SCI120 Concordia University Data Analysis of Cancer Patients IntroductionIn this activity you will be working with data from REAL cancer cases. For each pa

SCI120 Concordia University Data Analysis of Cancer Patients IntroductionIn this activity you will be working with data from REAL cancer cases. For each patient, you’ll be provided with the genes that are contributing to their specific cancer. You’ll be asked to analyze this data in several different ways that will reveal the similarities, differences, and trends within cancer patients. It has been this sort of analysis that has change our approach to treating cancer, finally allowing us to turn the tide on this deadly condition. Locating Cancer-Causing Genes on the Human Chromosome Map
1
Oncogene
Cell Survival
Tumor Supressor Gene
Cell Fate
Genome Maintenance
2
ARID1A
MYCL1
MPL
CDKN2C
amp
MYCN
DNMT3A
del
amp
3
4
ALK
JAK1
VHL
MSH2
FUBP1
FGFR3
MLH1
MSH6
5
MYD88
SKP2
CTNNB1
NRAS
SETD2
NOTCH2
6
amp
HIST1H3B
PDGFRA
BAP1
KIT
PBRM1
7
CARD11
DAXX
MAP3K1
IKZF1
PIK3R1
TET2
SF3B1
NFE2L2
CDC73
MDM4
PRDM1
FOXL2
amp
H3F3A
10
9
MET
CASP8
CSF1R
IDH1
JAK2
CDKN2A
NPM1
11
12
LMO1
PAX5
amp
RET
CCND1
PTCH1
PTEN
ABL1
TSC1
amp
FLT3
ACVR1B
BRCA2
CBL
amp
MYC
EZH2
ARID1B
MLL3
14
MLL2
MDM2
ATM
FGFR2
NOTCH1
ARID2
MEN1
GNAQ
BRAF
15
KRAS
WT1
KLF4
13
HRAS
SMO
TNFAIP3
FBXW7
PIK3CA
GATA3
del
EGFR
APC
GATA2
8
16
AXIN1
TRAF7
NKX2-1
CREBBP
amp
SOCS1
B2M
RB1
PTPN11
TSHR
HNF1A
AKT1
CYLD
MAP2K1
CDH1
IDH2
X
CRLF2
BCOR
17
TP53
MAP2K4
NCOR1
KDM6A
18
19
del
STK11
BRCA1
SPOP
RNF43
SOX9
SRSF2
20
21
GNA11
NF1
ERBB2
GATA1
SETBP1
SMAD2
SMAD4
BCL2
DNMT1
ASXL1
JAK3
NCOA3
SMARCA4
CEBPA
CIC
PPP2R1A
GNAS
KDM5C
22
FAM123B
AR
MED12
ATRX
amp
RUNX1
U2AF1
Y
SMARCB1
NF2
EP300
STAG2
PHF6
CRLF2
amp
Lung Cancer
Gene
Patient LC1
Location
Type
Chr17
TS
CS
SETBP1 Chr18
O
CF
NF1
TP53
Chr17
Lung Cancer
Gene
Location
TS
Lung Cancer
Function Gene
Location
Type
Function
EGFR
Chr7
O
CS
MLL2
Chr12
TS
CF
CS
Patient LC3
Type
Patient LC2
Lung Cancer
Function Gene
Patient LC4
Location
Type
Function
CTNNB1 Chr3
O
CF
KIT
Chr4
O
CS
KRAS
Chr12
O
CS
MEN1
Chr11
TS
CF
NF2
Chr22
TS
CF
MLL3
Chr7
TS
CF
TP53
Chr17
TS
CS
TP53
Chr17
TS
CS
Breast Cancer
Gene
Patient BC1
Location
Type
BRCA1
Chr17
TS
GM
TP53
Chr17
TS
CS
Breast Cancer
Function Gene
Patient BC2
Location
Type
Function
CDH1
Chr16
TS
CF
PIK3CA
Chr3
O
CS
Breast Cancer
Gene
Patient BC3
Location
Type
ARID1B
Chr6
TS
CF
TP53
Chr17
TS
CS
Colorectal Cancer
Gene
APC
TP53
Location
Chr5
Chr17
Colorectal Cancer
Gene
APC
ATM
Location
Chr5
Chr11
Function Gene
Patient CC1
Type
TS
TS
TS
TS
Patient BC4
Location
Type
Function
FGFR2
Chr10
O
CS
GATA3
Chr10
TS
CF
Colorectal Cancer
Function Gene
Patient CC2
Location
Type
Function
KDM6A
ChrX
TS
CF
KRAS
Chr12
O
CS
PIK3CA
Chr3
O
CS
SMAD4
Chr18
TS
CS
CF
CS
Patient CC3
Type
Breast Cancer
Colorectal Cancer
Function Gene
CF
GM
Patient CC4
Location
Type
Function
BRAF
Chr7
O
CS
CARD11
Chr7
O
CS
GNAS
