Stable Detection Research Articles

Biochemical and molecular detection of genetic instability of liver cancer

Biochemical and molecular detection of genetic instability of liver cancer

The Development of Machine Learning Models for Radial Compressor Monitoring With Instability Detection

Abstract Modern radial compressors are designed to operate with high performance and a wide range of applications while minimizing their environmental impact. It is necessary to study the machine near the stability limit and gain a comprehensive understanding of the fluid dynamic mechanisms that trigger the instability in the system to extend the operating range at high efficiency. Machine learning aids in developing pattern identification models for detecting compressor instability. In a prior study, a two-stage radial compressor for refrigerant gas underwent extensive simulation, capturing unsteady RANS conditions and generating a substantial dataset of pressure signals from multiple probes. Selected signals, coupled with detailed computational fluid dynamics (CFD) post-processing, revealed fluid dynamic structures near surge conditions. This study utilizes all pressure signals to assess the stage's operational state (stable, transient, or unstable/surge). An additional goal is to develop an algorithm predicting flow patterns in different stage sections with minimal pressure signals at the rotor inlet. This is a preliminary step toward the development of a smart monitoring and diagnostic model for the compressor installed in a plant. The developed model consists of three submodels, trained with CFD results obtained from unsteady Reynolds averaged Navier–Stokes (URANS) simulations. The three submodels are a regression submodule, a classification submodule, and a forecasting algorithm. The regression submodule predicts diffuser static pressure fields, the classification submodule categorizes whether the compressor is in a critical condition or not, and the forecasting algorithm predicts the inducer pressure signals in the future impeller rotations according to the actual operation history. These sub-modules work together to forecast whether the compressor, according to the operation strategy, is going into instability conditions.

Safe, simple, and valid position for obtaining flexion-extension radiographs to assess instability in patients with lumbar spondylolisthesis: one specific instruction can make a difference.

In lumbar spondylolisthesis, conventional standing flexion-extension radiography can yield varying results depending on the patient's effort and cooperation. Previous research suggested that assisted flexion radiography provides larger flexion with a significantly greater change in lumbar lordosis (ΔLL) and increased sagittal translation (ST), posterior opening (PO), segmental angulation (SA), and instability detection rates. In this study, the authors aimed to identify a safe, simple, and valid position for obtaining functional radiographs to evaluate abnormal instability in lumbar spondylolisthesis. Consecutive patients diagnosed with L4-5 degenerative lumbar spondylolisthesis were included. The patients underwent upright and extension radiography and three different flexion radiography positions: conventional flexion (CF), hand-knee (HK), and hand-ankle (HA). Measurements included ΔLL, ST, PO, and SA, with instability rates compared between the three flexion techniques. This study included 117 patients (81 women, mean age of the study sample 76.8 years). The median ΔLL values were 10.8° (interquartile range [IQR] 5.2°-18.2°) in the CF position, 30.0° (IQR 21.0°-41.1°) in the HK position, and 32.1° (IQR 23.0°-42.4°) in the HA position, with significant differences noted (p < 0.05). For PO and SA, significant differences were observed between the techniques (p < 0.05). ST medians were CF 5.5% (IQR 3.6%-8.1%), HK 9.5% (IQR 7.7%-11.1%), and HA 9.7% (IQR 7.4%-11.4%), with HK and HA positions differing significantly from the CF position (p < 0.001), but not the HK from the HA position (p = 0.15). Instability detection rates were 29.1% in the CF position, 76.1% in the HK position, and 76.9% in the HA position, with significant differences between the HK, HA, and CF positions (p < 0.001), but not between the HK and HA positions (p > 0.99). The study showed that HK and HA flexion radiographs provided greater ΔLL, ST, PO, SA, and better instability detection than the CF position. Given its safety and simplicity, the HK position is suitable for detecting abnormal lumbar mobility in degenerative lumbar spondylolisthesis.

