Genome-Wide Functional Synergy between Amplified

and Mutated Genes in Human Breast Cancer

Ariel KaluzhnyMuniba Iqbal

Katherine Kyman

Amplicon-  the source or product of DNA or RNA amplification or replication events◦ Amplicome- set of all amplicons

Mutacon- the site of mutation◦ Mutacome- set of all mutacons

Synergy-  multiple elements in a system working together to produce an effect greater than the sum of their individual effects

GO process- a series of molecular events or functions with a defined beginning and end (often disrupted by mutant phenotypes)

Definitions

“multiple genetic events, including copy number gains and somatic mutations, are necessary for establishing the malignant cell phenotype”

Hypothesis

“to characterize copy number alterations in primary breast cancer carcinomas and to define functional interactions among genetically altered genes in breast cancer”

“to lay the groundwork for future translational studies exploring the potential therapeutic targeting of key regulators of the malignant cellular phenotype”

Reasons for Research

The most frequent amplicons were found on chromosomes 1q, 8p12, 8q24, 11q13, 12p13, 16p13, 17q11-q21, 17q22-q23, and 20q13◦ Nomenclature: p is the short arm, q is the long

arm and the numbers are the map units Previous studies show that breast tumor

subtypes correlate with the types and numbers of amplicons

Some information…

specific gene combinations are rarely the same in two different tumors, but “very few genes are mutated in a high fraction of tumors”

A single amplicon does not encode any processes and pathways, but is highly synergistic for encoding for genes with roles in tumorigenisis

Some information…

Collected from multiple universities and hospitals using protocols approved by the Institutional Review Boards

Only immunomagnetic bead purified or microdissected tumor samples

Snap frozen and stored at -80°C

Breast Tumor Samples

SNP Array Analysis Synthesis of 24-40

different oligomers of 25 bases per locus◦ There are 3-5 groups of

probes◦ Each group has:

a perfect match for one allelic state

A single-based mismatch for one allele

A perfect match for the other allelic state

A single-based mismatch for the second allele

Also has the complementary sequences for each of the above

http://www-microarrays.u-strasbg.fr/base.php?page=affySNPsE.php

Subtypes of tumors: Luminal, Basal-like (triple negative), and HER2 type◦The subtypes were based on the

expression of ER (Estrogen Receptors) and HER2

173 out of 191 samples had at least one gained chromosomal region or gene

Observed an enrichment of specific amplicons:◦ Luminal - 16p13◦ Basal-like - 8p11-12

and 8q◦ HER2+ - 17q

Total of 15,145 genes gained in at least one sample Narrowed it down to 1,747 genes that were amplified at

least five-fold in at least seven cases of the 191 samples◦ Organized into 30 amplicons distributed over 16 chromosomes

= amplicome Number of genes in individual amplicons ranged from 4 to

336 and are not equally distrubed throughout the chromosomes

Defining the Amplicome

Supplementary Figure 1

All the of the amplicons found had different outcomes in terms of protein functions◦ Encoded for transcription factors (TF), receptors,

secreted proteins, kinases, phosphates, proteases, and metabolic enzymes

◦ Ex: amplicon 7p15 was made up of TFs encompassing the HOXA homebox genes whereas amplicon 20p13 had enrichment in receptors

Some amplicons had unrelated TFs but included for similar function◦ Ex. 20q12-13

Amplicons are highly variable

Looked for 5 functional ontologies◦ GO processes, GO molecular functions, canonoical

pathway maps, cellular process networks, and disease biomarkers

The amplicons all encoded multiple pathways and processes without showing distinct specialization for them

Individual amplicons were highly synergistic for encoding for tumorigenisis

Individual amplicons are not self-sustaining functional entities

IGF-RI signaling canonocial pathway map composed of genes derived from 11 amplicons shows synergy in tumorigenisis

Figure 2

Supplementary Figure S5

Individual amplicons did not form concise networks◦ Calculated by comparison with the global

interactome which was used as a control◦ Only 6 of the 30 displayed any significant amount

of intraamplicon connectivity but was still significantly lower than the global human interactome

Most amplicons were regulated (received signal in) rather than regulating◦ Although four of them send more signal out

Individual amplicons are not self-sustaining functional entities

Used the Analyze Networks (AN) algorithms to further evaluate functional interactions among 1,360 amplified genes◦ AN Transciption Factors (ANTF) algorithm

JUN oncogene is the central hub although it wasn’t amplified itself but has amplified targets

◦ AN Receptors (ANR) algorithm Highest scored ANR subnetwork is centered at ESR1

nuclear hormone receptor (not amplified in this data set but had many direct targets of amplified genes)

Network analysis

Figure 3

Connectivity- protein interaction Intraconnections and interconnections of the

amplicons were evaluated compared with the expected number of connection based on the size of the data

