scholarly journals Are we there yet? A machine learning architecture to predict organotropic metastases

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Michael Skaro ◽  
Marcus Hill ◽  
Yi Zhou ◽  
Shannon Quinn ◽  
Melissa B. Davis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Metastasis ◽  
Driving Forces ◽  
The Cancer Genome Atlas ◽  
Tumor Type ◽  
Metastatic Progression ◽  
Statistical Machine Learning ◽  
Primary Tumors ◽  
Site Specific ◽  
Tissue Specific

Abstract Background & Aims Cancer metastasis into distant organs is an evolutionarily selective process. A better understanding of the driving forces endowing proliferative plasticity of tumor seeds in distant soils is required to develop and adapt better treatment systems for this lethal stage of the disease. To this end, we aimed to utilize transcript expression profiling features to predict the site-specific metastases of primary tumors and second, to identify the determinants of tissue specific progression. Methods We used statistical machine learning for transcript feature selection to optimize classification and built tree-based classifiers to predict tissue specific sites of metastatic progression. Results We developed a novel machine learning architecture that analyzes 33 types of RNA transcriptome profiles from The Cancer Genome Atlas (TCGA) database. Our classifier identifies the tumor type, derives synthetic instances of primary tumors metastasizing to distant organs and classifies the site-specific metastases in 16 types of cancers metastasizing to 12 locations. Conclusions We have demonstrated that site specific metastatic progression is predictable using transcriptomic profiling data from primary tumors and that the overrepresented biological processes in tumors metastasizing to congruent distant loci are highly overlapping. These results indicate site-specific progression was organotropic and core features of biological signaling pathways are identifiable that may describe proliferative plasticity in distant soils.

scholarly journals Identification of the innate normal tissue specific genes and acquired tumor specific genes in determining the tumor transcriptional profiles

2019 ◽  
Author(s):  
Haiwei Wang ◽  
Xinrui Wang ◽  
Liangpu Xu ◽  
Ji Zhang ◽  
Hua Cao
Keyword(s):  
Dna Methylation ◽  
Cell Carcinoma ◽  
Normal Tissue ◽  
Expression Profiles ◽  
Lung Squamous Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Tumor Type ◽  
Tissue Specific ◽  
Tumor Tissues

Abstract Background: For a specific cancer type, the transcriptional profile is determined by the combination of innate transcriptional features of the original normal tissue and the acquired transcriptional characteristics mediated by genomic and epigenetic aberrations in the tumor development. However, the classification of innate normal tissue specific genes and acquired tumor specific genes is not studied in a pan-cancer manner. Methods: The innate and acquired gene expression profiles in each tumor type were studied using The Cancer Genome Atlas (TCGA) RNA-seq dataset. The prognostic effects of the tumor acquired genes were determined by “survival” package in R software. The methylation of the tumor acquired genes was delineated using TCGA HumanMethylation450 microarray data. Results: 90% liver hepatocellular carcinoma (LIHC) specific genes are derived from innate normal liver specific genes. On the contrary, 90.3% kidney clear cell carcinoma (KIRC) specific genes and 90.9 % lung squamous cell carcinoma (LUSC) specific genes are acquired in the tumor developmental progress. The innate normal tissue specific genes are down regulated in tumor tissues, while, the tumor acquired specific genes are up regulated in the tumor tissues. The innate normal tissue specific genes and the tumors acquired specific genes are both associated with the tumor overall survival in some tumor types. The hyper-DNA methylation of normal tissue specific genes is contributing to the inhibition of normal tissue specific genes expression in cancer cells. And the tumor acquired specific genes are activated by hypo-DNA methylation and genomic aberrations. Conclusions: Our results provide descriptions of the specific transcriptional features across cancer types and suggest that the tumor acquired specific genes are potential targets for anti-cancer therapy.

scholarly journals Deregulation of ribosomal protein expression and translation promotes breast cancer metastasis

Science ◽  
2020 ◽  
Vol 367 (6485) ◽  
pp. 1468-1473 ◽  
Author(s):  
Richard Y. Ebright ◽  
Sooncheol Lee ◽  
Ben S. Wittner ◽  
Kira L. Niederhoffer ◽  
Benjamin T. Nicholson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Ribosomal Proteins ◽  
Cancer Metastasis ◽  
Ribosomal Subunit ◽  
Small Subset ◽  
Cell Cycle Regulators ◽  
Metastatic Progression ◽  
Breast Cancer Patients ◽  
Primary Tumors

