Alison Meynert

Whole-genome sequencing (WGS) projects for rare disease diagnosis typically yield a diagnostic rate of 25–41%, depending on the methods for patient selection and the extent of prior genetic testing. The Scottish Genomes Partnership (SGP) is a collaborative programme using genome sequencing to diagnose rare disease patients with presumed monogenic aetiology in the Scottish NHS. Within SGP, short-read sequencing (SRS) had previously achieved a diagnostic rate of 23% in affected families. To increase diagnostic yield, we applied Oxford Nanopore Technologies (ONT) long-read sequencing (LRS) to a cohort of 24 SGP families (74 individuals) that remained undiagnosed after SRS-based SNV/indel analysis. We also retrospectively reviewed SRS-derived structural variant (SV) calls to assess whether LRS results could have been detected in SRS data. After quality, rarity, panel-based and inheritance-based filtering, 392 candidate SVs were retained across de novo, homozygous recessive, compound heterozygous and X-linked inheritance models, of which 8 overlapped PanelApp diagnostic-grade “green” genes. Pathogenic or likely pathogenic de novo SVs were identified in 3 of 24 families: an AUTS2 inversion, a DLX5/6 locus inversion, and an FN1 deletion. All three SVs were independently confirmed by retrospective SRS re-analysis using SVRare. These findings demonstrate that LRS-based SV analysis, supported by orthogonal SRS re-analysis, can resolve clinically significant SVs in families who remain unsolved after standard rare disease testing.

CTNNB1 , the gene encoding β-catenin, is a frequent target for oncogenic mutations activating the canonical Wnt signaling pathway, typically through missense mutations within a degron hotspot motif in exon 3. Here, we combine saturation genome editing with a fluorescent reporter assay to quantify signaling phenotypes for all 342 possible missense mutations in the mutation hotspot. Our data define the genetic requirements for β-catenin degron function, refine the consensus motif for substrate recognition by β-TRCP and reveal diverse levels of signal activation among known driver mutations. Tumorigenesis in different human tissues involves selection for CTNNB1 mutations spanning distinct ranges of predicted activity. In hepatocellular carcinoma, mutation effect scores distinguish two tumor subclasses with different levels of β-catenin signaling, and weaker mutations predict greater immune cell infiltration in the tumor microenvironment. Our work provides a resource to understand mutational diversity within a pan-cancer mutation hotspot, with potential implications for targeted therapy.

Relationship between subtyping methodologies. A, Comparison of transcriptomic subgrouping approaches: composition of Tothill subtypes across each of the TCGA subtypes; labeled P value represents comparison of Tothill subtype frequency across all TCGA subtypes by the Chi-squared test. B, Distribution of homologous recombination repair (HRR)-centric subtypes across each of the TCGA transcriptomic subtypes; labeled P value represents comparison of BRCA1/2m frequency across all groups by Chi-squared test; Bonferroni-adjusted P = 0.009. C, Distribution of HRR-centric subtypes across each of the Tothill transcriptomic subtypes; labeled P value represents comparison of BRCA1/2m frequency across all groups by the Chi-squared test; Bonferroni-adjusted P = 0.003. BRCA2m, BRCA2 mutant; BRCA1m, BRCA1 mutant; EMSY-overxp; overexpression of EMSY; nBRCA-HRRm, non-BRCA1/2 HRR gene mutation; CCNE1g, gain of CCNE1; HRRwt, non-CCNE1g HRR wild-type.

PTEN and RB loss in HGSOC. A, Frequency of loss of PTEN protein expression across HRR-centric subtypes. B, Frequency of loss of RB protein expression across HRR-centric subtypes. C, Impact of RB loss on survival in patients based on HRR status. Multivariable hazard ratio (mHR) for HRR-ab: RB loss vs. HRR-ab RB-intact = 0.50; 95% CI, 0.30–0.84; mHR for HRRwt/CCNE1g: RB loss vs. HRRwt/CCNE1g: RB-intact = 0.71; 95% CI, 0.48–1.06. BRCA2m, BRCA2 mutant; BRCA1m, BRCA1 mutant; EMSY-overxp; overexpression of EMSY; nBRCA-HRRm, non-BRCA1/2 HRR gene mutation; CCNE1g, gain of CCNE1; HRRwt, non-CCNE1g HRR wild-type. HRR-ab, HRR-aberrant: BRCA1m, BRCA2m, EMSY-overxp, or nBRCA-HRRm.

Clinicopathologic features of patients with HGSOC.

Molecular landscape of HGSOC. 1Mutation in non-BRCA1/2 HRR genes: 3 BRIP1, 2 CHEK2, 1 RAD51C, 1 PALB2, 1 concurrent BAP1 and NBN. CCNE1 CN gain, ≥4 copies by TaqMan CN assay. EMSY CN amplification, ≥6 copies by TaqMan CN assay.

