The genomic profile underlying an individual tumor can be highly informative in the creation of a personalized cancer treatment strategy for a given patient; a practice known as precision oncology. This involves next generation sequencing of a tumor sample and the subsequent identification of genomic aberrations, such as somatic mutations, to provide potential candidates of targeted therapy. The identification of these aberrations from sequencing noise and germline variant background poses a classic classification-style problem. This has been previously broached with many different supervised machine learning methods, including deep-learning neural networks. However, these neural networks have thus far not been tailored to give any indication of confidence in the mutation call, meaning an oncologist could be targeting a mutation with a low probability of being true. To address this, we present here a deep bayesian recurrent neural network for cancer variant calling, which shows no degradation in performance compared to standard neural networks. This approach enables greater flexibility through different priors to avoid overfitting to a single dataset. We will be incorporating this approach into software for oncologists to obtain safe, robust, and statistically confident somatic mutation calls for precision oncology treatment choices.
Cambridge Cancer Genomics（剑桥癌症基因组学公司）；The University of Cambridge（剑桥大学）
The emerging field of precision oncology relies on the accurate pinpointing of alterations in the molecular profile of a tumor to provide personalized targeted treatments. Current methodologies in the field commonly include the application of next generation sequencing technologies to a tumor sample, followed by the identification of mutations in the DNA known as somatic variants. The differentiation of these variants from sequencing error poses a classic classification problem, which has traditionally been approached with Bayesian statistics, and more recently with supervised machine learning…
Identifying genes underlying cancer development is critical to cancer biology and has important implications across prevention, diagnosis and treatment. Cancer sequencing studies aim at discovering genes with high frequencies of somatic mutations in specific types of cancer, as these genes are potential driving factors (drivers) for cancer development. We introduce a hierarchical Bayesian methodology to estimate gene-specific mutation rates and driver probabilities from somatic mutation data and to shed light on the overall proportion of drivers among sequenced genes. Our methodology applies t…
We built a novel Bayesian hierarchical survival model based on the somatic mutation profile of patients across 50 genes and 27 cancer types. The pan-cancer quality allows for the model to "borrow" information across cancer types, motivated by the assumption that similar mutation profiles may have similar (but not necessarily identical) effects on survival across different tissues-of-origin or tumor types. The effect of a mutation at each gene was allowed to vary by cancer type while the mean effect of each gene was shared across cancers. Within this framework we considered four parametric surv…
Recent studies of cancer somatic mutation profiles seek to identify mutations for targeted therapy in personalized medicine. Analysis of profiles, however, is not trivial, as each profile is heterogeneous and there are multiple confounding factors that influence the cause-and-effect relationships between cancer genes such as cancer (sub)type, biological processes, total number of mutations, and non-linear mutation interactions. Moreover, cancer is biologically redundant, i.e., distinct mutations can result in the alteration of similar biological processes, so it is important to identify all po…
Cambridge Cancer Genomics（剑桥癌症基因组学）；University of Cambridge（剑桥大学）；University College London（伦敦大学学院）
DNA sequencing to identify genetic variants is becoming increasingly valuable in clinical settings. Assessment of variants in such sequencing data is commonly implemented through Bayesian heuristic algorithms. Machine learning has shown great promise in improving on these variant calls, but the input for these is still a standardized "pile-up" image, which is not always best suited. In this paper, we present a novel method for generating images from DNA sequencing data, which interlaces the human reference genome with personalized sequencing output, to maximize usage of sequencing reads and im…
University of Toronto（多伦多大学）；Ontario Institute for Cancer Research（安大略癌症研究所）
High-throughput sequencing allows the detection and quantification of frequencies of somatic single nucleotide variants (SNV) in heterogeneous tumor cell populations. In some cases, the evolutionary history and population frequency of the subclonal lineages of tumor cells present in the sample can be reconstructed from these SNV frequency measurements. However, automated methods to do this reconstruction are not available and the conditions under which reconstruction is possible have not been described. We describe the conditions under which the evolutionary history can be uniquely reconstruct…
Cambridge Cancer Genomics（剑桥癌症基因组学公司）；University of Lancaster（兰卡斯特大学）；University of Cambridge（剑桥大学）
