Back

Insertional mutagenesis defines drivers and evolutionary relationships in pancreatic cancer metastasis

Govindaraju, S.; Maurin, M.; Newberg, J. Y.; Jones, D. J.; Lee, H.; Markovitz, J. E.; Mann, M. B.; Black, M. A.; Mann, K. M.

2020-09-29 cancer biology
10.1101/2020.09.28.317701 bioRxiv
Show abstract

Metastasis is a defining feature of pancreatic cancer, impacting patient quality of life and therapeutic outcomes. While recurrent mutations in KRAS and TP53 play important roles in primary tumor development and persist in metastases, the contributions of other genes to the metastatic process is understudied. Here, we define a network of metastasis-promoting genes and uncover positively selected genes in metastatic lesions from our Sleeping Beauty mouse model of pancreatic cancer using our recently described SB Driver Analysis statistical pipeline. We show that loss of single genes DLG1, PARD3, PKP4 and PTPRK promote pancreas cancer progression in a context-specific manner. Finally, we define clonal and sub-clonal insertion events that distinguish primary and individual metastatic tumors and use single cell sequencing to gain insight into the evolutionary relationships between sub-regions of primary pancreatic tumors and related metastases, which has important implications for pancreatic disease progression and therapeutic opportunities for patients.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
eLife
● 5828 papers in training set
Top 6%
13.0%
Similar papers in this journal
2
Cell Reports
○ 1498 papers in training set
Top 2%
9.9%
Similar papers in this journal
3
Cancer Research
● 130 papers in training set
Top 0.3%
6.8%
Similar papers in this journal
4
Genes & Development
● 90 papers in training set
Top 0.1%
5.6%
Similar papers in this journal
5
Gastroenterology
○ 42 papers in training set
Top 0.2%
4.1%
Similar papers in this journal
6
Cancer Cell
○ 42 papers in training set
Top 0.4%
3.3%
7
Molecular Cancer Research
● 49 papers in training set
Top 0.3%
3.3%
8
Disease Models & Mechanisms
139 papers in training set
Top 0.6%
3.2%
9
Cancer Discovery
● 66 papers in training set
Top 0.9%
2.7%
50% of probability mass above
10
JCI Insight
● 277 papers in training set
Top 3%
2.5%
11
Cancer Research Communications
● 51 papers in training set
Top 0.6%
2.1%
12
The Journal of Pathology
○ 26 papers in training set
Top 0.2%
2.1%
13
Cellular and Molecular Gastroenterology and Hepatology
○ 46 papers in training set
Top 0.5%
2.1%
14
Genome Medicine
○ 183 papers in training set
Top 2%
1.9%
15
Nature Communications
○ 5641 papers in training set
Top 43%
1.9%
16
The American Journal of Pathology
○ 32 papers in training set
Top 0.3%
1.7%
17
Scientific Reports
○ 3612 papers in training set
Top 56%
1.5%
18
BMC Cancer
○ 67 papers in training set
Top 1%
1.4%
19
iScience
○ 1154 papers in training set
Top 22%
1.4%
20
Journal of Experimental Medicine
● 119 papers in training set
Top 2%
1.4%
21
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 32%
1.3%
22
Translational Oncology
○ 21 papers in training set
Top 0.5%
1.1%
23
Cell Reports Medicine
○ 153 papers in training set
Top 3%
1.1%
24
Gut
● 40 papers in training set
Top 0.6%
1.1%
25
Cancer Letters
○ 35 papers in training set
Top 0.8%
1.1%
26
NAR Cancer
◐ 37 papers in training set
Top 0.5%
1.1%
27
Oncogene
○ 85 papers in training set
Top 1%
1.1%
28
Communications Biology
○ 993 papers in training set
Top 26%
1.0%
29
Journal of Clinical Investigation
● 179 papers in training set
Top 6%
0.9%
30
Cancers
○ 213 papers in training set
Top 5%
0.9%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.