Is the derived neutrophil to lymphocyte ratio (dNLR) an independent prognostic marker in patients with metastatic colorectal cancer (mCRC)? Analysis of the CO.17 and CO.20 studies

Diakos, Connie I.; Tu, D.; Gebski, Val; Yip, Sze Fai; Wilson, Kathleen; Karapetis, C.; O’Callaghan, Chris; Shapiro, Joël; Tebbutt, N. C.; Jonker, Derek J.; SIu, Lopukhin; Wong, R.; Doyle, C. T.; Strickland, A. H.; Price, Timothy; Simes, John; Clarke, Stephen

doi:10.1093/annonc/mdw370.136

Cited by 3 publications

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“…Increasing evidence shows that an elevated neutrophil-to-lymphocyte ratio (NLR) is closely related to a poor prognosis in ovarian cancer, cholangiocarcinoma, and elective colorectal cancer (CRC)[12-14]. The overexpression of circulating derived NLR, an effective biomarker for the diagnosis of early pancreatic cancer[15], was accompanied by increasing distal organ invasion in metastatic CRC[16]. An elevated preoperative lymphocyte-to-monocyte ratio (LMR), as a superior existing biomarker, was positively correlated with the survival outcomes of patients with resectable CRC and presented better overall survival[17].…”

Section: Introductionmentioning

confidence: 99%

Lymphocyte-to-monocyte ratio effectively predicts survival outcome of patients with obstructive colorectal cancer

Chen

Xue

Hou

et al. 2019

WJG

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BACKGROUNDObstructive colorectal cancer (OCC) is always accompanied by severe complications, and the optimal strategy for patients with OCC remains undetermined. Different from emergency surgery (ES), self-expandable metal stents (SEMS) as a bridge to surgery (BTS), could increase the likelihood of primary anastomosis. However, the stent failure and related complications might give rise to a high recurrence rate. Few studies have focused on the indications for either method, and the relationship between preoperative inflammation indexes and the prognosis of OCC is still underestimated.AIMTo explore the indications for ES and BTS in OCCs based on preoperative inflammation indexes.METHODSOne hundred and twenty-eight patients who underwent ES or BTS from 2008 to 2015 were enrolled. Receiver operating characteristic (ROC) curve analysis was used to define the optimal preoperative inflammation index and its cutoff point. Kaplan–Meier analyses and Cox proportional hazards models were applied to assess the association between the preoperative inflammation indexes and the survival outcomes [overall survival (OS) and disease-free survival (DFS)]. Stratification analysis was performed to identify the subgroups that would benefit from ES or BTS.RESULTSOS and DFS were comparable between the ES and BTS groups (P > 0.05). ROC curve analysis showed derived neutrophil-to-lymphocyte ratio (dNLR) as the optimal biomarker for the prediction of DFS in ES (P < 0.05). Lymphocyte-to-monocyte ratio (LMR) was recommended for BTS with regard to OS and DFS (P < 0.05). dNLR was related to stoma construction (P = 0.001), pneumonia (P = 0.054), and DFS (P = 0.009) in ES. LMR was closely related to lymph node invasion (LVI) (P = 0.009), OS (P = 0.020), and DFS (P = 0.046) in the BTS group. dNLR was an independent risk factor for ES in both OS (P = 0.032) and DFS (P = 0.016). LMR affected OS (P = 0.053) and DFS (P = 0.052) in the BTS group. LMR could differentiate the OS between the ES and BTS groups (P < 0.05).CONCLUSIONPreoperative dNLR and LMR could predict OS and DFS in patients undergoing ES and BTS, respectively. For OCC, as the potential benefit group, patients with a low LMR might be preferred for BTS via SEMS insertion.

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Section: Introductionmentioning

confidence: 99%

Lymphocyte-to-monocyte ratio effectively predicts survival outcome of patients with obstructive colorectal cancer

Chen

Xue

Hou

et al. 2019

WJG

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“…Neutrophil to lymphocyte ratio (NLR) at baseline has also been investigated, and its decrease was associated with better OS, progression-free survival (PFS) and response [ 6 ]. The derived NLR (dNLR = absolute neutrophils count/[white blood count—absolute neutrophils count]) is another ratio which has already been an alternative to NLR in melanomas [ 7 , 8 ] and metastatic colorectal cancer [ 9 ]. Furthermore, for lung cancer, in a Chinese meta-analysis published in 2016 [ 10 ], high levels of platelet to lymphocytes ratio (PLR) at baseline were associated with poor OS and PFS, but in all types of treatment.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Prediction of Immunotherapy Efficacy in Non-Small Cell Lung Cancer from Simple Clinical and Biological Data

