Standardization of the preanalytical phase of DNA extraction from fixed tissue for next-generation sequencing analyses

Advances in diagnostic liquid-based cytology

Liquid-based cytology (LBC) has changed the landscape of gynaecological cytology. A growing demand exists for LBC in diagnostic cytology, particularly for ancillary testing, such as immunocytochemistry and molecular testing. Ancillary testing solely based on conventional preparation (CP) methods remains challenging. Recently, the increased demand for specialist testing and minimally invasive techniques, such as endoscopic ultrasonography fine-needle aspiration, to obtain cellular samples has led to an increasing demand for ancillary testing on cytology LBC supernatant, slides and cell block (CB). This facilitates the diagnosis and prognosis in cytology samples enabling personalized treatment. An understanding of the history and future prospects of LBC is crucial for its application in routine diagnostics by cytopathologists and cytotechnologists. In this review, we initiated an internet search using the keyword 'liquid-based cytology', and we conducted a literature review to discuss the usefulness of combined diagnosis of LBC and CP, immunocytochemistry and molecular testing and assessed the quality of nucleic acids in diagnostic LBC. High-quality and cell-rich diagnostic LBC surpassed the CP method alone in terms of reliability and versatility of ancillary testing in cytological diagnosis. Conclusively, diagnostic LBC lends itself to various new technologies and is expected to continue evolving with innovations in the future.

Prognostic value of genomic mutation signature associated with immune microenvironment in southern Chinese patients with esophageal squamous cell carcinoma

The genomic landscape of esophageal squamous cell cancer (ESCC), as well as its impact on the regulation of immune microenvironment, is not well understood. Thus, tumor samples from 92 patients were collected from two centers and subjected to targeted-gene sequencing. We identified frequently mutated genes, including TP53, KMT2C, KMT2D, LRP1B, and FAT1. The most frequent mutation sites were ALOX12B (c.1565C > T), SLX4 (c.2786C > T), LRIG1 (c.746A > G), and SPEN (c.6915_6917del) (6.5%). Pathway analysis revealed dysregulation of cell cycle regulation, epigenetic regulation, PI3K/AKT signaling, and NOTCH signaling. A 17-mutated gene-related risk model was constructed using random survival forest analysis and showed significant prognostic value in both our cohort and the validation cohort. Based on the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression (ESTIMATE) algorithm, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm, and the MCPcounter algorithm, we found that the risk score calculated by the risk model was significantly correlated with stimulatory immune checkpoints (TNFSF4, ITGB2, CXCL10, CXCL9, and BTN3A1; p < 0.05). Additionally, it was significantly associated with markers that are important in predicting response to immunotherapy (CD274, IFNG, and TAMM2; p < 0.05). Furthermore, the results of immunofluorescence double staining showed that patients with high risk scores had a significantly higher level of M2 macrophage than those with low risk scores (p < 0.05). In conclusion, our study provides insights into the genomic landscape of ESCC and highlights the prognostic value of a genomic mutation signature associated with the immune microenvironment in southern Chinese patients with ESCC.

Review of Molecular Technologies for Investigating Canine Cancer

Genetic molecular testing is starting to gain traction as part of standard clinical practice for dogs with cancer due to its multi-faceted benefits, such as potentially being able to provide diagnostic, prognostic and/or therapeutic information. However, the benefits and ultimate success of genomic analysis in the clinical setting are reliant on the robustness of the tools used to generate the results, which continually expand as new technologies are developed. To this end, we review the different materials from which tumour cells, DNA, RNA and the relevant proteins can be isolated and what methods are available for interrogating their molecular profile, including analysis of the genetic alterations (both somatic and germline), transcriptional changes and epigenetic modifications (including DNA methylation/acetylation and microRNAs). We also look to the future and the tools that are currently being developed, such as using artificial intelligence (AI) to identify genetic mutations from histomorphological criteria. In summary, we find that the molecular genetic characterisation of canine neoplasms has made a promising start. As we understand more of the genetics underlying these tumours and more targeted therapies become available, it will no doubt become a mainstay in the delivery of precision veterinary care to dogs with cancer.

