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Genome-wide loss of heterozygosity predicts aggressive, treatment-refractory behavior in pituitary neuroendocrine
Andrew L Lin1,2,3, Vasilisa A Rudneva4, Allison L Richards4
1Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.
Abstract:
Pituitary neuroendocrine tumors (PitNETs) exhibiting aggressive, treatment-refractory behavior are the rare subset that progress after surgery, conventional medical therapies, and an initial course of radiation and are characterized by unrelenting growth and/or metastatic dissemination. Two groups of patients with PitNETs were sequenced: a prospective group of patients (n = 66) who consented to sequencing prior to surgery and a retrospective group (n = 26) comprised of aggressive/higher risk PitNETs. A higher mutational burden and fraction of loss of heterozygosity (LOH) was found in the aggressive, treatment-refractory PitNETs compared to the benign tumors (p = 1.3 × 10-10 and p = 8.5 × 10-9, respectively). Within the corticotroph lineage, a characteristic pattern of recurrent chromosomal LOH in 12 specific chromosomes was associated with treatment-refractoriness (occurring in 11 of 14 treatment-refractory versus 1 of 14 benign corticotroph PitNETs, p = 1.7 × 10-4). Across the cohort, a higher fraction of LOH was identified in tumors with TP53 mutations (p = 3.3 × 10-8). A machine learning approach identified loss of heterozygosity as the most predictive variable for aggressive, treatment-refractory behavior, outperforming the most common gene-level alteration, TP53, with an accuracy of 0.88 (95% CI: 0.70-0.96). Aggressive, treatment-refractory PitNETs are characterized by significant aneuploidy due to widespread chromosomal LOH, most prominently in the corticotroph tumors. This LOH predicts treatment-refractoriness with high accuracy and represents a novel biomarker for this poorly defined PitNET category.
Insights
Aggressive pituitary neuroendocrine tumors (PitNETs) show higher loss of heterozygosity (LOH), a key indicator of treatment resistance. This chromosomal instability, particularly in corticotroph PitNETs, accurately predicts refractory behavior, offering a new biomarker.
Area of Science:
- Endocrinology and Oncology
- Genetics and Genomics of Neuroendocrine Tumors
Background:
- Pituitary neuroendocrine tumors (PitNETs) are a diverse group, with a rare subset exhibiting aggressive, treatment-refractory behavior.
- These aggressive PitNETs are characterized by relentless growth and/or metastatic dissemination, often progressing despite standard treatments like surgery, medication, and radiation.
Purpose of the Study:
- To investigate the genomic underpinnings of aggressive, treatment-refractory PitNETs.
- To identify potential biomarkers for predicting treatment resistance in PitNETs.
Main Methods:
- Whole-genome sequencing was performed on two groups of PitNET patients: a prospective cohort (n=66) and a retrospective cohort of aggressive/higher-risk PitNETs (n=26).
- Analysis focused on mutational burden and fraction of loss of heterozygosity (LOH).
- Machine learning models were employed to assess the predictive value of genomic alterations for aggressive behavior.
Main Results:
- Aggressive, treatment-refractory PitNETs demonstrated a significantly higher mutational burden and fraction of LOH compared to benign tumors.
- A specific pattern of chromosomal LOH on 12 chromosomes was strongly associated with treatment-refractoriness in corticotroph PitNETs.
- Loss of heterozygosity was identified as the most accurate predictor of aggressive, treatment-refractory behavior, outperforming TP53 mutations, with a machine learning model achieving 88% accuracy.
Conclusions:
- Aggressive, treatment-refractory PitNETs are characterized by significant aneuploidy driven by widespread chromosomal LOH.
- Widespread chromosomal LOH, especially in corticotroph PitNETs, serves as a highly accurate predictive biomarker for treatment refractoriness.
- This finding offers a novel approach for identifying and managing this challenging category of PitNETs.
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