Benzekry

Grangeon

Karlsen

et al. 2021

Cancers

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Background: Immune checkpoint inhibitors (ICIs) are now a therapeutic standard in advanced non-small cell lung cancer (NSCLC), but strong predictive markers for ICIs efficacy are still lacking. We evaluated machine learning models built on simple clinical and biological data to individually predict response to ICIs. Methods: Patients with metastatic NSCLC who received ICI in second line or later were included. We collected clinical and hematological data and studied the association of this data with disease control rate (DCR), progression free survival (PFS) and overall survival (OS). Multiple machine learning (ML) algorithms were assessed for their ability to predict response. Results: Overall, 298 patients were enrolled. The overall response rate and DCR were 15.3% and 53%, respectively. Median PFS and OS were 3.3 and 11.4 months, respectively. In multivariable analysis, DCR was significantly associated with performance status (PS) and hemoglobin level (OR 0.58, p < 0.0001; OR 1.8, p < 0.001). These variables were also associated with PFS and OS and ranked top in random forest-based feature importance. Neutrophil-to-lymphocyte ratio was also associated with DCR, PFS and OS. The best ML algorithm was a random forest. It could predict DCR with satisfactory efficacy based on these three variables. Ten-fold cross-validated performances were: accuracy 0.68 ± 0.04, sensitivity 0.58 ± 0.08; specificity 0.78 ± 0.06; positive predictive value 0.70 ± 0.08; negative predictive value 0.68 ± 0.06; AUC 0.74 ± 0.03. Conclusion: Combination of simple clinical and biological data could accurately predict disease control rate at the individual level.

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“…Neutrophil to lymphocyte ratio (NLR) at baseline has also been investigated, and its decrease was associated with better OS, progression-free survival (PFS) and response 6 . The derived NLR (dNLR = absolute neutrophils count / [white blood countabsolute neutrophils count]) is another ratio which has already been an alternative to NLR in melanomas 7,8 and metastatic colorectal cancer 9 . Furthermore, for lung cancer, in a Chinese meta-analysis published in 2016 10 , high levels of platelet to lymphocytes ratio (PLR) at baseline were associated with poor OS and PFS, but in all types of treatment.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for prediction of immunotherapy efficacy in non-small cell lung cancer from simple clinical and biological data

Benzekry

Grangeon

Karlsen

et al. 2021

Preprint

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BackgroundImmune checkpoint inhibitors (ICIs) are now a therapeutic standard in advanced non-small cell lung cancer (NSCLC), but strong predictive markers for ICIs efficacy are still lacking. We evaluated machine learning models built on simple clinical and biological data to individually predict response to ICIs.MethodsPatients with metastatic NSCLC who received ICI in second line or later were included. We collected clinical and hematological data and studied the association of this data with disease control rate (DCR), progression free survival (PFS) and overall survival (OS). Multiple machine learning (ML) algorithms were assessed for their ability to predict response.ResultsOverall, 298 patients were enrolled. The overall response rate and DCR were 15.3 % and 53%, respectively. Median PFS and OS were 3.3 and 11.4 months, respectively. In multivariable analysis, DCR was significantly associated with performance status (PS) and hemoglobin level (OR 0.58, p<0.0001; OR 1.8, p<0.001). These variables were also associated with PFS and OS and ranked top in random forest-based feature importance. Neutrophils-to-lymphocytes ratio was also associated with DCR, PFS and OS. The best ML algorithm was a random forest. It could predict DCR with satisfactory efficacy based on these three variables. Ten-fold cross-validated performances were: accuracy 0.68 ± 0.04, sensitivity 0.58 ± 0.08; specificity 0.78 ± 0.06; positive predictive value 0.70 ± 0.08; negative predictive value 0.68 ± 0.06; AUC 0.74 ± 0.03.ConclusionCombination of simple clinical and biological data could accurately predict disease control rate at the individual level.Highlights-Machine learning applied to a large set of NSCLC patients could predict efficacy of immunotherapy with a 69% accuracy using simple routine data-Hemoglobin levels and performance status were the strongest predictors and significantly associated with DCR, PFS and OS-Neutrophils-to-lymphocyte ratio was also associated with outcome-Benchmark of 8 machine learning models

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Is the derived neutrophil to lymphocyte ratio (dNLR) an independent prognostic marker in patients with metastatic colorectal cancer (mCRC)? Analysis of the CO.17 and CO.20 studies

Cited by 3 publications

References 0 publications

Lymphocyte-to-monocyte ratio effectively predicts survival outcome of patients with obstructive colorectal cancer

Lymphocyte-to-monocyte ratio effectively predicts survival outcome of patients with obstructive colorectal cancer

Machine Learning for Prediction of Immunotherapy Efficacy in Non-Small Cell Lung Cancer from Simple Clinical and Biological Data

Machine learning for prediction of immunotherapy efficacy in non-small cell lung cancer from simple clinical and biological data

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