Low-coverage Whole-genome Sequencing Research Articles

Comparative Analysis of Two NGS-Based Platforms for Product-of-Conception Karyotyping.

Cytogenetic information about the product of conception (POC) is important to determine the presence of recurrent chromosomal abnormalities that are an indication for preimplantation genetic testing for aneuploidy or structural rearrangements. Although microscopic examination by G-staining has long been used for such an evaluation, detection failures are relatively common with this method, due to cell-culture-related issues. The utility of low-coverage whole-genome sequencing (lcWGS) using short-read next-generation sequencing (NGS) has been highlighted recently as an alternative cytogenomic approach for POC analysis. We, here, performed comparative analysis of two NGS-based protocols for this purpose based on different short-read sequencers (the Illumina VeriSeq system using a MiSeq sequencer and the Thermo Fisher ReproSeq system using an Ion S5 sequencer). The cytogenomic diagnosis obtained with each NGS method was equivalent in each of 20 POC samples analyzed. Notably, X chromosome sequence reads were reduced in some female samples with both systems. The possibility of low-level mosaicism for monosomy X as an explanation for this was excluded by FISH analysis. Additional data from samples with various degrees of X chromosome aneuploidy suggested that it was a technical artifact related to X chromosome inactivation. Indeed, subsequent nanopore sequencing indicated that the DNA in the samples showing the artifact was predominantly unmethylated. Our current findings indicate that although X chromosome data must be interpreted with caution, both the systems we tested for NGS-based lcWGS are useful alternatives for the karyotyping of POC samples.

Optimizing Strategy for Whole-Genome Genotype Imputation in Scallops

Advancements in sequencing technologies have facilitated low-coverage whole-genome sequencing (lcWGS) for detecting millions of single nucleotide polymorphisms (SNPs) in large populations at low cost. The effectiveness of this approach relies on genotype imputation following lcWGS. While several imputation methods have been applied in human and other terrestrial animals, their performance in aquaculture organisms, particularly mollusks, remains unassessed. Mollusks contribute 70% of mariculture production in China and are characterized by high genetic polymorphism. In this study, we aimed to optimize reference panel construction and evaluate imputation strategies for SNP detection in two scallops, Chlamys farreri and Patinopecten yessoensis, both important bivalve species. High-coverage whole-genome sequencing (hcWGS) data were used to construct haplotype reference panels, and three imputation methods were applied: Beagle5.4, GLIMPSE2, and QUILT. QUILT outperformed other methods, achieving imputation quality scores (IQS) exceeding 0.930 in C. farreri and 0.946 in P. yessoensis, and yielded the highest number of imputed loci in both species. Furthermore, panel construction was optimized by adjusting for genotype quality (GQ) and minor allele frequency (MAF); we determined that a GQ threshold > 20 and an MAF threshold > 0.01 were optimal. We observed that imputation quality improved with increased sequencing depth, plateauing at 0.5×, and varied with sample size depending on the imputation method. A negative correlation was observed between imputation quality and the ratio of non-synonymous to synonymous substitution rates (Ka/Ks) across chromosomal segments, highlighting the influence of selection pressure on imputation efficacy. Collectively, our findings provide an optimized framework for SNP genotyping using lcWGS in scallops, with implications for aquaculture breeding and genetic research.

Early detection of uterine corpus endometrial carcinoma utilizing plasma cfDNA fragmentomics

BackgroundUterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignancy with a favorable prognosis if detected early. However, there is a lack of accurate and reliable early detection tests for UCEC. This study aims to develop a precise and non-invasive diagnostic method for UCEC using circulating cell-free DNA (cfDNA) fragmentomics.MethodsPeripheral blood samples were collected from all participants, and cfDNA was extracted for analysis. Low-coverage whole-genome sequencing was performed to obtain cfDNA fragmentomics data. A robust machine learning model was developed using these features to differentiate between UCEC and healthy conditions.ResultsThe cfDNA fragmentomics-based model showed high predictive power for UCEC detection in training (n = 133; AUC 0.991) and validation cohorts (n = 89; AUC 0.994). The model manifested a specificity of 95.5% and a sensitivity of 98.5% in the training cohort, and a specificity of 95.5% and a sensitivity of 97.8% in the validation cohort. Physiological variables and preanalytical procedures had no significant impact on the classifier’s outcomes. In terms of clinical benefit, our model would identify 99% of Chinese UCEC patients at stage I, compared to 21% under standard care, potentially raising the 5-year survival rate from 84 to 95%.ConclusionThis study presents a novel approach for the early detection of UCEC using cfDNA fragmentomics and machine learning showing promising sensitivity and specificity. Using this model in clinical practice could significantly improve UCEC management and control, enabling early intervention and better patient outcomes. Further optimization and validation of this approach are warranted to establish its clinical utility.

