Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.07.15.664871

Using machine learning to improve cancer diagnosis accuracy through genetic data analysis

Elzaghmouri, B. M.·Abo Zanoneh, M.·Mashakbah, F. F. A.·Ahmed, S. M. A.
View original source ↗Export metadata ↓

Abstract

Withdrawal StatementThe authors have withdrawn this manuscript because : 1- After careful consideration, my co-authors and I have decided to pursue significant revisions that fundamentally change the scope of the study. 2- Due to unforeseen circumstances, we are unable to proceed with the current version of the manuscript. 3- We have identified errors in the data analysis that require additional time to address. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

Explore related subjects

cancer biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Elzaghmouri, B. M., Abo Zanoneh, M., Mashakbah, F. F. A., Ahmed, S. M. A.. 2025-07-18. Using machine learning to improve cancer diagnosis accuracy through genetic data analysis. https://doi.org/10.1101/2025.07.15.664871

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Ex vivo human tumor slices more accurately predict patient responses to an oncolytic virus than in vivo mouse models

Immunotherapies, including oncolytic viruses (OV), are promising therapies that can enhance anti-tumor immune responses. However, preclinical success of immunotherapies in mouse models has not always translated to clinical benefit in cancer patients. This study compared preclinical efficacy and mechanism of action for ASP9801, a vaccinia virus expressing IL-7 and IL-12, using mouse models of colorectal cancer (CRC) in vivo and in human organotypic tumor slice models ex vivo. The murine surrogate for ASP9801 significantly reduced tumor volumes in treated and abscopal tumors in two different CRC models in vivo (MC38 and RO100). Treatment efficacy was accentuated when combined with anti-PD1 treatment, and single-cell RNA sequencing analysis revealed depletion of tumor cells and increased T cell infiltration and activation in both treated and abscopal tumors. However, human tissue analysis ex vivo (E-slices) using PDX models and patient samples showed that ASP9801 is not effective in CRC, consistent with clinical trial results. On the other hand, ASP9801 was highly effective in GBM, indicating indication-specific efficacy of ASP9801, and how E-slice assays can be used to identify treatment-sensitive indications. This study demonstrates the superiority of E-slices over mouse models for predicting clinical response and its utility in planning clinical trials.

cancer biology↗
bioRxiv2026

Immune-cell depleted diffuse large B-cell lymphomas have reduced expression of MHC class I

Immunotherapy has transformed treatment for many cancers. In the aggressive and genetically heterogeneous diffuse large B-cell lymphoma (DLBCL), CD19 CAR T-cell therapy is highly effective, whereas immune checkpoint blockade has shown limited benefit. Loss of MHC expression is a common mechanism to escape T-cell cytotoxicity, and loss of MHC class I (MHC-I) and II are frequent in DLBCL. We applied imaging mass cytometry to diagnostic biopsies from younger, high-risk DLBCL patients to map the tumor microenvironment (TME) spatial architecture in relation to tumor cell MHC expression, mutational status, transcriptomic and proteomic profiles. Neighborhood analyses identified four TME subtypes: immune-cell depleted and three immune-infiltrated types (mixed, CD4 T cell-rich, CD8 T-cell/macrophage-rich). Depleted cases had shorter overall survival (p = 0.033) and increased expression of proteins involved in DNA replication and proliferation markers compared to infiltrated cases. Tumor cell MHC-I expression was heterogeneous. Cases with low frequency of MHC-I-pos tumor cells were enriched for the depleted TME type. MHC-I-pos tumor cells were surrounded by CD4 and CD8 T cells and M1 macrophages, whereas MHC-I-neg tumor cells were closer to other MHC-I-neg tumor cells. These findings suggest that TME-based classification incorporating tumor cell MHC-I status may improve individualized immunotherapy selection.

cancer biology↗
bioRxiv2026

Cross-species analysis links cell-cell communication rewiring to NOTCH2 during serous endometrial carcinogenesis

Cell-cell interactions shape the fate of mutant cells during cancer initiation but how these interactions evolve during progression to pathologically recognizable lesions remain poorly understood. Here, we investigated cell-cell communication during serous endometrial carcinoma (SEC; also known as uterine serous carcinoma) development using a lineage-traceable mouse model and cross-species analyses of the mouse and human neoplastic endometrium. In mice, the early, pre-dysplastic stage was marked by a global decrease in inferred cell-cell interactions, followed by extensive communication network rewiring during neoplastic progression. Pathway-specific analysis revealed a similar pattern for NOTCH signaling, with NOTCH2 emerging as the dominant NOTCH receptor in Trp53/Rb1-mutant immature epithelial cells. Functionally, NOTCH2 promoted the outgrowth of more proliferative mutant organoids. Cross-species transcriptomic analysis identified conserved immature epithelial states in mouse and human neoplastic endometrial epithelium. In human tissues, NOTCH2 was overexpressed in serous endometrial intraepithelial carcinoma, a precursor of SEC, and in overt SEC. Furthermore, elevated NOTCH2 expression was associated with poor patient survival. These findings link cell-cell communication rewiring during experimental SEC development to conserved neoplastic epithelial states and identify NOTCH2 as an early marker and a potential target of disease interception.

cancer biology↗