AbstractBackgroundBreast cancer's complex transcriptional landscape requires an improved understanding of cellular diversity to identify effective treatments. The study of genetic variations among breast cancer subtypes at single‐cell resolution has potential to deepen our insights into cancer progression.MethodsIn this study, we amalgamate single‐cell RNA sequencing data from patient tumours and matched lymph metastasis, reduction mammoplasties, breast cancer patient‐derived xenografts (PDXs), PDX‐derived organoids (PDXOs), and cell lines resulting in a diverse dataset of 117 samples with 506 719 total cells. These samples encompass hormone receptor positive (HR+), human epidermal growth factor receptor 2 positive (HER2+), and triple‐negative breast cancer (TNBC) subtypes, including isogenic model pairs. Herein, we delineated similarities and distinctions across models and patient samples and explore therapeutic drug efficacy based on subtype proportions.ResultsPDX models more closely resemble patient samples in terms of tumour heterogeneity and cell cycle characteristics when compared with TNBC cell lines. Acquired drug resistance was associated with an increase in basal‐like cell proportions within TNBC PDX tumours as defined with SCSubtype and TNBCtype cell typing predictors. All patient samples contained a mixture of subtypes; compared to primary tumours HR+ lymph node metastases had lower proportions of HER2‐Enriched cells. PDXOs exhibited differences in metabolic‐related transcripts compared to PDX tumours. Correlative analyses of cytotoxic drugs on PDX cells identified therapeutic efficacy was based on subtype proportion.ConclusionsWe present a substantial multimodel dataset, a dynamic approach to cell‐wise sample annotation, and a comprehensive interrogation of models within systems of human breast cancer. This analysis and reference will facilitate informed decision‐making in preclinical research and therapeutic development through its elucidation of model limitations, subtype‐specific insights and novel targetable pathways.Key points Patient‐derived xenografts models more closely resemble patient samples in tumour heterogeneity and cell cycle characteristics when compared with cell lines. 3D organoid models exhibit differences in metabolic profiles compared to their in vivo counterparts. A valuable multimodel reference dataset that can be useful in elucidating model differences and novel targetable pathways.

Abstract Breast cancer's complex transcriptional landscape requires a deep understanding of sample and cell diversity to identify effective treatments. In this study, we amalgamate single-cell RNA sequencing data from breast cancer patient-derived xenografts (PDX), organoids, cell lines, patient tumors and reduction mammoplasties resulting in a comprehensive dataset of 117 samples with 506,719 total cells. These samples encompass hormone receptor positive (HR+), human epidermal growth factor receptor 2 enriched (HER2E), and triple-negative breast cancer (TNBC) subtypes. We aimed to delineate similarities and distinctions across model systems and patient samples while also exploring stratification of therapeutic drug efficacy based on subtype proportions within tumors. Mammary tumor PDXs, organoids, and established cell lines exhibited higher proliferation and lower heterogeneity observed via UMAP dimensionality reduction compared to most patient tumors or normal breast epithelium. TNBCs had elevated proliferative and pro-metastatic signatures compared to HR+ and HER2E samples. Interestingly, compared to matched PDX tumors, organoids from these same models were found to exhibit stark differences in gene expression, including upregulation of metabolically active aldo-keto reductase family genes, highlighting differences in the model systems with implications for pre-clinical drug testing. Single-cell tumor subtyping analyses with scSubtype and TNBCtype methods found that therapeutically treated samples had shifts in the proportions of cell-wise subtype annotations when compared to matched untreated samples. Similarly, patient lymph node metastasis when compared with matched primary tumors were significantly linked to decreases in Basal-like and HER2-enriched cell-wise annotations in untreated ER+ samples. In vitro assessment of anti-cancer compounds on PDX cells showed significant correlation of subtype proportion with cell viability following treatment with targeted therapeutic agents. This subtyping methodology offers a powerful tool to monitor the evolving gene expression landscape within samples and predict responses to therapeutic agents. We present here a dynamic approach to cell-wise sample annotation and a substantial multi-model dataset for use facilitating informed decision-making in preclinical research and therapeutic development. Citation Format: Julia E. Altman, Carson J. Walker, Emily K. Zboril, Nicole S. Hairr, Rachel K. Myrick, David C. Boyd, Jennifer E. Koblenski, Madhavi Puchalapalli, Bin Hu, Mikhail G. Dozmorov, Xi Chen, Yunshun Chen, Charles M. Perou, Brian D. Lehmann, Jane E. Visvader, Amy L. Olex, J. Chuck Harrell. Decoding breast cancer: Unraveling subtype and model differences through multi-model single-cell RNA sequencing data integration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1750.

