Somatic cancer mutation detection at ultra-low variant allele frequencies (VAFs) is an unmet challenge that is intractable with current state-of-the-art mutation calling methods. Specifically, the limit of VAF detection is closely related to the depth of coverage, due to the requirement of multiple supporting reads in extant methods, precluding the detection of mutations at VAFs that are orders of magnitude lower than the depth of coverage. Nevertheless, the ability to detect cancer-associated mutations in ultra low VAFs is a fundamental requirement for low-tumor burden cancer diagnostics applications such as early detection, monitoring, and therapy nomination using liquid biopsy methods (cell-free DNA). Here we defined a spatial representation of sequencing information adapted for convolutional architecture that enables variant detection at VAFs, in a manner independent of the depth of sequencing. This method enables the detection of cancer mutations even in VAFs as low as 10x-4^, >2 orders of magnitude below the current state-of-the-art. We validated our method on both simulated plasma and on clinical cfDNA plasma samples from cancer patients and non-cancer controls. This method introduces a new domain within bioinformatics and personalized medicine – somatic whole genome mutation calling for liquid biopsy. The cancer genome acquires somatic mutations which drive its proliferative capacity BID8 . Mutations in the cancer genome also provide critical information regarding the evolutionary history and mutational processes active in each cancer (Martincorena et al., 2017; BID0 . Cancer mutation calling in patient tumor biopsies has become a pivotal step in determining patient outcomes and nomination of personalized therapeutics.Identifying cancer mutations in liquid biopsy techniques, such as cell-free circulating DNA (cfDNA), has been suggested as a transformative platform for early-stage cancer screening and residual disease monitoring. cfDNA released from dying tumor cells enables surveys the somatic genome dynamically over time for clinical purposes, empowered by the ability to obtain cancer-related genetic material non-invasively through a simple blood draw. Circulating tumor DNA (ctDNA) can be found and measured in the plasma cfDNA of cancer patients. ctDNA was shown to correlate with tumor burden and change in response to treatment or surgery BID4 . For example, ctDNA can be detected even in early stage non-small cell lung cancer (NSCLC) and therefore has the potential to transform NSCLC diagnosis and treatment BID15 BID17 BID1 BID20 . Nevertheless, the fraction of ctDNA of the total cfDNA is typically exceedingly low, especially in low disease-burden contexts such as early detection or detection of residual disease after therapeutic interventions. While detection of cancer through cfDNA in the low disease-burden setting may be of significant clinical benefit, it challenges our current methods for identifying somatic mutations due to the ultra-low VAFs compared with the available depth of sequencing.The most common type of somatic mutations is single-nucleotide variants (SNVs) , which occur at a frequency of 1-100 per million bases. These variants are typically identified in sequencing data through a careful comparison of the DNA sequencing reads which map to a particular genomic locus in both the cancer DNA and the matched germline DNA. This process has been enabled through tools of ever-increasing sophistication that refine the statistical comparison between the number of reads supporting a candidate mutated variant in the cancer vs. the germline sample BID39 BID42 .These statistical methods fundamentally require multiple independent observations (supporting reads) of the somatic variant at any given genomic location to distinguish true mutations from sequencing artifacts. Mutect , a state-of-the-art low-allele frequency somatic mutation caller, subjects each SNV to Bayesian classifiers that assume that the SNV either results from sequencing noise or that the site contains a true cancer variant. A true cancer-related SNV call is made when the log-likelihood ratio from the two models strongly favors the true cancer Bayesian classifier. This " locus-centric" type of cancer mutation detection can be readily achieved through increased depth of sequencing -so long as the tumor sample contains a high proportion of tumor DNA. However , these methods are significantly challenged in the ctDNA setting where the VAF is expected to be well below 1%. For example , a decrease of VAF to 5% and sequencing depth to 10X resulted in a decreased in the sensitivity of Mutect to below 0.1 BID39 BID42 . Thus, locus-centric mutation callers are unable to perform effective mutation calling in the ultra-low VAFs observed in low disease-burden cfDNA settings.We reasoned that to tackle this challenge, we would need a novel mutation detection framework. Specifically, we would need methodology to accurately distinguish true somatic cancer mutations from sequencing artifacts, even in ultra low tumor fractions that preclude the presence of multiple supporting