Faculty Bibliography

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

Brain metastasis is a significant cause of morbidity and mortality in multiple cancer types and represents an unmet clinical need. The mechanisms that mediate metastatic cancer growth in the brain parenchyma are largely unknown. Melanoma, which has the highest rate of brain metastasis among common cancer types, is an ideal model to study how cancer cells adapt to the brain parenchyma. Our unbiased proteomics analysis of melanoma short-term cultures revealed that proteins implicated in neurodegenerative pathologies are differentially expressed in melanoma cells explanted from brain metastases compared to those derived from extracranial metastases. We showed that melanoma cells require amyloid beta (AB) for growth and survival in the brain parenchyma. Melanoma-secreted AB activates surrounding astrocytes to a pro-metastatic, anti-inflammatory phenotype and prevents phagocytosis of melanoma by microglia. Finally, we demonstrate that pharmacological inhibition of AB decreases brain metastatic burden.

PURPOSE/OBJECTIVE:-truncating mutations. Conversely, the molecular underpinnings of the rare juvenile granulosa cell tumor (JGCT) have not been well elucidated. To this end, we applied a tumor-only integrated approach to investigate the genomic, transcriptomic, and epigenomic landscape of 31 JGCTs to identify putative oncogenic drivers. EXPERIMENTAL DESIGN/METHODS:Multipronged analyses of 31 JGCTs were performed utilizing a clinically validated next-generation sequencing (NGS)-panel targeting 580 cancer-related genes for genomic interrogation, in addition to targeted RNA NGS for transcriptomic exploration. Genome-wide DNA methylation profiling was conducted using an Infinium Methylation EPIC array targeting 866,562 CpG methylation sites. RESULTS:non-rearranged JGCTs under direct promoter control. Genome-wide DNA methylation rendered a clear delineation between AGCTs and JGCTs at the epigenomic level further supporting its diagnostic utility in distinguishing among these tumors. CONCLUSIONS:rearrangements in a subset of tumors. Our findings further offer insights into possible targeted therapies in a rare entity.

Primary sarcomas of the lung are extremely uncommon. A diverse group of round cell sarcomas has been reported to originate in this location, including Ewing sarcoma, desmoplastic small round cell tumor, rhabdomyosarcoma, and poorly differentiated synovial sarcoma. The rarity of these tumors presents a potential pitfall; without careful study, they may easily be misidentified as the significantly more common poorly differentiated carcinoma. While histomorphology is a key aspect of correctly identifying a sarcoma, ancillary testing has become increasingly important in making a definitive diagnosis, as more and more recurrent genetic alterations are discovered and new entities are defined. We present three cases of primary round cell sarcomas of the lung that proved diagnostically challenging, describe the features and ancillary testing that led to the correct diagnoses, and discuss classic and evolving entities among sarcomas with round cell morphology.

Background: Digital papillary adenocarcinoma (DPAC) is a rare but aggressive cutaneous malignant sweat gland neoplasm that occurs on acral sites and mimics other benign entities leading to diagnostic dilemmas. We investigate genomic and transcriptomic signatures unique to DPAC that would help differentiate it from other benign entities and aim to unveil unique transcriptomic signatures inherent to its biology.
Design(s): 9 DPAC and 10 hidradenoma (HD) cases were selected (Table 1). DNA analysis used targeted 607 gene FDA validated panel (clinically validated). Customized RNA panel targeting 104 genes (FusionSeeqer) was used for fusion analysis. All pipelines used are clinically validated. Transcriptomic analysis used nCounter Pan Cancer IO 360TM panel (770 genes) with subsequent analysis using our own pipelines n R studio. Accurate transcript quantification (lg2FC) was performed after normalization to standard housekeeping genes. DESEQ2 was used for the gene level differential gene expression (DGE) analysis after normalization to reference group benign HDs (FDR<0.01, fold change: > 2 or < -2) with subsequent KEGG pathway analysis.
Result(s): DPAC cases showed very low tumor mutational burden on genomic analysis (range 0-1 mut/mb). Gene rearrangements were more frequent in HD compared to DPAC (4/4 vs 2/7, p= 0.03). Mastermind-like family of protein genes (MAML2) rearrangements were more frequent in HD, while DPAC showed novel zinc finger gene (PLAG1) rearrangements (TRPS1- PLAG1; n=1) (Fig 1A). Unsupervised clustering analysis and subsequent DGE analysis revealed 100 significantly differentially expressed genes between DPAC and HD. MAGEA4, IL2, IFNG, and COL11A2 showed a significant upregulation in DPAC (lg2FC =2.6, 2.93, 2.98, 3.04, FDR range = 0.0001 to 0.00004) respectively (Fig 1B). Pathway analysis identified enrichment of JAK/STAT pathway, which is triggered by the upregulated cytokines in DPAC (Fig 1C).
Conclusion(s): While morphologically overlapping, DPAC and HD are biologically distinct. MAGEA4 upregulation seen in DPAC could serve as potential marker for both diagnostic and therapeutic purposes, since it could be potentially targeted with adoptive Tcell therapy (ADP-A2M4). Validation of the findings on a larger cohort is underway

Background: There is paucity of transcriptomic data concerning primary cutaneous CD8+ lymphoproliferative disorders. Herein we aim to investigate the transcriptomic profile of CD8+ mycosis fungoides (CD8+ MF) and type D lymphomatoid papulosis (Type D LyP), in order to better characterize them at and assess whether the two entities are biologically related.
Design(s): Adequate RNA was successfully extracted from formalin-fixed paraffin embedded sections derived from 7 CD4+ MF, 8 CD8+ MF and 4 type D LyP. After passing quality control, whole transcriptome sequencing (WTS) was performed. Fusion detection was performed using star-fusion (version 1.10.0). Accurate transcript quantification from RNASEQ data was performed using RSEM (version 1.3.1). DESEQ2 was used for differential gene expression (DGE) analysis, comparing CD8+ MF vs CD4+MF, CD8+ MF vs type D LyP groups, after normalization against normal skin controls.
Result(s): Unsupervised clustering classified CD4+ MF and CD8+ MF into two distinct groups and identified 100 genes that were differentially expressed at a significant level between both entities (Figure 1 a). CD8+ MF shows a profound down regulation of skin-homing chemokine receptors, immune signaling-related molecules or transcription factors and upregulation of long non-coding RNAs (lncRNAs) and small nuclear RNAs (snRNAs) (Figure 1a). KEGG pathway analysis showed an upregulation of pathways related to linoleic acid metabolism, as well as retinoid acid, PPAR and adipocytokine signaling in CD8+ MF (Figure 1b). Unsupervised clustering did not segregate CD8+ MF and Type D LyP. Nevertheless, DGE analysis identified 546 genes that were differentially expressed between both (FDR<0.01, fold change: > 1 or < -1, Figure 1c). KEGG pathway analysis showed that Wnt signaling is significantly upregulated in CD8+ MF compared to Type D LyP. A recurrent mRNA-lncRNA fusion secondary to interstitial deletion involving RP11-367G6.3-FAM65B (Figure 1d) was identified in 5 cases from 4 unique patients, including 2 type D LyP and 2 CD8+ MF patients.
Conclusion(s): WTS shows that CD8+ MF has unique signatures compared to CD4+MF pertaining to lncRNAs and snRNAs. Type D LyP and CD8+ MF display similar genomic rearrangements, yet pathway analysis highlights different genes contributing to their pathogenesis