Quantitative Analysis of Connectivity

Supplementary Figure 6

Quantitative Analysis of Connectivity

r= number of transcription interaction of the transcription factor in the set of interestR= number of regulation interactions of the transcription factor in the whole networkn= number of transcription targets in the sample of interestN= number of transcriptional targets in the global network

Largest number of interactions in the overconnected pair 8q12-q22 and 17q11-q21 (15 links) and the underconnected pair 8q23-q24 and 17q21-q25 (39 links)◦ c-Myc, a transcription fator, had the most outgoing

TR links in 8q23-q24 (which is the amplicon with the most outgoing interactions)

In general, interconnectivity was higher than intraconnectivity

Among 17 interactions tested, they were particularly enriched in transcription regulation (TR) links

Quantitative Analysis of Connectivity

Defined as the nonredundant union of 1,188 somatically mutated genes

Also analyzed 140 CAN (candidate cancer) genes that likely play a role in tumorigenesis

Mutated genes were randomly distributed through the genome (unlike the amplicons)

The Mutome

Only 94 genes were part of both the mutome and the amplicome◦ Statistically smaller than expected◦ Maybe because mutational analysis was conducted on

a smaller set of tumors◦ Maybe some gene categories are preferentially

affected by copy number gain versus mutation Mutome was closely interlinked with individual

amplicons More interactions were from the mutome to the

amplicome◦ Most prominent and statistically significant is 15q26

with 8 interactions from mutome to amplicome targets

Comparative analysis of the breast cancer amplicome and mutome

Figure S7. Interconnectivity between mutome and individual amplicons. The length of the line representing each amplicon reflects the number of genes in each amplicon. The thickness of arrows is proportional to the number of outgoing and incoming interactions between mutome and amplicons.

The mutome was enriched for TF’s and the amplicome for their targets◦ Applied Transcription Target Modeling (TTM) algorithm

to investigate Mutated genes were always upstream of the

amplified ones 23 mutated TFs were overconnected with

amplicome genes Seven of the CAN TFs had targets on amplified

genes◦ Mutated genes BRAC1 and HDAZ4 had the highest

number of interactions with mutated genes

Comparative analysis of the breast cancer amplicome and mutome

Both show higher connectivity than the global human interactome◦ Both had higher connectivity for outgoing links◦ CAN genes especially had many outgoing

interactions (2.6 times more than expected) Evaluated specific types of proteins

◦ Mutome had fewer ligands but more kinases and receptors

◦ Amplicome had fewer receptors◦ TFs similar in both

Connectivity of amplicome and mutome proteins

Most important trascription hub for the amplicome but underconnected with the mutome◦ Could be due to the distribution of ER+ or ER-

tumors within the amplicome and mutome Amplicome enriched in ESR1 target genes

but not binding sites

ESR1

Used enrichment analysis in the five functional ontologies

Both enriched for processes and genes involved in tumorigenesis

Mutome enriched for cell-adhesion, cell cycle, and DNA damage pathways◦ CAN genes were especially enriched in DNA damage

pathways◦ Suprisingly, 15 top disease markers were not cancer

causing except leukemia Amplicome enriched for developmental pathways

◦ Enriched for disease biomarkers of breast cancer and skin disease

Ontology Enrichment analysis

Analyzed synergy in ontology enrichment pathways to deterine if the mutome and amplicome were working cooperatively to change certain pathways or networks

Found synergy in some funtional ontologies canonoical pathways and processes networks◦ Suggest close interactions between mutated and

amplified genes

Synergy in ontology enrichment distributions

Figure 4

Some of the highest synergy found in pathways involving:• Cell adhesion• Cytoskeloton

remodeling• Cell Cycle

regulation of the G1-S checkpoint

Red rectangles are amplified genes and Blue boxes are mutated genes

The fact that the amplicome had more disease biomarkers for cancer suggests that gene amplification events are more significant in activating tumorigenenises than somatic mutations

Combination of multiple genetic alterations (both amplification and somatic mutation) necessary for multiple tumorigenetic signaling pathways in breast cancer

Further research can be done to explore therapeutic targeting of synergystic pathways

Conclusions

Is there selection for or against amplification in certain places in the genome? Is there something about the sequence or gene that allows amplification to occur?

Why do you think that the mutated genes were always found to be upstream of the amplified genes? What effect does this have on their roles in pathways?

Discussion

http://cancerres.aacrjournals.org/content/68/22/9532.full

http://cancerres.aacrjournals.org/content/68/22/9532/suppl/DC1

http://macf-web.dfci.harvard.edu/index.php?option=com_content&task=viewid=20&ltemid=88

http://media.affymetrix.com/support/technical/appnotes/microarrays_cancer_research_appnote.pdf

Sources