The Speaker Lineup and Schedule will be released closer to the event. Please check back at a later date.
Click on the dates below to see each day's program | Available from October 2023.
Last updated on 06.09.2023 | The listing of a proposal does not imply an approval of a presentation. The final programme will be released here closer to the event.
Previously, detection and quantifying DNA methylations have relied on conversions of DNA mainly using bisulfite, which is a time-consuming and labor-demanding procedure, requiring a high quantity of DNA. Our new and innovative qPCR technology, EpiDirect® allows for direct quantification of cytosine methylation using natural untreated DNA. In just two hours, methylated DNA is amplified by a special primer, EpiPrimer™, which has a high affinity for methylated cytosines, thus allowing selective amplification of methylated DNA over unmethylated DNA. We show results from the CE-IVD EpiDirect® MGMT Methylation qPCR Assay and the EpiDirect® MLH1 Assay which is currently under development. EpiDirect® MGMT detects and quantifies from 100% to 3% methylated DNA, and preliminary results from the EpiDirect® MLH1 show equally promising potential.
Small molecule inhibitors targeting Siglec-15 are not explored alongside characterised regulatory mechanisms involving microRNAs in CRC progression. Therefore, a small molecule inhibitor to target Siglec-15 was elucidated in vitro and microRNA-mediated inhibitor effects were investigated. We demonstrated that the SHG-8 molecule exerted significant cytotoxicity on cell viability, migration, and colony formation, with an IC50 value of 20µM. Notably, miR-6715b-3p was the most upregulated miRNA via high-throughput sequencing, which was validated via RT-qPCR. Additionally, molecular docking studies revealed SHG-8 interactions with the Siglec-15 binding pocket with the binding affinity of -5.4 kcal/mol, highlighting its role as a small molecule inhibitor. Importantly, Siglec-15 and PD-L1 are expressed on mutually exclusive cancer cell populations, suggesting the potential for combination therapies with PD-L1 antagonists.
Glioblastoma multiforme (GBM) is an aggressive brain malignancy with current treatments resulting in poor prognoses. β-amino carbonyl compounds (β-ACs) have gained attention due to their potential anti-cancerous properties. In vitro assays were performed to evaluate the effects of an in-house synthesised β-AC compound, named SHG-8, upon GBM cells. Small RNA-sequencing (sRNA-seq) and biocomputational analyses investigated SHG-8’s effects upon the miRNome and its bioavailability within the human body. SHG-8 exhibited significant cytotoxicity in U87MG cells, inhibiting migration, proliferation, and inducing apoptosis. sRNA-seq revealed a dramatic shift in miRNA expression upon SHG-8 treatment and significant downregulation of miR-21. RT-qPCR demonstrated a significant downregulation of CORO1C, a target gene of miR-21. Therefore, SHG-8's inhibitory effects on GBM cells may involve mi R-21-mediated CORO1C inhibition.
Information regarding a hand-held, real-time fluorescence detection device will be presented along with the development of a simple sample collection and analysis system. The sample, like a swab, goes into a standard flexible tube and a few drops are squeezed into two standard PCR tubes (with lyophilized reagents) in a holder and the holder is placed in the device. No cartridges or DNA or RNA purification steps are necessary. The Start button is pressed to initiate the reaction. Data is displayed at the end of the run on a LED bar, indicating an intuitive positive, not detected, or invalid result. The device provides a quantitative Ct value. The system is also capable of detecting mutations and therefore can be used for detection of single gene genetic diseases.
The main objective of this study is to explore data assimilation techniques for predicting malignant brain tumor growth, focusing on glioblastoma. We use MRI images as crucial observation data and integrate them into our numerical model using data assimilation methodologies. By assimilating the MRI observations into our model, we aim to provide accurate and up-to-date estimates of the tumor's state and forecast its development and progression over both short-term and long-term periods. We introduce two pivotal data assimilation operators, the Volume Element Data Assimilation Operator (VEDA) and the Nodal Point Operator (DANP), which enhance the accuracy and reliability of the tumor growth forecasts. Our research provides the existence and uniqueness of solutions and validates the convergence of the data assimilation algorithm. Accurate predictions of malignant brain tumor growth, particularly glioblastoma, hold significant clinical implications for treatment planning and patient guidance, potentially leading to improved treatment strategies and patient outcomes in brain cancer management.
About 25% of stage II colon cancers relapse within 5 years in the absence of adjuvant chemotherapy. We identify major markers of risk of relapse within a homogeneous population of stage II, pT3N0 microsatelitte-stable colon cancers and show that a gene-expression predictive signature could be used to identify the candidate patients for chemotherapy.
