Search bioRxiv⌕ Search

Biology subjects

Ahuja, M.

Publications and source records attributed to Ahuja, M..

2 recordsLinked to original sources

Neoadjuvant chemotherapy induces transient, regimen-specific immune stromal reprogramming in pancreatic ductal adenocarcinoma

Background: Neoadjuvant chemotherapy (NAC) is increasingly incorporated into the management of pancreatic ductal adenocarcinoma (PDAC), yet how treatment regimen and timing reshape the tumour-immune microenvironment remains poorly defined. Methods: Targeted immune transcriptomic profiling was performed using NanoString on spatially annotated tumour, stromal, and immune-enriched regions from resected PDAC specimens from 23 patients, including NAC-treated and treatment-naive cohorts. Key findings were validated and spatially localised using Xenium in situ transcriptomics. Results: NAC induced extensive transcriptional remodelling of the tumour microenvironment in a regimen- and time-dependent manner. Tumours resected shortly after FOLFIRINOX exhibited coordinated upregulation and strong spatial coupling of the CXCL12-CXCR4 axis. These correlations were attenuated or reversed at longer post-treatment intervals and following gemcitabine-based therapy. Disrupted stromal coordination of the CCL2-CCR2 axis further highlighted context-dependent effects of chemotherapy on chemokine signalling. Across regions, NT5E (CD73) expression inversely correlated with CD8A, consistent with spatially restricted T cell exclusion. Conclusions: NAC dynamically reprograms the immunestromal landscape of PDAC in a regimen- and timing-dependent manner, revealing a transient post-chemotherapy window of immune remodelling. The clinical significance of this phenomenon requires further investigation to determine whether temporally optimised integration of immunomodulatory and stroma-targeted therapies with NAC can improve patient outcomes.

cancer biology↗

Latent-Based Imputation of Laboratory Measures from Electronic Health Records: Case for Complex Diseases

Imputation is a key step in Electronic Health Records-mining as it can significantly affect the conclusions derived from the downstream analysis. There are three main categories that explain the missingness in clinical settings-incompleteness, inconsistency, and inaccuracy-and these can capture a variety of situations: the patient did not seek treatment, the health care provider did not enter the information, etc. We used EHR data from patients diagnosed with Inflammatory Bowel Disease from Geisinger Health System to design a novel imputation that focuses on a complex phenotype. Our approach is based on latent-based analysis integrated with clustering to group patients based on their comorbidities before imputation. IBD is a chronic illness of unclear etiology and without a complete cure. We have taken advantage of the complexity of IBD to pre-process the EHR data of 10,498 IBD patients and show that imputation can be improved using shared latent comorbidities. The R code and sample simulated input data will be available at a future time.

bioinformatics↗