Imputation strategies for low-coverage whole-genome sequencing data and their effects on genomic prediction and genome-wide association studies in pigs

The uncertainty resulting from missing genotypes in low-coverage whole-genome sequencing (LCWGS) data complicates genotype imputation. The aim of this study is to find out an optimal strategy for accurately imputing LCWGS data and assess its effectiveness for genomic prediction (GP) and genome-wide association study (GWAS) on economically important traits of Large White pigs. The LCWGS data of 1 423 Large White pigs were imputed using three different strategies: (1) using the high-coverage whole-genome sequencing (HCWGS) of 30 key progenitors as the reference panel (Ref_LG); (2) mixing HCWGS of key progenitors with LCWGS (Mix_HLG) and (3) self-imputation in LCWGS (Within_LG). Additionally, to compare the imputation effects of LCWGS, we also imputed SNP chip data of 1 423 Large White pigs to the whole-genome sequencing level using the reference panel consisting of key progenitors (Ref_SNP). To evaluate effects of the imputed sequencing data, we compared the accuracies of GP and statistical power of GWAS for four reproductive traits based on the chip data, sequencing data imputed from chip data and LCWGS data using an optimal strategy. The average imputation accuracies of the Within_LG, Ref_LG and Mix_HLG were 0.9893, 0.9899 and 0.9875, respectively, which were higher than that of the Ref_SNP (0.8522). Using the imputed sequencing data from LCWGS with the Ref_LG imputation strategy, the accuracies of GP for four traits improved by approximately 0.31–1.04% compared to the chip data, and by 0.7–1.05% compared to the imputed sequencing data from chip data. Furthermore, by using the sequence data imputed from LCWGS with the Ref_LG, 18 candidate genes were identified to be associated with the four reproductive traits of interest in Large White pigs: Total number of piglets born - EPC2, MBD5, ORC4 and ACVR2A; number of piglets born healthy - IKBKE; total litter weight of piglets born alive - HSPA13 and CPA1; gestation length - GTF2H5, ITGAV, NFE2L2, CALCRL, ITGA4, STAT1, HOXD10, MSTN, COL5A2 and STAT4. With the exception of EPC2, ORC4, ACVR2A and MSTN, others represent novel candidates. Our findings can provide a reference for the application of LCWGS data in livestock and poultry.

Accurate genotype imputation from low-coverage whole-genome sequencing data of rainbow trout.

With the rapid and significant cost reduction of next-generation sequencing, low-coverage whole-genome sequencing (lcWGS) followed by genotype imputation is becoming a cost-effective alternative to SNP (single nucleotide polymorphism) array genotyping. The objectives of this study were two-fold: 1) construct a haplotype reference panel for genotype imputation from lcWGS data in rainbow trout (Oncorhynchus mykiss); and 2) evaluate the concordance between imputed genotypes and SNP-array genotypes in two breeding populations. Medium-coverage (12x) whole-genome sequences were obtained from a total of 410 fish representing five breeding populations with various spawning dates. The short-read sequences were mapped to the rainbow trout reference genome, and genetic variants were identified using GATK. After data filtering, 20,434,612 biallelic SNPs were retained. The reference panel was phased with SHAPEIT5, and was used as a reference to impute genotypes from lcWGS data using GLIMPSE2. A total of 90 fish from the Troutlodge November breeding population were sequenced with an average coverage of 1.3x, and these fish were also genotyped with the Axiom 57K rainbow trout SNP array. The concordance between array-based genotypes and imputed genotypes was 99.1%. After downsampling the coverage to 0.5x, 0.2x and 0.1x, the concordance between array-based genotypes and imputed genotypes was 98.7%, 97.8% and 96.7%, respectively. In the USDA odd-year breeding population, the concordance between array-based genotypes and imputed genotypes was 97.8% for 109 fish downsampled to 0.5x coverage. Therefore, the reference haplotype panel reported in this study can be used to accurately impute genotypes from lcWGS data in rainbow trout breeding populations.

Genome-wide association study for age-related hearing loss in CFW mice.

