Humaira Noor

All Publications

• Response to anti-angiogenic therapy is associated with AIMP protein family expression in glioblastoma and lower-grade gliomas. Cancer research communications Noor, H., Zheng, Y., Itakura, H., Gevaert, O. 2025

  Abstract

  Glioblastoma (GBM) is a highly vascularized, heterogeneous tumor, yet anti-angiogenic therapies have yielded limited survival benefits. The lack of validated predictive biomarkers for treatment response stratification remains a major challenge. Aminoacyl tRNA synthetase complex-interacting multicomplex proteins (AIMPs) 1/2/3 have been implicated in CNS diseases, but their roles in gliomas remain unexplored. We investigated their association with angiogenesis and their significance as predictive biomarkers for anti-angiogenic treatment response. In this multi-cohort retrospective study we analyzed glioma samples from TCGA, CGGA, Rembrandt, Gravendeel, BELOB and REGOMA trials, and four single-cell transcriptomic datasets. Multi-omic analyses incorporated transcriptomic, epigenetic, and proteomic data. Kaplan-Meier and Cox proportional hazards models were used to assess the potential prognostic value of AIMPs in heterogeneous and homogeneous treatment-groups. Using single-cell transcriptomics, we explored spatial and cell-type-specific AIMP2 expression in GBM. AIMP1/2/3 expressions correlated significantly with angiogenesis across TCGA cancers. In gliomas, AIMPs were upregulated in tumor vs. normal tissues, higher- vs. lower-grade gliomas, and recurrent vs. primary tumors (p<0.05). Upon retrospective analysis of two clinical trials assessing different anti-angiogenic drugs, we found that high-AIMP2 subgroups had improved response to therapies in GBM (REGOMA: HR 4.75 [1.96-11.5], p<0.001; BELOB: HR 2.3 [1.17-4.49], p=0.015). AIMP2-cg04317940 methylation emerged as a clinically applicable stratification marker. Single-cell analysis revealed homogeneous AIMP2 expression in tumor tissues, particularly in AC-like cells, suggesting a mechanistic link to tumor angiogenesis. These findings provide novel insights into the role of AIMPs in angiogenesis, offering improved patient stratification and therapeutic outcomes in recurrent GBM.

  View details for DOI 10.1158/2767-9764.CRC-25-0170

  View details for PubMedID 40874786

• A 20-feature radiomic signature of triple-negative breast cancer identifies patients at high risk of death. NPJ breast cancer Noor, H., Zheng, Y., Mantz, A. B., Zhou, R., Kozlov, A., DeMartini, W. B., Chen, S. T., Okamoto, S., Ikeda, D. M., Telli, M. L., Kurian, A. W., Ford, J. M., Vinayak, S., Satoyoshi, M., Joshi, V., Mattonen, S. A., Lee, K., Gevaert, O., Sledge, G. W., Itakura, H. 2025; 11 (1): 79

  Abstract

  A substantial proportion of patients with non-metastatic triple-negative breast cancer (TNBC) experience disease progression and death despite treatment. However, no tool currently exists to discriminate those at higher risk of death. To identify high-risk TNBC, we conducted a retrospective analysis of 749 patients from two independent cohorts. We built a prediction model that leverages breast magnetic resonance imaging (MRI) features to predict risk groups based on a 50-gene Transcriptomics Signature (TS). The TS distinguished patients with high-risk for death in multivariate survival analysis (Transcriptomic cohort: [HR] = 13.6, 95% confidence interval [CI] = 1.56-1, p = 0.02; SCAN-B cohort: HR = 1.45, CI 1.04-2.03, p = 0.02). The model identified a 20-feature radiomic signature derived from breast MRI that predicted the TS-based risk groups. This imaging-based classifier was applied to a validation cohort (log rank p = 0.013, accuracy 0.72, AUC 0.71, F1 0.74, precision 0.67, and recall 0.82), detecting a 25% absolute survival difference between high- and low-risk groups after 5 years.

