Machine learning for prediction of immunotherapeutic outcome in non-small-cell lung cancer based on circulating cytokine signatures

Wei, Feifei; Azuma, Koichi; Nakahara, Yoshiaki; Saito, Haruhiro; Matsuo, Norikazu; Tagami, Tomoyuki; Kouro, Taku; Igarashi, Yuka; Tokito, Takaaki; Kato, Terufumi; Kondo, Tetsuro; Murakami, Shuji; Usui, Ryo; Himuro, Hidetomo; Horaguchi, Shun; Tsuji, Kiichiro; Murotani, Kenta; Ban, Tomohiro; Tamura, Tomohide; Miyagi, Yohei; Sasada, Tetsuro

doi:10.1136/jitc-2023-006788

Cited by 10 publications

(4 citation statements)

References 60 publications

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“…In lung cancer, machine learning models have been developed to predict the response to immunotherapy. These models use data from imaging studies, clinical parameters, and molecular markers to predict which patients are likely to respond to treatment [ 30 , 31 , 32 , 33 , 34 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

Daye,

Parker,

Tripathi

et al. 2024

Cancers

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This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine learning algorithm, XGBoost. The algorithm’s performance was assessed using confusion matrix metrics and the area under the Receiver Operating Characteristics (ROC) curve. The study included 140 patients (mean age 67.7 ± 8.9 years), and the algorithm was found to be predictive of all eight treatment recommendations made by the board. The model’s predictions were more accurate than those based on published therapeutic guidelines by ESMO and NCCN. The study concludes that a machine learning model incorporating clinical and imaging variables can predict treatment recommendations made by an expert multidisciplinary tumor board, potentially aiding clinical decision-making in settings lacking subspecialty expertise.

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Section: Discussionmentioning

confidence: 99%

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

Daye,

Parker,

Tripathi

et al. 2024

Cancers

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“…In this study, patients with advanced or recurrent NSCLC treated with anti-PD-1 Ab (pembrolizumab or nivolumab) or anti-PD-L1 Ab (atezolizumab) were enrolled at Kanagawa Cancer Center (Yokohama, Japan) or Kurume University (Kurume, Japan) between March 2017 and February 2021. This study was conducted in accordance with the provisions of the Declaration of Helsinki, and was approved by the Institutional Review Boards of Kurume University (approval numbers: 15210 and 19240) and Kanagawa Cancer Center (approval number: 2019-131) ( Wei et al, 2023 ). Written informed consent was obtained from all participants prior to enrollment after the nature and possible consequences of this study were explained.…”

Section: Methodsmentioning

confidence: 99%

Circulating IL-6 and not its circulating signaling components sIL-6R and sgp130 demonstrate clinical significance in NSCLC patients treated with immune checkpoint inhibitors

Nakahara,

Kouro,

Motoyama

et al. 2024

Front. Cell Dev. Biol.

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Introduction: Clinical roles of plasma IL-6 levels have been reported in patients with various cancers, including non-small cell lung cancer (NSCLC), treated with immune checkpoint inhibitors (ICIs). However, the roles of other IL-6 signaling components, soluble IL-6 receptor (sIL-6R) and soluble gp130 (sgp130), in the plasma have not been elucidated.Methods: Blood was collected from 106 patients with NSCLC before initiation of ICI treatment (anti-PD-1 or anti-PD-L1 antibody). Plasma levels of IL-6, sIL-6R, sgp130, and their complexes were assessed by Cox regression hazard model to evaluate their clinical significance. The clinical role of IL-6 or IL-6R genetic polymorphisms was also analyzed.Results: Cox regression analysis showed that higher plasma IL-6 levels significantly predicted unfavorable overall survival (OS; hazard ratio [HR] 1.34, 95% confidence interval [CI] 1.05–1.68, p = 0.012) in NSCLC patients treated with ICIs. However, plasma sIL-6R and sgp130 levels showed no prognostic significance (p = 0.882 and p = 0.934, respectively). In addition, the estimated concentrations of binary IL-6:sIL-6R and ternary IL-6:sIL-6R:sgp130 complexes and their ratios (binary/ternary complex) were not significantly associated with OS (p = 0.647, p = 0.727, and p = 0.273, respectively). Furthermore, the genetic polymorphisms of IL-6 (−634G>C) and IL-6R (48892A>C) showed no clinical role by Kaplan-Meier survival analysis (p = 0.908 and p = 0.639, respectively).Discussion: These findings demonstrated the clinical significance of plasma levels of IL-6, but not of other IL-6 signaling components, sIL-6R and sgp130, suggesting that classical IL-6 signaling, but not trans-signaling, may be related to anti-tumor immune responses in cancer patients treated with ICIs.

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“…Several serum proteomic tests based on pre-therapy levels of soluble analytes have been developed, using machine learning algorithms to stratify patients with NSCLC treated with ICI to predict clinical outcomes [ 59 – 61 ] (Supplemental Table 2 ). Taguchi et al developed a test based on eight mass spectral features called the Host Immune Classifier (HIC), in which patients with NSCLC were classified as HIC-hot (HIC-H), or HIC-cold (HIC-C) to represent patient immune status and predict response to epidermal growth factor receptor tyrosine kinase inhibitors, a test that is now commercially marketed as the VeriStrat test [ 62 ].…”

Section: Serum Proteomic Tests At Baseline Associate With Response To...mentioning

confidence: 99%

Blood-based biomarkers in patients with non-small cell lung cancer treated with immune checkpoint blockade

Tsai,

Schlom,

Donahue

2024

J Exp Clin Cancer Res

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The paradigm of non-small cell lung cancer (NSCLC) treatment has been profoundly influenced by the development of immune checkpoint inhibitors (ICI), but the range of clinical responses observed among patients poses significant challenges. To date, analyses of tumor biopsies are the only parameter used to guide prognosis to ICI therapy. Tumor biopsies, however, are often difficult to obtain and tissue-based biomarkers are limited by intratumoral heterogeneity and temporal variability. In response, there has been a growing emphasis on the development of “liquid biopsy”‒ derived biomarkers, which offer a minimally invasive means to dynamically monitor the immune status of NSCLC patients either before and/or during the course of treatment. Here we review studies in which multiple blood-based biomarkers encompassing circulating soluble analytes, immune cell subsets, circulating tumor DNA, blood-based tumor mutational burden, and circulating tumor cells have shown promising associations with the clinical response of NSCLC patients to ICI therapy. These investigations have unveiled compelling correlations between the peripheral immune status of patients both before and during ICI therapy and patient outcomes, which include response rates, progression-free survival, and overall survival. There is need for rigorous validation and standardization of these blood-based assays for broader clinical application. Integration of multiple blood-based biomarkers into comprehensive panels or algorithms also has the potential to enhance predictive accuracy. Further research aimed at longitudinal monitoring of circulating biomarkers is also crucial to comprehend immune dynamics and resistance mechanisms and should be used alongside tissue-based methods that interrogate the tumor microenvironment to guide treatment decisions and may inform on the development of novel therapeutic strategies. The data reviewed here reinforce the opportunity to refine patient stratification, optimize treatments, and improve outcomes not only in NSCLC but also in the wider spectrum of solid tumors undergoing immunotherapy.

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Machine learning for prediction of immunotherapeutic outcome in non-small-cell lung cancer based on circulating cytokine signatures

Cited by 10 publications

References 60 publications

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

Circulating IL-6 and not its circulating signaling components sIL-6R and sgp130 demonstrate clinical significance in NSCLC patients treated with immune checkpoint inhibitors

Blood-based biomarkers in patients with non-small cell lung cancer treated with immune checkpoint blockade

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