Association of Germline Variants in Natural Killer Cells With Tumor Immune Microenvironment Subtypes, Tumor-Infiltrating Lymphocytes, Immunotherapy Response, Clinical Outcomes, and Cancer Risk.

Importance: Only a small fraction of patients with cancer receiving immune checkpoint therapy (ICT) respond, which is associated with tumor immune microenvironment (TIME) subtypes and tumor-infiltrating lymphocytes (TILs). Objective: To examine whether germline variants of natural killer (NK) cells, a key component of the immune system, are associated with TIME subtypes, the abundance of TILs, response to ICT, clinical outcomes, and cancer risk. Design, Setting, and Participants: This genetic association study explored TIME subtypes and examined the association of the germline genomic information of patients with cancer with TIME subtypes, abundance of TILs, response to ICT, prognosis, and cancer risk. Clinical information, tumor RNA sequencing, and whole-exome sequencing (WES) data of paired normal samples of patients with 13 common cancers (n = 5883) were obtained from the Cancer Genome Atlas. The WES data of individuals with no cancer (n = 4500) were obtained from the database of Genotypes and Phenotypes. Data collection and analysis took place in March 2017. Main Outcomes and Measures: Associations between the number of germline defective genes in NK cells and survival time and the abundance of TILs. Results: Based on tumor RNA sequencing data, tumors were stratified into TIME-rich, TIME-intermediate, and TIME-poor subtypes. Tumors of TIME-rich subtype had more TILs (TIL-NK cells in TIME-rich head and neck squamous cell carcinoma [HNSC] tumors: t = 4.85; 95% CI of the difference, 0.01-0.03; P = 2.19 × 10-6) compared with TIME-intermediate HNSC tumors (t = 3.70; 95% CI of the difference, 0.01-0.03; P < .001), better prognosis (patients with HNSC: hazard ratio, 0.65; 95% CI, 0.41-1.02; P = .054) compared with TIME-intermediate and TIME-poor subtypes, and better ICT response (patients with melanoma: odds ratio [OR], 4.45; 95% CI, 0.99-27.08; P = .04). Patients with TIME-rich tumors had significantly fewer inherited defective genes in NK cells than patients with TIME-intermediate and TIME-poor tumors (patients with HNSC: OR, 0.49; 95% CI, 0.26-1.07; P = .005). Similarly, patients with cancer had significantly more inherited defective genes in NK cells than individuals with no cancer (patients with HNSC: OR, 19.09; 95% CI, 4.30-315.96; P = 6.21 × 10-4). Among 11 of 13 common cancers, the number of heritable defective genes in NK cells was significantly negatively associated with survival (patients with HNSC: hazard ratio, 1.77; 95% CI, 1.18-2.66; P = .005), abundance of TILs (patients with HNSC: R = -0.25; 95% CI, -0.65-2.17; P = 0.02), and response to ICT (patients with melanoma: OR, 4.45; 95% CI, 0.99-27.08; P = .04). Conclusions and Relevance: These results suggest that individuals who have more inherited defective genes in NK cells had a higher risk of developing cancer and that these inherited defects were associated with TIME subtypes, recruitment of TILs, ICT response, and clinical outcomes. The findings have implications for identifying individuals at risk for developing cancer of many types based on germline variants of NK cells and for improving existing ICT and chimeric antigen receptor-T cell therapy by adoptive transfer of healthy NK cells to patients with TIME-intermediate and TIME-poor tumors.

1000 human genomes carry widespread signatures of GC biased gene conversion.

BACKGROUND: GC-Biased Gene Conversion (gBGC) is one of the important theories put forward to explain profound long-range non-randomness in nucleotide compositions along mammalian chromosomes. Nucleotide changes due to gBGC are hard to distinguish from regular mutations. Here, we present an algorithm for analysis of millions of known SNPs that detects a subset of so-called "SNP flip-over" events representing recent gBGC nucleotide changes, which occurred in previous generations via non-crossover meiotic recombination. RESULTS: This algorithm has been applied in a large-scale analysis of 1092 sequenced human genomes. Altogether, 56,328 regions on all autosomes have been examined, which revealed 223,955 putative gBGC cases leading to SNP flip-overs. We detected a strong bias (11.7% ± 0.2% excess) in AT- > GC over GC- > AT base pair changes within the entire set of putative gBGC cases. CONCLUSIONS: On average, a human gamete acquires 7 SNP flip-over events, in which one allele is replaced by its complementary allele during the process of meiotic non-crossover recombination. In each meiosis event, on average, gBGC results in replacement of 7 AT base pairs by GC base pairs, while only 6 GC pairs are replaced by AT pairs. Therefore, every human gamete is enriched by one GC pair. Happening over millions of years of evolution, this bias may be a noticeable force in changing the nucleotide composition landscape along chromosomes.