Abstract Estrogen receptor (ER) signaling is the main driver of tumorigenesis in ER+ breast cancers by inducing proliferation and survival through genomic and non-genomic means, therefore inhibition of ER signaling has been a mainstay of treatment for decades. Roughly 30 percent of patients will develop treatment-refractory recurrence or metastasis within their lifetime, which may include metastasis to the bone, lung, liver, and brain. Although advances in treatment approaches have prolonged average progression free survival, metastatic breast cancer remains incurable. A new endocrine therapy, Elacestrant, has been approved by the FDA for the treatment of ER+, ESR1-mutant advanced or metastatic breast cancer. The mechanism of action of Elacestrant involves both degradation of ER and modulation of ER signaling through non-degradative means. The central hypothesis of this study was that Elacestrant may prolong progression free survival in animal models of breast to bone metastasis, because modulation of ER in the bone would decrease the incidence of osteoporosis which is induced by other endocrine therapies. However, we discovered that prolonged Elacestrant treatment induces destruction of the trabecular bone structure and increased adipocyte mass within the bone, consistent with an osteoporotic phenotype. Ongoing studies will seek to determine if other endocrine therapies will provide better protection to the bone architecture while reducing metastatic burden in animal models. Additionally, we will evaluate organotropic efficacy of new endocrine therapies on multi-organ metastasis to determine if utility is contingent upon the metastatic site. Citation Format: Emily Kate Zboril, Julia E. Altman, Nicole S. Hairr, Rachel K. Myrick, Amy L. Olex, Mikhail G. Dozmorov, J. Chuck Harrell. Assessment of new generation endocrine therapies for the treatment of breast to bone metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6269.

Abstract Sacituzumab Govitecan (SG) is an antibody drug conjugate that targets the epithelial glycoprotein Trop-2. SG has been approved for the treatment of patients with triple negative breast cancer (TNBC) or estrogen receptor positive (ER+) breast cancer after they have received at least two previous systemic treatments. The objectives of this study were to identify biomarkers of SG response using breast cancer patient-derived xenografts (PDXs) and determine if SG efficacy changes when cells become carboplatin resistant (CR). Analysis of transcriptomic and proteomic levels of Trop2 using bulk RNAseq, scRNA-seq, and tandem mass tag spectrometry found that overall ER+ PDXs had significantly lower RNA (p-value less than 0.001) and protein (p-value less than 0.001) expression of Trop-2 than TNBC PDXs. Immunohistochemical assessment of 42 TNBC and 27 ER+ patient tumor samples identified variation in Trop-2 expression; within each cohort, expression varied from highly positive to completely negative. To test the association of Trop-2 with drug responsiveness, cells from 19 different PDXs were cultured in vitro and administered increasing doses of SG. TNBC cells were more responsive to SG treatments compared to ER+ cells (p-value equaling 0.005). Correlation analysis identified a positive relationship of drug response with protein abundance which was stronger in TNBC samples. In vivo studies with 7 TNBC PDXs found that over 70% were highly susceptible to SG treatments, with some tumors being completely eradicated or exhibiting a total inhibition of tumor growth which resulted in a long-term durable response after 10 weeks of treatment. In vivo across all the PDXs, Trop-2 expression alone did not strongly correlate with SG treatment success. Interestingly, CR sublines derived from 2 carboplatin sensitive PDXs were significantly less responsive to SG than their parental PDXs (p-value less than 0.001). These two CR sublines were also significantly less sensitive to the majority of 555 other drugs identified by the NCI compared to their parental PDXs. Ongoing studies are defining mechanisms of reduced SG efficacy in CR models. Overall, these studies find that SG is highly effective in many TNBC PDX models and should be utilized earlier in treatment protocols, especially in metastatic patients. Citation Format: Carson J. Walker, Julia E. Altman, Emily K. Zboril, Rachel K. Myrick, Nicole S. Hairr, David C. Boyd, Bin Hu, Mikhail G. Dozmorov, J. Chuck Harrell. Acquisition of carboplatin resistance corresponds with reduced sacituzumab govitecan efficacy in triple negative breast cancer patient derived xenografts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1986.