Chr20
O
CS
PIK3CA
Chr3
O
CS
SMAD4
Chr18
TS
CS
TP53
Chr17
TS
CS
Glioma
Gene
Patient G1
Location
Type
CIC
Chr19
TS
CS
IDH1
Chr2
O
CF
PIK3CA
Chr3
Glioma
Gene
ALK
O
Function Gene
Type
Chr2
O
TS
GM
BRCA1
Chr17
TS
GM
TP53
Chr17
TS
CS
Patient M1
Location
Type
BRAF
Chr7
O
CREBBP Chr16
EP300
Chr22
TS
TS
Type
Function
CBL
Chr11
O
CS
TP53
Chr17
TS
CS
Glioma
Patient G4
Location
Type
Function
HNF1A
Chr12
TS
CF
PTEN
Chr10
TS
CS
CS
Chr11
Gene
Location

Function Gene
ATM
Melanoma
Patient G2
CS
Patient G3
Location
Glioma
Melanoma
Function Gene
CS
Patient M2
Location
Type
Function
FGFR2
Chr10
O
CS
MLL3
Chr7
TS
CF
NRAS
Chr1
O
CS
Chr9
TS
CF
CF
CF CS
PTCH1
Melanoma
Patient M3
Melanoma
Gene
Location
Type
Location
Type
Function
BRAF
Chr7
O
CS
BRAF
Chr7
O
CS
APC
Chr5
TS
CF
CDKN2A Chr9
TS
CS
BCOR
ChrX
TS
CF
MLL3
Chr7
TS
CF
JAK3
Chr19
O
CS
ATM
Chr11
TS
GM
MLL2
Chr12
TS
CF
PAX5
Chr9
TS
CF
Hepatic Cancer
Gene
Function Gene
Patient HC1
Location
Type
ARID1A
Chr1
TS
CF
ARID2
Chr12
TS
CF
BRCA1
Chr17
TS
GM
RB1
Chr13
TS
CS
TP53
Chr17
TS
CS
Hepatic Cancer
Gene
HNF1A
TP53
Patient M4
Location
Chr12
Chr17
Function Gene
Patient HC3
Type
TS
TS
Hepatic Cancer
Patient HC2
Type
Function
CTNNB1 Chr3
O
CF
ChrX
O
CS
MED12
Location
Hepatic Cancer
Function Gene
Patient HC4
Location
Type
Function
ARID2
Chr12
TS
CF
AXIN1
Chr16
TS
CF
JAK2
Chr9
O
CS
TP53
Chr17
TS
CS
CF
CS
Pancreatic Cancer
Patient PC1
Gene
Location
Type
APC
Chr5
TS
CF
GNAS
Chr20
O
CF CS
Function Gene
KRAS
Chr12
O
CS
RNF43
Chr17
TS
CF
Pancreatic Cancer
Gene
Location
Patient PC3
Type
TS
CS
Chr12
O
CS
MPL
Chr1
O
CS
TP53
Chr17
TS
CS
Leukemia
Gene
Patient L1
Patient PC2
Location
Type
Function
KRAS
Chr12
O
CS
TRAF7
Chr16
TS
CS
TP53
Chr17
TS
CS
Pancreatic Cancer
Function Gene
CDKN2A Chr9
KRAS
Pancreatic Cancer
Patient PC4
Location
Type
Function
KRAS
Chr12
O
CS
SMAD4
Chr18
TS
CS
TP53
Chr17
TS
CS
Leukemia
Location
Type
Function Gene
MYD88
Chr3
O
CS
BCOR
SETD2
Chr3
TS
CF
Patient L2
Location
Type
Function
ChrX
TS
CF
NOTCH1 Chr9
TS
CF
Leukemia
Patient L3
Gene
Location
Type
BRAF
Chr7
O
CS
FAM123B ChrX
TS
CF
Chr12
O
CS
KRAS
Function Gene
Legend
Gene Classification
Oncogene
O
Tumor Suppressor Gene
TS
Leukemia
Function
Cell Fate
CF
Cell Survival
CS
Genome Maintenance
GM
Patient L4
Type
Function
NOTCH1 Chr9
TS
CF
PIK3CA
Chr3
O
CS
TP53
Chr17
TS
CS
Location
Finding the Patterns: Data Analysis of Cancer Patients
Introduction
In this activity you will be working with data from REAL cancer cases. For each patient,
you’ll be provided with the genes that are contributing to their specific cancer. You’ll be
asked to analyze this data in several different ways that will reveal the similarities,
differences, and trends within cancer patients. It has been this sort of analysis that has
change our approach to treating cancer, finally allowing us to turn the tide on this deadly
condition.
Goal
The primary goal of this lab is to help you understand how large data sets are distilled in
