Simple and complex carcinomas are the most common type of malignant Canine Mammary Tumors (CMTs), with simple carcinomas exhibiting aggressive behavior and poorer prognostic. Stemness is an ability associated with cancer initiation, malignancy, and therapeutic resistance, but is still few elucidated in canine mammary tumor subtypes. Here, we first validated, using CMT samples, a previously published canine one-class logistic regression machine learning algorithm (OCLR) to predict stemness (mRNAsi) in canine cancer cells. Then, using the canine mRNAsi, we observed that simple carcinomas exhibit higher stemness than complex carcinomas and other histological subtypes. Also, we confirmed that stemness is higher and associated with basal-like CMTs and with NMF2 metagene signature, a tumor-specific DNA-repair metagene signature. Using correlation analysis, we selected the top 50 genes correlated with higher stemness, and the top 50 genes correlated with lower stemness and further performed a gene set enrichment analysis to observe the biological processes enriched for these genes. Finally, we suggested two promise stemness-associated targets in CMTs, POLA2 and APEX1, especially in simple carcinomas. Thus, our work elucidates stemness as a potential mechanism behind the aggressiveness and development of canine mammary tumors, especially in simple carcinomas, describing evidence of a promising strategy to target this disease.

Hematopoietic stem cells (HSCs) sustain life-long hematopoiesis and emerge during mid-gestation from hemogenic endothelial progenitors via an endothelial-to-hematopoietic transition (EHT). The full scope of molecular mechanisms governing this process remains unclear. The NR4A subfamily of orphan nuclear receptors act as tumor suppressors in myeloid leukemogenesis and have never been implicated in HSC specification. Here, we report that Nr4a1 and Nr4a2 expression is upregulated in hemogenic endothelium during EHT. Progressive genetic ablation of Nr4a gene dosage results in a gradual decrease in numbers of nascent c-Kit+ hematopoietic progenitors in developing embryos, c-Kit+ cell cluster size in the dorsal aorta, and a block in HSC maturation, revealed by an accumulation of pro-HSCs and pre-HSC-type I cells and decreased numbers of pre-HSC-type II cells. Consistent with these observations, cells isolated from embryonic day 11.5 Nr4a1-/-; Nr4a2-/- aorta-gonads-mesonephros are devoid of in vivo long-term hematopoietic repopulating potential. Molecularly, employing spatial transcriptomic analysis we determined that the genetic ablation of Nr4a1 and Nr4a2 prevents Notch signaling from being downregulated in intra-aortic clusters and thus for pro-HSCs to mature into HSCs. Interestingly, this defect is partially rescued by ex vivo culture of dissected aorta-gonads-mesonephros with SCF, IL3 and FLT3L, which may bypass Notch-dependent regulation. Overall, our data reveal a role for the NR4A family of orphan nuclear receptors in EHT.

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment.