High-precision 3D Modeling of Construction Waste Pile Bodies by Integrating Multi-source Data

Abstract. The instability of construction waste pile bodies, as an increasingly concerning disaster, poses significant risks to the safety of people's lives and property. Currently, there is limited research on high-precision 3D modeling techniques for construction waste pile bodies, which significantly hinders the accuracy and reliability of early detection and risk assessment of pile body instability. Therefore, constructing high-precision 3D models of construction waste pile bodies using multi-source data plays a crucial role in improving the accuracy and timeliness of early warning systems for pile body instability, offering significant theoretical research and practical application value. Building 3D models of construction waste pile bodies solely based on unmanned aerial vehicle (UAV) oblique photography data faces challenges, such as various degrees of data voids and insufficient model completeness, and few methods currently address the construction of 3D models of construction waste pile bodies through the fusion of multi-source data. This paper attempts to use the Iterative Closest Point (ICP) algorithm, combining SLAM laser point cloud data with UAV oblique photography data to fill data voids, and utilizes the fused point cloud to reconstruct the triangulation network, achieving the transformation from 3D point cloud models to 3D surface models. On the basis of texture mapping, a high-precision 3D model of the construction waste pile body is constructed. The research results show that the 3D model of the construction waste pile body, integrated with multi-source data, has a planar error of 0.0187m and an elevation error of 0.0368m, meeting the corresponding model accuracy requirements. It can more realistically restore the fine 3D features of the construction waste pile body, effectively compensating for the shortcomings of single-source data in 3D modeling of construction waste pile bodies, providing a new method for the 3D model reconstruction of construction waste pile bodies, and offering effective data support for construction waste research.

Fanconi anemia diagnosed by genetic testing

Background : Fanconi anemia (FA) is a rare genetic disorder inherited in an autosomal recessive manner. The clinical phenotype varies depending on the involvement of different genes. Major physical abnormalities mainly affect the limbs and spine.. Methods/Observation : We report the case of an 11-year-old girl from a first-degree consanguineous marriage. The classic triad of short stature, malformation syndrome and early bone marrow failure suggested AF, which was confirmed by the detection of significant chromosomal instability after culture with Mitomycin C, compared with a normal control. Conclusion : This case highlights the crucial role of cytogenetics in the diagnosis of Fanconi anemia and genetic counseling to improve the management of affected children and their families. Keywords : Fanconi anemia; chromosomal breaks; mitomycin C.

Integrating deep learning for accurate gastrointestinal cancer classification: a comprehensive analysis of MSI and MSS patterns using histopathology data

Early detection of microsatellite instability (MSI) and microsatellite stability (MSS) is crucial in the fight against gastrointestinal (GI) cancer. MSI is a sign of genetic instability often associated with DNA repair mechanism deficiencies, which can cause (GI) cancers. On the other hand, MSS signifies genomic stability in microsatellite regions. Differentiating between these two states is pivotal in clinical decision-making as it provides prognostic and predictive information and treatment strategies. Rapid identification of MSI and MSS enables oncologists to tailor therapies more accurately, potentially saving patients from unnecessary treatments and guiding them toward regimens with the highest likelihood of success. Detecting these microsatellite status markers at an initial stage can improve patient outcomes and quality of life in GI cancer management. Our research paper introduces a cutting-edge method for detecting early GI cancer using deep learning (DL). Our goal is to identify the optimal model for GI cancer detection that surpasses previous works. Our proposed model comprises four stages: data acquisition, image processing, feature extraction, and classification. We use histopathology images from the Cancer Genome Atlas (TCGA) and Kaggle website with some modifications for data acquisition. In the image processing stage, we apply various operations such as color transformation, resizing, normalization, and labeling to prepare the input image for enrollment in our DL models. We present five different DL models, including convolutional neural networks (CNNs), a hybrid of CNNs-simple RNN (recurrent neural network), a hybrid of CNNs with long short-term memory (LSTM) (CNNs-LSTM), a hybrid of CNNs with gated recurrent unit (GRU) (CNNs-GRU), and a hybrid of CNNs-SimpleRNN-LSTM-GRU. Our empirical results demonstrate that CNNs-SimpleRNN-LSTM-GRU outperforms other models in accuracy, specificity, recall, precision, AUC, and F1, achieving an accuracy of 99.90%. Our proposed methodology offers significant improvements in GI cancer detection compared to recent techniques, highlighting the potential of DL-based approaches for histopathology data. We expect our findings to inspire future research in DL-based GI cancer detection.

