Comprehensive prenatal diagnostics: Exome versus genome sequencing.

OBJECTIVE: This study aimed to assess the diagnostic yield of prenatal genetic testing using trio whole exome sequencing (WES) and trio whole genome sequencing (WGS) in pregnancies with fetal anomalies by comparing the results with conventional chromosomal microarray (CMA) analysis. METHODS: A total of 40 pregnancies with fetal anomalies or increased nuchal translucency (NT ≥ 5 mm) were included between the 12th and 21st week of gestation. Trio WES/WGS and CMA were performed in all cases. RESULTS: The trio WES/WGS analysis increased the diagnostic yield by 25% in cases with negative CMA results. Furthermore, all six chromosomal aberrations identified by CMA were independently detected by WES/WGS analysis. In total, 16 out of 40 cases obtained a genetic sequence variant, copy number variant, or aneuploidy explaining the phenotype, resulting in an overall WES/WGS diagnostic yield of 40%. WES analysis provided a more reliable identification of mosaic sequence variants than WGS because of its higher sequencing depth. CONCLUSIONS: Prenatal WES/WGS proved to be powerful diagnostic tools for fetal anomalies, surpassing the diagnostic yield of CMA. They have the potential to serve as standalone methods for prenatal diagnosis. The study highlighted the limitations of WGS in accurately detecting mosaic variants, which is particularly relevant when analyzing chorionic villus samples.

Is it time to report carrier state for recessive disorders in every microarray analysis?-A pilot model based on hearing loss genes deletions.

This study aimed to examine the implications of reporting heterozygous losses of recessive genes in Chromosomal Microarray Analysis (CMA), based on the incidence of microdeletions of three common hearing impairment genes in the local cohort and the prevalence of sequence variants in these genes in worldwide databases. Prevalence of heterozygous microdeletions in OTOA and STRC genes, as well as deletions in the DFNB1 locus encompassing GJB6 gene, was determined using electronic database of Rabin Medical Center. ClinVar archive and Deafness Variation Database were used to generate a list of clinically significant sequence variants in these three genes, as well as GJB2 gene, and estimation of the frequency of sequence variants was performed. Of the 19,189 CMA tests were performed in our laboratory, 107 STRC microdeletions were found (0.56%), followed in frequency by OTOA deletions (39, 0.2%), and DFNB1 locus deletions (10, 0.05%). The estimated risk for a hearing loss in the examined individual carrying the microdeletion was estimated as 0.11-0.67% for STRC, 0.016-0.13% for OTOA, and 1.9-7.5% in the DFNB1 locus (including double heterozygocity with GJB2 clinically significant sequence variants). The risks were higher in specific populations. In conclusion, we believe that that general decision whether to report or to disregard such incidental findings cannot be part of a uniform policy, but rather based on a detailed evaluation of origin-specific variants for each gene, with a careful consideration and discussion whether to include the microdeletion in the final report for each patient.

Dealing with uncertain results from chromosomal microarray and exome sequencing in the prenatal setting: An international cross-sectional study with healthcare professionals.

OBJECTIVES: To conduct qualitative interviews with healthcare providers working in different countries to understand their experiences of dealing with uncertain results from prenatal chromosome microarray analysis (CMA) and exome sequencing (ES). METHODS: Semi-structured interviews with 31 healthcare providers who report or return prenatal CMA and/or ES results (clinicians, genetic counsellors and clinical scientists) in six countries with differing healthcare systems; Australia (4), Denmark (5), Netherlands (6), Singapore (4), Sweden (6) and United Kingdom (6). The topic guide explored the main sources of uncertainty and their management. RESULTS: There was variation in reporting practices both between and across countries for variants of uncertain significance, however, there was broad agreement on reporting practices for incidental findings. There was also variation in who decides what results are reported (clinical scientists or clinicians). Technical limitations and lack of knowledge (to classify variants and of prenatal phenotypes) were significant challenges, as were turnaround times and lack of guidelines. CONCLUSION: Health professionals around the globe are dealing with similar sources of uncertainty, but managing them in different ways, Continued dialogue with international colleagues on ways of managing uncertain results is important to compare and contrast the benefits and limitations of the different approaches.

Identification of potential gene drivers of cutaneous squamous cell carcinoma: Analysis of microarray data.

Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer with an increasing incidence. As a pre-cancerous condition, actinic keratosis (AK) has an up to 20% risk of progression to cSCC. This study aims to define the potential genes that associated with genesis and progression of cSCC, thereby further identify critical biomarkers for the prevention, early diagnosis, and effective treatment of cSCC.Two datasets GSE42677 and GSE45216 were downloaded from the GEO. Microarray data analysis was applied to explore the differentially expressed genes (DEGs) between cSCC samples and AK samples. Then functional enrichment analysis, protein-protein interaction (PPI) network, and drug-gene interaction analysis were performed to screen key genes.A total of 711 DEGs, including 238 upregulated genes and 473 downregulated genes, were screened out. DEGs mainly involved in pathways as extracellular matrix (ECM)-receptor interaction, hematopoietic cell lineage, phosphatidylinositol 3-kinase (PI3K-Akt) signaling pathway, and focal adhesion. Candidate genes, including upregulated genes as JUN, filamin A (FLNA), casein kinase 1 delta (CSNK1D), and histone cluster 1 H3 family member f (HIST1H3F), and downregulated genes as androgen receptor (AR), heat shock protein family H member 1 (HSPH1), tropomyosin 1 (TPM1), pyruvate kinase, muscle (PKM), LIM domain and actin binding 1 (LIMA1), and synaptopodin (SYNPO) were screened out. In drug-gene interaction analysis, 13 genes and 44 drugs were identified.This study demonstrates that genes JUN, FLNA, AR, HSPH1, and CSNK1D have the potential to function as targets for diagnosis and treatment of cSCC.

Integrating modification and detection in acoustic microchip for in-situ analysis.

Ultrasound as a biocompatible and powerful approach has been advanced in biotechnology. Here we present an acoustic microchip integrating modification and detection for in-situ analysis. Such microchip employs two pairs of piezoelectric transducers (PZTs) for acoustic field generation and a polydimethylsiloxane (PDMS) microcavity on a polyethylene terephthalate (PET) substrate for producing microparticle array. The applying of acoustic field results in rapidly forming microparticle array by adjusting the inputting frequency and voltage. In-situ modification and detection are accelerated due to the dynamic ultrasonic streaming around the ultrasound induced microparticle array. Such array also benefits from reducing the detection errors by coupling of multiple points. With this strategy, biomarkers (e.g. miRNA) can be enriched, and achieve in-situ modification and detection via simple two steps with excellent specificity. After the detection, samples are regained from the output channel by releasing the acoustic field, which is benefit for further analysis. Such integrated modification and detection acoustic microchip shows great potential in visual in-situ analysis and enriching ultratrace biomarkers for clinical diagnosis.

[Application value of chromosome microarray analysis for prenatal diagnosis of fetus with ultrasonic abnormalities].

OBJECTIVE: To assess the application value of chromosomal microarray analysis (CMA) for prenatal diagnosis of fetus with ultrasound abnormalities. METHODS: For 293 fetuses with ultrasound abnormalities (including 168 with structural abnormalities and 125 with non-structured abnormalities) but no common chromosomal abnormalities, CMA assay was performed. RESULTS: Sixteen pathogenic copy number variants (pCNVs) were detected by CMA with a detection rate of 5.46%. The detection rates were 5.95% (10/168) for those with structural abnormalities and 4.80% (6/125) for those with non-structural abnormalities. CONCLUSION: Compared with conventional karyotyping analysis, CMA can improve the detection of fetal chromosomal abnormality and provide an effective means for prenatal diagnosis.

The impact of different sources of heterogeneity on loss of accuracy from genomic prediction models.

Cross-study validation (CSV) of prediction models is an alternative to traditional cross-validation (CV) in domains where multiple comparable datasets are available. Although many studies have noted potential sources of heterogeneity in genomic studies, to our knowledge none have systematically investigated their intertwined impacts on prediction accuracy across studies. We employ a hybrid parametric/non-parametric bootstrap method to realistically simulate publicly available compendia of microarray, RNA-seq, and whole metagenome shotgun microbiome studies of health outcomes. Three types of heterogeneity between studies are manipulated and studied: (i) imbalances in the prevalence of clinical and pathological covariates, (ii) differences in gene covariance that could be caused by batch, platform, or tumor purity effects, and (iii) differences in the "true" model that associates gene expression and clinical factors to outcome. We assess model accuracy, while altering these factors. Lower accuracy is seen in CSV than in CV. Surprisingly, heterogeneity in known clinical covariates and differences in gene covariance structure have very limited contributions in the loss of accuracy when validating in new studies. However, forcing identical generative models greatly reduces the within/across study difference. These results, observed consistently for multiple disease outcomes and omics platforms, suggest that the most easily identifiable sources of study heterogeneity are not necessarily the primary ones that undermine the ability to accurately replicate the accuracy of omics prediction models in new studies. Unidentified heterogeneity, such as could arise from unmeasured confounding, may be more important.

