Treatment resistance in ovarian cancer (OC) is common. Recent efforts have identified new treatment approaches with potential to improve its management, but their therapeutic effects are limited due to treatment resistances. Tumor microenvironment (TME) is emerging as a key contributor to treatment resistance, being the metabolic cross-talk between TME and OC one of the recently proposed mechanisms. The hosting lab has evidence that platinum-resistant OC cells have defects in serine biosynthesis which leads to an enhance uptake of serine. Importantly, exogenous serine is also required for some TME components, suggesting a possible competition for it and affecting their anti-cancer function. Thus, my working hypothesis is that metabolic cross-talk between TME and OC could involve serine metabolism, which could mediate treatment resistance.
Unfortunately, there is a lack of preclinical models of OC that recapitulate TME to study its role. In this regard, the secondment lab developed a new ex vivo platform based on patient-derived explants (PDEs) that allows the study of TME interactions with cancer cells. In OVADEX, we will use PDEs to dissect the role of TME components in treatment response in OC, and particularly, in the metabolic reprogramming of cancer cells.
The project’s objectives are to generate OC PDEs and expose them to standard and emerging therapies, to define metabolic changes in OC cells and cellular and phenotypic changes in TME before and after treatment and to associate changes in TME and OC cells to serine metabolism and treatment resistance. To this end, we will integrate innovative techniques and the access of the hosting team to a unique registry of OC patients.
OVADEX
Grant agreement ID: 101064216
DOI
EC signature date
9 June 2022
Start date
1 September 2022
End date
31 August 2024
Funded under
Marie Skłodowska-Curie Actions (MSCA)
EU contribution
€ 175 920,00
Summary of the context and overall objectives of the project.
Treatment resistance in ovarian cancer (OC) is common. Recently, new treatment approaches with the potential to improve OC management have been identified, but their therapeutic efficacy is limited due to treatment resistance. In this regard, the tumor microenvironment (TME) is emerging as a key contributor to treatment resistance, being the metabolic crosstalk between TME and OC one of the recently proposed mechanisms of contribution. Evidence shows that platinum-resistant OC cells have defects in serine biosynthesis, leading to an enhanced serine uptake (Nat Commun 2022;13(1):4578). Importantly, exogenous serine is also required for some TME components, suggesting a possible competition that could affect their anti-cancer function. Unfortunately, the lack of knowledge on the exact functional role of TME in treatment resistance in OC is mostly because current preclinical models of OC do not fully recapitulate the TME.
In this context, OVADEX established four scientific objectives:
• To generate patient-derived explants (PDEs) from 50 treatment naïve OC patients and evaluate their response to different treatments, including standard chemotherapy and emerging therapies.
• To define metabolic changes, especially in serine biosynthesis, in the tumor before and after treatment.
• To define cellular and phenotypic changes in TME components before and after treatment.
• To associate changes in TME and OC cells to serine metabolism and treatment resistance.
Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far.
Tumor resections were collected from 36 OC patients. Solid tumor lesions from different areas of the resected tissue were selected by an experienced pathologist and brought to the lab where I processed them by manually cutting into small tumor fragments of 1–2 mm3 size. Fragments were subsequently frozen according to Nat Med. 27(7) 2021:1250-1261, which allows for the cryopreservation of viable tissue. In each vial, we added 10 to 20 fragments to maintain tumor heterogeneity. We also created a RedCap project to store clinical and preclinical patient data, including demographic data, diagnosis, histology and translational evaluation of the tumor, treatment, follow-up, and survival. Clinical and preclinical data is continuously registered and updated in the database.
