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Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities. Significance: Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.

We propose a method to leverage spatial transcriptomics and bioinformatic analysis to identify sub-clonal loss of heterozygosity that could otherwise be missed by standard bulk sequencing in glioblastoma tumors. Glioblastomas are highly invasive and aggressive brain tumors with a low five-year survival rate of less than 10%. Current treatment strategies are not sufficient for long term disease outcome, and innovative approaches to analyze glioblastoma are imperative for future improved options. Many factors contribute to difficulty of treatment, though one critical consideration is the diffuse nature and known intratumoral heterogeneity of this cancer. It is important to determine LOH heterogeneity, as this genomic alteration is associated with copy number variation and specific classifications of gliomas. Loss of heterozygosity is an irreversible deletion of DNA that is a common alteration in cancer. Application of spatial transcriptomics can increase our understanding of genomic complexity across a tissue, and unsupervised graph clustering can computationally separate spatial regions with similar gene expression patterns. Our method investigates spatial clusters individually at known heterozygous SNP sites to identify evidence for LOH with Bayesian inference and a hidden Markov model. Our analysis centers on the balance of reference (A) and non-reference (B) spatial transcriptomics read counts at SNPs of interest. B allele frequency is the number of reads aligned to the alternative allele divided by the total number of reads counted at the SNP. At a normal two-copy state at a heterozygous site, B allele frequency is ideally 50 percent. LOH can be identified as this value deviates toward 0 or 100, and copy number alterations impact this frequency. Our method utilizes Bayes factor values, or likelihood ratios between two models, using allele counts to provide support for either LOH or a heterozygous event. For our analysis, spatial transcriptomics BAMs are processed with a graph clustering algorithm and split into cluster-specific BAMs. We calculate allele count coverage for each cluster at pre-selected heterozygous SNP positions identified through germline exome sequencing. Bayes factor values are then calculated at each SNP, resulting in evidence for LOH or a heterozygous event. Chromosomal regions are segmented by a hidden Markov approach. Cumulative metrics for each segment are evaluated to determine a “state” label of heterozygous, LOH, or undefined. We tested our method on three WHO grade 4 IDH wild-type EGFRvIII positive fresh frozen glioblastoma tissue samples. Sub-clonal heterogeneity was identified in two samples and confirmed by bulk exome sequencing, bulk RNA sequencing, and analysis of initial clinical reports. Citation Format: Michelle G. Webb, Frances Chow, Carmel McCullough, Bohan Zhang, Kyle Hurth, John Carpten, Gabriel Zada, David W. Craig. Identification of sub-clonal loss of heterozygosity (LOH) in glioblastomas analyzed with spatial transcriptomics [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 3124.

