. The NNMT, LCP1, and NM23A triple marker assay could be a powerful diagnostic tumor marker assay to screen the early stage of RCC.

Present status and technique for early detection of renal cell carcinoma and urinary tract cancer in US screening

Choonpa Igaku ◽  
2010 ◽  
Vol 37 (2) ◽  
pp. 107-114
Author(s):  
Shuichi MIHARA ◽  
Kouji OTAKE ◽  
Hiroyuki KOBA ◽  
Shinji TANAKA ◽  
Shinichi HIRAO
Keyword(s):  
Renal Cell Carcinoma ◽  
Urinary Tract ◽  
Early Detection ◽  
Cell Carcinoma ◽  
Present Status ◽  
Renal Cell ◽  
Tract Cancer ◽  
Urinary Tract Cancer

scholarly journals Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

2020 ◽  
Vol 20 (1) ◽  
pp. 841-857
Author(s):  
Malena Manzi ◽  
Martín Palazzo ◽  
María Elena Knott ◽  
Pierre Beauseroy ◽  
Patricio Yankilevich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Renal Cell Carcinoma ◽  
Early Detection ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Learning Approach ◽  
Cell Renal Cell Carcinoma ◽  
Machine Learning Approach

Proteomic identification of Reticulocalbin 1 as potential tumor marker in renal cell carcinoma

2013 ◽  
Vol 91 ◽  
pp. 385-392 ◽  
Author(s):  
Giuliana Giribaldi ◽  
Giovanna Barbero ◽  
Giorgia Mandili ◽  
Lorenzo Daniele ◽  
Amina Khadjavi ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Tumor Marker ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Proteomic Identification

Ferritin: A Tumor Marker Expressed by Renal Cell Carcinoma

1995 ◽  
Vol 28 (2) ◽  
pp. 131-134 ◽  
Author(s):  
Ziya Kirkali ◽  
A. Adii Esen ◽  
Güldal Kirkalt ◽  
Gül Güner
Keyword(s):  
Renal Cell Carcinoma ◽  
Tumor Marker ◽  
Cell Carcinoma ◽  
Renal Cell

Durable complete responses and near complete responses to sunitinib in metastatic renal cell carcinoma

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 15514-15514
Author(s):  
D. Y. Heng ◽  
B. I. Rini ◽  
J. Garcia ◽  
L. Wood ◽  
R. M. Bukowski
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Tumor Volume ◽  
Pulmonary Metastases ◽  
Cleveland Clinic ◽  
Cancer Center ◽  
Composite Tumor ◽  
Number Of Patients ◽  
Metastatic Rcc

15514 Introduction: Sunitinib is a tyrosine kinase inhibitor with activity against VEGFR and PDGFR recently approved by the FDA for the treatment of advanced renal cell carcinoma (RCC). There is no existing literature that details complete responses (CRs) in patients taking sunitinib for metastatic RCC. Methods: Seventy-four patients with metastatic RCC receiving sunitinib at the Cleveland Clinic Taussig Cancer Center on clinical trials were reviewed to determine the number of patients with RECIST-defined CRs. Additionally, patients who achieved near-CRs defined as a greater than 90% reduction in composite tumor volume or residual disease of less than or equal to 1 cm were reviewed. Results: Two patients (2.7%) achieved a RECIST-defined CR lasting >15 months. The patients who obtained CRs had non-bulky pulmonary metastases, favorable or intermediate MSKCC risk profiles, were treated with sunitinib in the first-line setting and had a significant reduction in composite tumor measurements within the first two cycles. An additional 2 patients achieved near-CRs, including one patient that previously progressed on bevacizumab. These 2 near-CR patients remain progression-free for more than 19 months. Finally, 1 patient achieved sufficient downstaging and reduction of tumor volume such that the remaining lesion could be excised, resulting in a surgical CR. This patient is currently off sunitinib and remains progression-free 4 months after surgery. Conclusion: Sunitinib is capable of producing durable CRs in cytokine-naïve metastatic RCC patients with non-bulky pulmonary metastases. Additionally, near-CRs can be seen despite non-pulmonary metastatic sites and prior VEGF-targeted therapy. No significant financial relationships to disclose.

Avoidance of futile treatment and adverse events by using angiogenesis-specific imaging for early detection of disease progression in patients with first-line metastatic renal cell carcinoma.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 134-134
Author(s):  
Stephen K. Gruschkus ◽  
Carolyn Bodnar ◽  
Amol Dhamane ◽  
Manan Shah
Keyword(s):  
Renal Cell Carcinoma ◽  
Early Detection ◽  
Cell Carcinoma ◽  
Metastatic Renal Cell Carcinoma ◽  
Renal Cell ◽  
First Line ◽  
Early Detection Of Disease ◽  
First Line Therapy ◽  
Line Therapy ◽  
Patients Experience

134 Background: Although sunitinib is effective first-line therapy (1LT) for metastatic renal cell carcinoma (mRCC), ~20% of patients experience rapid progressive disease (PD). Traditional RECIST monitoring often does not detect PD until 90 days after 1LT initiation. Investigational angiogenesis-specific imaging (AI) may identify PD as early as 14 days post-1LT initiation, thus allowing a switch to a potentially more effective second-line therapy and avoiding unnecessary risk of AEs. This study’s goal was to quantify the potential reduction in futile 1LT length, AEs, and costs by using AI for early detection of PD. Methods: Decision modeling with a 90-day horizon evaluated a comparator arm using RECIST monitoring at 90 days and an intervention arm using AI at 14 days. Sunitinib costs were $21,250 for the comparator arm and $13,282 for the intervention arm. RECIST costs were $619 and AI costs were tested as a breakeven analysis. A literature review quantified AE rates associated with 1LT sunitinib and claims data were used to determine costs. Early PD detection was estimated based on a 20% rapid PD rate. Results: For AI sensitivity of 50% to predict rapid PD, a 38-day reduction in futile 1LT could be achieved per PD patient by using AI vs. RECIST (AI sensitivity of 75%/100% yielded 57/76 fewer days). The potential number of AEs avoided through early PD identification is shown below. Costs saved per 1,000 mRCC patients were $684,566 for AEs and $3,187,400 for futile 1LT. Based on these results, $3,872 per mRCC patient would be freed up for AI. Conclusions: Continuing 1LT after PD brings unnecessary risk of AEs and delays potentially effective 2nd-line therapy. Results of this study indicate that early PD identification using AI may improve quality of care by minimizing duration of futile 1LT and avoiding unnecessary AEs. [Table: see text]

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