scholarly journals Quantified CIN Score From Cell-free DNA as a Novel Noninvasive Predictor of Survival in Patients With Spinal Metastasis

2021 ◽  
Vol 9 ◽  
Author(s):  
Su Chen ◽  
Minglei Yang ◽  
Nanzhe Zhong ◽  
Dong Yu ◽  
Jiao Jian ◽  
...  
Keyword(s):  
Spinal Metastasis ◽  
Metastatic Cancer ◽  
Chromosome Instability ◽  
Validation Dataset ◽  
Survival Prediction ◽  
Survival Prognosis ◽  
Tokuhashi Score ◽  
Number Variation ◽  
Cancer Types ◽  
Low Coverage

Purpose: Most currently available scores for survival prediction of patients with bone metastasis lack accuracy. In this study, we present a novel quantified CIN (Chromosome Instability) score modeled from cfDNA copy number variation (CNV) for survival prediction.Experimental Design: Plasma samples collected from 67 patients with bone metastases from 11 different cancer types between November 2015 and May 2016 were sent through low-coverage whole genome sequencing followed by CIN computation to make a correlation analysis between the CIN score and survival prognosis. The results were validated in an independent cohort of 213 patients.Results: During the median follow-up period of 598 (95% CI 364–832) days until December 25, 2018, 124 (44.3%) of the total 280 patients died. Analysis of the discovery dataset showed that CIN score = 12 was the optimal CIN cutoff. Validation dataset showed that CIN was elevated (score ≥12) in 87 (40.8%) patients, including 5 (5.75%) with head and neck cancer, 11 (12.6%) with liver and gallbladder cancer, 11 (12.6%) with cancer from unidentified sites, 21 (24.1%) with lung cancer, 7 (8.05%) with breast cancer, 4 (4.60%) with thyroid cancer, 6 (6.90%) with colorectal cancer, 4 (4.60%) with kidney cancer, 2 (2.30%) with prostate cancer, and 16 (18.4%) with other types of cancer. Further analysis showed that patients with elevated CIN were associated with worse survival (p < 0.001). For patients with low Tokuhashi score (≤8) who had predictive survival of less than 6 months, the CIN score was able to distinguish patients with a median overall survival (OS) of 443 days (95% CI 301–585) from those with a median OS of 258 days (95% CI 184–332).Conclusion: CNV examination in bone metastatic cancer from cfDNA is superior to the traditional predictive model in that it provides a noninvasive and objective method of monitoring the survival of patients with spine metastasis.

scholarly journals Glioblastoma Multiforme Patient Survival Prediction

2021 ◽  
Author(s):  
Snehal Rajput ◽  
Rupal Agravat ◽  
Mohendra Roy ◽  
Mehul S Raval
Keyword(s):  
Brain Tumor ◽  
Glioblastoma Multiforme ◽  
Image Features ◽  
Validation Dataset ◽  
Gradient Boosting ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Cancer Tissue ◽  
Shape Features ◽  
Survival Prognosis

Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to spatial and temporal intra-tissue inhomogeneity, location and the extent of the cancer tissue, it is difficult to detect and dissect the tumor regions. In this paper, we propose survival prognosis models using four regressors operating on handcrafted image-based and radiomics features. We hypothesize that the radiomics shape features have the highest correlation with survival prediction. The proposed approaches were assessed on the Brain Tumor Segmentation (BraTS-2020) challenge dataset. The highest accuracy of image features with random forest regressor approach was 51.5\% for the training and 51.7\% for the validation dataset. The gradient boosting regressor with shape features gave an accuracy of 91.5\% and 62.1\% on training and validation datasets respectively. It is better than the BraTS 2020 survival prediction challenge winners on the training and validation datasets. Our work shows that handcrafted features exhibit a strong correlation with survival prediction. The consensus based regressor with gradient boosting and radiomics shape features is the best combination for survival prediction.

scholarly journals Usefulness of Tokuhashi Score in Survival Prediction of Patients Operated for Vertebral Metastatic Disease

2017 ◽  
Vol 7 (3) ◽  
pp. 260-265 ◽  
Author(s):  
Marcelo Gruenberg ◽  
Maximiliano E. Mereles ◽  
Gastón O. Camino Willhuber ◽  
Marcelo Valacco ◽  
Matias G. Petracchi ◽  
...  
Keyword(s):  
Surgical Treatment ◽  
Spinal Metastasis ◽  
Survival Prediction ◽  
Survival Times ◽  
Primary Tumor Site ◽  
Tokuhashi Score ◽  
Group 3 ◽  
Group 2 ◽  
Almost All ◽  
Group 1

