Biology subjects

Hasanov, E.

Publications and source records attributed to Hasanov, E..

3 recordsLinked to original sources

bioRxiv2026

OmniT2I: a unified table-to-image framework for survival prediction and cross-modal inference with incomplete multimodal cancer data

Molecular profiles and histopathology provide complementary information about cancer prognosis, but these data are often incomplete for individual patients. Here we present OmniT2I, a table-to-image framework for survival prediction and cross-modal inference from incomplete multimodal cancer data. Each molecular profile was converted into a three-channel image representation with correlation-based feature placement. OmniT2I combines available molecular and histopathology embeddings through availability-aware fusion and predicts missing modalities from those observed. Across 10,373 patients with 32 cancer types, the C-index was 0.631 within cancer types and 0.783 with all cancers pooled (0.660 and 0.787 in 5,605 patients with all five modalities). A single model handled all 31 modality combinations and, compared with nine survival methods trained separately for each, achieved higher C-indices in 240 of 279 pooled and 226 of 279 within-cancer comparisons. Adding predictions of withheld modalities to observed inputs improved pooled C-indices in all 180 pairings of observed and withheld modality sets and within-cancer C-indices in 164. Adding measured histopathology to RNA produced the largest within-cancer C-index gain among the four additional modalities evaluated, whereas adding copy number variation reduced it. In seven external cohorts, frozen RNA-only C-indices differed from test performance in the corresponding cancer type in TCGA by a median of -0.003 (range, -0.092 to +0.079). OmniT2I enables prognostic modeling with incomplete multimodal data.

bioinformatics↗

bioRxiv2026

Single-Cell Atlas of Renal Cell Carcinoma Brain Metastasis Uncovers Mechanisms of Immune Dysfunction and Resistance

Brain metastasis (BM) in renal cell carcinoma (RCC) remains poorly understood and often resistant to immune checkpoint inhibitors. We generated a large single-nucleus RNA-seq data of RCC BM, profiling 14 BM samples alongside matched extracranial metastases and primary tumors. Tumor cells in BM displayed neuronal infiltration, neural-like adaptation, and marked remodeling of the microenvironment, including expansion of immunosuppressive myeloid cells and depletion of antigen-presenting dendritic cells. Tumor, immune, and stromal cells exhibited metabolic rewiring characterized by fatty-acid metabolism, oxidative phosphorylation, and MYC-driven programs. CD8 T cells showed terminal exhaustion and impaired proliferative capacity, and tertiary lymphoid structures were absent. Spatial profiling of 12 BM samples (13,128 cells) validated key cellular interactions, while ligand-receptor analysis revealed immunoregulatory circuits between tumor, stromal, and immune cells. These findings define BM-specific adaptations that promote immune evasion and resistance, revealing therapeutic vulnerabilities in RCC BM. SIGNIFICANCESingle-nucleus RNA-sequencing profiling reveals tumor, immune, and metabolic adaptations in renal cell carcinoma (RCC) brain metastases, including neuroglial remodelling and immunosuppressive niche formation. These findings identify immune evasion mechanisms that could contribute to therapeutic resistance, providing new avenues for site-specific therapeutic interventions to improve treatment efficacy and outcomes in patients with RCC BM.

cancer biology↗

bioRxiv2026

Deciphering the Genomic Landscape of Renal Cell Carcinoma Brain Metastases

Brain metastases from renal cell carcinoma (RCC) remain a major cause of morbidity and mortality, yet the genomic features associated with metastatic dissemination remain poorly understood. Whole-exome sequencing was performed on 72 RCC brain metastasis samples with matched normal. To identify candidate genomic alterations associated with brain metastasis, the genomic alterations detected in the brain metastases were compared against alterations in extracranial metastases from the MSK-ECM cohort (n=137) and primary RCC tumors from TCGA (n=432) by case-control analyses. Candidate alterations were also identified through matched-pair analyses comparing brain metastases with matched primary tumors or extracranial metastases from the same patient (n=25). A random survival forest model incorporating the candidate CNA events was developed to predict overall survival. The candidate CNAs were further evaluated using functional experimental data from MetMap and DepMap. Survival analyses were conducted to assess the prognostic relevance of these alterations. We identified recurrent CNAs enriched in RCC brain metastases, including 4q loss, 7p gain, 7q gain, 8p loss, 8q gain, 9p21.3 deletion, 12q15 amplification, and 14q loss. These alterations were associated with significantly poorer patient survival among RCC patients. A random survival forest model based on these CNA events stratified TCGA-KIRC patients into prognostically distinct risk groups (C-index = 0.64). Among the recurrent CNAs, 8p loss, 8q gain, 9p21.3 deletion were associated with increased incidence of brain metastases across multiple primary cancer types in xenograft mouse models. These alterations were also strongly associated with metastatic progression and poor prognosis across RCC, lung adenocarcinoma, breast cancer, and melanoma. These findings indicate a shared genomic basis for brain tropism and highlight the potential utility of copy-number alterations as biomarkers for risk stratification and clinical decision-making.

genetics↗