Computational Molecular Diagnostics

Module Analysis of Molecular Biomarkers

Knowledge and Understanding: The student will acquire in-depth knowledge of biomarker analysis obtained from various biotechnological approaches in order to identify and validate new biomarkers for clinical use.

Applying Knowledge and Understanding: The student will develop the skills necessary to independently conduct molecular data analysis aimed at identifying biomarkers. Additionally, the student will acquire the foundational knowledge for biomarker validation using molecular biology techniques and interpreting results in a translational context, such as oncology or precision medicine.

Making judgements: The student will develop critical thinking skills in biomarker analysis, recognizing methodological and application limitations.

Communication Skills: The student will acquire communication skills, both written and oral, using scientific language appropriate for the subject matter of the course.

Learning Skills: The student will be guided to develop autonomous learning strategies in order to stay updated and improve their knowledge.

The lessons will be delivered in a lecture-based format (classroom lesson). The teacher will present the theoretical content using slides, a whiteboard to facilitate understanding, and specific online platforms for biomarker analysis, including clinical case to allow students to apply the concepts presented. During the course, active participation from students will be encouraged through questions, discussion and debate in the classroom. If the course will be performed by remote format, necessary adjustments may be made to the previously stated approach to ensure the syllabus and program requirements are met.

Knowledge of Molecular and Cellular Biology

Attendance to the courses is not mandatory, however, to ensure the understanding of the content and methodologies presented, regular and active participation in the lectures is strongly recommended.

The program will address the basic concepts of precision medicine, biomarkers, and targeted therapy, the role of advanced molecular analysis, as well as the application of omics sciences in clinical and translational research settings. The course aims to provide students with theoretical knowledge and practical skills in biomarker analysis using various methods (e.g., PCR I, II, and III generation), Sanger sequencing, with particular emphasis on data obtained from gene sequencing of DNA and fusion genes on RNA through the use of the IonTorrent platform with AmpliSeq technology. Students will learn to use the main databases for clinical variant classification and bioinformatics tools, such as Integrative Genomics Viewer (IGV), for variant annotation and sequencing read analysis through BAM file examination. They will consult databases and apply bioinformatic tools to interpret clinical variants, including those of uncertain significance, and reclassify them using in-silico predictive algorithms. Additionally, databases will be queried for Protein-Protein Interaction (PPI) network analysis and pathway enrichment studies. Throughout the course, students will be guided in designing and implementing workflows based on NGS methodologies, such as designing gene panels for target regions and hotspot files for variant calling. Finally, the course will cover liquid biopsy, with a particular focus on the analysis of Circulating Tumor Cells (CTCs) at the single-cell level (single-cell analysis) for identifying new biomarkers in oncology. The course will include theoretical and practical exercises based on real clinical cases, organized in groups to foster the practical application of the acquired skills.

These exams may be held remotely, should the conditions require it. The oral exam may take place on the same day as the written exam or a few days afterward.

The purpose of the exam is to thoroughly assess the student’s preparation, their analytical and critical thinking abilities on the topics covered during the course, as well as their mastery and appropriateness in using technical language.

The final evaluation will be based on the following indicative criteria:

Exam not passed: Insufficient knowledge of fundamental concepts, inadequate use of language and/or presentation, inability to solve the required questions.

18–23: Basic knowledge of essential content; limited presentation and connection skills; partial solution of the required questions, limited technical language proficiency.

24–27: Good mastery of the topics covered; clear presentation and adequate ability to make connections; correct resolution of most of the required questions, good technical language proficiency.

28–30 with honors: Complete mastery of the content; articulate and conscious presentation; critical ability to connect concepts; full resolution of the required questions, excellent mastery and appropriateness in using technical language.

Students with disabilities and/or Specific Learning Disabilities (DSA) must contact the professor, the CInAP representative of the DMI (Professor Daniele), and the CInAP in advance of the exam date to inform them that they intend to take the exam using appropriate compensatory measures.

1. Concept of Precision Medicine

2. Role of Biomarkers in Precision Medicine

3. Variant Calling

4. Predictive Algorithm for Reclassification of Clinical Variants

5. Analysis of a BAM File using IGV

6. Design of a Gene Panel for Target Regions

7. Diagnostic Algorithms

It is important to note that these questions are purely indicative: the actual questions asked during the exam may differ, potentially in a significant way, from those listed here.

Throughout the lectures, exercises similar to those encountered in the final exam will be provided. Additionally, further exercises will be made available during the course.