Adult diffuse gliomas are heterogeneous and the most common primary brain tumors. Gliomas have high tissue invasion, proliferation and therapeutic resistance potential. Although much knowledge has been recently gained regarding glioma biology and evolution, many questions are still open, such as which is the cell of origin, what are its characteristics and how the tumor propagation occurs. The cancer stem cell (CSC) model proposes a population of cells with high self-renew capacity and capable of propagating the tumor with more differentiated cells, generating intratumoral heterogeneity. Based on the CSC model, our work aims to integrate the most advanced techniques available such as scRNAseq and machine learning models to estimate the enrichment of glioma stem cells (GSCs) in tumors, by defining a GSC-Stemness Index (GSCsi). To build the prediction model, we used public scRNA-seq data from glioblastoma (GBM)-enriched GSCs. We used the standard Seurat pipeline for quality control, normalization and downstream analysis. The GSC single cell data was used to train a prediction model using the One Class Logistic Regression (OCLR) algorithm. Several models were tested, including overdispersed genes, differentially expressed genes between GSCs and the whole tumor, GSCs from each patient individually and a model with all GSCs. The prediction models were applied to gene expression data from TCGA, GLASS, and publicly available scRNA-seq data from different glioma subtypes. The model built with all the GSCs and all genes showed the best performance, being able to identify with higher indices grade 4 gliomas and IDHwt in the TCGA and GLASS data. Survival analysis resulted in a hazard ratio greater than 20, indicating a high correlation between increased GSCsi and poor prognosis. By applying the model to scRNA-seq data from gliomas, clusters of high GSCsi were identified. We can partially conclude that the GSCsi obtained with the model is capable of identifying grade 4 gliomas and IDHwt and we are performing analyzes with the genes with the highest correlation (positive and negative) with the GSCsi in the TCGA and GLASS data. The analysis of these genes together with genes differentially expressed in the high GSCsi clusters in scRNA-seq data can elucidate pathways responsible for the therapeutic resistance and propagation of gliomas, in addition to proposing theories about the cell of origin and potential therapeutic targets to improve the diagnosis and treatment of these patients. Citation Format: Renan L. Simões, Maycon Marção, Tathiane M. Malta. Glioma stem cell index recapitulates grade, IDH mutation status and correlates with survival of glioma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6563.

The US Black population consists of both US-born Black and immigrant Black populations from the Caribbean and Africa. Normal tissues in Black individuals, independent of their country of birth or residence, are woefully understudied. Yet, Black individuals disproportionately develop aggressive pathologic diseases and are treatment refractory or resistant, thus leaing to premature deaths. In women, breast cancer is more common among US Black women, and the most common non-viral driven cancer in African and Caribbean countries. Furthermore, Black women develop this disease younger than other ancestral groups and have a higher incidence aggressive pathologies, such as metaplastic and triple-negative breast cancer. With the African-Caribbean Cancer Consortium (AC3) and Transatlantic Gynecologic Cancer Research Consortium, we have created a multiomic spatial atlas of triple-negative and other breast cancers across Africa, the Caribbean, and among US-Black individuals. We performed ultrahigh-plex RNA and protein spatial phenotyping on the PhenoCycler-Fusion (PCF). The PCF is a fast end-to-end spatial biology platform that enables whole-slide spatial readouts of RNA and protein moieties at single-cell resolution. Multiomic spatial phenotyping of tissues allowed for the detection of novel cell populations associated with a given African ancestry linking unique immune/stromal cell types to the outcome and severity of breast cancer. Here, we aim to develop a benchmark that confidently measures and interprets ancestral genomic differences at the cellular level. Deciphering the relationship between African ancestry, aggressive disease biology, and early onset will enable the characterization of the tissue composition and the proportion of cell sub-populations implicated in tumorigenesis and the interplay with germline genetics. Citation Format: Jasmine T. Plummer, Nadezhda Nikulina, Ayodele Omotoso, Destiny Burnett, Priscilla Coelho, Simone Badal, Judith Hurley, Carmen Gomez, Ha Yeun Ji, Maycon Marcao, Felipe Dezem Segato, Oliver Braubach, Sophia George. A multiomic spatial atlas of breast cancer in women of African ancestry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1968.