Deep Learning for Detecting BRCA Mutations in High-Grade Ovarian Cancer Based on an Innovative Tumor Segmentation Method From Whole Slide Images

BRCA1 and BRCA2 genes play a crucial role in repairing DNA double-strand breaks through homologous recombination. Their mutations represent a significant proportion of homologous recombination deficiency and are a reliable effective predictor of sensitivity of high-grade ovarian cancer (HGOC) to poly(ADP-ribose) polymerase inhibitors. However, their testing by next-generation sequencing is costly and time-consuming and can be affected by various preanalytical factors. In this study, we present a deep learning classifier for BRCA mutational status prediction from hematoxylin-eosin-safran-stained whole slide images (WSI) of HGOC. We constituted the OvarIA cohort composed of 867 patients with HGOC with known BRCA somatic mutational status from 2 different pathology departments. We first developed a tumor segmentation model according to dynamic sampling and then trained a visual representation encoder with momentum contrastive learning on the predicted tumor tiles. We finally trained a BRCA classifier on more than a million tumor tiles in multiple instance learning with an attention-based mechanism. The tumor segmentation model trained on 8 WSI obtained a dice score of 0.915 and an intersection-over-union score of 0.847 on a test set of 50 WSI, while the BRCA classifier achieved the state-of-the-art area under the receiver operating characteristic curve of 0.739 in 5-fold cross-validation and 0.681 on the testing set. An additional multiscale approach indicates that the relevant information for predicting BRCA mutations is located more in the tumor context than in the cell morphology. Our results suggest that BRCA somatic mutations have a discernible phenotypic effect that could be detected by deep learning and could be used as a prescreening tool in the future.

A critical spotlight on the paradigms of FFPE-DNA sequencing

In the late 19th century, formalin fixation with paraffin-embedding (FFPE) of tissues was developed as a fixation and conservation method and is still used to this day in routine clinical and pathological practice. The implementation of state-of-the-art nucleic acid sequencing technologies has sparked much interest for using historical FFPE samples stored in biobanks as they hold promise in extracting new information from these valuable samples. However, formalin fixation chemically modifies DNA, which potentially leads to incorrect sequences or misinterpretations in downstream processing and data analysis. Many publications have concentrated on one type of DNA damage, but few have addressed the complete spectrum of FFPE-DNA damage. Here, we review mitigation strategies in (I) pre-analytical sample quality control, (II) DNA repair treatments, (III) analytical sample preparation and (IV) bioinformatic analysis of FFPE-DNA. We then provide recommendations that are tested and illustrated with DNA from 13-year-old liver specimens, one FFPE preserved and one fresh frozen, applying target-enriched sequencing. Thus, we show how DNA damage can be compensated, even when using low quantities (50 ng) of fragmented FFPE-DNA (DNA integrity number 2.0) that cannot be amplified well (Q129 bp/Q41 bp = 5%). Finally, we provide a checklist called 'ERROR-FFPE-DNA' that summarises recommendations for the minimal information in publications required for assessing fitness-for-purpose and inter-study comparison when using FFPE samples.

The ideal reporting of RAS testing in colorectal adenocarcinoma: a pathologists' perspective

RAS gene mutational status represents an imperative predictive biomarker to be tested in the clinical management of metastatic colorectal adenocarcinoma. Even if it is one of the most studied biomarkers in the era of precision medicine, several pre-analytical and analytical factors may still impasse an adequate reporting of RAS status in clinical practice, with significant therapeutic consequences. Thus, pathologists should be aware on the main topics related to this molecular evaluation: (i) adopt diagnostic limit of detections adequate to avoid the interference of sub-clonal cancer cell populations; (ii) choose the most adequate diagnostic strategy according to the available sample and its qualification for molecular testing; (iii) provide all the information regarding the mutation detected, since many RAS mutation-specific targeted therapeutic approaches are in development and will enter into routine clinical practice. In this review, we give a comprehensive description of the current scenario about RAS gene mutational testing in the clinic focusing on the pathologist's role in patient selection for targeted therapies.

Cost-Effectiveness Analysis of Germline and Somatic BRCA Testing in Patients With Advanced Ovarian Cancer

Background

BRCA testing is necessary for establishing a management strategy for ovarian cancer. Several BRCA testing strategies, including germline and somatic testing, are implemented in clinical practice in Korea. We aimed to comparatively evaluate their cost-effectiveness from patients' perspective.

Methods

We developed a decision model comprising five BRCA testing strategies implemented in Korea: (1) germline testing first, followed by somatic tumor testing for patients without a germline variant; (2) somatic testing first, followed by germline testing for patients with a variant detected by somatic testing; (3) both germline and somatic testing; (4) germline testing alone; and (5) somatic testing alone, with no testing as the comparator. One-way sensitivity analysis was conducted to test the uncertainty of key parameters.

Results

Assuming a willingness-to-pay of $20,000 per progression-free life-year gain (PF-LYG), all five strategies were considered cost-effective. Strategy 4 was the most cost-effective option, with an incremental cost-effectiveness ratio (ICER) of $2,547.7 per PF-LYG, followed by strategy 1, with an ICER of $3,978.4 per PF-LYG. Even when the parameter values were varied within the possible range, the ICERs of all strategies did not exceed the willingness-to-pay threshold.