Detection of a low-frequency ion instability in a magnetic nozzle

Abstract A low-frequency ion instability, with frequency f I between the ion gyrofrequency and the lower hybrid frequency f c , i &lt; f I ≪ f LH , is detected in an argon plasma expanding in a magnetic nozzle for magnetic fields between 240 &lt; B z , max &lt; 700 G. The frequency of the instability exhibits a linear dependence with magnetic field strength, and the wave amplitude has a radial maximum that would match the location of a conical density structure, i.e. high-density cones. For all of the magnetic field cases analysed, the high-frequency spectra showed upper and lower sidebands centred around the driving frequency and at a separation equal to the instability frequency, 27.12 MHz ± f I kHz. Measurements of the perpendicular wavenumber would satisfy, for certain magnetic field strengths, the dispersion relation of both an electrostatic ion cyclotron wave (ICW) and of an ion acoustic wave. It is hypothesised that the observed low-frequency wave could be an acoustic-like instability propagating perpendicular to the magnetic field, which develops as an ICW at some magnetic field strengths. From the data collected, it is suggested that the high-frequency sidebands may be caused by modulation of the low-frequency wave.

Artificial Intelligence Models for the Detection of Microsatellite Instability from Whole-Slide Imaging of Colorectal Cancer.

With the advent of whole-slide imaging (WSI), a technology that can digitally scan whole slides in high resolution, pathology is undergoing a digital revolution. Detecting microsatellite instability (MSI) in colorectal cancer is crucial for proper treatment, as it identifies patients responsible for immunotherapy. Even though universal testing for MSI is recommended, particularly in patients affected by colorectal cancer (CRC), many patients remain untested, and they reside mainly in low-income countries. A critical need exists for accessible, low-cost tools to perform MSI pre-screening. Here, the potential predictive role of the most relevant artificial intelligence-driven models in predicting microsatellite instability directly from histology alone is discussed, focusing on CRC. The role of deep learning (DL) models in identifying the MSI status is here analyzed in the most relevant studies reporting the development of algorithms trained to this end. The most important performance and the most relevant deficiencies are discussed for every AI method. The models proposed for algorithm sharing among multiple research and clinical centers, including federal learning (FL) and swarm learning (SL), are reported. According to all the studies reported here, AI models are valuable tools for predicting MSI status on WSI alone in CRC. The use of digitized H&E-stained sections and a trained algorithm allow the extraction of relevant molecular information, such as MSI status, in a short time and at a low cost. The possible advantages related to introducing DL methods in routine surgical pathology are underlined here, and the acceleration of the digital transformation of pathology departments and services is recommended.

A pilot study on the detection of microsatellite instability using long mononucleotide repeats in solid tumors.

Microsatellite instability (MSI) status is a prognostic biomarker for immunotherapy in certain types of cancers, such as colorectal cancers (CRCs) and endometrial cancers (ECs). Tumors that are categorized as having high MSI (MSI-H) express high levels of neoantigens for immune recognition. The typical MSI test measures the length of short mononucleotide repeats (SMR) poly(A) 21-27; however, a limitation of this test is the difficulty in determining the shift size, particularly in endometrial cancer. To investigate an MSI detection assay with improved performance, the present study analyzed the use of poly(A) 40-44 mononucleotide repeats to detect the MSI status of 100 patients with either CRC (n=50) or EC (n=50). Capillary electrophoresis was used to evaluate five long mononucleotide repeat (LMR) markers, including poly(A) 40-A, 40-B, 40-C, 40-D and 44. The concordance rate of the LMR-MSI assay compared with an immunohistochemistry MSI detection assay was 96.0 and 95.1% for CRCs and ECs respectively, with the detection limit of the LMR-MSI assay demonstrated to be 2.5% MSI-H in HCT116 colorectal carcinoma cell lines. The LMR-MSI assay yielded a 95.1% concordance rate in ECs compared with that in the SMR-MSI test (87.8%). The LMR-MSI test identified a significantly higher mean shift size (13 bp) in MSI-H tumors compared with the SMR-MSI test (10 bp), in both EC and CRC tissue samples. Together, the present study suggested that the LMR-MSI test could potentially be a sensitive and practical technology for molecular laboratory testing, particularly in the use of immunotherapy for patients with CRCs and ECs.