Age-related hearing impairment is the most common cause of hearing loss and is one of the most prevalent conditions affecting the elderly globally. It is influenced by a combination of environmental and genetic factors. The mouse and human inner ears are functionally and genetically homologous. Investigating the genetic basis of age-related hearing loss (ARHL) in an outbred mouse model may lead to a better understanding of the molecular mechanisms of this condition. We used Carworth Farms White (CFW) outbred mice, because they are genetically diverse and exhibit variation in the onset and severity of ARHL. The goal of this study was to identify genetic loci involved in regulating ARHL. Hearing at a range of frequencies was measured using Auditory Brainstem Response (ABR) thresholds in 946 male and female CFW mice at the age of 1, 6, and 10 months. We obtained genotypes at 4.18 million single nucleotide polymorphisms (SNP) using low-coverage (mean coverage 0.27x) whole-genome sequencing followed by imputation using STITCH. To determine the accuracy of the genotypes we sequenced 8 samples at >30x coverage and used calls from those samples to estimate the discordance rate, which was 0.45%. We performed genetic analysis for the ABR thresholds for each frequency at each age, and for the time of onset of deafness for each frequency. The SNP heritability ranged from 0 to 42% for different traits. Genome-wide association analysis identified several regions associated with ARHL that contained potential candidate genes, including Dnah11 , Rapgef5 , Cpne4 , Prkag2 , and Nek11 . We confirmed, using functional study, that Prkag2 deficiency causes age-related hearing loss at high frequency in mice; this makes Prkag2 a candidate gene for further studies. This work helps to identify genetic risk factors for ARHL and to define novel therapeutic targets for the treatment and prevention of ARHL.

Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing

Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (∼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.

Longitudinal cell-free DNA characterization by low-coverage whole-genome sequencing in patients undergoing high-dose radiotherapy

Background and PurposeCurrent radiotherapy guidelines rely heavily on imaging-based monitoring. Liquid biopsy monitoring promises to complement imaging by providing frequent systemic information about the tumor. In particular, cell-free DNA (cfDNA) sequencing offers a tumor-agnostic approach, which lends itself to monitoring heterogeneous cohorts of cancer patients. MethodsWe collected plasma cfDNA from oligometastatic patients (OMD) and head-and-neck cancer patients (SCCHN) at six time points before, during, and after radiotherapy, and compared them to the plasma samples of healthy and polymetastatic volunteers. We performed low-pass (on average 7x) whole-genome sequencing on 93 plasma cfDNA samples and correlated copy number alterations and fragment length distributions to clinical and imaging findings. ResultsWe observed copy number alterations in 4/7 polymetastatic cancer patients, 1/7 OMD and 1/7 SCCHN patients, these patients’ imaging showed progression following radiotherapy. Using unsupervised learning, we identified cancer-specific fragment length features that showed a strong correlation with copy number-based tumor fraction estimates. In 4/4 HPV-positive SCCHN patient samples, we detected viral DNA that enabled the monitoring of very low tumor fraction samples. ConclusionsOur results indicate that an elevated tumor fraction is associated with tumor aggressiveness and systemic tumor spread. This information may be used to adapt treatment strategies. Further, we show that by detecting specific sequences such as viral DNA, the sensitivity of detecting cancer from cell-free DNA sequencing data can be greatly increased.

Non-invasive colorectal cancer detection using multimodal deep learning ensemble classifier.

3066 Background: The demand for alternative, non-invasive methods for colorectal cancer (CRC) screening is substantial. Cell-free DNA (cfDNA) whole genome sequencing (WGS) offers a promising avenue, utilizing diverse fragmentomic data. We aimed to develop a new approach: integrating fragment end motif by size (FEMS) with genomic coverage (COV) of cfDNA to enhance CRC screening. Methods: Participants were comprised of 1,506 colonoscopy verified normal samples, 130 advanced adenoma (AA) patients, 302 CRC patients (stage I: 28.5%, stage II: 25.5%, stage III: 31.1%, stage IV: 14.2%, unknown: 0.7%). We generated low depth cfDNA-WGS data (a minimum 40 million reads per sample) using the Novaseq 6000 sequencer. We minimized batch bias by normalizing with the same samples for each experimental batch. The development set comprised 1,332 samples (1,023 normal, 103 AA, and 206 CRC), while the validation set consisted of 606 samples (483 normal, 27 AA, and 96 CRC). Results: Our new algorithm achieved 84.0% sensitivity (95% CI: 81%-86%) for CRC and 69.9% (95% CI: 66%-81%) for AA in the development set, with a specificity of 90.0%. These findings were consistent in the external set with 84.9% specificity (95% CI: 82%-88%) and sensitivities of 80.2% (95% CI: 72%-88%) and 63.0% (95% CI: 44%-82%) for CRC and AA, respectively. The sensitivities across stages (Stage I: 80%, Stage II: 86.8%, Stage III: 78.8%, Stage IV: 96.7%) were similarly reflected in the validation sets (Stage I: 74%, Stage II: 83%, Stage III: 79%, Stage IV: 92%). Conclusions: Our multimodal deep learning model shows promise for accurate non-invasive colon cancer and AA detection using low-coverage cfDNA WGS. Its strong performance in detecting not only CRC but also AA lesions suggests its potential for early intervention and improved patient outcomes. The use of colonoscopy-verified normal samples strengthens the study's credibility and paves the way for future clinical translation.

