Significance of longitudinal Epstein-Barr virus DNA combined with multipoint tumor response for dynamic risk stratification and treatment adaptation in nasopharyngeal carcinoma.

Dynamic therapy response is strongly associated with cancer outcomes. This study aimed to evaluate the significance of longitudinal Epstein-Barr virus (EBV) DNA and radiological tumor regression in risk stratification and response-adaptive treatment in locally-advanced nasopharyngeal carcinoma (LA-NPC). In total, 1312 patients from two centers were assigned to the training and validation cohorts. Based on the multipoint examination of EBV-DNA and tumor response, four post-induction chemotherapy, four mid-radiotherapy, and four post-radiotherapy subgroups were established. Then seven phenotypes were further generated according to different permutations and combinations. These phenotypes were subsequently congregated into four response clusters, which reflect distinct biological treatment responses. The four response clusters correlated with an evident 5-year progression-free survival in both the training and external validation cohorts (5-year: training cohort 91.1 %, 82.8 %, 30.6 %, and 10.0 %; external validation 94.4 %, 55.6 %, 40.0 %, and 12.7 %) had superior prognostic performance compared to TNM staging and nomogram model (concordance index: training cohort-0.825 vs. 0.603 vs. 0.756 and external validation-0.834 vs. 0.606 vs. 0.789). Importantly, the response clusters exhibited an excellent capability in selecting candidates who can benefit from adjuvant chemotherapy. In conclusion, risk stratification based on the dynamic assessment of both radiological and biological responses can significantly enhance prognostic insights and shed light on individualized treatment modifications in LA-NPCs.

Liu Y ，Yan W ，Qi X ，Zhang Y ，Wang K ，Qu Y ，Chen X ，Zhang J ，Luo J ，Li YX ，Huang X ，Wu R ，Wang J ，Yi J ... -  《-》

Liquid biopsy with plasma Epstein-Barr virus DNA characterizes biological relapse for the prediction of cancer recurrence in non-disseminated nasopharyngeal carcinoma.

To investigate whether a bounce in plasma Epstein-Barr virus (EBV) DNA during posttreatment surveillance of nasopharyngeal carcinoma (NPC) informs the risk of clinical recurrence and its implication for early therapeutic intervention. 950 non-disseminated NPC patients with completed remission in 3 months after treatment were retrospectively screened. Detectable EBV DNA with no evidence of clinical relapse during follow-up was deemed as DNA bounce. The diagnostic and prognostic performance of EBV DNA bounce was assessed for subsequent failures. Tumor recurrence occurred in 6.6 %, 10.1 % and 65.8 % in the group with persistently negative EBV DNA, single positive test and ≥ 2 positive tests, respectively. EBV DNA bounce over twice was associated with worse disease-free survival (DFS), locoregional recurrence-free survival (LRRFS), and distant metastasis-free survival (DMFS) than the other two groups. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy for the prediction of recurrence were 0.56, 0.95, 0.66, 0.93 and 0.90 using two positive tests, which were hence deemed as biological relapse. Serial cutoffs (EBV DNA 1 ≥ 40 copies/ml or EBV DNA 2 ≥100 copies/ml) further defined a high-risk subgroup with an eventual recurrence rate of 77.9 % and 3-year DFS of merely 20.5 %. Prophylactic medical intervention with capecitabine or S1 significantly improved the 3-year DFS when compared to those with observation. The earliest two positive tests of EBV DNA represent a biomarker of biological relapse that allows early detection of clinical recurrence in EBV-related NPC. For high-risk biological relapse, preemptive intervention provides potential survival benefits.

Zhang Q ，Zhu L ，Lv W ，Xu T ，Shen C ，Qian W ，Liu P ，Ying H ，He X ，Hu C ，Zhou X ，Lu X ... -  《-》

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 ... -  《-》

Evaluation of Risk Factors, and Development and Validation of Prognostic Prediction Models for Distant Metastasis in Patients With Rectal Cancer: A Study Based on the SEER Database and a Chinese Population.

Zhang H ，Wang H ，Yao Y ，Liu L ，Feng F ，Li H ，Sun C ... -  《-》

Recursive partitioning analysis model for de novo metastatic nasopharyngeal carcinoma treated with locoregional radiotherapy following chemoimmunotherapy.

Chemoimmunotherapy is the first-line treatment of de novo metastatic nasopharyngeal carcinoma (dmNPC), with additional locoregional radiotherapy (LRRT) significantly prolonging patient survival. De novo metastatic nasopharyngeal carcinoma, however, demonstrates considerable heterogeneity, resulting in significant variability in patient outcomes. We developed and validated a prognostic tool for patients undergoing first-line chemoimmunotherapy plus LRRT and to evaluate the benefit of local therapy (LT) for distant metastases across different risk levels. We studied 364 dmNPC patients receiving initial platinum-based chemotherapy and anti-programmed cell death protein 1 immunotherapy followed by LRRT. Patients were randomly divided into training and validation cohorts (7 : 3 ratio). The primary endpoint was progression-free survival (PFS). A prognostic model for PFS was developed using recursive partitioning analysis (RPA). An RPA model categorized patients into five prognostic groups based on number of metastatic lesions, liver metastasis status, and post-treatment Epstein-Barr virus DNA levels. Survival analysis identified three distinct risk groups. High-risk patients had significantly poorer PFS compared with medium- and low-risk groups (2-year PFS rate: training cohort: 13.7% versus 69.4% versus 94.4%, P < 0.001; validation cohort: 7.8% versus 65.1% versus 87.3%, P < 0.001). We investigated the impact of LT for distant metastases across these risk groups and found that only patients in the medium-risk group derived benefit from LT (2-year PFS rate: 77.5% versus 64.0%; hazard ratio = 0.535, 95% confidence interval 0.297-0.966, P = 0.035). Conversely, no survival benefit from LT for distant metastases was observed in the low-risk (P = 0.218) and high-risk subgroups (P = 0.793). Our RPA-based prognostic model integrates number of metastatic lesions, liver metastasis status, and post-treatment Epstein-Barr virus DNA levels to predict PFS in dmNPC patients undergoing chemoimmunotherapy plus LRRT. This model offers personalized treatment guidance, suggesting that patients in the medium-risk group may benefit from LT for distant metastases, while those in high- and low-risk groups may not.

Wen D ，Gu L ，Long H ，Liu S ，Luo M ，Li R ，Liu R ，Lin J ，Jin J ，Xiong L ，Tang L ，Mai H ，Liu L ，Liang Y ，Chen Q ，Guo S ... -  《ESMO Open》