Real-world performance of blood-based proteomic profiling in first-line immunotherapy treatment in advanced stage non-small cell lung cancer

Rich, Patricia; Mitchell, R. Brian; Schaefer, Eric; Walker, Paul R.; Dubay, John W.; Boyd, J.; Oubre, D.; Page, Ray D.; Khalil, Mazen; Sinha, Suman; Boniol, Scott; Halawani, H.; Santos, Edgardo S.; Brenner, W.; Orsini, James; Pauli, Emily K.; Goldberg, Jonathan M.; Veatch, A.L.; Haut, Mitchell; Ghabach, Bassam; Bidyasar, Savita; Quejada, M. I.; Khan, W.; Huang, Kan; Traylor, Linda; Akerley, Wallace

doi:10.1136/jitc-2021-002989

Cited by 17 publications

(21 citation statements)

References 40 publications

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“…Our model analyzes a large set of proteomic biomarkers, termed RAPs, that collectively provide a robust prediction of treatment benefit. Using supervised computational models to analyze omic-level data, we and others have previously identified blood-based signatures predictive of ICI outcomes [11][12][13][14] . However, successful validation can be challenging due to several intrinsic limitations, including overfitting and inter-and intra-patient heterogeneity, while large sample sizes are necessary to achieve acceptable model performance.…”

Section: Discussionmentioning

confidence: 99%

A novel decision-making tool for first-line treatment selection in metastatic non-small cell lung cancer based on plasma proteome profiling

Christopoulos

Harel²,

Lahav³

et al. 2022

Preprint

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Importance: Advanced stage non-small cell lung cancer (NSCLC) patients with no driver mutations are typically treated with immune checkpoint inhibitor (ICI)-based therapy, either in the form of monotherapy or concurrently with chemotherapy, while treatment modality selection is based mainly on programmed death ligand 1 (PD-L1) expression levels in the tumor. However, PD-L1 assays are only moderately predictive of therapeutic benefit. Objective: To develop a novel decision-making tool for physicians treating NSCLC patients on whether to administer immune checkpoint inhibitor (ICI) therapy alone or in combination with chemotherapy. Design, setting, and participants: This multicenter observational study includes patients from an ongoing clinical trial (PROPHETIC;NCT04056247). Patients were recruited from 13 different centers (total n=425; 58 patients were excluded) from June 2016 and June 2021. Plasma samples were obtained prior to treatment initiation, and deep proteomic profiling was conducted. PROphet computational model for predicting clinical benefit (CB) probability at 12 months was developed based on the plasma proteomic profile. The model performance was validated in a blinded manner. Following validation, training and prediction was performed over the entire cohort using cross-validation methodology. The patients were divided into four groups based on their PD-L1 expression level combined with their CB probability, and the survival outcome was examined for each group. The data were analyzed from July to October 2022. Main outcome and measures: Clinical benefit from ICI-based treatment, overall survival (OS) and progression-free survival (PFS). Results: The model displayed strong predictive capability with an AUC of 0.78 (p-value = 5.00e-05), outperforming a PD-L1-based predictive model (AUC = 0.62; p-value 2.76e-01), and exhibited a significant difference in OS and PFS between patients with low and high CB probabilities. When combining CB probability with PD-L1 expression levels, four patient subgroups were identified; (i) patients with PD-L1>=50% and a negative PROphet result who significantly benefit from ICI-chemotherapy combination therapy compared to ICI monotherapy; (ii) patients with PD-L1≥50% and a positive PROphet result who benefit similarly from either treatment modalities; (iii) patients with PD-L1<50% and a negative PROphet result who do not benefit from either treatment modalities; (iv) patients with PD-L1<50% and a positive PROphet score who benefit from combination therapy. Conclusions and relevance: The PROphet model displayed good performance for prediction of CB at 12 months based on a plasma sample obtained prior to treatment. Our findings further demonstrate a potential clinical utility for informing treatment decisions for NSCLC patients treated with ICIs by adding resolution to the PD-L1 biomarker currently used to guide treatment selection, thereby enabling to select the most suitable treatment modality for each patient.

