Back

Extending structural surfaceomics to identify aberrant conformations of tumor surface proteins as potential immunotherapy targets

Kishishita, A.; Cismoski, S.; Grant, T.; Deo, R.; Prudhvi, S.; Sue, C.; Barpanda, A.; Yu, C.; Shenoy, S.; Berman, S.; Reeves, A. G.; Li, H.; Liu, T.; Naik, A.; Biswas, D.; Jiao, F.; He, Y.; Hancock, M.; Dalal, R.; Zalevsky, A.; Hoopmann, M. R.; Ye, C. J.; Viner, R. I.; Feng, F.; Mandal, K.; Moritz, R. L.; Echeverria Riesco, I.; Sali, A.; Wells, J. A.; Srivastava, S.; Huang, L.; Wiita, A. P.

2026-05-18 cancer biology
10.64898/2026.05.15.721813 bioRxiv
Show abstract

The complement of tumor cell surface proteins, or "surfaceome", is a rich source of potential immunotherapy targets. To move beyond expression-based target discovery, we previously described "structural surfaceomics," combining crosslinking mass spectrometry (XL-MS) with surface protein biotinylation to identify conformation-selective targets. In our prior work, we applied this method to a single model of acute myeloid leukemia (AML), identifying active integrin beta-2 as a promising target. Here, we expand structural surfaceomics to identify additional immunotherapy targets and surface protein biology across additional models of AML, multiple myeloma, and prostate cancer, as well as donor peripheral blood mononuclear cells. Utilizing these models and different chemical crosslinkers, we compile an extensive database of 5,209 crosslinks. We characterize both shared and unique crosslink-based features, identifying 1,612 disease model-specific crosslinks, including 212 potentially defining tumor-specific conformations based on distance constraint violations relative to AlphaFold predictions. We further implement a suite of emerging modeling tools to predict tumor-specific protein structures. We probe crosslinking patterns suggesting multiple myeloma-specific CD48 and AML-specific integrin 1/{beta}4 heterodimer conformations. This work establishes a resource for cancer structural biology by implementation of structural surfaceomics. Our findings also point toward more realistic protein design models, potentially enabling systematic detection of targetable cancer-specific epitopes for next-generation immunotherapies.

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

1
Antibody Therapeutics
16 papers in training set
Top 0.1%
6.7%
2
Molecular & Cellular Proteomics
158 papers in training set
Top 0.4%
6.7%
3
eLife
5422 papers in training set
Top 15%
6.2%
4
Nature Communications
4913 papers in training set
Top 36%
4.2%
5
PLOS Computational Biology
1633 papers in training set
Top 10%
3.5%
6
Cell Reports Methods
141 papers in training set
Top 1.0%
3.5%
7
Cell Systems
167 papers in training set
Top 4%
3.5%
8
Genome Medicine
154 papers in training set
Top 2%
3.5%
9
Cell Reports
1338 papers in training set
Top 16%
3.5%
10
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 23%
3.0%
11
Journal of Proteome Research
215 papers in training set
Top 0.8%
3.0%
12
Scientific Reports
3102 papers in training set
Top 48%
2.3%
13
PROTEOMICS
35 papers in training set
Top 0.3%
2.3%
50% of probability mass above
14
Nature Methods
336 papers in training set
Top 4%
2.0%
15
PLOS ONE
4510 papers in training set
Top 50%
1.9%
16
Cell Reports Medicine
140 papers in training set
Top 4%
1.7%
17
Cancer Research
116 papers in training set
Top 2%
1.7%
18
iScience
1063 papers in training set
Top 18%
1.5%
19
Bioinformatics Advances
184 papers in training set
Top 3%
1.5%
20
Cell Genomics
162 papers in training set
Top 4%
1.3%
21
Bioinformatics
1061 papers in training set
Top 8%
1.3%
22
Cancer Research Communications
46 papers in training set
Top 0.6%
1.3%
23
Nucleic Acids Research
1128 papers in training set
Top 14%
1.2%
24
Cancer Cell
38 papers in training set
Top 1%
1.2%
25
Patterns
70 papers in training set
Top 2%
1.2%
26
Communications Biology
886 papers in training set
Top 19%
0.9%
27
Analytical Chemistry
205 papers in training set
Top 2%
0.8%
28
Molecular Biology of the Cell
272 papers in training set
Top 2%
0.8%
29
Computational and Structural Biotechnology Journal
216 papers in training set
Top 10%
0.7%
30
npj Systems Biology and Applications
99 papers in training set
Top 3%
0.7%