Faculty Bibliography

Chromosomal structural variants (SVs) are major contributors to cancer development. Although multiple methods exist for detecting SVs, they are limited in throughput, such as fluorescent in situ hybridization and targeted panels, and use RNA, which degrades in formalin-fixed, paraffin-embedded (FFPE) blocks and is unable to detect SVs that do not produce a fusion transcript. High-throughput chromosome conformation capture (Hi-C) is a DNA-based next-generation sequencing (NGS) method that preserves the spatial conformation of the genome, capturing long-range genetic interactions and SVs. Herein, a retrospective study analyzing 71 FFPE specimens from 10 different solid tumors was performed. Results showed high concordance (98%) with clinical fluorescent in situ hybridization and RNA NGS in detecting known SVs. Furthermore, Hi-C provided insight into the mechanism of SV formation, including chromothripsis and extrachromosomal DNA, and detected rearrangements between genes and regulatory regions, all of which are undetectable by RNA NGS. Lastly, SVs were detected in 71% of cases in which previous clinical methods failed to identify a driver. Of these, 14% were clinically actionable based on current medical guidelines, and an additional 14% were not in medical guidelines but involve targetable biomarkers. Current data suggest that Hi-C is a robust and accurate method for genome-wide SV analyses from FFPE tissue and can be incorporated into current clinical NGS workflows.

Soft tissue sarcomas (STSs) are a diverse group of malignant tumors derived from mesenchymal tissues [...].

BACKGROUND:Digital papillary adenocarcinoma (DPAC) is a rare but aggressive cutaneous malignant sweat gland neoplasm that occurs on acral sites. Despite its clinical significance, the cellular and genetic characteristics of DPAC remain incompletely understood. METHODS:We conducted a comprehensive genomic and transcriptomic analysis of DPAC (n = 14) using targeted next-generation DNA and RNA sequencing, along with gene expression profiling employing the Nanostring Technologies nCounter IO 360 Panel. Gene expression in DPAC was compared to that in hidradenoma (n = 10). Immunohistochemistry was employed to validate gene expression. RESULTS:Two out of eight DPACs showed fusion gene rearrangements (CRTC3::MAML2 and TRPS1::PLAG1). No uniform mutational signature was detected in DPAC. Comparative gene expression analysis revealed an enrichment of genes related to matrix remodeling, metabolism, and DNA damage repair. Hallmark pathway analysis demonstrated significant upregulation of E2F target genes in DPAC compared to hidradenoma (p = 0.00710). Human papillomavirus-42 was found to be positive in all of our tested DPAC cases. Immunohistochemistry confirmed increased protein expression of CD56, CDC20, and SOX10 in DPAC. Notably, most DPAC tumors also exhibited B-cell infiltration, as indicated by CD20 staining. CONCLUSIONS:Our findings reveal novel fusions and validate altered replication pathways related to HPV42 in DPAC.

PURPOSE/OBJECTIVE:Necrosis quantification in the neoadjuvant setting using pathology slide review is the most important validated prognostic marker in conventional osteosarcoma. Herein, we explored three deep learning strategies on histology samples to predict outcome for OSA in the neoadjuvant setting. EXPERIMENTAL DESIGN/METHODS:Our study relies on a training cohort from New York University (New York, NY) and an external cohort from Charles university (Prague, Czechia). We trained and validated the performance of a supervised approach that integrates neural network predictions of necrosis/tumor content, and compared predicted overall survival (OS) using Kaplan-Meier curves. Furthermore, we explored morphology-based supervised and self-supervised approaches to determine whether intrinsic histomorphological features could serve as a potential marker for OS in the setting of neoadjuvant. RESULTS:Excellent correlation between the trained network and the pathologists was obtained for the quantification of necrosis content (R2=0.899, r=0.949, p < 0.0001). OS prediction cutoffs were consistent between pathologists and the neural network (22% and 30% of necrosis, respectively). Morphology-based supervised approach predicted OS with p-value=0.0028, HR=2.43 [1.10-5.38]. The self-supervised approach corroborated the findings with clusters enriched in necrosis, fibroblastic stroma, and osteoblastic morphology associating with better OS (lg2HR; -2.366; -1.164; -1.175; 95% CI=[-2.996; -0.514]). Viable/partially viable tumor and fat necrosis were associated with worse OS (lg2HR;1.287;0.822;0.828; 95% CI=[0.38-1.974]). CONCLUSIONS:Neural networks can be used to automatically estimate the necrosis to tumor ratio, a quantitative metric predictive of survival. Furthermore, we identified alternate histomorphological biomarkers specific to the necrotic and tumor regions themselves which can be used as predictors.

