Periodic Reporting for period 1 - MaCaROM (Mapping Cancer Response using Organoids and Mass cytometry)
Reporting period: 2021-04-01 to 2023-03-31
Advances in breast cancer drug discovery are limited by the absence of a relevant research model. Cancer cell lines and mice are the most widely used models but they fail at recapitulating several cancer features including drug responses. Patient-derived organoids (PDOs) have been demonstrated to capture the main biological features of the primary tumor. Furthermore, PDOs recapitulate the patients’ response to anticancer treatment which makes them the new gold standard model in the study of cancer response to drugs.
Using such a model to study cancer would be an unvaluable asset to better understand drug response and predict which drug should be used for each patient. This would improve cancer healthcare by increasing the overall survival of cancer patients, and improve the healthcare system by giving the best drug possible to each patient.
Here, we created breast cancer PDOs in order to better understand drug responses of breast cancer patients. We used several quality controls to make sure that the generated PDOs recapitulate the main features of their tissue of origin and we studied them in order to better understand the correspondence between a tumor and its probability to be sensitive to a given drug treatment. We will then reference all these response data in order to make them available to clinicians so that it can be a new resource to help guide the treatment course of future cancer patients.
Using such a model to study cancer would be an unvaluable asset to better understand drug response and predict which drug should be used for each patient. This would improve cancer healthcare by increasing the overall survival of cancer patients, and improve the healthcare system by giving the best drug possible to each patient.
Here, we created breast cancer PDOs in order to better understand drug responses of breast cancer patients. We used several quality controls to make sure that the generated PDOs recapitulate the main features of their tissue of origin and we studied them in order to better understand the correspondence between a tumor and its probability to be sensitive to a given drug treatment. We will then reference all these response data in order to make them available to clinicians so that it can be a new resource to help guide the treatment course of future cancer patients.
For the MACAROM project, a biobank of breast cancer PDOs was generated using a protocol that was optimized during one year.
Several experiments were then performed in order to demonstrate the closeness of these PDOs to their tissue of origin, which is an essential condition to their use in a preclinical setting. Precisely, we performed a comparison of breast cancer PDOs with their tissues of origin using a technology called ‘mass cytometry’ that allows to quantify the protein expression of each sample at the single-cell level. This approach revealed that breast cancer PDOs recapitulate the main protein expression features of their tissues of origin and that PDOs formation can be predicted using the expression of only two proteins of the primary tissue.
Single-cell RNA sequencing of the breast cancer PDOs was also performed in order to better capture the whole expression profile of the generated tissues. This experiment uncovered the fact that every PDOs sample had a unique expression profile which is reminiscent of the intertumor heterogeneity observed in primary tumors. Moreover, comparison to our breast cancer PDOs dataset to external primary tissues datasets revealed that breast cancer PDOs recapitulate the main breast cancer lineages expression profiles observed all breast cancer primary tissues datasets tested.
We also inferred the CNV profiles of our generated PDOs to perform a quality control aiming at ensuring that our PDOs are cancer tissues. This experiment confirmed that breast cancer PDOs contained massive CNV alterations compared to the healthy breast tissues that was used in control.
After performing all these quality checks to ensure that our generated PDOs samples are of the highest quality, we did a screening using 43 drugs targeting the main cancer-related proteins. This experiment will show the ability of each breast cancer PDOs to respond or resist to these 43 anticancer drugs, and will make allow us to study the drug response of cancer models that recapitulate their tissue of origin at the single-cell level. Moreover, this study will also include the RNA profile of the untreated breast cancer PDOs and the clinical data of each patient to evaluate if these data can be used to predict the response to specific drugs.
The results of the MACAROM project were presented in different conferences and events. I attended the ETH retreat in Davos and Single-cell Genomics 2022 in Utrecht which gave me the opportunity to communicate my results and get the scientific community’s attention. I also presented my work in regular meetings in the University of Zurich department seminars, the ETH department seminars and the PATH ‘Patients Tumors Bank of Hope’ meetings which all allowed me to directly reach the potential users of the results of the project as the audience was mostly made of clinicians and researchers in cancer biology.
More international conferences will be attended to ensure the international dissemination of the MACAROM project.
The results of the project will also be published in different scientific journals. We will be split the results into two different manuscripts which will be submitted for publication in the coming months. The first study will be about the generation of high-quality breast cancer PDOs and is almost ready to be submitted. The second study will be about the screening and will take more time to be prepared and submitted, it will be submitted at the end of this year.
Other planned dissemination activities will be implemented once the results of the project are finalized. A website will be created as the core of the ‘mapping’ concept. This will allow anyone to access the data in a simple and interactive way in order to see the correspondence between sample information, RNA profiles, drug response and clinical data. This will be presented in a way that is easily understandable and that highlights the fact that results can be referenced in order to help guide future patients’ treatments.
Several experiments were then performed in order to demonstrate the closeness of these PDOs to their tissue of origin, which is an essential condition to their use in a preclinical setting. Precisely, we performed a comparison of breast cancer PDOs with their tissues of origin using a technology called ‘mass cytometry’ that allows to quantify the protein expression of each sample at the single-cell level. This approach revealed that breast cancer PDOs recapitulate the main protein expression features of their tissues of origin and that PDOs formation can be predicted using the expression of only two proteins of the primary tissue.
