Whole-genome characterization of myoepithelial carcinomas of the soft tissue

Myoepithelial carcinomas (MECs) of soft tissue are rare and aggressive tumors affecting young adults and children, but their molecular landscape has not been comprehensively explored through genome sequencing. Here, we present the whole-exome sequencing (WES), whole-genome sequencing (WGS), and RNA sequencing findings of two MECs. Patients 1 and 2 (P1, P2), both male, were diagnosed at 27 and 37 yr of age, respectively, with shoulder (P1) and inguinal (P2) soft tissue tumors. Both patients developed metastatic disease, and P2 died of disease. P1 tumor showed a rhabdoid cytomorphology and a complete loss of INI1 (SMARCB1) expression, associated with a homozygous SMARCB1 deletion. The tumor from P2 showed a clear cell/small cell morphology, retained INI1 expression and strong S100 positivity. By WES and WGS, tumors from both patients displayed low tumor mutation burdens, and no targetable alterations in cancer genes were detected. P2's tumor harbored an EWSR1::KLF15 rearrangement, whereas the tumor from P1 showed a novel ASCC2::GGNBP2 fusion. WGS evidenced a complex genomic event involving mainly Chromosomes 17 and 22 in the tumor from P1, which was consistent with chromoplexy. These findings are consistent with previous reports of EWSR1 rearrangements (50% of cases) in MECs and provide a genetic basis for the loss of SMARCB1 protein expression observed through immunohistochemistry in 10% of 40% of MEC cases. The lack of additional driver mutations in these tumors supports the hypothesis that these alterations are the key molecular events in MEC evolution. Furthermore, the presence of complex structural variant patterns, invisible to WES, highlights the novel biological insights that can be gained through the application of WGS to rare cancers

Small Cell Carcinoma of the Ovary, Hypercalcemic Type (SCCOHT) beyond SMARCA4 Mutations: A Comprehensive Genomic Analysis.

Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is an aggressive malignancy that occurs in young women, is characterized by recurrent loss-of-function mutations in the SMARCA4 gene, and for which effective treatments options are lacking. The aim of this study was to broaden the knowledge on this rare malignancy by reporting a comprehensive molecular analysis of an independent cohort of SCCOHT cases. We conducted Whole Exome Sequencing in six SCCOHT, and RNA-sequencing and array comparative genomic hybridization in eight SCCOHT. Additional immunohistochemical, Sanger sequencing and functional data are also provided. SCCOHTs showed remarkable genomic stability, with diploid profiles and low mutation load (mean, 5.43 mutations/Mb), including in the three chemotherapy-exposed tumors. All but one SCCOHT cases exhibited 19p13.2-3 copy-neutral LOH. SMARCA4 deleterious mutations were recurrent and accompanied by loss of expression of the SMARCA2 paralog. Variants in a few other genes located in 19p13.2-3 (e.g., PLK5) were detected. Putative therapeutic targets, including MAGEA4, AURKB and CLDN6, were found to be overexpressed in SCCOHT by RNA-seq as compared to benign ovarian tissue. Lastly, we provide additional evidence for sensitivity of SCCOHT to HDAC, DNMT and EZH2 inhibitors. Despite their aggressive clinical course, SCCOHT show remarkable inter-tumor homogeneity and display genomic stability, low mutation burden and few somatic copy number alterations. These findings and preliminary functional data support further exploration of epigenetic therapies in this lethal disease

Kaiso (ZBTB33) subcellular partitioning functionally links LC3A/B, the tumor microenvironment, and breast cancer survival

The use of digital pathology for the histomorphologic profiling of pathological specimens is expanding the precision and specificity of quantitative tissue analysis at an unprecedented scale; thus, enabling the discovery of new and functionally relevant histological features of both predictive and prognostic significance. In this study, we apply quantitative automated image processing and computational methods to profile the subcellular distribution of the multi-functional transcriptional regulator, Kaiso (ZBTB33), in the tumors of a large racially diverse breast cancer cohort from a designated health disparities region in the United States. Multiplex multivariate analysis of the association of Kaiso’s subcellular distribution with other breast cancer biomarkers reveals novel functional and predictive linkages between Kaiso and the autophagy-related proteins, LC3A/B, that are associated with features of the tumor immune microenvironment, survival, and race. These findings identify effective modalities of Kaiso biomarker assessment and uncover unanticipated insights into Kaiso’s role in breast cancer progression.Fil: Singhal, Sandeep K.. North Dakota State University; Estados UnidosFil: Byun, Jung S.. National Institutes of Health; Estados UnidosFil: Park, Samson. National Institutes of Health; Estados UnidosFil: Yan, Tingfen. National Institutes of Health; Estados UnidosFil: Yancey, Ryan. Columbia University; Estados UnidosFil: Caban, Ambar. Columbia University; Estados UnidosFil: Hernandez, Sara Gil. National Institutes of Health; Estados UnidosFil: Hewitt, Stephen M.. U.S. Department of Health & Human Services. National Institute of Health. National Cancer Institute; Estados UnidosFil: Boisvert, Heike. Ultivue, Inc; Reino UnidoFil: Hennek, Stephanie. Ultivue Inc.; Reino UnidoFil: Bobrow, Mark. Ultivue Inc.; Reino UnidoFil: Ahmed, Md Shakir Uddin. Tuskegee University; Estados UnidosFil: White, Jason. Tuskegee University; Estados UnidosFil: Yates, Clayton. Tuskegee University; Estados UnidosFil: Aukerman, Andrew. Columbia University; Estados UnidosFil: Vanguri, Rami. Columbia University; Estados UnidosFil: Bareja, Rohan. Columbia University; Estados UnidosFil: Lenci, Romina. Columbia University; Estados UnidosFil: Farré, Paula Lucía. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: de Siervi, Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Nápoles, Anna María. National Institutes of Health; Estados UnidosFil: Vohra, Nasreen. East Carolina University; Estados UnidosFil: Gardner, Kevin. Columbia University; Estados Unido

