Back

Deep Visual Proteomics Unveils Precision Medicine Insights in Composite Small Lymphocytic and Classical Hodgkin Lymphoma

Mann, M.; Zheng, X.; Pedersen, L. M.; Bzorek, M.; Mund, A.; Gjerdrum, L. M. R.

2024-06-13 cancer biology
10.1101/2024.06.12.598635 bioRxiv
Show abstract

Coexistence of two cancer types in the same organ presents challenges for clinical decision-making, calling for personalized treatment strategies. Deep Visual Proteomics (DVP) combines AI driven single cell type analysis with laser microdissection and ultrasensitive mass spectrometry. In a composite case of classical Hodgkin lymphoma (cHL) and small lymphocytic lymphoma (SLL) in a single patient, we investigated the potential of DVP to inform precision oncology. We quantified the proteomic landscapes in the cHL and SLL to a depth of thousands of proteins. Our analysis revealed distinct proteome profiles in cHL and SLL populations, highlighting their clonal unrelatedness. Our data suggested standardized chemotherapy and interleukin-4 inhibition as potential strategies to manage chemo-resistance - instead of bone marrow transplantation. DVP highlighted minichromosome maintenance protein and proteasome inhibitors for cHL and H3K27 methylation and receptor tyrosine kinase inhibitors for SLL as subtype-specific treatments. Thus cell-type specific insights of DVP can guide personalized oncological treatments.

Matching journals

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

1
Blood Advances
● 62 papers in training set
Top 0.1%
13.1%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 20%
8.0%
Similar papers in this journal
3
Blood
● 74 papers in training set
Top 0.3%
6.4%
Similar papers in this journal
4
Cancer Research Communications
● 51 papers in training set
Top 0.1%
5.6%
Similar papers in this journal
5
Cancers
○ 213 papers in training set
Top 1%
4.4%
Similar papers in this journal
6
Cell Reports Medicine
○ 153 papers in training set
Top 0.6%
4.1%
7
npj Precision Oncology
○ 53 papers in training set
Top 0.4%
3.3%
8
eLife
● 5828 papers in training set
Top 34%
3.3%
9
HemaSphere
○ 16 papers in training set
Top 0.1%
2.5%
50% of probability mass above
10
Leukemia
○ 42 papers in training set
Top 0.4%
2.4%
11
Frontiers in Oncology
○ 103 papers in training set
Top 1%
2.4%
12
Blood Cancer Journal
○ 14 papers in training set
Top 0.1%
2.4%
13
eBioMedicine
○ 183 papers in training set
Top 2%
2.2%
14
Communications Biology
○ 993 papers in training set
Top 10%
2.2%
15
iScience
○ 1154 papers in training set
Top 18%
1.7%
16
Cell Reports
○ 1498 papers in training set
Top 21%
1.4%
17
Scientific Reports
○ 3612 papers in training set
Top 59%
1.4%
18
Oncogene
○ 85 papers in training set
Top 1%
1.4%
19
Molecular Cancer Research
● 49 papers in training set
Top 0.9%
1.1%
20
Cancer Research
● 130 papers in training set
Top 3%
1.1%
21
Journal of Experimental & Clinical Cancer Research
○ 25 papers in training set
Top 0.6%
1.1%
22
Laboratory Investigation
○ 13 papers in training set
Top 0.2%
1.1%
23
Clinical Cancer Research
● 64 papers in training set
Top 2%
1.1%
24
Science Advances
● 1243 papers in training set
Top 28%
1.0%
25
Molecular Oncology
○ 55 papers in training set
Top 1%
0.9%
26
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 40%
0.9%
27
Breast Cancer Research
○ 36 papers in training set
Top 0.6%
0.6%
28
EMBO Molecular Medicine
○ 95 papers in training set
Top 3%
0.6%
29
Journal of Clinical Investigation
● 179 papers in training set
Top 6%
0.6%
30
Neoplasia
○ 23 papers in training set
Top 0.9%
0.6%

"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.