Analysis of cell free DNA to predict outcome to bevacizumab therapy in colorectal cancer patients

To predict outcome to combination bevacizumab (BVZ) therapy, we employed cell-free DNA (cfDNA) to determine chromosomal instability (CIN), nucleosome footprints (NF) and methylation profiles in metastatic colorectal cancer (mCRC) patients. Low-coverage whole-genome sequencing (LC-WGS) was performed on matched tumor and plasma samples, collected from 74 mCRC patients from the AC-ANGIOPREDICT Phase II trial (NCT01822444), and analysed for CIN and NFs. A validation cohort of plasma samples from the University Medical Center Mannheim (UMM) was similarly profiled. 61 AC-ANGIOPREDICT plasma samples collected before and following BVZ treatment were selected for targeted methylation sequencing. Using cfDNA CIN profiles, AC-ANGIOPREDICT samples were subtyped with 92.3% accuracy into low and high CIN clusters, with good concordance observed between matched plasma and tumor. Improved survival was observed in CIN-high patients. Plasma-based CIN clustering was validated in the UMM cohort. Methylation profiling identified differences in CIN-low vs. CIN high (AUC = 0.87). Moreover, significant methylation score decreases following BVZ was associated with improved outcome (p = 0.013). Analysis of CIN, NFs and methylation profiles from cfDNA in plasma samples facilitates stratification into CIN clusters which inform patient response to treatment.

Detection and characterization of pancreatic and biliary tract cancers using cell-free DNA fragmentomics

BackgroundPlasma cell-free DNA (cfDNA) fragmentomics has demonstrated significant differentiation power between cancer patients and healthy individuals, but little is known in pancreatic and biliary tract cancers. The aim of this study is to characterize the cfDNA fragmentomics in biliopancreatic cancers and develop an accurate method for cancer detection.MethodsOne hundred forty-seven patients with biliopancreatic cancers and 71 non-cancer volunteers were enrolled, including 55 patients with cholangiocarcinoma, 30 with gallbladder cancer, and 62 with pancreatic cancer. Low-coverage whole-genome sequencing (median coverage: 2.9 ×) was performed on plasma cfDNA. Three cfDNA fragmentomic features, including fragment size, end motif and nucleosome footprint, were subjected to construct a stacked machine learning model for cancer detection. Integration of carbohydrate antigen 19–9 (CA19-9) was explored to improve model performance.ResultsThe stacked model presented robust performance for cancer detection (area under curve (AUC) of 0.978 in the training cohort, and AUC of 0.941 in the validation cohort), and remained consistent even when using extremely low-coverage sequencing depth of 0.5 × (AUC: 0.905). Besides, our method could also help differentiate biliopancreatic cancer subtypes. By integrating the stacked model and CA19-9 to generate the final detection model, a high accuracy in distinguishing biliopancreatic cancers from non-cancer samples with an AUC of 0.995 was achieved.ConclusionsOur model demonstrated ultrasensitivity of plasma cfDNA fragementomics in detecting biliopancreatic cancers, fulfilling the unmet accuracy of widely-used serum biomarker CA19-9, and provided an affordable way for accurate noninvasive biliopancreatic cancer screening in clinical practice.

Robustness in population-structure and demographic-inference results derived from the Aedes aegypti genotyping chip and whole-genome sequencing data.

The mosquito Aedes aegypti is the primary vector of many human arboviruses such as dengue, yellow fever, chikungunya, and Zika, which affect millions of people worldwide. Population genetic studies on this mosquito have been important in understanding its invasion pathways and success as a vector of human disease. The Axiom aegypti1 SNP chip was developed from a sample of geographically diverse A. aegypti populations to facilitate genomic studies on this species. We evaluate the utility of the Axiom aegypti1 SNP chip for population genetics and compare it with a low-depth shotgun sequencing approach using mosquitoes from the native (Africa) and invasive ranges (outside Africa). These analyses indicate that results from the SNP chip are highly reproducible and have a higher sensitivity to capture alternative alleles than a low-coverage whole-genome sequencing approach. Although the SNP chip suffers from ascertainment bias, results from population structure, ancestry, demographic, and phylogenetic analyses using the SNP chip were congruent with those derived from low-coverage whole-genome sequencing, and consistent with previous reports on Africa and outside Africa populations using microsatellites. More importantly, we identified a subset of SNPs that can be reliably used to generate merged databases, opening the door to combined analyses. We conclude that the Axiom aegypti1 SNP chip is a convenient, more accurate, low-cost alternative to low-depth whole-genome sequencing for population genetic studies of A. aegypti that do not rely on full allelic frequency spectra. Whole-genome sequencing and SNP chip data can be easily merged, extending the usefulness of both approaches.

