Combining imaging- and gene-based hypoxia biomarkers in cervical cancer improves prediction of treatment failure independent of intratumor heterogeneity
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PROVIDER: PRJNA609323 | ENA |
REPOSITORIES: ENA
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Project description:Emerging biomarkers based on medical images and molecular characterization of tumor biopsies open up for combining the two disciplines and exploiting their synergy in treatment planning. We compared pretreatment classification of cervical cancer patients by two previously validated imaging- and gene-based hypoxia biomarkers, evaluated the influence of intratumor heterogeneity, and investigated the benefit of combining them in prediction of treatment failure. The imaging-based biomarker was hypoxic fraction, determined from diagnostic dynamic contrast enhanced (DCE)-MR images. The gene-based biomarker was a hypoxia gene expression signature determined from tumor biopsies. Paired data were available for 118 patients. Intratumor heterogeneity was assessed by variance analysis of MR images and multiple biopsies from the same tumor. The two biomarkers were combined using a dimension-reduction procedure. The biomarkers classified 75% of the tumors with the same hypoxia status. Both intratumor heterogeneity and distribution pattern of hypoxia from imaging were unrelated to inconsistent classification by the two biomarkers, and the hypoxia status of the slice covering the biopsy region was representative of the whole tumor. Hypoxia by genes was independent on tumor cell fraction and showed minor heterogeneity across multiple biopsies in 9 tumors. This suggested that the two biomarkers could contain complementary biological information. Combination of the biomarkers into a composite score led to improved prediction of treatment failure (HR:7.3) compared to imaging (HR:3.8) and genes (HR:3.0) and prognostic impact in multivariate analysis with clinical variables. In conclusion, combining imaging- and gene-based biomarkers enables more precise and informative assessment of hypoxia-related treatment resistance in cervical cancer, independent of intratumor heterogeneity.
2020-06-26 | GSE146114 | GEO
Project description:To explore intratumor heterogeneity in gene expression profiles from patients with cervical cancer. Experimental Design: A total of 33 biopsies were obtained from 11 patients, sampling between two and five different areas for each tumor.The extracted RNA was hybridized onto the Affymetrix U133 Plus 2.0 oligonucleotide chip. The variance of expression within a patient (W), between patients (B) and the total variance (T =W + B) were calculated for each ProbeSet, and the ratio W/T was used as a measure of intratumor heterogeneity. Gene Ontology functional analysis was done to assess the function of genes that had high W/T (top 10%) and low W/T (bottom 10%) values.
2007-11-11 | E-GEOD-5787 | biostudies-arrayexpress
Project description:Multi-omics Prediction from High-content Cellular Imaging with Deep Learning
| PRJNA995437 | ENA
Project description:Spatially resolved characterization of proteoforms has great potential to significantly advance our understanding of physiological and disease mechanisms. However, it faces challenges regarding throughput and coverage. Herein, we developed a robust method for high-throughput proteoform imaging (HTPi) by combining matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) and region-specific top-down proteomic analysis.
2024-12-03 | PXD058552 |
Project description:High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and function. However, the biological determinants through which changes in multi-omics measurements influence cellular morphology have not yet been systematically explored, and the degree to which cell imaging could potentially enable the prediction of multi-omics directly from cell imaging data is therefore currently unclear. Here, we address the question of whether it is possible to predict bulk multi-omics measurements directly from cell images using Image2Omics -- a deep learning approach that predicts multi-omics in a cell population directly from high-content images stained with multiplexed fluorescent dyes. We perform an experimental evaluation in gene-edited macrophages derived from human induced pluripotent stem cell (hiPSC) under multiple stimulation conditions and demonstrate that Image2Omics achieves significantly better performance in predicting transcriptomics and proteomics measurements directly from cell images than predictors based on the mean observed training set abundance. We observed significant predictability of abundances for 5903 (22.43%; 95% CI: 8.77%, 38.88%) and 5819 (22.11%; 95% CI: 10.40%, 38.08%) transcripts out of 26137 in M1 and M2-stimulated macrophages respectively and for 1933 (38.77%; 95% CI: 36.94%, 39.85%) and 2055 (41.22%; 95% CI: 39.31%, 42.42%) proteins out of 4986 in M1 and M2-stimulated macrophages respectively. Our results show that some transcript and protein abundances are predictable from cell imaging and that cell imaging may potentially, in some settings and depending on the mechanisms of interest and desired performance threshold, even be a scalable and resource-efficient substitute for multi-omics measurements.
