scholarly journals Tumor cell total mRNA expression shapes the molecular and clinical phenotype of cancer

2021 ◽  
Author(s):  
Shaolong Cao ◽  
Jennifer Wang ◽  
Shuangxi Ji ◽  
Peng Yang ◽  
Matthew Montierth ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Mrna Expression ◽  
Genetic Alterations ◽  
Individual Variability ◽  
Cancer Type ◽  
Sequencing Data ◽  
Increased Risk ◽  
Mrna Content ◽  
Risk Of Disease ◽  
Pan Cancer

Abstract Cancers can vary greatly in their transcriptomes. In contrast to alterations in specific genes or pathways, differences in tumor cell total mRNA content have not been comprehensively assessed. Technical and analytical challenges have impeded examination of total mRNA expression at scale across cancers. To address this, we developed a model for quantifying tumor-specific total mRNA expression (TmS) from bulk sequencing data, which performs transcriptomic deconvolution while adjusting for mixed genomes. We used single-cell RNA sequencing data to demonstrate total mRNA expression as a feature of tumor phenotype. We estimated and validated TmS in 5,015 patients across 15 cancer types identifying significant inter-individual variability. At a pan-cancer level, high TmS is associated with increased risk of disease progression and death. Cancer type-specific patterns of genetic alterations, intra-tumor genetic heterogeneity, as well as pan-cancer trends in metabolic dysregulation and hypoxia contribute to TmS. Taken together, our results suggest that measuring cell-type specific total mRNA expression offers a broader perspective of tracking cancer transcriptomes, which has important biological and clinical implications.

Role of DNA polymerases family members in cancer: The pan-cancer perspective.

2021 ◽  
Vol 39 (3_suppl) ◽  
pp. 475-475
Author(s):  
Zhiwen Luo ◽  
Xinyu Bi ◽  
Xingang Bi
Keyword(s):  
Expression Pattern ◽  
Drug Target ◽  
Dna Polymerases ◽  
Family Members ◽  
Prognostic Biomarkers ◽  
Cancer Type ◽  
Sequencing Data ◽  
Clinical Value ◽  
Bioinformatics Analyses ◽  
Pan Cancer

475 Background: DNA polymerases family (DNA pols) has a lengthy reported significant influence on the initiation, development, and progress of cancer. However, the pan-cancer value of whole family members was poorly done. Our study intends to demonstrate the expression pattern and clinical cancer value of DNA pols members as prognostic biomarkers and a therapeutic target of pan-cancer. Methods: Comprehensive bioinformatics analyses were done using data from TCGA and CCLE. MultiCox regression was done to select tumor prognosis-related members. Nomogram was constructed to predict the overall survival (OS) across cancer patients. Transcription factor, GO, IPA, and GSEA enrichments were done to explore regulatory mechanisms and functions. Results: A total of 22 DNA pols were identified to have a potential to diagnostic value, and 10 DNA pols have a pan-cancer prognostic value under various stages, and cancer type, among which overexpression of 6 DNA-pols (POLA2, POLD1, POLD2, POLE2, POLE4, and POLQ) was found to be significantly related to worse outcomes regarding OS, while 4 DNA-pols (POLH, POLL, POLN, and REV1) significantly related to better outcomes. A 5-DNA pols based risk score (POLQ, POLD2, POLL, POLH, and REV1) was generated by MultiCox regression with a nomogram validated an accurate predictive efficacy. MYB Proto-Oncogene Like 2 (MYBL2) was identified as transcription factors of prognostic DNA pols in pan-cancer, and IPA mimic experiment reveals inhibiting MYBL2 could be a drug target to recover and balance the dysregulated expression pattern of DNA pols in pan-cancer. GO, IPA, and GSEA enrichments revealed functions and pathways altered by DNA pols in cancer, and the results were supported by pan-cancer cell sequencing data. Conclusions: DNA pols have a pan-cancer clinical value and can work as potential prognostic biomarkers. Furthermore, MYBL2 could be a drug target for pan-cancer.

