Faculty Bibliography

AIMS/OBJECTIVE:We investigated key signalling pathways' activity and mutational status of early-stage breast carcinomas with low and intermediate 21-gene recurrence score (RS) to identify molecular features that may predict recurrence. METHODS:This is a retrospective case-control study of 18 patients with recurrent breast carcinoma with low and intermediate 21-gene RS (<25) and control group of 15 non-recurrent breast cancer patients. DNA and mRNA were extracted from tumour tissue. mRNA expression of genes involved in oestrogen receptor (ER), androgen receptor (AR), PI3K and MAPK signalling pathways was measured by real-time quantitative reverse transcription-qPCR (OncoSIGNal G4 test, InnoSIGN). Tumour mutational landscape was assessed by targeted DNA sequencing (Oncomine Precision Assay). RESULTS:mutations, may play a role in the recurrence of early-stage breast cancer with low and intermediate 21-gene RS. Pathway analysis can help to identify high-risk patients in this setting.

UNLABELLED:DNA methylation is an essential molecular assay for central nervous system (CNS) tumor diagnostics. While some fusions define specific brain tumors, others occur across many different diagnoses. We performed a retrospective analysis of 219 primary CNS tumors with whole genome DNA methylation and RNA next-generation sequencing. DNA methylation profiling results were compared with RNAseq detected gene fusions. We detected 105 rare fusions involving 31 driver genes, including 23 fusions previously not implicated in brain tumors. In addition, we identified 6 multi-fusion tumors. Rare fusions and multi-fusion events can impact the diagnostic accuracy of DNA methylation by decreasing confidence in the result, such as BRAF, RAF, or FGFR1 fusions, or result in a complete mismatch, such as NTRK, EWSR1, FGFR, and ALK fusions. IMPLICATIONS/UNASSIGNED:DNA methylation signatures need to be interpreted in the context of pathology and discordant results warrant testing for novel and rare gene fusions.

There is currently no targeted therapy to treat NF1-mutant melanomas. Herein, we compared the genomic and transcriptomic signatures of NF1-mutant and NF1-WT melanoma to reveal potential treatment targets for this subset of patients. Genomic alterations were verified using qPCR, and differentially expressed genes were independently validated using TCGA data, and immunohistochemistry (IHC). Digital spatial profiling (DSP) with multiplex IHC and immunofluorescence (IF) were used to validate the signatures. The efficacy of combinational regimens driven by these signatures was tested through in vitro assays using low-passage cell lines. Pathogenic NF1 mutations were identified in 27% cases. NF1-mutant melanoma expressed higher proliferative markers MK167 and CDC20 compared to NF1-WT (P=0.008), which was independently validated both in the TCGA dataset (P=0.01, P=0.03) and with IHC (P=0.013, P=0.036), respectively. DSP analysis showed upregulation of LY6E within the tumor cells [FDR<0.01, lg2FC>1], confirmed with multiplex IF showing co-localization of LY6E in melanoma cells. The combination of MEK and CDC20 co-inhibition induced both cytotoxic and cytostatic effects, decreasing CDC20 expression in multiple NF1-MUT cell lines. In conclusion, NF1-mutant melanoma is associated with a distinct genomic and transcriptomic profile. Our data support investigating CDC20 inhibition with MAPK pathway inhibitors as a targeted regimen in this melanoma subtype.

BACKGROUND:A 64-year-old man presented with a 7.8 cm lipomatous thyroid mass discovered on magnetic resonance imaging. METHODS:After two non-diagnostic fine needle aspirations (FNAs) were performed, computed tomography (CT) revealed features concerning for malignancy including central necrosis and infiltrative borders. A third FNA was still non-diagnostic. Total thyroidectomy was performed. RESULTS:Upon pathologic examination, the final diagnosis was primary thyroid angiolipoma. The lesion contained central fat necrosis with ischemic features, attributable to the FNAs. CONCLUSION/CONCLUSIONS:Ours is the third published case report of this rare entity. To date, no lipomatous thyroid tumor has undergone extensive genomic testing. Next-generation sequencing of our case revealed multiple genetic alterations, supporting the concept of angiolipomas being true neoplasms. Whereas the two previously reported cases in the literature were radiographically much smaller and appeared indolent, the large tumor in our case exhibited radiographic features concerning for liposarcoma, which belied the benign final pathologic diagnosis. Our case demonstrates that conservative surgical management (partial thyroidectomy) may be considered for lipomatous thyroid tumors, with further interventions to be determined only after final pathologic diagnosis.

