Early Detection of Cancer Patients with Germline SDH Deficiency

Division Of Basic Sciences - Nci

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Abstract

The initial training set will include 40 patients with SDH-deficient GIST, 40 patients with SDH-deficient paraganglioma, and 40 patients matched for gender and SDH subunit mutation enrolled on the Rare Tumor Natural History Study. Peripheral blood and urine samples will be collected at the second void of the day at three separate time points separated by at least one month for each patient to minimize the impact of normal variability in the metabolic profiling. All patients will continue to be followed using the current standard screening recommendations. Cell free DNA will be isolated from plasma. Urine cfDNA will be isolated using a Q-Sepharose chromatography protocol which has been shown in the literature to outperform other published and commercial urine cfDNA isolation methods for trans-renal cfDNA yield. PDL-1 methylation has been demonstrated to regulate PDL-1 expression and DNA methylation patterns have been shown to match or exceed TMBs predictive value for PDL-1 inhibition. When sufficient cfDNA is isolated, PDL-1 methylation status will be assessed using previously published probes and digital PCR enhanced Methylight technology. This technology's minimal DNA input is 3ng with limits of detection approaching 0.03%. PDL-1 methylation status would be compared to objective response rates for predictive value as a stand-alone assay as well as when integrated with cfDNA derived TMB using a synergistic index. While the genome of SDH-deficient GIST typically contain very few somatic mutations, other cancer-associated genes including p53 and RB have been observed, particularly in patients with more aggressive tumor behavior. In addition to methylation analysis, cfDNA variant allele detection and copy number alterations will also be assessed using a CAPP-seq approach. Previously validated and optimized probe sequences will be incorporated into a targeted hybrid capture NGS panel (cancer personalized profiling by deep sequencing (CAPP-Seq)). Using integrated digital error suppression, CAPP-seq limit of detections for rare variants approaches 0.0025% (2.5 in 105 molecules). Metabolomic analysis will be performed in collaboration with the laboratory of Dr. Naomi Taylor through the Mass Spectrometry (Protein and Small Molecule) core at NCI at Frederick. Paired serum and urine samples will be collected and frozen within 2 hours of collection. Metabolic profiling of batched samples will be performed via HPLC-Mass spectrometry including quantitation of TCA cycle, pentose phosphate shunt, and glycolysis metabolites. AIM 2: Model building will be performed in collaboration with the NCI's Cancer Data Science Laboratory. The combination of DNA methylation and metabolomic data will provide a robust data set for model development using standard algorithms for unsupervised cluster analysis. If possible, the model will be refined and simplified to use only better-discriminating features. AIM 3: A testing data set will be generated through ongoing enrollment of patients with germline SDH deficiency on the Rare Tumor Natural History Study. Metabolomic and cfDNA methylation data will be collected from newly enrolled patients with GIST, PHEO/PGL, and without evidence of cancer (20 in each group). The model will be tested and refined using this dataset.