Abstract PO-007: Plasma-based detection of pancreatic cancer: A multiomics approach

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with an overall five-year survival rate of 11%. Potential curative resection is possible if the tumor is detected at an early stage, with a five-year survival rate of 42%. The only current FDA-cleared biomarker for PDAC is the carbohydrate antigen 19-9 (CA19-9), which is intended for monitoring response to therapy but not for early detection. CA19-9 blood tests have varying sensitivity to detect PDAC and are prone to false positives in the presence of other underlying pancreatic conditions and to false negatives in subpopulations unable to express CA19-9. The goal of our pilot study was to determine if a multiomics approach using cell-free DNA and CA19-9 would be better than CA19-9 alone in detecting PDAC. In this retrospective study of 75 participants, we performed targeted methylation profiling of circulating cell-free DNA and quantitation of plasma CA19-9 abundance. Participants with PDAC (n=39) were 51% male with a mean age of 74.9 years, and consisted of stage II (n=9), stage III (n=11) and stage IV (n=19) pancreatic cancer. Controls (n=36) were 33% male with a mean age of 74.2 years, and included both healthy control/normal pancreas (n=17) and various benign abnormalities of the pancreas or biliary system (n=19). We developed a novel machine learning model that combines CA19-9 and methylation signals to build a joint multiomics prediction. We compared the joint predictions to those based on methylation or CA19-9 alone. Five resamplings of three-fold cross-validation were performed, and sensitivity was calculated for decision thresholds that achieved the desired test set specificity. Across all stages, the multiomics approach achieved a sensitivity of 93% at a specificity of 96%, which was greater than methylation or CA19-9 alone. At 96% specificity, methylation alone achieved a sensitivity of 74% while CA19-9 alone achieved a sensitivity of 87%. In stages II, III and IV, the multiomics approach achieved a sensitivity of 82%, 89%, and 100%, respectively and was also more sensitive than either methylation or CA19-9 alone. These proof-of-concept data demonstrate the promise of using a multiomics approach to develop a highly sensitive and specific test for the early detection of pancreatic cancer. Additional studies are underway, focusing on early-stage disease (stage I/II), to validate these results.

Citation Format: Teng-Kuei Hsu, Tzu-Yu Liu, Billie Gould, Christine Decapite, Amer Zureikat, Alessandro Paniccia, Eric Ariazi, Marvin Bertin, Richard Bourgon, Kaitlyn Coil, Hayley Donnella, Adam Drake, Julie M. Granka, Preet Kaur, Maggie C. Louie, Shivani Mahajan, Amit Pasupathy, Ofer Shapira, Peter Ulz, Chun Yang, C. Jimmy Lin, Randall Brand. Plasma-based detection of pancreatic cancer: A multiomics approach [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-007.

The history and geographic distribution of a KCNQ1 atrial fibrillation risk allele

The genetic architecture of atrial fibrillation (AF) encompasses low impact, common genetic variants and high impact, rare variants. Here, we characterize a high impact AF-susceptibility allele, KCNQ1 R231H, and describe its transcontinental geographic distribution and history. Induced pluripotent stem cell-derived cardiomyocytes procured from risk allele carriers exhibit abbreviated action potential duration, consistent with a gain-of-function effect. Using identity-by-descent (IBD) networks, we estimate the broad- and fine-scale population ancestry of risk allele carriers and their relatives. Analysis of ancestral migration routes reveals ancestors who inhabited Denmark in the 1700s, migrated to the Northeastern United States in the early 1800s, and traveled across the Midwest to arrive in Utah in the late 1800s. IBD/coalescent-based allele dating analysis reveals a relatively recent origin of the AF risk allele (~5000 years). Thus, our approach broadens the scope of study for disease susceptibility alleles to the context of human migration and ancestral origins.

Different Selected Mechanisms Attenuated the Inhibitory Interaction of KIR2DL1 with C2+ HLA-C in Two Indigenous Human Populations in Southern Africa

The functions of human NK cells in defense against pathogens and placental development during reproduction are modulated by interactions of killer cell Ig-like receptors (KIRs) with HLA-A, -B and -C class I ligands. Both receptors and ligands are highly polymorphic and exhibit extensive differences between human populations. Indigenous to southern Africa are the KhoeSan, the most ancient group of modern human populations, who have highest genomic diversity worldwide. We studied two KhoeSan populations, the Nama pastoralists and the ≠Khomani San hunter-gatherers. Comprehensive next-generation sequence analysis of HLA-A, -B, and -C and all KIR genes identified 248 different KIR and 137 HLA class I, which assort into ∼200 haplotypes for each gene family. All 74 Nama and 78 ≠Khomani San studied have different genotypes. Numerous novel KIR alleles were identified, including three arising by intergenic recombination. On average, KhoeSan individuals have seven to eight pairs of interacting KIR and HLA class I ligands, the highest diversity and divergence of polymorphic NK cell receptors and ligands observed to date. In this context of high genetic diversity, both the Nama and the ≠Khomani San have an unusually conserved, centromeric KIR haplotype that has arisen to high frequency and is different in the two KhoeSan populations. Distinguishing these haplotypes are independent mutations in KIR2DL1, which both prevent KIR2DL1 from functioning as an inhibitory receptor for C2⁺ HLA-C. The relatively high frequency of C2⁺ HLA-C in the Nama and the ≠Khomani San appears to have led to natural selection against strong inhibitory C2-specific KIR.

