Biology subjects

Saghapour, E.

Publications and source records attributed to Saghapour, E..

3 recordsLinked to original sources

bioRxiv2026

Induced ERBB response and standing FAK dependency nominate separable KRAS-combination hypotheses in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is driven by oncogenic KRAS in roughly 90% of cases, and KRAS-pathway inhibition has finally become clinically active. Durable benefit, however, will require identifying the adaptive and baseline vulnerabilities that shape response to KRAS inhibition. Two resistance mechanisms have been proposed separately in the literature -- receptor-tyrosine-kinase bypass of KRAS, and dependence on the adhesion kinase FAK -- but whether they are one target class or two, and which should partner a KRAS inhibitor, is unresolved. We integrate public perturbation, dependency, and survival data to nominate them as mechanistically separable candidate combination partners. Two findings define the separation. First, KRAS loss increases ERBB2/3 receptor expression. This appeared in both an inducible genetic KRAS-extinction model and, independently, in five PDAC lines treated with pharmacological KRAS-G12C/D inhibitors, while MAPK output collapsed as expected. The signal was clearest for ERBB2 and in the genetic model; in the small pharmacological cohort the effect was modest and its confidence intervals crossed zero, so we treat ERBB2/3 up-regulation as a candidate adaptive response -- ERBB2-dominant and ERBB3-compatible -- not a proven resistance mechanism. Second, focal adhesion kinase (FAK/PTK2) is the top-ranked standing druggable dependency within the KRAS/Src/RTK/adhesion network we examined (essential in 58% of pancreatic lines), yet it is not induced by KRAS shutdown. FAK dependency is present at baseline and, in DepMap, is statistically independent of a lines KRAS dependency (Spearman {rho} = +0.05, n.s.) -- a genuinely standing vulnerability rather than a KRAS-rebound effect. The candidate adaptive response and the standing dependency are not positively co-regulated across the perturbed lines (pooled Spearman {rho} = -0.43, but n = 8 and n.s., so this cannot by itself establish independence); we therefore treat them as separable on mechanistic grounds -- each nominated by different data and engaged by a different drug -- rather than as statistically demonstrated independent programs. A Src-centered signaling-landscape analysis associates patient prognosis with the coordinated invasion-and-RTK program these nodes organize, rather than with any single transcript; this program remains prognostic after adjustment for a conventional EMT/stromal signature, which does not (Src-neighborhood per-standard-deviation OS hazard ratio 1.9, p {approx} 3 x 10-; EMT signature null on adjustment). Together these results motivate a concrete, testable hypothesis: that FAK inhibition (a standing dependency) and ERBB inhibition (a candidate induced adaptive response) are separable candidate partners for a KRAS inhibitor, best evaluated as distinct arms of a biomarker-stratified platform. They also clarify why single-agent Src inhibition -- a non-oncogene dependency tested as monotherapy, without a KRAS backbone, in advanced rather than micro-metastatic disease -- was not positioned to surface either mechanism. No protein-level, phospho-signaling, or combination-response validation is performed here; all findings are computational nominations that require experimental validation before any clinical inference. HighlightsO_LIKRAS inhibition is associated with an induced ERBB2/3 up-regulation -- ERBB2-dominant, ERBB3-compatible -- directionally reproduced across genetic KRAS extinction and pharmacological KRAS-G12C/D inhibition; the pharmacological effect is modest and underpowered C_LIO_LIGenome-wide dependency nominates FAK -- essential in 58% of pancreatic lines -- as the top-ranked standing candidate co-target within the KRAS network; FAK dependency is present at baseline, statistically independent of KRAS dependency, and not co-regulated with the induced ERBB response C_LIO_LIPatient prognosis associates with a Src-organized invasion-and-RTK program, not with SRC, KRAS, or any single-gene transcript, and this program stays prognostic after adjustment for a conventional EMT/stromal signature C_LIO_LIThe two mechanisms are separable on mechanistic grounds -- nominated by different data and not positively co-regulated (though the direct correlation is underpowered, n = 8, n.s.) -- motivating a multi-arm platform that could test FAK and ERBB partner arms as distinct hypotheses rather than one bundled combination C_LI In briefBlocking KRAS in pancreatic cancer is now clinically feasible, but resistance is the obstacle. Using only public data, Chen and colleagues nominate two mechanistically separable candidate combination partners for KRAS inhibitors: a candidate ERBB2-dominant adaptive (putative escape) response that is induced when KRAS is blocked, and FAK, the top-ranked standing dependency in the KRAS network -- present at baseline and independent of a tumors KRAS dependency. Because the two are nominated by different data and are not positively co-regulated, they argue for a multi-arm KRAS-combination trial that tests each as a separate hypothesis -- and they explain why the earlier single-agent Src trials, run without a KRAS backbone and in the wrong disease setting, were not positioned to detect either. The findings are computational nominations that require experimental validation.

cancer biology↗

bioRxiv2026

MondrianMap: Navigating Gene Set Hierarchies with Multi-Resolution Enrichment Maps

