Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients

Enhancer mutations modulate the severity of chemotherapy-induced myelosuppression

Non-small cell lung cancer is often diagnosed at advanced stages, and many patients are still treated with classical chemotherapy. The unselective nature of chemotherapy often results in severe myelosuppression. Previous studies showed that protein-coding mutations could not fully explain the predisposition to myelosuppression. Here, we investigate the possible role of enhancer mutations in myelosuppression susceptibility. We produced transcriptome and promoter-interaction maps (using HiCap) of three blood stem-like cell lines treated with carboplatin or gemcitabine. Taking advantage of publicly available enhancer datasets, we validated HiCap results in silico and in living cells using epigenetic CRISPR technology. We also developed a network approach for interactome analysis and detection of differentially interacting genes. Differential interaction analysis provided additional information on relevant genes and pathways for myelosuppression compared with differential gene expression analysis at the bulk level. Moreover, we showed that enhancers of differentially interacting genes are highly enriched for variants associated with differing levels of myelosuppression. Altogether, our work represents a prominent example of integrative transcriptome and gene regulatory datasets analysis for the functional annotation of noncoding mutations.

Adjuvant role of Salvia miltiorrhiza bunge in cancer chemotherapy: A review of its bioactive components, health-promotion effect and mechanisms

ETHNOPHARMACOLOGICAL RELEVANCE

Chemotherapy is a common cancer treatment strategy. However, its effectiveness is constrained by toxicity and adverse effects. The Lamiaceae herb Salvia miltiorrhiza Bunge has a long history of therapeutic use in the treatment of blood stasis illnesses, which are believed by traditional Chinese medicine to be connected to cancer.

AIM OF THE STUDY

This review summarized the common toxicity of chemotherapy and the potential chemo-adjuvant effect and mechanisms of active ingredients from S. miltiorrhiza, hoping to provide valuable information for the development and application of S. miltiorrhiza resources.

MATERIALS AND METHODS

The literatures were retrieved from PubMed, Web of Science, Baidu Scholar and Google Scholar databases from 2002 to 2022. The inclusion criteria were studies reporting that S. miltiorrhiza or its constituents enhanced the efficiency of chemotherapy drugs or reduced the side effects.

RESULTS

Salvianolic acid A, salvianolic acid B, salvianolic acid C, rosmarinic acid, tanshinone I, tanshinone IIA, cryptotanshinone, dihydrotanshinone I and miltirone are the primary adjuvant chemotherapy components of S. miltiorrhiza. The mechanisms mainly involve inhibiting proliferation, metastasis, and angiogenesis, inducing apoptosis, regulating autophagy and tumor microenvironment. In addition, they also improve chemotherapy drug-induced side effects.

CONCLUSIONS

The bioactive compounds of S. miltiorrhiza are shown to inhibit proliferation, metastasis, and angiogenesis, induce apoptosis and autophagy, regulate immunity and tumor microenvironment when combined with chemotherapy drugs. However, further clinical studies are required to validate the current studies.

A nomogram for predicting severe myelosuppression in small cell lung cancer patients following the first-line chemotherapy

This study aimed at establishing and validating a nomogram to predict the probability of severe myelosuppression in small cell lung cancer (SCLC) patients following the first-line chemotherapy. A total of 179 SCLC cases were screened as the training group and another 124 patients were used for the validation group. Predictors were determined by the smallest Akaike's information criterion (AIC) in multivariate logistic regression analysis, leading to a new nomogram. The nomogram was validated in both training and validation groups and the predicting value was evaluated by area under the receiver operating characteristics (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). Age and tumor staging were extracted as predictors to establish a nomogram, which displayed the AUC values as 0.725 and 0.727 in the training and validation groups, respectively. This nomogram exhibited acceptable calibration curves in the two groups and its prediction added more net benefits than the treat-all scheme and treat-none scheme if the range of threshold probability in the DCA was between 15 and 60% in the training and validation groups. Therefore, the nomogram objectively and accurately predict the occurrence of severe myelosuppression in SCLC patients following the first-line chemotherapy.