  View details for DOI 10.1038/s41523-025-00790-3

  View details for PubMedID 40715116

  View details for PubMedCentralID 8824427

• Brain tumor segmentation using deep learning: high performance with minimized MRI data. Frontiers in radiology Huang, J., Yagmurlu, B., Molleti, P., Lee, R., VanderPloeg, A., Noor, H., Bareja, R., Li, Y., Iv, M., Itakura, H. 2025; 5: 1616293

  Abstract

  Brain tumor segmentation with MRI is a challenging task, traditionally relying on manual delineation of regions-of-interest across multiple imaging sequences. However, this data-intensive approach is time-consuming. We aimed to optimize the process by using a deep learning (DL) based model while minimizing the number of MRI sequences required to segment gliomas.We trained a 3D U-Net DL model using the annotated 2018 MICCAI BraTS dataset (training dataset, n = 285), focusing on sub-segmenting enhancing tumor (ET) and tumor core (TC). We compared the performances of models trained on four different combinations of MRI sequences: T1C-only, FLAIR-only, T1C + FLAIR and T1 + T2 + T1C + FLAIR to evaluate whether a smaller MRI data subset could achieve comparable performance. We evaluated the performance on the four different sequence combinations using 5-fold cross-validation on the training dataset, then on our test dataset (n = 358) consisting of samples from a separately held-out 2018 BraTS validation set (n = 66) and 2021 BraTS datasets (n = 292). Dice scores on both cross-validation and test datasets were assessed to measure model performance.Dice scores on cross-validation showed that T1C + FLAIR (ET: 0.814, TC: 0.856) matched or outperformed those of T1 + T2 + T1C + FLAIR (ET: 0.785, TC: 0.841), T1C-only (ET: 0.781, TC: 0.852) and FLAIR-only (ET: 0.008, TC: 0.619). Results on the test dataset also showed that T1C + FLAIR (ET: 0.867, TC: 0.926) matched or outperformed those of T1 + T2 + T1C + FLAIR (ET: 0.835, TC: 0.908), T1C-only (ET: 0.726, TC: 0.928), and FLAIR-only (ET: 0.056, TC: 0.543). T1C + FLAIR excelled in both ET and TC, exceeding the performance of the four-sequence dataset. T1C-only matched T1C + FLAIR in TC performance. Similarly, T1C and T1C + FLAIR also outperformed in ET delineation by sensitivity (0.829) and Hausdorff distance (5.964) on the test set. Across all configurations, specificity remained high (≥0.958). T1C performed well in TC delineation (sensitivity: 0.737), but the inclusion of all sequences led to improvement (0.754). Hausdorff distances clustered in a narrow range (17.622-33.812) for TC delineation across the configurations.DL-based brain tumor segmentation can achieve high accuracy using only two MRI sequences (T1C + FLAIR). Reduction of multiple sequence dependency may enhance DL generalizability and dissemination in both clinical and research contexts. Our findings may ultimately help mitigate human labor intensity of a complex task integral to medical imaging analysis.

  View details for DOI 10.3389/fradi.2025.1616293

  View details for PubMedID 40697313

  View details for PubMedCentralID PMC12281592

• Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight. PLoS computational biology Nourbakhsh, M., Zheng, Y., Noor, H., Chen, H., Akhuli, S., Tiberti, M., Gevaert, O., Papaleo, E. 2025; 21 (4): e1012999

  Abstract

  Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data.

  View details for DOI 10.1371/journal.pcbi.1012999

  View details for PubMedID 40258059

• Response to anti-angiogenic therapy is affected by AIMP protein family activity in glioblastoma and lower-grade gliomas. bioRxiv : the preprint server for biology Noor, H., Zheng, Y., Itakura, H., Gevaert, O. 2025

  Abstract

  Glioblastoma (GBM) is a highly vascularized, heterogeneous tumor, yet anti-angiogenic therapies have yielded limited survival benefits. The lack of validated predictive biomarkers for treatment response stratification remains a major challenge. Aminoacyl tRNA synthetase complex-interacting multicomplex proteins (AIMPs) 1/2/3 have been implicated in CNS diseases, but their roles in gliomas remain unexplored. We investigated their association with angiogenesis and their significance as predictive biomarkers for anti-angiogenic treatment response.In this multi-cohort retrospective study we analyzed glioma samples from TCGA, CGGA, Rembrandt, Gravendeel, BELOB and REGOMA trials, and four single-cell transcriptomic datasets. Multi-omic analyses incorporated transcriptomic, epigenetic, and proteomic data. Kaplan-Meier and Cox proportional hazards models were used to assess the prognostic value of AIMPs in heterogeneous and homogeneous treatment-groups. Using single-cell transcriptomics, we explored spatial and cell-type-specific AIMP2 expression in GBM.AIMP1/2/3 expressions correlated significantly with angiogenesis across TCGA cancers. In gliomas, AIMPs were upregulated in tumor vs. normal tissues, higher- vs. lower-grade gliomas, and recurrent vs. primary tumors (p<0.05). Upon retrospective analysis of two clinical trials assessing different anti-angiogenic drugs, we found that high-AIMP2 subgroups had improved response to therapies in GBM (REGOMA: HR 4.75 [1.96-11.5], p<0.001; BELOB: HR 2.3 [1.17-4.49], p=0.015). AIMP2-cg04317940 methylation emerged as a clinically applicable stratification marker. Single-cell analysis revealed homogeneous AIMP2 expression in tumor tissues, particularly in AC-like cells, suggesting a mechanistic link to tumor angiogenesis.These findings provide novel insights into the role of AIMPs in angiogenesis, offering improved patient stratification and therapeutic outcomes in recurrent GBM.

