February 27, 2020/Cancer

NSCLC Patients with Brain Metastases: Look to Molecular Data for Prognostication

Number of brain metastases doesn’t matter for ALK/EGFR+ patients


The number of brain metastases does not impact progression-free or overall survival for patients with epidermal growth factor receptor (EGFR) mutations or anaplastic lymphoma kinase (ALK) translocations and non-small cell lung cancer (NSCLC), according to a Cleveland Clinic study recently published in Neuro-Oncology (2020;22:267-277).


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Traditional prognostic factors

Historically, the number of brain metastases has been considered a key prognostic factor for patients with NSCLC; generally, the more a patient has, the poorer the prognosis. It is one of several factors considered in the disease-specific graded prognostic assessment (DS-GPA) tool for NSCLC; others include age, Karnofsky performance status (KPS) and extracranial metastases, all shown to significantly impact patient outcomes. A recent update to the DS-GPA, called Lung-molGPA, incorporates advances in molecular and genetic data as a prognostic factor in brain metastases.

“The move toward incorporating molecular and genetic data into prognostic tools is the right one, but we lack large studies evaluating the role of EGFR and ALK mutations and alterations in NSCLC patients with brain metastases,” says Manmeet Ahluwalia, MD, Director of the Brain Metastasis Research Program at Cleveland Clinic’s Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center and senior author of the study. “This investigation provides that necessary data and, interestingly, shows that the number of brain metastases and outcomes correlate primarily in patients without these actionable molecular targets.”

Targeted therapies and prognostication

The advent of targeted therapies for NSCLC patients with EGFR mutations and ALK translocations has improved outcomes in this population, but patients without actionable molecular targets (wild type) rely more heavily on surgery, radiation and, more recently, immunotherapy. The current study analyzed 257 wild type patients in comparison with 91 patients with actionable targets (ALK, N = 23; EGFR, N = 68) from Cleveland Clinic’s Burkhardt Brain Tumor and Neuro-Oncology Center database. The researchers sought to assess the prognostic significance of tumor, treatment and patient characteristics in the respective cohorts.


Median patient age was 60 years, and about half had a KPS score greater than 80. The median number of brain metastases was two (range, 1 to ≥ 99). Patients received stereotactic radiosurgery (SRS) with or without surgery, whole brain radiation therapy (WBRT) with or without surgery, or WBRT plus SRS. Patients in the actionable targets group also received tyrosine kinase inhibitors.

Median overall survival for patients with EGFR/ALK+ NSCLC was 19.93 months compared with 9.87 months for wild type (P = 0.028). Patients in the wild type cohort with fewer brain metastases had significantly better overall survival (P = 0.006) after adjusting for KPS, synchronicity, age and symptoms of brain metastases. For patients with ALK or EGFR+ NSCLC, the number of brain metastases didn’t matter.

“Our findings are an important departure from prior studies that have not accounted for molecular data in determining the role that the number of brain metastases plays in prognosis,” says Dr. Ahluwalia. “Before, the number of brain metastases mattered in all NSCLC patients; now we have data to support a more nuanced approach.”


Prognostic assessments and molecular data

Other findings from this study are consistent with current literature, including the independent impacts of extracranial metastases and KPS on overall and progression-free survival in all patients. Overall, the results encourage a continued refinement of available prognostic tools as therapies become more precise.

“From recursive partitioning analysis to the DS-GPA based on primary malignancies and now to the new Lung-molGPA, our ability to offer patients some ideas about what is to come has greatly improved over time,” says Dr. Ahluwalia. “The more we learn, the more complex our prognostic tools may need to become.”

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