CancerEMC: frontline non-invasive cancer screening from circulating protein biomarkers and mutations in cell-free DNA

Towards precision oncology: a multi-level cancer classification system integrating liquid biopsy and machine learning

BACKGROUND

Millions of people die from cancer every year. Early cancer detection is crucial for ensuring higher survival rates, as it provides an opportunity for timely medical interventions. This paper proposes a multi-level cancer classification system that uses plasma cfDNA/ctDNA mutations and protein biomarkers to identify seven distinct cancer types: colorectal, breast, upper gastrointestinal, lung, pancreas, ovarian, and liver.

RESULTS

The proposed system employs a multi-stage binary classification framework where each stage is customized for a specific cancer type. A majority vote feature selection process is employed by combining six feature selectors: Information Value, Chi-Square, Random Forest Feature Importance, Extra Tree Feature Importance, Recursive Feature Elimination, and L1 Regularization. Following the feature selection process, classifiers-including eXtreme Gradient Boosting, Random Forest, Extra Tree, and Quadratic Discriminant Analysis-are customized for each cancer type individually or in an ensemble soft voting setup to optimize predictive accuracy. The proposed system outperformed previously published results, achieving an AUC of 98.2% and an accuracy of 96.21%. To ensure reproducibility of the results, the trained models and the dataset used in this study are made publicly available via the GitHub repository ( https://github.com/SaraEl-Metwally/Towards-Precision-Oncology ).

CONCLUSION

The identified biomarkers enhance the interpretability of the diagnosis, facilitating more informed decision-making. The system's performance underscores its effectiveness in tissue localization, contributing to improved patient outcomes through timely medical interventions.

Impact of clinical factors on accuracy of ovarian cancer detection via platelet RNA profiling

ABSTRACT

Ovarian cancer (OC) presents a diagnostic challenge, often resulting in poor patient outcomes. Platelet RNA sequencing, which reflects host response to disease, shows promise for earlier OC detection. This study examines the impact of sex, age, platelet count, and the training on cancer types other than OC on classification accuracy achieved in the previous platelet-alone training data set. A total of 339 samples from healthy donors and 1396 samples from patients with cancer, spanning 18 cancer types (including 135 OC cases) were analyzed. Logistic regression was applied to verify our classifiers' performance and interpretability. Models were tested at 100% specificity and 100% sensitivity levels. Incorporating patient age as an additional feature along with gene expression increased sensitivity from 68.6% to 72.6%. Models trained on data from both sexes and on female-only data achieved a sensitivity of 68.6% and 74.5%, respectively. Training solely on OC data reduced late-stage sensitivity from 69.1% to 44.1% but increased early-stage sensitivity from 66.7% to 69.7%. This study highlights the potential of platelet RNA profiling for OC detection and the importance of clinical variables in refining classification accuracy. Incorporating age with gene expression data may enhance OC diagnostic accuracy. The inclusion of male samples deteriorates classifier performance. Data from diverse cancer types improves advanced cancer detection but negatively affects early-stage diagnosis.

Liquid Biopsy in Pancreatic Ductal Adenocarcinoma: A Review of Methods and Applications

Pancreatic ductal adenocarcinoma (PDAC) remains a malignancy with one of the highest mortality rates. One limitation in the diagnosis and treatment of PDAC is the lack of an early and universal biomarker. Extensive research performed recently to develop new assays which could fit this role is available. In this review, we will discuss the current landscape of liquid biopsy in patients with PDAC. Specifically, we will review the various methods of liquid biopsy, focusing on circulating tumor DNA (ctDNA) and exosomes and future opportunities for improvement using artificial intelligence or machine learning to analyze results from a multi-omic approach. We will also consider applications which have been evaluated, including the utility of liquid biopsy for screening and staging patients at diagnosis as well as before and after surgery. We will also examine the potential for liquid biopsy to monitor patient treatment response in the setting of clinical trial development.