Conclusions

Considering the importance of knowing a patient's BRCA gene status, germline testing first, followed by somatic testing, may be a reasonable option.

External Quality Assurance programs for processing methods provide evidence on impact of preanalytical variables

An annual External Quality Assurance (EQA) program has been provided to processing laboratories over the last ten years, allowing them to assess the performance of their processing methods, such as nucleic acid extractions or peripheral blood mononuclear cell (PBMC) isolation and cryopreservation. The objective of this study was to perform a global analysis on almost 1000 EQA scheme/participant data in order to assess (i) the impact of critical preanalytical factors on quantitative or qualitative attributes of different types of specimens and (ii) laboratory performance pattern over time. Statistical analysis was performed within each EQA scheme based on categorized preanalytical data provided by the participants and on centralized measurements of relevant quality attributes of the produced specimens (z-scores): DNA, cell-free (cf)DNA or RNA extraction from blood, DNA or RNA extraction from formalin fixed tissue, DNA or RNA extraction from frozen tissue, DNA extraction from saliva or stool, viable PBMC isolation and cryopreservation. The most critical preanalytical factors in nucleic acid extraction schemes were the nucleic acid extraction method and kit, the elution buffer, the enzymes used during extraction, the input material quantity and the storage temperature. Several indications of laboratory performance improvement over time could be seen. The conclusions are that EQA for processing methods provides unique evidence-based insights into the impact of preanalytical factors and the comparative performance of different processing methods and kits, while supporting laboratories in validating their processing methods.

Real-life data from standardized preanalytical coding (SPREC) in tissue biobanking and its dual use for sample characterization and process optimization

The standardized preanalytical code (SPREC) aggregates warm ischemia (WIT), cold ischemia (CIT), and fixation times (FIT) in a precise format. Despite its growing importance underpinned by the European in vitro diagnostics regulation or broad preanalytical programs by the National Institutes of Health, little is known about its empirical occurrence in biobanked surgical specimen. In several steps, the Tissue Bank Bern achieved a fully informative SPREC code with insights from 10,555 CIT, 4,740 WIT, and 3,121 FIT values. During process optimization according to LEAN six sigma principles, we identified a dual role of the SPREC code as a sample characteristic and a traceable process parameter. With this preanalytical study, we outlined real-life data in a variety of organs with specific differences in WIT, CIT, and FIT values. Furthermore, our FIT data indicate the potential to adapt the SPREC fixation toward concrete paraffin-embedding time points and to extend its categories beyond 72 h due to weekend delays. Additionally, we identified dependencies of preanalytical variables from workload, daytime, and clinics that were actionable with LEAN process management. Thus, streamlined biobanking workflows during the day were significantly resilient to workload peaks, diminishing the turnaround times of native tissue processing (i.e. CIT) from 74.6 to 46.1 min under heavily stressed conditions. In conclusion, there are surgery-specific preanalytics that are surgico-pathologically limited even under process optimization, which might affect biomarker transfer from one entity to another. Beyond sample characteristics, SPREC coding is highly beneficial for tissue banks and Institutes of Pathology to track WIT, CIT, and FIT for process optimization and monitoring measurements.

Preanalytical Variables and Sample Quality Control for Clinical Variant Analysis

Broad molecular profiling by next-generation sequencing of solid tumors has become a critical tool for clinical decision-making in the era of precision oncology. In addition to many already approved targeted therapies, more than half of ongoing oncology-related clinical trials are biomarker-driven. Therefore, accurate and reliable assays are needed to assess the genetic make-up of tumor cells and guide clinicians in the therapy decision process. In order to obtain high-quality NGS data for variant detection, certain preanalytical steps and quality metrics should be followed. These include assessment of sample types, choice of extraction method, library preparation technology, sequencing platform, and finally sequencing quality control. Each of these steps has certain challenges and pitfalls that need to be addressed and overcome, respectively. In this chapter, we address the preanalytical quality control and how each of the involved steps may influence the final result. Following these guidelines and QC metrics may help in obtaining optimal results that will allow the precise and robust assessment of genetic variants in a clinical setting.

Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides

To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed an artificial intelligence (AI)-augmented smart pathology review system (SmartPath) to empower pathologists with quantitative metrics for accurately determining tissue extraction parameters. SmartPath uses two deep learning architectures, a U-Net based network for cell segmentation and a multi-field-of-view convolutional network for tumor area segmentation, to extract features from digitized H&E-stained formalin-fixed paraffin-embedded slides. From the segmented tumor area, SmartPath suggests a macrodissection area. To predict DNA yield per slide, the extracted features from within the macrodissection area are correlated with known DNA yields to fit a regularized linear model (R = 0.85). Then, a pathologist-defined target yield divided by the predicted DNA yield per slide gives the number of slides to scrape. Following model development, an internal validation trial was conducted within the Tempus Labs molecular sequencing laboratory. We evaluated our system on 501 clinical colorectal cancer slides, where half received SmartPath-augmented review and half traditional pathologist review. The SmartPath cohort had 25% more DNA yields within a desired target range of 100-2000 ng. The number of extraction attempts was statistically unchanged between cohorts. The SmartPath system recommended fewer slides to scrape for large tissue sections, saving tissue in these cases. Conversely, SmartPath recommended more slides to scrape for samples with scant tissue sections, especially those with degraded DNA, helping prevent costly re-extraction due to insufficient extraction yield. A statistical analysis was performed to measure the impact of covariates on the results, offering insights on how to improve future applications of SmartPath. With these improvements, AI-augmented histopathologic review has the potential to decrease tissue waste, sequencing time, and laboratory costs by optimizing DNA yields, especially for samples with scant tissue and/or degraded DNA.

Concordance Analysis of ALK Gene Fusion Detection Methods in Patients with Non-Small-Cell Lung Cancer from Chile, Brazil, and Peru

About 4% to 7% of the non-small-cell lung cancer patients have anaplastic lymphoma kinase (ALK) rearrangements, and specific targeted therapies improve patients' outcomes significantly. ALK gene fusions are detected by immunohistochemistry or fluorescent in situ hybridization as gold standards in South America. Next-generation sequencing-based assays are a reliable alternative, able to perform simultaneous detection of multiple events from a single sample. We analyzed 4240 non-small-cell lung cancer samples collected in 37 hospitals from Chile, Brazil, and Peru, where ALK rearrangements were determined as part of their standard of care (SofC) using either immunohistochemistry or fluorescent in situ hybridization. A subset of 1450 samples was sequenced with the Oncomine Focus Assay (OFA), and the concordance with the SofC tests was measured. An orthogonal analysis was performed using a real-time quantitative PCR echinoderm microtubule-associated protein-like 4-ALK fusion detection kit. ALK fusion prevalence is similar for Chile (3.67%; N = 2142), Brazil (4.05%; N = 1013), and Peru (4.59%; N = 675). Although a comparison between OFA and SofC assays showed similar sensitivity, OFA had significantly higher specificity and higher positive predictive value, which opens new opportunities for a more specific determination of ALK gene rearrangements.

[Next generation sequencing in histopathology : Applications and methodological challenges]

The enormous increase in sequencing capacity due to the development of next generation sequencing technologies opens up new opportunities in the fields of histopathology, research, and diagnostics, but also poses huge challenges.The identification of genomic aberrations (point mutations, small insertions and deletions, fusion transcripts, and tumor mutation burden (TMB)) have already become a reliable part of routine molecular diagnostics. This will be supplemented by additional applications, namely gene amplifications, microsatellite instability, genomic signatures like homologous recombination deficiency (HRD), mRNA expression patterns, B‑ and T‑cell clonality, and DNA methylation. Challenges in preanalytics and the evaluation of assay sensitivity and specificity as well as proper curation of identified aberrations, which requires a new type of specialist, are presented and discussed.

Chromosomal Junction Detection from Whole-Genome Sequencing on Formalin-Fixed, Paraffin-Embedded Tumors

DNA junctions (DNAJs) frequently impact clinically relevant genes in tumors and are important for diagnostic and therapeutic purposes. Although routinely screened through fluorescence in situ hybridization assays, such testing only allows the interrogation of single-gene regions or known fusion partners. Comprehensive assessment of DNAJs present across the entire genome can only be determined from whole-genome sequencing. Structural variance analysis from whole-genome paired-end sequencing data is, however, frequently restricted to copy number changes without DNAJ detection. Through optimized whole-genome sequencing and specialized bioinformatics algorithms, complete structural variance analysis is reported, including DNAJs, from formalin-fixed DNA. Selective library assembly from larger fragments (>500 bp) and economical sequencing depths (300 to 400 million reads) provide representative genomic coverage profiles and increased allelic coverage to levels compatible with DNAJ calling (40× to 60×). Although consistently fragmented, more recently formalin-fixed, specimens (<2 years' storage) revealed consistent populations of larger DNA fragments. Optimized bioinformatics efficiently detected >90% of DNAJs in two prostate tumors (approximately 60% tumor) previously analyzed by mate-pair sequencing on fresh frozen tissue, with evidence of at least one spanning-read in 99% of DNAJs. Rigorous masking with data from unrelated formalin-fixed tissue progressively eliminated many false-positive DNAJs, without loss of true positives, resulting in low numbers of false-positive passing current filters. This methodology enables more comprehensive clinical genomics testing on formalin-fixed clinical specimens.