Mitogenomes do not substantially improve phylogenetic resolution in a young non-model adaptive radiation of freshwater gastropods

BackgroundSpecies flocks in ancient lakes, and particularly those arising from adaptive radiation, make up the bulk of overall taxonomic and morphological diversity in these insular ecosystems. For these mostly young species assemblages, classical mitochondrial barcoding markers have so far been key to disentangle interspecific relationships. However, with the rise and further development of next-generation sequencing (NGS) methods and mapping tools, genome-wide data have become an increasingly important source of information even for non-model groups.ResultsHere, we provide, for the first time, a comprehensive mitogenome dataset of freshwater gastropods endemic to Sulawesi and thus of an ancient lake invertebrate species flock in general. We applied low-coverage whole-genome sequencing for a total of 78 individuals including 27 out of the 28 Tylomelania morphospecies from the Malili lake system as well as selected representatives from Lake Poso and adjacent catchments. Our aim was to assess whether mitogenomes considerably contribute to the phylogenetic resolution within this young species flock. Interestingly, we identified a high number of variable and parsimony-informative sites across the other ‘non-traditional’ mitochondrial loci. However, although the overall support was very high, the topology obtained was largely congruent with previously published single-locus phylogenies. Several clades remained unresolved and a large number of species was recovered polyphyletic, indicative of both rapid diversification and mitochondrial introgression.ConclusionsThis once again illustrates that, despite the higher number of characters available, mitogenomes behave like a single locus and thus can only make a limited contribution to resolving species boundaries, particularly when introgression events are involved.

Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits.

The selection and breeding of good meat rabbit breeds are fundamental to their industrial development, and genomic selection (GS) can employ genomic information to make up for the shortcomings of traditional phenotype-based breeding methods. For the practical implementation of GS in meat rabbit breeding, it is necessary to assess different marker densities and GS models. Here, we obtained low-coverage whole-genome sequencing (lcWGS) data from 1515 meat rabbits (including parent herd and half-sibling offspring). The specific objectives were (1) to derive a baseline for heritability estimates and genomic predictions based on randomly selected marker densities and (2) to assess the accuracy of genomic predictions for single- and multiple-trait linear mixed models. We found that a marker density of 50 K can be used as a baseline for heritability estimation and genomic prediction. For GS, the multi-trait genomic best linear unbiased prediction (GBLUP) model results in more accurate predictions for virtually all traits compared to the single-trait model, with improvements greater than 15% for all of them, which may be attributed to the use of information on genetically related traits. In addition, we discovered a positive correlation between the performance of the multi-trait GBLUP and the genetic correlation between the traits. We anticipate that this approach will provide solutions for GS, as well as optimize breeding programs, in meat rabbits.

Abstract 6929: Inter- and intratumoral PIK3CA subclonal diversity in breast cancer contextualized by single-cell multiomics

Abstract Rare clonotypes within pre-cancerous tissues can drive progression to cancer. However, the evolution of rare clonotypes in tumors or normal tissue cannot be defined in the absence of single-cell resolution. At this single-cell level, multiomic interrogation across the Central Dogma of Biology provides enhanced power to reconstruct such evolutionary trajectories, defining the mutational profile, cell identify, and receptor expression within each subpopulation. Leveraging a multiomic approach, we aimed to define how different mutations in the same oncogenic driver observed in the same tumor resection associate with copy number variation (CNV) across the genome. We analyzed individual ductal carcinoma in situ/invasive ductal carcinoma cells using a unified whole-genome and full-transcript RNAseq workflow (ResolveOME™, BioSkryb Genomics) coupled with panel-level extracellular protein information through oligo-conjugated antibodies (BioLegend). We sequenced the exomes and transcriptomes of ResolveOME-amplified single cells from mastectomy samples from twelve patients. At the single nucleotide variant (SNV) level, we identified an allelic series of PIK3CA oncogenic driver mutations in the same tumor resection. A single amino acid, in-frame deletion of E109 dominated the sample, followed by H1047R and K111E in decreasing subclonal abundance. A fourth mutation, E345T, not present in the first sample, was detected as the sole PIK3CA variant in the second tumor sample. Intriguingly, each respective PIK3CA mutation class was associated with a distinct copy number alteration profile revealed by low-coverage whole-genome sequencing: Cells harboring the predominant ΔE109 mutation displayed chromosome 8p,16p, and 17 loss while the less abundant H1047R mutation was in single cells harboring 1q gain, 4q loss, and 22 loss in addition to the 8p and 16q loss present in the ΔE109 cells. PIK3CA K111E had a quiescent, 2n copy number profile. The transcriptomic arm of ResolveOME, containing an oligo-conjugated antibody readout of surface protein expression, jointly confirmed the epithelial identity for the cells harboring the oncogenic PIK3CA mutations. A subpopulation of cells harboring prototypical breast cancer CNV were typed as non-epithelial with increased stemness characteristics, indicative of the ability to resolve phenotypic cellular states. These results suggest a tight interrelationship between CNV and SNV influencing the relative rate of clonal expansion. They also provide the opportunity to explore CNV:SNV signature association with loci exclusive of PIK3CA, and to exploit the power of multiomic integration for lineage reconstruction and for defining common oncogenic signatures of the evolving tumors. Citation Format: Jon Zawistowski, Isai Salas-Gonzalez, Tia Tate, Tatiana Morozova, Katherine Kennedy, Durga Arvapalli, Jamie Remington, Jeffrey Marks, E. Shelley Hwang, Gary Harton, Victor Weigman, Jay A. West. Inter- and intratumoral PIK3CA subclonal diversity in breast cancer contextualized by single-cell multiomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6929.

