Tracing back primed resistance in cancer via sister cells
Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods coupling cell states to future fates. However, transcriptional changes that drive pre-treatment cells into resistant fates may be subtle, necessitating high resolution analysis. We developed ReSisTrace that uses shared transcriptomic features of synchronised sister cells to predict the states that prime treatment resistance, and allows identification of asymmetric features that drive phenotypic heterogeneity. Applying ReSisTrace in ovarian cancer cells revealed that BRCAness transcriptional signatures are associated with pre-existing vulnerability not only to olaparib and carboplatin treatments, but also to natural killer cell-mediated cytotoxicity. This novel connection between DNA repair defect and susceptibility to natural killer cells was further validated both functionally and in a clinical cohort. The high-resolution analysis by ReSisTrace enables resolving pre-existing transcriptional features of treatment vulnerability, facilitating molecular patient stratification for personalised therapies.
A platform for efficient establishment, expansion and drug response profiling of high-grade serous ovarian cancer organoids
The broad research use of organoids from high-grade serous ovarian carcinoma (HGSC) has been hampered by low culture success rates and limited availability of fresh tumor material. Here we describe a method for generation and long-term expansion of HGSC organoids with efficacy markedly improved over previous reports (55% vs. 23-38%). We established organoids from cryopreserved material, demonstrating the feasibility of using viably biobanked tissue for HGSC organoid derivation. Genomic, histologic and single-cell transcriptomic analyses revealed that organoids recapitulated genetic and phenotypic features of original tumors. Organoid drug responses correlated with clinical treatment outcomes, although in culture conditions-dependent manner and only in organoids maintained in human plasma-like medium (HPLM). Organoids from consenting patients are available to the research community through a public biobank and organoid genomic data explorable through an interactive online tool. Taken together, this resource facilitates the application of HGSC organoids in basic and translational ovarian cancer research.
Longitudinal single-cell RNA-seq analysis reveals stress-promoted chemoresistance in metastatic ovarian cancer
To characterize chemotherapy resistance processes in high-grade serous ovarian cancer, the authors prospectively collected tissue samples before and after chemotherapy and analyzed their transcriptomic profiles at a single-cell resolution. After removing patient-specific signals by a novel analysis approach, PRIMUS, they found a consistent increase in stress-associated cell state during chemotherapy, which was validated by RNA in situ hybridization and bulk RNA sequencing. The stress-associated state exists before chemotherapy, is subclonally enriched during the treatment, and associates with poor progression-free survival. Co-occurrence with an inflammatory cancer–associated fibroblast subtype in tumors implies that chemotherapy is associated with stress response in both cancer cells and stroma, driving a paracrine feed-forward loop. In summary, the authors have found a resistant state that integrates stromal signaling and subclonal evolution and offers targets to overcome chemotherapy resistance.
Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer
To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, the authors have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, they show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.
miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data
In this study the authors propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. Then demonstrates how their QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses. The software package is available at https://bioconductor.org/packages/miQC.
PRISM: Recovering cell type specific expression profiles from individual composite RNA-seq samples
In this study the authors developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. The results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. The findings were validated in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments.
A Functional Homologous Recombination Assay Predicts Primary Chemotherapy Response and Long-Term Survival in Ovarian Cancer Patients
In this study, the authors aimed to fill the gap of HRD diagnostic by developing a clinically relevant tool to detect functional HRD. Then established an ex vivo test in primary HGSOC and quantified the tumor HRD in an HR score. Finally demonstrated that a low HR score significantly predicts platinum sensitivity and correlates with improved overall survival.
The Emerging Role of the Single-Cell and Spatial Tumor Microenvironment in High-Grade Serous Ovarian Cancer.
The development of single-cell and spatial technologies has enabled a more detailed understanding of the tumor microenvironment and its role in therapy response and clinical outcome of high-grade serous ovarian cancer (HGSC). Interestingly, emerging evidence suggests that HGSCs with different genetic drivers harbor distinct tumor-immune microenvironments. Further, spatial cell-cell interactions have been shown to shape the CD8+ T-cell phenotypes and responses to immune checkpoint blockade therapies. The heterogeneous stroma consisting of cancer-associated fibroblast (CAF) subtypes, endothelia, and site-specific stromal types such as mesothelium modulates treatment responses via increasing stiffness and by producing ligands that promote drug resistance, angiogenesis, or immune escape. Chemotherapy itself shifts CAFs toward an inflammatory phenotype that associates with poor survival and immune-suppressive signaling. New emerging immunotherapies include combinational approaches and agents targeting, for example, the tumor-intrinsic endoplasmic reticulum pathway. A more detailed understanding of the spatial interplay of tumor, immune, and stromal cells in the tumor microenvironment is needed to develop more efficient immunotherapeutic strategies for HGSC.
Counting absolute numbers of molecules using unique molecular identifiers.
This paper describes a universal method that can be applied to counting the absolute number of molecules in a given sample. This stems from the idea that if each molecule in a sample is made unique prior amplification - for example with addition of a random sequence tag - one can simply count the number of unique molecules from an amplified sample to obtain the original number of molecules. The method completely eliminates PCR bias, a common problem in accurately determining the number of RNA or DNA molecules in a cell. In this paper, the method was applied to RNA-seq and the authors showed that it can be can be used to improve accuracy of almost any next generation sequencing method, including ChIP- sequencing, genome assembly, diagnostic applications and manufacturing process control and monitoring. and has become a golden standard in quantitative single-cell RNA-sequencing. For this paper, Anna developed a custom RNA-seq library preparation method to include UMIs and performed the RNA-seq experiments (Cited 339 times; Source: Google Scholar).
Mice Lacking a Myc Enhancer That Includes Human SNP rs6983267 Are Resistant to Intestinal Tumours.
In this paper, the authors generated mice lacking Myc- 335, a putative Myc regulatory element that contains rs6983267. rs6983267 is a colon-cancer associated SNP that accounts for more human cancer-related morbidity than any other genetic variant or mutation. In Myc-335 null mice, Myc transcripts were expressed at modestly reduced levels with a pattern similar to that of wild-type mice. The mutant mice displayed no overt phenotype but when crossed with APCmin mice, mutant mice were markedly resistant to intestinal tumourigenesis. These results highlight the fact that although a disease-associated polymorphism typically has a relatively modest effect size, the element that it affects can be critically important for the underlying pathological process. Thus, we may harbor switches that might not compromise the normal development but can be critical for disease pathogenesis. For this paper, Anna analyzed transcriptomics data from which the authors identified a modest decrease in Myc exon expression (Cited 151 times, Source: Google Scholar).
Transcriptional networks controlling the cell cycle.
In this paper, the authors studied transcriptional networks that regulate the cell cycle in Drosophila melanogaster, and found two interconnected feedback circuits, of which one controls overall protein homeostasis and connects mribosome and proteasome, and another that controls protein synthesis capacity and connects the ribosome and Myc/Max. For this study, Anna developed the basic library preparation methodology that she later adapted to molecule counting and applied this initial version to a large number of custom RNA-seq libraries for Drosophila RNAi samples (Cited 21 times; Source: Google Scholar).
Perspective: Cancer by super-enhancer.