Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome

Sun, Dongdong; Li, Ao; Tang, Bo; Wang, Minghui

doi:10.1016/j.cmpb.2018.04.008

Cited by 88 publications

(94 citation statements)

References 40 publications

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“…Cropping and randomly selecting images are widely used methods in studies with whole slide image processing. [9,16,19] Secondly, we applied CellPro ler [25] to extract features from each sub-image. CellPro ler is an opensource modular analysis software that can process cell images.…”

Section: Extraction Of Histopathological Imaging Featuresmentioning

confidence: 99%

“…[17] It is also feasible to combine pathologic features with oncological omics to optimize prognostic models. At present, the method of establishing the prognostic model of cancer by using genomic data and histopathological image features has been applied to renal cell carcinoma, [18] breast cancer [19] and other early-stage cancers [20], etc., and superior prediction models has been obtained.…”

Section: Introductionmentioning

confidence: 99%

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Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Zeng

Chen

et al. 2020

Preprint

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Background: Colon adenocarcinoma (COAD) is one of the highest morbidity cancers all over the world. Its 5-year survival is no more than 60% even in European countries with the highest survival rates. The histopathological information is crucial for the prognosis and therapy of COAD. Application of the digital whole slide imaging system enables us to read histopathological sections digitally. Apart from that, cancer genomics is also an important prognostic factor.Methods: To identify prognosis biomarkers of COAD, we downloaded whole-slide histopathological images from TCIA database. After processing these images, histopathological features were extracted by CellProfiler. Least Absolute Shrinkage and Selection Operator and Support Vector Machine Recursive Feature Elimination were followed applied, screening out 5 prognosis-related features. Weighted gene co-expression network analysis (WGCNA) was operated to find co-expression gene module correlated with prognosis-related features. The samples were divided into a training set and a testing set on a scale of 70% and 30%. Random forest was applied to construct histopathologic-genomic prognosis factor (HGPF) using prognosis-related features and genomic data. After that, we combined HGPF and clinical characteristics with nomogram and verify its predictive efficacy.Results: The time-dependent ROC was drawn to evaluate the efficacy of prognostic model. In the training set, 1-year, 3-year and 5-year AUCs are respectively 0.948, 0.916, 0.933. In the testing set, 1-year, 3-year and 5-year AUCs are respectively 0.913, 0.894, 0.924. In addition, patients were separated into high-risk survival group and low-risk survival group by HGPF. Survival analysis indicates that the low-risk patients’ survival was significantly better than high-risk patients’ in both training set and testing set. It is suggested that histopathological image features have certain ability to predict COAD survival, which can be further improved by means of multi-omics combination.Conclusions: In conclusion, this study constructs an integrative prognosis model based on histopathological and genomic features, which may have some guidance effect on prognosis and clinical decision of COAD patients. Furthermore, the underlying biological mechanisms of this multi-omics model require further study.

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Section: Extraction Of Histopathological Imaging Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Zeng

Chen

et al. 2020

Preprint

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“…These studies usually present a model with own and specific datasets and their results are presented as the accuracy or sensitivity to predict outcomes, such as survival rates. As new studies also underline, the usage of other data sources, such as genetic data and image data can also improve the outcome prediction [9].…”

Section: A Data Mining Techniquesmentioning

confidence: 99%

Exploring Breast Cancer Patterns for Different Outcomes using Artificial Intelligence

Larburu¹,

Arrue²,

Muro³

et al. 2018

2018 IEEE 20th International Conference on E-Health Networking, Applications and Services (Healthcom)

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Breast Cancer is a complex disease characterized by multiple variables obtained from several data-sources, such as clinical, genetic or image sources. Over the past decades, various studies have tried to predict the outcome of breast cancer with the support of these data, and big advances have been done in this direction. However, only a few reports describe the causal relationships among the variables and outcomes, such as adverse events and survival rate, and usually they are very limited to a specific dataset. This research work presents a novel system that using data mining and visual analytics tools depicts in an intuitive way the patterns associated with different outcomes, such as treatment response and adverse events related to a treatment. For that the system processes heterogenous data coming from a real setting for primary breast cancer. This way clinicians can explore in a dynamic, fast and intuitive way whether certain group of patients are prone to certain outcomes.

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“…Their multi-omics model, excluding imaging data, has an AUC of 0.802 ± 0.032. When incorporating the imaging data, the AUC goes up slightly to 0.828 ± 0.034 [14]. Ma et al have applied factorization autoencoder to integrate gene expression, miRNA expression, DNA methylation, and protein expression for progression-free interval event prediction and achieve an AUC of 0.74 on bladder cancer and an AUC of 0.825 on brain glioma [15].…”

Section: Introductionmentioning

confidence: 99%

Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis

Mitchel

Chatlin

et al. 2020

BMC Med Inform Decis Mak

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Background Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival lengths, indicating a need to identify prognostic biomarkers for personalized diagnosis and treatment. With the development of new technologies such as next-generation sequencing, multi-omics information are becoming available for a more thorough evaluation of a patient’s condition. In this study, we aim to improve breast cancer overall survival prediction by integrating multi-omics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)). Methods Motivated by multi-view learning, we propose a novel strategy to integrate multi-omics data for breast cancer survival prediction by applying complementary and consensus principles. The complementary principle assumes each -omics data contains modality-unique information. To preserve such information, we develop a concatenation autoencoder (ConcatAE) that concatenates the hidden features learned from each modality for integration. The consensus principle assumes that the disagreements among modalities upper bound the model errors. To get rid of the noises or discrepancies among modalities, we develop a cross-modality autoencoder (CrossAE) to maximize the agreement among modalities to achieve a modality-invariant representation. We first validate the effectiveness of our proposed models on the MNIST simulated data. We then apply these models to the TCCA breast cancer multi-omics data for overall survival prediction. Results For breast cancer overall survival prediction, the integration of DNA methylation and miRNA expression achieves the best overall performance of 0.641 ± 0.031 with ConcatAE, and 0.63 ± 0.081 with CrossAE. Both strategies outperform baseline single-modality models using only DNA methylation (0.583 ± 0.058) or miRNA expression (0.616 ± 0.057). Conclusions In conclusion, we achieve improved overall survival prediction performance by utilizing either the complementary or consensus information among multi-omics data. The proposed ConcatAE and CrossAE models can inspire future deep representation-based multi-omics integration techniques. We believe these novel multi-omics integration models can benefit the personalized diagnosis and treatment of breast cancer patients.

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Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome

Cited by 88 publications

References 40 publications

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Exploring Breast Cancer Patterns for Different Outcomes using Artificial Intelligence

Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis

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