Abstract 1764: Long read sequencing allows comprehensive molecular profiling of complex karyotype acute myeloid leukemia (CKAML) with 5q deletions

Abstract Background Acute myeloid leukemia with complex karyotype (CKAML) does frequently show del(5q), deletion of chromosome 5q. Previous work in MDS and AML showed that CKAML form clusters distinguishing del(5q) cases with commonly retained regions (CRRs) in the telomeric (≥5q34) and centromeric (≤5q14.2) ends from cases with loss of the telomeric and/or centromeric regions. Interestingly, del(5q) patients with CRRs were shown to have less genomic lesions and were associated with a better prognosis than other 5q-deleted CKAML cases. Aim: Combining the advantages of two different long read sequencing technologies, Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), we aimed to more comprehensively characterize the molecular landscape of CKAML with del(5q). Our integrative workflow enabled an in-depth characterization of copy number variations (CNV), genomic rearrangements, transcriptomic and epigenomic changes. Methods: Low coverage whole genome sequencing (WGS) using ONT was performed on 209 patients with CKAML to identify del(5q) cases for further in-depth analysis. For 10 selected cases with known CRR status, genomic deep sequencing using PacBio Revio and ONT PromethION was performed, as well as PacBio Revio MAS-seq for fusion transcript detection. Results: A mean sequencing depth of 3-fold (range 0.4-17.3-fold) was obtained by low coverage WGS of the 209 CKAML cases. With a resolution of 0.1 Mbp n=134 del(5q) cases were identified in our CKAML cohort (64%) and commonly deleted regions on 5q could be resolved at 5q21.2 to 5q23.3 and 5q33.3 to 5q35 (Fisher test, FDR Benjamini-Hochberg, p<10−30). These regions separated del(5q) in n=52 cases showing CRR retention and n=82 showing partially no CRR, (either the centromeric or telomeric regions, n=74, or of both regions, n=8 involved). Subsequent co-occurrence analysis provided evidence of an association between the loss of telomeric 5q region with a 3p12-25 deletion. In-depth analysis (30-50 fold) of ten distinct del(5q) cases by PacBio and ONT revealed additional insights into the complex clonal composition of del(5q) cases. For example, one case showed two distinct subclones with 5q14.2-5q35.1 deletion and with translocation t(3;5)(p14;q14) respectively. Moreover, we could demonstrate that 5q-aberrations are far more complex than initially thought showing, e.g. inverted terminal duplications that resulted in fusion transcripts involving PDE4D and leading to its deregulated expression. Conclusion: Using our long read deep sequencing approach we could further delineate the structural complexity of del(5q) CKAML. While gene expression profiling, methylation analysis and correlation of findings with clinical outcome are ongoing and will be presented at the meeting, our results help to further pinpoint the biology underlying del(5q) and to further refine this heterogeneous CKAML subgroup. Citation Format: Felix Schick, Ekaterina Slonova, Eric Straeng, Sarah Huet, Maurizio Raso, Marlon Tilgner, Frederik Damm, Pierre Sujobert, Olga Blau, Lars Bullinger, Anna Dolnik. Long read sequencing allows comprehensive molecular profiling of complex karyotype acute myeloid leukemia (CKAML) with 5q deletions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1764.

Abstract 1266: Development and performance of a multi-cancer early detection test utilizing plasma cfDNA fragmentomics: A large-scale, prospective, multicenter study

