A Risk Model to Predict Recurrence in Early-Stage Lung Adeno
Now open: Certificate Course in Management of Covid-19 by Govt. Of Gujarat and PlexusMDKnow more...Now open: Certificate Course in Management of Covid-19 by Govt. Of Gujarat and PlexusMDKnow more...
This JAMA Surgery study suggests that the integration of genomic and clinicopathologic factors are associated with the risk of recurrence in surgically resected Lung Adenocarcinoma (LUAD), potentially enriching and increasing accrual to adjuvant therapy clinical trials.

The aim was to identify tumor genomic factors independently associated with recurrence and to develop a computational machine-learning prediction model (PRecur) to determine whether the integration of genomic and clinicopathologic features could better predict the risk of recurrence, compared with the TNM system.

This prospective cohort study included 426 patients. 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). The study endpoint consisted of relapse-free survival (RFS).

The Result was;
--Association analysis showed that alterations in SMARCA4 and TP53 and the fraction of genome altered were independently associated with RFS.

--The PRecur prediction model outperformed the TNM-based model for the prediction of RFS.

--To validate the prediction model, PRecur was applied to the TCGA LUAD data set, and a clear separation of risk groups was noted, confirming external validation.

Conclusively, the 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.

Source: https://jamanetwork.com/journals/jamasurgery/article-abstract/2774477
Like
Comment
Share