Use of comprehensive genomic profiling for biomarker discovery for the management of non-small cell lung cancer brain metastases.

Background: Clinical biomarkers for brain metastases remain elusive. Increased availability of genomic profiling has brought discovery of these biomarkers to the forefront of research interests. Method: In this single institution retrospective series, 130 patients presenting with brain metastasis secondary to Non-Small Cell Lung Cancer (NSCLC) underwent comprehensive genomic profiling conducted using next generation circulating tumor deoxyribonucleic acid (DNA) (Guardant Health, Redwood City, CA). A total of 77 genetic mutation identified and correlated with nine clinical outcomes using appropriate statistical tests (general linear models, Mantel-Haenzel Chi Square test, and Cox proportional hazard regression models). For each outcome, a genetic signature composite score was created by summing the total genes wherein genes predictive of a clinically unfavorable outcome assigned a positive score, and genes with favorable clinical outcome assigned negative score. Results: Seventy-two genes appeared in at least one gene signature including: 14 genes had only unfavorable associations, 36 genes had only favorable associations, and 22 genes had mixed effects. Statistically significant associated signatures were found for the clinical endpoints of brain metastasis velocity, time to distant brain failure, lowest radiosurgery dose, extent of extracranial metastatic disease, concurrent diagnosis of brain metastasis and NSCLC, number of brain metastases at diagnosis as well as distant brain failure. Some genes were solely associated with multiple favorable or unfavorable outcomes. Conclusion: Genetic signatures were derived that showed strong associations with different clinical outcomes in NSCLC brain metastases patients. While these data remain to be validated, they may have prognostic and/or therapeutic impact in the future. Statement of translation relevance: Using Liquid biopsy in NSCLC brain metastases patients, the genetic signatures identified in this series are associated with multiple clinical outcomes particularly these ones that lead to early or more numerous metastases. These findings can be reverse-translated in laboratory studies to determine if they are part of the genetic pathway leading to brain metastasis formation.

Prognostic Mutational Signatures of NSCLC Patients treated with chemotherapy, immunotherapy and chemoimmunotherapy.

Different types of therapy are currently being used to treat non-small cell lung cancer (NSCLC) depending on the stage of tumor and the presence of potentially druggable mutations. However, few biomarkers are available to guide clinicians in selecting the most effective therapy for all patients with various genetic backgrounds. To examine whether patients' mutation profiles are associated with the response to a specific treatment, we collected comprehensive clinical characteristics and sequencing data from 524 patients with stage III and IV NSCLC treated at Atrium Health Wake Forest Baptist. Overall survival based Cox-proportional hazard regression models were applied to identify mutations that were "beneficial" (HR < 1) or "detrimental" (HR > 1) for patients treated with chemotherapy (chemo), immune checkpoint inhibitor (ICI) and chemo+ICI combination therapy (Chemo+ICI) followed by the generation of mutation composite scores (MCS) for each treatment. We also found that MCS is highly treatment specific that MCS derived from one treatment group failed to predict the response in others. Receiver operating characteristics (ROC) analyses showed a superior predictive power of MCS compared to TMB and PD-L1 status for immune therapy-treated patients. Mutation interaction analysis also identified novel co-occurring and mutually exclusive mutations in each treatment group. Our work highlights how patients' sequencing data facilitates the clinical selection of optimized treatment strategies.

Collagen deposition within brain metastases is associated with leptomeningeal failure after cavity-directed radiosurgery.

Background: Leptomeningeal failure (LMF) represents a devastating progression of disease following resection of brain metastases (BrM). We sought to identify a biomarker at time of BrM resection that predicts for LMF using mass spectrometry-based proteomic analysis of resected BrM and to translate this finding with histochemical assays. Methods: We retrospectively reviewed 39 patients with proteomic data available from resected BrM. We performed an unsupervised analysis with false discovery rate adjustment (FDR) to compare proteomic signature of BrM from patients that developed LMF versus those that did not. Based on proteomic analysis, we applied trichrome stain to a total of 55 patients who specifically underwent resection and adjuvant radiosurgery. We used competing risks regression to assess predictors of LMF. Results: Of 39 patients with proteomic data, FDR revealed type I collagen-alpha-1 (COL1A1, P = .045) was associated with LMF. The degree of trichrome stain in each block correlated with COL1A1 expression (ß = 1.849, P = .001). In a cohort of 55 patients, a higher degree of trichrome staining was associated with an increased hazard of LMF in resected BrM (Hazard Ratio 1.58, 95% CI 1.11-2.26, P = .01). Conclusion: The degree of trichrome staining correlated with COL1A1 and portended a higher risk of LMF in patients with resected brain metastases treated with adjuvant radiosurgery. Collagen deposition and degree of fibrosis may be able to serve as a biomarker for LMF.