Single-cell RNA sequencing of the breast cancer PDOs was also performed in order to better capture the whole expression profile of the generated tissues. This experiment uncovered the fact that every PDOs sample had a unique expression profile which is reminiscent of the intertumor heterogeneity observed in primary tumors. Moreover, comparison to our breast cancer PDOs dataset to external primary tissues datasets revealed that breast cancer PDOs recapitulate the main breast cancer lineages expression profiles observed all breast cancer primary tissues datasets tested.
We also inferred the CNV profiles of our generated PDOs to perform a quality control aiming at ensuring that our PDOs are cancer tissues. This experiment confirmed that breast cancer PDOs contained massive CNV alterations compared to the healthy breast tissues that was used in control.
After performing all these quality checks to ensure that our generated PDOs samples are of the highest quality, we did a screening using 43 drugs targeting the main cancer-related proteins. This experiment will show the ability of each breast cancer PDOs to respond or resist to these 43 anticancer drugs, and will make allow us to study the drug response of cancer models that recapitulate their tissue of origin at the single-cell level. Moreover, this study will also include the RNA profile of the untreated breast cancer PDOs and the clinical data of each patient to evaluate if these data can be used to predict the response to specific drugs.
The results of the MACAROM project were presented in different conferences and events. I attended the ETH retreat in Davos and Single-cell Genomics 2022 in Utrecht which gave me the opportunity to communicate my results and get the scientific community’s attention. I also presented my work in regular meetings in the University of Zurich department seminars, the ETH department seminars and the PATH ‘Patients Tumors Bank of Hope’ meetings which all allowed me to directly reach the potential users of the results of the project as the audience was mostly made of clinicians and researchers in cancer biology.
More international conferences will be attended to ensure the international dissemination of the MACAROM project.
The results of the project will also be published in different scientific journals. We will be split the results into two different manuscripts which will be submitted for publication in the coming months. The first study will be about the generation of high-quality breast cancer PDOs and is almost ready to be submitted. The second study will be about the screening and will take more time to be prepared and submitted, it will be submitted at the end of this year.
Other planned dissemination activities will be implemented once the results of the project are finalized. A website will be created as the core of the ‘mapping’ concept. This will allow anyone to access the data in a simple and interactive way in order to see the correspondence between sample information, RNA profiles, drug response and clinical data. This will be presented in a way that is easily understandable and that highlights the fact that results can be referenced in order to help guide future patients’ treatments.
The MACAROM project led to the discovery of groundbreaking concepts.
The mass cytometric comparison of breast cancer PDOs with their tissues of origin represent the first description of the biases induced by ex vivo culturing and shows that several proteins expression are modulated systematically due to the PDOs generation protocol. This will lead to the new concept where ex vivo-generated tissues will have to be extensively described before being studied. Moreover, this experiment uncovered that PDOs formation can be predicted using single-cell expression of two proteins of the primary tissue, leading to a breakthrough where patients’ compatibility with the ex vivo culturing-associated therapies can be evaluated by clinicians using only a dual protein staining as routinely done in pathology.
The screening that has been performed and is currently being analysed will uncover the single-cell drug response of PDOs that are recapitulating their tissues of origin. We anticipate that this part of the study will lead to a better understanding of drug response in complex biological models, to a better understanding of the correspondence between a response and the overall RNA and protein composition of a sample and to new therapeutic option that have not been explored so far due to the lack of a biological model that recapitulates the complexity of primary tumors.
All the above-mentioned discoveries are related to the concept of personalized medicine that aims at finding the right cure for the right patient. Here, after rigorously validating that our generated PDOs samples recapitulate primary tumor heterogeneity, we compared PDOs to their tissues of origin leading to the ability to precisely identify primary tissues that can form PDOs; and we are currently working on better understanding the drug response at the single-cell level in PDOs that capture primary tissues complexity and heterogeneity, leading to the ability to map the existing cancer response and to possibly guide future patients’ treatment.
The mass cytometric comparison of breast cancer PDOs with their tissues of origin represent the first description of the biases induced by ex vivo culturing and shows that several proteins expression are modulated systematically due to the PDOs generation protocol. This will lead to the new concept where ex vivo-generated tissues will have to be extensively described before being studied. Moreover, this experiment uncovered that PDOs formation can be predicted using single-cell expression of two proteins of the primary tissue, leading to a breakthrough where patients’ compatibility with the ex vivo culturing-associated therapies can be evaluated by clinicians using only a dual protein staining as routinely done in pathology.
The screening that has been performed and is currently being analysed will uncover the single-cell drug response of PDOs that are recapitulating their tissues of origin. We anticipate that this part of the study will lead to a better understanding of drug response in complex biological models, to a better understanding of the correspondence between a response and the overall RNA and protein composition of a sample and to new therapeutic option that have not been explored so far due to the lack of a biological model that recapitulates the complexity of primary tumors.
All the above-mentioned discoveries are related to the concept of personalized medicine that aims at finding the right cure for the right patient. Here, after rigorously validating that our generated PDOs samples recapitulate primary tumor heterogeneity, we compared PDOs to their tissues of origin leading to the ability to precisely identify primary tissues that can form PDOs; and we are currently working on better understanding the drug response at the single-cell level in PDOs that capture primary tissues complexity and heterogeneity, leading to the ability to map the existing cancer response and to possibly guide future patients’ treatment.