Computational GenomicsModule Computational Approaches for Precision Medicine in Oncology
Academic Year 2025/2026 - Teacher: GABRIELLA PELLEGRITIExpected Learning Outcomes
SYLLABUS TEMPLATE – MASTER’S DEGREE PROGRAM LM18
Course Title: Computational Approaches for Precision Medicine in Oncology (6 ECTS)
Expected Learning Outcomes
Knowledge and Understanding
Students will acquire foundational knowledge of the main molecular mechanisms underlying oncogenesis, key aspects of cancer epidemiology (incidence, prevalence, risk factors, prevention, early diagnosis, and survival), and the role of genetic alterations and critical biomarkers in major cancer types (breast, lung, colorectal, gastric, biliary tract, thyroid, central nervous system, gynecological cancers, and sarcomas). The course will also illustrate the potential and limitations of selected technologies such as liquid biopsy and high-throughput analyses, as well as the role of molecular data in guiding personalized therapeutic decisions.
Applying Knowledge and Understanding
Students will be able to identify prognostic and predictive biomarkers specific to each tumor type, interpret results obtained from Next Generation Sequencing (NGS) and liquid biopsy techniques, and integrate clinical, pathological, and molecular data to prepare case reports and propose clinical trials in the field of precision oncology.
Making Judgements
Students will develop critical skills for assessing the quality and reliability of oncological datasets. They will also become familiar with weighing risks and benefits—particularly in relation to ethical considerations—when implementing molecular tests in clinical and research contexts.
Communication Skills
Students will learn to present complex results from molecular and computational analyses clearly and coherently to multidisciplinary audiences (biologists, clinicians, data scientists). They will be able to draft technical and scientific reports in accordance with international standards, and effectively communicate the clinical implications of novel biomarkers or liquid biopsy protocols through the preparation of posters, oral presentations, and manuscripts suitable for peer-reviewed journals.
Learning Skills
Students will demonstrate an autonomous and continuous learning approach, maintaining up-to-date knowledge of innovations in bioinformatics, sequencing technologies, and preclinical cancer models. They will be able to identify relevant scientific resources and datasets, actively participate in study groups and specialized seminars, and adapt their skills to emerging research projects and clinical trials in the field of precision medicine in oncology.
Course Structure
Teaching Methods
Lectures will be delivered in person in a traditional classroom setting. Theoretical content will be presented by the instructor with the support of slides and blackboard explanations.
Active student participation will be encouraged through questions, classroom discussions, the analysis of clinical case studies, and the reading of selected scientific literature.
Should the course be delivered in hybrid or online mode, the necessary adjustments will be introduced in order to ensure consistency with the planned program and syllabus.
Special Provisions
Students with disabilities and/or specific learning disorders (SLD) must contact the instructor, the CInAP representative of the Department of Mathematics and Computer Science (Prof. Daniele), and the CInAP office well in advance of the examination date to request appropriate compensatory measures.
Required Prerequisites
Prerequisites
Principles of biochemistry and clinical biochemistry
Principles of molecular and cellular biology
Principles of general and clinical pathology
Attendance of Lessons
Attendance
Regular attendance is strongly recommended, as it is essential for developing a thorough understanding of the topics covered and the methodologies presented.
Detailed Course Content
Course Content
The course Computational Approaches for Precision Medicine in Oncology integrates the fundamentals of molecular biology and cancer epidemiology with advanced computational methodologies for the analysis of clinical and molecular data. Following an overview of the general principles of different neoplasms—from breast and lung cancer to colorectal, gastric, biliary tract, thyroid, brain, and gynecological tumors, as well as sarcomas—the course provides a detailed description of the molecular alterations characteristic of each cancer type, along with the prognostic and predictive role of biomarkers used in clinical practice.
Special emphasis is placed on the practical application of liquid biopsy techniques, which allow dynamic monitoring of disease progression and the adjustment of therapeutic strategies. Each module includes the discussion of real clinical cases, where students will be required to identify therapeutic targets and design precision medicine clinical trials.
