Digital spatial profiling to predict recurrence in grade 3 stage I lung adenocarcinoma

Area of Science:

• Oncology
• Genomics
• Cancer Research

Background:

• Early-stage lung adenocarcinoma (LADC) treatment often involves local therapy.
• However, high-grade Stage I LADC presents a significant recurrence risk (50% at 5 years).
• Predictive models for recurrence are needed to guide treatment decisions.

Purpose of the Study:

• To investigate the potential of tumor microenvironment analysis for predicting recurrence in Stage I LADC.
• To develop a predictive model for recurrence based on genomic and proteomic profiling.

Main Methods:

• Digital spatial profiling was employed for genomic and proteomic analysis of tumor cells from 34 Stage I LADC patients.
• K-means clustering was applied to identify differential genes and proteins associated with recurrence.
• Kaplan-Meier curves were used to assess recurrence prediction, with external validation of key genes.

Main Results:

• No significant clinicopathologic differences were observed between recurrent and non-recurrent groups.
• Genomic clustering of pancytokeratin-positive cells yielded a predictive model with a concordance index of 0.75.
• Proteomic data showed internal validation of genomic findings, though not statistically significant on its own.

Conclusions:

• Genomic alterations within lung adenocarcinoma tumors show promise in predicting recurrence risk.
• Further validation is required to stratify patients and guide adjuvant therapy selection.
• This approach could personalize treatment strategies for early-stage lung adenocarcinoma.