Computational Genomics

Module Computational Approaches for Precision Medicine in Oncology

Knowledge and understanding: The student will acquire basic knowledge of the main molecular mechanisms involved in 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 the most common cancers (breast, lung, colorectal, stomach, biliary tract, thyroid, central nervous system, gynecological cancers, sarcomas). The potential and limitations of certain techniques such as liquid biopsy and high-throughput analyses, as well as the role of molecular data in personalized therapeutic decision-making, will also be illustrated.

Applying knowledge and understanding: The student will be able to identify prognostic and predictive biomarkers specific to each tumor type, understand results provided by next-generation sequencing (NGS) procedures and liquid biopsy techniques, and integrate clinical-pathological and molecular data for the preparation of case reports and the proposal of clinical trials in the field of precision medicine.

Making judgements: The student will develop critical skills to evaluate the quality and reliability of oncological datasets. They will also gain familiarity with weighing risks and benefits, with particular reference to ethical aspects, in the implementation of molecular tests in clinical and research contexts.

Communication skills: The student will be able to clearly and coherently present complex results of molecular and computational analyses to multidisciplinary audiences (biologists, clinicians, data scientists), draft technical and scientific reports in accordance with international standards, and effectively communicate the clinical implications of new biomarkers or liquid biopsy protocols through the preparation of posters, oral presentations, and manuscripts suitable for peer-reviewed journals.

Learning skills: The student will demonstrate an autonomous and continuous learning approach, with ongoing updates on 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 quickly adapt their skills to emerging research projects and clinical trials in the field of precision oncology.

Course Program

• Principles of molecular biology of cancer

• Principles of cancer epidemiology and methodology

• Clinical trials in oncology

Breast cancer:

• General principles

• Molecular alterations in breast carcinoma

• Prognostic and predictive biomarkers

• Liquid biopsy in breast carcinoma

• Clinical case discussion

Lung cancer:

• General principles

• Molecular alterations in lung carcinoma

• Prognostic and predictive biomarkers

• Liquid biopsy in lung carcinoma

• Clinical case discussion

Colorectal cancer:

• General principles

• Molecular alterations in colorectal carcinoma

• Prognostic and predictive biomarkers

• Liquid biopsy in colorectal carcinoma

• Clinical case discussion

Stomach and biliary tract cancers:

• General principles

• Molecular alterations in gastric and biliary tract carcinoma

• Prognostic and predictive biomarkers

• Clinical case discussion

Thyroid tumors:

• General principles

• Molecular alterations in thyroid carcinoma

• Prognostic and predictive biomarkers

• Clinical case discussion

Brain tumors:

• General principles

• Molecular alterations in malignant gliomas

• Prognostic and predictive biomarkers

• Clinical case discussion

Gynecological and hereditary cancers:

• General principles of hereditary cancers

• Molecular alterations in ovarian and endometrial cancers

• Prognostic and predictive biomarkers in ovarian and endometrial cancers

• Principles of oncogenetics and hereditary cancer syndromes

• Clinical case discussion

Soft tissue sarcomas:

• General principles

• Molecular alterations in soft tissue sarcomas

• Prognostic and predictive biomarkers

• Clinical case discussion

• 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

All lectures will be made available to students through the online portal.

The exam is designed to assess the student’s preparation, analytical skills, and reasoning ability regarding the topics covered during the course.

Assessment will be conducted through a written exam with multiple-choice questions. The exam will consist of 32 questions with 4 possible answers, only one of which is correct. Each correct answer is worth 2 points, while each wrong answer results in a penalty of -0.25 points.

Students who pass the written exam with a score of at least 18 and wish to improve their grade may take an oral exam. The oral exam may take place on the same day as the written test or within a few days.

Final grading criteria:

• Fail: the student has not acquired the basic concepts and is unable to solve the exercises.

• 18–23: the student demonstrates minimal mastery of the basic concepts, modest presentation and content-linking skills, and can solve only simple exercises.

• 24–27: the student demonstrates good mastery of course content, good presentation and linking skills, and solves exercises with few errors.

• 28–30 with honors: the student has acquired all course content, can present it comprehensively and critically, and solves exercises fully and without errors.

Students with disabilities and/or specific learning disorders (SLD) must contact, well in advance of the exam date, the instructor, the CInAP DMI contact person (Prof. Daniele), and the CInAP office to request appropriate compensatory measures.

To take part in the final exam, it is necessary to register through the SmartEdu portal. For any technical issues related to registration, students must contact the Teaching Office.

Examples of typical questions/exercises

In a patient with newly diagnosed advanced colorectal carcinoma, the choice of biological therapy to combine with chemotherapy depends on:
A. The level of PD-L1 expression
B. The presence of HER2 amplification
C. The mutational status of EGFR
D. The mutational status of KRAS, NRAS, and BRAF

A breast neoplasm with the following characteristics: ER 45%; PgR 0%; HER2 1+; Ki67 25%
A. Classified as Luminal A
B. Classified as Luminal B
C. Classified as HER2-positive
D. Classified as Triple-negative