Periodic Reporting for period 1 - BCRES-CLL (B cell receptor engagement and signalling in chronic lymphocytic leukemia: identify the structural and functional requirements for disease development and progression.)
Reporting period: 2022-03-31 to 2024-03-30
B-cell chronic lymphocytic leukemia (CLL) is a common type of cancer where a specific type of white blood cell (called B cells or B lymphocytes) grows out of control. It's characterized by the presence, on the leukemic cells, of a protein called CD5. The disease is quite challenging to treat, and we still don't fully understand why it develops or how it progresses. This project aimed to uncover part of the mysteries behind the role of a key protein called the B-cell receptor (BCR) in CLL. A better understanding of how the BCR works in this disease could lead to new treatments.
Current therapies offer some relief and extend the lives of patients with CLL, but they also come with side effects, unintended effects, and the problem of tumors becoming resistant to treatment. Moreover, since these treatments don't provide a complete cure, they also put a heavy burden on healthcare systems worldwide. Thus, a better understanding of the cellular mechanisms involved in CLL will allows for the development of novel, more potent therapies, directly benefit patients by reducing side effects and prolonging remission periods, and indirectly by easing the financial strain on healthcare systems.
The goal of the project included the identification of several features implicated with the key protein of the CLL leukemic cells, the BCR, the link of these feature with the various ways a CLL cell can uncontrollably grow in response to agents present in our bodies, and the identification of genes involved in this process that could be potentially exploited for the development of novel therapies. Concomitantly, to carry this goal out, the project aimed to develop a machine learning algorithm that could help to identify non-obvious relationships among the different clinical and biological features evaluated.
Current therapies offer some relief and extend the lives of patients with CLL, but they also come with side effects, unintended effects, and the problem of tumors becoming resistant to treatment. Moreover, since these treatments don't provide a complete cure, they also put a heavy burden on healthcare systems worldwide. Thus, a better understanding of the cellular mechanisms involved in CLL will allows for the development of novel, more potent therapies, directly benefit patients by reducing side effects and prolonging remission periods, and indirectly by easing the financial strain on healthcare systems.
The goal of the project included the identification of several features implicated with the key protein of the CLL leukemic cells, the BCR, the link of these feature with the various ways a CLL cell can uncontrollably grow in response to agents present in our bodies, and the identification of genes involved in this process that could be potentially exploited for the development of novel therapies. Concomitantly, to carry this goal out, the project aimed to develop a machine learning algorithm that could help to identify non-obvious relationships among the different clinical and biological features evaluated.
During the project, the CLL cells were stimulated in different conditions that mimic the activation of the BCR happening in the patient. These cells were then evaluated for the levels of key biological markers such as the increased levels of messengers associated with the CLL ability to grow out of control. Similarly, we dissected the overall profile of genes that the CLL cells use or inhibit during the growth associated with the stimulation.
In parallel, we developed a machine learning technology that can take all these measurements and check, in a way more detailed than any human could do, specific associations between some of these parameters. For example, CLL cells growing quicker in response to BCR stimulation presented a link between intracellular calcium ions, production of reactive oxygen species, production of energy, and shorter time between diagnosis and begin of treatment. Hence, by identifying these associations, we can potentially develop novel drugs that target (and block) this axis, from stimulation to growth.
In parallel, we developed a machine learning technology that can take all these measurements and check, in a way more detailed than any human could do, specific associations between some of these parameters. For example, CLL cells growing quicker in response to BCR stimulation presented a link between intracellular calcium ions, production of reactive oxygen species, production of energy, and shorter time between diagnosis and begin of treatment. Hence, by identifying these associations, we can potentially develop novel drugs that target (and block) this axis, from stimulation to growth.
The findings of these project were published in eight articles in international scientific journals and presented at four scientific conferences, marking a significant advancement toward the improvement of the prognosis and treatment of Chronic Lymphocytic Leukemia (CLL).
Particularly noteworthy was the discovery of connections between the BCR, specific signals within the leukemic cells, and the ability of these signaling in promoting the growth of the leukemia. These mechanisms will be leveraged to develop new drugs that enhance the quality of life for CLL patients and alleviate the strain on healthcare systems caused by the high cost of existing treatments that don't offer a cure.
Furthermore, the machine learning techniques developed in this research hold promise for improving the analysis of future studies and translating findings into clinical practice. This could lead to better prognoses and personalized treatment options based on biological markers that can be routinely tested. Additionally, this analytical approach could be adapted for similar purposes in other types of blood cancers.
Particularly noteworthy was the discovery of connections between the BCR, specific signals within the leukemic cells, and the ability of these signaling in promoting the growth of the leukemia. These mechanisms will be leveraged to develop new drugs that enhance the quality of life for CLL patients and alleviate the strain on healthcare systems caused by the high cost of existing treatments that don't offer a cure.
Furthermore, the machine learning techniques developed in this research hold promise for improving the analysis of future studies and translating findings into clinical practice. This could lead to better prognoses and personalized treatment options based on biological markers that can be routinely tested. Additionally, this analytical approach could be adapted for similar purposes in other types of blood cancers.