independent observations (reads) in any given genomic location. We propose a "readcentric " alternative approach, and developed a convolutional neural network classifier -Kittyhawk -trained to discriminate between individual sequencing reads containing sequencing artifacts and sequencing reads harboring somatic cancer mutations. We take advantage of the fact that both cancer mutations and sequencing errors are systemic and governed by distinct signatures that can be learned and used for efficient signal to noise discrimination (e.g., mutagenesis processes such as exposure to tobacco or UV light are enriched in specific sequence contexts; BID0 ) 0.01%-1%, as well as with cfDNA samples from patients with early stage lung cancer and an individual with non-malignant lung nodules as controls. Ultra-low tumor fraction such as observed in cfDNA fundamentally challenge the prevailing mutation calling paradigm. State-of-the-art mutation callers share a common unifying principle: mutation calling at a particular genomic location based on the observation of the cancer variant in multiple FIG6 : PPV, enrichment, and sensitivity of CA0044 synthetic cfDNA.overlapping reads. However, in the ultra-low tumor fraction context, at best, only a single mutated read is observed, limiting the ability of traditional mutation calling.The need for extending the mutation-calling framework to ultra-low tumor fraction contexts motivated us to rethink the mutation calling process from a locus-centric approach to a read-centric approach. This approach uses every individual read as input for a classifier and lends itself to the application of convolutional neuronal network learning. To realize this novel methodology, we embodied the information captured in the sequencing read (nucleotide sequence, context, quality metrics) in a spatial representation typically applied for image analysis. While we anticipate that our ongoing efforts to include larger training datasets, will result in further performance improvement, even at this proof-of-principle stage the algorithm is providing a 30-fold enrichment in a manner that is completely independent from variant allele fraction or depth of coverage, a unique performance feature that addresses a major emerging unmet need. Indeed, stable enrichment performance extends to tumor fractions as low as 10 4 .While Kittyhawk captures position in the read by using a fully connected sigmoid layer, there are other architectures, which may be suited for capturing relative position on the read. Additionally , we have excluded an extra source of information contained in the read-pair that comes from the DNA fragment. The read pair can be used to determine both the strand of origin (Watson or Crick) and to estimate the DNA fragment size. It has been observed that ctDNA have a distinct fragment size distribution compared to other cfDNA from normal cells BID19 . It has been shown that recurrent neural networks (RNN) are a powerful tool for using length as a feature in bioinformatics at distances even up to 1kb, far beyond the size of a ctDNA fragment BID6 . These results suggest that integrating an RNN instead of a logistic regression layer could increase performance even further. In addition, while Kittyhawk was developed for the context of low tumor fraction mutation calling in cfDNA, we note that this framework can be adapted to other contexts. For example, it may be used in mutation (or germline SNP) detection in low pass genome sequencing (0.01-1X) across a wide range of applications. Furthermore, a read-centric approach may be also integrated with a more traditional locus-centric mutation calling approach, by adding Kittyhawk predictions as an additional input metric for extant statistical or machine learning mutation calling algorithms.In summary, Kittyhawk is the first somatic mutation caller designed specifically to function in the ultra-low allele frequency setting where at best a single supporting read is available for candidate mutation identification, such as liquid biopsy for early stage cancer detection. We apply a novel representation of a read together with a hand-engineered architecture to capture the entirety of informative features associated with a read and its alignment. This work sets the stage for a new family of somatic mutation callers to aid detection in liquid biopsy, paving the way for pivotal non-invasive screening and prognosis.
 Current somatic mutation methods do not work with liquid biopsies (ie low coverage sequencing), we apply a CNN architecture to a unique representation of a read and its ailgnment, we show significant improvement over previous methods in the low frequency setting. Proposes a CNN based solution called Kittyhawk for somatic mutation calling at ultra low allele frequencies. A new algorithm to detect cancer mutations from sequencing cell free DNA that will identify the sequence context that characterize sequencing errors from true mutations. This paper proposes a deep learning framework to predict somatic mutations at extremely low frequencies which occurs in detecting tumor from cell-free DNA