HRR pathway (HRR)–centric subtyping of high-grade serous carcinoma. A, HRR-centric classification taxonomy. B, OS profile of HRR-centric subtypes. C, Chemosensitivity of HRR-centric subtypes at first-line treatment (left) and treatment for disease relapse (right) as determined by CA125 tumor marker (top) and radiology (bottom). BRCA2m, BRCA2 mutant; BRCA1m, BRCA1 mutant; EMSY-overxp; overexpression of EMSY; CCNE1g, gain of CCNE1; HRRwt, non-CCNE1g HRR wild-type.

Tumor-infiltrating immune cells across high-grade serous carcinoma subtypes. A, CD3+ infiltration across HRR-centric subtypes; labeled P value represents comparison of BRCA2m and CCNE1g groups using the Mann–Whitney U test. B, CD3+ infiltration across TCGA transcriptomic subtypes; labeled P value represents comparison of IMR and PRO groups using the Mann–Whitney U test. C, CD3+ infiltration across Tothill transcriptomic subtypes; labeled P value represents comparison of C2 and C5 groups using the Mann–Whitney U test. BRCA2m, BRCA2 mutant; BRCA1m, BRCA1 mutant; EMSY-overxp; overexpression of EMSY; CCNE1g, gain of CCNE1; HRRwt, non-CCNE1g HRR wild-type.

Therapeutic coinhibition of Wnt and PI3K signaling reduces tumor growth in ICC. A, RNA-seq data of human ICC demonstrating a positive correlation between the activity of canonical Wnt signaling and Akt signaling. B, Schematic representation of the KPPTom cholangiocarcinoma model where CreERT expression in Keratin-19–positive cholangiocytes results in the inactivation of Trp53 and Pten, whereas labeling transformed cells with tdTomato. C, Representative IHC staining of KPPTom model following tamoxifen administration (day 0) and following 4 and 8 weeks of thioacetamide administration. tdTomato (red) denotes recombined cholangiocytes (denoted by Keratin-19; green). Blue, DNA. Top, whole mount FUNGI images; bottom, 2D histologic sections. Scale bar, 200 μm. White arrows, tdTomato-positive cells. D, Quantification of liver tissue occupied by tumor in the KPPTom ICC model. E, IHC showing that KPPTom ICC has activated canonical Wnt signaling [by staining for dephosphorylated (active) β-catenin] and PI3K activity (through pAKTSer647 positivity). Red arrows, positive cells. Scale bar, 100 μm. F, A schematic representation of how the KPPTom model was used to test the effectiveness of Wnt and Pi3K inhibitor combinations on ICC progression. G, IHC staining for tdTomato-positive cancer cells in vehicle-treated animals compared with those treated with a combination of LGK974 and pictilisib. Scale bar, 100 μm. H, Number of tdTomato-positive cells in KPPTom animals given vehicle or LGK974 and pictilisib in combination. I, Proportion of KPPTom animals containing macroscopic tumors in KPPTom animals treated with vehicle versus combination treatment. b.d., bile duct; p.v., portal vein.

Nf2 loss results in RasG12D-induced oncogenesis and cooperates with Trp53 loss to accelerate ICC formation. A, Kaplan–Meier curve demonstrating the relative survival proportions of mice with KRASG12D and gRNAs targeting Trp53 (N = 12), Nf2 (N = 5), Nf2;Trp53 (N = 13), or nontargeting control (scrm, N = 5). B and C, Proportion of liver occupied by tumor (B) and number of tumors per mouse (C). D, Hematoxylin and eosin (H&E) staining of KRASG12D tumors with Trp53, Nf2, or Trp53;Nf2 loss. Scale bar, 100 μm. Dotted line, tumor-stroma boundary. E, Comparison of RNA-seq analysis when the transcriptomes from Nf2;Trp53 versus Trp53 alone tumors (blue) are compared with transcripts from Nf2;Trp53 versus Nf2 alone (yellow) tumors. Each group contains N = 4 regionally distinct tumors. F, Analysis of RPPA data demonstrating the changes in the proportion of phosphorylated GSK3α/β, β-catenin, and pAKT relative to total protein levels in KRASG12D;Trp53KO (gray points), KRASG12D;Nf2KO (yellow points), KRASG12D;Trp53KO;Nf2KO (blue points). G, IHC of active, dephosphorylated β-catenin (top) and phosphorylated AKTSer647 (bottom) in KRASG12D;Trp53KO, KRASG12D;Nf2KO, KRASG12D;Trp53KO/Nf2KO tumors. Scale bar, 50 μm. H. Immunoblot for dephosphorylated (active) β-catenin (β-cateninSer33/37/Thr41) in tumors isolated from mice baring KrasG12D -driven ICC with Trp53, Nf2, or Trp53;Nf2 co-loss. GAPDH was used as a loading control. I, Schematic representing our dosing approach to determine whether Wnt inhibition, PI3K inhibition, or a combination of the two is effective in improving the survival of mice with KRASG12D;Trp53KO;Nf2KO ICC. J, Kaplan–Meier curve demonstrating the survival changes when KRASG12D;Trp53KO;Nf2KO animals are treated with vehicle (yellow line), LGK974 (Wnt-inhibitor; blue line), pictilisib (PI3K inhibitor; orange line), or a combination (green line; N = 5 per group).