The overarching aim of the course is to train professionals capable of working within multidisciplinary teams and of translating complex molecular evidence into personalized clinical decisions.
Textbook Information
Reference Texts
A. Laganà, Computational Methods for Precision Oncology, Springer
B. Alberts, Molecular Biology of the Cell, 7th Edition, W.W. Norton & Company
R. A. Weinberg, The Biology of Cancer, 3rd Edition, W.W. Norton & Company
Abeloff’s Clinical Oncology, 6th Edition, Elsevier Health
Collegio degli Oncologi Medici Universitari, Manuale di Oncologia Medica, Edizioni Minerva Medica
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Course SchedulePrinciples of molecular biology of cancerPrinciples of cancer epidemiology and methodologyClinical trials in oncologyBreast cancerGeneral principlesMolecular alterations in breast carcinomaPrognostic and predictive biomarkersLiquid biopsy in breast cancerClinical case discussionLung cancerGeneral principlesMolecular alterations in lung carcinomaPrognostic and predictive biomarkersLiquid biopsy in lung cancerClinical case discussionColorectal cancerGeneral principlesMolecular alterations in colorectal carcinomaPrognostic and predictive biomarkersLiquid biopsy in colorectal cancerClinical case discussionGastric and biliary tract cancersGeneral principlesMolecular alterations in gastric and biliary tract carcinomaPrognostic and predictive biomarkersClinical case discussionThyroid tumorsGeneral principlesMolecular alterations in thyroid carcinomaPrognostic and predictive biomarkersClinical case discussionBrain tumorsGeneral principlesMolecular alterations in malignant gliomasPrognostic and predictive biomarkersClinical case discussionGynecological cancers and hereditary tumorsGeneral principles of hereditary cancersMolecular alterations in ovarian and endometrial cancersPrognostic and predictive biomarkers in ovarian and endometrial cancersPrinciples of oncogenetics and hereditary cancer syndromesClinical case discussionSoft tissue sarcomasGeneral principlesMolecular alterations in soft tissue sarcomasPrognostic and predictive biomarkersClinical case discussion |
Learning Assessment
Learning Assessment Procedures
Assessment of Learning
Assessment Methods
The examination is designed to thoroughly evaluate the student’s preparation, analytical skills, and ability to reason about the topics covered in the course.
Assessment will be conducted through a written test consisting of 32 multiple-choice questions, each with four options, only one of which is correct. Each correct answer is awarded 2 points, while each incorrect answer results in a deduction of 0.25 points.
Students who achieve a minimum passing grade of 18/30 in the written exam and wish to improve their result may take an oral examination. The oral exam may be held on the same day as the written test or within a few days thereafter.
Grading Criteria
Final grades will normally be assigned according to the following standards:
Fail: the student has not acquired the basic concepts and is unable to solve exercises.
18–23: the student demonstrates minimal mastery of basic concepts; their ability to present and connect topics is limited; able to solve only simple exercises.
24–27: the student shows good mastery of course content; demonstrates solid presentation and reasoning skills; solves exercises with only minor errors.
28–30 with honors: the student has fully acquired the course content and can present it comprehensively and critically; able to solve exercises completely and without errors.
Special Provisions
Students with disabilities and/or specific learning disorders (SLD) must contact the instructor, the CInAP representative for the Department of Mathematics and Computer Science (Prof. Daniele), and the CInAP office well in advance of the examination date to request appropriate compensatory measures.
Examples of frequently asked questions and / or exercises
Sample Questions and Exercises
1. In a patient with newly diagnosed advanced colorectal cancer, the choice of biological therapy to be used in combination with chemotherapy depends on:
A. PD-L1 expression level
B. HER2 amplification status
C. EGFR mutation status
D. KRAS, NRAS, and BRAF mutation status
2. A breast tumor with the following characteristics: ER 45%; PgR 0%; HER2 1+; Ki67 25% is classified as:
A. Luminal A
B. Luminal B
C. HER2-positive
D. Triple-negative