scholarly journals Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

2021 ◽  
Vol 20 ◽  
pp. 117693512110024
Author(s):  
Jason D Wells ◽  
Jacqueline R Griffin ◽  
Todd W Miller
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Human Tumors ◽  
Molecular Characteristics ◽  
Survival Times ◽  
Chemotherapy Drugs ◽  
Testing Dataset ◽  
Cancer Models ◽  
Cancer Types ◽  
Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

scholarly journals Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines

2021 ◽  
Vol 9 ◽  
Author(s):  
ShiJian Ding ◽  
Hao Li ◽  
Yu-Hang Zhang ◽  
XianChao Zhou ◽  
KaiYan Feng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Cell Line ◽  
Cancer Patients ◽  
Cell Lines ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Biomarkers ◽  
Cancer Types ◽  
Pan Cancer

There are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer types? After the Cancer Genome Atlas (TCGA) project, there are more and more pan-cancer studies. Researchers want to get robust gene expression signature from pan-cancer patients. But there is large variance in cancer patients due to heterogeneity. To get robust results, the sample size will be too large to recruit. In this study, we tried another approach to get robust pan-cancer biomarkers by using the cell line data to reduce the variance. We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types. Two feature selection methods, including Boruta, and max-relevance and min-redundancy methods, were applied to the cell line gene expression data one by one, generating a feature list. Such list was fed into incremental feature selection method, incorporating one classification algorithm, to extract biomarkers, construct optimal classifiers and decision rules. The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. NCKAP1, TNFRSF12A, LAMB2, FKBP9, PFN2, TOM1L1) and rules identified in this work may provide a meaningful and precise reference for differentiating multiple types of cancer and contribute to the personalized treatment of tumors.

scholarly journals A Pan-Cancer Study of Epidermal Growth Factor-Like Domains 6/7/8 as Therapeutic Targets in Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Shanping Shi ◽  
Ting Ma ◽  
Yang Xi
Keyword(s):  
Gene Expression ◽  
Growth Factor ◽  
Tumor Microenvironment ◽  
Epidermal Growth Factor ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cancer Study ◽  
Cancer Types ◽  
Epidermal Growth ◽  
Pan Cancer

With highly homologous epidermal growth factor (EGF)-like (EGFL) domains, the members of the EGFL family play crucial roles in growth, invasion, and metastasis of tumors and are closely associated with the apoptosis of tumor cells and tumor angiogenesis. Furthermore, their contribution to immunoreaction and tumor microenvironment is highly known. In this study, a comprehensive analysis of EGFL6, −7, and −8 was performed on the basis of their expression profiles and their relationship with the rate of patient survival. Through a pan-cancer study, their effects were correlated with immune subtypes, tumor microenvironment, and drug resistance. Using The Cancer Genome Atlas pan-cancer data, expression profiles of EGFL6, −7, and −8, and their association with the patient survival rate and tumor microenvironment were analyzed in 33 types of cancers. The expression of the EGFL family was different in different cancer types, revealing the heterogeneity among cancers. The results showed that the expression of EGFL8 was lower than EGFL6 and EGFL7 among all cancer types, wherein EGFL7 had the highest expression. The univariate Cox proportional hazard regression model showed that EGFL6 and EGFL7 were the risk factors to predict poor prognosis of cancers. Survival analysis was then used to verify the relationship between gene expression and patient survival. Furthermore, EGFL6, EGFL7, and EGFL8 genes revealed a clear association with immune infiltrate subtypes; they were also related to the infiltration level of stromal cells and immune cells with different degrees. Moreover, they were negatively correlated with the characteristics of cancer stem cells measured by DNAs and RNAs. In addition, EGFL6, −7, and −8 were more likely to contribute to the resistance of cancer cells. Our systematic analysis of EGFL gene expression and their correlation with immune infiltration, tumor microenvironment, and prognosis of cancer patients emphasized the necessity of studying each EGFL member as a separate entity within each particular type of cancer. Simultaneously, EGFL6, −7, and −8 signals were verified as promising targets for cancer therapies, although further laboratory validation is still required.