Triple negative breast cancer (TNBC) is an aggressive and clinically challenging disease that disproportionately affects African-American (AA) women. Despite this well-known disparity, AA women have historically been underrepresented in large clinical and genomic studies of TNBC. Furthermore, while a genetic association of disease risk with African ancestry has been established, the tumor biology of AA TNBC has not been well characterized. We therefore sought to investigate the contribution of transcriptional substructure and tumor heterogeneity to TNBC disparities. We applied 10x Genomics Visium spatial transcriptomics (ST) technology to 28 samples representing primary TNBC tumors from 14 patients, 7 of whom were AA and 7 who were Caucasian. ST allows collection of whole transcriptome information from up to 5,000 spatially resolved coordinates within a tissue sample, providing remarkable insight into cellular organization within the larger framework of tissue architecture. Normalization of our ST expression data followed by clustering analysis confirmed extensive intra-tumoral subclonality, consistent with the genetic and molecular heterogeneity of TNBC. Most samples contained several transcriptionally distinct tumor clusters, characterized by distinctive activation of oncogenic and immunogenic signaling pathways revealed by gene set enrichment analysis (GSEA). For direct comparison of AA and Caucasian ST data, we performed an integration of all samples to consolidate the variation across genetic backgrounds. The integrated dataset was subject to clustering analysis to produce nine integrated clusters (ICs) representing shared cell populations that were present across all samples in the cohort. Interestingly, while all nine ICs were present in all individual samples, the representation of ICs varied by race. Molecular annotation of ICs using tools such as GSEA, ESTIMATE, and CIBERSORTx revealed that ICs enriched in Caucasian samples were immune-rich, while those enriched in AA samples were immunodeficient and distinctly hypoxic. These results support previous studies that have posited a unique tumor microenvironment in AA TNBC. Furthermore, investigation of the spatial organization of ICs using join count analysis revealed consistent non-random spatial incompatibility of hypoxic regions with immune infiltrate, which is consistent with established roles for hypoxia in immunosuppression. To examine the clinical significance of IC representation in TNBC, we used top marker genes of relevant ICs to perform survival analysis in publicly available breast cancer datasets (4929 samples). We found that high expression of IGKC, which was associated with Caucasian samples in our cohort, is significantly protective in basal-like breast cancer. Conversely, high expression of NDRG1, a hypoxia-related gene overrepresented in AA samples in our cohort, was significantly associated with poor survival. Thus, our study utilizes advanced molecular profiling to provide novel characterizations of AA TNBC with clinical implications in TNBC outcomes disparities. Citation Format: Rania Bassiouni, Michael O. Idowu, Lee D. Gibbs, Valentina Robila, Pamela J. Grizzard, Michelle G. Webb, Jiarong Song, Ashley Noriega, David W. Craig, John D. Carpten. Spatial transcriptomics reveals racial disparities in the transcriptional substructure of triple negative breast cancer [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PR012.

Background: Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer that disproportionately affects African-American and black (AAB) women, who have higher rates of incidence and mortality. To understand this disparity, we must apply advanced molecular profiling to racially diverse cohorts. We propose the use of spatial transcriptomics to map whole transcriptome data to several thousand coordinates within tissue sections, for comprehensive and unbiased characterization of TNBC tumors and their microenvironment. Methods: We applied the Visium Spatial Gene Expression method (10x Genomics) to 28 fresh frozen TNBC tissue sections obtained from 14 patients (7 AAB and 7 Non-Hispanic White). Sections were stained with H&E, and regions of tumor and stroma were annotated by a pathologist. The assay yielded whole transcriptome data from over 36,000 spatially-defined features, averaging 1,380 features per section. Expression data from each sample was normalized and subject to dimensionality reduction and clustering analysis. All sections were also integrated into a single dataset, from which integrated clusters (IC) were defined. Several bioinformatics tools were used to annotate the data at the feature, cluster, and IC level. Join count statistical analysis was employed to quantify patterns of spatial aggregation or dispersion of ICs across samples. Results: Clustering analysis revealed that all samples exhibited a great deal of heterogeneity; most contained several transcriptionally distinct regions of both tumor and stroma. Application of the ESTIMATE gene sets at the feature level accurately defined tumor and non-malignant regions when compared to histopathological annotation. Gene set enrichment analysis at the cluster level allowed further classification of biological processes within each sample. Following integration of all 28 samples, 9 ICs were defined from transcriptional data. Although all ICs were present in all samples, IC5 - characterized by a strong hypoxic signature - was overrepresented in AAB samples. ICs were then mapped back to individual samples and subject to join count statistics. This revealed strong spatial autocorrelation within each IC, as well as significant spatial pairings of ICs. The fibrotic clusters IC3 and IC7 were strongly contiguous in all samples (z-score > 5), as were tumor clusters IC1 and IC4. Conversely, we found spatial exclusion between IC3 and tumor clusters IC5 and IC2. Conclusion: Our study provides novel evidence of spatially related populations across diverse TNBC samples. Our findings suggest that, while TNBC tumors are highly heterogeneous, they exhibit elements of a common spatio-transcriptional architecture. Moreover, this provides a new framework in which to evaluate transcriptional differences between racial groups within a broader spatial context. Citation Format: Rania Bassiouni, Michael Idowu, Lee D. Gibbs, Pamela J. Grizzard, Michelle G. Webb, Ashley Noriega, Valentina Robila, David W. Craig, John D. Carpten. Comprehensive spatial transcriptomic analysis of an integrated, diverse cohort reveals distinct molecular topographic patterns in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2032.