Netie: inferring the evolution of neoantigen-T cell interactions in tumors.

Neoantigens are the key targets of antitumor immune responses from cytotoxic T cells and play a critical role in affecting tumor progressions and immunotherapy treatment responses. However, little is known about how the interaction between neoantigens and T cells ultimately affects the evolution of cancerous masses. Here, we develop a hierarchical Bayesian model, named neoantigen-T cell interaction estimation (netie) to infer the history of neoantigen-CD8+ T cell interactions in tumors. Netie was systematically validated and applied to examine the molecular patterns of 3,219 tumors, compiled from a panel of 18 cancer types. We showed that tumors with an increase in immune selection pressure over time are associated with T cells that have an activation-related expression signature. We also identified a subset of exhausted cytotoxic T cells postimmunotherapy associated with tumor clones that newly arise after treatment. These analyses demonstrate how netie enables the interrogation of the relationship between individual neoantigen repertoires and the tumor molecular profiles. We found that a T cell inflammation gene expression profile (TIGEP) is more predictive of patient outcomes in the tumors with an increase in immune pressure over time, which reveals a curious synergy between T cells and neoantigen distributions. Overall, we provide a new tool that is capable of revealing the imprints left by neoantigens during each tumor's developmental process and of predicting how tumors will progress under further pressure of the host's immune system.

PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data.

High-throughput oligonucleotide microarrays are commonly employed to investigate genetic disease, including cancer. The algorithms employed to extract genotypes and copy number variation function optimally for diploid genomes usually associated with inherited disease. However, cancer genomes are aneuploid in nature leading to systematic errors when using these techniques. We introduce a preprocessing transformation and hidden Markov model algorithm bespoke to cancer. This produces genotype classification, specification of regions of loss of heterozygosity, and absolute allelic copy number segmentation. Accurate prediction is demonstrated with a combination of independent experimental techniques. These methods are exemplified with affymetrix genome-wide SNP6.0 data from 755 cancer cell lines, enabling inference upon a number of features of biological interest. These data and the coded algorithm are freely available for download.

Subclonal phylogenetic structures in cancer revealed by ultra-deep sequencing.

During the clonal expansion of cancer from an ancestral cell with an initiating oncogenic mutation to symptomatic neoplasm, the occurrence of somatic mutations (both driver and passenger) can be used to track the on-going evolution of the neoplasm. All subclones within a cancer are phylogenetically related, with the prevalence of each subclone determined by its evolutionary fitness and the timing of its origin relative to other subclones. Recently developed massively parallel sequencing platforms promise the ability to detect rare subclones of genetic variants without a priori knowledge of the mutations involved. We used ultra-deep pyrosequencing to investigate intraclonal diversification at the Ig heavy chain locus in 22 patients with B-cell chronic lymphocytic leukemia. Analysis of a non-polymorphic control locus revealed artifactual insertions and deletions resulting from sequencing errors and base substitutions caused by polymerase misincorporation during PCR amplification. We developed an algorithm to differentiate genuine haplotypes of somatic hypermutations from such artifacts. This proved capable of detecting multiple rare subclones with frequencies as low as 1 in 5000 copies and allowed the characterization of phylogenetic interrelationships among subclones within each patient. This study demonstrates the potential for ultra-deep resequencing to recapitulate the dynamics of clonal evolution in cancer cell populations.

The genetic heterogeneity and mutational burden of engineered melanomas in zebrafish models.

BACKGROUND: Melanoma is the most deadly form of skin cancer. Expression of oncogenic BRAF or NRAS, which are frequently mutated in human melanomas, promote the formation of nevi but are not sufficient for tumorigenesis. Even with germline mutated p53, these engineered melanomas present with variable onset and pathology, implicating additional somatic mutations in a multi-hit tumorigenic process. RESULTS: To decipher the genetics of these melanomas, we sequence the protein coding exons of 53 primary melanomas generated from several BRAF(V600E) or NRAS(Q61K) driven transgenic zebrafish lines. We find that engineered zebrafish melanomas show an overall low mutation burden, which has a strong, inverse association with the number of initiating germline drivers. Although tumors reveal distinct mutation spectrums, they show mostly C > T transitions without UV light exposure, and enrichment of mutations in melanogenesis, p53 and MAPK signaling. Importantly, a recurrent amplification occurring with pre-configured drivers BRAF(V600E) and p53-/- suggests a novel path of BRAF cooperativity through the protein kinase A pathway. CONCLUSION: This is the first analysis of a melanoma mutational landscape in the absence of UV light, where tumors manifest with remarkably low mutation burden and high heterogeneity. Genotype specific amplification of protein kinase A in cooperation with BRAF and p53 mutation suggests the involvement of melanogenesis in these tumors. This work is important for defining the spectrum of events in BRAF or NRAS driven melanoma in the absence of UV light, and for informed exploitation of models such as transgenic zebrafish to better understand mechanisms leading to human melanoma formation.

Novel candidate cancer genes identified by a large-scale cross-species comparative oncogenomics approach.

Comparative genomic hybridization (CGH) can reveal important disease genes but the large regions identified could sometimes contain hundreds of genes. Here we combine high-resolution CGH analysis of 598 human cancer cell lines with insertion sites isolated from 1,005 mouse tumors induced with the murine leukemia virus (MuLV). This cross-species oncogenomic analysis revealed candidate tumor suppressor genes and oncogenes mutated in both human and mouse tumors, making them strong candidates for novel cancer genes. A significant number of these genes contained binding sites for the stem cell transcription factors Oct4 and Nanog. Notably, mice carrying tumors with insertions in or near stem cell module genes, which are thought to participate in cell self-renewal, died significantly faster than mice without these insertions. A comparison of the profile we identified to that induced with the Sleeping Beauty (SB) transposon system revealed significant differences in the profile of recurrently mutated genes. Collectively, this work provides a rich catalogue of new candidate cancer genes for functional analysis.