Gliomas are the most common central nervous system neoplasm and despite the past significant progress, its diagnostics faces suboptimal classification which impacts patient management. The stem cell-like phenotype of various cancers is correlated with the worst overall prognosis. We propose a Stemness prediction model based on gene expression signatures of neural progenitors that can be used to measure the dedifferentiation state (or Stemness) of glioma samples. To built the model, publicly available single-cell RNA sequencing data was used to identify gene expression from the fetal astrocyte (AST) population. Subpopulations of interest were identified through the expression of marker genes. We applied a one-class logistic regression to built the prediction model using the AST population. The model was applied to glioma bulk transcriptomic data to generate an fetal astrocyte stemness index (ASTsi). The ASTsi was able to stratify gliomas based on grade, histology, and molecular subtypes. Grade 4, glioblastoma, IDHwt, and the mitochondrial and proliferative functional subtypes had the highest stemness. When applied to longitudinal samples we observed an increase of ASTsi in IDHmut recurrent and a decrease in IDHwt recurrent tumors, compared to primary samples. Additionally, we applied the model to single-cell RNAseq of adult IDHwt glioblastomas and found clusters of high-stemness cells (ASTsi > 0.8). A differential gene expression combined with pathway analysis between high- and low-stemness cells revealed cell cycle, DNA repair mechanisms and histone modifications upregulated in the high-stemness population. The balance between histone methylation and demethylation may be directly related to the phenotype of these cells. More in-depth analysis of these genes and pathways are being carried out and may provide important information about the oncogenesis and phenotypic characterization of glioma stemness. Our stemness prediction model stratified glioma samples by pathological and molecular features and revealed tumor subpopulations with distinct stemness degree in gliomas IDHwt.

Tumor progression and therapeutic resistance in cancer have been strongly associated with stemness. Rather than focus on a discrete subpopulation for stemness and tumor maintenance, we might explore stemness as a continuous variable through machine learning algorithms. We chose One Class Logistic Regression algorithm to define an epigenetic signature to assess stemness in brain tumors using induced Neural Stem Cell (iNSC) through public DNA methylation data. In order to keep DNA methylation features most related to iNSC, we perform Wilcoxon test between 9 iNSC and 128 non-tumor brain tissue (methylation difference |0.3|, FDR < 0.01) and mapped the resultant features to genome regions. Our model revealed a positive correlation (r2 = 0.86 p < 0.001) with the pluripotent model from Malta et al. (2018) applied on TCGA gliomas. More remarkable, the iNSC model stratified IDHwt glioma survival by the median of stemness both on TCGA and GLASS cohorts (TCGA: p < 0.001, GLASS: p = 0.086; Likelihood ratio test). Having shown its potential, we next applied the iNSC model in other Central Nervous System (CNS) tumors using cohorts studied by Capper et al. (2016). The iNSC model stratified distinct DNA methylation-based subtypes, such as: i) ependymoma-RELA presented a very distinct and lowest stemness among all ependymal subtypes; ii) low stemness in lymphoma and high stemness in plasmacytoma; iii) low stemness pituitary-ACTH and high stemness pituitary-STH-DNS-B. Interestingly, gliomas IDHmutant have the lowest stemness. Additionally, among putative new entities identified by Capper, our iNSC model stratified high-grade-neuroepithelial-BCOR with the lowest stemness among embryonal tumors and infantile-hemispheric-glioma, high-grade-neuroepithelial-MN1, and anaplastic-pilocytic-astrocytoma showing differences among each other in “Other glioma” group. Our results indicate prognosis prediction for gliomas and recapitulate CNS tumors subgroups, which might suggest the iNSC model as a surrogate strategy to explore the stemness in brain tumors from an epigenetic perspective.

Stemness is a phenotype associated with cancer initiation and progression, malignancy, and therapeutic resistance, exhibiting particular molecular signatures. Targeting stemness has been proposed as a promising strategy against breast cancer stem cells that can play a key role in breast cancer progression, metastasis, and multiple drug resistance. Here, using a previously published one-class logistic regression machine learning algorithm (OCLR) built on pluripotent stem cells to predict stemness in human cancer samples, we provide the stemness index (mRNAsi) of different canine non-tumor and mammary cancer cells. Then, we confirmed that inhibition of BET proteins by (+)-JQ1 reduces stemness in a high mRNAsi canine cancer cell. Furthermore, using public data, we observed that (+)-JQ1 can also decrease stemness in human triple-negative breast cancer cells. Our work suggests that mRNAsi can be used to estimate stemness in different species and confirm epigenetic modulation by BET inhibition as a promising strategy for modulating the stemness phenotype in canine and human mammary cancer cells.