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Jones GD, Brandt WS, Shen R, et al. A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma. JAMA Surg. 2021;156(2):e205601doi: 10.1001/jamasurg.2020.5601.
Jones, G. D., Brandt, W. S., Shen, R., Sanchez-Vega, F., Tan, K. S., Martin, A., Zhou, J., Berger, M., Solit, D. B., Schultz, N., Rizvi, H., Liu, Y., Adamski, A., Chaft, J. E., Riely, G. J., Rocco, G., Bott, M. J., Molena, D., Ladanyi, M., Travis, W. D., Rekhtman, N., Park, B. J., Adusumilli, P. S., Lyden, D., Imielinski, M., Mayo, M. W., Li, B. T., & Jones, D. R. (2021). A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma. JAMA surgery, 156(2), e205601. https://doi.org/10.1001/jamasurg.2020.5601
Jones, Gregory D, et al. "A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma." JAMA surgery vol. 156,2 (2021): e205601. doi: https://doi.org/10.1001/jamasurg.2020.5601
Jones GD, Brandt WS, Shen R, Sanchez-Vega F, Tan KS, Martin A, Zhou J, Berger M, Solit DB, Schultz N, Rizvi H, Liu Y, Adamski A, Chaft JE, Riely GJ, Rocco G, Bott MJ, Molena D, Ladanyi M, Travis WD, Rekhtman N, Park BJ, Adusumilli PS, Lyden D, Imielinski M, Mayo MW, Li BT, Jones DR. A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma. JAMA Surg. 2021 Feb 01;156(2):e205601. doi: 10.1001/jamasurg.2020.5601. Epub 2021 Feb 10. PMID: 33355651; PMCID: PMC7758824.
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JAMA Surg. 2021 Feb 01;156(2):e205601. doi: 10.1001/jamasurg.2020.5601. Epub 2021 Feb 10.

A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma.

JAMA surgery

Gregory D Jones, Whitney S Brandt, Ronglai Shen, Francisco Sanchez-Vega, Kay See Tan, Axel Martin, Jian Zhou, Michael Berger, David B Solit, Nikolaus Schultz, Hira Rizvi, Yuan Liu, Ariana Adamski, Jamie E Chaft, Gregory J Riely, Gaetano Rocco, Matthew J Bott, Daniela Molena, Marc Ladanyi, William D Travis, Natasha Rekhtman, Bernard J Park, Prasad S Adusumilli, David Lyden, Marcin Imielinski, Marty W Mayo, Bob T Li, David R Jones

Affiliations

  1. Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
  2. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York.
  3. Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York.
  4. Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
  5. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
  6. Weill Cornell Medicine, New York, New York.
  7. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.
  8. Department of Pediatrics, Meyer Cancer Center, Weill Cornell Medicine, New York, New York.
  9. Department of Pathology, Weill Cornell Medicine, New York Genome Center, New York.
  10. Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville.

PMID: 33355651 PMCID: PMC7758824 DOI: 10.1001/jamasurg.2020.5601

Abstract

IMPORTANCE: Recommendations for adjuvant therapy after surgical resection of lung adenocarcinoma (LUAD) are based solely on TNM classification but are agnostic to genomic and high-risk clinicopathologic factors. Creation of a prediction model that integrates tumor genomic and clinicopathologic factors may better identify patients at risk for recurrence.

OBJECTIVE: To identify tumor genomic factors independently associated with recurrence, even in the presence of aggressive, high-risk clinicopathologic variables, in patients with completely resected stages I to III LUAD, and to develop a computational machine-learning prediction model (PRecur) to determine whether the integration of genomic and clinicopathologic features could better predict risk of recurrence, compared with the TNM system.

DESIGN, SETTING, AND PARTICIPANTS: This prospective cohort study included 426 patients treated from January 1, 2008, to December 31, 2017, at a single large cancer center and selected in consecutive samples. Eligibility criteria included complete surgical resection of stages I to III LUAD, broad-panel next-generation sequencing data with matched clinicopathologic data, and no neoadjuvant therapy. External validation of the PRecur prediction model was performed using The Cancer Genome Atlas (TCGA). Data were analyzed from 2014 to 2018.

MAIN OUTCOMES AND MEASURES: The study end point consisted of relapse-free survival (RFS), estimated using the Kaplan-Meier approach. Associations among clinicopathologic factors, genomic alterations, and RFS were established using Cox proportional hazards regression. The PRecur prediction model integrated genomic and clinicopathologic factors using gradient-boosting survival regression for risk group generation and prediction of RFS. A concordance probability estimate (CPE) was used to assess the predictive ability of the PRecur model.

RESULTS: Of the 426 patients included in the analysis (286 women [67%]; median age at surgery, 69 [interquartile range, 62-75] years), 318 (75%) had stage I cancer. Association analysis showed that alterations in SMARCA4 (clinicopathologic-adjusted hazard ratio [HR], 2.44; 95% CI, 1.03-5.77; P = .042) and TP53 (clinicopathologic-adjusted HR, 1.73; 95% CI, 1.09-2.73; P = .02) and the fraction of genome altered (clinicopathologic-adjusted HR, 1.03; 95% CI, 1.10-1.04; P = .005) were independently associated with RFS. The PRecur prediction model outperformed the TNM-based model (CPE, 0.73 vs 0.61; difference, 0.12 [95% CI, 0.05-0.19]; P < .001) for prediction of RFS. To validate the prediction model, PRecur was applied to the TCGA LUAD data set (n = 360), and a clear separation of risk groups was noted (log-rank statistic, 7.5; P = .02), confirming external validation.

CONCLUSIONS AND RELEVANCE: The findings suggest that integration of tumor genomics and clinicopathologic features improves risk stratification and prediction of recurrence after surgical resection of early-stage LUAD. Improved identification of patients at risk for recurrence could enrich and enhance accrual to adjuvant therapy clinical trials.

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