A pan-cancer somatic mutation embedding using autoencoders

Palazzo, Martín; Beauseroy, Pierre; Yankilevich, Patricio

doi:10.1186/s12859-019-3298-z

Cited by 12 publications

(10 citation statements)

References 22 publications

(24 reference statements)

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“…There are many works which have used VAE in their studies. However, they are mostly mono-omics studies of individual cancer 28,32,33 or pancancer 29,34,35 . OmiVAE 29 is the only work that considered VAE for integrated multi-omics (di-omics) analysis of pancancer.…”

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confidence: 99%

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Integrated multi-omics analysis of ovarian cancer using variational autoencoders

Hira

Razzaque

Angione

et al. 2021

Sci Rep

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Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi-omics analysis of cancer data. However, high dimensional multi-omics data are generally imbalanced with too many molecular features and relatively few patient samples. This imbalance makes a DL based integrated multi-omics analysis difficult. DL-based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi-omics data. However, there are few VAE-based integrated multi-omics analyses, and they are limited to pancancer. In this work, we did an integrated multi-omics analysis of ovarian cancer using the compressed features learned through VAE and an improved version of VAE, namely Maximum Mean Discrepancy VAE (MMD-VAE). First, we designed and developed a DL architecture for VAE and MMD-VAE. Then we used the architecture for mono-omics, integrated di-omics and tri-omics data analysis of ovarian cancer through cancer samples identification, molecular subtypes clustering and classification, and survival analysis. The results show that MMD-VAE and VAE-based compressed features can respectively classify the transcriptional subtypes of the TCGA datasets with an accuracy in the range of 93.2-95.5% and 87.1-95.7%. Also, survival analysis results show that VAE and MMD-VAE based compressed representation of omics data can be used in cancer prognosis. Based on the results, we can conclude that (i) VAE and MMD-VAE outperform existing dimensionality reduction techniques, (ii) integrated multi-omics analyses perform better or similar compared to their mono-omics counterparts, and (iii) MMD-VAE performs better than VAE in most omics dataset.

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confidence: 99%

“…Moreover, most of the existing works 28,32,33,35 use unsupervised dimensionality reduction methods, separating the downstream analysis from the reduction method. However, dimensionality reduction in cancer multi-omics analysis is an intermediate step toward the downstream analysis, like classification (e.g., cancer vs normal cell).…”

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confidence: 99%

Integrated multi-omics analysis of ovarian cancer using variational autoencoders

Hira

Razzaque

Angione

et al. 2021

Sci Rep

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“…In contrast, some AE based models are being developed but no DK is used in PRE, ARCH nor POSTHOC [91,92,93,94]. The architecture proposed by Hira et al [83] can integrate multi-omics data (genomics, epigenomics, transcriptomics).…”

Section: Posthoc Explanationsmentioning

confidence: 99%

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

Wysocka

Wysocki

Zufferey

et al. 2022

Preprint

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Background There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings. Methods This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. It focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the biomedical domain. For this, we retrieved and analyzed 42 studies focusing on emerging architectural and methodological advances, the encoding of biological domain knowledge and the integration of explainability methods. Results We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction networks) and interpretability. This represents a fundamental functional shift towards models which can integrate mechanistic and statistical inference aspects. We introduce a concept of bio-centric interpretability and according to its taxonomy, we discuss representational methodologies for the integration of domain prior knowledge in such models. Conclusions The paper provides a critical outlook into contemporary methods for explainability and interpretabiltiy used in DL for cancer. The analysis points in the direction of a convergence between encoding prior knowledge and improved interpretability. We introduce bio-centric interpretability which is an important step towards formalisation of biological interpretability of DL models and developing methods that are less problem- or application-specific.

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“…Comparison of tumor types analyzed by The Cancer Genome Atlas (TCGA) through the Pan-Cancer Atlas can further supplement and summarize the completed TCGA results ( 2 ). The integration of these data sets provides a comprehensive picture of somatic mutations ( 3 , 4 ), copy number changes ( 5 , 6 ), mutational signatures ( 7 ), and other genetic variations in tumors, furthering the understanding of cancer mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Pan-Cancer Patients With Ultrahigh Tumor Mutation Burden

Shi

et al. 2021

Front. Oncol.

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BackgroundTumor mutation burden has been proven to be a good predictor for the efficacy of immunotherapy, especially in patients with hypermutation. However, most research focused on the analysis of hypermutation in individual tumors, and there is a lack of integrated research on the hypermutation across different cancers. This study aimed to characterize hypermutated patients to distinguish between these patients and non-hypermutated patients.MethodsA total of 5,980 tumor samples involving 23 types of solid tumors from the in-house database were included in the study. Based on the cutoff value of tumor mutation burden (TMB), all samples were divided into hypermutated or non-hypermutated groups. Microsatellite instability status, PD-L1 expression and other mutation-related indicators were analyzed.ResultsAmong the 5,980 tumor samples, 1,164 were selected as samples with hypermutation. Compared with the non-hypermutated group, a significant increase in the mutation rates of DNA mismatch repair genes and polymerase genes was detected in the hypermutated group, and there was an overlap between high TMB and high microsatellite instability or high PD-L1. In addition, we found that EGFR, KRAS and PIK3CA had a high frequency of both single nucleotide variation and copy number variation mutations. These identified mutant genes were enriched in the oncogenic signaling pathway and the DNA damage repair pathway. At the same time, the somatic cell characteristics and distribution of the two groups were significantly different.ConclusionsThis study identified genetic and phenotypic characteristics of hypermutated tumors and demonstrated that DNA damage repair is critically involved in hypermutation.

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A pan-cancer somatic mutation embedding using autoencoders

Cited by 12 publications

References 22 publications

Integrated multi-omics analysis of ovarian cancer using variational autoencoders

Integrated multi-omics analysis of ovarian cancer using variational autoencoders

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

Characteristics of Pan-Cancer Patients With Ultrahigh Tumor Mutation Burden

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