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Section: Discussionmentioning

confidence: 99%

A novel decision-making tool for first-line treatment selection in metastatic non-small cell lung cancer based on plasma proteome profiling

Christopoulos

Harel²,

Lahav³

et al. 2022

Preprint

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“…described a blood-based host immune classifier (HIC) proteomic testing to classify NSCLC patients. HIC classification could predict survival with ICI-based therapy and select NSCLC patients who were responding to immunotherapy ( 94 ). Zhang et al.…”

Section: Cutting-edge Progress In Biomarker Explorationmentioning

confidence: 99%

Advances in efficacy prediction and monitoring of neoadjuvant immunotherapy for non-small cell lung cancer

Wang

Feng

et al. 2023

Front. Oncol.

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The use of immune checkpoint inhibitors (ICIs) has become mainstream in the treatment of non-small cell lung cancer (NSCLC). The idea of harnessing the immune system to fight cancer is fast developing. Neoadjuvant treatment in NSCLC is undergoing unprecedented change. Chemo-immunotherapy combinations not only seem to achieve population-wide treating coverage irrespective of PD-L1 expression but also enable achieving a pathological complete response (pCR). Despite these recent advancements in neoadjuvant chemo-immunotherapy, not all patients respond favorably to treatment with ICIs plus chemo and may even suffer from severe immune-related adverse effects (irAEs). Similar to selection for target therapy, identifying patients most likely to benefit from chemo-immunotherapy may be valuable. Recently, several prognostic and predictive factors associated with the efficacy of neoadjuvant immunotherapy in NSCLC, such as tumor-intrinsic biomarkers, tumor microenvironment biomarkers, liquid biopsies, microbiota, metabolic profiles, and clinical characteristics, have been described. However, a specific and sensitive biomarker remains to be identified. Recently, the construction of prediction models for ICI therapy using novel tools, such as multi-omics factors, proteomic tests, host immune classifiers, and machine learning algorithms, has gained attention. In this review, we provide a comprehensive overview of the different positive prognostic and predictive factors in treating preoperative patients with ICIs, highlight the recent advances made in the efficacy prediction of neoadjuvant immunotherapy, and provide an outlook for joint predictors.

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“…44 45 More recent analyses have shown that HIC is also predictive, when considering immunotherapy alone or in combination with chemotherapy for patients with high PD-L1 status. 46 …”

Section: Current State Of Serum Proteomic Tests In Clinicmentioning

confidence: 99%

Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy

Park

Kim

Choi

et al. 2022

J Immunother Cancer

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Immunotherapy has fundamentally changed the landscape of cancer treatment. However, only a subset of patients respond to immunotherapy, and a significant portion experience immune-related adverse events (irAEs). In addition, the predictive ability of current biomarkers such as programmed death-ligand 1 (PD-L1) remains unreliable and establishing better potential candidate markers is of great importance in selecting patients who would benefit from immunotherapy. Here, we focus on the role of serum-based proteomic tests in predicting the response and toxicity of immunotherapy. Serum proteomic signatures refer to unique patterns of proteins which are associated with immune response in patients with cancer. These protein signatures are derived from patient serum samples based on mass spectrometry and act as biomarkers to predict response to immunotherapy. Using machine learning algorithms, serum proteomic tests were developed through training data sets from advanced non-small cell lung cancer (Host Immune Classifier, Primary Immune Response) and malignant melanoma patients (PerspectIV test). The tests effectively stratified patients into groups with good and poor treatment outcomes independent of PD-L1 expression. Here, we review current evidence in the published literature on three liquid biopsy tests that use biomarkers derived from proteomics and machine learning for use in immuno-oncology. We discuss how these tests may inform patient prognosis as well as guide treatment decisions and predict irAE of immunotherapy. Thus, mass spectrometry-based serum proteomics signatures play an important role in predicting clinical outcomes and toxicity.

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Real-world performance of blood-based proteomic profiling in first-line immunotherapy treatment in advanced stage non-small cell lung cancer

Cited by 17 publications

References 40 publications

A novel decision-making tool for first-line treatment selection in metastatic non-small cell lung cancer based on plasma proteome profiling

A novel decision-making tool for first-line treatment selection in metastatic non-small cell lung cancer based on plasma proteome profiling

Advances in efficacy prediction and monitoring of neoadjuvant immunotherapy for non-small cell lung cancer

Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy

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