In this study, we present Flatsomatic - a Variational Auto Encoder (VAE) optimized to compress somatic mutations that allow for unbiased data compression whilst maintaining the signal. We compared two different neural network architectures for the VAE: Multilayer Perceptron (MLP) and bidirectional LSTM. The somatic profiles we used to train our models consisted of 8,062 Pan-Cancer patients from The Cancer Genome Atlas and 989 cell lines from the COSMIC cell line project. The profiles for each patient were represented by the genomic loci where somatic mutations occurred and, to reduce sparsity,…
Cambridge Cancer Genomics（剑桥癌症基因组学公司）；University of Lancaster（兰卡斯特大学）；University of Cambridge（剑桥大学）
Analysis of somatic mutation profiles from cancer patients is essential in the development of cancer research. However, the low frequency of most mutations and the varying rates of mutations across patients makes the data extremely challenging to statistically analyze as well as difficult to use in classification problems, for clustering, visualization or for learning useful information. Thus, the creation of low dimensional representations of somatic mutation profiles that hold useful information about the DNA of cancer cells will facilitate the use of such data in applications that will prog…
Statistical inference on the cancer-site specificities of collective ultra-rare whole genome somatic mutations is an open problem. Traditional statistical methods cannot handle whole-genome mutation data due to their ultra-high-dimensionality and extreme data sparsity -- e.g., >30 million unique variants are observed in the ~1700 whole-genome tumor dataset considered herein, of which >99% variants are encountered only once. To harness information in these rare variants we have recently proposed the "hidden genome model", a formal multilevel multi-logistic model that mines information in ultra-…
University of Trieste（的里雅斯特大学）；University of Milan-Bicocca（米兰比可卡大学）
Learning the structure of dependencies among multiple random variables is a problem of considerable theoretical and practical interest. Within the context of Bayesian Networks, a practical and surprisingly successful solution to this learning problem is achieved by adopting score-functions optimisation schema, augmented with multiple restarts to avoid local optima. Yet, the conditions under which such strategies work well are poorly understood, and there are also some intrinsic limitations to learning the directionality of the interaction among the variables. Following an early intuition of Fr…
City University of New York（纽约城市大学）；Hunter College, City University of New York（纽约城市大学亨特学院）
An unsolved fundamental problem in biology and ecology is to predict observable traits (phenotypes) from a new genetic constitution (genotype) of an organism under environmental perturbations (e.g., drug treatment). The emergence of multiple omics data provides new opportunities but imposes great challenges in the predictive modeling of genotype-phenotype associations. Firstly, the high-dimensionality of genomics data and the lack of labeled data often make the existing supervised learning techniques less successful. Secondly, it is a challenging task to integrate heterogeneous omics data from…
Universidade Federal do Rio Grande do Sul（南里奥格兰德联邦大学）；Hospital de Clínicas de Porto Alegre（阿雷格里港临床医院）
Identifying the genes and mutations that drive the emergence of tumors is a major step to improve understanding of cancer and identify new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in identifying genomic patterns associated with cancer drivers and developing models to predict driver events. Machine learning (ML) has been the…
University of Toronto（多伦多大学）；Ontario Institute for Cancer Research（安大略癌症研究所）
Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, that can be applied to WGS data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods.
Characterizing patient somatic mutations through next-generation sequencing technologies opens up possibilities for refining cancer subtypes. However, catalogues of mutations reveal that only a small fraction of the genes are altered frequently in patients. On the other hand different genomic alterations may perturb the same pathways. We propose a novel clustering procedure that quantifies the similarities of patients from their mutational profile on pathways via a novel graph kernel. We represent each KEGG pathway as an undirected graph. For each patient the vertex labels are assigned based o…
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
University of Augsburg（奥格斯堡大学）；University Hospital Augsburg（奥格斯堡大学医院）；Neu-Ulm University of Applied Sciences（新乌尔姆应用科学大学）；Bavarian Cancer Research Center（巴伐利亚癌症研究中心）；BioM Biotech Cluster Development GmbH（BioM生物技术集群发展有限公司）
Johannes Kepler University（约翰内斯·开普勒大学）；Software Competence Center Hagenberg（哈根贝格软件能力中心）；Children’s Cancer Research Institute（儿童癌症研究所）
Diagnosis and risk stratification of cancer and many other diseases require the detection of genomic breakpoints as a prerequisite of calling copy number alterations (CNA). This, however, is still challenging and requires time-consuming manual curation. As deep-learning methods outperformed classical state-of-the-art algorithms in various domains and have also been successfully applied to life science problems including medicine and biology, we here propose Deep SNP, a novel Deep Neural Network to learn from genomic data. Specifically, we used a manually curated dataset from 12 genomic single …
Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction
Technical University of Munich（慕尼黑工业大学）；TUM University Hospital Rechts der Isar（慕尼黑工业大学伊萨尔河右岸大学医院）；Helmholtz Munich（慕尼黑亥姆霍兹中心）；Universitat de Barcelona（巴塞罗那大学）；Medical University of Vienna（维也纳医科大学）；King’s College London（伦敦国王学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）