  View details for DOI 10.1101/2025.03.13.643116

  View details for PubMedID 40161601

  View details for PubMedCentralID PMC11952521

• Digital profiling of gene expression from histology images with linearized attention. Nature communications Pizurica, M., Zheng, Y., Carrillo-Perez, F., Noor, H., Yao, W., Wohlfart, C., Vladimirova, A., Marchal, K., Gevaert, O. 2024; 15 (1): 9886

  Abstract

  Cancer is a heterogeneous disease requiring costly genetic profiling for better understanding and management. Recent advances in deep learning have enabled cost-effective predictions of genetic alterations from whole slide images (WSIs). While transformers have driven significant progress in non-medical domains, their application to WSIs lags behind due to high model complexity and limited dataset sizes. Here, we introduce SEQUOIA, a linearized transformer model that predicts cancer transcriptomic profiles from WSIs. SEQUOIA is developed using 7584 tumor samples across 16 cancer types, with its generalization capacity validated on two independent cohorts comprising 1368 tumors. Accurately predicted genes are associated with key cancer processes, including inflammatory response, cell cycles and metabolism. Further, we demonstrate the value of SEQUOIA in stratifying the risk of breast cancer recurrence and in resolving spatial gene expression at loco-regional levels. SEQUOIA hence deciphers clinically relevant information from WSIs, opening avenues for personalized cancer management.

  View details for DOI 10.1038/s41467-024-54182-5

  View details for PubMedID 39543087

• Digital profiling of cancer transcriptomes from histology images with grouped vision attention. bioRxiv : the preprint server for biology Zheng, Y., Pizurica, M., Carrillo-Perez, F., Noor, H., Yao, W., Wohlfart, C., Marchal, K., Vladimirova, A., Gevaert, O. 2023

  Abstract

  Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. RNA-sequencing has emerged as a potent tool to unravel the transcriptional heterogeneity. However, large-scale characterization of cancer transcriptomes is hindered by the limitations of costs and tissue accessibility. Here, we develop SEQUOIA , a deep learning model employing a transformer architecture to predict cancer transcriptomes from whole-slide histology images. We pre-train the model using data from 2,242 normal tissues, and the model is fine-tuned and evaluated in 4,218 tumor samples across nine cancer types. The results are further validated across two independent cohorts compromising 1,305 tumors. The highest performance was observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicted 13,798, 10,922 and 9,735 genes, respectively. The well predicted genes are associated with the regulation of inflammatory response, cell cycles and hypoxia-related metabolic pathways. Leveraging the well predicted genes, we develop a digital signature to predict the risk of recurrence in breast cancer. While the model is trained at the tissue-level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.

  View details for DOI 10.1101/2023.09.28.560068

  View details for PubMedID 37808782

• DLL3 expression and methylation are associated with lower-grade glioma immune microenvironment and prognosis. Genomics Noor, H., Whittaker, S., McDonald, K. L. 2022; 114 (2): 110289

  Abstract

  Notch signalling pathway, particularly its ligand delta-ligand 3 (DLL3), is important in glioma, however, little is known about DLL3 regulation and prognostic effects. Immunohistochemistry on a cohort of 163 gliomas revealed DLL3 upregulation in IDH1 mutant gliomas, where it was associated with a favourable prognosis (HR[95% CI]: 0.28[0.09-0.87]; p = 0.021). We investigated the epigenetic regulation of DLL3, and identified individual CpG sites correlating with DLL3 mRNA expression, which were significant prognostic markers in LGG. In silico analysis revealed that infiltrating immune cells significantly correlated with DLL3 expression, methylation and somatic copy number alterations. The prognostic effects of DLL3 expression was significantly affected by infiltration of immune cells. RNA Sequencing of 83 LGGs and GO Term analysis of differentially expressed genes showed that low DLL3 expression was related to ciliogenesis, which was confirmed by TCGA LGG analysis. Thus, DLL3 may play an important role in the immune microenvironment and prognosis of LGGs.

