SummaryImmune evasion is a hallmark of cancer. Losing the ability to present neoantigens through human leukocyte antigen (HLA) loss may facilitate immune evasion. However, the polymorphic nature of the locus has precluded accurate HLA copy-number analysis. Here, we present loss of heterozygosity in human leukocyte antigen (LOHHLA), a computational tool to determine HLA allele-specific copy number from sequencing data. Using LOHHLA, we find that HLA LOH occurs in 40% of non-small-cell lung cancers (NSCLCs) and is associated with a high subclonal neoantigen burden, APOBEC-mediated mutagenesis, upregulation of cytolytic activity, and PD-L1 positivity. The focal nature of HLA LOH alterations, their subclonal frequencies, enrichment in metastatic sites, and occurrence as parallel events suggests that HLA LOH is an immune escape mechanism that is subject to strong microenvironmental selection pressures later in tumor evolution. Characterizing HLA LOH with LOHHLA refines neoantigen prediction and may have implications for our understanding of resistance mechanisms and immunotherapeutic approaches targeting neoantigens.Video Abstract

The shift to digital systems for the creation, transmission and storage of information has led to increasing complexity in archiving, requiring active, ongoing maintenance of the digital media. DNA is an attractive target for information storage 1 because of its capacity for high density information encoding, longevity under easily-achieved conditions 2-4 and proven track record as an information bearer. Previous DNA-based information storage approaches have encoded only trivial amounts of information 5-7 or were not amenable to scaling-up 8 , and used no robust errorcorrection and lacked examination of their cost-efficiency for large-scale information archival 9 . Here we describe a scalable method that can reliably store more information than has been handled before. We encoded computer files totalling 739 kB of hard disk storage and with an estimated Shannon information 10 of 5.2 × 10 6 bits into a DNA code, synthesised this DNA, sequenced it and reconstructed the original files with 100% accuracy. Theoretical analysis indicates that our DNA-storage scheme scales far beyond current global information volumes. These results demonstrate DNA-storage to be a realistic technology for large-scale digital archiving that may already be cost-effective for low access, multi-century-long archiving tasks. Within a decade, as costs fall rapidly under realistic scenarios for technological advances, it may be cost-effective for sub-50-year archival.Although techniques for manipulating, storing and copying large amounts of DNA have been established for many years [11][12][13] , these rely on the availability of initial copies of the DNA molecule to be processed, and one of the main challenges for practical information storage in DNA is the difficulty of synthesising long sequences of DNA de novo to an exactly-specified design. Instead, we developed an in vitro approach that represents the information being stored as a hypothetical long DNA molecule and encodes this using shorter DNA fragments. A similar approach was proposed by Church et al. 9 in a report * To whom correspondence should be addressed; goldman@ebi.ac.uk. SupplementaryInformation is provided as a number of separate files accompanying this document.Author Contributions N.G. and E.B. conceived and planned the project and devised the information encoding methods. P.B. advised on NGS protocols, prepared the DNA library and managed the sequencing process. S.C. and E.M.L. provided custom oligonucleotides. N.G. wrote the software for encoding and decoding information into/from DNA and analysed the data. N.G., E.B., C.D. and B.S. modelled the scaling properties of DNA-storage. N.G. wrote the paper with discussions and contributions from all other authors. N.G. and C.D. produced the figures.Author Information Data are available online at http://www.ebi.ac.uk/goldman-srv/DNA-storage and in the Sequence Read Archive (SRA) with accession number ERP002040 (to be confirmed). Correspondence and requests for materials should be addressed to N.G. (goldman@ebi.ac.uk). Co...

Phylogenetic inference and evaluating support for inferred relationships is at the core of many studies testing evolutionary hypotheses. Despite the popularity of nonparametric bootstrap frequencies and Bayesian posterior probabilities, the interpretation of these measures of tree branch support remains a source of discussion. Furthermore, both methods are computationally expensive and become prohibitive for large data sets. Recent fast approximate likelihood-based measures of branch supports (approximate likelihood ratio test [aLRT] and Shimodaira–Hasegawa [SH]-aLRT) provide a compelling alternative to these slower conventional methods, offering not only speed advantages but also excellent levels of accuracy and power. Here we propose an additional method: a Bayesian-like transformation of aLRT (aBayes). Considering both probabilistic and frequentist frameworks, we compare the performance of the three fast likelihood-based methods with the standard bootstrap (SBS), the Bayesian approach, and the recently introduced rapid bootstrap. Our simulations and real data analyses show that with moderate model violations, all tests are sufficiently accurate, but aLRT and aBayes offer the highest statistical power and are very fast. With severe model violations aLRT, aBayes and Bayesian posteriors can produce elevated false-positive rates. With data sets for which such violation can be detected, we recommend using SH-aLRT, the nonparametric version of aLRT based on a procedure similar to the Shimodaira–Hasegawa tree selection. In general, the SBS seems to be excessively conservative and is much slower than our approximate likelihood-based methods.