Circulating tumor cells (CTCs) are shed into the bloodstream from primary tumors, but only a small subset of these cells generates metastases. We conducted an in vivo genome-wide CRISPR activation screen in CTCs from breast cancer patients to identify genes that promote distant metastasis in mice. Genes coding for ribosomal proteins and regulators of translation were enriched in this screen. Overexpression of RPL15, which encodes a component of the large ribosomal subunit, increased metastatic growth in multiple organs and selectively enhanced translation of other ribosomal proteins and cell cycle regulators. RNA sequencing of freshly isolated CTCs from breast cancer patients revealed a subset with strong ribosome and protein synthesis signatures; these CTCs expressed proliferation and epithelial markers and correlated with poor clinical outcome. Therapies targeting this aggressive subset of CTCs may merit exploration as potential suppressors of metastatic progression.

scholarly journals MYC controls metastatic heterogeneity in pancreatic cancer

2021 ◽  
Author(s):  
Ravikanth Maddipati ◽  
Robert J. Norgard ◽  
Timour Baslan ◽  
Komal S. Rathi ◽  
Amy Zhang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Mouse Model ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cancer Patients ◽  
Ductal Adenocarcinoma ◽  
Tumor Type ◽  
Metastatic Progression ◽  
Primary Tumors ◽  
Functional Differences ◽  
Metastatic Burden

AbstractThe degree of metastatic disease varies widely amongst cancer patients and impacts clinical outcomes. However, the biological and functional differences that drive the extent of metastasis are poorly understood. We analyzed primary tumors and paired metastases using a multi-fluorescent lineage-labeled mouse model of pancreatic ductal adenocarcinoma (PDAC) – a tumor type where most patients present with metastases. Genomic and transcriptomic analysis revealed an association between metastatic burden and gene amplification or transcriptional upregulation of MYC and its downstream targets. Functional experiments showed that MYC promotes metastasis by recruiting tumor associated macrophages (TAMs), leading to greater bloodstream intravasation. Consistent with these findings, metastatic progression in human PDAC was associated with activation of MYC signaling pathways and enrichment for MYC amplifications specifically in metastatic patients. Collectively, these results implicate MYC activity as a major determinant of metastatic burden in advanced PDAC.

scholarly journals A cross-cancer metastasis signature in the microRNA-mRNA axis of paired tissue samples

2018 ◽  
Author(s):  
Samuel C. Lee ◽  
Alistair Quinn ◽  
Thin Nguyen ◽  
Svetha Venkatesh ◽  
Thomas P. Quinn
Keyword(s):  
Cancer Progression ◽  
Cancer Metastasis ◽  
Primary Tumour ◽  
Clinical Diagnostics ◽  
The Body ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Metastatic Progression ◽  
Tissue Samples ◽  
Cancer Types

AbstractIn the progression of cancer, cells acquire genetic mutations that cause uncontrolled growth. Over time, the primary tumour may undergo additional mutations that allow for the cancerous cells to spread throughout the body as metastases. Since metastatic development typically results in markedly worse patient outcomes, research into the identity and function of metastasisassociated biomarkers could eventually translate into clinical diagnostics or novel therapeutics. Although the general processes underpinning metastatic progression are understood, no consistent nor clear cross-cancer biomarker profile has yet emerged. However, the literature suggests that some microRNAs (miRNAs) may play an important role in the metastatic progression of several cancer types. Using a subset of The Cancer Genome Atlas (TCGA) data, we performed an integrated analysis of mRNA and miRNA expression with paired metastatic and primary tumour samples to interrogate how the miRNA-mRNA regulatory axis influences metastatic progression. From this, we successfully built mRNAand miRNA-specific classifiers that can discriminate pairs of metastatic and primary samples across 11 cancer types. In addition, we identified a number of miRNAs whose metastasis-associated dysregulation could predict mRNA metastasis-associated dysregulation. Among the most predictive miRNAs, we found several previously implicated in cancer progression, including miR-301b, miR-1296, and miR-423. Taken together, our results suggest that cross-cancer metastatic samples have unique biomarker signatures when compared with paired primary tumours, and that these miRNA biomarkers can be used to predict both metastatic status and mRNA expression.

scholarly journals Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type

2021 ◽  
Vol 14 (3) ◽  
pp. 101016 ◽  
Author(s):  
Jim Abraham ◽  
Amy B. Heimberger ◽  
John Marshall ◽  
Elisabeth Heath ◽  
Joseph Drabick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Type ◽  
Learning Analysis

scholarly journals Machine learning for perovskite materials design and discovery

