Prognostic Nomogram for Childhood Acute Lymphoblastic Leukemia: A Comprehensive Analysis of 673 Patients

A scoring system based on fusion genes to predict treatment outcomes of the non-acute promyelocytic leukemia pediatric acute myeloid leukemia

Background

Fusion genes are considered to be one of the major drivers behind cancer initiation and progression. Meanwhile, non-acute promyelocytic leukemia (APL) pediatric patients with acute myeloid leukemia (AML) in children had limited treatment efficacy. Hence, we developed and validated a simple clinical scoring system for predicting outcomes in non-APL pediatric patients with AML.

Method

A total of 184 non-APL pediatric patients with AML who were admitted to our hospital and an independent dataset (318 patients) from the TARGET database were included. Least absolute shrinkage and selection operation (LASSO) and Cox regression analysis were used to identify prognostic factors. Then, a nomogram score was developed to predict the 1, 3, and 5 years overall survival (OS) based on their clinical characteristics and fusion genes. The accuracy of the nomogram score was determined by calibration curves and receiver operating characteristic (ROC) curves. Additionally, an internal verification cohort was used to assess its applicability.

Results

Based on Cox and LASSO regression analyses, a nomogram score was constructed using clinical characteristics and OS-related fusion genes (CBFβ::MYH11, RUNX1::RUNX1T1, KMT2A::ELL, and KMT2A::MLLT10), yielded good calibration and concordance for predicting OS of non-APL pediatric patients with AML. Furthermore, patients with higher scores exhibited worse outcomes. The nomogram score also demonstrated good discrimination and calibration in the whole cohort and internal validation. Furthermore, artificial neural networks demonstrated that this nomogram score exhibits good predictive performance.

Conclusion

Our model based on the fusion gene is a prognostic biomarker for non-APL pediatric patients with AML. The nomogram score can provide personalized prognosis prediction, thereby benefiting clinical decision-making.

A novel prognostic nomogram for adult acute lymphoblastic leukemia: a comprehensive analysis of 321 patients

The cure rate of acute lymphoblastic leukemia (ALL) in adolescents and adults remains poor. This study aimed to establish a prognostic model for ≥14-year-old patients with ALL to guide treatment decisions. We retrospectively analyzed the data of 321 ALL patients between January 2017 and June 2020. Patients were randomly (2:1 ratio) divided into either the training or validation set. A nomogram was used to construct a prognostic model. Multivariate Cox analysis of the training set showed that age > 50 years, white blood cell count > 28.52×10⁹/L, and MLL rearrangement were independent risk factors for overall survival (OS), while platelet count >37×10⁹/L was an independent protective factor. The nomogram was established according to these independent prognostic factors in the training set, where patients were grouped into two categories: low-risk (≤13.15) and high-risk (>13.15). The survival analysis, for either total patients or sub-group patients, showed that both OS and progression-free survival (PFS) of low-risk patients was significantly better than that of high-risk patients. Moreover, treatment analysis showed that both OS and progression-free survival (PFS) of ALL with stem cell transplantation (SCT) were significantly better than that of ALL without SCT. Further stratified analysis showed that in low-risk patients, the OS and PFS of patients with SCT were significantly better than those of patients without SCT. In contrast, in high-risk patients, compared with non-SCT patients, receiving SCT can only significantly prolong the PFS, but it does not benefit the OS. We established a simple and effective prognostic model for ≥ 14-year-old patients with ALL that can provide accurate risk stratification and determine the clinical strategy.

Leveraging a cuproptosis-based signature to predict the prognosis and drug sensitivity of cutaneous melanoma

Immunotherapy is a vital treatment for patients with cutaneous melanoma (CM), but effective predictors to guide clinical immunotherapy are lacking. Cuproptosis is a newly discovered mode of cell death related to tumorigenesis. Exploring the relationship between the mode of cuproptosis and the effect of immunotherapy on CM could better guide clinical management. We clustered all patients with CM in the Cancer Genome Atlas (TCGA) database based on cuproptosis-related genes (CRGs). Prognosis, immunotherapeutic effect, tumor microenvironment score, expression of CD274, CTLA4, and PDCD1, and abundance of CD8 + T infiltration in group A were higher than in group B. Using a combination of LASSO and COX regression analysis, we identified 10 molecules significant to prognosis from differentially expressed genes between the two groups and constructed a cuproptosis-related scoring system (CRSS). Compared with the American Joint Committee on Cancer (AJCC) staging system, CRSS more accurately stratified CM patient risk and guided immunotherapy. CRSS successfully stratified risk and predicted the effect of immunotherapy in 869 patients with eight CM immunotherapy datasets and multiple other tumor immunotherapy cohorts. The nomogram model, which combined AJCC stage and CRSS, greatly improved the ability and accuracy of prognosis prediction. In general, our cuproptosis-related scoring system and nomogram model accurately stratified risk in CM patients and effectively predicted prognosis and the effect of immunotherapy in CM patients.