Study Design: Retrospective study. Objective: Spinal metastasis can produce pain, deformity, neurological compromise and can decrease life expectancy. Surgical management is usually indicated for pain control, neurological decompression, and to avoid deformity progression. Tokuhashi et al created a scoring system to estimate survival and stratify surgical treatment based on established parameters. Our objective was to evaluate the usefulness of Tokuhashi scoring (TS) system by comparing the predicted and real survival times and analyze the survival time according to the type of tumor. Methods: From 2004 to 2014, 105 patients with vertebral metastasis who underwent surgical treatment were enrolled and retrospectively analyzed. Preoperative TS was performed in all cases. Patients were classified into 3 groups according to TS; group 1 (TS 0-8), group 2 (TS 9-11), and group 3 (TS 12-15). Patients’ average age was 61.5 years, main primary tumor site were as follows: kidney (23%), lung (19%), and breast (18%). Results: The Tokuhashi general concordance was 67.6%. Per group concordance was as follows: group 1 80%, in group 2, only 33% of concordance was observed. In group 3, 100% of concordance was observed. In group 2, the most common primary sites were breast and kidney and the mean survival was 20 and 22.3 months, respectively, both longer than that expected for this group. Conclusions: Tokuhashi concordance was acceptable in our study, particularly in lower and higher scores. The lesser concordance observed in group 2 (33.3%) was observed in almost all tumors. For our practice, TS constitutes an acceptable tool to define survival, particularly in lower and higher scores.

scholarly journals Glioblastoma Multiforme Patient Survival Prediction

2021 ◽  
Author(s):  
Snehal Rajput ◽  
Rupal Agravat ◽  
Mohendra Roy ◽  
Mehul S Raval
Keyword(s):  
Brain Tumor ◽  
Glioblastoma Multiforme ◽  
Image Features ◽  
Validation Dataset ◽  
Gradient Boosting ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Cancer Tissue ◽  
Shape Features ◽  
Survival Prognosis

Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to spatial and temporal intra-tissue inhomogeneity, location and the extent of the cancer tissue, it is difficult to detect and dissect the tumor regions. In this paper, we propose survival prognosis models using four regressors operating on handcrafted image-based and radiomics features. We hypothesize that the radiomics shape features have the highest correlation with survival prediction. The proposed approaches were assessed on the Brain Tumor Segmentation (BraTS-2020) challenge dataset. The highest accuracy of image features with random forest regressor approach was 51.5\% for the training and 51.7\% for the validation dataset. The gradient boosting regressor with shape features gave an accuracy of 91.5\% and 62.1\% on training and validation datasets respectively. It is better than the BraTS 2020 survival prediction challenge winners on the training and validation datasets. Our work shows that handcrafted features exhibit a strong correlation with survival prediction. The consensus based regressor with gradient boosting and radiomics shape features is the best combination for survival prediction.

scholarly journals Glioblastoma Multiforme Patient Survival Prediction

2021 ◽  
Author(s):  
Mehul Raval ◽  
Snehal Rajput ◽  
Mohendra Roy ◽  
Rupal Agravat
Keyword(s):  
Brain Tumor ◽  
Glioblastoma Multiforme ◽  
Image Features ◽  
Validation Dataset ◽  
Gradient Boosting ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Cancer Tissue ◽  
Shape Features ◽  
Survival Prognosis

Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to spatial and temporal intra-tissue inhomogeneity, location, the extent of cancer tissue, it is difficult to detect and dissect tumor regions. In this paper, we propose survival prognosis models using four regressors operating on handcrafted image-based and radiomics features. We hypothesize that radiomics shape features have highest correlation with survival prediction. The proposed approaches were evaluated on Brain Tumor Segmentation (BraTS-2020) challenge dataset. The image features with random forest regressor approach’s highest accuracy was 51.5% and 51.7% for the training and validation dataset. The gradient boosting regressor with shape features gave the accuracy of 91.5% and 62.1% on training and validation datasets. It is better than the BraTS 2020 survival prediction challenge winners on training and validation datasets. Our work shows that handcrafted features exhibit strong correlation with survival prediction. The consensus based regressor with gradient boosting and radiomics shape features is the best combination for survival prediction.

