The ultrasensitive detection of clinically relevant somatic alterations in cancer genomes has great potential for impacting patient care, e.g. for early detection, establishing diagnoses, refining prognoses, guiding treatment, and monitoring recurrence. However, current technologies are poorly suited to the robust detection of somatic mutations present at very low frequencies. Massively parallel sequencing represents one path forward, but its sensitivity to detect very rare events is fundamentally constrained by the sequencing error rate. Our goal is to develop a new experimental paradigm that overcomes this limitation. In our approach, each copy of a target sequence that is present in a sample is molecularly tagged during the first cycle of a multiplex capture reaction with a unique barcode sequence. After amplification, target amplicons and their corresponding barcodes are subjected to massively parallel sequencing. During analysis, the barcodes are used to associate sequence reads sharing a common origin. Through oversampling, barcode-associated reads error-correct one another to yield an independent haploid consensus for each progenitor molecule, i.e. """"""""molecular counting"""""""". Furthermore, the collapsing of commonly derived reads inherently corrects for any allele-specific bias during amplification, such that estimates of mutant allele frequency can be accompanied by precise confidence bounds. In our first aim, we will develop experimental methods and analytical tools that enable the robust detection of targeted somatic mutations via molecular counting to frequencies as low as 1 mutated copy in a background of 100,000 unmutated copies. In our second aim, we will develop three ultrasensitive, multiplex molecular counting assays that are specifically targeted at panels of clinically relevant cancer mutations or genes, and rigorously evaluate these for reproducibility. The availability of robust, cost-effective, generically applicable tools for the ultrasensitive, multiplex detection of rare somatic events will be a transformative step forward for the translation of discoveries in cancer genetics to a clinical setting.
As we enter an era of personalized medicine, DNA sequencing technology will be increasingly important to public health, contributing towards the unraveling of the genetic basis of human disease, as well as for clinical diagnostics. This proposal aims to develop ultrasensitive methods for detecting cancer-relevant mutations in tumor samples. These technologies have the potential to directly enable the translation of discoveries made in cancer genetics to clinical applications such as the early detection of cancer and the monitoring of patients for cancer recurrence.
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