The levels, chemical modifications, and relative ratios of biomolecules circulating in systemic bioliquids, like blood plasma, are indicators of various physiological and pathological states. Capitalizing on broadband optics, ultrafast laser sources, and precision femtosecond-attosecond field-resolving technologies, we recently developed electric-field molecular fingerprinting (EMF) to detect changes in molecular composition of bioliquids, establishing EMF as a new in vitro diagnostic analytical technique to uncover characteristic molecular traces of diseases in blood samples. EMF, as advanced infrared spectroscopy, has the inherent capacity to sensitively and robustly probe across different types of chemical bonds and molecular classes within one measurement, providing deep cross-molecular description of physiological states. Here, we present data from our multi-centric multi-cancer study in which we analyzed infrared molecular fingerprints of plasma and serum from several thousands of individuals, involving cancer patients with different solid tumors and matched reference individuals. Focusing on four common cancers - breast, bladder, prostate, and lung cancer - we find that infrared molecular fingerprinting is capable of detecting therapy-naïve malignant conditions. Employing machine learning data analytics, we obtain binary classification performances in the range of 0.78–0.89 (area under the receiver operating characteristic curve [AUC]), and additionally demonstrate correlation between AUC and tumor load. Intriguingly, we find that the spectral signatures differ between different cancer types, thus allowing to distinguish between different cancers in a single measurement. Our studies lay the foundation for using EMF in health monitoring, in particular for onco-infrared-fingerprinting, providing a novel high-throughput, cost-effective technology platform that can be used as a complementary analytical tool for cancer detection and screening.
This cross-national study investigates and contrasts validation standards for early cancer detection biomarkers during regulatory approvals across the United States, Europe, Japan, and South Korea. Analyzing criteria set by regulatory bodies in each country, the research delves into healthcare infrastructure, regulatory benchmarks, and technological evaluations. The study sheds light on disparities in validation standards. Thorough analysis of regulatory guidelines and case studies informs the examination of early cancer detection biomarker validation processes. The study aims to enhance understanding of cross-national differences and promote international cooperation, contributing to the advancement of early cancer detection globally.
The presentation will delve into the utilisation of microRNA expression as biomarkers for the early detection of PDAC. This research study will concentrate on a selection of microRNAs to elucidate their functions and significance in the context of PDAC by using in vitro and in vivo data.
Liquid biopsy-based nucleic acid diagnostics (LD) is transformative in clinical diagnostics, yet oncological LD development predominantly relies on clinical samples, yielding limited correlative insights. Functional precision oncology, integrated to disease modeling (DM), bridges clinical-experimental gaps, offering mechanistic understanding to discern tumor therapy resistances and inflammation effects beyond perfusion imaging. Aligned with 3R principles, our focus on low 5-year survival rate tumors employs NGS to assess RNA content in cancer stem cells (CSC) and their disease model environments (DME). Diverse computational tools and machine learning discover potential CSC-RNA sheddome candidates, empowered by coherent epigenomic signals shared between in vitro and animal models. This provides therapy optimization insights, prompting a reconsideration of the necessity for animal perfusion studies in early LD development.
Cancer metastasis is the most lethal attribute of cancer. We identified the novel, previously undescribed gene Metastasis-Associated in Colon Cancer 1 (MACC1) induces fundamental processes like cell proliferation, migration, invasiveness and metastasis. MACC1 has been established as key player, prognostic and predictive biomarker for tumor progression and metastasis in >20 solid cancers. We identified repositioned drugs and novel compounds as transcriptional and post-translational small molecule inhibitors, restricting MACC1-induced metastasis in mice. All these endeavors are performed in translational approaches by bridging our experimental data to their clinical relevance and clinical use of patient tumor tissue and patient blood. The ultimate goal is the signaling-based establishment of new therapeutic concepts for metastasis restriction and prevention.
A novel method of high-thoughput targeted RNA sequencing (ciRNAseq) was used to profile over 2,000 HPV-DNA positive tested cervical scrapes collected from women participating in the Dutch screening program. The technique identifies activity of different high risk Human Papilloma Virus (hrHPV) oncogenes and their splice variants in infected cells, but does not detect dormant or latent, clinically nonsignificant, HPV infections. In 70% of HPV-DNA positive scrapes with normal cytology, no HPV RNA was found, and this percentage decreased with increased PAP-score. The test further identifies aberrant activity of genes involved in cell cycle regulation, which is a direct consequence of HPV-E6 and HPV-E7 protein activity. Machine learning on the data resulted in an algorithm to detect CIN2+ with high specificity.
The detection of cancer recurrence and treatment response monitoring are vital in cancer care. Current image-based methods lack sensitivity and speed. Cell-free tumour DNA (cfDNA) detection offers a non-invasive and sensitive alternative for assessing treatment response and identifying cancer recurrence. Our groundbreaking technique, CyclomicsSeq, utilises Oxford Nanopore sequencing to precisely detect TP53 mutations in low abundance. To assess sensitivity, we measured TP53 mutation frequency in post-neoadjuvant chemoradiotherapy esophageal cancer patients. We accurately detected mutations as low as 0.02%, highlighting CyclomicsSeq's reliability and sensitivity. This reinforces its potential as a powerful tool for personalised cancer management and monitoring, addressing significant clinical needs in cancer care.
Proper stratification of patients with high risk of relapse in early stage of estrogen-positive breast cancer is a known clinical problem. HAX1 protein has been described by our group as an important factor implicated in progression and metastasis of breast cancer. Diagnostic potential of HAX1 IHC staining of primary tumor samples in prediction of distant metastasis may be comparable with Oncotype DX multiple genes recurrence score assay. If further substantiated, HAX1 IHC staining may be a new, simple in use and inexpensive prognostic factor of disease recurrence in luminal breast cancer.