An Unexpectedly Complex Architecture for Skin Pigmentation in Africans

Approximately 15 genes have been directly associated with skin pigmentation variation in humans, leading to its characterization as a relatively simple trait. However, by assembling a global survey of quantitative skin pigmentation phenotypes, we demonstrate that pigmentation is more complex than previously assumed, with genetic architecture varying by latitude. We investigate polygenicity in the KhoeSan populations indigenous to southern Africa who have considerably lighter skin than equatorial Africans. We demonstrate that skin pigmentation is highly heritable, but known pigmentation loci explain only a small fraction of the variance. Rather, baseline skin pigmentation is a complex, polygenic trait in the KhoeSan. Despite this, we identify canonical and non-canonical skin pigmentation loci, including near SLC24A5, TYRP1, SMARCA2/VLDLR, and SNX13, using a genome-wide association approach complemented by targeted resequencing. By considering diverse, under-studied African populations, we show how the architecture of skin pigmentation can vary across humans subject to different local evolutionary pressures.

Clustering of 770,000 genomes reveals post-colonial population structure of North America

Despite strides in characterizing human history from genetic polymorphism data, progress in identifying genetic signatures of recent demography has been limited. Here we identify very recent fine-scale population structure in North America from a network of over 500 million genetic (identity-by-descent, IBD) connections among 770,000 genotyped individuals of US origin. We detect densely connected clusters within the network and annotate these clusters using a database of over 20 million genealogical records. Recent population patterns captured by IBD clustering include immigrants such as Scandinavians and French Canadians; groups with continental admixture such as Puerto Ricans; settlers such as the Amish and Appalachians who experienced geographic or cultural isolation; and broad historical trends, including reduced north-south gene flow. Our results yield a detailed historical portrait of North America after European settlement and support substantial genetic heterogeneity in the United States beyond that uncovered by previous studies.

Genetics and the history of the Samaritans: Y-chromosomal microsatellites and genetic affinity between Samaritans and Cohanim

The Samaritans are a group of some 750 indigenous Middle Eastern people, about half of whom live in Holon, a suburb of Tel Aviv, and the other half near Nablus. The Samaritan population is believed to have numbered more than a million in late Roman times but less than 150 in 1917. The ancestry of the Samaritans has been subject to controversy from late Biblical times to the present. In this study, liquid chromatography/electrospray ionization/quadrupole ion trap mass spectrometry was used to allelotype 13 Y-chromosomal and 15 autosomal microsatellites in a sample of 12 Samaritans chosen to have as low a level of relationship as possible, and 461 Jews and non-Jews. Estimation of genetic distances between the Samaritans and seven Jewish and three non-Jewish populations from Israel, as well as populations from Africa, Pakistan, Turkey, and Europe, revealed that the Samaritans were closely related to Cohanim. This result supports the position of the Samaritans that they are descendants from the tribes of Israel dating to before the Assyrian exile in 722-720 BCE. In concordance with previously published single-nucleotide polymorphism haplotypes, each Samaritan family, with the exception of the Samaritan Cohen lineage, was observed to carry a distinctive Y-chromosome short tandem repeat haplotype that was not more than one mutation removed from the six-marker Cohen modal haplotype.

On the stability of the Bayenv method in assessing human SNP-environment associations

Background

Phenotypic variation along environmental gradients has been documented among and within many species, and in some cases, genetic variation has been shown to be associated with these gradients. Bayenv is a relatively new method developed to detect patterns of polymorphisms associated with environmental gradients. Using a Bayesian Markov Chain Monte Carlo (MCMC) approach, Bayenv evaluates whether a linear model relating population allele frequencies to environmental variables is more probable than a null model based on observed frequencies of neutral markers. Although this method has been used to detect environmental adaptation in a number of species, including humans, plants, fish, and mosquitoes, stability between independent runs of this MCMC algorithm has not been characterized. In this paper, we explore the variability of results between runs and the factors contributing to it.

Results

Independent runs of the Bayenv program were carried out using genome-wide single-nucleotide polymorphism (SNP) data from samples from 60 worldwide human populations following previous applications of the Bayenv method. To assess factors contributing to the method's stability, we used varying numbers of MCMC iterations and also analyzed a second modified data set that excluded two Siberian populations with extreme climate variables. Between any two runs, correlations between Bayes factors and the overlap of SNPs in the empirical p value tails were surprisingly low. Enrichments of genic versus non-genic SNPs in the empirical tails were more robust than the empirical p values; however, the significance of the enrichments for some environmental variables still varied among runs, contradicting previously published conclusions. Runs with a greater number of MCMC iterations slightly reduced run-to-run variability, and excluding the Siberian populations did not have a large effect on the stability of the runs.

