Which is a suggestive model, by the way, not direct evidence. The best direct evidence would be to restore the pace of immune cell creation - such as via regeneration of the thymus (via FOXN1, or BMP4, or tissue engineering), cell therapy to replace or increase the pool of hematopoietic stem cells, etc. - and then see what happens. This sort of thing is five years out; there are people working on it, though more of those and more funding would certainly be a good thing. It would certainly be possible to push forward a startup today based on thymic regeneration or hematopoietic stem cell transplantation; the science is more or less ready for it.
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T cells develop from hematopoietic stem cells as part of the lymphoid lineage and have the ability to detect foreign antigens and neoantigens arising from cancer cells. In the thymus, lymphoid progenitors commit to a specific T cell receptor and undergo selection events that screen against self-reactivity. Cells that pass these selection gates then leave the thymus, clonally expanding to form the patrolling naive T cell pool.
The vast majority of vertebrates experience thymic involution (or atrophy) in which thymic epithelial tissue is replaced with adipose tissue, resulting in decreasing T cell export from the thymus. In humans, this is thought to begin as early as 1 year of age. The rate of thymic T cell production is estimated to decline exponentially over time with a half-life of ∼15.7 years. Declining production of new naive T cells is thought to be a significant component of immunosenescence, the age-related decline in immune system function. With the recent successes of T cell-based immunotherapies, it is timely to assess how thymic involution may affect cancer and infectious disease incidence.
It is clear from epidemiological data that incidence of infectious disease and cancer increases dramatically with age, and, specifically, that many cancer incidence curves follow an apparent power law. The simplest model to account for this assumes that cancer initiation is the result of a gradual accumulation of rare "driver" mutations in one single cell. Furthermore, the fitting of this power law model (PLM) can be used to estimate the number of such mutations. Exponential curves have also been used to fit cancer incidence data, resulting in worse fits than the PLM overall. Nevertheless, it is worth noting that exponential rates close to the declining curve for thymic T cell production can be seen to emerge from the incidence data, indicating the relevance of the thymic involution timescale. While the PLM fits well, it does not account for changes in the immune system with age. To better determine the processes underlying carcinogenesis, we asked whether an alternative model, based only on age-related changes in immune system function, might partly or entirely explain cancer incidence.
Our model outperforms the power law model with the same number of fitting parameters in describing cancer incidence data across a wide spectrum of different cancers, and provides excellent fits to infectious disease data. Our hypothesis and results add to the understanding of infectious disease and cancer incidence, suggesting in the latter case that immunosenescence, rather than gradual accumulation of mutations, serves as the predominant reason for an increase in cancer incidence with age for many cancers. For future therapies, including preventative therapies, strengthening the functionality of the aging immune system appears to be more feasible than limiting genetic mutations, which raises hope for effective new treatments.
Which is a suggestive model, by the way, not direct evidence. The best direct evidence would be to restore the pace of immune cell creation - such as via regeneration of the thymus (via FOXN1, or BMP4, or tissue engineering), cell therapy to replace or increase the pool of hematopoietic stem cells, etc. - and then see what happens. This sort of thing is five years out; there are people working on it, though more of those and more funding would certainly be a good thing. It would certainly be possible to push forward a startup today based on thymic regeneration or hematopoietic stem cell transplantation; the science is more or less ready for it.
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T cells develop from hematopoietic stem cells as part of the lymphoid lineage and have the ability to detect foreign antigens and neoantigens arising from cancer cells. In the thymus, lymphoid progenitors commit to a specific T cell receptor and undergo selection events that screen against self-reactivity. Cells that pass these selection gates then leave the thymus, clonally expanding to form the patrolling naive T cell pool.
The vast majority of vertebrates experience thymic involution (or atrophy) in which thymic epithelial tissue is replaced with adipose tissue, resulting in decreasing T cell export from the thymus. In humans, this is thought to begin as early as 1 year of age. The rate of thymic T cell production is estimated to decline exponentially over time with a half-life of ∼15.7 years. Declining production of new naive T cells is thought to be a significant component of immunosenescence, the age-related decline in immune system function. With the recent successes of T cell-based immunotherapies, it is timely to assess how thymic involution may affect cancer and infectious disease incidence.
It is clear from epidemiological data that incidence of infectious disease and cancer increases dramatically with age, and, specifically, that many cancer incidence curves follow an apparent power law. The simplest model to account for this assumes that cancer initiation is the result of a gradual accumulation of rare "driver" mutations in one single cell. Furthermore, the fitting of this power law model (PLM) can be used to estimate the number of such mutations. Exponential curves have also been used to fit cancer incidence data, resulting in worse fits than the PLM overall. Nevertheless, it is worth noting that exponential rates close to the declining curve for thymic T cell production can be seen to emerge from the incidence data, indicating the relevance of the thymic involution timescale. While the PLM fits well, it does not account for changes in the immune system with age. To better determine the processes underlying carcinogenesis, we asked whether an alternative model, based only on age-related changes in immune system function, might partly or entirely explain cancer incidence.
Our model outperforms the power law model with the same number of fitting parameters in describing cancer incidence data across a wide spectrum of different cancers, and provides excellent fits to infectious disease data. Our hypothesis and results add to the understanding of infectious disease and cancer incidence, suggesting in the latter case that immunosenescence, rather than gradual accumulation of mutations, serves as the predominant reason for an increase in cancer incidence with age for many cancers. For future therapies, including preventative therapies, strengthening the functionality of the aging immune system appears to be more feasible than limiting genetic mutations, which raises hope for effective new treatments.
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