Identification of immune and stromal cell infiltration-related gene signature for prognosis prediction in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a common and life-threatening hematologic malignancy, its occurrence and progression are closely related to immune/stromal cell infiltration in the bone marrow (BM) microenvironment. However, no studies have described an immune/stromal cell infiltration-related gene (ISCIRG)-based prognostic signature for ALL. A total of 444 patients involving 437 bulk and 7 single-cell RNA-seq datasets were included in this study. Eligible datasets were searched and reviewed from the database of TCGA, TARGET project and GEO. Then an integrated bioinformatics analysis was performed to select optimal prognosis-related genes from ISCIRGs, construct a nomogram model for predicting prognosis, and assess the predictive power. After LASSO and multivariate Cox regression analyses, a seven ISCIRGs-based signature was proved to be able to significantly stratify patients into high- and low-risk groups in terms of OS. The seven genes were confirmed that directly related to the composition and status of immune/stromal cells in BM microenvironment by analyzing bulk and single-cell RNA-seq datasets. The calibration plot showed that the predicted results of the nomogram were consistent with the actual observation results of training/validation cohort. This study offers a reference for future research regarding the role of ISCIRGs in ALL and the clinical care of patients.

Construction and validation of a novel gene signature for predicting the prognosis of osteosarcoma

Osteosarcoma (OS) is the most common type of primary malignant bone tumor. The high-throughput sequencing technology has shown potential abilities to illuminate the pathogenic genes in OS. This study was designed to find a powerful gene signature that can predict clinical outcomes. We selected OS cases with gene expression and survival data in the TARGET-OS dataset and GSE21257 datasets as training cohort and validation cohort, respectively. The univariate Cox regression and Kaplan–Meier analysis were conducted to determine potential prognostic genes from the training cohort. These potential prognostic genes underwent a LASSO regression, which then generated a gene signature. The harvested signature’s predictive ability was further examined by the Kaplan–Meier analysis, Cox analysis, and receiver operating characteristic (ROC curve). More importantly, we listed similar studies in the most recent year and compared theirs with ours. Finally, we performed functional annotation, immune relevant signature correlation identification, and immune infiltrating analysis to better study he functional mechanism of the signature and the immune cells’ roles in the gene signature’s prognosis ability. A seventeen-gene signature (UBE2L3, PLD3, SLC45A4, CLTC, CTNNBIP1, FBXL5, MKL2, SELPLG, C3orf14, WDR53, ZFP90, UHRF2, ARX, CORT, DDX26B, MYC, and SLC16A3) was generated from the LASSO regression. The signature was then confirmed having strong and stable prognostic capacity in all studied cohorts by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GO and KEGG annotations uncovered the specifically mechanism of action related to the gene signature. Six immune signatures, including PRF1, CD8A, HAVCR2, LAG3, CD274, and GZMA were identified associating with our signature. The immune-infiltrating analysis recognized the vital roles of T cells CD8 and Mast cells activated, which potentially support the seventeen-gene signature’s prognosis ability. We identified a robust seventeen-gene signature that can accurately predict OS prognosis. We identified potential immunotherapy targets to the gene signature. The T cells CD8 and Mast cells activated were identified linked with the seventeen-gene signature predictive power.

Analysis of cancer omics data: a selective review of statistical techniques

Cancer is an omics disease. The development in high-throughput profiling has fundamentally changed cancer research and clinical practice. Compared with clinical, demographic and environmental data, the analysis of omics data-which has higher dimensionality, weaker signals and more complex distributional properties-is much more challenging. Developments in the literature are often 'scattered', with individual studies focused on one or a few closely related methods. The goal of this review is to assist cancer researchers with limited statistical expertise in establishing the 'overall framework' of cancer omics data analysis. To facilitate understanding, we mainly focus on intuition, concepts and key steps, and refer readers to the original publications for mathematical details. This review broadly covers unsupervised and supervised analysis, as well as individual-gene-based, gene-set-based and gene-network-based analysis. We also briefly discuss 'special topics' including interaction analysis, multi-datasets analysis and multi-omics analysis.