Inference of distant genetic relations in humans using "1000 genomes".

Nucleotide sequence differences on the whole-genome scale have been computed for 1,092 people from 14 populations publicly available by the 1000 Genomes Project. Total number of differences in genetic variants between 96,464 human pairs has been calculated. The distributions of these differences for individuals within European, Asian, or African origin were characterized by narrow unimodal peaks with mean values of 3.8, 3.5, and 5.1 million, respectively, and standard deviations of 0.1-0.03 million. The total numbers of genomic differences between pairs of all known relatives were found to be significantly lower than their respective population means and in reverse proportion to the distance of their consanguinity. By counting the total number of genomic differences it is possible to infer familial relations for people that share down to 6% of common loci identical-by-descent. Detection of familial relations can be radically improved when only very rare genetic variants are taken into account. Counting of total number of shared very rare single nucleotide polymorphisms (SNPs) from whole-genome sequences allows establishing distant familial relations for persons with eighth and ninth degrees of relationship. Using this analysis we predicted 271 distant familial pairwise relations among 1,092 individuals that have not been declared by 1000 Genomes Project. Particularly, among 89 British and 97 Chinese individuals we found three British-Chinese pairs with distant genetic relationships. Individuals from these pairs share identical-by-descent DNA fragments that represent 0.001%, 0.004%, and 0.01% of their genomes. With affordable whole-genome sequencing techniques, very rare SNPs should become important genetic markers for familial relationships and population stratification.

Bioinformatics analysis of plant orthologous introns: identification of an intronic tRNA-like sequence.

Orthologous introns have identical positions relative to the coding sequence in orthologous genes of different species. By analyzing the complete genomes of five plants we generated a database of 40,512 orthologous intron groups of dicotyledonous plants, 28,519 orthologous intron groups of angiosperms, and 15,726 of land plants (moss and angiosperms). Multiple sequence alignments of each orthologous intron group were obtained using the Mafft algorithm. The number of conserved regions in plant introns appeared to be hundreds of times fewer than that in mammals or vertebrates. Approximately three quarters of conserved intronic regions among angiosperms and dicots, in particular, correspond to alternatively-spliced exonic sequences. We registered only a handful of conserved intronic ncRNAs of flowering plants. However, the most evolutionarily conserved intronic region, which is ubiquitous for all plants examined in this study, including moss, possessed multiple structural features of tRNAs, which caused us to classify it as a putative tRNA-like ncRNA. Intronic sequences encoding tRNA-like structures are not unique to plants. Bioinformatics examination of the presence of tRNA inside introns revealed an unusually long-term association of four glycine tRNAs inside the Vac14 gene of fish, amniotes, and mammals.

Genome evolution by matrix algorithms: cellular automata approach to population genetics.

Mammalian genomes are replete with millions of polymorphic sites, among which those genetic variants that are colocated on the same chromosome and exist close to one another form blocks of closely linked mutations known as haplotypes. The linkage within haplotypes is constantly disrupted due to meiotic recombination events. Whole ensembles of such numerous haplotypes are subjected to evolutionary pressure, where mutations influence each other and should be considered as a whole entity-a gigantic matrix, unique for each individual genome. This idea was implemented into a computational approach, named Genome Evolution by Matrix Algorithms (GEMA) to model genomic changes taking into account all mutations in a population. GEMA has been tested for modeling of entire human chromosomes. The program can precisely mimic real biological processes that have influence on genome evolution such as: 1) Authentic arrangements of genes and functional genomic elements, 2) frequencies of various types of mutations in different nucleotide contexts, and 3) nonrandom distribution of meiotic recombination events along chromosomes. Computer modeling with GEMA has demonstrated that the number of meiotic recombination events per gamete is among the most crucial factors influencing population fitness. In humans, these recombinations create a gamete genome consisting on an average of 48 pieces of corresponding parental chromosomes. Such highly mosaic gamete structure allows preserving fitness of population under the intense influx of novel mutations (40 per individual) even when the number of mutations with deleterious effects is up to ten times more abundant than those with beneficial effects.