Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies, largely because it is rarely caught early and diagnosed too late for curative treatment. The five-year survival rate remains around 12%, and currently tools-such as imaging and CA19-9 blood test-often miss early-stage cases. There is currently no recommended population-wide screening method for PDAC.

The new test model from Geneseeq analyzes cfDNA fragmentomics-specific patterns of DNA fragments shed into the bloodstream by cancer cells. By applying advanced machine learning algorithm to shallow whole-genome sequencing data, the test can detect subtle genomic and epigenetic changes associated with early-stage PDAC.

Key clinical results:

тАв Achieved 93.4% sensitivity and 95.2% specificity in the training cohort
тАв Reached 90.91-97.3% sensitivity and 92.8-94.5% specificity in multiple validation cohorts
тАв Demonstrated strong performance even in early-state cancers
тАв Outperformed CA19-9, especially in individuals with normal bilirubin levels

тАЬOur cfDNA fragmentomics model offers a practical, highly accurate, and non-invasive option for detecting pancreatic cancer early,тАЭ said Dr. Hua Bao, VP of R&D at Geneseeq. тАЬIt could support earlier identification of at-risk individuals, allowing timely clinical follow-up and potentially improving outcomes.тАЭ

What makes this approach especially promising is its clinical feasibility. The test uses low-coverage sequencing (as little as 0.5├Ч), making it cost-effective and suitable for broader population screening. The test also showed high stability, even with lower DNA sequencing data, and could be used to monitor high-risk patients or suspicious pancreatic lesions. The researchers also estimated that applying this test at the population level could reduce pancreatic cancer mortality by up to 27%, by catching more cancers at a treatable stage.

Further research is underway to refine the modelтАЩs application in screening programs and to validate its effectiveness in more diverse populations. Clinicians may soon have a powerful new tool to help combat one of the hardest-to-detect cancers.

About Geneseeq

Geneseeq Technology Inc. (Geneseeq) is a research-driven leader in precision oncology, utilizing cutting-edge next-generation sequencing (NGS) technologies to advance cancer care. The company provides comprehensive genomic profiling solutions for all tumor types, including pan-cancer and cancer-specific panels, alongside cutting-edge tools for minimal residual disease (MRD) monitoring and multi-cancer early detection (MCED). Geneseeq has reached key regulatory milestones to date, including CE-IVD certification for its solid tumor and hematologic cancer panels, and FDA Breakthrough Device Designation for its MCED test, CanScan┬о. The company has also received approval from the National Medical Products Administration (NMPA) for GeneseeqPrime, designed for tumor mutational burden (TMB) detection in lung cancer.

The post Geneseeq Unveils Groundbreaking Blood Test for Early Detection of Pancreatic Cancer appeared first on Geneseeq Technology Inc. | A Precision Oncology Company.

The following is a summary of our studies that will be presented at ASCO 2024я╝Ъ

Poster 1 (Bd# 276) A multi-center study for colorectal cancer early detection among patients with high-risk disease using a cell-free fragmentomics assay.

Poster 2 (Bd# 34) Leveraging cfDNA fragmentomic features in a stacked ensemble model for early detection of esophageal squamous cell carcinoma.

Poster 3 (Bd# 153) Fragmentomics of cell-free DNA as a sensitive biomarker for early detection of pancreatic cancer.

Poster 4 (Bd# 83) Molecular characterization and biomarker identification in pediatric B-cell acute lymphoblastic leukemia.

Poster 5 (Bd# 460) Genomic profiling of non-small cell lung cancer with rare aberrations in EGFR codon L858 and the survival outcome under real-world first-line EGFR tyrosine kinase inhibitor treatment compared to classic EGFR^L858R

The post Geneseeq AT ASCO 2024 appeared first on Geneseeq Technology Inc. | A Precision Oncology Company.

Geneseeq’s mini-oral presentations will release the most recent results from multi-cancer early detection(MCED) case-control study and the тАЬJinling cohort,тАЭ a 15,000-participant perspective MCED cohort study. In addition to the mini-oral presentations, Geneseeq will present ten posters featuring a diverse range of studies covering various aspects of minimal residual disease (MRD), cancer genomics and biomarker identification.

The post Geneseeq to Showcase Twelve Studies at 2024 American Association for Cancer Research (AACR) Annual Meeting appeared first on Geneseeq Technology Inc. | A Precision Oncology Company.

CanScan utilizes low-depth whole-genome sequencing (WGS) on circulating cell-free DNA (cfDNA) from a single tube of peripheral blood, extracting genetic and fragmentomic features to detect early cancer signals with 99% specificity and predict the tissue of origin (TOO) of cancers to help guide next steps for cancer diagnosis. CanScan exhibits promising potential to address unmet medical needs in clinical diagnosis and treatment, particularly for individuals aged 50 and above with an average risk of cancer. The test outperforms current standard of care (SOC) screening methods in common cancer types, such as prostate, lung and liver cancers. It also detects cancer types currently without effective SOC screening methods, such as esophagus, endometrial, gastric, pancreatic cancers and lymphoma.

