Keith Nykamp

The D4Z4 macrosatellite repeat encompasses some of the most difficult-to-resolve disease-related variations in the human genome. D4Z4 has a repeat unit of 3.3 kb (encoding the DUX4 gene) that is present in up to 100 copies on two chromosomes (4 and 10), while DUX4 can only be expressed in somatic cells from the permissive A haplotype that usually occurs on chromosome 4. Facioscapulohumeral muscular dystrophy (FSHD) is caused by chromatin relaxation and ectopic expression of DUX4 in skeletal muscle, mediated by contraction of D4Z4 to 1-10 copies (FSHD1, 95% of FSHD cases) or mutations in chromatin factor genes such as SMCHD1 (FSHD2, 5% of FSHD cases). Due to its large size, disease specific haplotypes and sequence homology between chromosomes, D4Z4 is challenging to resolve by current sequencing technologies. We report a computational tool, Kivvi, to genotype D4Z4 using PacBio whole-genome long-read sequence data. Kivvi detects all D4Z4 alleles in a sample, reporting the repeat size, chromosome (4 vs. 10), distal haplotype (A vs. non-permissive haplotypes) and the methylation level of each allele. We validated Kivvi against gold standard assays for FSHD diagnostics, detecting 100% of contracted alleles and correctly classifying 90% of noncontracted alleles. We showed differential methylation signals between FSHD1 and candidate FSHD2 samples. We profiled D4Z4 across 601 individuals from five ancestral populations, revealing extensive genetic diversity. We identified common haplotypes of D4Z4 alleles and characterized hybrid repeat units, hybrid repeat arrays, and translocation alleles. Combined with HiFi long reads, Kivvi enables the consolidation of multiple FSHD assays into a single workflow and facilitates the discovery of novel genetic modifiers of FSHD through population-scale studies.

Supplementary Fig. S1: Analysis of FH variants and their associated interpretations of pathogenicity. Supplementary Fig. S2: Kinetic parameters for active/partially-active VUS/CI. Supplementary Fig. S3: Recombinant FH produced for 74 VUS/CI. Supplementary Fig. S4: Michaelis-Menten graphs for VUS/CI incubated with fumarate. Supplementary Fig. S5: Michaelis-Menten graphs for VUS/CI incubated with malate. Supplementary Fig. S6: Broad analysis of recombinant FH. Supplementary Fig. S7: Multimerization status of 74 VUS/CI. Supplementary Fig. S8: Tetramerization correlates with catalytic activity. Supplementary Fig. S9: Expression of FH variants in HLRCC cells. Supplementary Fig. S10: Fumarate accumulation decreases TCA intermediates. Supplementary Fig. S11: FH loss enhances de novo glutathione biosynthesis. Supplementary Fig. S12: Coessentiality of FH and nucleotide biosynthesis genes. Supplementary Fig. S13: Loss of FH activity disrupts de novo purine biosynthesis. Supplementary Fig. S14: Dysregulated purine metabolism is independent of HLRCC cell proliferation rate. Supplementary Fig. S15: FH-deficient cells use the purine salvage pathway to maintain purine nucleotide levels. Supplementary Fig. S16: Pyrimidine metabolism in FH-deficient and FH-expressing cells. Supplementary Fig. S17: Disrupting expression of purine salvage enzymes impairs tumor growth. Supplementary Fig. S18: Purine salvage inhibitor 6-mercaptopurine (6-MP) impairs tumor growth.

Supplementary Table S1: FH variants with population frequencies, interpretations of pathogenicity, catalytic efficiencies, and multimerization states

Fumarate accumulation due to loss of fumarate hydratase (FH) drives cellular transformation. Germline FH alterations lead to hereditary leiomyomatosis and renal cell cancer (HLRCC) where patients are predisposed to an aggressive form of kidney cancer. There is an unmet need to classify FH variants by cancer-associated risk. We quantified catalytic efficiencies of 74 variants of uncertain significance. Over half were enzymatically inactive, which is strong evidence of pathogenicity. We next generated a panel of HLRCC cell lines expressing FH variants with a range of catalytic activities, then correlated fumarate levels with metabolic features. We found that fumarate accumulation blocks de novo purine biosynthesis, rendering FH-deficient cells reliant on purine salvage for proliferation. Genetic or pharmacologic inhibition of the purine salvage pathway reduced HLRCC tumor growth in vivo. These findings suggest the pathogenicity of patient-associated FH variants and reveal purine salvage as a targetable vulnerability in FH-deficient tumors.Significance:This study functionally characterizes patient-associated FH variants with unknown significance for pathogenicity. This study also reveals nucleotide salvage pathways as a targetable feature of FH-deficient cancers, which are shown to be sensitive to the purine salvage pathway inhibitor 6-mercaptopurine. This presents a new rapidly translatable treatment strategy for FH-deficient cancers.This article is featured in Selected Articles from This Issue, p. 1949

Supplementary Table S1: FH variants with population frequencies, interpretations of pathogenicity, catalytic efficiencies, and multimerization states

Fumarate accumulation due to loss of fumarate hydratase (FH) drives cellular transformation. Germline FH alterations lead to hereditary leiomyomatosis and renal cell cancer (HLRCC) where patients are predisposed to an aggressive form of kidney cancer. There is an unmet need to classify FH variants by cancer-associated risk. We quantified catalytic efficiencies of 74 variants of uncertain significance. Over half were enzymatically inactive which is strong evidence of pathogenicity. We next generated a panel of HLRCC cell lines expressing FH variants with a range of catalytic activities, then correlated fumarate levels with metabolic features. We found that fumarate accumulation blocks de novo purine biosynthesis, rendering FH-deficient cells reliant on purine salvage for proliferation. Genetic or pharmacologic inhibition of the purine salvage pathway reduced HLRCC tumor growth in vivo. These findings suggest pathogenicity of patient-associated FH variants and reveal purine salvage as a targetable vulnerability in FH-deficient tumors.