Upfront immunotherapy leads to lower brain metastasis velocity in patients undergoing stereotactic radiosurgery for brain metastases.

Background: While immunotherapy has been shown to improve survival and decrease neurologic death in patients with brain metastases, it remains unclear whether this improvement is due to prevention of new metastasis to the brain. Method: We performed a retrospective review of patients presenting with brain metastases simultaneously with the first diagnosis of metastatic disease and were treated with upfront immunotherapy as part of their treatment regimen and stereotactic radiosurgery (SRS) to the brain metastases. We compared this cohort with a historical control population (prior to the immunotherapy era) who were treated with pre-immunotherapy standard of care systemic therapy and with SRS to the brain metastases. Results: Median overall survival time was improved in the patients receiving upfront immunotherapy compared to the historical cohort (48 months vs 8.4 months, p=0.001). Median time to distant brain failure was statistically equivalent (p=0.3) between the upfront immunotherapy cohort and historical control cohort (10.3 vs 12.6 months). Brain metastasis velocity was lower in the upfront immunotherapy cohort (median 3.72 metastases per year) than in the historical controls (median 9.48 metastases per year, p=0.001). Cumulative incidence of neurologic death at one year was 12% in the upfront immunotherapy cohort and 28% in the historical control cohort (p=0.1). Conclusions: Upfront immunotherapy appears to improve overall survival and decrease BMV compared to historical controls. While these data remain to be validated, they suggest that brain metastasis patients may benefit from concurrent immunotherapy with SRS.

Local control outcomes for combination of stereotactic radiosurgery and immunotherapy for non-small cell lung cancer brain metastases.

BACKGROUND: Previous series have demonstrated CNS activity for immune checkpoint inhibitors, yet no prior data exists regarding whether this activity can improve outcomes of stereotactic radiosurgery. METHODS: In this single institution retrospective series, the clinical outcomes of 80 consecutive lung cancer patients treated with concurrent immune checkpoint inhibitors and stereotactic radiosurgery were compared to 235 in the historical control cohort in which patients were treated prior to immune checkpoint inhibition being standard upfront therapy. Overall survival was estimated using the Kaplan Meier method. Cumulative incidence of local progression was estimated using a competing risk model. RESULTS: Median overall survival time was improved in patients receiving upfront immunotherapy compared to the historical control group (40 months vs 8 months, p < 0.001). Factors affected overall survival include concurrent immunotherapy (HR 0.23, p < 0.0001) and KPS (HR 0.97, p = 0.0001). Cumulative incidence of local failure in the historical control group was 10% at 1 year, compared to 1.1% at 1 year in the concurrent immunotherapy group (p = 0.025). Factors affected local control included use of concurrent immunotherapy (HR 0.09, p = 0.012), and lowest margin dose delivered to a metastasis (HR 0.8, p = 0.0018). CONCLUSION: Local control and overall survival were both improved in patients receiving concurrent immune checkpoint inhibitors with radiosurgery compared to historical controls. While these data remain to be validated, they suggest that brain metastasis patients may benefit from concurrent use of immunotherapy with SRS.

Brain metastasis prognostic nomograms and brain metastasis velocity: a narrative review.

OBJECTIVE: To provide a review of the current status of predictive nomograms and brain metastasis velocity (BMV) in the prognostication of brain metastasis outcomes. BACKGROUND: Statistical analyses have been used for many years in an attempt to predict clinical outcomes of brain metastasis patients. Such models have attempted to predict such endpoints as survival and which patients would most benefit from stereotactic radiosurgery (SRS) or whole brain radiotherapy (WBRT). METHODS: This narrative review includes documents the history of statistical models and nomograms through the stage migration of the brain metastasis population from a population with large symptomatic brain metastases to the modern population with small asymptomatic metastases found on surveillance imaging. It also tells the history of the derivation and validation of BMV, a recently identified biomarker for survival and neurologic death in the brain metastasis population treated with SRS. CONCLUSIONS: Statistical models predicting brain metastasis behavior continue to evolve with the changing landscape of systemic therapy and the more aggressive use of SRS. Previous models with ultimately need to integrate biologic data and will continue to be updated.