Protein Signatures Could Improve Patient Outcome Predictions in Soft Tissue Sarcomas

For high-risk soft tissue sarcomas (STS), adding a specific protein signature to the existing Sarculator tool helps better predict patient outcomes by identifying important biological differences, according to a study published in Cancer Medicine.¹

Although the findings are preliminary and exploratory, the authors noted that the study highlights the value of incorporating protein signatures in creating more advanced outcome prediction tools for patients with STS.

Incorporating protein signature screening into existing tools could help providers better predict patient outcomes and treatment reactions in soft-tissue sarcoma. | Image credit: VadimGuzhva - stock.adobe.com

STS is a rare cancer that originates in the body's soft tissues, which include muscle, fat, blood vessels, nerves, tendons, and joint linings.² This type of cancer can occur anywhere in the body but is most commonly found in the arms, legs, and abdomen. There are over 50 types of STS. For 2024, the American Cancer Society estimates that approximately 13,590 new cases of STS will be diagnosed in the US.³ Additionally, about 5200 people are expected to die from STS.

This study aimed to improve prognostic accuracy for STS of the extremities or trunk wall by exploring biological features that may explain varying responses to treatment. Although the Sarculator nomogram predicts survival outcomes, incorporating additional biological factors could enhance its accuracy. By investigating protein networks and evaluating the Sarcoma Proteomic Module 6 (SPM6) as a complementary tool, the study seeks to identify new drug targets and improve treatment strategies for high-risk patients with STS.

Clinical data were retrospectively collected and analyzed. Proteomic data were obtained from ProteomeXchange and analyzed using various bioinformatics tools to identify proteins associated with different risk levels. Functional genomic data and RNA sequencing were used to further explore potential drug targets and gene expression patterns. Statistical analyses, including Cox regression models for 5-year overall survival (OS) Sarculator nomogram predicted probabilities (pr-OS) and Kaplan–Meier survival curves, were performed to assess the impact of the SPM6 on risk stratification and overall survival outcomes.

Researchers enrolled 123 patients with primary STS of the extremities or trunk wall from The Royal Marsden Hospital in London, UK. The patients’ median age was 64.78 years, and 55.3% of the cohort were women. The median follow-up time was 72.5 months, with a 5-year OS probability of 50.3%. Using the Sarculator-defined pr-OS quartiles, patients were categorized into 4 risk groups: high risk (pr-OS ≤ 39%), intermediate risk (pr-OS = 39%-52%), low risk (pr-OS = 52%-69%), and very low risk (pr-OS > 69%). Kaplan–Meier survival curves showed significant differences among these risk groups (P < .001), with particularly notable differences between the very-low-risk group and all other groups (low risk, P = .024; intermediate risk, P < .0001; high risk, P < .0001).

The study used mass spectrometry to analyze tumor proteomic profiles from a cohort of patients, identifying 44 proteins upregulated in both the high-risk and very-low-risk groups. High-risk group proteins included the MCM complex, CDK1, and proteins involved in collagen crosslinking, while the very-low-risk group had proteins involved in oxidative phosphorylation and fatty acid oxidation. Functional analysis of cell lines highlighted essential genes, such as the MCM complex and CDK1, as potential drug targets.

The SPM6 protein signature, associated with DNA replication, was enriched in high-risk patients and linked to poorer OS. Although SPM6 alone did not significantly predict OS, the authors noted that it slightly improved the predictive accuracy of the Sarculator nomogram. Additionally, SPM6's prognostic value varied with tumor size, suggesting it provides unique biological insights, particularly for smaller tumors with better outcomes.

Study limitations included the study’s retrospective design, a potential selection bias due to tissue availability, a small patient cohort limiting detailed analysis among STS histologies, and the absence of grade 1 tumors making the cohort relatively homogenous and higher risk.

The authors recommended that, “Future proteomic analysis of grade 1 tumours may identify additional pathways that will improve our mechanistic understanding of the biology of low-risk STS patients.”

References

1. Chadha M, Iadecola S, Jenks A, et al. Proteomic profiling improves prognostic risk stratification of the Sarculator nomogram in soft tissue sarcomas of the extremities and trunk wall. Cancer Med. 2024;12(14):e70026. doi:10.1002/cam4.70026

2. Soft tissue sarcoma. Mayo Clinic. April 27, 2023. Accessed July 26, 2024. https://www.mayoclinic.org/diseases-conditions/soft-tissue-sarcoma/symptoms-causes/syc-20377725

3. Key statistics for soft tissue sarcomas. American Cancer Society. Accessed July 26, 2024. https://www.cancer.org/cancer/types/soft-tissue-sarcoma/about/key-statistics.html