scholarly journals Multi-cancer classification; an analysis of neural network complexity

2022 ◽  
Author(s):  
James W. Webber ◽  
Kevin M. Elias
Keyword(s):  
Neural Network ◽  
The Body ◽  
Cancer Classification ◽  
Control Group ◽  
Specific Information ◽  
Cancer Type ◽  
Feed Forward ◽  
Cancer Prediction ◽  
Cancer Types ◽  
Pan Cancer

Background: Cancer identification is generally framed as binary classification, normally discrimination of a control group from a single cancer group. However, such models lack any cancer-specific information, as they are only trained on one cancer type. The models fail to account for competing cancer risks. For example, an ostensibly healthy individual may have any number of different cancer types, and a tumor may originate from one of several primary sites. Pan-cancer evaluation requires a model trained on multiple cancer types, and controls, simultaneously, so that a physician can be directed to the correct area of the body for further testing. Methods: We introduce novel neural network models to address multi-cancer classification problems across several data types commonly applied in cancer prediction, including circulating miRNA expression, protein, and mRNA. In particular, we present an analysis of neural network depth and complexity, and investigate how this relates to classification performance. Comparisons of our models with state-of-the-art neural networks from the literature are also presented. Results: Our analysis evidences that shallow, feed-forward neural net architectures offer greater performance when compared to more complex deep feed-forward, Convolutional Neural Network (CNN), and Graph CNN (GCNN) architectures considered in the literature. Conclusion: The results show that multiple cancers and controls can be classified accurately using the proposed models, across a range of expression technologies in cancer prediction. Impact: This study addresses the important problem of pan-cancer classification, which is often overlooked in the literature. The promising results highlight the urgency for further research.

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

2019 ◽  
Author(s):  
John P. Lloyd ◽  
Matthew 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

ABSTRACTIncreased 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 with MEK inhibitor (MEKi) response and RNA, SNP, and CNV 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 we estimate that exclusion of between-tissue signals leads to a 22% decrease in performance metrics. 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 the 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 Estimating the predictive power of silent mutations on cancer classification and prognosis

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Tal Gutman ◽  
Guy Goren ◽  
Omri Efroni ◽  
Tamir Tuller
Keyword(s):  
Gene Expression ◽  
Predictive Power ◽  
Predictive Ability ◽  
Cancer Classification ◽  
Silent Mutation ◽  
Cancer Type ◽  
Coding Region ◽  
Probability Of Survival ◽  
Diagnosis And Prognosis ◽  
Cancer Types

AbstractIn recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and non-silent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and its ramifications on cancer fitness.

Exploration of the MSI landscape in Chinese pan-cancer patient by Next-Generation Sequencing.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e14576-e14576
Author(s):  
Xinlu Liu ◽  
Jiasheng Xu ◽  
Jian Sun ◽  
Deng Wei ◽  
Xinsheng Zhang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Correlation Analysis ◽  
Cancer Patients ◽  
Cancer Type ◽  
Multiple Tumor ◽  
Treatment Plans ◽  
Cancer Types ◽  
Chinese Cancer Patients ◽  
Colorectal Cancer Patients ◽  
Pan Cancer

