Explore the vast range of titles available.

High-grade endometrial stromal sarcoma likely encompasses underrecognized tumors harboring genetic abnormalities besides YWHAE–NUTM2 fusion. Triggered by three initial endometrial stromal sarcomas with ZC3H7B–BCOR fusion characterized by high-grade morphology and aggressive clinical behavior, we herein investigate the clinicopathologic features of this genetic subset by expanding the analysis to 17 such tumors. All of them occurred in adult women with a median age of 54 (range, 28–71) years. They were predominantly based in the endomyometrium and demonstrated tongue-like and/or pushing myometrial invasion. Most were uniformly cellular and displayed haphazard fascicles of spindle cells with mild to moderate nuclear atypia. Myxoid matrix was seen in 14 of 17 (82%) tumors, and collagen plaques were seen in 8 (47%). The mitotic index was ≥10 mitotic figures/10 high-power fields (HPFs) in 14 of 17 (82%) tumors with a median of 14.5 mitotic figures/10 HPFs. No foci of conventional or variant low-grade endometrial stromal sarcoma were seen. All tumors expressed CD10 with only limited or absent desmin, SMA and/or h-caldesmon staining. ER and PR expression in >5% of cells was seen in 4 of 12 (33%) tumors. Diffuse cyclin D1 and BCOR immunoreactivity was present in 7 of 8 (88%) and 7 of 14 (50%) tumors, respectively. Fluorescence in situ hybridization or targeted RNA sequencing confirmed ZC3H7B–BCOR fusion in all tumors, including four and two previously diagnosed as myxoid leiomyosarcoma and undifferentiated uterine sarcoma, respectively. Limited clinical data suggest that patients present at higher stage and have worse prognosis compared with published outcomes in low-grade endometrial stromal sarcoma. Tumors with ZC3H7B–BCOR fusion constitute a distinct group of endometrial stromal sarcomas with high-grade morphology that should be distinguished from other uterine mesenchymal neoplasms that may demonstrate myxoid morphology.

To evaluate whether radiomic features from contrast-enhanced computed tomography (CE-CT) can identify DNA mismatch repair deficient (MMR-D) and/or tumor mutational burden-high (TMB-H) endometrial cancers (ECs). Patients who underwent targeted massively parallel sequencing of primary ECs between 2014 and 2018 and preoperative CE-CT were included (n = 150). Molecular subtypes of EC were assigned using DNA polymerase epsilon ( POLE ) hotspot mutations and immunohistochemistry-based p53 and MMR protein expression. TMB was derived from sequencing, with > 15.5 mutations-per-megabase as a cut-point to define TMB-H tumors. After radiomic feature extraction and selection, radiomic features and clinical variables were processed with the recursive feature elimination random forest classifier. Classification models constructed using the training dataset (n = 105) were then validated on the holdout test dataset (n = 45). Integrated radiomic-clinical classification distinguished MMR-D from copy number (CN)-low-like and CN-high-like ECs with an area under the receiver operating characteristic curve (AUROC) of 0.78 (95% CI 0.58–0.91). The model further differentiated TMB-H from TMB-low (TMB-L) tumors with an AUROC of 0.87 (95% CI 0.73–0.95). Peritumoral-rim radiomic features were most relevant to both classifications (p ≤ 0.044). Radiomic analysis achieved moderate accuracy in identifying MMR-D and TMB-H ECs directly from CE-CT. Radiomics may provide an adjunct tool to molecular profiling, especially given its potential advantage in the setting of intratumor heterogeneity.

Non-mammary metastases to the breast and axilla are rare occurrences. However, they are important diagnostic considerations as their treatment and prognosis differ significantly from primary breast cancer. Between 1990 and 2010, we identified a total of 85 patients, 72 women and 13 men, with non-mammary malignancies involving the breast, axilla, or both. The tumor types consisted of carcinoma (58%), melanoma (22%) and sarcoma (20%). Ovary was the most common site of origin for carcinoma, and metastatic high-grade ovarian serous carcinoma was most frequently misdiagnosed as a primary breast carcinoma. Melanoma was the single most common non-carcinomatous tumor type to involve the breast and/or axilla, and uterine leiomyosarcoma was the most common type of sarcoma. Most patients (77%) had other metastases at the time of diagnosis of the tumor, but in 11% the breast or axillary lesion was the first presentation. Without a clinical history, non-mammary metastases were difficult to diagnose because the majority of cases presented with a solitary nodule and lacked pathognomonic pathologic features. There were, however, certain recurrent histological findings identified, such as the often relatively well-circumscribed growth pattern of the metastatic lesion surrounded by a fibrous pseudocapsule, and the absence of an in situ carcinoma. Overall, these patients had poor survival; 96% of patients with follow-up available are dead of disease, with a median survival of 15 months after the diagnosis of the breast or axillary lesion. This finding emphasizes the need to accurately identify these tumors as metastases in order to avoid unnecessary procedures and treatments in these patients.