Next, I learned to establish and evaluate treatment responses in PDEs during a secondment. During this formative period, I was trained in (i) the culture technique, including thawing of the tissue, preparation of the culture matrix, establishment of cultures, ex vivo treatment, harvesting of fragments for analysis, (ii) read-outs, including sample processing for fluorescence-activated cell sorting (FACS), cytokine/chemokine analysis and scRNA-seq, and (iii) experimental design, especially about number of fragments required and tumor heterogeneity. Back in my lab, adaptation of the protocol to gynecological samples and non-immunologic therapies was required. The first optimization consisted of the identification of the culture conditions that lead to higher viability over time but preserving the original tumor microenvironment as much as possible. This included the usage of human plasma-like medium (HPLM), serum, and hormone supplementation when required. The second stage included the determination of appropriate drug concentrations and readout methods. In this regard, we set up two methods to evaluate tumor response: AlamarBlue and ToxiLight assays. Using AlamarBlue we measured the metabolic capacity in terms of reduction capacity over time, whereas the usage of ToxiLight allowed the determination of cell death. Treated and control (untreated) PDEs were collected and i) carboxymethylcellulose embedded for spatial metabolomics (WP3), ii) formalin-fixed and paraffin-embedded for IHC (WP3 and WP4) or iii) digested for FACS analysis.
During the project, we managed to create tissue microarrays from the cultured fragments to evaluate the evolution of the fragment composition and cellularity, tumor architecture, tissue morphology, and the expression of several markers. Preliminary data shows that we will be able to use these microarrays to measure PHGDH levels using IHC, but the optimization of the staining is not complete. To further investigate the metabolic changes associated with resistance and serine biosynthesis, we performed spatial metabolomics using mass spectrometry imaging (MSI). In contrast to bulk metabolites analysis by mass spectrometry, this platform allows the mapping of the spatial distribution of metabolites, drugs, and proteins from a tissue section at the single-cell level. To do so, we combined two state-of-the-art techniques: Matrix Assisted Laser Desorption Ionization (MALDI)-MSI and Desorption Electrospray Ionization (DESI)-MSI. Treated and untreated PDEs were embedded in optimal cutting temperature (OCT) compound. Cryosections were obtained following standard procedures using the Epredia™ Microm HM525 NX cryostat. For MALDI-MSI, we applied a matrix directly to cryosections, forming co-crystals with metabolites. Upon radiation with a laser beam, the matrix was ionized and charges were transferred to the metabolites, resulting in their desorption and ionization. Using this approach, we detected crucial metabolomics pathways, including serine, arginine, glutamine, nucleotides, free fatty acids, and many others. Complementary, we used DESI-MSI. DESI-MSI uses electrospray ionization, whereby a fine spray of charged solvent droplets extracts metabolites from tissue. Importantly, DESI-MSI imaging provided information on the spatial distribution of small metabolites (glucose, lactate, amino acids, etc.) without any additional sample preparation.
We characterize tumor samples collected at baseline, and after 3 days of culture to determine cell composition and viability. HE slides were generated from the tumor tissue reserved to be formalin-fixed and paraffin-embedded (FFPE), uncultured tumor fragments, and all treated and untreated PDEs. In addition, ki67 (cell proliferation) and Caspase-3 (apoptosis) were evaluated using standard IHC methods. To further evaluate cell composition, T cell state and activation, and the presence of myeloid, fibroblast, and endothelial cells, cultured and uncultured PDEs were digested into a single-cell suspension using a digestion mix (RPMI medium supplemented with 1% P/S, 1 mg/mL DNAse I, and 100 mg/mL collagenase type IV) for 1 h at 37 °C under slow rotation. Cells were analyzed using antibody panels that I set up in the hosting lab. Data was collected using BD FACSDiva 8.0.1 software and further analyzed with FlowJo v10.8.2 (FlowJo LLC). Unfortunately, the application of multiplex IHC using the MILAN platform and single-cell RNA sequencing has been delayed until sample collection is completed.
Finally, we also looked for associations between changes in the metabolism of cancer cells (WP3), TME changes (WP4), and patients’ responses, expecting to find differences in TME components and OC metabolism in the PDEs obtained from the resistant and refractory patients in comparison to the PDEs established from responders.
