Research
Publications
Featured Interview / Article with CSE
On May 29, 2025, BioMark Diagnostics Inc. was featured on The Canadian Securities Exchange Magazine – June Issue, focusing on the health sector, here is the link to review the article:
https://issuu.com/thecse/docs/canadian_securities_exchange_magazine_june_2025/22
Clinical Validation of Plasma Metabolite Markers for Early Lung Cancer Detection
Journal: International Journal of Molecular Sciences Special Issue
Published on May 9, 2025
Abstract: Early detection of lung cancer significantly improves survival, yet current screening methods have limitations. This study aimed to identify a robust panel of plasma metabolites for early-stage non-small cell lung cancer (NSCLC) diagnosis using a large, clinically diverse patient cohort. A total of 680 archived plasma samples from biopsy-confirmed NSCLC patients and controls (including healthy individuals and patients with noncancerous lung diseases) were analyzed using targeted, quantitative mass spectrometry-based metabolomics and used as the discovery cohort. An independent set of 216 plasma samples served as the validation cohort. Logistic regression (LR) models developed from the discovery set using ten metabolites achieved area under the receiver-operating characteristic curve (AUROC) values of 93.63%, 93.74%, and 93.91% for distinguishing all-stage, stage I–II, and stage I NSCLC patients from controls, respectively. Incorporating smoking history further improved model performance. The validation cohort confirmed the model’s robustness, demonstrating high sensitivity and specificity for early-stage detection. These results support the potential of metabolomic biomarkers as a minimally invasive, accurate tool for early NSCLC diagnosis. This approach may complement current screening methods, enabling earlier intervention and improved patient outcomes. Further studies are warranted to validate these findings in more diverse populations and real-world clinical settings.
Identification of a Novel Biomarker Panel for Breast Cancer Screening
Journal: International Journal of Molecular Sciences
Published on December 4, 2024
Abstract: Breast cancer is a global concern as a leading cause of death for women. Early and precise diagnosis can be vital in handling the disease efficiently. Breast cancer subtyping based on estrogen receptor (ER) status is crucial for determining prognosis and treatment. This study uses metabolomics data from plasma samples to detect metabolite biomarkers that could distinguish ERpositive from ER-negative breast cancers in a non-invasive manner. The dataset includes demographic information, ER status, and metabolite levels from 188 breast cancer patients and 73 healthy controls. Recursive Feature Elimination (RFE) with a Random Forest (RF) classifier identified an optimal subset of 30 features—29 biomarkers and age—that achieved the highest area under the curve (AUC). To address the class imbalance, Gaussian noise-based augmentation and Adaptive Synthetic Oversampling (ADASYN) were applied, ensuring balanced representation during training. Four machine learning (ML) algorithms—Random Forest, Support Vector Classifier (SVC), XGBoost, and Logistic Regression (LR)—were evaluated using grid search. The Random Forest classifier emerged as the top performer, achieving an AUC of 0.95 and an accuracy of 93%. These results suggest that ML has great promise for identifying specific metabolites linked to ER expression, paving the development of a novel analytical tool that can minimize current challenges in identifying ER status, and improve the precision of breast cancer subtyping.
Identification of a Novel Biomarker Panel for Breast Cancer Screening
Journal: International Journal of Molecular Sciences
Published on November 4, 2024
Abstract: Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved. Unlike previous studies that rely on singular feature selection techniques such as Partial Least Square (PLS) or LASSO regression, this research combines supervised and unsupervised machine learning methods with random sampling strategies, offering a more robust and interpretable approach to feature selection. This study aimed to identify a parsimonious and robust set of biomarkers for breast cancer diagnosis using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (from the Cooperative Human Tissue Network, USA) were analyzed. This study also overcomes the common issue of dataset imbalance by using propensity score matching (PSM), which ensures reliable comparisons between cancer and control groups. We employed Univariate Naïve Bayes, L2-regularized Support Vector Classifier (SVC), Principal Component Analysis (PCA), and feature engineering techniques to refine and select the most informative features. Our best-performing feature set comprised 11 biomarkers, including 9 metabolites (SM(OH) C22:2, SM C18:0, C0, C3OH, C14:2OH, C16:2OH, LysoPC a C18:1, PC aa C36:0 and Asparagine), a metabolite ratio (Kynurenine-to-Tryptophan), and 1 demographic variable (Age), achieving an area under the ROC curve (AUC) of 98%. These results demonstrate the potential for a robust, cost-effective, and non-invasive breast cancer screening and diagnostic tool, offering significant clinical value for early detection and personalized patient management.