e14576 Background: Clinically, MSI had been used as an important molecular marker for the prognosis of colorectal cancer and other solid tumors and the formulation of adjuvant treatment plans, and it had been used to assist in the screening of Lynch syndrome. However, there were currently few reports on the incidence of MSI-H in Chinese pan-cancer patients. This study described the occurrence of MSI in a large multi-center pan-cancer cohort in China, and explored the correlation between MSI and patients' TMB, age, PD-L1 expression and other indicators. Methods: The study included 8361 patients with 8 cancer types from multiple tumor centers. Use immunohistochemistry to detect the expression of MMR protein (MLH1, MSH2, MSH6 and PMS2) in patients with various cancer types to determine the MSI status and detect the expression of PD-L1 in patients. Through NGS technology, 831 genes of 8361 Chinese cancer patients were sequenced and the tumor mutation load of the patients was calculated. The MSI mutations of patients in 8 cancer types were analyzed and the correlation between MSI mutations of patients and the patient's age, TMB and PD-L1 expression was analyzed. Results: The test results showed that MSI patients accounted for 1.66% of pan-cancers. Among them, MSI-H patients accounted for the highest proportion in intestinal cancer, reaching 7.2%. The correlation analysis between MSI and TMB was performed on patients of various cancer types. The results showed that: in each cancer type, MSI-H patients had TMB greater than 10, and 26.83% of MSI-H patients had TMB greater than 100 in colorectal cancer patients. The result of correlation analysis showed that there was no significant correlation between the patient's age and the risk of MSI mutation ( P> 0.05). In addition to PAAD and LUAD, the expression of PD-L1 in MSI-H patients was higher than that in MSS patients in other cancer types( P< 0.05). The correlation analysis between PD-L1 expression and TMB in patients found that in colorectal cancer, the higher the expression of PD-L1, the higher the patient's TMB ( P< 0.05). Conclusions: In this study, we explored the incidence of MSI-H in pan-cancer patients in China and found that the TMB was greater than 10 in patients with MSI-H. Compared with MSS patients, MSI-H patients have higher PD-L1 expression, and the higher the PD-L1 expression in colorectal cancer, the higher the TMB value of patients.

scholarly journals Putatively cancer-specific exon–exon junctions are shared across patients and present in developmental and other non-cancer cells

NAR Cancer ◽  
2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Julianne K David ◽  
Sean K Maden ◽  
Benjamin R Weeder ◽  
Reid F Thompson ◽  
Abhinav Nellore
Keyword(s):  
Rna Splicing ◽  
Tissue Expression ◽  
The Body ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Rna Seq ◽  
Normal Tissues ◽  
Cancer Types ◽  
Exon Junctions ◽  
Cancer Tissues

Abstract This study probes the distribution of putatively cancer-specific junctions across a broad set of publicly available non-cancer human RNA sequencing (RNA-seq) datasets. We compared cancer and non-cancer RNA-seq data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) Project and the Sequence Read Archive. We found that (i) averaging across cancer types, 80.6% of exon–exon junctions thought to be cancer-specific based on comparison with tissue-matched samples (σ = 13.0%) are in fact present in other adult non-cancer tissues throughout the body; (ii) 30.8% of junctions not present in any GTEx or TCGA normal tissues are shared by multiple samples within at least one cancer type cohort, and 87.4% of these distinguish between different cancer types; and (iii) many of these junctions not found in GTEx or TCGA normal tissues (15.4% on average, σ = 2.4%) are also found in embryological and other developmentally associated cells. These findings refine the meaning of RNA splicing event novelty, particularly with respect to the human neoepitope repertoire. Ultimately, cancer-specific exon–exon junctions may have a substantial causal relationship with the biology of disease.

scholarly journals Characterization of Class-3 Semaphorin Receptors, Neuropilins and Plexins, as Therapeutic Targets in a Pan-Cancer Study

Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1816
Author(s):  
Xiaoli Zhang ◽  
Shuai Shao ◽  
Lang Li
Keyword(s):  
Patient Survival ◽  
Cancer Type ◽  
Dependent Manner ◽  
Metastatic Progression ◽  
Drug Responses ◽  
Signal Transducers ◽  
Surface Receptors ◽  
Downstream Signaling ◽  
Cancer Types ◽  
Pan Cancer