Currently Applied Molecular Assays for Identifying ESR1 Mutations in Patients with Advanced Breast Cancer

Approximately 70% of breast cancers, the leading cause of cancer-related mortality worldwide, are positive for the estrogen receptor (ER). Treatment of patients with luminal subtypes is mainly based on endocrine therapy. However, ER positivity is reduced and ESR1 mutations play an important role in resistance to endocrine therapy, leading to advanced breast cancer. Various methodologies for the detection of ESR1 mutations have been developed, and the most commonly used method is next-generation sequencing (NGS)-based assays (50.0%) followed by droplet digital PCR (ddPCR) (45.5%). Regarding the sample type, tissue (50.0%) was more frequently used than plasma (27.3%). However, plasma (46.2%) became the most used method in 2016-2019, in contrast to 2012-2015 (22.2%). In 2016-2019, ddPCR (61.5%), rather than NGS (30.8%), became a more popular method than it was in 2012-2015. The easy accessibility, non-invasiveness, and demonstrated usefulness with high sensitivity of ddPCR using plasma have changed the trends. When using these assays, there should be a comprehensive understanding of the principles, advantages, vulnerability, and precautions for interpretation. In the future, advanced NGS platforms and modified ddPCR will benefit patients by facilitating treatment decisions efficiently based on information regarding ESR1 mutations.

Implementing NGS-based BRCA tumour tissue testing in FFPE ovarian carcinoma specimens: hints from a real-life experience within the framework of expert recommendations

AIMS

Next Generation Sequencing (NGS)-based BRCA tumour tissue testing poses several challenges. As a first step of its implementation within a regional health service network, an in-house validation study was compared with published recommendations.

METHODS

Epithelial ovarian cancer (EOC) formalin-fixed paraffin-embedded specimens stored in the archives of the eight regional pathology units were selected from a consecutive series of patients with known BRCA germline status. Two expert pathologists evaluated tumour cell content for manual macrodissection. DNA extraction, library preparation and NGS analyses were performed blinded to the germinal status. Parameters used in the study were confronted with guidelines for the validation of NGS-based oncology panels and for BRCA tumour tissue testing.

RESULTS

NGS analyses were successful in 66 of 67 EOC specimens, with good quality metrics and high reproducibility among different runs. In all, 19 BRCA pathogenic variants were identified: 12 were germline and 7 were somatic. A 100% concordance with blood tests was detected for germline variants. A BRCA1 variant showed a controversial classification. In different areas of two early stage EOCs showing somatic variants, intratumour heterogeneity not relevant for test results (variant allele frequency >5%) was observed. Compared with expert recommendations, main limitations of the study were absence of controls with known somatic BRCA status and exclusion from the validation of BRCA copy number variations (CNV).

CONCLUSIONS

A close collaboration between pathology and genetics units provides advantages in the implementation of BRCA tumour tissue testing. The development of tools for designing and interpreting complex testing in-house validation could improve process quality.

Developing Single Nucleotide Polymorphisms for Identification of Cod Products by RAD-Seq

The increase in the rate of seafood fraud, particularly in the expensive fishes, forces us to verify the identity of marine products. Meanwhile, the definition of cod lacks consistency at the international level, as few standards and effective application methods are capable of accurately detecting cod species. Genetic fingerprinting is important for both certifying authenticity and traceability of fish species. In this study, we developed a method that combines DNA barcoding and the restriction-site associated DNA sequencing (RAD-Seq) approach for the identification of cod products. We first obtained 6941 high-quality single nucleotide polymorphism (SNP)s from 65.6 gigabases (Gb) of RAD-Seq raw data, and two sequences that contain SNPs were finally used to successfully identify three different cod product species, which are Atlantic cod (Gadus morhua), Greenland turbot (Reinhardtius hippoglossoides), and Patagonian toothfish (Dissostichus eleginoides). This SNP-based method will help us to identify the products, which are sold under the name of "Xue Yu" (Cod) in China, and works in parallel with existing fish identification techniques to establish an efficient framework to detect and prevent fraud at all points of the seafood supply chain.