TMBserval: a statistical explainable learning model reveals weighted tumor mutation burden better categorizing therapeutic benefits

A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study

BACKGROUND

Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC.

METHODS

This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017-2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018-2021) and another from center 2 (n = 43, 2020-2021), were utilized to assess the signature's association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data.

RESULTS

Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression.

CONCLUSION

This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC's MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications.

DeepLION2: deep multi-instance contrastive learning framework enhancing the prediction of cancer-associated T cell receptors by attention strategy on motifs

Introduction

T cell receptor (TCR) repertoires provide valuable insights into complex human diseases, including cancers. Recent advancements in immune sequencing technology have significantly improved our understanding of TCR repertoire. Some computational methods have been devised to identify cancer-associated TCRs and enable cancer detection using TCR sequencing data. However, the existing methods are often limited by their inadequate consideration of the correlations among TCRs within a repertoire, hindering the identification of crucial TCRs. Additionally, the sparsity of cancer-associated TCR distribution presents a challenge in accurate prediction.

Methods

To address these issues, we presented DeepLION2, an innovative deep multi-instance contrastive learning framework specifically designed to enhance cancer-associated TCR prediction. DeepLION2 leveraged content-based sparse self-attention, focusing on the top k related TCRs for each TCR, to effectively model inter-TCR correlations. Furthermore, it adopted a contrastive learning strategy for bootstrapping parameter updates of the attention matrix, preventing the model from fixating on non-cancer-associated TCRs.

Results

Extensive experimentation on diverse patient cohorts, encompassing over ten cancer types, demonstrated that DeepLION2 significantly outperformed current state-of-the-art methods in terms of accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the curve (AUC). Notably, DeepLION2 achieved impressive AUC values of 0.933, 0.880, and 0.763 on thyroid, lung, and gastrointestinal cancer cohorts, respectively. Furthermore, it effectively identified cancer-associated TCRs along with their key motifs, highlighting the amino acids that play a crucial role in TCR-peptide binding.

Conclusion

These compelling results underscore DeepLION2's potential for enhancing cancer detection and facilitating personalized cancer immunotherapy. DeepLION2 is publicly available on GitHub, at https://github.com/Bioinformatics7181/DeepLION2, for academic use only.

Development of a risk assessment model for cardiac injury in patients newly diagnosed with acute myeloid leukemia based on a multicenter, real-world analysis in China

BACKGROUND

Studies have revealed that acute myeloid leukemia (AML) patients are prone to combined cardiac injury. We aimed to identify hematological risk factors associated with cardiac injury in newly diagnosed AML patients before chemotherapy and develop a personalized predictive model.

METHODS

The population baseline, blood test, electrocardiogram, echocardiograph, and genetic and cytogenetic data were collected from newly diagnosed AML patients. The data were subdivided into training and validation cohorts. The independent risk factors were explored by univariate and multivariate logistic regression analysis respectively, and data dimension reduction and variable selection were performed using the least absolute shrinkage and selection operator (LASSO) regression models. The nomogram was generated and the reliability and generalizability were verified by receiver operating characteristic (ROC) curves, the area under the curve (AUC) and calibration curves in an external validation cohort.

RESULTS

Finally, 499 AML patients were included. After univariate logistic regression, LASSO regression and multivariate logistic regression analysis, abnormal NT-proBNP, NPM1 mutation, WBC, and RBC were independent risk factors for cardiac injury in AML patients (all P < 0.05). The nomogram was constructed based on the above four variables with high accuracy. The area under the curve was 0.742, 0.750, and 0.706 in the training, internal validation, and external validation cohort, respectively. The calibration curve indicated that the model has good testing capability. The Kaplan-Meier curve showed that the higher the risk of combined cardiac injury in AML patients, the lower their probability of survival.

CONCLUSIONS

This prediction nomogram identifies hematological risk factors associated with cardiac injury in newly diagnosed AML patients and can help hematologists identify the risk and provide precise treatment options.

An association between ATP7B expression and human cancer prognosis and immunotherapy: a pan-cancer perspective

BACKGROUND

ATP7B is a copper-transporting protein that contributes to the chemo-resistance of human cancer cells. It remains unclear what the molecular mechanisms behind ATP7B are in cancer, as well as its role in human pan-cancer studies.