order to see underlying patterns in the biology. It may be somewhat tedious, but the
essence of scientific discovery is in the details. Often times, arranging the data in
different ways helps investigators see important trends and features of data.
In the Case Study Cards there are 32 cases with 8 different types of cancer (lung,
breast, colorectal, pancreatic, leukemia, glioma, melanoma, and hepatic). Choose 4 of
those cancer types and analyze the genetic mutations that are contributing to the
cancerous growth in each patient.
Cancers you are analyzing:
1)
2)
3)
4)
Part I – The Multiple Hit Hypothesis
Fill in the table below calculating the average number of mutations in your cancer
patients. First, do this by cancer type, then calculate an overall average.
Cancer Type
Average number of
Mutations per patient
Overall
Average
1.
2.
3.
4.
1. Is there any patient in your set that has a single mutation? What does that tell
you about cancer? Can we ever “blame” a single gene?
Part II – Gene Type and Address
Download and print the Chromosome Map from Blackboard. On it, you see all of the
genes that have been discovered to play a role in the progression of cancer (~140 total),
along with their chromosomal “address” (the chromosomal location at which the gene
can be found in all of us). Using the chromosome map provided, find the genes
associated with the cancer patients you’re working with. Highlight those genes, color
coding by whether or not the gene is an oncogene or a tumor suppressor. Take a
picture of your map and include it in your assignment. Answer the following
questions.
2. Using your color coded map as a reference, what can you say about the number
oncogenes vs. the number of tumor suppressors in cancer (i.e. do we see a lot
more oncogene mutations contributing to cancer or do we see more tumor
suppressors?)
3. Do all the genes associated with a specific cancer cluster onto a particular
chromosome or are they on different chromosomes?
4. According to the American Cancer Society, “nearly half of all men and a little
more than one-third of all women in the United States will have cancer during
their lifetimes.” In other words, more men get cancer than women. Provide a
hypothesis as to why that is that is based on the Chromosome Map. Hint: Look
at the X chromosome and think about chromosomal make up of men and
women.
Part III – Common Cancer-causing Mutations
5. Looking at each TYPE of cancer individually, are there any genes that show up in
more than 1 patient? Looking at ALL of the patients you’re working with, what is
the overall most common gene?
Cancer Type
Most Common Gene
Overall Most
Common Gene
1.
2.
3.
4.
6. Do a quick Google search on your overall most common gene. Briefly, what
does it do? DO NOT copy and paste. Use your own words. Understand what
the gene does.

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