Correlation between JAK1/2 expression and immune-related genes and JAK2 gene variants: A pan-cancer analysis.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15057-e15057
Author(s):  
Lichao Xu ◽  
Ding Zhang ◽  
Guoqiang Wang ◽  
Chao Chen ◽  
Ying Wang ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Copy Number ◽  
Checkpoint Inhibitors ◽  
The Cancer Genome Atlas ◽  
Gene Variants ◽  
Missense Mutations ◽  
Sequencing Data ◽  
Primary Resistance ◽  
Immune Related Genes ◽  
Pan Cancer

e15057 Background: Loss of function mutations for Janus kinases 1/2 (JAK1/2) have shown to be the underling mechanism of primary resistance to immune checkpoint inhibitors (ICIs). However, the correlation between JAK1/2 expression and immune-related genes have not been studied. Methods: Survival, mRNA expression and whole-exome sequencing data from 32 pan-cancer atlas studies were obtained from The Cancer Genome Atlas (TCGA). Correlations between JAK1/2 expression and immune-related genes were depicted in heatmaps. We also analyzed the association between JAK2 gene variants and JAK2 expression. Results: In total, 10071 samples with mRNA expression data were included for analysis. Expression of 46 immune-related genes were positively correlated with JAK2 expression in 25 tumors instead of JAK1 expression. Patients with higher expression of JAK2 had better prognosis than patients with lower expression of JAK2 in 13 tumors. Among 10071 patients, 363 (3.60%) patients harbored JAK2 variants, including 8 with frame shift mutations, 44 with nonsense mutations, 142 with missense mutations, 11 with splices, 8 with fusions, 90 with copy-number reduction and 116 with copy-number amplification. There was no difference in JAK2 expression between patients with JAK2 variants and those without JAK2 variants. However, JAK2 fusion (2.20%, 8/363) and amplification (31.96%, 116/363) were associated with higher JAK2 expression. Conclusions: Our pan-cancer analysis found that JAK2 expression was correlated with immune-related genes and the prognosis of cancer patients. JAK2 fusion and amplification increased the expression of JAK2. Altogether, patients with high JAK2 expression may benefit from ICIs.

Mutational profile of HPV-driven head and neck cancers according to tobacco consumption.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e17535-e17535
Author(s):  
Haitham Mirghani ◽  
Ludovic Lacroix ◽  
Odile Casiraghi ◽  
Anne Auperin ◽  
Caroline Rossoni ◽  
...  
Keyword(s):  
Smoking Status ◽  
Tobacco Consumption ◽  
Genetic Alterations ◽  
Smoking History ◽  
Targeted Next Generation Sequencing ◽  
Significant Survival ◽  
Increased Risk ◽  
Significant Difference ◽  
Study Population ◽  
Risk Of Disease

e17535 Background: HPV-driven oropharyngeal cancer (OPC) patients are characterized by a better prognosis than their HPV-negative counterparts. However, this significant survival advantage is not homogeneous and studies have highlighted that among HPV-positive patients those with a smoking history have a significantly increased risk of disease progression and death compared to those who have never smoked. The reason why tobacco consumption impacts negatively the prognosis is still elusive. Tobacco might induce additional genetic alterations leading to a more aggressive phenotype. The purpose of this study is to characterize the mutational profile of HPV-positive OPC by smoking status. We hypothesize a higher frequency of mutations affecting among smokers. Methods: Targeted next-generation sequencing of 38 oncogenes/tumor suppressor genes that are commonly mutated in cancers caused by tobacco/alcohol consumption was performed in 62 HPV-driven OPC cases stratified by smoking status. Results: The study population included 37 (60%) non-smokers and 25 (40%) smokers distributed as follows: 1 (4%) patient smoked <10 pack-year (PY), 8 (32%) patients between 10-20 PY and 16 (64%) >20 PY. Twenty (31%) patients had no mutation, 14 (23%) had 1 mutation and 28 (46%) had 2 or more mutations. The most commonly mutated genes regardless of tobacco consumption were PIK3CA (20%), MLL2 (20%), TP53 (8%), FAT 1 (15%), FBXW7 (16%), NOTCH 1 (9%) and FGFR3 (9%). Mutation rate was not significantly different in smokers compared to non-smokers even when analyses focused on heavy smokers (>20 pack-years compared to <20 pack-years). Similarly there was no significant difference in mutations patterns according to tobacco consumption. The 3 years overall survival, disease-specific-survival and loco-regional-control rates for the whole cohort are respectively 88% (95% CI: 76.4-94.1), 88% (95% CI: 76.4-94.1) and 80.6% (95% CI: 67.5-88.8). Despite a median follow-up was 4.5 years (6 months to 11.7 years), the few number of events (13 relapses, 13 deaths including 10 due to OPC) precludes detailed prognosis analyses. Conclusions: HPV-driven OPC patients with a smoking history have a comparable mutational rate than non-smokers. Smoking impact on the prognosis isn’t attributable to the mutational burden. Further studies are warranted.

