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Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone.

ABSTRACT: High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-year survival of less than 50%. Beyond determining and confirming the diagnosis itself, pathologist review of histologic slides provides no prognostic or predictive information, which is in sharp contrast to almost all other carcinoma types. Deep-learning based image analysis has recently been able to predict outcome and/or identify morphology-based representations of underlying molecular alterations in other tumor types, such as colorectal carcinoma, lung carcinoma, breast carcinoma, and melanoma. Using a carefully stratified HGSC patient cohort consisting of women (n = 30) with similar presentations who experienced very different treatment responses (platinum free intervals of either ≤ 6 months or ≥ 18 months), we used whole slide images (WSI, n = 205) to train a convolutional neural network. The neural network was trained, in three steps, to identify morphologic regions (digital biomarkers) that are highly associating with one or the other treatment response group. We tested the classifier using a separate 22 slide test set, and 18/22 slides were correctly classified. We show that a neural network based approach can discriminate extremes in patient response to primary platinum-based chemotherapy with high sensitivity (73%) and specificity (91%). These proof-of-concept results are novel, because for the first time, prospective prognostic information is identified specifically within HGSC tumor morphology.

SUBMITTER: Laury AR

PROVIDER: S-EPMC8476598 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeMolecular cancer subtyping is an important tool in predicting prognosis and developing novel precision medicine approaches. We developed a novel platform-independent gene expression-based classification system for molecular subtyping of patients with high-grade serous ovarian carcinoma (HGSOC).MethodsUnprocessed exon array (569 tumor and nine normal) and RNA sequencing (RNA-seq; 376 tumor) HGSOC data sets, with clinical annotations, were downloaded from the Genomic Data Commons portal. Sample clustering was performed by non-negative matrix factorization by using isoform-level expression estimates. The association between the subtypes and overall survival was evaluated by Cox proportional hazards regression model after adjusting for the covariates. A novel classification system was developed for HGSOC molecular subtyping. Robustness and generalizability of the gene signatures were validated using independent microarray and RNA-seq data sets.ResultsSample clustering recaptured the four known The Cancer Genome Atlas molecular subtypes but switched the subtype for 22% of the cases, which resulted in significant (P = .006) survival differences among the refined subgroups. After adjusting for covariate effects, the mesenchymal subgroup was found to be at an increased hazard for death compared with the immunoreactive subgroup. Both gene- and isoform-level signatures achieved more than 92% prediction accuracy when tested on independent samples profiled on the exon array platform. When the classifier was applied to RNA-seq data, the subtyping calls agreed with the predictions made from exon array data for 95% of the 279 samples profiled by both platforms.ConclusionIsoform-level expression analysis successfully stratifies patients with HGSOC into groups with differing prognosis and has led to the development of robust, platform-independent gene signatures for HGSOC molecular subtyping. The association of the refined The Cancer Genome Atlas HGSOC subtypes with overall survival, independent of covariates, enhances the clinical annotation of the HGSOC cohort.

Project description:BackgroundInsulin-like growth factor binding protein 7 (IGFBP7) has been suggested to act as a tumour suppressor gene in various human cancers, yet its role in epithelial ovarian cancer (EOC) has not yet been investigated. We previously observed that IGFBP7 was one of several genes found significantly upregulated in an EOC cell line model rendered non-tumourigenic as consequence of genetic manipulation. The aim of the present study was to investigate the role of IGFBP7 in high-grade serous ovarian carcinomas (HGSC), the most common type of EOC.MethodsWe analysed IGFBP7 gene expression in 11 normal ovarian surface epithelial cells (NOSE), 79 high-grade serous ovarian carcinomas (HGSC), and seven EOC cell lines using a custom gene expression array platform. IGFBP7 mRNA expression profiles were also extracted from publicly available databases. Protein expression was assessed by immunohistochemistry of 175 HGSC and 10 normal fallopian tube samples using tissue microarray and related to disease outcome. We used EOC cells to investigate possible mechanisms of gene inactivation and describe various in vitro growth effects of exposing EOC cell lines to human recombinant IGFBP7 protein and conditioned media.ResultsAll HGSCs exhibited IGFBP7 expression levels that were significantly (p = 0.001) lower than the mean of the expression value of NOSE samples and that of a whole ovary sample. IGFBP7 gene and protein expression were lower in tumourigenic EOC cell lines relative to a non-tumourigenic EOC cell line. None of the EOC cell lines harboured a somatic mutation in IGFBP7, although loss of heterozygosity (LOH) of the IGFBP7 locus and epigenetic methylation silencing of the IGFBP7 promoter was observed in two of the cell lines exhibiting loss of gene/protein expression. In vitro functional assays revealed an alteration of the EOC cell migration capacity. Protein expression analysis of HGSC samples revealed that the large majority of tumour cores (72.6%) showed low or absence of IGFBP7 staining and revealed a significant correlation between IGFBP7 protein expression and a prolonged overall survival (p = 0.044).ConclusionThe low levels of IGFPB7 in HGSC relative to normal tissues, and association with survival are consistent with a purported role in tumour suppressor pathways.

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Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone.

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