Conclusions

Because of high run-to-run variability, we advise against making conclusions about genome-wide patterns of adaptation based on only one run of the Bayenv algorithm and recommend caution in interpreting previous studies that have used only one run. Moving forward, we suggest carrying out multiple independent runs of Bayenv and averaging Bayes factors between runs to produce more stable and reliable results. With these modifications, future discoveries of environmental adaptation within species using the Bayenv method will be more accurate, interpretable, and easily compared between studies.

The role of selection in shaping diversity of natural M. tuberculosis populations

Mycobacterium tuberculosis (M.tb), the cause of tuberculosis (TB), is estimated to infect a new host every second. While analyses of genetic data from natural populations of M.tb have emphasized the role of genetic drift in shaping patterns of diversity, the influence of natural selection on this successful pathogen is less well understood. We investigated the effects of natural selection on patterns of diversity in 63 globally extant genomes of M.tb and related pathogenic mycobacteria. We found evidence of strong purifying selection, with an estimated genome-wide selection coefficient equal to −9.5×10⁻⁴ (95% CI −1.1×10⁻³ to −6.8×10⁻⁴); this is several orders of magnitude higher than recent estimates for eukaryotic and prokaryotic organisms. We also identified different patterns of variation across categories of gene function. Genes involved in transport and metabolism of inorganic ions exhibited very low levels of non-synonymous polymorphism, equivalent to categories under strong purifying selection (essential and translation-associated genes). The highest levels of non-synonymous variation were seen in a group of transporter genes, likely due to either diversifying selection or local selective sweeps. In addition to selection, we identified other important influences on M.tb genetic diversity, such as a 25-fold expansion of global M.tb populations coincident with explosive growth in human populations (estimated timing 1684 C.E., 95% CI 1620–1713 C.E.). These results emphasize the parallel demographic histories of this obligate pathogen and its human host, and suggest that the dominant effect of selection on M.tb is removal of novel variants, with exceptions in an interesting group of genes involved in transportation and defense. We speculate that the hostile environment within a host imposes strict demands on M.tb physiology, and thus a substantial fitness cost for most new mutations. In this respect, obligate bacterial pathogens may differ from other host-associated microbes such as symbionts.

Mycobacterium tuberculosis (M.tb), the etiologic agent of tuberculosis (TB), is a highly prevalent pathogen of humans, estimated to infect one-third of the world's population. Previous investigations of M.tb evolution have emphasized the influence of chance events on populations of these bacteria. To understand why this organism is so well adapted to its niche, we sought to characterize the influence of natural selection on M.tb. DNA sequence data from M.tb populations appeared to be strongly influenced by genome-wide selection against deleterious mutations. This type of selection was particularly evident in three functional categories: genes essential for infection (identified in an animal model of TB), genes involved in protein translation, and genes involved in trafficking and metabolism of inorganic ions. By contrast, a fourth category (‘defense’ genes) exhibited high levels of diversity, consistent with selection for advantageous mutations. In addition to effects of selection on M.tb genomic data, we identified the influence of a pronounced recent expansion in M.tb populations, coincident with explosive growth of human populations around the world. Our results suggest that growth of M.tb populations parallels that of its human host population, and that complex influences lead to emergence and maintenance of adaptive traits in M.tb.

On the classification of epistatic interactions

Modern genomewide association studies are characterized by the problem of "missing heritability." Epistasis, or genetic interaction, has been suggested as a possible explanation for the relatively small contribution of single significant associations to the fraction of variance explained. Of particular concern to investigators of genetic interactions is how to best represent and define epistasis. Previous studies have found that the use of different quantitative definitions for genetic interaction can lead to different conclusions when constructing genetic interaction networks and when addressing evolutionary questions. We suggest that instead, multiple representations of epistasis, or epistatic "subtypes," may be valid within a given system. Selecting among these epistatic subtypes may provide additional insight into the biological and functional relationships among pairs of genes. In this study, we propose maximum-likelihood and model selection methods in a hypothesis-testing framework to choose epistatic subtypes that best represent functional relationships for pairs of genes on the basis of fitness data from both single and double mutants in haploid systems. We gauge the performance of our method with extensive simulations under various interaction scenarios. Our approach performs reasonably well in detecting the most likely epistatic subtype for pairs of genes, as well as in reducing bias when estimating the epistatic parameter (epsilon). We apply our approach to two available data sets from yeast (Saccharomyces cerevisiae) and demonstrate through overlap of our identified epistatic pairs with experimentally verified interactions and functional links that our results are likely of biological significance in understanding interaction mechanisms. We anticipate that our method will improve detection of epistatic interactions and will help to unravel the mysteries of complex biological systems.