Basal-like triple-negative breast cancer (TNBC) tumor cells are difficult to eliminate due to resistance mechanisms that promote survival. While this breast cancer subtype has low PIK3CA mutation rates when compared to estrogen receptor-positive (ER+) breast cancers, most basal-like TNBCs have an overactive PI3K pathway due to gene amplification or high gene expression. BYL-719 is a PIK3CA inhibitor that has been found to have low drug-drug interactions, which increases the likelihood that it could be useful for combinatorial therapy. Alpelisib (BYL-719) with fulvestrant was recently approved for treating ER+ breast cancer patients whose cancer had developed resistance to ER-targeting therapy. In these studies, a set of basal-like patient-derived xenograft (PDX) models was transcriptionally defined with bulk and single-cell RNA-sequencing and clinically actionable mutation profiles defined with Oncomine mutational profiling. This information was overlaid onto therapeutic drug screening results. BYL-719-based, synergistic two-drug combinations were identified with 20 different compounds, including everolimus, afatinib, and dronedarone, which were also found to be effective at minimizing tumor growth. These data support the use of these drug combinations towards cancers with activating PIK3CA mutations/gene amplifications or PTEN deficient/PI3K overactive pathways.

The goals of this study were to identify transcriptomic changes that arise in basal-like breast cancer cells during the development of resistance to epidermal growth factor receptor inhibitors (EGFRi) and to identify drugs that are cytotoxic once EGFRi resistance occurs. Human patient-derived xenografts (PDXs) were grown in immunodeficient mice and treated with a set of EGFRi; the EGFRi erlotinib was selected for more expansive in vivo studies. Single-cell RNA sequencing was performed on mammary tumors from the basal-like PDX WHIM2 that was treated with vehicle or erlotinib for 9 weeks. The PDX was then subjected to long-term erlotinib treatment in vivo. Through serial passaging, an erlotinib-resistant subline of WHIM2 was generated. Bulk RNA-sequencing was performed on parental and erlotinib-resistant tumors. In vitro high-throughput drug screening with > 500 clinically used compounds was performed on parental and erlotinib-resistant cells. Previously published bulk gene expression microarray data from MMTV-Wnt1 tumors were contrasted with the WHIM2 PDX data. Erlotinib effectively inhibited WHIM2 tumor growth for approximately 4 weeks. Compared to untreated cells, single-cell RNA sequencing revealed that a greater proportion of erlotinib-treated cells were in the G1 phase of the cell cycle. Comparison of WHIM2 and MMTV-Wnt1 gene expression data revealed a set of 38 overlapping genes that were differentially expressed in the erlotinib-resistant WHIM2 and MMTV-Wnt1 tumors. Comparison of all three data types revealed five genes that were upregulated across all erlotinib-resistant samples: IL19, KLK7, LCN2, SAA1, and SAA2. Of these five genes, LCN2 was most abundantly expressed in triple-negative breast cancers, and its knockdown restored erlotinib sensitivity in vitro. Despite transcriptomic differences, parental and erlotinib-resistant WHIM2 displayed similar responses to the majority of drugs assessed for cytotoxicity in vitro. This study identified transcriptomic changes arising in erlotinib-resistant basal-like breast cancer. These data could be used to identify a biomarker or develop a gene signature predictive of patient response to EGFRi. Future studies should explore the predictive capacity of these gene signatures as well as how LCN2 contributes to the development of EGFRi resistance.