scholarly journals Pan-cancer detection of driver genes at the single-patient resolution

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Joel Nulsen ◽  
Hrvoje Misetic ◽  
Christopher Yau ◽  
Francesca D. Ciccarelli
Keyword(s):  
False Positive Rate ◽  
Genetic Alterations ◽  
Therapeutic Interventions ◽  
Precision Oncology ◽  
Cancer Type ◽  
Rare Cancer ◽  
Driver Genes ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

Abstract Background Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. Results We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. Conclusions sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

scholarly journals Differing total mRNA expression shapes the molecular and clinical phenotype of cancer

2020 ◽  
Author(s):  
Shaolong Cao ◽  
Jennifer R. Wang ◽  
Shuangxi Ji ◽  
Peng Yang ◽  
Jingxiao Chen ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Single Cell ◽  
Computational Method ◽  
Model Systems ◽  
Intratumor Heterogeneity ◽  
Tumor Type ◽  
Single Cell Sequencing ◽  
Molecular Features ◽  
Increased Risk ◽  
Mrna Content

AbstractCancers can vary greatly in their transcriptomes. In contrast to alterations in specific genes or pathways, the significance of differences in tumor cell total mRNA content is poorly understood. Studies using single-cell sequencing or model systems have suggested a role for total mRNA content in regulating cellular phenotypes. However, analytical challenges related to technical artifacts and cellular admixture have impeded examination of total mRNA expression at scale across cancers. To address this, we evaluated total mRNA expression using single cell sequencing, and developed a computational method for quantifying tumor-specific total mRNA expression (TmS) from bulk sequencing data. We systematically estimated TmS in 5,181 patients across 15 cancer types and observed close correlations with clinicopathologic characteristics and molecular features, where high TmS generally accompanies high-risk disease. At a pan-cancer level, high TmS is associated with increased risk of disease progression and death. Moreover, TmS captures tumor type-specific effects of somatic mutations, chromosomal instability, and hypoxia, as well as aspects of intratumor heterogeneity. Taken together, our results suggest that measuring total mRNA expression offers a broader perspective of tracking cancer transcriptomes, which has important clinical and biological implications.

scholarly journals Pan-cancer analyses of human nuclear receptors reveal transcriptome diversity and prognostic value across cancer types

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Toshima Z. Parris
Keyword(s):  
Human Cancer ◽  
The Cancer Genome Atlas ◽  
Transcriptional Networks ◽  
Cancer Type ◽  
Sequencing Data ◽  
Pairwise Correlation ◽  
Organ Systems ◽  
Cancer Types ◽  
Disease Stratification ◽  
Pan Cancer

AbstractThe human nuclear receptor (NR) superfamily comprises 48 ligand-dependent transcription factors that play regulatory roles in physiology and pathophysiology. In cancer, NRs have long served as predictors of disease stratification, treatment response, and clinical outcome. The Cancer Genome Atlas (TCGA) Pan-Cancer project provides a wealth of genetic data for a large number of human cancer types. Here, we examined NR transcriptional activity in 8,526 patient samples from 33 TCGA ‘Pan-Cancer’ diseases and 11 ‘Pan-Cancer’ organ systems using RNA sequencing data. The web-based Kaplan-Meier (KM) plotter tool was then used to evaluate the prognostic potential of NR gene expression in 21/33 cancer types. Although, most NRs were significantly underexpressed in cancer, NR expression (moderate to high expression levels) was predominantly restricted (46%) to specific tissues, particularly cancers representing gynecologic, urologic, and gastrointestinal ‘Pan-Cancer’ organ systems. Intriguingly, a relationship emerged between recurrent positive pairwise correlation of Class IV NRs in most cancers. NR expression was also revealed to play a profound effect on patient overall survival rates, with ≥5 prognostic NRs identified per cancer type. Taken together, these findings highlighted the complexity of NR transcriptional networks in cancer and identified novel therapeutic targets for specific cancer types.