Clinical performance of the TrueMark™ MSI assay for microsatellite instability detection in a Chinese colorectal cancer cohort

BackgroundMicrosatellite instability (MSI) and mismatch repair (MMR) detection is valuable in assessing prognosis and treatment options. However, the conventional detection methods such as immunohistochemistry (IHC) are limited by not fully consistent results as well as a long turnaround time. TrueMark™ MSI Assay is a novel solution for MSI analysis, but lack of research support in the Chinese colorectal cancer (CRC) patients. Materials and methods60 dMMR and 60 pMMR CRC samples identified by IHC were collected and their MSI status were detected using TrueMark™ MSI assay with an expanded panel of 13 markers. The overall performance and diagnostic concordance between TrueMark™ MSI test and MMR IHC analysis were assessed and analyzed. ResultsAccording to the TrueMark™ test, 55 out of the 120 (45.8 %) CRCs were identified as MSI-high (MSI-H) with an instability at ≥ 4/13 markers. Compared with the MMR IHC analysis, an overall percent agreement of 94.2 % and a Kappa of 0.883 were achieved. For the seven inconsistent samples, tumor mutation burden analysis was performed and the results supported the diagnosis by TrueMark™ test. To confirm the robustness of the above findings, a validation was performed in an independent cohort comprising 51 consecutive CRCs. Furthermore, an optimized panel composed of NR-21, NR-24, NR-27, ABI-16, ABI-17 and ABI-20B was developed by multivariate logistic regression model, and showed 100 % agreement with the 13-marker panel for MSI detection in both the derivation and validation sets. ConclusionTrueMark™ MSI provides a fast, reliable and highly automated solution to MSI detection in Chinese CRC patients, and the new 6-marker panel we established shows promise deserving further evaluation.

Validation and Implementation of Long Mononucleotide Repeat Testing for the Detection of Microsatellite Instability: A Single Institution Experience

Introduction: Microsatellites are short, repetitive portions of DNA that are error prone during replication; several mismatch repair proteins recognize and repair these errors. Deficient MMR (dMMR) results in microsatellite instability (MSI), a well-recognized carcinogenic pathway. In Lynch syndrome (LS), germline mutations in MMR genes predispose to colorectal (CRC) and endometrial carcinoma (EC), among others. Immune checkpoint inhibitors (ICI), such as pembrolizumab, are approved to treat dMMR or MSI-high (MSI-H) cancers regardless of tumor type. Therefore, sensitive and specific methods to detect MSI are of critical importance to patient care. Multiple methods are used to detect MSI status, including immunohistochemistry (IHC) for MMR proteins and molecular assays. The Detroit Medical Center utilizes both IHC and PCR-based assays to maximize MSI detection sensitivity. We attempted validation of a new fluorescent PCR-based assay for MSI tumor testing which compares allelic profiles of microsatellite markers from matching normal and tumor samples. The assay utilizes four traditional MSI markers in addition to four long mononucleotide repeat (LMR) markers. LMR markers produce more pronounced fragment length changes, and thus may improve detection of MSI. Methods: Our validation set included 46 carcinoma patient samples with prior PCR-based MSI and MMR IHC testing. These included MSI-high, MSI-low (MSI-L), and microsatellite stable (MSS) cases. DNA was isolated from formalin-fixed, paraffin embedded (FFPE) tissue and amplified. Stutter peak patterns were analyzed, comparing allele sizes and stutter patterns. All cases were reviewed by two directors, with any discrepancies discussed and reviewed to achieve consensus. MSS samples showed identical stutter patterns between tumor and normal pairs. MSI-L samples had a single discrepant marker, whereas MSI-H samples had 2 or more discrepant markers. The results of the LMR assay were compared with the original PCR-based MSI and IHC results to determine test concordance. Results: 42 cases were successfully amplified and included in our results. Fourteen cases were classified as MSI-H by consensus, with a range of 3 to 8 unstable markers out of 8 total markers. IHC results for the MSI-H cases were available in 13/14 cases; thirteen were classified as abnormal. Notably, in one EC MSI-H case, a normal IHC result was reported. Three cases were classified as MSI-L. All were CRC samples, and 2 of 3 were also designated MSI-L using the traditional PCR MSI assay. The remaining MSI-L case was formerly interpreted as MSS. The IHC results for the MSI-L cases were all available and reported as normal. The remaining cases were designated as MSS. IHC results were normal in all but one EC case. Conclusions: Our study validated the LMR MSI Analysis System for detection of MSI; it is now used in clinical practice in our molecular laboratory at DMC, and challenging real-world cases from our assay rollout will be presented graphically. Our validation results demonstrate high concordance (42/42 cases) between the LMR and traditional MSI panels, highlighting the reliability of LMR markers. IHC and PCR results were largely concordant, with occasional discrepancies revealing the importance of a combination approach for MSI detection.