Distinctive gene expression profiles of CD34 cells from patients with myelodysplastic syndrome characterized by specific chromosomal abnormalities

Blood ◽  
2004 ◽  
Vol 104 (13) ◽  
pp. 4210-4218 ◽  
Author(s):  
Guibin Chen ◽  
Weihua Zeng ◽  
Akira Miyazato ◽  
Eric Billings ◽  
Jaroslaw P. Maciejewski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Small Sample ◽  
Hematopoietic Progenitor Cell ◽  
Molecular Characteristics ◽  
Hematopoietic Progenitor ◽  
Trisomy 8 ◽  
Monosomy 7 ◽  
Cd34 Cells

Abstract Aneuploidy, especially monosomy 7 and trisomy 8, is a frequent cytogenetic abnormality in the myelodysplastic syndromes (MDSs). Patients with monosomy 7 and trisomy 8 have distinctly different clinical courses, responses to therapy, and survival probabilities. To determine disease-specific molecular characteristics, we analyzed the gene expression pattern in purified CD34 hematopoietic progenitor cells obtained from MDS patients with monosomy 7 and trisomy 8 using Affymetrix GeneChips. Two methods were employed: standard hybridization and a small-sample RNA amplification protocol for the limited amounts of RNA available from individual cases; results were comparable between these 2 techniques. Microarray data were confirmed by gene amplification and flow cytometry using individual patient samples. Genes related to hematopoietic progenitor cell proliferation and blood cell function were dysregulated in CD34 cells of both monosomy 7 and trisomy 8 MDS. In trisomy 8, up-regulated genes were primarily involved in immune and inflammatory responses, and down-regulated genes have been implicated in apoptosis inhibition. CD34 cells in monosomy 7 showed up-regulation of genes inducing leukemia transformation and tumorigenesis and apoptosis and down-regulation of genes controlling cell growth and differentiation. These results imply distinct molecular mechanisms for monosomy 7 and trisomy 8 MDS and implicate specific pathogenic pathways.

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

AbstractThe genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

scholarly journals Integrative Analysis Reveals Comprehensive Altered Metabolic Genes Linking with Tumor Epigenetics Modification in Pan-Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yahui Shi ◽  
Jinfen Wei ◽  
Zixi Chen ◽  
Yuchen Yuan ◽  
Xingsong Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Histone Acetylation ◽  
Epigenetic Modification ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Altered Expression ◽  
Metabolic Genes ◽  
Cancer Types ◽  
Pan Cancer

Background. Cancer cells undergo various rewiring of metabolism and dysfunction of epigenetic modification to support their biosynthetic needs. Although the major features of metabolic reprogramming have been elucidated, the global metabolic genes linking epigenetics were overlooked in pan-cancer. Objectives. Identifying the critical metabolic signatures with differential expressions which contributes to the epigenetic alternations across cancer types is an urgent issue for providing the potential targets for cancer therapy. Method. The differential gene expression and DNA methylation were analyzed by using the 5726 samples data from the Cancer Genome Atlas (TCGA). Results. Firstly, we analyzed the differential expression of metabolic genes and found that cancer underwent overall metabolism reprogramming, which exhibited a similar expression trend with the data from the Gene Expression Omnibus (GEO) database. Secondly, the regulatory network of histone acetylation and DNA methylation according to altered expression of metabolism genes was summarized in our results. Then, the survival analysis showed that high expression of DNMT3B had a poorer overall survival in 5 cancer types. Integrative altered methylation and expression revealed specific genes influenced by DNMT3B through DNA methylation across cancers. These genes do not overlap across various cancer types and are involved in different function annotations depending on the tissues, which indicated DNMT3B might influence DNA methylation in tissue specificity. Conclusions. Our research clarifies some key metabolic genes, ACLY, SLC2A1, KAT2A, and DNMT3B, which are most disordered and indirectly contribute to the dysfunction of histone acetylation and DNA methylation in cancer. We also found some potential genes in different cancer types influenced by DNMT3B. Our study highlights possible epigenetic disorders resulting from the deregulation of metabolic genes in pan-cancer and provides potential therapy in the clinical treatment of human cancer.

scholarly journals Molecular characteristics of papillary thyroid carcinomas without BRAF mutation or RET/PTC rearrangement: relationship with clinico-pathological features

2009 ◽  
Vol 16 (2) ◽  
pp. 467-481 ◽  
Author(s):  
Stéphanie Durand ◽  
Carole Ferraro-Peyret ◽  
Mireille Joufre ◽  
Annie Chave ◽  
Françoise Borson-Chazot ◽  
...  
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Expression Profiles ◽  
Strong Association ◽  
The Other ◽  
Marker Genes ◽  
Molecular Characteristics ◽  
Papillary Thyroid ◽  
Thyroid Carcinomas ◽  
Gene Alteration