scholarly journals Pan-cancer detection of driver genes at the single-patient resolution

2020 ◽  
Author(s):  
Joel Nulsen ◽  
Hrvoje Misetic ◽  
Christopher Yau ◽  
Francesca D. Ciccarelli
Keyword(s):  
False Positive Rate ◽  
Genetic Alterations ◽  
Therapeutic Interventions ◽  
Precision Oncology ◽  
Cancer Type ◽  
Rare Cancer ◽  
Driver Genes ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

ABSTRACTBackgroundIdentifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions.ResultsWe present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways.ConclusionssysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

scholarly journals Integrated molecular characterisation of endometrioid ovarian carcinoma identifies opportunities for stratification

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Robert L. Hollis ◽  
Barbara Stanley ◽  
John P. Thomson ◽  
Michael Churchman ◽  
Ian Croy ◽  
...  
Keyword(s):  
High Risk ◽  
Ovarian Carcinoma ◽  
Expression Patterns ◽  
Parp Inhibitors ◽  
Receptor Expression ◽  
Cancer Type ◽  
Sequencing Data ◽  
Molecular Features ◽  
Endometrioid Ovarian Carcinoma ◽  
Molecular Landscape

AbstractEndometrioid ovarian carcinoma (EnOC) is an under-investigated ovarian cancer type. Recent studies have described disease subtypes defined by genomics and hormone receptor expression patterns; here, we determine the relationship between these subtyping layers to define the molecular landscape of EnOC with high granularity and identify therapeutic vulnerabilities in high-risk cases. Whole exome sequencing data were integrated with progesterone and oestrogen receptor (PR and ER) expression-defined subtypes in 90 EnOC cases following robust pathological assessment, revealing dominant clinical and molecular features in the resulting integrated subtypes. We demonstrate significant correlation between subtyping approaches: PR-high (PR + /ER + , PR + /ER−) cases were predominantly CTNNB1-mutant (73.2% vs 18.4%, P < 0.001), while PR-low (PR−/ER + , PR−/ER−) cases displayed higher TP53 mutation frequency (38.8% vs 7.3%, P = 0.001), greater genomic complexity (P = 0.007) and more frequent copy number alterations (P = 0.001). PR-high EnOC patients experience favourable disease-specific survival independent of clinicopathological and genomic features (HR = 0.16, 95% CI 0.04–0.71). TP53 mutation further delineates the outcome of patients with PR-low tumours (HR = 2.56, 95% CI 1.14–5.75). A simple, routinely applicable, classification algorithm utilising immunohistochemistry for PR and p53 recapitulated these subtypes and their survival profiles. The genomic profile of high-risk EnOC subtypes suggests that inhibitors of the MAPK and PI3K-AKT pathways, alongside PARP inhibitors, represent promising candidate agents for improving patient survival. Patients with PR-low TP53-mutant EnOC have the greatest unmet clinical need, while PR-high tumours—which are typically CTNNB1-mutant and TP53 wild-type—experience excellent survival and may represent candidates for trials investigating de-escalation of adjuvant chemotherapy to agents such as endocrine therapy.

scholarly journals Genetic Determinants for Prediction of Outcome of Patients with Papillary Thyroid Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2048
Author(s):  
Antónia Afonso Póvoa ◽  
Elisabete Teixeira ◽  
Maria Rosa Bella-Cueto ◽  
Rui Batista ◽  
Ana Pestana ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Patient Outcomes ◽  
Genetic Alterations ◽  
Papillary Thyroid ◽  
Tissue Samples ◽  
Primary Tumors ◽  
Persistent Disease ◽  
Increased Risk ◽  
Structural Disease