Abstract Background: The implementation of the multi-cancer early detection (MCED) test offers a valuable adjunct to existing screening methods, enabling more efficient detection of cancer and potentially leading to improved treatment outcomes and prognoses for patients. Here, we report on the performance of an MCED test, which utilizes plasma cfDNA and leverages genome-wide fragmentomics-based characteristics to identify cancer signals and predict the signal origin across a diverse range of cancer types. Methods: Plasma cfDNA from evaluable blood samples was analyzed using an MCED blood test called MERCURY, a robust machine learning classifier leveraging the low-coverage whole-genome sequencing and a comprehensive set of genome-wide features derived from cfDNA fragmentomics. A carefully selected cohort of 3076 cancer patients representing 13 cancer types, in addition to 3477 healthy controls, were pre-specified into the training and internal validation sets to train and internally validate the models to assess cancer and tissue of origin (TOO). The classifier was trained to a target specificity of 99% and locked before analysis of the independent validation set. The independent validation was enrolled prospectively and consists of 1465 participants (cancer: n= 732; non-cancer: n= 733). Results: The performance metrics in the internal validation set demonstrated that the sensitivity and specificity for cancer detection were 0.865 (95% CI [0.840, 0.887]) and 0.989 (95% CI [0.980, 0.994]), respectively. These impressive results were further substantiated in the independent validation set, where the overall sensitivity and specificity were found to be 0.874 (95% CI [0.848, 0.897]) and 0.978 (95% CI [0.965, 0.987]) respectively. Notably, the sensitivity showed an incremental increase with the stage of cancer (Stage I: 0.769, 95% CI [0.708, 0.821]; Stage II: 0.840, 95% CI [0.784, 0.886]; Stage III: 0.923, 95% CI [0.874, 0.954]; Stage IV: 0.971, 95% CI [0.901, 0.995]. Regarding the TOO model, a total of 10 cancer types with more than 100 patients in the model construction cohort were considered. The TOO model achieved a prediction accuracy of 83.5% (95% CI [80.7%, 86.6%]) and 91.8% (95% CI [89.6%, 94.1%]) for the top predicted origin and the top two predicted origins, respectively, amongst the true positive cases in the independent validation set. Conclusions: In this pre-specified, large-scale study, the MCED test, utilizing cfDNA fragmentomics, demonstrates its remarkable ability to assess cancer signal with an elevated level of sensitivity and specificity across 13 distinct types of cancer. Moreover, it displays noteworthy accuracy in predicting the tissue of origin. The performances are to be further validated in a prospective cohort study (NCT06011694). Citation Format: Hua Bao, Xiaoxi Chen, Min Wu, Shiting Tang, Xuxiaochen Wu, Wanxiangfu Tang, Dongqin Zhu, Shanshan Yang, Shuang Chang, Peng He, Xiuxiu Xu, JinPeng Zhang, Yi Shen, Shuyu Wu, Ya Jiang, Sisi Liu, Xian Zhang, Xue Wu, Yang Shao. Development and performance of a multi-cancer early detection test utilizing plasma cfDNA fragmentomics: A large-scale, prospective, multicenter study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1266.

Abstract 6093: Evaluation of preanalytical and physiological variables affecting cfDNA-based multi-cancer early detection test

Abstract Background: The multi-cancer early detection (MCED) tests utilizing blood-based circulating cell-free DNA (cfDNA) have gained significant attention in the market as a promising approach for early cancer detection. However, potential effects of preanalytical and physiological variables, including extraction methods, preservation techniques, and physiological factors such as diet, on the accuracy and reliability of MCED results remains poorly investigated. In this study, we aimed to evaluate the impact of physiological variables during blood collection and preanalytical procedures on the outcomes of a MCED test. Methods: A total of 105 plasma samples from 19 healthy donors was collected and analyzed using a MCED test (MERCURY) which leverages the low-coverage whole-genome sequencing and a set of genome-wide features based on cfDNA fragmentomics. Samples were controlled for preanalytical procedures, including transportation condition and preservation time or physiological conditions (before/after meal or exercise) at blood collection. Results: Repeat blood collection (N=4) from the 5 healthy participants consistently yielded negative results for cancer (PPA: 100.0%; 95% CI [56.6%,100.0%]). Among the 5 healthy participants, plasma samples frozen within 1 year (7 days, 3 months, 9 months) showed the same agreement with the reference condition as non-frozen samples (PPA: 100.0%; 95% CI [56.6%,100.0%]), except for one sample frozen for 1 year, which showed a higher risk score and became a positive signal (PPA:80.0%; 95% CI [37.6%,96.4%]). Transportation conditions within 2 hours, 24 hours, 48 hours, and 96 hours at room temperature or 4℃ did not affect the test outcomes, as all samples remained in agreement with the reference condition as of 2 hours at 4℃ (PPA:100.0%; 95% CI [61.0%,100.0%]). Physiological conditions, including pre- and post-meal as well as pre- and post-exercise states, did not exert any influence on the test results, as all samples exhibited agreement with the reference condition (PPA:100.0%; 95% CI [43.9%,100.0%]). Conclusions: The results of this study suggest that variables such as time of blood collection and plasma freezing time may not significantly affect the accuracy of the MCED test in healthy participants. Transportation conditions and physiological conditions evaluated in this study did not have a notable influence on the test outcomes. Nevertheless, it is advised that the freezing duration of plasma samples should not exceed one year. Citation Format: Hua Bao, Xiaoxi Chen, Xuxiaochen Wu, Wanxiangfu Tang, Min Wu, Shiting Tang, Xue Wu, Yang Shao. Evaluation of preanalytical and physiological variables affecting cfDNA-based multi-cancer early detection test [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6093.