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Qiuling Tao ◽  
Pengcheng Xu ◽  
Minjie Li ◽  
Wencong Lu
Keyword(s):  
Machine Learning ◽  
Technological Innovation ◽  
Future Development ◽  
Rational Design ◽  
Driving Forces ◽  
Scientific Progress ◽  
Double Perovskites ◽  
Perovskite Materials ◽  
Development Tendency ◽  
Development Prospects

AbstractThe development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.

scholarly journals Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua E. Lewis ◽  
Melissa L. Kemp
Keyword(s):  
Machine Learning ◽  
Metabolic Flux ◽  
Metabolic Modeling ◽  
The Cancer Genome Atlas ◽  
Radiation Response ◽  
Metabolomics Data ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Metabolic Biomarkers ◽  
Genome Scale

AbstractResistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

scholarly journals Multiregional genetic evolution of metastatic uveal melanoma

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Daniel A. Rodriguez ◽  
Jessica Yang ◽  
Michael A. Durante ◽  
Alexander N. Shoushtari ◽  
Stergios J. Moschos ◽  
...  
Keyword(s):  
Uveal Melanoma ◽  
Tumor Development ◽  
Genetic Evolution ◽  
Metastatic Progression ◽  
Therapeutic Approaches ◽  
Primary Tumors ◽  
Early Tumor ◽  
Rapid Autopsy ◽  
Genetic Events ◽  
Novel Therapeutic

AbstractUveal melanoma (UM) is the most common primary intraocular malignancy in adults and leads to deadly metastases for which there is no approved treatment. Genetic events driving early tumor development are well-described, but those occurring later during metastatic progression remain poorly understood. We performed multiregional genomic sequencing on 22 tumors collected from two patients with widely metastatic UM who underwent rapid autopsy. We observed multiple seeding events from the primary tumors, metastasis-to-metastasis seeding, polyclonal seeding, and late driver variants in ATM, KRAS, and other genes previously unreported in UM. These findings reveal previously unrecognized temporal and anatomic complexity in the genetic evolution of metastatic uveal melanoma, and they highlight the distinction between early and late phases of UM genetic evolution with implications for novel therapeutic approaches.

scholarly journals Tissue-specific tregs in cancer metastasis: opportunities for precision immunotherapy

2021 ◽  
Author(s):  
Laura A. Huppert ◽  
Michael D. Green ◽  
Luke Kim ◽  
Christine Chow ◽  
Yan Leyfman ◽  
...  
Keyword(s):  
Cancer Immunotherapy ◽  
Cancer Metastasis ◽  
Metastatic Cancer ◽  
High Volume ◽  
Therapy Resistance ◽  
Term Treatment ◽  
The Body ◽  
Tissue Specific ◽  
Tissue Repair And Regeneration ◽  
Long Term Treatment

AbstractDecades of advancements in immuno-oncology have enabled the development of current immunotherapies, which provide long-term treatment responses in certain metastatic cancer patients. However, cures remain infrequent, and most patients ultimately succumb to treatment-refractory metastatic disease. Recent insights suggest that tumors at certain organ sites exhibit distinctive response patterns to immunotherapy and can even reduce antitumor immunity within anatomically distant tumors, suggesting the activation of tissue-specific immune tolerogenic mechanisms in some cases of therapy resistance. Specialized immune cells known as regulatory T cells (Tregs) are present within all tissues in the body and coordinate the suppression of excessive immune activation to curb autoimmunity and maintain immune homeostasis. Despite the high volume of research on Tregs, the findings have failed to reconcile tissue-specific Treg functions in organs, such as tolerance, tissue repair, and regeneration, with their suppression of local and systemic tumor immunity in the context of immunotherapy resistance. To improve the understanding of how the tissue-specific functions of Tregs impact cancer immunotherapy, we review the specialized role of Tregs in clinically common and challenging organ sites of cancer metastasis, highlight research that describes Treg impacts on tissue-specific and systemic immune regulation in the context of immunotherapy, and summarize ongoing work reporting clinically feasible strategies that combine the specific targeting of Tregs with systemic cancer immunotherapy. Improved knowledge of Tregs in the framework of their tissue-specific biology and clinical sites of organ metastasis will enable more precise targeting of immunotherapy and have profound implications for treating patients with metastatic cancer.

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