Nimra Qaisar

Leveraging AI and Spatial Transcriptomics in predicting Non-small Cell Lung Cancer (NSCLC) Treatment Response

Nimra Qaisar

This PhD aims to build robust survival models for non-small cell lung cancer using H&E histology images and spatial omics data.

Spatial transcriptomics offers unprecedented resolution into the tumour microenvironment, yet its clinical adoption remains limited by cost and accessibility. This project leverages routinely collected haematoxylin and eosin (H&E) histology images to predict spatial gene expression profiles in non-small cell lung cancer (NSCLC) and using these inferred transcriptomic signatures to build prediction models for outcomes such as overall survival and recurrence. A central challenge addressed in this work is the identification of prognostic features that exhibit stable association across different batch environments and cohorts, ensuring the generalisability and transferability of the models. By integrating computational pathology with multi-omics methods, this research aims to advance data-driven approaches to patient stratification and survival analysis in NSCLC.

Principal Supervisor’s name: A/Prof. Agus Salim
Co-supervisors’ names: Dr. Chin Wee Tan, Dr. James Dowty, Dr. Dharmesh Bhuva

Source of funding: Melbourne Research Scholarship