Low-pass genome sequencing versus chromosomal microarray analysis: implementation in prenatal diagnosis.

PURPOSE: Emerging studies suggest that low-pass genome sequencing (GS) provides additional diagnostic yield of clinically significant copy-number variants (CNVs) compared with chromosomal microarray analysis (CMA). However, a prospective back-to-back comparison evaluating accuracy, efficacy, and incremental yield of low-pass GS compared with CMA is warranted. METHODS: A total of 1023 women undergoing prenatal diagnosis were enrolled. Each sample was subjected to low-pass GS and CMA for CNV analysis in parallel. CNVs were classified according to guidelines of the American College of Medical Genetics and Genomics. RESULTS: Low-pass GS not only identified all 124 numerical disorders or pathogenic or likely pathogenic (P/LP) CNVs detected by CMA in 121 cases (11.8%, 121/1023), but also defined 17 additional and clinically relevant P/LP CNVs in 17 cases (1.7%, 17/1023). In addition, low-pass GS significantly reduced the technical repeat rate from 4.6% (47/1023) for CMA to 0.5% (5/1023) and required less DNA (50 ng) as input. CONCLUSION: In the context of prenatal diagnosis, low-pass GS identified additional and clinically significant information with enhanced resolution and increased sensitivity of detecting mosaicism as compared with the CMA platform used. This study provides strong evidence for applying low-pass GS as an alternative prenatal diagnostic test.

Comparison of microfluid sperm sorting chip and density gradient methods for use in intrauterine insemination cycles.

OBJECTIVE: To compare the effect of microfluiding sperm sorting chip and density gradient methods on ongoing pregnancy rates (PRs) of patients undergoing IUI. DESIGN: Retrospective cohort study. SETTING: Hospital IVF unit. PATIENT(S): Couples with infertility undergoing IUI cycles between 2017 and 2018. INTERVENTION(S): Not applicable. MAIN OUTCOME MEASURE(S): Ongoing PRs. RESULT(S): A total of 265 patients were included in the study. Microfluid sperm sorting and density gradient were used to prepare sperm in 133 and 132 patients, respectively. Baseline spermiogram parameters, including volume, concentration, motility, and morphology, were similar between the two groups. Total motile sperm count was lower in the microfluiding sperm sorting group at baseline (35.96 ± 37.69 vs. 70.66 ± 61.65). After sperm preparation sperm motility was higher in the microfluid group (96.34 ± 7.29 vs. 84.42 ± 10.87). Pregnancy rates were 18.04% in the microfluid group and 15.15% in the density gradient group, and ongoing PRs were 15.03% and 9.09%, respectively. After using multivariable logistic regression and controling for confounding factors, there was a significant increase in ongoing PRs in the microfluid sperm sorting group. The adjusted odds ratio for ongoing pregnancy in the microfluid group compared with the density gradient group was 3.49 (95% confidence interval 1.12-10.89). CONCLUSION(S): The microfluid sperm sorting method significantly increased the ongoing PRs compared with the density gradient group in IUI cycles.

Improving classification accuracy of cancer types using parallel hybrid feature selection on microarray gene expression data.

BACKGROUND: Data mining techniques are used to mine unknown knowledge from huge data. Microarray gene expression (MGE) data plays a major role in predicting type of cancer. But as MGE data is huge in volume, applying traditional data mining approaches is time consuming. Hence parallel programming frameworks like Hadoop, Spark and Mahout are necessary to ease the task of computation. OBJECTIVE: Not all the gene expressions are necessary in prediction, it is very essential to select important genes for improving classification accuracy. So feature selection algorithms are parallelized and executed on Spark framework to eliminate unnecessary genes and identify only predictive genes in very less time without affecting prediction accuracy. METHODS: Parallelized hybrid feature selection (HFS) method is proposed to serve the purpose. This method includes parallelized correlation feature subset selection followed by rank-based feature selection methods. The selected subset of genes is evaluated using parallel classification algorithms. The accuracy values obtained are compared with existing rank-weight feature selection, parallelized recursive feature selection methods and also with the values obtained by executing parallelized HFS on DistributedWekaSpark. RESULTS: The classification accuracy obtained with the proposed parallelized HFS method is 97% and 79% for gastric cancer and childhood leukemia respectively. The proposed parallelized HFS method produced ~ 4% to ~ 15% improvement in classification accuracy when compared with previous methods. CONCLUSION: The results reveal the fact that the proposed parallelized feature selection algorithm is scalable to growing medical data and predicts cancer sub-types in lesser time with higher accuracy.