Gene fusions involving JAZF1 are a recurrent event in low grade endometrial stromal sarcoma, and have been more recently described in few instances of endometrial stromal sarcoma-like tumors in the genitourinary tract of men. In this article, we describe a previously unreported spindle cell sarcoma harboring an in-frame JAZF1::NUDT5 gene fusion, arising in the chest wall of a 51-year-old man. The tumor had unique morphologic features resembling both endometrial stromal sarcoma and endometrial stromal sarcoma-like tumors, consisting of a mixture of cytologically bland and pleomorphic spindle cells with brisk mitotic activity, within an alternating myxoid and fibrous stroma. It had diffuse immunohistochemical expression of CD10, CD34 and CD56, and variable expression of androgen receptor. To our knowledge, neoplasms with these clinico-pathologic characteristics and novel gene fusion have not been previously reported in the English language literature.

As efforts to study the mechanisms of melanoma metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate the effects on tumor burden resulting from specific interventions. We show that automated quantification of tumor content from whole slide images is a compelling solution to assess in vivo experiments. In order to increase the outflow of data collection from preclinical studies, we assembled a large dataset with annotations and trained a deep neural network for the quantitative analysis of melanoma tumor content on histopathological sections of murine models. After assessing its performance in segmenting these images, the tool obtained consistent results with an orthogonal method (bioluminescence) of measuring metastasis in an experimental setting. This AI-based algorithm, made freely available to academic laboratories through a web-interface called MetFinder, promises to become an asset for melanoma researchers and pathologists interested in accurate, quantitative assessment of metastasis burden.

CIC-rearranged sarcomas comprise a group of exceptionally aggressive round-cell sarcomas. These tumors most commonly demonstrate CIC::DUX4 fusion and show similar histopathology to Ewing sarcomas, though lesions mimicking vascular neoplasms have recently been described. Here, we describe a case of a patient with CIC::DUX4 fusion sarcoma identified using RNA-based molecular testing who was initially diagnosed with an endothelial neoplasm. The tumor showed extensive vasoformative growth, complete WT1 negativity, and global positive staining for ERG, CD31, and DUX4 by immunohistochemistry. Methylation testing of the tumor clustered more closely with angiosarcomas than with CIC-rearranged sarcomas. Our findings suggest that CIC::DUX4 fused neoplasms may demonstrate a more diverse phenotypic range than previously appreciated and offer evidence that both molecular and immunohistochemical studies are needed for accurate diagnosis.

Diverse cellular insults converge on activation of the heat shock factor 1 (HSF1), which regulates the proteotoxic stress response to maintain protein homoeostasis. HSF1 regulates numerous gene programmes beyond the proteotoxic stress response in a cell-type- and context-specific manner to promote malignancy. However, the role(s) of HSF1 in immune populations of the tumour microenvironment remain elusive. Here, we leverage an in vivo model of HSF1 activation and single-cell transcriptomic tumour profiling to show that augmented HSF1 activity in natural killer (NK) cells impairs cytotoxicity, cytokine production and subsequent anti-tumour immunity. Mechanistically, HSF1 directly binds and regulates the expression of key mediators of NK cell effector function. This work demonstrates that HSF1 regulates the immune response under the stress conditions of the tumour microenvironment. These findings have important implications for enhancing the efficacy of adoptive NK cell therapies and for designing combinatorial strategies including modulators of NK cell-mediated tumour killing.

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.