Identification of 8 candidate microsatellite instability loci in colorectal cancer and validation of the ACVR2A mechanism in the tumor progression

This study probes the utility of biomarkers for microsatellite instability (MSI) detection and elucidates the molecular dynamics propelling colorectal cancer (CRC) progression. We synthesized a primer panel targeting 725 MSI loci, informed by The Cancer Genome Atlas (TCGA) and ancillary databases, to construct an amplicon library for next-generation sequencing (NGS). K-means clustering facilitated the distillation of 8 prime MSI loci, including activin A receptor type 2A (ACVR2A). Subsequently, we explored ACVR2A’s influence on CRC advancement through in vivo tumor experiments and hematoxylin–eosin (HE) staining. Transwell assays gauged ACVR2A’s role in CRC cell migration and invasion, while colony formation assays appraised cell proliferation. Western blotting illuminated the impact of ACVR2A suppression on CRC’s PI3K/AKT/mTOR pathway protein expressions under hypoxia. Additionally, ACVR2A’s influence on CRC-induced angiogenesis was quantified via angiogenesis assays. K-means clustering of NGS data pinpointed 32 MSI loci specific to tumor and DNA mismatch repair deficiency (dMMR) tissues. ACVR2A emerged as a pivotal biomarker, discerning MSI-H tissues with 90.97% sensitivity. A curated 8-loci set demonstrated 100% sensitivity and specificity for MSI-H detection in CRC. In vitro analyses corroborated ACVR2A’s critical role, revealing its suppression of CRC proliferation, migration, and invasion. Moreover, ACVR2A inhibition under CRC-induced hypoxia markedly escalated MMP3, CyclinA, CyclinD1, and HIF1α protein expressions, alongside angiogenesis, by triggering the PI3K/AKT/mTOR cascade. The 8-loci ensemble stands as the optimal marker for MSI-H identification in CRC. ACVR2A, a central element within this group, deters CRC progression, while its suppression amplifies PI3K/AKT/mTOR signaling and angiogenesis under hypoxic stress.

A machine learning algorithm based on multi-omics biomarkers for the detection of tumor microsatellite instability.

1554 Background: Microsatellite instability (MSI) occurs via defects in mismatch repair (MMR) and is characterized by high tumor mutational burden (TMB) and a robust inflammatory response. Accurate MSI-high classification of immune-evasive tumors is vital due to the effectiveness of immune checkpoint inhibitor (ICI) therapy, such as pembrolizumab, which is FDA approved for all MSI-high tumors irrespective of anatomic origin. Detection methods of MSI-status vary widely, including immunohistochemistry (IHC), polymerase chain reaction (PCR), and next-generation sequencing (NGS). We developed a machine learning (ML) model to predict MSI status in patients with solid tumors using comprehensive genomic and immune profiling (CGIP), independent of direct sequencing data from microsatellite sites. Methods: We analyzed samples from 1,838 patients with colorectal cancer (CRC) by CGIP, which included DNA panel testing (523 genes) for pathogenic single nucleotide variants (pSNV) and determination of TMB (mut/Mb), and RNA sequencing (397 genes) for gene expression (GEX). The Boruta algorithm was used to select key genomic and GEX changes associated with MSI status. A distributed gradient boosting algorithm created a predictive model that was trained on 70% of the cohort. The model was tested on the remaining CRC cohort (Test), and in the PanCancer Atlas CRCs (TCGA), endometrial adenocarcinomas (EMCA), and CRC with indeterminate MSI testing by CGIP. Performance of the trained model was assessed using sensitivity, specificity, and positive (PPV) and negative (NPV) predictive value. Results: Feature selection identified 79 genes with pSNVs, 63 GEX changes, and TMB as informative for MSI prediction. The model showed high predictive accuracy in differentiating MSI-H tumors. Of the 39 cases that failed MSI component of CGIP (Indeterminate), 17 had MMR IHC results available, of which 1 demonstrated loss of MLH1/PMS2 and the other 16 showed intact (normal) expression. Testing of this cohort identified 3 cases as MSI-high, at least one of which was confirmed by IHC. No cases with intact MMR protein IHC were identified as MSI-high by the algorithm. Conclusions: Our ML-driven approach accurately assessed MSI status of CRC and EMCA using CGIP data. This approach also identified potential cases with MSI-high status where direct sequencing of microsatellites failed. This study highlights a method to identify patients with potential MSI-high status for orthogonal screening with the MSI component of a test fails. [Table: see text]