Therapeutic Effects of Coumarins with Different Substitution Patterns

The use of derivatives of natural and synthetic origin has gained attention because of their therapeutic effects against human diseases. Coumarins are one of the most common organic molecules and are used in medicine for their pharmacological and biological effects, such as anti-inflammatory, anticoagulant, antihypertensive, anticonvulsant, antioxidant, antimicrobial, and neuroprotective, among others. In addition, coumarin derivates can modulate signaling pathways that impact several cell processes. The objective of this review is to provide a narrative overview of the use of coumarin-derived compounds as potential therapeutic agents, as it has been shown that substituents on the basic core of coumarin have therapeutic effects against several human diseases and types of cancer, including breast, lung, colorectal, liver, and kidney cancer. In published studies, molecular docking has represented a powerful tool to evaluate and explain how these compounds selectively bind to proteins involved in various cellular processes, leading to specific interactions with a beneficial impact on human health. We also included studies that evaluated molecular interactions to identify potential biological targets with beneficial effects against human diseases.

Development of a Machine Learning-Based Prediction Model for Chemotherapy-Induced Myelosuppression in Children with Wilms' Tumor

Purpose: Develop and validate an accessible prediction model using machine learning (ML) to predict the risk of chemotherapy-induced myelosuppression (CIM) in children with Wilms' tumor (WT) before chemotherapy is administered, enabling early preventive management. Methods: A total of 1433 chemotherapy cycles in 437 children with WT who received chemotherapy in our hospital from January 2009 to March 2022 were retrospectively analyzed. Demographic data, clinicopathological characteristics, hematology and blood biochemistry baseline results, and medication information were collected. Six ML algorithms were used to construct prediction models, and the predictive efficacy of these models was evaluated to select the best model to predict the risk of grade ≥ 2 CIM in children with WT. A series of methods, such as the area under the receiver operating characteristic curve (AUROC), the calibration curve, and the decision curve analysis (DCA) were used to test the model's accuracy, discrimination, and clinical practicability. Results: Grade ≥ 2 CIM occurred in 58.5% (839/1433) of chemotherapy cycles. Based on the results of the training and validation cohorts, we finally identified that the extreme gradient boosting (XGB) model has the best predictive efficiency and stability, with an AUROC of up to 0.981 in the training set and up to 0.896 in the test set. In addition, the calibration curve and the DCA showed that the XGB model had the best discrimination and clinical practicability. The variables were ranked according to the feature importance, and the five variables contributing the most to the model were hemoglobin (Hgb), white blood cell count (WBC), alkaline phosphatase, coadministration of highly toxic chemotherapy drugs, and albumin. Conclusions: The incidence of grade ≥ 2 CIM was not low in children with WT, which needs attention. The XGB model was developed to predict the risk of grade ≥ 2 CIM in children with WT for the first time. The model has good predictive performance and stability and has the potential to be translated into clinical applications. Based on this modeling and application approach, the extension of CIM prediction models to other pediatric malignancies could be expected.

JMJD1C knockdown affects myeloid cell lines proliferation, viability, and gemcitabine/carboplatin-sensitivity

OBJECTIVES

Chemotherapy-induced hematological toxicities are potentially life-threatening adverse drug reactions that vary between individuals. Recently, JMJD1C has been associated with gemcitabine/carboplatin-induced thrombocytopenia in non-small-cell lung cancer patients, making it a candidate marker for predicting the risk of toxicity. This study investigates if JMJD1C knockdown affects gemcitabine/carboplatin-sensitivity in cell lines.

METHODS

Lentiviral transduction-mediated shRNA knockdown of JMJD1C in the cell lines K562 and MEG-01 were performed using shRNA#32 and shRNA#33. The knockdown was evaluated using qPCR. Cell proliferation, viability, and gemcitabine/carboplatin-sensitivity were subsequently determined using cell counts, trypan blue, and the MTT assay.

RESULTS

ShRNA#33 resulted in JMJD1C downregulation by 56.24% in K562 and 68.10% in MEG-01. Despite incomplete knockdown, proliferation (reduction of cell numbers by 61-68%, day 7 post-transduction) and viability (reduction by 21-53%, day 7 post-transduction) were impaired in K562 and MEG-01 cells. Moreover, JMJD1C knockdown reduced the gemcitabine IC50-value for K562 cells (P < 0.01) and MEG-01 cells (P < 0.05) compared to scrambled shRNA control transduced cells.

CONCLUSIONS

Our results suggest that JMJD1C is essential for proliferation, survival, and viability of K562 and MEG-01 cells. Further, JMJD1C also potentially affects the cells gemcitabine/carboplatin-sensitivity. Although further research is required, the findings show that JMJD1C could have an influential role for gemcitabine/carboplatin-sensitivity.