Periodic Reporting for period 1 - MITHAML (MITHAML – Exploring the role of Metabolic IntraTumoral Heterogeneity in drug resistance of Acute Myeloid Leukemia in vivo)
Período documentado: 2021-02-01 hasta 2023-01-31
Acute Myeloid Leukemia (AML) patient prognosis remains dramatic with a 5-year overall survival of only 28%. However, several new-drugs have been approved by the FDA during the last 2 years. This includes venetoclax (VEN), a selective inhibitor of the anti-apoptotic protein BCL2, which showed 62% of overall clinical response in combination with conventional chemotherapy cytarabine (AraC) in Phase 2/3 clinical trials. However, several mechanisms of resistance have already been reported. Different subpopulations coexist within the same tumor and differ in their ability to acquire, or because they already possess, features to resist treatments. Thus, intratumoral heterogeneity may underlie innate or acquired resistance, and disease relapses. Sources of intratumoral heterogeneity are diverse and lead to different metabolic status at the cellular level. Indeed, metabolism plays a critical role in AML relapse by conferring unique drug-resistant properties. In order to therapeutically impact the incidence of AML relapse, we must understand the metabolic intratumoral heterogeneity (MITH).
Our project aimed to develop a novel approach using mass cytometry (CyTOF) for single-cell proteomics to explore MITH in AML at diagnosis and following treatment with VEN alone or in combination with AraC or hypomethylating agent azacytidine (AZA) ex vivo. This could help better understand the mechanisms of resistance to these new regimens already approved in clinic and identify complementary drug combinations able to circumvent them. Moreover, the identification of biomarkers of response to the different therapies is a crucial point to make progress towards precision medicine.
The main conclusions of the project so far are:
1/AML patients present a high metabolic heterogeneity which is not restrained to developmental state.
2/Adult and pediatric AML patients display distinct and unique features, in particular metabolic ones, which could lead to different response to metabolic inhibitors.
3/VEN treatment abrogates immature clusters but spares other clusters, monocytic or not, with specific metabolic features which represent promising targets for more efficient combinations.
Our project aimed to develop a novel approach using mass cytometry (CyTOF) for single-cell proteomics to explore MITH in AML at diagnosis and following treatment with VEN alone or in combination with AraC or hypomethylating agent azacytidine (AZA) ex vivo. This could help better understand the mechanisms of resistance to these new regimens already approved in clinic and identify complementary drug combinations able to circumvent them. Moreover, the identification of biomarkers of response to the different therapies is a crucial point to make progress towards precision medicine.
The main conclusions of the project so far are:
1/AML patients present a high metabolic heterogeneity which is not restrained to developmental state.
2/Adult and pediatric AML patients display distinct and unique features, in particular metabolic ones, which could lead to different response to metabolic inhibitors.
3/VEN treatment abrogates immature clusters but spares other clusters, monocytic or not, with specific metabolic features which represent promising targets for more efficient combinations.
1/ We have designed 2 CyTOF panels including phenotypic, signaling markers, transcription factors but mostly metabolic antibodies associated with the central metabolic pathways. In total, the 2 panels allowed the measurement of 76 unique markers. The entire optimized panels have been validated on AML and B-ALL patient cells.
2/ We used the new CyTOF panels on BM cells isolated from 6 healthy donors. Unexpectedly, when we performed dimension reduction using only the metabolic markers, we were also able to identify the B and myeloid trajectories going from the more immature to the more differentiated subpopulations, highlighting that metabolic markers capture the heterogeneity of the subpopulations belonging to the different lineages.
3/ We then applied these panels to a cohort of 27 adult and 9 pediatric AML patients. Importantly, when we looked at the distribution of the different clusters in each patient, we observed that while there is definitely heterogeneity of cluster abundance between patients, most of the clusters are shared among most of the patients, arguing that these metabolic clustering allows to capture the metabolic heterogeneity among our cohort. We observed few overlaps between adults and children. The only clusters really shared between adult and pediatric samples are the clusters expressing the highest stemness score. Importantly, other stem-like clusters were also very specific to adult or pediatric samples. We observed that in adults these immature clusters highly express anti-apoptotic protein BCL2 (VEN target) and not MCL1 while in clusters specific to children it was exactly the contrary. These differences highlight distinct regulations and metabolic activity of immature subpopulations in adults and pediatrics and therefore potentially response to metabolic inhibitors.
4/ The cells of the whole cohort of 36 AML patients has also been treated ex vivo with VEN, AraC, AZA or combos and analyzed with the 2 metabolic CyTOF panels. In concordance with the literature, all the immature clusters were strongly targeted by VEN. However, we also observed clusters persisting or even being enriched post-VEN. Some of them were highly monocytic but not all of them. VEN being a selective BCL2 inhibitor and targeting cysteine/amino acid uptake and degradation that support mitochondrial metabolism, we wondered how BCL2 expression was affected by VEN treatment. If there was no difference at the bulk level, we observed a significant increase in BCL2 expression in the persistent clusters, suggesting that cells resisting the therapy are able to counteract BCL2 inhibition by VEN.
