Mutational signature learning with supervised negative binomial non-negative matrix factorization

ROBUST DISCOVERY OF MUTATIONAL SIGNATURES USING POWER POSTERIORS

Mutational processes, such as the molecular effects of carcinogenic agents or defective DNA repair mechanisms, are known to produce different mutation types with characteristic frequency profiles, referred to as mutational signatures. Non-negative matrix factorization (NMF) has successfully been used to discover many mutational signatures, yielding novel insights into cancer etiology and targeted therapies. However, the NMF model is only a rough approximation to reality, and even small departures from this assumed model can have large negative effects on the accuracy and reliability of the results. We propose a new approach to mutational signatures analysis that improves robustness to misspecification by using a power posterior for a fully Bayesian NMF model, while employing a sparsity-inducing prior to automatically infer the number of active signatures. In extensive simulation studies, we find that our proposed approach recovers more true signatures with greater accuracy than current leading methods. On whole-genome sequencing data for six cancer types from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, we find that our method is able to accurately recover more signatures than the current state-of-the-art.

Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization

BACKGROUND

The spectrum of mutations in a collection of cancer genomes can be described by a mixture of a few mutational signatures. The mutational signatures can be found using non-negative matrix factorization (NMF). To extract the mutational signatures we have to assume a distribution for the observed mutational counts and a number of mutational signatures. In most applications, the mutational counts are assumed to be Poisson distributed, and the rank is chosen by comparing the fit of several models with the same underlying distribution and different values for the rank using classical model selection procedures. However, the counts are often overdispersed, and thus the Negative Binomial distribution is more appropriate.

RESULTS

We propose a Negative Binomial NMF with a patient specific dispersion parameter to capture the variation across patients and derive the corresponding update rules for parameter estimation. We also introduce a novel model selection procedure inspired by cross-validation to determine the number of signatures. Using simulations, we study the influence of the distributional assumption on our method together with other classical model selection procedures. We also present a simulation study with a method comparison where we show that state-of-the-art methods are highly overestimating the number of signatures when overdispersion is present. We apply our proposed analysis on a wide range of simulated data and on two real data sets from breast and prostate cancer patients. On the real data we describe a residual analysis to investigate and validate the model choice.

CONCLUSIONS

With our results on simulated and real data we show that our model selection procedure is more robust at determining the correct number of signatures under model misspecification. We also show that our model selection procedure is more accurate than the available methods in the literature for finding the true number of signatures. Lastly, the residual analysis clearly emphasizes the overdispersion in the mutational count data. The code for our model selection procedure and Negative Binomial NMF is available in the R package SigMoS and can be found at https://github.com/MartaPelizzola/SigMoS .

Ensemble classification based signature discovery for cancer diagnosis in RNA expression profiles across different platforms

Molecular signatures have been excessively reported for diagnosis of many cancers during the last 20 years. However, false-positive signatures are always found using statistical methods or machine learning approaches, and that makes subsequent biological experiments fail. Therefore, signature discovery has gradually become a non-mainstream work in bioinformatics. Actually, there are three critical weaknesses that make the identified signature unreliable. First of all, a signature is wrongly thought to be a gene set, each component of which keeps differential expressions between or among sample groups. Second, there may be many false-positive genes expressed differentially found, even if samples derived from cancer or normal group can be separated in one-dimensional space. Third, cross-platform validation results of a discovered signature are always poor. In order to solve these problems, we propose a new feature selection framework based on ensemble classification to discover signatures for cancer diagnosis. Meanwhile, a procedure for data transform among different expression profiles across different platforms is also designed. Signatures are found on simulation and real data representing different carcinomas across different platforms. Besides, false positives are suppressed. The experimental results demonstrate the effectiveness of our method.

Premetastatic shifts of endogenous and exogenous mutational processes support consolidative therapy in EGFR-driven lung adenocarcinoma

The progression of cancer is an evolutionary process that is challenging to assess between sampling timepoints. However, investigation of cancer evolution over specific time periods is crucial to the elucidation of key events such as the acquisition of therapeutic resistance and subsequent fatal metastatic spread of therapy-resistant cell populations. Here we apply mutational signature analyses within clinically annotated cancer chronograms to detect and describe the shifting mutational processes caused by both endogenous (e.g. mutator gene mutation) and exogenous (e.g. mutagenic therapeutics) factors between tumor sampling timepoints. In one patient, we find that cisplatin therapy can introduce mutations that confer genetic resistance to subsequent targeted therapy with Erlotinib. In another patient, we trace detection of defective mismatch-repair associated mutational signature SBS3 to the emergence of known driver mutation CTNNB1 S37C. In both of these patients, metastatic lineages emerged from a single ancestral lineage that arose during therapy-a finding that argues for the consideration of local consolidative therapy over other therapeutic approaches in EGFR-positive non-small cell lung cancer. Broadly, these results demonstrate the utility of phylogenetic analysis that incorporates clinical time course and mutational signature deconvolution to inform therapeutic decision making and retrospective assessment of disease etiology.