Elevated plasma cell-free DNA copy number variations to predict survival for cancer patient with spinal metastasis.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14522-e14522
Author(s):  
Su Chen ◽  
Minglei Yang ◽  
Nanzhe Zhong ◽  
Qian Ziliang ◽  
Jian Jiao ◽  
...  
Keyword(s):  
Overall Survival ◽  
Copy Number ◽  
Spinal Metastasis ◽  
Metastatic Cancer ◽  
Copy Number Variations ◽  
Hazard Ratio ◽  
Intractable Pain ◽  
Tokuhashi Score ◽  
Common Location

e14522 Background: Approximately 70% of patients with cancer have evidence of spinal metastatic disease at the time of their deaths. The spinal column is the most common location among osseous sites for metastatic deposits. Expected overall survival is the major determinant of treatments for those ones suffering from intractable pain, neurologic deficits, and even paralysis. We sought to evaluate copy number variations (CNV) in spinal metastatic cancer via cfDNA and determine if CNV is associated with prognosis and treatment decision. Methods: In this study, 314 patients with pathologically confirmed spinal metastatic cancers were recruited since November 2015. 314 plasma samples were sent to low-coverage genome-wide sequencing of cfDNA from plasma followed by a customized bioinformatics workflow UCAD. Tokuhashi score was also evaluated for each patient before surgery. Statistical correlation with clinical index like prognosis was estimated. Results: 280 evaluable data were collected (34 samples failed Quality Control). The median follow-up time is 276 days. 114 (40.7%) patients died during follow up till December the 25th, 2018. Elevated CNVs was found in 109 (38.9%) patients, including 9(69.2%) head&neck, 15(46.9%) liver and gallbladder, 27(44.3%) lung, 7(38.9%) breast, 2(28.6%) prostate, 5(19.2%) thyroid cancer, 4 (10.5%) kidney and 20(40.0%) cancer with unknown primary site. Further analyses showed that patients with elevated CNVs were found with worse survival. The median overall survival (OS) was 298 (95% CI: 258-422) days, as compared with those low-CNVs status with median OS 657 (95% CI:433-NA) days (Hazard ratio = 3.73 [95% CI: 2.22-6.27], P < 0.01). Especially for patients with low Tokuhashi score (≤8) who have the predictive survival less than 6 months, CNVs score distinguish those well-prognosis patients with median OS 433 (95% CI: 308-NA) days from the worse survival group with median OS 285(95% CI:243-348) days (Hazard ratio = 2.42 [95% CI:1.38-4.25], P = 0.013). Conclusions: We present the largest cfDNA genomic characterization of spinal metastatic cancers. Specific CNVs features are enriched in spinal metastasis cancers with different primary sites. Elevated CNVs in plasma cfDNA is significantly associated with worse survival in a large spinal metastatic cancer cohort. This demonstrates that cfDNA CNVs could be a useful marker in estimating the survival time of spinal metastasis cancer patients for whom the outcome is mainly dependent on the selection of proper treatment.

Integration of gene interaction information into a reweighted Lasso-Cox model for accurate survival prediction

2020 ◽  
Author(s):  
Wei Wang ◽  
Wei Liu
Keyword(s):  
Cox Model ◽  
Gene Interaction ◽  
Supplementary Information ◽  
Survival Prediction ◽  
Gene Interaction Network ◽  
Clinical Cancer Research ◽  
Prognostic Performance ◽  
Interaction Information ◽  
Cancer Types ◽  
Risk Of Cancer

Abstract Motivation Accurately predicting the risk of cancer patients is a central challenge for clinical cancer research. For high-dimensional gene expression data, Cox proportional hazard model with the least absolute shrinkage and selection operator for variable selection (Lasso-Cox) is one of the most popular feature selection and risk prediction algorithms. However, the Lasso-Cox model treats all genes equally, ignoring the biological characteristics of the genes themselves. This often encounters the problem of poor prognostic performance on independent datasets. Results Here, we propose a Reweighted Lasso-Cox (RLasso-Cox) model to ameliorate this problem by integrating gene interaction information. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. We used random walk to evaluate the topological weight of genes, and then highlighted topologically important genes to improve the generalization ability of the RLasso-Cox model. Experiments on datasets of three cancer types showed that the RLasso-Cox model improves the prognostic accuracy and robustness compared with the Lasso-Cox model and several existing network-based methods. More importantly, the RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types. Availability and implementation http://bioconductor.org/packages/devel/bioc/html/RLassoCox.html Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Learning mutational signatures and their multidimensional genomic properties with TensorSignatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Harald Vöhringer ◽  
Arne Van Hoeck ◽  
Edwin Cuppen ◽  
Moritz Gerstung
Keyword(s):  
Somatic Hypermutation ◽  
Excision Repair ◽  
Metastatic Cancer ◽  
Strand Break ◽  
Lymphoid Leukaemia ◽  
Mutational Signatures ◽  
Transcription Start Sites ◽  
Nucleotide Excision ◽  
Cancer Types ◽  
Underlying Processes