Metabolomic Profiling of Pulmonary Neuroendocrine Neoplasms
Journal: International Journal of Molecular Sciences
Published on September 17, 2024
Abstract: Pulmonary neuroendocrine neoplasms (NENs) account for 20% f malignant lung tumors. Their management is challenging due to their diverse clinical features and aggressive nature. Currently, metabolomics offers a range of potential cancer biomarkers for diagnosis, monitoring tumor progression, and assessing therapeutic response. However, a specific metabolomic profile for early diagnosis of lung NENs has yet to be identified. This study aims to identify specific metabolomic profiles that can serve as biomarkers for early diagnosis of lung NENs. Methods: We measured 153 metabolites using liquid chromatography combined with mass spectrometry (LC-MS) in the plasma of 120 NEN patients and compared them with those of 71 healthy individuals. Additionally, we compared these profiles with those of 466 patients with non-small-cell lung cancers (NSCLCs) to ensure clinical relevance. Results: We identified 21 metabolites with consistently altered plasma concentrations in NENs. Compared to healthy controls, 18 metabolites were specific to carcinoid tumors, 5 to small-cell lung carcinomas (SCLCs), and 10 to large-cell neuroendocrine carcinomas (LCNECs). These findings revealed alterations in various metabolic pathways, such as fatty acid biosynthesis and beta-oxidation, the Warburg effect, and the citric acid cycle. Conclusions: Our study identified biomarker metabolites in the plasma of patients with each subtype of lung NENs and demonstrated significant alterations in several metabolic pathways. These metabolomic profiles could potentially serve as biomarkers for early diagnosis and better management of lung NENs.
Spermidine/Spermine N1-Acetyltransferase 1 (SAT1)—A Potential Gene Target for Selective Sensitization of Glioblastoma Cells Using an Ionizable Lipid Nanoparticle to Deliver siRNA
Journal: Cancers
(This article belongs to the Special Issue Novel Techniques and Technology for Treatment of Brain Tumors)
Published on October 22, 2022
Abstract: Spermidine/spermine N1-acetyltransferase 1 (SAT1) responsible for cell polyamine catabolism is overexpressed in glioblastoma multiforme (GB). Its role in tumor survival and promoting resistance towards radiation therapy has made it an interesting target for therapy. In this study, we prepared a lipid nanoparticle-based siRNA delivery system (LNP-siSAT1) to selectively knockdown (KD) SAT1 enzyme in a human glioblastoma cell line. The LNP-siSAT1 containing ionizable DODAP lipid was prepared following a microfluidics mixing method and the resulting nanoparticles had a hydrodynamic size of around 80 nm and a neutral surface charge. The LNP-siSAT1 effectively knocked down the SAT1 expression in U251, LN229, and 42MGBA GB cells, and other brain-relevant endothelial (hCMEC/D3), astrocyte (HA) and macrophage (ANA-1) cells at the mRNA and protein levels. SAT1 KD in U251 cells resulted in a 40% loss in cell viability. Furthermore, SAT1 KD in U251, LN229 and 42MGBA cells sensitized them towards radiation and chemotherapy treatments. In contrast, despite similar SAT1 KD in other brain-relevant cells no significant effect on cytotoxic response, either alone or in combination, was observed. A major roadblock for brain therapeutics is their ability to cross the highly restrictive blood–brain barrier (BBB) presented by the brain microcapillary endothelial cells. Here, we used the BBB circumventing approach to enhance the delivery of LNP-siSAT1 across a BBB cell culture model. A cadherin binding peptide (ADTC5) was used to transiently open the BBB tight junctions to promote paracellular diffusion of LNP-siSAT1. These results suggest LNP-siSAT1 may provide a safe and effective method for reducing SAT1 and sensitizing GB cells to radiation and chemotherapeutic agents.
Metabolomic Profiling for the Early Detection of Lung Cancer
Journal: Annals of Oncology
Published: 2022/07
Background: Currently, the five-year survival rate of lung cancer patients is very low, largely attributed to newly diagnosed patients presenting with locally advanced or metastatic disease. The lung cancer five-year survival rate (18.6%) is lower than many other leading cancer sites, such as colorectal (64.5%), breast (89.6%) and prostate (98.2%). The five-year survival rate for lung cancer is 56% for cases detected when the disease is still localized (within the lungs). However, only 16% of lung cancer cases are diagnosed at an early stage. For distant tumors (spread to other organs) the five-year survival rate is only 5%. More than 50% of lung cancer cases die within one year of being diagnosed. Accordingly, early diagnosis is key to the successful treatment, management and care of lung cancer.
Versatility of Amantadine and Rimantadine for Detection of Cancer
Journal: Novel Approaches in Cancer Study;
Published on April 21, 2021
Our Approach: We have developed a simple test that evaluates SSAT- 1 activity by measuring acetylated products in the urine. On this basis, we believe that SSAT-1 activity can predict the presence of cancer and possibly disease progression.
