Integration of next generation sequencing data to inform survival prediction of patients with spine metastasis Journal Article
| Authors: | Giantini-Larsen, A.; Ramos, A. D.; Martin, A.; Panageas, K. S.; Kostrzewa, C. E.; Abou-Mrad, Z.; Schmitt, A.; Bromberg, J. F.; Safonov, A.; Rudin, C. M.; Newman, W. C.; Bilsky, M. H.; Barzilai, O. |
| Article Title: | Integration of next generation sequencing data to inform survival prediction of patients with spine metastasis |
| Abstract: | Background/Objectives: Spinal metastatic disease is a life-altering problem for individuals with cancer. Prognostication is key for tailored treatment of spinal metastases. This manuscript provides a comprehensive overview of the genomic profiles of metastatic spine tumors and investigates the potential of mutational data to stratify overall survival (OS) across various histologies. Methods: This is a cohort study of consecutive patients with spine metastatic disease whose tumors were sequenced on a next generation sequencing platform; a machine learning (ML) algorithm was used to stratify OS risk. Results: Targeted sequencing and stratification of OS risk of 282 spine metastases (breast (84), non-small cell lung (56), prostate (49), other (93)) was performed. TP53 (HR 1.80; 95% CI 1.26, 2.56) and KEAP1 (HR 3.95, 95% CI 2.24, 6.98) mutations were associated with poor survival across the entire cohort in univariate Cox proportional hazards models. The ML algorithm categorized breast cancer metastasis into low- and high-risk groups, revealing a median OS of 71 compared to 22 months (HR 3.3, p < 0.001). TP53 mutations and ESR1 mutations conferred poor prognosis. In lung cancer, low- and high-risk groups with median OS of 30 and 6 months (HR 8.3, p < 0.001), respectively, were identified with poor prognosis linked to MET amplification. No significant prognostic associations were identified for spinal prostate metastases. Conclusions: Metastatic spine tumor molecular data allows for the identification of prognostic groups. We present an open-source machine learning algorithm utilizing genomic mutational data that may aid in prognostication and tailored decision making. © 2025 by the authors. |
| Keywords: | adult; human tissue; aged; overall survival; cancer patient; breast cancer; cohort analysis; retrospective study; protein p53; uvomorulin; risk factor; histology; prostate cancer; genomics; k ras protein; metastatic breast cancer; decision making; spine metastasis; non small cell lung cancer; clinical outcome; bone biopsy; demographics; survival prediction; machine learning; cancer prognosis; high throughput sequencing; metastatic prostate cancer; kelch like ech associated protein 1; human; male; female; article; genetic profile; cross validation; machine learning algorithm |
| Journal Title: | Cancers |
| Volume: | 17 |
| Issue: | 13 |
| ISSN: | 2072-6694 |
| Publisher: | MDPI |
| Date Published: | 2025-07-01 |
| Start Page: | 2218 |
| Language: | English |
| DOI: | 10.3390/cancers17132218 |
| PROVIDER: | scopus |
| PMCID: | PMC12249426 |
| PUBMED: | 40647516 |
| DOI/URL: | |
| Notes: | Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PubMed and PDF -- MSK corresponding author is Ori Barzilai -- Source: Scopus |
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