Rapid and Ultrasensitive Quantification of Multiplex Respiratory Tract Infection Pathogen via Lateral Flow Microarray based on SERS Nanotags.

Respiratory tract infections (RTIs) are severe acute infectious diseases, which require the timely and accurate identification of the pathogens involved so that the individual treatment plan can be selected, including optimized use of antibiotics. However, high throughput and ultrasensitive quantification of multiple nucleic acids is a challenge in a point of care testing (POCT) device. Methods: Herein, we developed a 2×3 microarray on a lateral flow strip with surface enhanced Raman scattering (SERS) nanotags encoding the nucleic acids of 11 common RTI pathogens. On account of the signal magnification of encoded SERS nanotags in addition to the high surface area to volume ratio of the nitrocellulose (NC) membrane, rapid quantification of the 11 pathogens with a broad linear dynamic range (LDR) and ultra-high sensitivity was achieved on one lateral flow microarray. Results: The limit of detection (LOD) for influenza A, parainfluenza 1, parainfluenza 3, respiratory syncytial virus, coxiella burnetii, legionella pneumophila, influenza B, parainfluenza 2, adenovirus, chlamydophila pneumoniae, and mycoplasma pneumoniae were calculated to be 0.031 pM, 0.030 pM, 0.038 pM, 0.038 pM, 0.040 pM, 0.039 pM, 0.035 pM, 0.032 pM, 0.040 pM, 0.039 pM, and 0.041 pM, respectively. The LDR of measurement of the target nucleic acids of the eleven RTI pathogens were 1 pM-50 nM, which span 5 orders of magnitude. Conclusions: We anticipate this novel approach could be widely adopted in the early and precise diagnosis of RTI and other diseases.

Analytical evaluation of the microarray-based FluChip-8G Influenza A+B Assay.

BACKGROUND: Influenza causes a significant annual disease burden, with characterization of the infecting virus important in clinical and public health settings. Rapid immunoassays are fast but insensitive, whereas real-time RT-PCR is sensitive but susceptible to genetic mutations and often requires multiple serial assays. The FluChip-8G Influenza A+B Assay provides type and subtype/lineage identification of influenza A and B, including non-seasonal A viruses, in a single microarray-based assay with same day turnaround time. OBJECTIVE: To evaluate key analytical performance characteristics of the FluChip-8G Influenza A+B Assay. STUDY DESIGN: Analytical sensitivity, cross-reactivity, and multi-site reproducibility were evaluated. RESULTS: The limit of detection (LOD) for the FluChip-8G influenza A+B Assay ranged from 5.8 × 102-1.5 × 105 genome copies/mL, with most samples ∼2 × 103 genome copies/mL (∼160 genome copies/reaction). Fifty two (52) additional strains were correctly identified near the LOD, demonstrating robust reactivity. Two variant viruses (H1N1v and H3N2v) resulted in dual identification as both "non-seasonal influenza A" and A/H1N1pdm09. No reproducible cross-reactivity was observed for the 34 organisms tested, however, challenges with internal control inhibition due to crude growth matrix were observed. Lastly, samples tested near the LOD showed high reproducibility (97.0% (95% CI 94.7-98.7)) regardless of operator, site, reagent lot, or testing day. CONCLUSION: The FluChip-8G Influenza A+B Assay is an effective new method for detecting and identifying both seasonal and non-seasonal influenza viruses, as revealed by good sensitivity and robust reactivity to 52 unique strains of influenza virus. In addition, the lack of cross-reactivity to non-influenza pathogens and high lab-to-lab reproducibility highlight the analytical performance of the assay as an alternative to real-time RT-PCR and sequencing-based assays. Clinical validation of the technology in a multi-site clinical study is the subject of a separate investigation.

Copy number variant and runs of homozygosity detection by microarrays enabled more precise molecular diagnoses in 11,020 clinical exome cases.