Papillary thyroid carcinoma (PTC) usually presents an excellent prognosis, but some patients present with aggressive metastatic disease. BRAF, RAS, and TERT promoter (TERTp) genes are altered in PTC, and their impact on patient outcomes remains controversial. We aimed to determine the role of genetic alterations in PTC patient outcomes (recurrent/persistent disease, structural disease, and disease-specific mortality (DSM)). The series included 241 PTC patients submitted to surgery, between 2002–2015, in a single hospital. DNA was extracted from tissue samples of 287 lesions (primary tumors and metastases). Molecular alterations were detected by Sanger sequencing. Primary tumors presented 143 BRAF, 16 TERTp, and 13 RAS mutations. Isolated TERTpmut showed increased risk of structural disease (HR = 7.0, p < 0.001) and DSM (HR = 10.1, p = 0.001). Combined genotypes, BRAFwt/TERTpmut (HR = 6.8, p = 0.003), BRAFmut/TERTpmut (HR = 3.2, p = 0.056) and BRAFmut/TERTpwt (HR = 2.2, p = 0.023) showed increased risk of recurrent/persistent disease. Patients with tumors BRAFwt/TERTpmut (HR = 24.2, p < 0.001) and BRAFmut/TERTpmut (HR = 11.5, p = 0.002) showed increased risk of structural disease. DSM was significantly increased in patients with TERTpmut regardless of BRAF status (BRAFmut/TERTpmut, log-rank p < 0.001; BRAFwt/TERTpmut, log-rank p < 0.001). Our results indicate that molecular markers may have a role in predicting PTC patients’ outcome. BRAFmut/TERTpwt tumors were prone to associate with local aggressiveness (recurrent/persistent disease), whereas TERTpmut tumors were predisposed to recurrent structural disease and DSM.

scholarly journals Evaluating of the association between ABO blood groups and coronavirus disease 2019 (COVID-19) in Iraqi patients

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Ali H. Ad’hiah ◽  
Risala H. Allami ◽  
Raghdan H. Mohsin ◽  
Maha H. Abdullah ◽  
Ali J. R. AL-Sa’ady ◽  
...  
Keyword(s):  
Blood Groups ◽  
Abo Blood Groups ◽  
Risk Of Death ◽  
Increased Risk ◽  
Fluorescence Probing ◽  
Group A ◽  
Risk Of Disease ◽  
Novel Coronavirus ◽  
Polymorphic System ◽  
Made In

Abstract Background Susceptibility to the pandemic coronavirus disease 2019 (COVID-19) has recently been associated with ABO blood groups in patients of different ethnicities. This study sought to understand the genetic association of this polymorphic system with risk of disease in Iraqi patients. Two outcomes of COVID-19, recovery and death, were also explored. ABO blood groups were determined in 300 hospitalized COVID-19 Iraqi patients (159 under therapy, 104 recovered, and 37 deceased) and 595 healthy blood donors. The detection kit for 2019 novel coronavirus (2019-nCoV) RNA (PCR-Fluorescence Probing) was used in the diagnosis of disease. Results Mean age was significantly increased in patients compared to controls (49.8 ± 11.7 vs. 28.9 ± 6.6 years; p < 0.001). A similar observation was made in recovered (42.1 ± 10.4 vs. 28.9 ± 6.6 years; p < 0.001) and deceased (53.6 ± 9.7 vs. 28.9 ± 6.6 years; p < 0.001) cases. The mean age was also significantly increased in deceased cases compared to recovered cases (53.6 ± 9.7 vs. 42.1 ± 10.4 years; p < 0.001). There were gender-dependent differences in COVID-19 prevalence. The percentage of COVID-19 was higher in males than in females (all cases: 59.7 vs. 40.3%; recovered cases: 55.8 vs. 44.2%). Such male-gender preponderance was more pronounced in deceased cases (67.6 vs. 32.4%). Logistic regression analysis revealed that groups AB and B + AB were significantly associated with increased risk to develop COVID-19 (OR = 3.10; 95% CI 1.59–6.05; pc = 0.007 and OR = 2.16; 95% CI 1.28–3.63; pc = 0.028, respectively). No ABO-associated risk was observed in recovered cases. On the contrary, groups A (OR = 14.60; 95% CI 2.85–74.88; pc = 0.007), AB (OR = 12.92; 95% CI 2.11–79.29; pc = 0.042), A + AB (OR = 14.67; 95% CI 2.98–72.33; pc = 0.007), and A + B + AB (OR = 9.67; 95% CI 2.02–46.24; pc = 0.035) were associated with increased risk of death in deceased cases. Conclusions The findings of this study suggest that group AB may be a susceptibility biomarker for COVID-19, while group A may be associated with increased risk of death.

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