Great post. I’m a clinical-translational lymphoma researcher and all of the issues you describe are a critical issue in moving our field forward.
I share your optimism that ML will be able to help us find features of cancer that humans would never be able to discover due to the sheer amount of data. In the past few years we have developed ML methods to decipher different subcategories of diffuse large B cell lymphoma (DLBCL, the most common lymphoma) using genomic and multi-omic strategies. We now have several competing systems of categorizing by both the lymphomas themselves and the way they interact with the other cells that play a critical role in their survival and outcomes of treatment (the microenvironment).
However, these are a) extremely expensive to do on any individual patient, b) time consuming, and c) are yet to be clinically actionable. Identifying smaller and smaller subgroups of lymphomas (we went from having basically “Hodgkin” and “non-Hodgkin lymphoma” only to having dozens of subclassifications of DLBCL in a span of only a few decades) is critical for prognostication- but we haven’t yet been able to actually say “drug X will work better than drug Y in Z subtype of DLBCL with micro environment signature A”. Critically, the more you subclassify the harder it is to actually get sufficient numbers for clinical trials.
I am hopeful that ML may help us to find those clues we haven’t found yet, and find actionable solutions with smaller ns for trials. As you say, cancer has a surprising amount of detail- but we need help incorporating all of the exponentially increasing classifiers.
Great post. I’m a clinical-translational lymphoma researcher and all of the issues you describe are a critical issue in moving our field forward.
I share your optimism that ML will be able to help us find features of cancer that humans would never be able to discover due to the sheer amount of data. In the past few years we have developed ML methods to decipher different subcategories of diffuse large B cell lymphoma (DLBCL, the most common lymphoma) using genomic and multi-omic strategies. We now have several competing systems of categorizing by both the lymphomas themselves and the way they interact with the other cells that play a critical role in their survival and outcomes of treatment (the microenvironment).
However, these are a) extremely expensive to do on any individual patient, b) time consuming, and c) are yet to be clinically actionable. Identifying smaller and smaller subgroups of lymphomas (we went from having basically “Hodgkin” and “non-Hodgkin lymphoma” only to having dozens of subclassifications of DLBCL in a span of only a few decades) is critical for prognostication- but we haven’t yet been able to actually say “drug X will work better than drug Y in Z subtype of DLBCL with micro environment signature A”. Critically, the more you subclassify the harder it is to actually get sufficient numbers for clinical trials.
I am hopeful that ML may help us to find those clues we haven’t found yet, and find actionable solutions with smaller ns for trials. As you say, cancer has a surprising amount of detail- but we need help incorporating all of the exponentially increasing classifiers.