Proteomic profiling of antibody-inducing immunogens in tumor tissue identifies PSMA1, LAP3, ANXA3, and maspin as colon cancer markers
Bibliographic record
Abstract
// Qian Yang 1, 2 , Michael H. Roehrl 1, 2, 3 and Julia Y. Wang 1, 2, 4 1 Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada 2 Ontario Cancer Institute, University Health Network, Toronto, Canada 3 Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA 4 Curandis, Boston, MA, USA Correspondence to: Michael H. Roehrl, email: roehrlm@mskcc.org Julia Y. Wang, email: julia@wangresearchlab.org Keywords: colon cancer; antibody; immunogen; precision diagnostics; immunotherapy Abbreviations: ANXA3: annexin A3; IHC: immunohistochemistry; LAP3: leucine aminopeptidase 3; MS: mass spectrometry; PSMA1: proteasome subunit alpha type-1 Received: June 05, 2017 Accepted: June 27, 2017 Published: December 22, 2017 ABSTRACT We hypothesized that cancer tissue immunogens – antigens capable of inducing specific antibody production in patients – are promising targets for development of precision diagnostics and humoral immunotherapies. We developed an innovative immuno-proteomic strategy and identified new immunogenic markers of colon cancer. Proteins from cancers and matched normal tissues were separated by 2D gel electrophoresis and blotted with serum antibodies from the same patients. Antibody-reactive proteins were sequenced by mass spectrometry and validated by Western blotting and immunohistochemistry. 170 serum antibody-reactive proteins were identified only in cancerous but not matched normal. Among these, proteasome subunit alpha type 1 (PSA1), leucine aminopeptidase 3 (LAP3), annexin A3 (ANXA3), and maspin (serpin B5) were reproducibly found in tissues from three patients. Differential expression patterns were confirmed in samples from eight patients with various stages of colon adenocarcinoma and liver metastases. These tumor-resident proteins and/or their associated serum antibodies may be promising markers for colon cancer screening and early diagnosis. Furthermore, tumor tissue-specific antibodies could potentially be exploited as immunotherapeutic targets against cancer. More generally, proteomic profiling of antibody-inducing cancer-associated immunogens represents a powerful generic method for uncovering the tumor antigen-ome, i.e., the totality of immunogenic tumor-associated proteins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".