Integrated genomic characterization of papillary thyroid carcinoma

Abstract

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Here, we describe the genomic landscape of 496 PTCs. We observed a low frequency of somatic alterations (relative to other carcinomas) and extended the set of known PTC driver alterations to include EIF1AX, PPM1D, and CHEK2 and diverse gene fusions. These discoveries reduced the fraction of PTC cases with unknown oncogenic driver from 25% to 3.5%. Combined analyses of genomic variants, gene expression, and methylation demonstrated that different driver groups lead to different pathologies with distinct signaling and differentiation characteristics. Similarly, we identified distinct molecular subgroups of BRAF-mutant tumors, and multidimensional analyses highlighted a potential involvement of oncomiRs in less-differentiated subgroups. Our results propose a reclassification of thyroid cancers into molecular subtypes that better reflect their underlying signaling and differentiation properties, which has the potential to improve their pathological classification and better inform the management of the disease.

Figure 1. Landscape of Genomic Alterations in 402 Papillary Thyroid Carcinomas

(A) Mutation density (mutations/Mb) across the cohort. (B) Tumor purity, patient age, gender, history of radiation exposure, risk of recurrence, MACIS score, histological type, and BRS score. (C) Number and frequency of recurrent mutations in genes (left) ranked by MutSig significance (right), gene-sample matrix of mutations (middle) with TERT promoter mutations (bottom). (D) Number and frequency of fusion events (left), gene-sample matrix of fusions across the cohort (middle). (E) Number and frequency of SCNAs (left), chromosome-sample matrix of SCNAs across the cohort (middle) with focal deletions in BRAF and PTEN (bottom), GISTIC2 significance (right). (F) Driving variant types across the cohort. Samples were sorted by driving variant type with dark matter on the left. See also Figures S1, S2, S3, S4 and Tables S1, S2, S3, S4A, and S5A,B,E.

Figure 2. TERT Promoter Mutations and Clonality Assessment of Driver Mutations

(A-C) Association of TERT promoter mutations with (A) risk of recurrence, (B) MACIS score, and (C) thyroid differentiation score (TDS). See also Table S2. (D) Mutation cancer cell fraction distribution. The majority of all mutations, including driver mutations BRAF, NRAS, HRAS, KRAS, and EIF1AX, have a calculated cancer cell fraction close to 1.0, indicating their presence all tumor cells.

Figure 3. Candidate “Driver” Gene Fusions in Papillary Thyroid Carcinoma

(A) RNA expression fusion plots for representative novel candidate genes involving RET, BRAF, ALK, NTRK3 and LTK fusions. Each gene in the fusion plot is drawn 5′ to 3′, exon specific relative expression data is represented with low (blue) and high expression (red), and the kinase domain is mapped with a green box. The pairs of numbers across the links indicate the number of split reads and paired-end supporting reads from RNA-seq. (B) Circos plots (http://circos.ca) of RET, BRAF and NTRK3 fusions. Red links represent recurrent fusions, black non-recurrent. See also Figures S3F and Table S5B.

Figure 4. The BRAF^V600E-RAS Score

(A) Thyroid samples (n=391) were ranked by BRAF^V600E-RAS score (BRS), with BRAF^V600E-like and RAS-like samples having negative (-1 to 0) and positive scores (0 to 1), respectively. The BRAF^V600E-RAS score is strongly associated with: (B) driver mutation status; (C) thyroid differentiation score (TDS); (D) single data-type clusters and (E) histology and follicular fraction. The RAS-like samples (normalized score > 0, in red on the top bar) consistently emerged as a distinct subgroup characterized by a higher TDS. See also Figures S6 and S7A, B and Tables S2 and S4B.

Figure 5. Role of Thyroid Differentiation in Papillary Thyroid Carcinomas

Thyroid Differentiation Score (TDS) across the cohort with tumors sorted by driver mutation and TDS. Below TDS are the BRAF^V600E-RAS score (BRS), ERK signature, histological type, MACIS score, risk of recurrence, driver mutations, gene expression data for nine thyroid genes used to derive the TDS (TG, TPO, SLC26A4 (pendrin), SLC5A5 (Na/I symporter), SLC5A8 (apical iodide transporter), DIO1, DIO2, DUOX1, DUOX2), four selected mRNAs correlated to TDS, and three selected miRs correlated to TDS. Featured mRNA (except for 16 thyroid genes) and miRNA genes were selected based on Spearman correlation to TDS in the BRAF^V600E cohort (*) and the full cohort (**) (see Supplement). See also Figures S7C-J and Table S5F.

Figure 6. Downstream Signaling of BVL and RL PTCs

(A) MAPK and PI3K pathways are differentially activated in the BVL and RL PTCs. (B) BRAF^V600E-mutated cases show robust activation of MAPK signaling resulting in higher output of the ERK transcriptional program, represented in particular by DUSP (DUSP4, 5 and 6) mRNAs. This may be due to insensitivity of BRAF^V600E to ERK inhibitory feedback. By contrast, RAS-like tumors activated both MAPK and PI3K/AKT signaling, as shown by higher pAKT levels in these tumors. The mechanism by which RAS-like tumors activated MAPK signaling was distinct from that of BRAF^V600E tumors, as they had higher CRAF phosphorylation, consistent with engagement of RAF dimers. Paradoxically, RL-PTCs had higher phosphorylation of the ERK substrate p90RSK, which was associated with mTOR activation, likely through phosphorylation and consequent inhibition of TSC2. RL-PTCs also showed activation of an anti-apoptotic program, characterized by S112-BAD phosphorylation (a target of P90RSK) and BCL2 over-expression. See also Figures S8 and Tables S4B,F.

Figure 7. Unsupervised clusters for miRNA-seq data

Heatmap showing discriminatory miRs (5p or 3p mature strands) with the largest 6% of metagene matrix scores (see Supplement), as well as miR-204-5p, 221-3p and 222-3p, which were highlighted in correlations to BRS and TDS scores (see Figure S10D). The scalebar shows log2 normalized (reads-per-million, RPM), median-centered miR abundance. miR names in red are discussed in the text. Gray vertical lines in the clinical information tracks mark samples without clinical data, and in the mutation tracks gray lines identify samples without sequence data. See also Figures S9, S10 and Tables S4C,D,E, 5G, 6.