About 60–70% of papillary thyroid carcinomas (PTC) present a BRAFT1799A gene mutation or a rearrangement of RET gene (RET/PTC). In this study, we examined whether PTC without BRAFT1799A mutation and without RET/PTC rearrangement named PTC-ga(−) were distinguishable from PTC-ga(+) (with one or the other gene alteration) on the basis of gene expression characteristics. We analyzed the mutational state of 116 PTC and we compared gene expression profiles of PTC-ga(+) and PTC-ga(−) from data of a 200 gene macroarray and quantitative PCR. Seventy five PTC were PTC-ga(+) and 41 were PTC-ga(−). Unsupervised analyses of macroarray data by hierarchical clustering led to a complete segregation of PTC-ga(+) and PTC-ga(−). In a series of 42 genes previously recognized as PTC ‘marker’ genes, 22 were found to be expressed at a comparable level in PTC-ga(−) and normal tissue. Thyroid-specific genes, TPO, TG, DIO1, and DIO2 were under-expressed in PTC-ga(+) but expressed at a normal level in PTC-ga(−). A few genes including DUOX1 and DUOX2 were selectively dys-regulated in PTC-ga(−). Tumor grade of PTC-ga(−) was lower than that of PTC-ga(+). There was a strong association between the mutational state and histiotype of PTC; 81% of PTC follicular variants were corresponded to PTC-ga(−), whereas 84% of PTC of classical form were PTC-ga(+). In conclusion, we show that PTC without BRAFT1799A mutation or RET/PTC rearrangement, mainly corresponding to follicular variants, maintain a thyroid differentiation expression level close to that of normal tissue and should be of better prognosis than PTC with one or the other gene alteration.

scholarly journals Pan-cancer machine learning predictors of primary site of origin and molecular subtype

2018 ◽  
Author(s):  
William F. Flynn ◽  
Sandeep Namburi ◽  
Carolyn A. Paisie ◽  
Honey V. Reddi ◽  
Sheng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Metastatic Cancer ◽  
Gene Expression Profiles ◽  
Primary Site ◽  
Cancer Types ◽  
Tissue Of Origin ◽  
Pan Cancer

ABSTRACTBackgroundIt is estimated by the American Cancer Society that approximately 5% of all metastatic tumors have no defined primary site (tissue) of origin and are classified as cancers of unknown primary (CUPs). The current standard of care for CUP patients depends on immunohistochemistry (IHC) based approaches to identify the primary site. The addition of post-mortem evaluation to IHC based tests helps to reveal the identity of the primary site for only 25% of the CUPs, emphasizing the acute need for better methods of determination of the site of origin. CUP patients are therefore given generic chemotherapeutic agents resulting in poor prognosis. When the tissue of origin is known, patients can be given site specific therapy with significant improvement in clinical outcome. Similarly, identifying the primary site of origin of metastatic cancer is of great importance for designing treatment.Identification of the primary site of origin is an import first step but may not be sufficient information for optimal treatment of the patient. Recent studies, primarily from The Cancer Genome Atlas (TCGA) project, and others, have revealed molecular subtypes in several cancer types with distinct clinical outcome. The molecular subtype captures the fundamental mechanisms driving the cancer and provides information that is essential for the optimal treatment of a cancer. Thus, along with primary site of origin, molecular subtype of a tumor is emerging as a criterion for personalized medicine and patient entry into clinical trials.However, there is no comprehensive toolset available for precise identification of tissue of origin or molecular subtype for precision medicine and translational research.Methods and FindingsWe posited that metastatic tumors will harbor the gene expression profiles of the primary site of origin of the cancer. Therefore, we decided to learn the molecular characteristics of the primary tumors using the large number of cancer genome profiles available from the TCGA project. Our predictors were trained for 33 cancer types and for the 11 cancers where there are established molecular subtypes. We estimated the accuracy of several machine learning models using cross-validation methods. The extensive testing using independent test sets revealed that the predictors had a median sensitivity and specificity of 97.2% and 99.9% respectively without losing classification of any tumor. Subtype classifiers achieved median sensitivity of 87.7% and specificity of 94.5% via cross validation and presented median sensitivity of 79.6% and specificity of 94.6% in two external datasets of 1,999 total samples. Importantly, these external data shows that our classifiers can robustly predict the primary site of origin from external microarray data, metastatic cancer data, and patient-derived xenograft (PDX) data.ConclusionWe have demonstrated the utility of gene expression profiles to solve the important clinical challenge of identifying the primary site of origin and the molecular subtype of cancers based on machine learning algorithms. We show, for the first time to our knowledge, that our pan-cancer classifiers can predict multiple cancers’ primary site of origin from metastatic samples. The predictors will be made available as open source software, freely available for academic non-commercial use.

scholarly journals Impact of between-tissue differences on pan-cancer predictions of drug sensitivity

