Periodic Reporting for period 4 - PROTEOMICAN (Discovery of breast cancer aggressiveness markers using topo-proteomics mapping)
Reporting period: 2020-01-01 to 2021-06-30
Breast cancer is the most prevalent cancer type in women and one of the most common death causes in women worldwide. In the last decades, breast tumors have been classified into 3-4 subtypes, in order to assign the appropriate treatment modality and to determine patient prognosis. However, in many of the cases, patient do not respond to these treatments, or develop resistance and tumor relapse. One of the underlying causes of this resistance is the heterogeneity of the tumors. It is now recognized that most tumors are not actually composed of a single subtype, but are rather a heterogeneous mass of cells, of different clones, each one with its distinct characteristics. These distinct populations are affected by their mutational profiles that evolved with tumor development, and by the microenvironment of healthy cells adjacent to the cancer cells. We hypothesized that characterization of the various clones within single tumors will highlight the key regulators of tumor development. This study aimed to understand tumor heterogeneity in breast cancer, using state of the art proteomics technologies. Mass spectrometry-based proteomics monitors the global profiles of protein changes. Determination of protein levels reflects the cellular phenotype much more closely than genomic approaches, and is therefore expected to reveal tumorigenic mechanisms that cannot be identified by other approaches. Identification of the key regulators of tumor aggressiveness, and their spatial distribution in breast tumors has the potential to translate into efficient drugs that overcome development of treatment resistance, and lead to durable treatment responses.
To achieve our major research goal, we combined state of the art proteomics, with histopathological analysis of tumors, and advanced computational analysis to create the first of its kind topological protein mapping of breast cancer. The major part of the project separated the tumors based on their histopathological profiles, followed with microscopy-guided laser microdissection of each region, and their mass spectrometry-based proteomic analysis. Overall, we analyzed >330 tumor regions, to understand the diversity of the proteomic profiles within tumors and between patients. Advanced computational analysis of the proteomic data unraveled multi-level heterogeneity. While molecular subtype heterogeneity decreased with cancer progression, proteomic heterogeneity increased with cancer progression. Interestingly, even tumors that seemed homogeneous based on their receptor expression pattern were found to have marked proteomic differences between distinct regions. This study clearly shows the limitation of the routine clinical diagnostics in perceiving cancer phenotypic complexity.
Beyond the characterization of cancer proteomic profiles, our research identified key regulators of cancer progression and drug response. We showed perturbation of these enzymes inhibits tumor growth and induces sensitivity to chemotherapy. Altogether, we show that the proteomic data is able to unravel cancer vulnerabilities that can be targeted in future anti-cancer treatment.
Beyond the characterization of cancer proteomic profiles, our research identified key regulators of cancer progression and drug response. We showed perturbation of these enzymes inhibits tumor growth and induces sensitivity to chemotherapy. Altogether, we show that the proteomic data is able to unravel cancer vulnerabilities that can be targeted in future anti-cancer treatment.
To achieve our scientific goals, we had to push the proteomic technology to its limit in terms of sample amounts and throughput. We tackled this challenge by combining method development with the biological aims that we are addressing. We improved the yield >10-fold using a novel sample preparation technique, and implemented an automated sample preparation pipeline to increase the throughput. This combination has never been done in proteomics, and opens new opportunities for large clinical proteomic projects. The combination of our proteomic and biological expertise provided a unique breast cancer topological mapping and opened the way to individualized therapy assignment that would lead complete therapeutic response.