Abstract
Current approach for the detection of cancer is based on identifying genetic mutations typical to tumor cells. This approach is effective only when cancer has already emerged, however, it might be in a stage too advanced for effective treatment. Cancer is caused by the continuous accumulation of mutations; is it possible to measure the time-dependent information of mutation accumulation and predict the emergence of cancer? We hypothesize that the mutation history derived from the tandem repeat regions in blood-derived DNA carries information about the accumulation of the cancer driver mutations in other tissues. To validate our hypothesis, we computed the mutation histories from the tandem repeat regions in blood-derived exomic DNA of 3874 TCGA patients with different cancer types and found a statistically significant signal with specificity ranging from 66% to 93% differentiating Glioblastoma patients from other cancer patients. Our approach and findings offer a new direction for future cancer prediction and early cancer detection based on information derived from blood-derived DNA.
Highlights
Cancer is the second leading cause of death in the world [1]
We search for associations between the mutation histories of tandem repeat regions of blood-derived DNA with the cancer of the individual. We conducted this experiment on 3874 samples obtained using Whole Exome Sequencing (WXS) on The Cancer Genome Atlas (TCGA) [15] and found that the mutation histories of tandem repeat regions in blood-derived DNA of patients with Glioblastoma (GBM) is statistically different from those of patients with other cancer types which we describe in detail
We reconstruct mutation histories for all the short tandem repeat regions in exomic DNA derived from blood cells using the algorithm stated in [22]
Summary
Cancer is the second leading cause of death in the world [1]. It has been widely accepted that cancer is caused by the continuous accumulation of mutations over an individual’s lifetime [2]. Most studies in the past have focused on detecting these cancer mutations by studying tumor DNA against normal DNA [3, 4] This approach has proven useful in identifying cancer genes like TP53, BRCA, HER2, to name a few [3, 5]. These works have shown that tumor genomes have significantly more genes with repeat instabilities, linking microsatellite instability to colorectal [6] and other cancers [6,7,8,9,10,11]. The molecular timing of different driver mutations in the tumor was estimated by analyzing their presence in copied segments spanning the tumor genome [13]
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