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Abstract Background Spatial chromatin structure is intricately linked with somatic aberrations, and somatic mutations of various cancer-related genes, termed co-mutations (CoMuts), occur in certain patterns during cancer initiation and progression. The functional mechanisms underlying these genetic events remain largely unclear in thyroid cancer (TC). With discrepant differentiation, papillary thyroid cancer (PTC) and anaplastic thyroid cancer (ATC) differ greatly in characteristics and prognosis. We aimed to reveal the spatial gene alterations and regulations between the two TC subtypes. Methods We systematically investigated and compared the spatial co-mutations between ATC (8305C), PTC (BCPAP and TPC-1), and normal thyroid cells (Nthy-ori-3–1). We constructed a framework integrating whole-genome sequencing (WGS), high-throughput chromosome conformation capture (Hi-C), and transcriptome sequencing, to systematically detect the associations between the somatic co-mutations of cancer-related genes, structural variations (SVs), copy number variations (CNVs), and high-order chromatin conformation. Results Spatial co-mutation hotspots were enriched around topologically associating domains (TADs) in TC. A common set of 227 boundaries were identified in both ATC and PTC, with significant overlaps between them. The spatial proximities of the co-mutated gene pairs in the two TC types were significantly greater than in the gene-level and overall backgrounds, and ATC cells had higher TAD contact frequency with CoMuts > 10 compared with PTC cells. Compared with normal thyroid cells, in ATC the number of the created novel three-dimensional chromatin structural domains increased by 10%, and the number of shifted TADs decreased by 7%. We found five TAD blocks with CoMut genes/events specific to ATC with certain mutations in genes including MAST-NSUN4, AM129B/TRUB2, COL5A1/PPP1R26, PPP1R26/GPSM1/CCDC183, and PRAC2/DLX4. For the majority of ATC and PTC cells, the HOXA10 and HIF2α signals close to the transcription start sites of CoMut genes within TADs were significantly stronger than those at the background. CNV breakpoints significantly overlapped with TAD boundaries in both TC subtypes. ATCs had more CNV losses overlapping with TAD boundaries, and noncoding SVs involved in intrachromosomal SVs, amplified inversions, and tandem duplication differed between ATC and PTC. TADs with short range were more abundant in ATC than PTC. More switches of A/B compartment types existed in ATC cells compared with PTC. Gene expression was significantly synchronized, and orchestrated by complex epigenetics and regulatory elements. Conclusion Chromatin interactions and gene alterations and regulations are largely heterogeneous in TC. CNVs and complex SVs may function in the TC genome by interplaying with TADs, and are largely different between ATC and PTC. Complexity of TC genomes, which are highly organized by 3D genome-wide interactions mediating mutational and structural variations and gene activation, may have been largely underappreciated. Our comprehensive analysis may provide key evidence and targets for more customized diagnosis and treatment of TC.

Abstract Background The breast cancer genome dynamically evolves during malignant progression and recurrence. We investigated the genomic profiles of primary early-stage breast cancers and matched relapses to elucidate the molecular underpinnings of the metastatic process, focusing on potentially actionable alterations in the recurrences. Methods A mono-institutional cohort of 128 patients with breast cancers (n = 68 luminal B HER2, n = 6 luminal B HER2+, n = 1 HER2+ non-luminal, n = 56 triple negative) and at least one recurrence in a timeframe of 17 years was evaluated. Next-generation sequencing comprehensive genomic profiling was performed on 289 formalin-fixed paraffin-embedded (FFPE) samples, including primary tumors and matched relapses. Correlations of genomic aberrations with clinicopathologic factors and time to breast cancer relapse were analyzed. Results Genomic data were available for 188 of 289 FFPE samples that achieved the sequencing quality parameters (failure rate 34.9%), including 106 primary tumors and 82 relapses. All primary and relapse samples harbored at least one genomic alteration, with a median number of six alterations per sample (range 1–16). The most frequent somatic genomic alterations were mutations of TP53 (primary tumors = 49%, relapses = 49%) and PIK3CA (primary tumors = 33%, relapses = 30%). Distinctive genomic alterations of primary tumors were significantly associated with molecular subtypes. TP53, PIK3R1, and NF1 somatic alterations were more frequently detected in triple negative tumors (p value

Abstract Background In lung cancer, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor sensitizing mutations co-existing with rare minor EGFR mutations are known as compound mutations. These minor EGFR mutations can lead to acquired resistance after EGFR tyrosine kinase inhibitor treatment, so determining the mutation status of patients is important. However, using amplicon-based targeted deep sequencing based on next-generation sequencing to characterize mutations is prone to sequencing error. We therefore assessed the benefit of incorporating molecular barcoding with high-throughput sequencing to investigate genomic heterogeneity in treatment-naïve patients who have undergone resection of their non-small cell lung cancer (NSCLC) EGFR mutations. Methods We performed amplicon-based targeted sequencing with the molecular barcoding system (MBS) to detect major common EGFR mutations and uncommon minor mutations at a 0.5% allele frequency in fresh–frozen lung cancer samples. Results Profiles of the common mutations of EGFR identified by MBS corresponded with the results of clinical testing in 63 (98.4%) out of 64 cases. Uncommon mutations of EGFR were detected in seven cases (10.9%). Among the three types of major EGFR mutations, patients with the G719X mutation had a significantly higher incidence of compound mutations than those with the L858R mutation or exon 19 deletion (p = 0.0052). This was validated in an independent cohort from the Cancer Genome Atlas dataset (p = 0.018). Conclusions Our findings demonstrate the feasibility of using the MBS to establish an accurate NSCLC patient genotype. This work will help understand the molecular basis of EGFR compound mutations in NSCLC, and could aid the development of new treatment modalities.