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Improving Risk Assessment for Metastatic Disease in Endometrioid Endometrial Cancer Patients Using Molecular and Clinical Features: An NRG Oncology/Gynecologic Oncology Group Study.

Publication ,  Journal Article
Casablanca, Y; Wang, G; Lankes, HA; Tian, C; Bateman, NW; Miller, CR; Chappell, NP; Havrilesky, LJ; Wallace, AH; Ramirez, NC; Miller, DS ...
Published in: Cancers (Basel)
August 23, 2022

Objectives: A risk assessment model for metastasis in endometrioid endometrial cancer (EEC) was developed using molecular and clinical features, and prognostic association was examined. Methods: Patients had stage I, IIIC, or IV EEC with tumor-derived RNA-sequencing or microarray-based data. Metastasis-associated transcripts and platform-centric diagnostic algorithms were selected and evaluated using regression modeling and receiver operating characteristic curves. Results: Seven metastasis-associated transcripts were selected from analysis in the training cohorts using 10-fold cross validation and incorporated into an MS7 classifier using platform-specific coefficients. The predictive accuracy of the MS7 classifier in Training-1 was superior to that of other clinical and molecular features, with an area under the curve (95% confidence interval) of 0.89 (0.80-0.98) for MS7 compared with 0.69 (0.59-0.80) and 0.71 (0.58-0.83) for the top evaluated clinical and molecular features, respectively. The performance of MS7 was independently validated in 245 patients using RNA sequencing and in 81 patients using microarray-based data. MS7 + MI (myometrial invasion) was preferrable to individual features and exhibited 100% sensitivity and negative predictive value. The MS7 classifier was associated with lower progression-free and overall survival (p ≤ 0.003). Conclusion: A risk assessment classifier for metastasis and prognosis in EEC patients with primary tumor derived MS7 + MI is available for further development and optimization as a companion clinical support tool.

Duke Scholars

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Published In

Cancers (Basel)

DOI

ISSN

2072-6694

Publication Date

August 23, 2022

Volume

14

Issue

17

Location

Switzerland

Related Subject Headings

  • 3211 Oncology and carcinogenesis
  • 1112 Oncology and Carcinogenesis
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Casablanca, Y., Wang, G., Lankes, H. A., Tian, C., Bateman, N. W., Miller, C. R., … Maxwell, G. L. (2022). Improving Risk Assessment for Metastatic Disease in Endometrioid Endometrial Cancer Patients Using Molecular and Clinical Features: An NRG Oncology/Gynecologic Oncology Group Study. Cancers (Basel), 14(17). https://doi.org/10.3390/cancers14174070
Casablanca, Yovanni, Guisong Wang, Heather A. Lankes, Chunqiao Tian, Nicholas W. Bateman, Caela R. Miller, Nicole P. Chappell, et al. “Improving Risk Assessment for Metastatic Disease in Endometrioid Endometrial Cancer Patients Using Molecular and Clinical Features: An NRG Oncology/Gynecologic Oncology Group Study.Cancers (Basel) 14, no. 17 (August 23, 2022). https://doi.org/10.3390/cancers14174070.
Casablanca Y, Wang G, Lankes HA, Tian C, Bateman NW, Miller CR, Chappell NP, Havrilesky LJ, Wallace AH, Ramirez NC, Miller DS, Oliver J, Mitchell D, Litzi T, Blanton BE, Lowery WJ, Risinger JI, Hamilton CA, Phippen NT, Conrads TP, Mutch D, Moxley K, Lee RB, Backes F, Birrer MJ, Darcy KM, Maxwell GL. Improving Risk Assessment for Metastatic Disease in Endometrioid Endometrial Cancer Patients Using Molecular and Clinical Features: An NRG Oncology/Gynecologic Oncology Group Study. Cancers (Basel). 2022 Aug 23;14(17).

Published In

Cancers (Basel)

DOI

ISSN

2072-6694

Publication Date

August 23, 2022

Volume

14

Issue

17

Location

Switzerland

Related Subject Headings

  • 3211 Oncology and carcinogenesis
  • 1112 Oncology and Carcinogenesis