2021 ◽  
Vol 17 (2) ◽  
pp. e1008720
Author(s):  
John P. Lloyd ◽  
Matthew B. Soellner ◽  
Sofia D. Merajver ◽  
Jun Z. Li
Keyword(s):  
Sample Size ◽  
Cell Lines ◽  
Drug Response ◽  
Joint Analysis ◽  
Cancer Type ◽  
Cancer Prediction ◽  
Cancer Models ◽  
Cancer Types ◽  
Tissue Differences ◽  
Pan Cancer

Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ = 0.88–0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ = 0.11–0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as exclusion of between-tissue signals leads to a decrease in Spearman’s ρ from a range of 0.43–0.62 to 0.30–0.51. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.

scholarly journals Utility of Homologous Recombination Deficiency Biomarkers Across Cancer Types

2021 ◽  
pp. 1270-1280
Author(s):  
Shiro Takamatsu ◽  
J.B. Brown ◽  
Ken Yamaguchi ◽  
Junzo Hamanishi ◽  
Koji Yamanoi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Homologous Recombination ◽  
Expression Profiles ◽  
Chemical Exposure ◽  
Clinical Samples ◽  
Precision Oncology ◽  
Tp53 Mutations ◽  
Pathway Gene ◽  
Cancer Types ◽  
Gene Alterations

PURPOSE Homologous recombination DNA repair deficiency (HRD) is associated with sensitivity to platinum and poly (ADP-ribose) polymerase inhibitors in certain cancer types, including breast, ovarian, pancreatic, and prostate. In these cancers, BRCA1/2 alterations and genomic scar signatures are useful indicators for assessing HRD. However, alterations in other homologous recombination repair (HRR)-related genes and their clinical significance in other cancer types have not been adequately and systematically investigated. METHODS We obtained data sets of all solid tumors in The Cancer Genome Atlas and Cancer Cell Line Encyclopedia, and comprehensively analyzed HRR pathway gene alterations, their loss-of-heterozygosity status, and per-sample genomic scar scores, that is, the HRD score and mutational signature 3 ratio, DNA methylation profiles, gene expression profiles, somatic TP53 mutations, sex, and clinical or in vitro response to chemical exposure. RESULTS Biallelic alterations in HRR genes other than BRCA1/2 were also associated with elevated genomic scar scores. The association between HRR-related gene alterations and genomic scar scores differed significantly by sex and the presence of somatic TP53 mutations. HRD tumors determined by a combination of indices also showed HRD features in gene expression analysis and exhibited significantly higher sensitivity to DNA-damaging agents than non-HRD cases in both clinical samples and cell lines. CONCLUSION This study provides evidence for the usefulness of HRD analysis in all cancer types, improves chemotherapy decision making and its efficacy in clinical settings, and represents a substantial advancement in precision oncology.

scholarly journals Pan-cancer Analysis Identifies LMNB1 as a Target to Redress Th1/Th2 Imbalance and Enhance PARP Inhibitor Response in Human Cancers

2021 ◽  
Author(s):  
Haixiang Qin ◽  
Yingqiang Lu ◽  
Lin Du ◽  
Jingyan Shi ◽  
Haoli Yin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parp Inhibitor ◽  
Homologous Recombination Repair ◽  
Human Cancers ◽  
Th2 Cell ◽  
Recombination Repair ◽  
Cancer Types ◽  
Pan Cancer ◽  
Inhibitor Treatment

Abstract Background: Emerging evidence suggests that LMNB1 is involved in the development of multiple cancer types. However, there is no study reporting the potential role of LMNB1 in a systematic pan-cancer manner.Methods: The gene expression level and potential oncogenic roles of LMNB1 in The Cancer Genome Atlas (TCGA) database were analyzed with Tumor Immune Estimation Resource version 2 (TIMER2.0), Gene Expression Profiling Interactive Analysis version 2 (GEPIA2), UALCAN and Sangerbox tools. Pathway enrichment analysis was carried out to explore the possible mechanism of LMNB1 on tumorigenesis and tumor progression. The therapeutic effects of LMNB1 knockdown combined with PARP inhibition on human cancers were further investigated in vitro. Results: LMNB1 upregulation is generally observed in the tumor tissues of most TCGA cancer types, and is verified in kidney renal clear cell carcinoma using clinical specimens of our institute. High level of LMNB1 expression usually predicts poor overall survival and disease free survival for patients with tumors. Mechanically, LMNB1 level is positively correlated with CD4+ Th2 cell infiltration and DNA homologous recombination repair gene expression. In vitro experiments reveal that targeting LMNB1 has a synergistic effect on prostate cancer with PARP inhibitor treatment. Conclusions: LMNB1 is a biomarker of CD4+ Th2 cell infiltration and DNA homologous recombination repair in human cancers. Blockage of LMNB1 combined with PARP inhibitor treatment could be a promising therapeutic strategy for patients with cancers.

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