Neural Network Deconvolution Method for Resolving Pathway-Level Progression of Tumor Clonal Expression Programs With Application to Breast Cancer Brain Metastases

Abstract

Metastasis is the primary mechanism by which cancer results in mortality and there are currently no reliable treatment options once it occurs, making the metastatic process a critical target for new diagnostics and therapeutics. Treating metastasis before it appears is challenging, however, in part because metastases may be quite distinct genomically from the primary tumors from which they presumably emerged. Phylogenetic studies of cancer development have suggested that changes in tumor genomics over stages of progression often result from shifts in the abundance of clonal cellular populations, as late stages of progression may derive from or select for clonal populations rare in the primary tumor. The present study develops computational methods to infer clonal heterogeneity and dynamics across progression stages via deconvolution and clonal phylogeny reconstruction of pathway-level expression signatures in order to reconstruct how these processes might influence average changes in genomic signatures over progression. We show, via application to a study of gene expression in a collection of matched breast primary tumor and metastatic samples, that the method can infer coarse-grained substructure and stromal infiltration across the metastatic transition. The results suggest that genomic changes observed in metastasis, such as gain of the ErbB signaling pathway, are likely caused by early events in clonal evolution followed by expansion of minor clonal populations in metastasis, a finding that may have translational implications for early detection or prevention of metastasis 1 .

COVID-19 deep classification network based on convolution and deconvolution local enhancement

“…Besides, the characteristics of the fireworks like glass shadow and capillaries in the CT image are also similar. Therefore, this paper proposes a deconvolution network model based on the ROI to enhance the infection characteristics of the novel coronaviruses [ 49 , 50 ].…”

Assessing the contribution of tumor mutational phenotypes to cancer progression risk

“…While some study designs might alternatively use targeted deep sequencing data, we would generally consider those data not suited to the present methods, which benefit from profiling larger fractions of the genome to estimate better aggregate mutation rates. We consider here only inference from bulk tumor data [38,39], although we note that the strategy might be applied to single-cell sequence data [40] or combinations of bulk and single-cell data [41][42][43], should such data become available for sufficiently large cohorts. The genomic data is preprocessed and passed to one or more variant callers, ideally including single nucleotide variations (SNVs) and copy number alterations (CNAs) calls as well as calls for diverse classes of structural variations (SVs) to produce a variant call format (VCF) file with detected variants and their variant allele frequencies (VAFs) per sample.…”

Semi-deconvolution of bulk and single-cell RNA-seq data with application to metastatic progression in breast cancer

“…See Avila Cobos et al (2020) for a comparison of different partial deconvolution algorithms and related data transformation methods. Examples of complete deconvolution methods include Geometric Unmixing ( Schwartz and Shackney, 2010 ), which proposed an archetype analysis method based on geometries of genomic point clouds; DSA ( Zhong et al , 2013 ), which treats deconvolution as a matrix factorization problem; LinSeed ( Zaitsev et al , 2019 ), which identifies a set of anchor genes through linear correlation and uses DSA to solve for the non-anchor genes; NND ( Tao et al , 2020a ), which poses partial deconvolution problem as a matrix factorization to be solved with gradient descent implemented through a neural network; and RAD ( Tao et al , 2020b ), which solve s the formulation of NND using a hybrid optimizer with improved accuracy and speed.…”