Class-3 semaphorins (SEMA3s), initially characterized as axon guidance cues, have been recognized as key regulators for immune responses, angiogenesis, tumorigenesis and drug responses. The functions of SEMA3s are attributed to the activation of downstream signaling cascades mainly mediated by cell surface receptors neuropilins (NRPs) and plexins (PLXNs), yet their roles in human cancers are not completely understood. Here, we provided a detailed pan-cancer analysis of NRPs and PLXNs in their expression, and association with key signal transducers, patient survival, tumor microenvironment (TME), and drug responses. The expression of NRPs and PLXNs were dysregulated in many cancer types, and the majority of them were further dysregulated in metastatic tumors, indicating a role in metastatic progression. Importantly, the expression of these genes was frequently associated with key transducers, patient survival, TME, and drug responses; however, the direction of the association varied for the particular gene queried and the specific cancer type/subtype tested. Specifically, NRP1, NRP2, PLXNA1, PLXNA3, PLXNB3, PLXNC1, and PLXND1 were primarily associated with aggressive phenotypes, whereas the rest were more associated with favorable prognosis. These data highlighted the need to study each as a separate entity in a cancer type- and subtype-dependent manner.

scholarly journals Parkinson‟s Disease Prediction using Modified Gauss-Newton Method in Feed-Forward Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 4986-4993
Keyword(s):  
Neural Network ◽  
Newton Method ◽  
Early Stage ◽  
Optimization Techniques ◽  
The Body ◽  
Probabilistic Methods ◽  
Weight Optimization ◽  
Feed Forward Neural Network ◽  
Brain Disorder ◽  
Feed Forward

Parkinson’s disease (PD) is a brain disorder, characterized by the relapse of the nervous system that spreads gradually in the body. The symptom of PD includes a loss of body control (moderate movement, resting tremors, postural shakiness etc.). So, it is required to detect at an early stage. Machine learning (ML) deals with a variety of probabilistic methods to identify a pattern in a dataset. Therefore, the research is carried out to predict the PD using Multilayer Feed-Forward Neural Network. In Neural Network (NN), weight optimization performed at each layer that plays a major role in the prediction. First-order weight optimization techniques are slow in computation because they reduce the sum of square error using parameter updating in the steepest descent way. In proposed work, a modified recursive Gauss-Newton method is used to optimize the weights for speed up the performance of Feed-Forward NN. This approach is compared with widely used optimization techniques. The Proposed method found better than other techniques and performs fast in Apache Spark than R-Studio framework.

scholarly journals Risk of New Primary Cancer in Patients with Posterior Uveal Melanoma: A National Cohort Study

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 284
Author(s):  
Mette Bagger ◽  
Vanna Albieri ◽  
Tine Gadegaard Hindso ◽  
Karin Wadt ◽  
Steffen Heegaard ◽  
...  
Keyword(s):  
Cohort Study ◽  
Uveal Melanoma ◽  
Population Based ◽  
Control Group ◽  
Cancer Type ◽  
Primary Cancer ◽  
Increased Risk ◽  
Specific Cancer ◽  
National Cohort ◽  
Cancer Types

Background: Studies on the risk of new primary cancer in patients with posterior uveal melanoma (UM) have produced conflicting results, and the role of socioeconomic status (SES) is unknown. The purpose of this population-based matched cohort study was to determine the risk of new primary cancer following the diagnosis of posterior UM. Methods: 2179 patients with posterior UM 1968–2016 and 22,717 matched controls without cancer were included. Incidence and time-dependent hazard ratio (HR) of new primary cancer were described, and the effect of SES was emphasized in a sub-cohort. Results: The incidence of new primary cancer was increased in patients with posterior UM, rate ratio (RR) 1.21 (95% CI: 1.08; 1.35), but the specific cancer types did not differ compared to the controls. The rate of new primary cancer following the diagnosis of posterior UM was significantly increased 2–5 years (HR 1.49 (95% CI: 1.23; 1.80)) and 11–15 years (HR: 1.49 (95% CI: 1.12; 1.99)), and adjusting for SES did not change the rate (HR 1.35 (95% CI:1.20; 1.55)). Conclusions: Patients with posterior UM have an increased risk of new primary cancer independent of SES. No difference in incidence of specific cancer type was observed compared to the control group.

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