A few weeks ago my dad was diagnosed with Stage 4 Cancer of Unknown Primary. He felt healthy other than a swollen lymph node in his neck. Despite multiple tests and images, no primary tumor has been found. Chemo starts next week.
Cancer of Unknown Primary isn’t a diagnosis anyone wants to hear. It means that no one knows where your cancer cells came from. It means no one knows their weak link, what they are most susceptible to, what chemo regimen is most likely to kill them. This makes for a poor prognosis.
Coincidentally, a few days before my dad’s diagnosis, I published a study which found that cancer datasets are less likely to be widely available for further research than similar datasets outside cancer.
That finding hurts. It hurt in theory then, and it hurts in practice now.
Combining and reanalyzing datasets may be one of the best ways we have to make big advances in rare, understudied, and underfunded diseases. Cancer of Unknown Primary is particularly well poised to benefit from reanalysis across diverse studies, putting data to use from sources totally unrelated to this particular diagnosis.
The research we do, as scientists? And the research we would do but we can’t, because we can’t build on work that others have done?
Edited to add: My dad died of cancer less than two years after diagnosis, on April 15 2013. The type of his cancer was never determined.