Gene Ontology encodes genes as a hierarchy, yet every enrichment visualization flattens it into a ranked list, discarding the ability to view the same process at different levels of abstraction. We present MondrianMap, a free interactive web application (https://mondrianmap.smartdrugdiscovery.org/) that organizes enrichment results into 13 semantically principled layers derived from the GOALS framework and renders them as color-encoded rectangular maps where block area reflects significance, color encodes effect direction, and spatial proximity preserves semantic relations, all within an interactive interface. Three case studies across the NIH Common Fund Data Ecosystem demonstrate that visualization facilitates recognition of patterns that are difficult to discern in flat outputs: (1) LINCS CRISPR perturbations reveal that TP53 and KRAS knockouts produce opposite color maps at a single semantic layer, the same immune recruitment processes suppressed by TP53 loss are activated by KRAS disruption; (2) GTEx aging signatures expose the inflammaging paradox as an immediate visual phenomenon, identical antimicrobial defense programs appear uniformly upregulated in aging blood yet uniformly downregulated in aging liver at matched semantic resolution; and (3) MoTrPAC exercise data capture temporal dynamics as color transitions where brown adipose tissue undergoes a threshold switch from two enriched terms to thirty-four at a single molecular layer, while cardiac tissue reverses from uniformly activated to uniformly suppressed glycolytic metabolism as adaptation progresses. MondrianMap facilitates hierarchical visual reasoning, complementing statistical enrichment reporting for biological discovery. HIGHLIGHTSO_LIMondrianMap provides multi-resolution enrichment visualization for Gene Ontology C_LIO_LILayer-specific views reveal directional oppositions difficult to discern in flat term lists C_LIO_LINavigating layers organizes one enrichment into a multi-scale biological narrative C_LIO_LIDemonstrated on cancer, aging, and exercise data across three CFDE programs C_LI IN BRIEFMondrianMap is a web application that transforms gene set enrichment results into layered visualizations encoding regulation, significance, and semantic hierarchy. Across cancer, aging, and exercise datasets, viewing enrichment at defined semantic layers exposes directional inversions, temporal switches, and multi-scale biological narratives that are difficult to extract from conventional flat enrichment outputs. THE BIGGER PICTUREWhen researchers measure gene expression changes in disease, aging, or drug response, they rely on enrichment analysis to translate thousands of molecular measurements into interpretable biological themes. The standard output is a ranked list of processes sorted by statistical significance. This format served the field well when studies examined a single condition; however, modern genomics routinely compares dozens of tissues, timepoints, and perturbations, each generating hundreds of enriched terms. The critical limitation is not statistical power; however, interpretive structure: a flat list cannot show whether two conditions activate the same biological process in opposite directions, whether a process visible at one level of abstraction disappears or transforms at another, or how a tissues functional response evolves across time. These are precisely the questions that define contemporary systems biology. Does a tumor suppressor gene silence the same immune program that an oncogene activates? Does aging drive the same defense pathway upward in the blood and downward in the liver? Does an exercise response flip from activation to suppression as the tissue adapts? Answering these questions requires a visualization framework that preserves hierarchy, encodes direction, and enables comparison at matched levels of biological resolution. MondrianMap provides this framework by organizing Gene Ontology terms into quantitatively defined semantic layers and rendering enrichment as color-encoded rectangular maps navigable from molecular mechanism to system-level theme. The result is a tool that extends enrichment analysis from a reporting step into an interactive framework for biological reasoning, hypothesis generation, and cross-dataset discovery.

Systems Biology↗

bioRxiv2025

LlamaAffinity: A Predictive Antibody Antigen Binding Model Integrating Antibody Sequences with Llama3 Backbone Architecture

Antibody-facilitated immune responses are central to the bodys defense against pathogens, viruses, and other foreign invaders. The ability of antibodies to specifically bind and neutralize antigens is vital for maintaining immunity. Over the past few decades, bioengineering advancements have significantly accelerated therapeutic antibody development. These antibody-derived drugs have shown remarkable efficacy, particularly in treating Cancer, SARS-Cov-2, autoimmune disorders, and infectious diseases. Traditionally, experimental methods for affinity measurement have been time-consuming and expensive. With the realm of Artificial Intelligence, in silico medicine has revolutionized; recent developments in machine learning, particularly the use of large language models (LLMs) for representing antibodies, have opened up new avenues for AI-based designing and improving affinity prediction. Herein, we present an advanced antibody-antigen binding affinity prediction model (LlamaAffinity), leveraging an open-source Llama 3 backbone and antibody sequence data employed from the Observed Antibody Space (OAS) database. The proposed approach significantly improved over existing state-of-the-art (SOTA) approaches (AntiFormer, AntiBERTa, AntiBERTy) across multiple evaluation metrics. Specifically, the model achieved an accuracy of 0.9640, an F1-score of 0.9643, a precision of 0.9702, a recall of 0.9586, and an AUC-ROC of 0.9936. Moreover, this strategy unveiled higher computational efficiency, with a five-fold average cumulative training time of only 0.46 hours, significantly lower than previous studies. LlamaAffinity defines a new benchmark for antibody-antigen binding affinity prediction, achieving advanced performance in the immunotherapies and immunoinformatics field. Furthermore, it can effectively assess binding affinities following novel antibody design, accelerating the discovery and optimization of therapeutic candidates.

bioinformatics↗