BACKGROUND: Exome sequencing (ES) has been successfully applied in clinical detection of single nucleotide variants (SNVs) and small indels. However, identification of copy number variants (CNVs) using ES data remains challenging. The purpose of this study is to understand the contribution of CNVs and copy neutral runs of homozygosity (ROH) in molecular diagnosis of patients referred for ES. METHODS: In a cohort of 11,020 consecutive ES patients, an Illumina SNP array analysis interrogating mostly coding SNPs was performed as a quality control (QC) measurement and for CNV/ROH detection. Among these patients, clinical chromosomal microarray analysis (CMA) was performed at Baylor Genetics (BG) on 3229 patients, either before, concurrently, or after ES. We retrospectively analyzed the findings from CMA and the QC array. RESULTS: The QC array can detect ~ 70% of pathogenic/likely pathogenic CNVs (PCNVs) detectable by CMA. Out of the 11,020 ES cases, the QC array identified PCNVs in 327 patients and uniparental disomy (UPD) disorder-related ROH in 10 patients. The overall PCNV/UPD detection rate was 5.9% in the 3229 ES patients who also had CMA at BG; PCNV/UPD detection rate was higher in concurrent ES and CMA than in ES with prior CMA (7.2% vs 4.6%). The PCNVs/UPD contributed to the molecular diagnoses in 17.4% (189/1089) of molecularly diagnosed ES cases with CMA and were estimated to contribute in 10.6% of all molecularly diagnosed ES cases. Dual diagnoses with both PCNVs and SNVs were detected in 38 patients. PCNVs affecting single recessive disorder genes in a compound heterozygous state with SNVs were detected in 4 patients, and homozygous deletions (mostly exonic deletions) were detected in 17 patients. A higher PCNV detection rate was observed for patients with syndromic phenotypes and/or cardiovascular abnormalities. CONCLUSIONS: Our clinical genomics study demonstrates that detection of PCNV/UPD through the QC array or CMA increases ES diagnostic rate, provides more precise molecular diagnosis for dominant as well as recessive traits, and enables more complete genetic diagnoses in patients with dual or multiple molecular diagnoses. Concurrent ES and CMA using an array with exonic coverage for disease genes enables most effective detection of both CNVs and SNVs and therefore is recommended especially in time-sensitive clinical situations.

Effects of platforms, size filter cutoffs, and targeted regions of cytogenomic microarray on detection of copy number variants and uniparental disomy in prenatal diagnosis: Results from 5026 pregnancies.

OBJECTIVE: We evaluated the effects of platforms, size filter cutoffs, and targeted regions of cytogenomic microarray (CMA) on the detection of copy number variants (CNVs) and uniparental disomy (UPD) in prenatal diagnosis. METHODS: Five thousand twenty-six consecutive prenatal specimens (>98% high-risk pregnancy) were studied by high-resolution CMA, with cutoffs of 50 kb for losses and 200 kb for gains in nontargeted regions and 20 kb for losses and 100 kb for gains in targeted regions. We assessed actual detection rates using the current assay as well as hypothetical detection rates using platforms with the same or lower resolution and smaller or larger cutoffs. RESULTS: The detection rate of our current assay was 11.2% (562 of 5026), including abnormal findings in 543 cases and likely pathogenic variants in 19. The hypothetical decrease in the overall detection of variants (excluding likely benign) and UPD ranged from 3.8% to 23.0%. For the subgroup of pathogenic and likely pathogenic CNVs < 1 Mb, the decrease of detection ranged from 2.7% to 24.3%. CONCLUSIONS: These findings underscore the significant effects of chosen CMA platform, as well as size filter cutoffs and targeted regions used in data analysis, on detection of CNVs and UPDs in a cohort of prenatal cases.

Independent validation of induced overexpression efficiency across 242 experiments shows a success rate of 39.

Although numerous studies containing induced gene expression have already been published, independent authentication of their results has not yet been performed. Here, we utilized available transcriptomic data to validate the achieved efficiency in overexpression studies. Microarray data of experiments containing cell lines with induced overexpression in one or more genes were analyzed. All together 342 studies were processed, these include 242 different genes overexpressed in 184 cell lines. The final database includes 4,755 treatment-control sample pairs. Successful gene induction (fold change induction over 1.44) was validated in 39.3% of all genes at p < 0.05. Number of repetitions within a study (p < 0.0001) and type of used vector (p = 0.023) had significant impact on successful overexpression efficacy. In summary, over 60% of studies failed to deliver a reproducible overexpression. To achieve higher efficiency, robust and strict study design with multi-level quality control will be necessary.

