Preclinical Analysis of JAA-F11, a Specific Anti–Thomsen-Friedenreich Antibody via Immunohistochemistry and In Vivo Imaging
Bibliographic record
Abstract
The tumor specificity of JAA-F11, a novel monoclonal antibody specific for the Thomsen-Friedenreich cancer antigen (TF-Ag-alpha linked), has been comprehensively studied by in vitro immunohistochemical (IHC) staining of human tumor and normal tissue microarrays and in vivo biodistribution and imaging by micro-positron emission tomography imaging in breast and lung tumor models in mice. The IHC analysis detailed herein is the comprehensive biological analysis of the tumor specificity of JAA-F11 antibody performed as JAA-F11 is progressing towards preclinical safety testing and clinical trials. Wide tumor reactivity of JAA-F11, relative to the matched mouse IgG 3 (control), was observed in 85% of 1269 cases of breast, lung, prostate, colon, bladder, and ovarian cancer. Staining on tissues from breast cancer cases was similar regardless of hormonal or Her2 status, and this is particularly important in finding a target on the currently untargetable triple-negative breast cancer subtype. Humanization of JAA-F11 was recently carried out as explained in a companion paper “Humanization of JAA-F11, a Highly Specific Anti–Thomsen-Friedenreich Pancarcinoma Antibody and In Vitro Efficacy Analysis” ( Neoplasia 19: 716-733, 2017), and it was confirmed that humanization did not affect chemical specificity. IHC studies with humanized JAA-F11 showed similar binding to human breast tumor tissues. In vivo imaging and biodistribution studies in a mouse syngeneic breast cancer model and in a mouse-human xenograft lung cancer model with humanized 124 I- JAA-F11 construct confirmed in vitro tumor reactivity and specificity. In conclusion, the tumor reactivity of JAA-F11 supports the continued development of JAA-F11 as a targeted cancer therapeutic for multiple cancers, including those with unmet need.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".