Constitutional mismatch repair deficiency mimicking Lynch syndrome is associated with hypomorphic mismatch repair gene variants

Lynch syndrome (LS) and constitutional mismatch repair deficiency (CMMRD) are distinct cancer syndromes caused, respectively, by mono- and bi-allelic germline mismatch repair (MMR) variants. LS predisposes to mainly gastrointestinal and genitourinary cancers in adulthood. CMMRD predisposes to brain, haematological, and LS-spectrum cancers from childhood. Two suspected LS patients with first cancer diagnosis aged 27 or 38 years were found to be homozygous for an MMR (likely) pathogenic variant, MSH6 c.3226C>T (p.(Arg1076Cys)), or variant of uncertain significance (VUS), MLH1 c.306G>A (p.(Glu102=)). MLH1 c.306G>A was shown to cause leaky exon 3 skipping. The apparent genotype-phenotype conflict was resolved by detection of constitutional microsatellite instability in both patients, a hallmark feature of CMMRD. A hypomorphic effect of these and other variants found in additional late onset CMMRD cases, identified by literature review, likely explains a LS-like phenotype. CMMRD testing in carriers of compound heterozygous or homozygous MMR VUS may find similar cases and novel hypomorphic variants. Individualised management of mono- and bi-allelic carriers of hypomorphic MMR variants is needed until we better characterise the associated phenotypes.

Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology

In the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI. This resemblance not only could facilitate their detection by a transformer-based Deep Learning (DL) system trained on MSI pathology slides, but also indicates the possibility for MSS patients with POLE mutations to access enhanced treatment options, which might otherwise be overlooked. To harness this potential, we trained a Deep Learning classifier on a large dataset with the ground truth for microsatellite status and subsequently validated its capabilities for MSI and POLE detection across three external cohorts. Our model accurately identified MSI status in both the internal and external resection cohorts using pathology images alone. Notably, with a classification threshold of 0.5, over 75% of POLE driver mutant patients in the external resection cohorts were flagged as “positive” by a DL system trained on MSI status. In a clinical setting, deploying this DL model as a preliminary screening tool could facilitate the efficient identification of clinically relevant MSI and POLE mutations in colorectal tumors, in one go.

Role of Dynamic Ultrasound Scan in Detection of Hip Instability and Changing Management Plan

Objectives: To show the vital role of dynamic ultrasound in hip instability screening and review the management plans available for DDH associated with instability with a view to reduce the incidence of complication from delayed diagnosis in our region. Place and Duration of the study: Helena governmental rehabilitation center for children with special needs, Erbil-Iraq, between Nov’21 to Jan’23. Material and Method: This study included 942 infants’ hips presenting with high-risk indicators or a positive clinical examination ranging in age from 6 weeks to 6 months. We performed the ultrasonography using the static Graf technique to determine the DDH Types and then apply the dynamic scan to find stability using Moren-Terjesen’s and Harcke methods. The management approaches were conducted considering both techniques. We used SPSS version 28 along with Chi-square test for data analysis and comparison of proportions. Results: In this retrospective cross-sectional study of 942 hips, the mean age of the infants was 11.9±4.8 weeks with male to female percentage of 46% to 54%. We found a correlation between family history and breech presentation to the dynamic scan stability. In the Graf technique 55% showed the absence of dysplasia while the dynamic scan showed 66% of the hips as normal and 34% as unstable. Further, the significant statistical association (P= 0.001) between the results of dynamic scan stability to the effect on the management plan was also found to advise the change for the unstable hips. Conclusion: It is crucial to apply both static and dynamic scans in the screening of DDH, so that we can reduce the possibility of late detection and elevate the level of diagnostic accuracy. We should follow a strict guideline for the management of all DDH cases (stable and unstable) and change the plan accordingly.