Profiling of Copy Number Alterations Using Low-Coverage Whole-Genome Sequencing Informs Differential Diagnosis and Prognosis in Primary Cutaneous Follicle Center Lymphoma

Primary cutaneous follicle center lymphoma (PCFCL) has an excellent prognosis using local treatment, whereas nodal follicular lymphoma (nFL), occasionally presenting with cutaneous spread, often requires systemic therapy. Distinction of the 2 diseases based on histopathology alone might be challenging. Copy number alterations (CNAs) have scarcely been explored on a genome-wide scale in PCFCL; however, they might serve as potential biomarkers during differential diagnosis and risk stratification. Low-coverage whole-genome sequencing is a robust, high-throughput method for genome-wide copy number profiling. In this study, we analyzed 28 PCFCL samples from 20 patients and compared the copy number profiles with a cohort of diagnostic samples of 64 nFL patients. Although the copy number profile of PCFCL was similar to that of nFL, PCFCL lacked amplifications of 18q, with the frequency peaking at 18q21.33 in nFL cases involving the BCL2 locus (PCFCL: 5.0% vs nFL: 31.3%, P = .018, Fisher exact test). Development of distant cutaneous spread was significantly associated with higher genomic instability including the proportion of genome altered (0.02 vs 0.13, P = .033) and number of CNAs (2 vs 9 P = .017), as well as the enrichment of 2p22.2-p15 amplification involving REL and XPO1 (6.3% vs 60.0%, P = .005), 3q23-q24 amplification (0.0% vs 50.0%, P = .004), 6q16.1-q23.3 deletion (6.3% vs 50.0%, P = .018), and 9p21.3 deletion covering CDKN2A and CDKN2B loci (0.0% vs 40.0%, P = .014, all Fisher exact test) in PCFCL. Analysis of sequential tumor samples in 2 cases harboring an unfavorable clinical course pointed to the acquisition of 2p amplification in the earliest common progenitor underlining its pivotal role in malignant transformation. By performing genome-wide copy number profiling on the largest patient cohort to date, we identified distinctive CNA alterations conceivably facilitating the differential diagnosis of PCFCL and secondary cutaneous involvement of nFL and potentially aiding the risk stratification of patients with PCFCL in the future.

A genotyping array for the globally invasive vector mosquito, Aedes albopictus

BackgroundAlthough whole-genome sequencing (WGS) is the preferred genotyping method for most genomic analyses, limitations are often experienced when studying genomes characterized by a high percentage of repetitive elements, high linkage, and recombination deserts. The Asian tiger mosquito (Aedes albopictus), for example, has a genome comprising up to 72% repetitive elements, and therefore we set out to develop a single-nucleotide polymorphism (SNP) chip to be more cost-effective. Aedes albopictus is an invasive species originating from Southeast Asia that has recently spread around the world and is a vector for many human diseases. Developing an accessible genotyping platform is essential in advancing biological control methods and understanding the population dynamics of this pest species, with significant implications for public health.MethodsWe designed a SNP chip for Ae. albopictus (Aealbo chip) based on approximately 2.7 million SNPs identified using WGS data from 819 worldwide samples. We validated the chip using laboratory single-pair crosses, comparing technical replicates, and comparing genotypes of samples genotyped by WGS and the SNP chip. We then used the chip for a population genomic analysis of 237 samples from 28 sites in the native range to evaluate its usefulness in describing patterns of genomic variation and tracing the origins of invasions.ResultsProbes on the Aealbo chip targeted 175,396 SNPs in coding and non-coding regions across all three chromosomes, with a density of 102 SNPs per 1 Mb window, and at least one SNP in each of the 17,461 protein-coding genes. Overall, 70% of the probes captured the genetic variation. Segregation analysis found that 98% of the SNPs followed expectations of single-copy Mendelian genes. Comparisons with WGS indicated that sites with genotype disagreements were mostly heterozygotes at loci with WGS read depth < 20, while there was near complete agreement with WGS read depths > 20, indicating that the chip more accurately detects heterozygotes than low-coverage WGS. Sample sizes did not affect the accuracy of the SNP chip genotype calls. Ancestry analyses identified four to five genetic clusters in the native range with various levels of admixture.ConclusionsThe Aealbo chip is highly accurate, is concordant with genotypes from WGS with high sequence coverage, and may be more accurate than low-coverage WGS.Graphical