2025-07-15 | GSE237485 | GEO
Project description:Cervical cancer (CC) remains a significant public health issue in low- and middle-income countries (LMICs), especially in Western sub-Saharan Africa and Nigeria. While global CC incidence and mortality have declined, these regions continue to face high rates due to inadequate screening and the high prevalence of HIV, which increases CC risk by promoting persistent HPV infections. This study aimed to identify DNA methylation (DNAm) biomarkers for cervical intraepithelial neoplasia (CIN) and CC in HIV-positive Nigerian women and to assess their potential for clinical risk prediction. From 2018 to 2020, 538 participants were recruited from Nigerian tertiary hospitals. Cervical tissue samples were analyzed for DNAm using the Infinium MethylationEPIC BeadChip array, and HPV genotyping was conducted via next-generation sequencing. An epigenome-wide association study revealed 24 significant DNAm biomarkers associated with CIN and CC. These biomarkers showed hypermethylation in tumor suppressor genes (e.g., PRMD8), hypomethylation in oncogenes (e.g., MIR520H), and aberrant methylation in genes related to HIV/HPV infection and oncogenesis (e.g., GNB5, LMO4, FOXK2, NMT1). A machine learning-based DNAm classifier achieved 92.9% sensitivity and 88.6% specificity in predicting CC risk, with higher risk observed in adjacent normal cervical samples from CIN/CC patients and HIV/HPV co-infected women. DNAm biomarkers offer a promising approach to enhancing CC screening and early detection, particularly for HIV-positive women in LMICs. The DNAm-based model developed in this study shows potential for more accurate CC risk stratification, highlighting the need for further optimization, validation, and implementation in low-resource settings.
2024-11-01 | GSE279982 | GEO
Project description:Tumor hypoxia levels range from mild to severe and have different biological and therapeutical consequences, but are not easily assessable in patients. We present a method based on diagnostic dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) that visualizes a continuous range of hypoxia levels in tumors of cervical cancer patients. Hypoxia images were generated using an established approach based on pixel-wise combination of the DCE-MRI parameters e and Ktrans, reflecting oxygen consumption and supply, respectively. An algorithm to retrieve hypoxia levels from the images was developed and validated in 28 xenograft tumors, by comparing the MRI-defined levels with hypoxia levels derived from pimonidazole stained histological sections. We further established an indicator of hypoxia levels in patient tumors based on expression of nine hypoxia responsive genes. A strong correlation was found between these indicator values and the MRI-defined hypoxia levels in 63 patients. Chemoradiotherapy outcome of 74 patients was most strongly predicted by moderate hypoxia levels, whereas more severe or milder levels were less predictive. By combining gene expression profiles and MRI-defined hypoxia levels in cancer hallmark analysis, we identified a distribution of levels associated with each hallmark; oxidative phosphorylation and G2/M checkpoint were associated with moderate hypoxia, and epithelial-to-mesenchymal transition and inflammatory responses with significantly more severe levels. At the mildest levels, interferon response hallmarks, together with stabilization of HIF1A protein by immunohistochemistry, appearred significant. Thus, our method visualizes the distribution of hypoxia levels within patient tumors and has potential to distinguish levels of different prognostic and biological significance.
2020-09-01 | GSE147384 | GEO
Project description:We compared the prediction powers for disease recurrence between gene set prognostic model and clinical prognostic model developed in a single large population to see whether genetic quantitative approach will have significant prognostic role in early cervical cancer patients who underwent radical hysterectomy with or without adjuvant therapies. Gene set model to predict disease free survival of early cervical cancer was developed using DASL assay dataset from the cohort of early cervical cancer patients who were treated with radical surgery with or without adjuvant therapies at the Samsung Medical Center of Sungkyunkwan University School of Medicine in Seoul, Korea, between January 2002 and September 2008. Clinical prediction model was also developed in the same cohort and the ability of predicting recurrence from each model was compared.
2013-05-01 | GSE44001 | GEO
Project description:DNA Methylation Biomarkers for Cervical Cancer Risk Prediction in HIV-Positive Nigerian Women
| PRJNA1175663 | ENA
Project description:Purpose: To explore intratumor heterogeneity in gene expression profiles from patients with cervical cancer. Experimental Design: A total of 33 biopsies were obtained from 11 patients, sampling between two and five different areas for each tumor.The extracted RNAwas hybridized onto the Affymetrix U133 Plus 2.0 oligonucleotide chip. The variance of expression within a patient (W), between patients (B) and the total variance (T =W + B) were calculated for each ProbeSet, and the ratio W/T was used as a measure of intratumor heterogeneity. Gene Ontology functional analysis was done to assess the function of genes that had high W/T (top 10%) and low W/T (bottom 10%) values. Results: In total, 448 ProbeSets (2.2% of the total) had W/T < 0.10, indicating low intratumor heterogeneity, and 537 ProbeSets (2.7% of the total) had W/T > 0.90, indicating high intratumor heterogeneity. In total 14,473 ProbeSets (72.4%) had higher intertumor than intratumor heterogeneity (W/T < 0.5). Genes with low intratumor heterogeneity were characterized by a statistically significant enrichment of immune-related functions (P < 0.0001). Genes with high intratumor heterogeneity were characterized by a significant tendency towards nuclear localization and nucleic acid binding (both P < 0.0001). For genes with W/T > 0.5, more than six biopsies would be required to minimize the intratumoral heterogeneity to <0.15; if W/T is 0.3 to 0.4, four biopsies are required; and for low W/T of 0.16 to 0.3, only two to three biopsies would be needed. Conclusion: Although the intratumor heterogeneity was low for the majority of the tested ProbeSets, for many genes, multiple biopsies are required to obtain a reliable estimate of gene expression. Keywords: disease state comparison
2006-10-01 | GSE5787 | GEO