AbstractWe present TensorSignatures, an algorithm to learn mutational signatures jointly across different variant categories and their genomic localisation and properties. The analysis of 2778 primary and 3824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that all signatures operate dynamically in response to genomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis in 7 different cancer types. The algorithm also extracts distinct signatures of replication- and double strand break repair-driven mutagenesis by APOBEC3A and 3B with differential numbers and length of mutation clusters. Finally, TensorSignatures reproduces a signature of somatic hypermutation generating highly clustered variants at transcription start sites of active genes in lymphoid leukaemia, distinct from a general and less clustered signature of Polη-driven translesion synthesis found in a broad range of cancer types. In summary, TensorSignatures elucidates complex mutational footprints by characterising their underlying processes with respect to a multitude of genomic variables.

scholarly journals Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Guoliang Jia ◽  
Zheyu Song ◽  
Zhonghang Xu ◽  
Youmao Tao ◽  
Yuanyu Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cutaneous Melanoma ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Validation Dataset ◽  
Survival Prognosis ◽  
Gene Markers ◽  
Clinical Prognosis ◽  
Logit Regression

Abstract Background Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis. Methods We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein–protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs. Results Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected. Conclusion The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.

An ultrasensitive method for noninvasive pan-cancer early detection based on targeted methylation sequencing of cell-free DNA.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10544-10544
Author(s):  
Tiancheng Han ◽  
Yuanyuan Hong ◽  
Pei Zhihua ◽  
Song Xiaofeng ◽  
Jianing Yu ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Cancer Detection ◽  
Real World ◽  
Early Stage ◽  
Validation Dataset ◽  
Cpg Sites ◽  
Free Dna ◽  
Cancer Types ◽  
Discovery Dataset ◽  
Pan Cancer

10544 Background: Screening the biomarkers from the cell-free DNA (cfDNA) of peripheral blood is a non-invasive and promising method for cancer diagnosis. Among diverse types of biomarkers, epigenetic biomarkers have been reported to be one of the most promising ones. Epigenetic modifications are widespread on the human genome and generally have strong signals due to the similar methylation patterns shared by adjacent CpG sites. Although some epigenetic diagnostic methods have been developed based on cfDNAs, few of them could be applied to pan-cancer and their sensitivities are barely satisfactory for early cancer detection. Methods: Targeted methylation sequencing was performed using our in-house-designed panel targeting regions with abundant cancer-specific methylation CpGs. The cfDNA samples from 80 healthy individuals and 549 cancer patients of 14 cancer types were separately sequenced. The dataset was randomly split into one discovery dataset and one validation dataset. Moreover, cfDNA samples from four cancer patients were diluted with the healthy cfDNAs to generate 12 in vitro simulated samples with low circulating tumor DNA (ctDNA) fraction. Additionally, DNAs extracted from 130 unmatched tumor formalin fixation and paraffin embedding (FFPE) samples of 10 cancer types were sequenced to screen the diagnostic biomarkers. Adjacent CpG sites were first merged into methylation-correlated blocks (MCB) according to their correlations of methylation levels in tumor DNAs. The MCBs with higher methylation levels in tumor DNAs than that of healthy cfDNAs (from the discovery dataset) were defined as our hypermethylation biomarkers. For each cfDNA sample, a hypermethylation score (HM-score) was computed to measure the overall methylation level difference of selected biomarkers. The performance of our method was evaluated with the real-world dataset, while the limit of detection was estimated using the simulated low-ctDNA samples. Results: Our model based on 37 hypermethylation MCB biomarkers achieved an area under the curve (AUC) of 0.89 and 0.86 in the real-world pan-cancer discovery and validation cfDNA datasets, respectively. Furthermore, the overall specificity and sensitivity are 100% and 76.19% in the discovery dataset, and 96.67% and 72.86% in the validation dataset. In the validation dataset, 28/40 (70%) of early-stage colorectal cancer patients and 10/20 (50%) of non-small-cell lung cancer patients were successfully diagnosed. Additionally, all the simulated samples with theoretical ctDNA factions over 0.5% were predicted as diseased, demonstrating the ability of our method to detect tumor signals at early stages. Conclusions: Our cfDNA-based epigenetic method outperforms currently available methods in various cancer types, and is promising to be applied to early-stage cancer detection and samples with low ctDNA fractions.

A Novel Nomogram for Survival Prediction of Patients with Spinal Metastasis from Prostate Cancer

Spine ◽  
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Yujie Liu ◽  
Lin Li ◽  
Dongjie Jiang ◽  
Minglei Yang ◽  
Xin Gao ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Spinal Metastasis ◽  
Survival Prediction
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