Inhibition of FGF receptor blocks adaptive resistance to RET inhibition in CCDC6-RET-rearranged thyroid cancer.

Genetic alterations in RET lead to activation of ERK and AKT signaling and are associated with hereditary and sporadic thyroid cancer and lung cancer. Highly selective RET inhibitors have recently entered clinical use after demonstrating efficacy in treating patients with diverse tumor types harboring RET gene rearrangements or activating mutations. In order to understand resistance mechanisms arising after treatment with RET inhibitors, we performed a comprehensive molecular and genomic analysis of a patient with RET-rearranged thyroid cancer. Using a combination of drug screening and proteomic and biochemical profiling, we identified an adaptive resistance to RET inhibitors that reactivates ERK signaling within hours of drug exposure. We found that activation of FGFR signaling is a mechanism of adaptive resistance to RET inhibitors that activates ERK signaling. Combined inhibition of FGFR and RET prevented the development of adaptive resistance to RET inhibitors, reduced cell viability, and decreased tumor growth in cellular and animal models of CCDC6-RET-rearranged thyroid cancer

Single-cell profiling reveals an endothelium-mediated immunomodulatory pathway in the eye choroid

The activity and survival of retinal photoreceptors depend on support functions performed by the retinal pigment epithelium (RPE) and on oxygen and nutrients delivered by blood vessels in the underlying choroid. By combining single-cell and bulk RNA sequencing, we categorized mouse RPE/choroid cell types and characterized the tissue-specific transcriptomic features of choroidal endothelial cells. We found that choroidal endothelium adjacent to the RPE expresses high levels of Indian Hedgehog and identified its downstream target as stromal GLI1+ mesenchymal stem cell-like cells. In vivo genetic impairment of Hedgehog signaling induced significant loss of choroidal mast cells, as well as an altered inflammatory response and exacerbated visual function defects after retinal damage. Our studies reveal the cellular and molecular landscape of adult RPE/choroid and uncover a Hedgehog-regulated choroidal immunomodulatory signaling circuit. These results open new avenues for the study and treatment of retinal vascular diseases and choroid-related inflammatory blinding disorders.Funding for this study was provided by National Institutes of Health grants EY08538 and GM34107 (E. Rodriguez-Boulan); EY027038 (R.F. Mullins); 1R21CA224391-01A1 (J.H. Zippin); and 1R01CA194547, 1U24CA210989, and P50CA211024 (O. Elemento); National Cancer Institute grant R01CA192176 and cancer center support grant P30 CA008748-48 (A.L. Joyner); Comunidad Autónoma de Madrid grant 2017-T1/BMD-5247 (I. Benedicto); Agencia Nacional Argentina de Promoción Cient´ıfica y Tecnológica grant PICT 2014-3687 and Fundación Sales (G.A. Rabinovich); a Pew Latin American Fellowship (G.L. Lehmann); Calder Research Scholar Award Vitiligo/Pigment Cell Disorders (J.H. Zippin); Starr Foundation Tri-Institutional Stem Cell Initiative award 2013-028; NYSTEM contract C32596GG; and Research to Prevent Blindness and Dyson Foundation departmental grants. The CNIC is supported by the Instituto de Salud Carlos III, the Ministerio de Ciencia e Innovación, and the Pro CNIC Foundation and is a Severo Ochoa Center of Excellence (SEV-2015-0505).S

Opposing transcriptional programs of KLF5 and AR emerge during therapy for advanced prostate cancer.

Endocrine therapies for prostate cancer inhibit the androgen receptor (AR) transcription factor. In most cases, AR activity resumes during therapy and drives progression to castration-resistant prostate cancer (CRPC). However, therapy can also promote lineage plasticity and select for AR-independent phenotypes that are uniformly lethal. Here, we demonstrate the stem cell transcription factor Krüppel-like factor 5 (KLF5) is low or absent in prostate cancers prior to endocrine therapy, but induced in a subset of CRPC, including CRPC displaying lineage plasticity. KLF5 and AR physically interact on chromatin and drive opposing transcriptional programs, with KLF5 promoting cellular migration, anchorage-independent growth, and basal epithelial cell phenotypes. We identify ERBB2 as a point of transcriptional convergence displaying activation by KLF5 and repression by AR. ERBB2 inhibitors preferentially block KLF5-driven oncogenic phenotypes. These findings implicate KLF5 as an oncogene that can be upregulated in CRPC to oppose AR activities and promote lineage plasticity

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing