Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer

BACKGROUND

Most primary Triple Negative Breast Cancers (TNBCs) show amplification of the Epidermal Growth Factor Receptor (EGFR) gene, leading to increased protein expression. However, unlike other EGFR-driven cancers, targeting this receptor in TNBC yields inconsistent therapeutic responses.

METHODS

To elucidate the underlying mechanisms of this variability, we employ cellular barcoding and single-cell transcriptomics to reconstruct the subclonal dynamics of EGFR-amplified TNBC cells in response to afatinib, a tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR.

RESULTS

Integrated lineage tracing analysis revealed a rare pre-existing subpopulation of cells with distinct biological signature, including elevated expression levels of Insulin-Like Growth Factor Binding Protein 2 (IGFBP2). We show that IGFBP2 overexpression is sufficient to render TNBC cells tolerant to afatinib treatment by activating the compensatory insulin-like growth factor I receptor (IGF1-R) signalling pathway. Finally, based on reconstructed mechanisms of resistance, we employ deep learning techniques to predict the afatinib sensitivity of TNBC cells.

CONCLUSIONS

Our strategy proved effective in reconstructing the complex signalling network driving EGFR-targeted therapy resistance, offering new insights for the development of individualized treatment strategies in TNBC.

Predicting drug response from single-cell expression profiles of tumours

BACKGROUND

Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention.

RESULTS

Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations.

CONCLUSIONS

DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP .

Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data

Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatize the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single-cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to re-purpose and contextualize new cells into a reference cell atlas. Using both simulated and real datasets, we show that scGSEA effectively recapitulates recurrent patterns of pathways' activity shared by cells from different experimental conditions. At the same time, we show that scMAP can reliably map and contextualize new single-cell profiles on a breast cancer atlas we recently released. Both tools are provided in an effective and straightforward workflow providing a framework to determine cell function and significantly improve annotation and interpretation of scRNA-seq data.

A single-cell analysis of breast cancer cell lines to study tumour heterogeneity and drug response

Cancer cells within a tumour have heterogeneous phenotypes and exhibit dynamic plasticity. How to evaluate such heterogeneity and its impact on outcome and drug response is still unclear. Here, we transcriptionally profile 35,276 individual cells from 32 breast cancer cell lines to yield a single cell atlas. We find high degree of heterogeneity in the expression of biomarkers. We then train a deconvolution algorithm on the atlas to determine cell line composition from bulk gene expression profiles of tumour biopsies, thus enabling cell line-based patient stratification. Finally, we link results from large-scale in vitro drug screening in cell lines to the single cell data to computationally predict drug responses starting from single-cell profiles. We find that transcriptional heterogeneity enables cells with differential drug sensitivity to co-exist in the same population. Our work provides a framework to determine tumour heterogeneity in terms of cell line composition and drug response.

Abstract 214: The Breast Single-Cell Atlas: Single-cell transcriptomics for personalised medicine

Breast Cancer (BC) patient stratification is driven by receptor status and histological grading and subtyping, with about 20% of patients for which absence of any actionable biomarkers results in no clear therapeutic intervention. Here, we evaluated the potentiality of single cell RNA-sequencing (scRNA-seq) for automated diagnosis and drug treatment of BC. We transcriptionally profiled 35,276 individual cells from 33 BC cell-lines covering all BC subtypes plus primary cancer cells from three patients to obtain a BC cell atlas. scRNA-seq successfully measured the expression of clinically relevant receptors and was used to automatically group cancer cell lines according to their tumor subtype. In most cell lines, as well as patients' cells, we observed a high degree of heterogeneity in the expression of BC receptors. We thus asked whether such heterogeneity impacts a cell line overall drug sensitivity. By correlating the percentage of cells expressing a given drug target (e.g. HER2, etc.) to the known IC50 of the relevant drug across the 33 cell lines, we observed a significant negative correlation (the higher the % of cells, the lower the IC50). This means that even within a genomically stable isogenic cell line, cells with differential drug sensitivity can co-exist. We then focused on the MDAMB361 cell-line of the luminal B subtype with a gain in genomic copy number of the locus containing the ERRB2 gene coding for HER2. Despite HER2 amplification, scRNA-seq showed that only 64% of cells express its mRNA. We used fluorescence-activating cell sorting to isolate HER2 expressing cells (HER2+) from non-expressing cells (HER2-). After a week, both subpopulations re-established the original heterogeneity, thus showing that heterogeneity in HER2 expression in these cells is dynamic. As expected, HER2 targeting drugs such as Afatinib and Erlotinib, have a higher IC50 in this cell line as compared to cell lines uniformly expressing HER2. We developed a bioinformatics approach named DEEP (Drug Estimation from Expression Profiles) to automatically predict responses to more than 450 anticancer agents starting from scRNA-seq and confirmed the validity of the approach using published large-scale studies on drug sensitivity. We then applied DEEP to the MDAMB361 cell-line to identify drugs able to selectively inhibit growth of the HER2- subpopulation. Etoposide was predicted to selectively inhibit growth of the HER2- cells but not HER2+ cells. We experimentally confirmed this prediction by performing cell viability assays at different dosages. Finally, we used DEEP to primary cancer cells of three patients end confirmed its validity experimentally. We found that scRNA-seq can be used to improve cancer diagnosis and predict drug sensitivity and transcriptional heterogeneity is common, dynamic and plays a relevant role in determining drug sensitivity. Our BC cell atlas and DEEP approach are a unique resource for the BC research community.

Citation Format: Gaetano Viscido, Gennaro Gambardella, Diego di Bernardo. The Breast Single-Cell Atlas: Single-cell transcriptomics for personalised medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 214.

TRPV4 and purinergic receptor signalling pathways are separately linked in airway epithelia to CFTR and TMEM16A chloride channels

KEY POINTS

Eact is a putative pharmacological activator of TMEM16A. Eact is strongly effective in recombinant Fischer rat thyroid (FRT) cells but not in airway epithelial cells with endogenous TMEM16A expression. Transcriptomic analysis, gene silencing and functional studies in FRT cells reveal that Eact is actually an activator of the Ca²⁺ -permeable TRPV4 channel. In airway epithelial cells TRPV4 and TMEM16A are expressed in separate cell types. Intracellular Ca²⁺ elevation by TRPV4 stimulation leads to CFTR channel activation.

ABSTRACT

TMEM16A is a Ca²⁺ -activated Cl^- channel expressed in airway epithelial cells, particularly under conditions of mucus hypersecretion. To investigate the role of TMEM16A, we used Eact, a putative TMEM16A pharmacological activator. However, in contrast to purinergic stimulation, we found little effect of Eact on bronchial epithelial cells under conditions of high TMEM16A expression. We hypothesized that Eact is an indirect activator of TMEM16A. By a combination of approaches, including short-circuit current recordings, bulk and single cell RNA sequencing, intracellular Ca²⁺ imaging and RNA interference, we found that Eact is actually an activator of the Ca²⁺ -permeable TRPV4 channel and that the modest effect of this compound in bronchial epithelial cells is due to a separate expression of TMEM16A and TRPV4 in different cell types. Importantly, we found that TRPV4 stimulation induced activation of the CFTR Cl^- channel. Our study reveals the existence of separate Ca²⁺ signalling pathways linked to different Cl^- secretory processes.

A mutant of phosphomannomutase1 retains full enzymatic activity, but is not activated by IMP: Possible implications for the disease PMM2-CDG

The most frequent disorder of glycosylation, PMM2-CDG, is caused by a deficiency of phosphomannomutase activity. In humans two paralogous enzymes exist, both of them require mannose 1,6-bis-phosphate or glucose 1,6-bis-phosphate as activators, but only phospho-mannomutase1 hydrolyzes bis-phosphate hexoses. Mutations in the gene encoding phosphomannomutase2 are responsible for PMM2-CDG. Although not directly causative of the disease, the role of the paralogous enzyme in the disease should be clarified. Phosphomannomutase1 could have a beneficial effect, contributing to mannose 6-phosphate isomerization, or a detrimental effect, hydrolyzing the bis-phosphate hexose activator. A pivotal role in regulating mannose-1phosphate production and ultimately protein glycosylation might be played by inosine monophosphate that enhances the phosphatase activity of phosphomannomutase1. In this paper we analyzed human phosphomannomutases by conventional enzymatic assays as well as by novel techniques such as 31P-NMR and thermal shift assay. We characterized a triple mutant of phospomannomutase1 that retains mutase and phosphatase activity, but is unable to bind inosine monophosphate.