Scientific Program

Day 1 :

Keynote Forum

Igor F. Tsigelny

CureMatch Inc. USA

Keynote: Cancer treatment in the era of precision medicine

Time : 10.00 - 10.40


Dr. Igor F. Tsigelny is an expert in structural biology, molecular modeling, bioinformatics, structure-based drug design and personalized cancer medicine. He published >200 articles, 4 scientific books and around 15 patents. The book ‘Protein Structure Prediction: Bioinformatic Approach’ that he edited has been called ‘The Bible of all current prediction techniques’ by BioPlanet Bioinformatics Forums. His computational study of molecular mechanisms of Parkinson’s disease was included in the US Department of Energy publication ‘Decade of Discovery’ where the best computational studies of the decade 1999–2009 have been described. He is a Professor in the UC San Diego and CSO of CureMatch Inc. (San Diego). 


Traditional approach to cancer treatment generally involves “one-size-fits-all” treatments and procedures (e.g., chemotherapy, radiation therapy, and surgery), which is focused largely at fighting a particular type of cancer (e.g., liver, lung, colorectal). However, this approach ignores the unique nature of an individual patient’s cancer, despite the fact that the complex genotypic and phenotypic heterogeneity of an individual patient’s cancer/tumor has a profound influence on the clinical responses to targeted anticancer therapies. Genetic sequencing of tumors is conducted for only a small number of patients (~2%), and the large number (>4.5 M) of options and potential for drug-drug interactions have precluded widespread adoption of combination therapies. Current approach to treatment response planning and assessment also lacks an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. Major goals of successful combination therapy include the ability to: (a) cover most of the patient’s aberrations with a minimal number of drugs, (b) achieve enhanced effectiveness through drug synergy, (c) reduce the frequency and severity of adverse events (AEs) and (d) minimize the potential to develop drug resistance. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. Development of effective combination therapy is also challenging because many cancer drugs act on intersecting signaling pathways and thus can potentially interfere or antagonize each other. One approach to identify effective combinations is by precise targeting of synergistic combinations, which exhibit enhanced therapeutic efficacy when combined at lower doses. However, identification of synergistic drug combinations is often a labor- and resource-intensive process. We developed a precise, multimodal computational model that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. 

Keynote Forum

Ondrej Slaby

Masaryk University, Czech Republic


Time : 10.40 - 11.20


Ondrej Slaby is a Professor of Medical Biochemistry at the the 1st Faculty of Medicine at Charles University in Prague, Czech Republic. He works as Research Group Leader at the Department of Molecular Medicine, Central European Institute of Technology (CEITEC), Masaryk University (Brno, Czech Republic) and as a Scientific Secretary at the Masaryk Memorial Cancer Institute in Brno. Prof. Slaby has published extensively in the field of non-coding RNAs and solid cancer with special focus on their translational potential in diagnostics and as the therapeutic targets (h-index 31, Sum of the times cited without auto-citations > 3500. Since 2018, prof. Slaby is a chair of Czechoslovak Biological Society. In 2010 and 2012 received Award of Czech Society for Oncology, in 2016 an Award of Czech Medical Society and 2014 and 2016 Award for Medical Research of Czech Minister of Health, Novartis Discovery Award 2017. 


For many years, central dogma of molecular biology has been that RNA functions mainly as an informational intermediate between DNA sequence and its encoded protein. One of the great surprises of modern biology was discovery that protein-coding genes represent less than 2% of total genome sequence, and subsequently that almost 90% of human genome is actively transcribed. Thus, human transcriptome was found to be more complex than collection of protein-coding gene transcripts and their splice variants. Recent evidences have clearly shown that non-coding RNAs (ncRNAs) play major biological roles in cellular development, physiology and pathologies. NcRNAs are grouped into two major classes based on transcript size; small ncRNAs and long ncRNAs. Each of these classes can be further divided, whereas novel subclasses are still being discovered and characterized. In last ten years, class of small ncRNAs called microRNAs was studied most intensively with more than fifty thousand hits at PubMed database. Huge amount of evidence has been accumulated to describe molecular mechanisms of novel RNA species functioning, providing insight into their functional roles in cellular biology and in human disease, especially in cancer. Knowledge regarding ncRNAs functioning in cancer biology and their translational potential to serve as disease biomarkers and novel therapeutic targets in cancer will be summarized and demonstrated on several examples based on our recent observations.

  • cancer

Nipaporn Ngernyuang has completed her Ph.D. in Biomedical sciences from Khon Khan University, Thailand. Currently, she is an Assistant Professor at Chulabhorn International College of Medicine, Thammasat University, Thailand. Her program of research focuses on molecular oncology and nanotechnology for cancer treatment. She has published about 8 papers in reputed journals. She has received researcher awards for her scholarly work from Thammasat University.


Statement of the Problem: Over the past several decades, accumulating evidence has revealed that highly metastatic cancers are intimately associated with vessel-like formation that is primarily derived from tumor cells, independent of endothelial cell-mediated angiogenesis. This alternative microvascular formation lacking endothelial cells is known as vasculogenic mimicry (VM). VM develops tumor vascular networks that associated tumor growth, metastasis, and short survival time of cancer patients. However, the
knowledge of VM in the vascularization of cervical cancer are not fully understood yet. Chitinase-3-like-1 (CHI3L1) has been reported to plays a critical role in angiogenesis of cervical cancer. Here, we explored a pathological function of CHI3L1 in tumor cell-mediated vascularization. Methodology & Theoretical Orientation: The sixtysix tissue samples of cervical cancer were collected to determine CHI3L1 expression and VM formation using immunohistochemistry and CD34-periodic acid-Schiff (PAS) dual staining. Findings: CHI3L1 expression was significantly correlated with VM formation (p = 0.031). Interestingly, patients with VM positive tumors tended to have decreased overall survival (OS) compared to those with VM negative samples (43.9 versus 64.6 months, p = 0.079). In addition, recombinant CHI3L1 enhanced cervical cancer cell lines to form tube-like structures, supporting the notion that CHI3L1 mediates VM in cervical cancer. Conclusion & Significance: Our present findings suggest the crucial role of CHI3L1 by promoting the formation, which may contribute to tumor aggressiveness. Therefore, CHI3L1 may represent a novel attractive therapeutic target for the reduction of cervical cancer vascularization and metastasis.