The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral Blood

T cells recognize tumor antigens and initiate an anticancer immune response in the very early stages of tumor development, and the antigen specificity of T cells is determined by the T-cell receptor (TCR). Therefore, monitoring changes in the TCR repertoire in peripheral blood may offer a strategy to detect various cancers at a relatively early stage. Here, we developed the deep learning framework iCanTCR to identify patients with cancer based on the TCR repertoire. The iCanTCR framework uses TCRβ sequences from an individual as an input and outputs the predicted cancer probability. The model was trained on over 2,000 publicly available TCR repertoires from 11 types of cancer and healthy controls. Analysis of several additional publicly available datasets validated the ability of iCanTCR to distinguish patients with cancer from noncancer individuals and demonstrated the capability of iCanTCR for the accurate classification of multiple cancers. Importantly, iCanTCR precisely identified individuals with early-stage cancer with an AUC of 86%. Altogether, this work provides a liquid biopsy approach to capture immune signals from peripheral blood for noninvasive cancer diagnosis.

SIGNIFICANCE

Development of a deep learning-based method for multicancer detection using the TCR repertoire in the peripheral blood establishes the potential of evaluating circulating immune signals for noninvasive early cancer detection.

Precision cancer classification using liquid biopsy and advanced machine learning techniques

Cancer presents a significant global health burden, resulting in millions of annual deaths. Timely detection is critical for improving survival rates, offering a crucial window for timely medical interventions. Liquid biopsy, analyzing genetic variations, and mutations in circulating cell-free, circulating tumor DNA (cfDNA/ctDNA) or molecular biomarkers, has emerged as a tool for early detection. This study focuses on cancer detection using mutations in plasma cfDNA/ctDNA and protein biomarker concentrations. The proposed system initially calculates the correlation coefficient to identify correlated features, while mutual information assesses each feature's relevance to the target variable, eliminating redundant features to improve efficiency. The eXtrem Gradient Boosting (XGBoost) feature importance method iteratively selects the top ten features, resulting in a 60% dataset dimensionality reduction. The Light Gradient Boosting Machine (LGBM) model is employed for classification, optimizing its performance through a random search for hyper-parameters. Final predictions are obtained by ensembling LGBM models from tenfold cross-validation, weighted by their respective balanced accuracy, and averaged to get final predictions. Applying this methodology, the proposed system achieves 99.45% accuracy and 99.95% AUC for detecting the presence of cancer while achieving 93.94% accuracy and 97.81% AUC for cancer-type classification. Our methodology leads to enhanced healthcare outcomes for cancer patients.

DNA methylation analysis of tumor suppressor genes in liquid biopsy components of early stage NSCLC: a promising tool for early detection

Purpose

Circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) analysis represents a liquid biopsy approach for real-time monitoring of tumor evolution. DNA methylation is considered to be an early event in the process of cancer development and progression. The aim of the present study was to evaluate whether detection of DNA methylation of selected tumor suppressor genes in CTC and matched ctDNA provides prognostic information in early stage NSCLC.

Experimental design

The methylation status of five selected gene promoters (APC, RASSFIA1, FOXA1, SLFN11, SHOX2) was examined by highly specific and sensitive real-time methylation specific PCR assays in: (a) a training group of 35 primary tumors and their corresponding adjacent non-cancerous tissues of early stage NSCLC patients, (b) a validation group of 22 primary tumor tissues (FFPEs) and 42 peripheral blood samples of early stage NSCLC patients. gDNA was isolated from FFPEs, CTCs (size-based enriched by Parsortix; Angle and plasma, and (c) a control group of healthy blood donors (n = 12).

Results

All five gene promoters tested were highly methylated in the training group; methylation of SHOX2 promoter in primary tumors was associated with unfavorable outcome. RASSFIA and APC were found methylated in plasma-cfDNA samples at 14.3% and 11.9%, respectively, whereas in the corresponding CTCs SLFN11 and APC promoters were methylated in 7.1%. The incidence of relapses was higher in patients with a) promoter methylation of APC and SLFN11 in plasma-cfDNA (P = 0.037 and P = 0.042 respectively) and b) at least one detected methylated gene promoter in CTC or plasma-cfDNA (P = 0.015).

Conclusions

DNA methylation of these five gene promoters was significantly lower in CTCs and plasma-cfDNA than in the primary tumors. Combination of DNA methylation analysis in CTC and plasma-cfDNA was associated with worse DFI of NSCLC patients. Additional studies are required to validate our findings in a large cohort of early stage NSCLC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01283-x.