2/ We used the new CyTOF panels on BM cells isolated from 6 healthy donors. Unexpectedly, when we performed dimension reduction using only the metabolic markers, we were also able to identify the B and myeloid trajectories going from the more immature to the more differentiated subpopulations, highlighting that metabolic markers capture the heterogeneity of the subpopulations belonging to the different lineages.
3/ We then applied these panels to a cohort of 27 adult and 9 pediatric AML patients. Importantly, when we looked at the distribution of the different clusters in each patient, we observed that while there is definitely heterogeneity of cluster abundance between patients, most of the clusters are shared among most of the patients, arguing that these metabolic clustering allows to capture the metabolic heterogeneity among our cohort. We observed few overlaps between adults and children. The only clusters really shared between adult and pediatric samples are the clusters expressing the highest stemness score. Importantly, other stem-like clusters were also very specific to adult or pediatric samples. We observed that in adults these immature clusters highly express anti-apoptotic protein BCL2 (VEN target) and not MCL1 while in clusters specific to children it was exactly the contrary. These differences highlight distinct regulations and metabolic activity of immature subpopulations in adults and pediatrics and therefore potentially response to metabolic inhibitors.
4/ The cells of the whole cohort of 36 AML patients has also been treated ex vivo with VEN, AraC, AZA or combos and analyzed with the 2 metabolic CyTOF panels. In concordance with the literature, all the immature clusters were strongly targeted by VEN. However, we also observed clusters persisting or even being enriched post-VEN. Some of them were highly monocytic but not all of them. VEN being a selective BCL2 inhibitor and targeting cysteine/amino acid uptake and degradation that support mitochondrial metabolism, we wondered how BCL2 expression was affected by VEN treatment. If there was no difference at the bulk level, we observed a significant increase in BCL2 expression in the persistent clusters, suggesting that cells resisting the therapy are able to counteract BCL2 inhibition by VEN.
A growing literature is highlighting the importance of intratumoral heterogeneity on one hand and metabolism rewiring on the other hand to understand drug resistance of cancer cells. However, approaches to study intratumoral heterogeneity and including metabolism are mostly lacking. Here, we developed a single-cell proteomic approach to investigate metabolism together with developmental state, signaling and transcription factors.
Using this approach first on healthy donor BM, we were able to depict the different lineages trajectories using only metabolic markers, highlighting the importance to integrate metabolism to better characterize the hematopoietic hierarchy. Then, we used the same approach to measure the expression of the 76 proteins in our AML cohort (27 adult and 9 pediatric). We observed major differences in the metabolic clusters abundances in pediatric compared to adult samples, highlighting key metabolic differences in function of the age of the patients. To our knowledge, metabolic dependencies of pediatric AML patients have never been reported. In addition, these 36 AML patient samples have been treated ex vivo with VEN and combinations with AraC or AZA. The results collected so far are very promising with some metabolic clusters being completely abrogated by VEN while others are enriched. The proteins characteristic of these enriched clusters post-treatments could therefore correspond to new targets to propose even more efficient therapeutic combinations and/or help monitoring the response to the treatment.
Importantly, all adult primary AML samples included in this cohort have already been characterized by Dr Sarry team as high (14 samples) or low (13 samples) responders to AraC in vivo using NSG mice-based PDX models. Therefore, we are currently analyzing deeper the data to evaluate if there is a correlation between enrichment in some of these subpopulations at diagnosis and the status of response to AraC in vivo. Identification of specific subpopulations at diagnosis in samples characterized as low responders could give further insights in the resistance to AraC. In addition, this could lead to design a flow cytometry panel combining the markers representing the clusters characteristic of resistance applicable in clinics to predict which patients will benefit from conventional chemotherapy. The same pipeline will be implemented for VEN and new combinations.
Using this approach first on healthy donor BM, we were able to depict the different lineages trajectories using only metabolic markers, highlighting the importance to integrate metabolism to better characterize the hematopoietic hierarchy. Then, we used the same approach to measure the expression of the 76 proteins in our AML cohort (27 adult and 9 pediatric). We observed major differences in the metabolic clusters abundances in pediatric compared to adult samples, highlighting key metabolic differences in function of the age of the patients. To our knowledge, metabolic dependencies of pediatric AML patients have never been reported. In addition, these 36 AML patient samples have been treated ex vivo with VEN and combinations with AraC or AZA. The results collected so far are very promising with some metabolic clusters being completely abrogated by VEN while others are enriched. The proteins characteristic of these enriched clusters post-treatments could therefore correspond to new targets to propose even more efficient therapeutic combinations and/or help monitoring the response to the treatment.
Importantly, all adult primary AML samples included in this cohort have already been characterized by Dr Sarry team as high (14 samples) or low (13 samples) responders to AraC in vivo using NSG mice-based PDX models. Therefore, we are currently analyzing deeper the data to evaluate if there is a correlation between enrichment in some of these subpopulations at diagnosis and the status of response to AraC in vivo. Identification of specific subpopulations at diagnosis in samples characterized as low responders could give further insights in the resistance to AraC. In addition, this could lead to design a flow cytometry panel combining the markers representing the clusters characteristic of resistance applicable in clinics to predict which patients will benefit from conventional chemotherapy. The same pipeline will be implemented for VEN and new combinations.