Built on Geneseeq’s highly sensitive MERCURY multi-omics technology, the performance of CanScan has been validated in large-scale clinical study series, DECIPHER (Detecting Early Cancer by Inspecting ctDNA Features), in over thirteen cancer types. CanScan is currently under real-world evaluation in the Jinling Cohort (NCT06011694), a large-scale prospective multi-center trial. The ongoing recruitment of 15,000 individuals for the phase I trial within the Jinling Cohort is approaching completion.

“The Jinling Cohort aims to further validate the technical performance of CanScan in the targeted screening population,” Dr.Xue Wu, Geneseeq Toronto’s CEO said in a statement, noting that “we will release more results of the Jinling Cohort in 2024.”

This FDA Breakthrough Device Designation follows the CanScan assay kit’s CE approval in January 2023, marking another significant recognition from an internationally authoritative institution.

The post FDA Grants Breakthrough Device Designation for GeneseeqтАЩs Multi-cancer Early Detection Solution appeared first on Geneseeq Technology Inc. | A Precision Oncology Company.

Here are the key highlights from these studies:

• Performance of cfDNA fragmentonics-based early detection models in gastric cancer and breast cancer populations. The findings shed light on the potential of this approach in improving early diagnosis and subsequent treatment outcomes.
• Novel drug resistance mechanism in ROS1-rearranged non-small cell lung cancer patients. This research provides valuable insights into the development of targeted therapies and the management of treatment resistance in this subset of patients.
• Comprehensive analysis of homologous recombination repair gene reversion mutations. By examining a large pan-cancer population, this study sheds light on the prevalence, clinical implications, and potential therapeutic implications of these mutations.

Abstracts of the studies to be presented:

• Multi-dimensional cell-free DNA-based liquid biopsy for sensitive early detection of gastric cancer.
• Detecting Early Stage Breast Cancer Using Low-Depth Cell-Free DNA Fragmentomics: A Multi-Center Cohort Study.
• Mechanisms of Resistance to Tyrosine Kinase Inhibitor Treatments in ROS1 Fusion-Positive Non-Small Cell Lung Cancer Patients.
• A four-tier classification system of studying homologous recombination repair gene reversion mutations as a mechanism of resistance to PARP inhibitors and platinum-chemotherapy.

The post GENESEEQ TO SHOWCASE NEW FINDINGS AT ASCO 2023 appeared first on Geneseeq Technology Inc. | A Precision Oncology Company.

This study enrolled 292 stage I LUAD patients from three medical centers in China as well as 230 healthy volunteers. The LUAD patients included invasive adenocarcinoma (ADC) and minimally invasive adenocarcinoma (MIA). The cfDNA was prepared from the plasma samples, followed by whole-genome sequencing (WGS). Multiple cfDNA fragmentomic motif features and machine learning algorithms were investigated to select the best model. During this process, the participants from Center I were randomly assigned to the training and internal validation cohorts for model construction and cutoff determination. The cancer patients from Centers II and III were assigned to the external validation cohort with 40 healthy controls for independent validation.

A novel 6bp-breakpoint-motif feature using the logistic regression model reached 98.0% sensitivity and 94.7% specificity in the internal validation cohort (Area Under the Curve AUC: 0.985), and 92.5% sensitivity and 90.0% specificity in the external validation cohort (AUC: 0.954), consistently outperforming other cfDNA-based methods for stage I lung adenocarcinoma detection. Notably, this assay is sensitive for early-stage (e.g., 100% sensitivity for MIA) and <1 cm (92.9%-97.7% sensitivity) tumors. The predictive power remained high with 0.5├Ч WGS (AUC: 0.977 and 0.931 for internal and external cohorts). These results justified the cfDNA breakpoint motif-based machine learning model for detecting early-stage LUAD, especially the MIA and very small-size tumors.

тАЬNoninvasive detection of early-stage lung cancer using plasma cfDNA has attracted increasing attention and is still in progress for leveraging its performance. Optimal cfDNA features and machine learning algorithms could improve the prediction power and need to be intensively tested in the clinical settingsтАЭ, says Dr. Hua Bao, author and the Associate Dean of Geneseeq Research Institute.

The post Geneseeq and collaborators publish new findings from DECIPHER series, exploring single cfDNA feature-based machine learning model for lung cancer early detection appeared first on Geneseeq Technology Inc. | A Precision Oncology Company.