Detection of chromosomal instability using ultrasensitive chromosomal aneuploidy detection in the diagnosis of precancerous lesions of gastric cancer.

The diagnosis of Precancerous Lesions of Gastric Cancer (PLGC) is challenging in clinical practice. We conducted a clinical study by analyzing the information of relevant chromosome copy number variations (CNV) in the TCGA database followed by the UCAD technique to evaluate the value of Chromosomal Instability (CIN) assay in the diagnosis of PLGC. Based on the screening of gastric cancer related data in TCGA database, CNV analysis was performed to explore the information of chromosome CNV related to gastric cancer. Based on the gastroscopic pathology results, 12 specimens of patients with severe atrophy were screened to analyze the paraffin specimens of gastric mucosa by UCAD technology, and to explore the influence of related factors on them. The results of CNV in TCGA database suggested that chromosome 7, 8, and 17 amplification was obvious in patients with gastric cancer. UCAD results confirmed that in 12 patients with pathologic diagnosis of severe atrophy, five of them had positive results of CIN, with a positive detection rate of 41.7%, which was mainly manifested in chromosome seven and chromosome eight segments amplification. We also found that intestinalization and HP infection were less associated with CIN. And the sensitivity of CIN measurement results was significantly better than that of tumor indicators. The findings suggest that the diagnosis of PLGC can be aided by UCAD detection of CIN, of which Chr7 and 8 may be closely related to PLGC.

Microsatellite Instability Detection in Cancer: A Multiplex qPCR Approach that Obviates the Need for Matching Normal Samples.

Microsatellite instability (MSI) indicates DNA mismatch repair deficiency in certain types of cancer, such as colorectal cancer. The current gold standard technique, PCR-capillary electrophoresis (CE), requires matching normal samples and specialized instrumentation. We developed VarTrace, a rapid and low-cost quantitative PCR (qPCR) assay, to evaluate MSI using solely the tumor sample DNA, obviating the requirement for matching normal samples. One hundred and one formalin-fixed paraffin-embedded (FFPE) tumor samples were tested using VarTrace and compared with the Promega OncoMate assay utilizing PCR-CE. Tumor percentage limit of detection was evaluated on contrived samples derived from clinical high MSI (MSI-H) samples. Analytical sensitivity, specificity, limit of detection, and input requirements were assessed using synthetic commercial reference standards. VarTrace successfully analyzed all 101 clinical FFPE samples, demonstrating 100% sensitivity and 98% specificity compared to OncoMate. It detected MSI-H with 97% accuracy down to 10% tumor. Analytical studies using synthetic samples showed a limit of detection of 5% variant allele frequency and a limit of input of 0.5 ng. This study validates VarTrace as a swift, accurate, and economical assay for MSI detection in samples with low tumor percentages without the need for matching normal DNA. VarTrace's capacity for highly sensitive MSI analysis holds potential for enhancing the efficiency of clinical work flows and broadening the availability of this test.

Abstract 7560: Microsatellite instability (MSI) detection using flow-based sequencing