Supplementary Fig. S1: Analysis of FH variants and their associated interpretations of pathogenicity. Supplementary Fig. S2: Kinetic parameters for active/partially-active VUS/CI. Supplementary Fig. S3: Recombinant FH produced for 74 VUS/CI. Supplementary Fig. S4: Michaelis-Menten graphs for VUS/CI incubated with fumarate. Supplementary Fig. S5: Michaelis-Menten graphs for VUS/CI incubated with malate. Supplementary Fig. S6: Broad analysis of recombinant FH. Supplementary Fig. S7: Multimerization status of 74 VUS/CI. Supplementary Fig. S8: Tetramerization correlates with catalytic activity. Supplementary Fig. S9: Expression of FH variants in HLRCC cells. Supplementary Fig. S10: Fumarate accumulation decreases TCA intermediates. Supplementary Fig. S11: FH loss enhances de novo glutathione biosynthesis. Supplementary Fig. S12: Coessentiality of FH and nucleotide biosynthesis genes. Supplementary Fig. S13: Loss of FH activity disrupts de novo purine biosynthesis. Supplementary Fig. S14: Dysregulated purine metabolism is independent of HLRCC cell proliferation rate. Supplementary Fig. S15: FH-deficient cells use the purine salvage pathway to maintain purine nucleotide levels. Supplementary Fig. S16: Pyrimidine metabolism in FH-deficient and FH-expressing cells. Supplementary Fig. S17: Disrupting expression of purine salvage enzymes impairs tumor growth. Supplementary Fig. S18: Purine salvage inhibitor 6-mercaptopurine (6-MP) impairs tumor growth.

Supplementary Table S1: FH variants with population frequencies, interpretations of pathogenicity, catalytic efficiencies, and multimerization states

Supplementary Fig. S1: Analysis of FH variants and their associated interpretations of pathogenicity. Supplementary Fig. S2: Kinetic parameters for active/partially-active VUS/CI. Supplementary Fig. S3: Recombinant FH produced for 74 VUS/CI. Supplementary Fig. S4: Michaelis-Menten graphs for VUS/CI incubated with fumarate. Supplementary Fig. S5: Michaelis-Menten graphs for VUS/CI incubated with malate. Supplementary Fig. S6: Broad analysis of recombinant FH. Supplementary Fig. S7: Multimerization status of 74 VUS/CI. Supplementary Fig. S8: Tetramerization correlates with catalytic activity. Supplementary Fig. S9: Expression of FH variants in HLRCC cells. Supplementary Fig. S10: Fumarate accumulation decreases TCA intermediates. Supplementary Fig. S11: FH loss enhances de novo glutathione biosynthesis. Supplementary Fig. S12: Coessentiality of FH and nucleotide biosynthesis genes. Supplementary Fig. S13: Loss of FH activity disrupts de novo purine biosynthesis. Supplementary Fig. S14: Dysregulated purine metabolism is independent of HLRCC cell proliferation rate. Supplementary Fig. S15: FH-deficient cells use the purine salvage pathway to maintain purine nucleotide levels. Supplementary Fig. S16: Pyrimidine metabolism in FH-deficient and FH-expressing cells. Supplementary Fig. S17: Disrupting expression of purine salvage enzymes impairs tumor growth. Supplementary Fig. S18: Purine salvage inhibitor 6-mercaptopurine (6-MP) impairs tumor growth.

As the adoption and scope of genetic testing continue to expand, interpreting the clinical significance of DNA sequence variants at scale remains a formidable challenge, with a high proportion classified as variants of uncertain significance (VUSs). Genetic testing laboratories have historically relied, in part, on functional data from academic literature to support variant classification. High-throughput functional assays or multiplex assays of variant effect (MAVEs), designed to assess the effects of DNA variants on protein stability and function, represent an important and increasingly available source of evidence for variant classification, but their potential is just beginning to be realized in clinical lab settings. Here, we describe a framework for generating, validating and incorporating data from MAVEs into a semi-quantitative variant classification method applied to clinical genetic testing. Using single-cell gene expression measurements, cellular evidence models were built to assess the effects of DNA variation in 44 genes of clinical interest. This framework was also applied to models for an additional 22 genes with previously published MAVE datasets. In total, modeling data was incorporated from 24 genes into our variant classification method. These data contributed evidence for classifying 4043 observed variants in over 57,000 individuals. Genetic testing laboratories are uniquely positioned to generate, analyze, validate, and incorporate evidence from high-throughput functional data and ultimately enable the use of these data to provide definitive clinical variant classifications for more patients.