Proteogenomic and targeted metabolomic analysis of ovarian cancer heterogeneity and its contribution to recurrence and therapy resistance
Background: Despite major progress in cancer therapy, mortality from ovarian cancer has changed only marginally in the past 30 years. While aggressive primary therapy including radical surgery and platinum-based chemotherapies gain response rates of >70%, most tumors relapse, leading to a dismal prognosis. Hypothesis: The relapse after an initial treatment response suggests that tumor heterogeneity may be involved in the therapy resistance of ovarian cancer. Specifically, differences between cells from the primary tumor and the metastatic disease in the same patient before and after therapy may explain why the initial response is mostly followed by tumor recurrence. Aims: Our aim is to develop minimally-invasive methods to monitor tumor evolution through biological fluids which are expected to better capture tumor heterogeneity than single tumor biopsies and thus to overcome tumor sampling bias. This will aid in early detection, monitoring of therapy efficacy and detection of recurrence in ovarian cancer. Methods: Plasma as well as tissue from the primary tumor and peritoneal metastases will be collected from patients with primary inoperative ovarian cancer who undergo surgery before and after systemic neo-adjuvant treatment. Tumor heterogeneity in the primary tumor and metastases before and after therapy will be measured by a novel proteogenomics, phospho-proteomics and targeted metabolomics approach using RNASeq as well as tandem mass spectrometry and chemical isobaric labelling with Tandem Mass Tags™ (TMT). TMT enables the simultaneous measurement of up to ten samples in one experiment. For biomarker identification, TMT-based simultaneous measurements of heterogeneous tumor tissues as well as biological fluids will be performed. These proteomics and targeted metabolomics measurements will identify the molecules present in both compartments while also dramatically increasing the sensitivity of the measurements. While most multi-omics studies neglect metabolomics, we will include targeted metabolomics with a focus on tryptophan metabolism, as we have preliminary evidence that this pathway plays an important role in ovarian cancer immune evasion. Tailored tools and computer pipelines will be developed for data analysis and the predicted biomarkers will be validated in prospectively collected biological fluids by targeted analyses. Expected results and potential impact: PROMETOV will enable an improved understanding of the contribution of ovarian cancer heterogeneity to therapy resistance and tumor recurrence and provide the basis to identify minimally-invasive sampling methods for early ovarian cancer detection, therapy monitoring and detection of relapse. If successful in the clinic such biomarkers would have an immense impact as they would enable early therapy, stratification of patients to treatments and control of treatment responses.
In ovarian cancer radical surgery and platinum-based chemotherapies gain response rates >70%, however most tumors relapse, leading to a dismal prognosis. The relapse after a treatment response suggests that tumor heterogeneity may be involved in tumor recurrence. While in the past most studies have focused on genetic tumor heterogeneity, PROMETOV investigated tumor heterogeneity by multi-level omics measurements. Comparison of the transcript, protein and metabolite levels of primary tumors, peritoneal metastases and healthy tube tissues revealed characteristic differences at all omics levels, highlighting the inter-tumor heterogeneity.
PROMETOV identified factors that help to understand the contribution of ovarian cancer heterogeneity to therapy resistance and recurrence and provide the basis to develop minimally-invasive methods for early detection of ovarian cancer, stratification of patients to therapies, therapy monitoring and detection of relapse.
The results of PROMETOV are expected to have an important clinical impact as the developed methods could also be applied to other tumors and may enable early and individualized treatment as well as control of therapy responses.
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This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 964264.