Analysis of time-course microarray data: Comparison of common tools.

High-throughput time-series data have a special value for studying the dynamism of biological systems. However, the interpretation of such complex data can be challenging. The aim of this study was to compare common algorithms recently developed for the detection of differentially expressed genes in time-course microarray data. Using different measures such as sensitivity, specificity, predictive values, and related signaling pathways, we found that limma, timecourse, and gprege have reasonably good performance for the analysis of datasets in which only test group is followed over time. However, limma has the additional advantage of being able to report significance cut off, making it a more practical tool. In addition, limma and TTCA can be satisfactorily used for datasets with time-series data for all experimental groups. These findings may assist investigators to select appropriate tools for the detection of differentially expressed genes as an initial step in the interpretation of time-course big data.

Weighted volume under the three-way receiver operating characteristic surface.

It is often necessary to differentiate subjects from multiple categories using medical tests. We may then adopt statistical measures to characterize the performance of these tests. The three-way ROC analysis has been proposed to evaluate the diagnostic accuracy of medical tests with three categories, reflecting the correct classification probabilities across all possible decision thresholds. The geometry of the ROC surface is carefully studied, leading to numerical summary measures such as the volume under the surface. This paper generalizes the global volume under the surface of three-way ROC analysis to the weighted volume under the surface (WVUS) by introducing a weight function emphasizing particular regions of correct classification probabilities. This generalization practically allows researchers to calculate the diagnostic accuracy for a medical or clinical biomarker while satisfactorily high probabilities of correct classification for one or two classes are conditionally ensured. We provide the asymptotic properties of the proposed nonparametric and parametric estimators of WVUS, which could easily lend support to statistical inferences. Some simulations have been conducted to assess the proposed estimators and also to demonstrate the necessity of WVUS. A real data analysis about liver cancer illustrates our methodology.

Application of Molecular Methods in the Identification of Ingredients in Chinese Herbal Medicines.

There are several kinds of Chinese herbal medicines originating from diverse sources. However, the rapid taxonomic identification of large quantities of Chinese herbal medicines is difficult using traditional methods, and the process of identification itself is prone to error. Therefore, the traditional methods of Chinese herbal medicine identification must meet higher standards of accuracy. With the rapid development of bioinformatics, methods relying on bioinformatics strategies offer advantages with respect to the speed and accuracy of the identification of Chinese herbal medicine ingredients. This article reviews the applicability and limitations of biochip and DNA barcoding technology in the identification of Chinese herbal medicines. Furthermore, the future development of the two technologies of interest is discussed.

Reference Size Matching, Whole-Genome Amplification, and Fluorescent Labeling as a Method for Chromosomal Microarray Analysis of Clinically Actionable Copy Number Alterations in Formalin-Fixed, Paraffin-Embedded Tumor Tissue.

Cancer genome copy number alterations (CNAs) assist clinicians in selecting targeted therapeutics. Solid tumor CNAs are most commonly evaluated in formalin-fixed, paraffin-embedded (FFPE) tissue by fluorescence in situ hybridization. Although fluorescence in situ hybridization is a sensitive and specific assay for interrogating preselected genomic regions, it provides no information about coexisting clinically significant copy number changes. Chromosomal microarray analysis is an alternative DNA-based method for interrogating genome-wide CNAs in solid tumors. However, DNA extracted from FFPE tumor tissue produces an essential, yet problematic, sample type. The College of American Pathologists/American Society of Clinical Oncology guidelines for optimal tumor tissue handling, published in 2007 for breast cancer and in 2016 for gastroesophageal adenocarcinomas, are lacking for other solid tumors. Thus, cold ischemia times are seldom monitored in non-breast cancer and non-gastroesophageal adenocarcinomas, and all tumor biospecimens are affected by chemical fixation. Although intended to preserve specimens for long-term storage, formalin fixation causes loss of genetic information through DNA damage. Herein, we describe a reference size matching, whole-genome amplification, and fluorescent labeling method for FFPE-derived DNA designed to improve chromosomal microarray results from suboptimal nucleic acids and salvage highly degraded samples. With this technological advance, whole-genome copy number analysis of tumor DNA can be reliably performed in the clinical laboratory for a wide variety of tissue conditions and tumor types.