METHODS

Our study evaluated the differential expression of ATP7B in cancer and paracancerous tissues based on RNA sequencing data from the GTEx and TCGA. Kaplan-Meier and Cox proportional hazards regressions were used to estimate prognostic factors associated with ATP7B.The correlations between the expression of ATP7B and immune cell infiltration, tumor mutation burden, microsatellite instability and immune checkpoint molecules were analyzed. Co-expression networks and mutations in ATP7B were analyzed using the web tools. An analysis of ATP7B expression difference on drug sensitivity on tumor cells was performed using the CTRP, GDSC and CMap database.

RESULTS

ATP7B expression differed significantly between cancerous and paracancerous tissues. The abnormal expression of ATP7B was linked to prognosis in LGG and KIRC. Infiltration of immune cells, tumor mutation burden, microsatellite instability and immunomodulators had all been linked to certain types of cancer. Cancer cells exhibited a correlation between ATP7B expression and drug sensitivity.

CONCLUSION

ATP7B might be an immunotherapeutic and prognostic biomarker based on its involvement in cancer occurrence and development.

Identification of the role of endoplasmic reticulum stress genes in endometrial cancer and their association with tumor immunity

BACKGROUND

Endometrial cancer (EC) is one of the worldwide gynecological malignancies. Endoplasmic reticulum (ER) stress is the cellular homeostasis disturbance that participates in cancer progression. However, the mechanisms of ER Stress on EC have not been fully elucidated.

METHOD

The ER Stress-related genes were obtained from Gene Set Enrichment Analysis (GSEA) and GeneCards, and the RNA-seq and clinical data were downloaded from The Cancer Genome Atlas (TCGA). The risk signature was constructed by the Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis. The significance of the risk signature and clinical factors were tested by time-dependent receiver operating characteristic (ROC) curves, and the selected were to build a nomogram. The immunity correlation was particularly analyzed, including the related immune cells, pathways, and immune checkpoints. Functional enrichment, potential chemotherapies, and in vitro validation were also conducted.

RESULT

An ER Stress-based risk signature, consisting of TRIB3, CREB3L3, XBP1, and PPP1R15A was established. Patients were randomly divided into training and testing groups with 1:1 ratio for subsequent calculation and validation. Based on risk scores, high- and low-risk subgroups were classified, and low-risk subgroup demonstrated better prognosis. The Area Under Curve (AUC) demonstrated a reliable predictive capability of the risk signature. The majority of significantly different immune cells and pathways were enriched more in low-risk subgroup. Similarly, several typical immune checkpoints, expressed higher in low-risk subgroup. Patients of the two subgroups responded differently to chemotherapies.

CONCLUSION

We established an ER Stress-based risk signature that could effectively predict EC patients' prognosis and their immune correlation.

OCRFinder: a noise-tolerance machine learning method for accurately estimating open chromatin regions

Open chromatin regions are the genomic regions associated with basic cellular physiological activities, while chromatin accessibility is reported to affect gene expressions and functions. A basic computational problem is to efficiently estimate open chromatin regions, which could facilitate both genomic and epigenetic studies. Currently, ATAC-seq and cfDNA-seq (plasma cell-free DNA sequencing) are two popular strategies to detect OCRs. As cfDNA-seq can obtain more biomarkers in one round of sequencing, it is considered more effective and convenient. However, in processing cfDNA-seq data, due to the dynamically variable chromatin accessibility, it is quite difficult to obtain the training data with pure OCRs or non-OCRs, and leads to a noise problem for either feature-based approaches or learning-based approaches. In this paper, we propose a learning-based OCR estimation approach with a noise-tolerance design. The proposed approach, named OCRFinder, incorporates the ideas of ensemble learning framework and semi-supervised strategy to avoid potential overfitting of noisy labels, which are the false positives on OCRs and non-OCRs. Compared to different noise control strategies and state-of-the-art approaches, OCRFinder achieved higher accuracies and sensitivities in the experiments. In addition, OCRFinder also has an excellent performance in ATAC-seq or DNase-seq comparison experiments.