Adult-type granulosa cell tumor (aGCT) is a rare malignant ovarian sex cord-stromal tumor, harboring recurrent FOXL2 c.C402G/p.C134W hotspot mutations in 97% of cases. These tumors are considered to have a favorable prognosis, however aGCTs have a tendency for local spread and late recurrences, which are associated with poor survival rates. We sought to determine the genetic alterations associated with aGCT disease progression. We subjected primary non-recurrent aGCTs ( n  = 7), primary aGCTs that subsequently recurred ( n  = 9) and their matched recurrences ( n  = 9), and aGCT recurrences without matched primary tumors ( n  = 10) to targeted massively parallel sequencing of ≥410 cancer-related genes. In addition, three primary non-recurrent aGCTs and nine aGCT recurrences were subjected to FOXL2 and TERT promoter Sanger sequencing analysis. All aGCTs harbored the FOXL2 C134W hotspot mutation. TERT promoter mutations were found to be significantly more frequent in recurrent (18/28, 64%) than primary aGCTs (5/19, 26%, p  = 0.017). In addition, mutations affecting TP53 , MED12 , and TET2 were restricted to aGCT recurrences. Pathway annotation of altered genes demonstrated that aGCT recurrences displayed an enrichment for genetic alterations affecting cell cycle pathway-related genes. Analysis of paired primary and recurrent aGCTs revealed that TERT promoter mutations were either present in both primary tumors and matched recurrences or were restricted to the recurrence and absent in the respective primary aGCT. Clonal composition analysis of these paired samples further revealed that aGCTs display intra-tumor genetic heterogeneity and harbor multiple clones at diagnosis and relapse. We observed that in a subset of cases, recurrences acquired additional genetic alterations not present in primary aGCTs, including TERT , MED12 , and TP53 mutations and CDKN2A/B homozygous deletions. Albeit harboring relatively simple genomes, our data provide evidence to suggest that aGCTs are genetically heterogeneous tumors and that TERT promoter mutations and/or genetic alterations affecting other cell cycle-related genes may be associated with disease progression and recurrences.

Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including ‘stress’, ‘interferon response’, ‘epithelial-mesenchymal transition’, ‘metal response’, ‘basal’ and ‘ciliated’. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis. Pan-cancer single-cell and spatial transcriptomic profiling identifies recurrent gene modules that underlie a continuum of cancer cell states. Tumor microenvironment influences the occurrence of these states.

Metastatic neuroendocrine neoplasms to the breast may show considerable morphologic overlap with primary mammary carcinomas, particularly those showing evidence of neuroendocrine differentiation, and may be misdiagnosed as such. Accurate distinction between these two entities is crucial for determination of appropriate clinical management. The histologic and immunohistochemical features of metastatic neuroendocrine neoplasms to the breast were studied and compared with the features of primary invasive mammary carcinomas with neuroendocrine differentiation, which served as controls. Of the metastatic neuroendocrine neoplasms, 15 were well-differentiated neuroendocrine tumors with carcinoid tumor-type morphology and 7 were poorly differentiated/high-grade neuroendocrine carcinomas with small-cell or large-cell neuroendocrine carcinoma morphology. The majority of the metastatic neoplasms originated in the lung and gastrointestinal tract. There were histologic similarities between metastatic neuroendocrine neoplasms and invasive mammary carcinomas with neuroendocrine differentiation, both of which exhibited neuroendocrine histologic features (nested and trabecular architecture, minimal tubular differentiation, and characteristic nuclear features). Only one case of the invasive mammary carcinomas with neuroendocrine differentiation was modified Bloom-Richardson grade 1 (largely due to minimal tubular differentiation on most such tumors), and the invasive mammary carcinomas with neuroendocrine differentiation were often associated with in situ carcinoma. Immunohistochemistry was helpful in distinguishing metastatic neuroendocrine neoplasms from invasive mammary carcinomas with neuroendocrine differentiation. Whereas the majority of invasive mammary carcinomas with neuroendocrine differentiation were positive for estrogen receptor and GATA3, metastatic neuroendocrine neoplasms were typically negative for estrogen receptor and GATA3, and metastatic well-differentiated neuroendocrine tumors often showed immunoreactivity for site-specific markers. Although the histologic and immunohistochemical features of a breast tumor may raise the suspicion of a metastatic neuroendocrine neoplasm, the pathologic findings should be interpreted in the context of the clinical history and imaging findings in order to establish an accurate diagnosis.

