Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.
Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided.
(1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS?
Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses.
Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS.
Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments.
Level III, diagnostic study.
Lee CC
，Chen CW
，Yen HK
，Lin YP
，Lai CY
，Wang JL
，Groot OQ
，Janssen SJ
，Schwab JH
，Hsu FM
，Lin WH
... -
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EGCG targeting STAT3 transcriptionally represses PLXNC1 to inhibit M2 polarization mediated by gastric cancer cell-derived exosomal miR-92b-5p.
M2-polarized tumor-associated macrophages (TAMs) predominate in tumor microenvironment (TME) and serve primary functions in tumor progression, including growth, angiogenesis, metastasis, immunosuppression, chemoresistance, and poor prognosis. The reversal of M2 polarization provides a new treatment strategy for cancer. Presently, the molecular mechanisms of M2 polarization have yet to be fully characterized, and there is a lack of effective therapeutic targets and drugs. Cancer cells initiate an immunosuppressive TME by recruiting macrophages and promoting M2 polarization through the secretion of inflammatory factors. Accordingly, blocking cancer cell-induced TAM M2 polarization may present a more effective strategy from the perspective of cancer cells. Hedyotis diffusa Willd (HDW) possesses immunomodulatory and antitumor properties, and is a precious and direct source of small molecule natural products with a dual function of inhibition of tumor growth and tumor cell-mediated M2 polarization.
To identify a new target promoting gastric cancer (GC) cell growth and GC cell-mediated M2 polarization from mRNA profiles of GC cells treated with HDW injection (HDI) and to excavate a natural product from HDI that can regulate related mRNA and inhibit the aforementioned effects.
RNA sequencing (RNA-seq) was used to analyze HDI-regulated differentially expressed mRNAs (HRmRNAs) in MKN45 cells. Weighted gene co-expression network analysis (WGCNA), univariate and multivariate Cox regression analysis, KM survival curves, and association analysis between HRmRNA and clinical characteristics/tumor infiltrating immune cells (TIICs) individually were utilized to screen out the target HRmRNA associated with prognosis and M2 macrophage infiltration in GC. shRNA lentiviral vectors were used for stably silencing, and transient overexpressing plasmids were constructed for overexpression. CCK8, EdU, colony formation, migration and invasion assays were used to validate the function of drugs and molecules in GC. HDI constituent analysis was performed using UHPLC-QE-MS. A network of HDI constituent-hub transcription factor (TF)-HRmRNA was constructed based on RNA-Seq, network pharmacology and TFs prediction. Exosome isolation and identification were performed using ultracentrifugation, NTA, TEM and western blot. Apoptosis and macrophage phenotypes were determined by flow cytometric analysis. Small RNA-Seq made exosomal miRNA identification. Small molecule interaction with targets were analyzed using molecular docking, SPR and CETSA. The direct relationship between transcription factors and promoters was verified using ChIP-QPCR and dual-luciferase reporter gene assay. A nude mice xenograft tumor model was established for vivo validation.
HDI inhibited MKN45 cell proliferation, migration, invasion and promoted apoptosis. RNA-Seq identified 2583 HRmRNAs. PLXNC1 was screened out as the target HRmRNA associated with prognosis and M2 macrophage infiltration in GC. PLXNC1 promoted GC cell proliferation and facilitated TAMs M2 polarization by transferring GC cell-derived exosomal miR-92b-5p, inhibiting SOCS7-STAT3 interactions and subsequently activating STAT3 in macrophages. M2 TAMs induced by PLXNC1-mediated GC cell-derived exosomes promoted GC cell migration and invasion. PLXNC1 regulated exosomal miR-92b-5p through the MEK1/MSK1/CREB1 pathway. STAT3 could transcriptionally regulate PLXNC1 expression in GC cells. The network of HDI constituent-hub TF-HRmRNA showed epigallocatechin gallate (EGCG) from HDI targeted STAT3 to transcriptionally regulate PLXNC1 expression. EGCG as a natural product directly bound to STAT3 to diminish its nuclear localization, resulting in the transcriptional repression of PLXNC1 and the reversal of M2 polarization induced by PLXNC1-mediated GC cell-derived exosomes.
PLXNC1 is a novel target exerting dual effects on GC cell proliferation and GC cell-mediated M2 polarization. EGCG derived from HDI inhibits GC cell proliferation and targets STAT3 to inhibit M2 polarization induced by PLXNC1-mediated exosomes derived from GC cells, which may be a multi-target therapeutic agent for GC cell proliferation and immune microenvironment.
Yi J
，Ye Z
，Xu H
，Zhang H
，Cao H
，Li X
，Wang T
，Dong C
，Du Y
，Dong S
，Zhou W
... -
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ResearchGate
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钛学术
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