Longitudinal single-cell RNA-seq analysis reveals stress-promoted chemoresistance in metastatic ovarian cancer

Abstract

Chemotherapy resistance is a critical contributor to cancer mortality and thus an urgent unmet challenge in oncology. To characterize chemotherapy resistance processes in high-grade serous ovarian cancer, we 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, we 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, we have found a resistant state that integrates stromal signaling and subclonal evolution and offers targets to overcome chemotherapy resistance.

A platform for efficient establishment, expansion and drug response profiling of high-grade serous ovarian cancer organoids

“…However, we found 7 out of the 11 non-concordant markers to be immediate-early genes (IEGs). IEG expression has been described to be rapidly induced by a number of stimuli, including protease treatment, tissue dissociation or inflammatory signals from the tumor microenvironment 31, 32, 33 . Of note, EOC382_pOme tumor sample preparation was extraordinarily harsh and involved multiple additional filtering and gradient purification steps, which could have induced additional cell stress.…”

Comparing the Secretomes of Chemorefractory and Chemoresistant Ovarian Cancer Cell Populations

“…Zhang et al identified several gene signatures among treatment-naïve HGSOC populations such as proliferative DNA repair, RNA processing, TCA cycle, among others. Strikingly, however, HGSOC populations post-neoadjuvant chemotherapy were enriched for a stress-associated profile (see Table 2) [56]. Pools of HGSOC cells with a similar stress profile have also been identified as likely sources of relapse-initiating cells [57].…”

“…Several modes of resistance have been identified in in vitro studies to either paclitaxel or platinum-based drugs, and studies using clinical samples have been critical in identifying the common traits of cancer cells; however, treatment efficacy and future drug development would greatly benefit from the ability to differentiate between resistant populations that are primed by the TME and those that are not. Critically, by characterizing 22 matched pre-and post-neoadjuvant chemotherapy-treated HGSOC patient samples, Zhang et al demonstrated that treatment enriches subpopulations with an initially increased transcriptomic stress response and primes HGSOC cells to resist chemotherapy [56]. Kan et al similarly identified that relapseinitiating HGSOC cells can largely originate from a subpopulation early in tumorigenesis with a high-stress signature.…”

“…While 25% of HGSOC cases are refractory and exhibit intrinsic resistance to chemotherapies, studies with a specific focus on refractory HGSOC are limited, with most using treatment-naïve samples [58]. By characterizing pre-and post-chemotherapy HGSOC patient samples using single-cell RNAseq, whole genome sequencing, and DNA copy number analysis, groups have found either no recurrent genomic changes in subpopulations primed to endure chemotherapy, BRCA1/2 mutations or amplification of 19q12, containing cyclin E (CCNE1) (Table 2) [56,58,59].…”

Tracing back primed resistance in cancer via sister cells

“…CNV profiles for the individual cancer cells for each sample were obtained with InferCNV (1.7.1) 49 using a common set of stromal cells from a previously published data 50 as references. The Leiden algorithm was used for determining the underlying subclonal populations based on the CNV profiles.…”