Abstract Background: Microsatellite instability (MSI) is an important genomic biomarker associated with various cancers and hereditary conditions. Accurate and efficient classification of MSI status is essential for diagnosis, prognosis, and treatment decisions. Different approaches for testing MSI have been presented in the literature, including NGS-based methods which either rely on comparison with a matched control sample, a set of reference control samples, or the reference genome (no control). In this study, we explore the feasibility of accurately identifying MSI status using the UG100 platform, which utilizes flow-based sequencing chemistry. Since MSI detection is typically based on long homopolymers, we examined here whether the flow-based sequencing data can be effectively used for MSI classification. Methods: We performed whole genome sequencing (WGS) on a cohort of 24 cell lines with known MSI status, as well as on 15 tumor tissue samples, encompassing various cancer types, including colon, lung, bladder, and endometrial cancer. To determine the MSI status, polyA/T homopolymer loci were considered. We measured the MSI status in these loci using two methods: 1) Counting short indel variants detected using somatic Deep Variant, in comparison with matched blood normal, and 2) a novel MSI score based on the difference of the polyA/T homopolymer length, in comparison to the reference genome. We assessed the ability to evaluate the score for the full sequenced coverage (~100X) as well as when limiting to exome boundaries, and when sampling down to 1X coverage. Results and Conclusions: We established the performance of the MSI score on the cell lines with known MSI status. The score was verified on cell line mixtures (simulating lower tumor purity) as well as on clinical samples from various tissue sources, including colon samples annotated as mismatch-repair deficient. We demonstrate the ability to use the cost-effective UG100 sequencing combined with the two methodologies to identify MSI-high samples in these scenarios. Citation Format: Gila Lithwick-Yanai, Hila Benjamin, Jorge Cano-Nistal, Shir Geisler, Ariel Jaimovich, Nika Iremadze, Dror Kessler, Ilya Soifer, Danit Lebanony, Keren Ben Simhon, Shlomit Gilad, Eti Meiri, Doron Lipson, Yosef E. Maruvka. Microsatellite instability (MSI) detection using flow-based sequencing [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 7560.

Abstract 972: Non-invasive detection of homologous recombination deficiency in metastatic prostate cancer patients

Abstract Background: The recent endorsement of poly ADP ribose polymerase inhibitors (PARPi) has been a breakthrough in managing prostate cancer (PCa) with homologous recombination deficiency (HRD). However, due to its propensity to metastasize to the bone, HRD assessment in tissue biopsy poses a challenge in advanced PCa. Circulating tumor DNA (ctDNA) as a tumor surrogate may be a promising substitute for pinpointing patients who may benefit from PARPi treatment. Methods: To test whether causes and consequences of HRD can be detected non-invasively in plasma, we pre-selected 139 plasma samples from PCa patients based on aneuploidy screening (mFAST-SeqS). We then applied targeted sequencing using a QIAseq panel including homologous recombination repair (HRR)-related genes as well as genes involved in cell cycle regulation (TP53, RB1, PTEN). Putative, pathogenic germline variants were sequencing in constitutional DNA using Sanger sequencing. Moreover, we performed low pass whole genome sequencing to determine the levels of genomic instability using the shallow HRD (sHRD) algorithm. Results: In our cohort, at least one pathogenic mutation was detected in 68/139 (48.9%) of the patients, of which the majority consisted of alterations in PTEN, TP53 or RB1 (44/139, 31.7%). Pathogenic BRCA1/2 mutations were detected in 13/139 patients (9.4%), the majority of which originated from the germline. In 8/139 patients (5.6%) a pathogenic variant in other HRR genes could be observed and in 3/139 patients (2.2%) CKD12 mutations were detected. As expected, samples with pathogenic HRR mutations and elevated tumor fraction, presented high sHRD scores (&amp;lt;20) in plasma DNA. In contrast, the majority of high ctDNA samples with mutations in PTEN, TP53 or RB1 had sHRD score below the cut-off of 20. However, the detection of genomic instability was clearly depending on tumor fractions. Conclusion: Our study indicates that a non-invasive assessment of root causes and subsequent effects of the HRD phenotype in prostate cancer is feasible, and that mutations in HRR correlate with a high genomic instability. However, to detect and quantify genomic instability in plasma, elevated ctDNA levels are required. Based on in-house data from mFAST-SeqS aneuploidy screening of over 900 plasma samples from advanced prostate cancer, approximately 30% of cfDNA samples would have sufficiently high ctDNA fractions for a combined assessment of causes and consequences of HRD, which might be most informative to direct PARPi treatment in PCa. Citation Format: Georgios Vlachos, Tina Moser, Anna Eberhard, Lisa Glaswitch, Jasmin Blatterer, Emil Bauernhofer, Nina Monsberger, Karl Kashofer, Jochen Geigl, Thomas Bauernhofer, Ellen Heitzer. Non-invasive detection of homologous recombination deficiency in metastatic prostate cancer patients [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 972.