While genetic tumor heterogeneity has long been recognized, recent work has revealed significant variation among cancer cells at the epigenetic and transcriptional levels. Profiling tumors at the single-cell level in individual cancer types has shown that transcriptional heterogeneity is organized into cancer cell states, implying that diverse cell states may represent stable and functional units with complementary roles in tumor maintenance and progression. However, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Furthermore, the role of cancer cell states in tumor progression and their specific interactions with cells of the tumor microenvironment remain to be elucidated. Here, we perform a pan-cancer single-cell RNA-Seq analysis across 15 cancer types and identify a catalog of 16 gene modules whose expression defines recurrent cancer cell states, including ‘stress’, ‘interferon response’, ‘epithelial-mesenchymal transition’, ‘metal response’, ‘basal’ and ‘ciliated’. Using mouse models, we find that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Moreover, spatial transcriptomic analysis further links the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance, and metastasis.

Microsatellite instability (MSI) is a critical phenotype of cancer genomes and an FDA-recognized biomarker that can guide treatment with immune checkpoint inhibitors. Previous work has demonstrated that next-generation sequencing data can be used to identify samples with MSI-high phenotype. However, low tumor purity, as frequently observed in routine clinical samples, poses a challenge to the sensitivity of existing algorithms. To overcome this critical issue, we developed MiMSI, an MSI classifier based on deep neural networks and trained using a dataset that included low tumor purity MSI cases in a multiple instance learning framework. On a challenging yet representative set of cases, MiMSI showed higher sensitivity (0.895) and auROC (0.971) than MSISensor (sensitivity: 0.67; auROC: 0.907), an open-source software previously validated for clinical use at our institution using MSK-IMPACT large panel targeted NGS data. In a separate, prospective cohort, MiMSI confirmed that it outperforms MSISensor in low purity cases ( P  = 8.244e-07). Identifying microsatellite instability (MSI) from routine next generation sequencing assays is an important part of clinical patient care. Here, authors develop a deep-learning based algorithm, highlighting its performance in a large validation cohort.

Abstract Microsatellite instability (MSI) is a critical phenotype of cancer genomes and an FDA-recognized biomarker that can guide treatment with immune checkpoint inhibitors. Recent work has demonstrated that next-generation sequencing data can be used to identify samples with MSI-high phenotype. However, low tumor purity, as frequently observed in routine clinical samples, poses a challenge to the sensitivity of existing algorithms. To overcome this critical issue, we developed MiMSI, an MSI classifier based on deep neural networks and trained using a dataset that included low tumor purity MSI cases in a multiple instance learning framework. On a challenging yet representative set of cases, MiMSI showed higher sensitivity (0.940) and auROC (0.988) than MSISensor(sensitivity: 0.57; auROC: 0.911), an open-source software previously validated for clinical use at our institution using MSK-IMPACT large panel targeted NGS data. Competing Interest Statement Ahmet Zehir received honoraria from Illumina. Marc Ladanyi has received advisory board compensation from Merck, Bristol-Myers Squibb, Takeda, and Bayer, Lilly Oncology, and Paige.AI, and research support from LOXO Oncology and Helsinn Healthcare. Michael F. Berger has received consulting fees from Roche and grant support from Illumina and Grail. Jaclyn Hechtman has received research funding from Bayer, Eli Lilly, and Boehringer Ingelheim; and honoraria or consulting fees from Axiom Healthcare Strategies, WebMD, Illumina, Bayer, and Cor2Ed. Thomas J. Fuchs is founder, chief scientist and shareholder of Paige.AI.