Today, we are excited to unveil another DECIPHER study published in Molecular Cancer, exploring the clinical utility of a novel multi-dimensional fragmentomics approach in the multi-cancer population through collaborations with multiple clinical facilities. This ultrasensitive model again demonstrated promising performance in the multi-cancer population, validating the performance in other cancer populations Geneseeq previously studied and published for.

In this study, a total of 1,214 participants were enrolled, including 381 primary liver cancer (PLC), 298 colorectal cancer (CRC), 292 lung adenocarcinoma (LUAD), and 243 healthy individuals. All participants were randomly split into training and testing datasets in a 1:1 ratio. Plasma samples were collected from the participants followed by cell-free DNA (cfDNA) extraction and low-coverage whole-genome sequencing. Five cfDNA fragmentomics features covering Fragment Size Coverage (FSC), Fragment Size Distribution (FSD), EnD Motif (EDM), BreakPoint Motif (BPM), and Copy Number Variation (CNV) were then extracted from the training dataset and implemented in five machine learning algorithms to build the ensemble stacked model. ┬аThe model was then evaluated in the testing dataset and true-positive cases were selected to validate the cancer origin model.

Our model showed an area under the curve (AUC) of 0.983 for differentiating cancer patients from healthy individuals. At 95% specificity, the sensitivities for detecting all cancer reached 95.5%, while 100%, 94.6%, and 90.4% for PLC, CRC, and LUAD, individually. The cancer origin model demonstrated an overall 93.1% accuracy for predicting cancer origin in the test cohort (97.4%, 94.3%, and 85.6% for PLC, CRC, and LUAD, respectively). Finally, the performance of the model is consistent (cancer detection тЙе 91.5% sensitivity at 95.0% specificity, cancer origin тЙе 91.6% accuracy) when sequencing depth was down-sampled to 1X coverage.

тАЬThis proof-of-concept study showed that our early detection model held great potential for developing accurate and affordable early detection assays for clinical practice. Our next step is to target a broader population and more cancer types including the less prevalent onesтАЭ, says Dr. Hua Bao, author and the Associate Dean of Geneseeq Research Institute.

The post Geneseeq Multicancer Early Detection Study Yields Promising Results appeared first on Geneseeq Technology Inc. | A Precision Oncology Company.

In this study, a total of 362 participants, including 192 PLC patients, 53 liver cirrhosis (LC) or hepatitis B virus (HBV) patients, and 117 healthy volunteers, were enrolled as the training cohort, which was used to construct a machine learning model. Plasma samples were collected from the participants followed by cell-free DNA (cfDNA) extraction and low-coverage whole-genome sequencing. Two cfDNA fragmentomics features, including fragment size ratio (FSR), fragment size distribution (FSD), were then extracted and employed by the ensemble stacked model incorporating three machine learning algorithms (gradient boosting machine, random forest, and deep learning). The model performance was then assessed in an independent testing cohort of 354 participants (189 PLC patients consisted of 157 HCC, 26 ICC, 6 cHCC-ICC, and 165 non-cancer controls).

The model showed excellent performance for cancer detection in the testing cohort (Area Under the Curve [AUC]:0.995, 96.8% sensitivity at 98.8% specificity). It also showed excellent sensitivities in detecting early-stages PLC (I: 95.9%, II: 97.9%), small tumors (<=3cm: 98.2%), and HCC (96.2%) or ICC (100%). The AUC for distinguishing PLC from LC/HBV reached 0.985 (96.8% specificity at 96.1% specificity). Promisingly, our model maintained consistent performances during the downsampling process, even using 1X coverage data (AUC: 0.994, 93.7% sensitivity at 98.8% specificity). A separate model for distinguishing ICC from HCC was also constructed using the same fragmentomic features, yielding an AUC of 0.776.

тАЬAn ultra-sensitive yet affordable non-invasive liquid-biopsy based assay can be used to massively screen high-risk population, which would greatly facilitate the diagnosis of HCC or PLC at an early stageтАЭ, says Dr. Bao Hua, author and the Director of GeneseeqтАШs R&D Department. The research team from Geneseeq investigated various liquid biopsy-based early cancer screening technologies such as cfDNA mutation, methylation, fragmentomics characteristics, metagenomics, and developed the multi-omics MERCURY technology. Through collaborations with clinicians, Geneseeq has carried out a series of cancer early screening studies DECIPHER using MERCURY in common solid tumor types. The DECIPHER-colon study has also been recently published in the Journal of Hematology & Oncology, demonstrating excellent ability to detect early-stage colorectal cancer (Stage 0/I) and advanced colorectal adenoma.

The post GeneseeqтАЩs cancer early detection study series DECIPHER yield another publication in HEPATOLOGY to explore the performance of cfDNA fragmentomics in liver cancer appeared first on Geneseeq Technology Inc. | A Precision Oncology Company.