We report about the development of an innovative class of tailor-made biomimetics with high affinity for the targeted molecule and suitable to tackle the early onset of molecular changes in cells and to sequester defined molecules. These biomimetics are called nanotraps and are soluble receptors, prepared starting from a biocompatible, non toxic material, already in use in regenerative medicine, that is silk fibroin. The molecular recognition is entailed to silk nanoparticles by means of the molecularly imprinted polymers (MIPs) technique, that is a template assisted synthesis. These nanotraps, called bioMIPs, are capable of recognizing and sequestering targeted analytes hence would find uses in imaging and therapy.
Thyroid nodules are frequent but mostly benign, and postoperative rate of benign nodules reaches 90%. Therefore, there is a need for identification of reliable preoperative diagnosis markers for patients with thyroid cytology. In particular, there is a gap in studies of interrelationships between diurnal profiles of expression of circadian clock genes and TSH receptor in thyroid tissue exposed to different concentrations of TSH. These interrelationships might be investigated in future in vitro experiments on benign and malignant thyrocytes cultivated under normal and challenged TSH levels. Results of this investigation might help to bridge previous studies of preoperative biomarkers for thyroid carcinoma that explore diagnostic value of diurnal profiles of serum TSH levels, expression of its receptor and circadian clock genes.
Rare tumor-specific mutations in patient samples serve as excellent markers to monitor the course of malignant disease and responses to therapy in clinical routine, and improved assay techniques are needed for broad adoption. We describe herein - superRCA assays - which provides for rapid and highly specific detection of DNA sequence variants present at very low frequencies in DNA samples. Using a standard flow cytometer we demonstrate precise, ultra-sensitive detection of single-nucleotide mutant sequences from malignant cells against a 100,000-fold excess of DNA, to follow the course of patients treated for acute myeloid leukemia (AML) and Lung Cancer patients.
Quanterix® has developed an ultra‐sensitive platform capable of measuring individual proteins at concentrations a thousand times lower than conventional immunoassays. The Single Molecule Array (Simoa®) technology at the foundation of this platform enables the detection and quantification of biomarkers previously difficult or impossible to measure, opening new applications to address significant unmet needs in life science research and diagnostics. Simoa® assays have the potential to be used to monitor cancer risk, identify early-stage cancers, and discriminate between benign and malignant cells. Simoa biomarkers also have the potential to be used prognostically to predict disease outcome, predict progression-free survival and monitor reoccurrence, as well as to be used to monitor sensitivity to therapy. Finally, Simoa technology and platforms have been designed with flexibility in mind to enable the discovery and testing of novel cancer relevant biomarkers. Simoa ultra-sensitive custom assays have been successfully developed and employed for several interesting applications ranging from tumor derived exosomes and miRNA quantification to study the PK/PD of anti-tumorigenic drugs.
The SuFIDA technology is a single molecule counting technology. It is an advanced ultrasensitive immunoassay technology that enables the detection of biomarkers that were previously undetectable down to lower femtomolar concentrations. SuFIDA is the evolution of the sandwich ELISA with greater selectivity, higher sensitivity and the digital readout using an optical imaging technique. In SuFIDA, capture antibodies are immobilized on a glass surface and bind the analyte from the sample. A fluorescence-labeled antibody is used as the detection probe, which recognizes an epitope on the target molecule that is not occupied by the capture antibody. Using highly sensitive TIRF microscopy, all individual fluorescence-labeled probes on the surface will be detected and virtually counted individually. By using an in-house developed analysis software, the huge image volumes are evaluated in a short time with various analysis functions.
Sepsis is a clinical syndrome characterized by dysregulated response to infection. It represents a leading cause of mortality in ICU patients worldwide. Although sepsis is in the point of interest of research for several decades, its clinical management and patient survival are improving slowly. Monitoring of the biomarkers and their combinations could help in early diagnosis, estimation of prognosis and patient’s stratification and development of new targeted treatment. This presentation recapitulates results of last 5 years of our collaborative translation research in the field. Promising new molecules such as Soluble Endoglin, Hepcidin, Ferritin or different cellular markers on immune cells could have predictive potential in Septic shock, critical Covid-19 and/or post-surgery patients.
Pancreatic cancer leads to a poor life expectancy (4-6 months) due to its silent nature. Thus, an early detection strategy would save a large number of lives. We focus on glycoprotein mucin 1 (MUC1), which is overexpressed in 90% of cancers while patients generate antibodies against it. We have developed a very sensitive detection system for these antibodies by generating an unnatural peptide with higher affinity for the antibodies, and binding the unnatural peptides to gold nanoparticles. In a clinical study, we found significant differences between pancreatic cancer patients and healthy volunteers (p<0.0001). A ROC analysis produced an area under the curve of 0.918, with 85% sensitivity and 90% specificity. We are looking for partners to commercialize this detection system.