  View details for DOI 10.1016/j.ygeno.2022.110289

  View details for PubMedID 35124175

• PODNL1 Methylation Serves as a Prognostic Biomarker and Associates with Immune Cell Infiltration and Immune Checkpoint Blockade Response in Lower-Grade Glioma. International journal of molecular sciences Noor, H., Zaman, A., Teo, C., Sughrue, M. E. 2021; 22 (22)

  Abstract

  Lower-grade glioma (LGG) is a diffuse infiltrative tumor of the central nervous system, which lacks targeted therapy. We investigated the role of Podocan-like 1 (PODNL1) methylation in LGG clinical outcomes using the TCGA-LGG transcriptomics dataset. We identified four PODNL1 CpG sites, cg07425555, cg26969888, cg18547299, and cg24354933, which were associated with unfavorable overall survival (OS) and disease-free survival (DFS) in univariate and multivariate analysis after adjusting for age, gender, tumor-grade, and IDH1-mutation. In multivariate analysis, the OS and DFS hazard ratios ranged from 0.44 to 0.58 (p < 0.001) and 0.62 to 0.72 (p < 0.001), respectively, for the four PODNL1 CpGs. Enrichment analysis of differential gene and protein expression and analysis of 24 infiltrating immune cell types showed significantly increased infiltration in LGGs and its histological subtypes with low-methylation levels of the PODNL1 CpGs. High PODNL1 expression and low-methylation subgroups of the PODNL1 CpG sites were associated with significantly increased PD-L1, PD-1, and CTLA4 expressions. PODNL1 methylation may thus be a potential indicator of immune checkpoint blockade response, and serve as a biomarker for determining prognosis and immune subtypes in LGG.

  View details for DOI 10.3390/ijms222212572

  View details for PubMedID 34830454

  View details for PubMedCentralID PMC8625785

• TP53 Mutation Is a Prognostic Factor in Lower Grade Glioma and May Influence Chemotherapy Efficacy. Cancers Noor, H., Briggs, N. E., McDonald, K. L., Holst, J., Vittorio, O. 2021; 13 (21)

  Abstract

  Identification of prognostic biomarkers in cancers is a crucial step to improve overall survival (OS). Although mutations in tumour protein 53 (TP53) is prevalent in astrocytoma, the prognostic effects of TP53 mutation are unclear.In this retrospective study, we sequenced TP53 exons 1 to 10 in a cohort of 102 lower-grade glioma (LGG) subtypes and determined the prognostic effects of TP53 mutation in astrocytoma and oligodendroglioma. Publicly available datasets were analysed to confirm the findings.In astrocytoma, mutations in TP53 codon 273 were associated with a significantly increased OS compared to the TP53 wild-type (HR (95% CI): 0.169 (0.036-0.766), p = 0.021). Public datasets confirmed these findings. TP53 codon 273 mutant astrocytomas were significantly more chemosensitive than TP53 wild-type astrocytomas (HR (95% CI): 0.344 (0.13-0.88), p = 0.0148). Post-chemotherapy, a significant correlation between TP53 and YAP1 mRNA was found (p = 0.01). In O (6)-methylguanine methyltransferase (MGMT) unmethylated chemotherapy-treated astrocytoma, both TP53 codon 273 and YAP1 mRNA were significant prognostic markers. In oligodendroglioma, TP53 mutations were associated with significantly decreased OS.Based on these findings, we propose that certain TP53 mutant astrocytomas are chemosensitive through the involvement of YAP1, and we outline a potential mechanism. Thus, TP53 mutations may be key drivers of astrocytoma therapeutic efficacy and influence survival outcomes.

  View details for DOI 10.3390/cancers13215362

  View details for PubMedID 34771529

  View details for PubMedCentralID PMC8582451