COURSE UNIT TITLE

: CANCEROMICS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
TRO 6004 CANCEROMICS ELECTIVE 1 0 0 6

Offered By

Translational Oncology

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSOCIATE PROFESSOR GIZEM ÇALIBAŞI KOÇAL

Offered to

Translational Oncology
Translational Oncology

Course Objective

Omics technology for cancer detection and their application in modern cancer clinics, Omics technologies, their use in the field of cancer, organo-specific canceromics, cancer genomics, cancer proteomics, cancer metabolamik,cancer epigenomic, cancer lipidomics, glycobiology, cancer pharmacogenomics, functional genomics, functional proteomic, system biology and clinical use of canceromics will be discussed

Learning Outcomes of the Course Unit

1   Knowledgeable about omics and Acquire knowledge of use of omic biomarkers in cancer
2   Be able to know genomics, proteomics, pharmacogenomics, lipdomics, glicobiology methods
3   Be able to expresse to the computational oncology and coregulomics methods
4   Be able to expresse to the concept of system biology
5   Be able to critically evaluate the clinical use of canceromics
6   Be able to critically evaluates the published literature in the field of functional genomics, functional proteomic

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Omics approaches in cancer research
2 Genomics in Cancer (New generation sequencing technologies, Cancer Genome Atlas, Single-cell genomics)
3 Oncogenomics in Cancer: From Genes to Pathway Analysis
4 Pharmacogenomics: From Genes to Drug Responses
5 Epigenomics in Cancer
6 Transcriptomics in Cancer
7 Interactome profiling in cancer
8 Metabolomics and Lipidomcs in Cancer
9 Proteomics in Cancer
10 Phenomics in Cancer
11 System Biology in Cancer
12 Microbiome in Cancer
13 Canceromics and Individualized Treatments
14 Integration of Omics data with Machine Learning and Artificial Intelligence
15 Canceromics and Biobanks

Recomended or Required Reading

An Omics Perspective on Cancer Research. Springer. 2010 Editors: Cho, William Chi-Sing (Ed.)
Molecular Biology of the Cell, 6th Edition. Ed. Bruce Alberts, Alexander Johnson, Julian Lewis, Martin Raff, Keith Roberts, Peter Walter. Garland Publishers.
Essential Cell Biology - 4th Edition Bruce Alberts, Dennis Bray, Karen Hopkin, Alexander D Johnson, Julian Lewis, Martin Raff, Keith Roberts, Peter Walter Garland Publishers.
The Biology of Cancer, 2nd Edition, Robert A. Weinberg, Garland Publishers.
Molecular Biology of the Gene, 7th edition, James Watson, Alexander Gann, Tania Baker, Michael Levine, Stephan Bell, Richard Losick, Stephan Harrison Pearson Cold Spring harbor Laboratory Press.
Principles of Molecular Diagnostics and Personalized Cancer Medicine, Dongfeng Tan, henry Lynch, Wolters Kluwer, Lippincott Williams&Wilkins.
The Handbook of Biomarkers, Kewal Jain, Humana Press.
Translational and Experimental Clinical Research, Daniel Schuster, William Powers, Lippincott Williams&Wilkins.
Klinik Araştırmalar Kitabı 2014, Hamdi Akan, Hilal Ilbars, Nurşah Çetinkaya, Bilimsel Tıp Yayınevi.
Designing Clinical Research 4th Edition, Steven R Cummings , Warren S Browner , Deborah G Grady , Thomas B Newman , Dr Stephen B Hulley, Lippincott Williams&Wilkins.
Cammarota, G., Ianiro, G., Ahern, A. et al. Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nat Rev Gastroenterol Hepatol 17, 635-648 (2020). https://doi.org/10.1038/s41575-020-0327-3.
Epstein RJ, Lin FP. Cancer and the omics revolution. Aust Fam Physician. 2017;46(4):189-193. PMID: 28376570.
Gemma L D Adamo1, James T Widdop, Edward M Giles. The future is now Clinical and translational aspects of Omics technologies. Immunology and Cell Biology. 2020.

Planned Learning Activities and Teaching Methods

Lectures, the topics with classroom activities (presentations and homework assignments) to consolidate and discussion

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 PRF PERFORMANCE
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.20 +PRF * 0.20 + FIN* 0.60
5 RST RESIT
6 FCGR FINAL COURSE GRADE MTE * 0.20 +PRF * 0.20 +RST* 0.60


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

1. The student will clearly define outlined LO1, LO2, LO3, and LO4 which will evaluate with exam.
2. The student will recognize LO5, LO6 will evaluate with homework, prepared presentations by themselves and exam.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

gizem.calibasi@deu.edu.tr ; 0 232 412 58 35

Office Hours

Thursday 14:00-15:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 15 1 15
Preparations before/after weekly lectures 15 5 75
Preparation for midterm exam 1 8 8
Preparation for final exam 1 10 10
Preparing assignments 5 4 20
Preparing presentations 2 10 20
Final 1 1 1
Midterm 1 1 1
TOTAL WORKLOAD (hours) 150

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12
LO.15555555
LO.25555555
LO.35555555
LO.45555555
LO.55555555
LO.65555555