A Nomogram for Predicting Event-Free Survival in Childhood Acute Lymphoblastic Leukemia: A Multicenter Retrospective Study

OBJECTIVE

Even though childhood acute lymphoblastic leukemia (ALL) has an encouraging survival rate in recent years, some patients are still at risk of relapse or even death. Therefore, we aimed to construct a nomogram to predict event-free survival (EFS) in patients with ALL.

METHOD

Children with newly diagnosed ALL between October 2016 and July 2021 from 18 hospitals participating in the South China children's leukemia Group (SCCLG) were recruited and randomly classified into two subsets in a 7:3 ratio (training set, n=1187; validation set, n=506). Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were adopted to screen independent prognostic factors. Then, a nomogram can be build based on these prognostic factors to predict 1-, 2-, and 3-year EFS. Concordance index (C-index), area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance and clinical utility of nomogram.

RESULT

The parameters that predicted EFS were age at diagnosis, white blood cell at diagnosis, immunophenotype, ETV6-RUNX1/TEL-AML1 gene fusion, bone marrow remission at day 15, and minimal residual disease at day 15. The nomogram incorporated the six factors and provided C-index values of 0.811 [95% confidence interval (CI) = 0.792-0.830] and 0.797 (95% CI = 0.769-0.825) in the training and validation set, respectively. The calibration curve and AUC revealed that the nomogram had good ability to predict 1-, 2-, and 3-year EFS. DCA also indicated that our nomogram had good clinical utility. Kaplan-Meier analysis showed that EFS in the different risk groups stratified by the nomogram scores was significant differentiated.

CONCLUSION

The nomogram for predicting EFS of children with ALL has good performance and clinical utility. The model could help clinical decision-making.

Landscape and Clinical Significance of Immune Checkpoint in Cutaneous Melanoma

Background

The incidence of cutaneous melanoma (CM) is increasing, and its prognosis is not optimistic. Although immune checkpoint (ICP) inhibitors are effective in the treatment of CM patients, they are not effective for all CM patients. There is an urgent need for a marker to predict both the prognosis and the immunotherapy effect in patients with CM.

Approaches

Two groups of patients with greatly different prognosis and response to immunotherapy were identified by unwatched cluster exploration of TCGA on the basis of 34 ICPs. The prognosis and immunotherapy effect of CM were predicted by developing a precise and given signature on the basis of ICPs, and a multivariate Cox risk regression model was established from the TCGA cohort consisting of 454 CM samples. The model was validated in 210 and 231 samples in the test and verification cohorts, respectively.

Results

The prognosis in clinical subgroups was predicted by the classification system. High-risk patients had poorer responses to chemotherapy and immunotherapy. Finally, the signature was recognized as an independent prognostic factor. Based on checkpoint-based signature (ICPBS) and clinical characteristics, we constructed a nomogram for the prognosis in patients with CM, which was superior to ICPBS in efficacy than ICPBS alone.

Conclusion

As a useful prognostic tool to further improve cancer immunotherapy, the signature can accurately predict recurrence and overall survival among patients with CM.

Identification of a nomogram based on an 8-lncRNA signature as a novel diagnostic biomarker for childhood acute lymphoblastic leukemia

Childhood acute lymphoblastic leukemia (cALL) still represents a major cause of disease-related death in children. This study aimed to explore the prognostic value of long non-coding RNAs (lncRNAs) in cALL. We downloaded lncRNA expression profiles from the TARGET and GEO databases. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to identify lncRNA-based signatures. We identified an eight-lncRNA signature (LINC00630, HDAC2-AS2, LINC01278, AL356599.1, AC114490.1, AL132639.3, FUT8.AS1, and TTC28.AS1), which separated the patients into two groups with significantly different overall survival rates. A nomogram based on the signature, BCR ABL1 status and white blood cell count at diagnosis was developed and showed good accuracy for predicting the 3-, 5- and 7-year survival probability of cALL patients. The C-index values of the nomogram in the training and internal validation set reached 0.8 (95% CI, 0.757 to 0.843) and 0.806 (95% CI, 0.728 to 0.884), respectively. The nomogram proposed in this study objectively and accurately predicted the prognosis of cALL. In vitro experiments suggested that LINC01278 promoted the proliferation of leukemic cells and inhibited leukemic cell apoptosis by targeting the inhibition of miR-500b-3p in cALL, and LINC01278 may be a biological target for the treatment of cALL in the future.