A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection
Journal: Caners;
Published on March 7, 2020
Abstract: The objective of this research is to use metabolomic techniques to discover and validate plasma metabolite biomarkers for the diagnosis of early-stage non-small cell lung cancer (NSCLC).The study included plasma samples from 156 patients with biopsy-confirmed NSCLC along with age and gender-matched plasma samples from 60 healthy controls. A fully quantitative targeted mass spectrometry (MS) analysis (targeting 138 metabolites) was performed on all samples. The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and smoking history were used to determine optimal sets of biomarkers and optimal regression models for identifying different stages of NSCLC using the discovery sets. The same biomarkers and regression models were used and assessed on the validation models. Univariate and multivariate statistical analysis identified β-hydroxybutyric acid, LysoPC 20:3, PC ae C40:6, citric acid, and fumaric acid as being significantly different between healthy controls and stage I/II NSCLC. Robust predictive models with areas under the curve (AUC) > 0.9 were developed and validated using these metabolites and other, easily measured clinical data for detecting different stages of NSCLC. This study successfully identified and validated a simple, high-performing, metabolite-based test for detecting early stage (I/II) NSCLC patients in plasma. While promising, further validation on larger and more diverse cohorts is still required.
Liquid Biopsy in Lung Cancer Screening: The Contribution of Metabolomics. Results of A Pilot Study
Journal: Cancers;
Published on July 29, 2019
Abstract: Background: Lung cancer is the most common cause of cancer-related deaths worldwide. Early diagnosis is crucial to increase the curability chance of the patients. Low dose CT screening can reduce lung cancer mortality, but it is associated with several limitations. Metabolomics is a promising technique for cancer diagnosis due to its ability to provide chemical phenotyping data. The intent of our study was to explore metabolomic effects and profiles of lung cancer patients to determine if metabolic perturbations in the SSAT-1/polyamine pathway can distinguish between healthy participants and lung cancer patients as a diagnostic and treatment monitoring tool. Patients and Methods: Plasma samples were collected as part of the SSAT1 Amantadine Cancer Study. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify and quantify metabolite concentrations in lung cancer patient and control samples. Standard statistical analyses were performed to determine whether metabolite concentrations could differentiate between healthy subjects and lung cancer patients, as well as risk prediction modeling applied to determine whether metabolic profiles could provide an indication of cancer progression in later stage patients. Results: A panel consisting of 14 metabolites, which included 6 metabolites in the polyamine pathway, was identified that correctly discriminated lung cancer patients from controls with an area under the curve of 0.97 (95% CI: 0.875-1.0). Conclusion: When used in conjunction with the SSAT-1/polyamine pathway, these metabolites may provide the specificity required for diagnosing lung cancer from other cancer types and could be used as a diagnostic and treatment monitoring tool.
Predictive value and clinical significance of increased SSAT-1 activity in healthy adults
Journal: Future Science OA;
Published on July 1, 2019
Abstract: This study describes the potential of a novel noninvasive urine test for screening of cancer using a safe and approved drug, amantadine. We have previously measured high urinary concentration of the acetylated form of amantadine in patients diagnosed with cancer. However, higher than expected acetylated amantadine concentration was also measured in some of the healthy adult volunteers. Subsequent clinical assessments revealed that these healthy individuals could have early clinical signs of cancer. This is a simple test, which may serve as a useful tool for routine screening in populations considered at high risk for cancer.
Follow-up evaluation of outliers with elevated spermine-spermidine acetyltransferase-1 activity
The 2019 American Society of Clinical Oncology (ASCO) Annual Meeting
Released on May 15, 2019
Use of amantadine as substrate for SSAT-1 activity as a reliable clinical diagnostic assay for breast and lung cancer
Journal: Future Science OA;
Published on December 11, 2018
Abstract: This study describes a novel noninvasive urine test for detecting and screening of breast and lung cancer using a safe and approved drug called amantadine. Higher concentration of the acetylated form of amantadine in the urine are detectable in the urine of both breast and lung cancer patients as compared with healthy adult volunteers. This test is simple and may serve as a useful tool for determining the presence of breast and lung cancer.
Spermidine/spermine N1-acetyltransferase-1 as a diagnostic biomarker in human cancer
Journal: Future Science OA;
Published on September 10, 2018
Abstract: In response to cancer, cells tend to overproduce specific enzymes as a self-defense mechanism. By using a safe and reliable method to capture and measure the excess enzyme spermidine/spermine N1-acetyltransferase-1, the presence of cancer can be established. This study describes a novel approach of detecting and screening cancer noninvasively in the urine of cancer patients using a safe and approved drug called amantadine that acts as a smart-tracking agent. Higher levels of the acetylated form of amantadine are detectable in the urine of cancer patients, which may serve as a detection tool. In addition, increases in the amount of spermidine/spermine N1-acetyltransferase-1 in tumor tissue may provide a tool for determining the presence of cancer during pathology assessment.