Erdheim-Chester disease (ECD) is a rare clonal histiocytic process that is characterized by a foamy (xanthomatous) proliferation often associated with Touton giant cells. The diagnosis is often challenging and not exclusively a histologic diagnosis, as it requires correlation with unique clinical, radiographic and recently described molecular findings. Activating mutations involving the MAPK pathway including BRAF, ARAF, N/KRAS and MEK are recurrent in the disease. However, it is increasingly being described that mutations associated with clonal hematopoiesis are also found in bone marrow specimens of patients with Erdheim-Chester disease (ECD), as well as higher frequency of overt concomitant myeloid malignancy including acute myeloid leukemia, myeloproliferative neoplasms, myelodysplastic syndromes, and mixed myeloproliferative neoplasms/myelodysplastic syndromes. Herein, we report a unique case of a patient presenting with BRAF-V600E-positive ECD with with peripheral blood findings consistent with a concurrent myeloid malignancy featuring co-occurrence of NRAS and IDH-2 mutations.

Cutaneous inflammatory myofibroblastic tumors (IMT) constitute a rare entity, generating a diagnostic pitfall when diagnosing spindle cell proliferation within the dermis. Raising awareness of this tumor among dermatopathologists remains vital in differentiating it from common cutaneous tumors such as fibrous histiocytoma, atypical fibroxanthoma, melanoma, poorly differentiated carcinoma, and other more aggressive tumors. Accurate diagnosis of IMT aid in ensuring appropriate management and follow-up for patients while preventing unnecessary harm and overtreatment. Here we report a case of a 38-year-old female with a painless, slow-growing nodule of the left posterior scalp initially diagnosed as a dermatofibroma. The histological examination revealed an ill-defined dermal nodule of spindled cells without connection or infiltration of the epidermis. At high power, the cells were arranged in fascicles with a prominent background of lymphocytic infiltrate. Immunohistochemical analysis showed strong diffuse immunoreactivity for anaplastic lymphoma kinase (ALK), targeted RNA sequencing identified a CARS-ALK fusion ultimately confirming the accurate diagnosis of a cutaneous IMT. This article is protected by copyright. All rights reserved.

Image-based analysis as a method for mutation detection can be advantageous in settings when tumor tissue is limited or unavailable for direct testing. Here, we utilize two distinct and complementary machine learning methods of analyzing whole slide images (WSI) for predicting mutated BRAF. In the first method, WSI of melanomas from 256 patients were used to train a deep convolutional neural network (CNN) in order to develop a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for mutated BRAF (AUC=0.71). Saliency mapping was performed and revealed that pixels corresponding to nuclei were the most relevant to network learning. In the second method, WSI were analyzed using a pathomics pipeline that first annotates nuclei and then quantifies nuclear features, demonstrating that mutated BRAF nuclei were significantly larger and rounder nuclei compared to BRAF WT nuclei. Lastly, we developed a model that combines clinical information, deep learning, and pathomics that improves the predictive performance for mutated BRAF to AUC=0.89. Not only does this provide additional insights on how BRAF mutations affect tumor structural characteristics, machine learning-based analysis of WSI has the potential to be integrated into higher order models for understanding tumor biology.

Introduction: Gene rearrangements play a critical role in the development of brain tumors. RNA next-generation sequencing (NGS) panels cover a limited number of genes, are rarely successful in FFPE samples > 5 years old, and cannot detect rearrangements between genes and non-coding regulatory regions. We evaluated whole genome Hi-C NGS for detection of gene fusions and cryptic rearrangements.
Method(s): DNA was extracted from FFPE scrolls of 55 glial and non-glial brain tumors and processed using Arima-HiC+ FFPE Sample protocol, consisting of chromatin fragmentation, labeling, and re-ligation, followed by DNA purification and library preparation for paired-end Illumina sequencing with an average of 10X genome coverage (100M PE reads per sample). Data were analyzed using the Arima-SV pipeline using Juicer and HiCUP, SV detection using HiC-Breakfinder, loop calling using Juicer Tools, and integrative data visualization using Juicebox. Overexpression of putative driver genes was confirmed by immunohistochemistry.
Result(s): Hi-C libraries were prepared and sequenced from FFPE tissues including samples that failed RNA NGS. Hi-C successfully detected gene-gene fusions including actionable EML4-NTRK3, ETV6-NTRK3, fusions. We detected rearrangements missed by RNA NGS (i.e., complex MYBL1 rearrangement) or between non-coding regions and known cancer genes (i.e. PDL1, PAX5, NRAS, TERT, KAT6A, GATA6, and ARID1B). Since Hi-C data captures 3D genome structural features such as chromatin loops and topological domains, datasets were of high quality and capable of detecting up to 13,000 chromatin loops per tumor.
Conclusion(s): Genome-wide Hi-C NGS is successful in detecting gene fusions and cryptic rearrangements between coding and non-coding regions in archival FFPE tissue including degraded samples. Because Hi-C data captures 3D genome structures, these datasets simultaneously inform gene regulatory mechanisms that may play a role in oncogenesis or tumor progression. Whole-genome Hi-C NGS expands our ability to detect actionable and novel drivers, and potentially new therapeutic targets in a single NGS workflow