# The Rechallenge Benefit Score: A Clinical Decision Tool for Patients Progressing After Immunotherapy

Caroline Plazy, Mohamed Boussena, Linh Nguyen Phuong, Baptiste Assié
Jean, Emmanuel Grolleau, Valerie Gounant, Nouha Chaabane, Chantal
Decroisette, Eric Dansin, Hélène Babey, et al.

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► To cite this version:

Caroline Plazy, Mohamed Boussena, Linh Nguyen Phuong, Baptiste Assié Jean, Emmanuel Grolleau, et al.. The Rechallenge Benefit Score: A Clinical Decision Tool for Patients Progressing After Immunotherapy. European Journal of Cancer, 2025. hal-05240613v1

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Submitted on 5 Sep 2025 (v1), last revised 28 Nov 2025 (v2)

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# Title: The Rechallenge Benefit Score: A Clinical Decision Tool for Patients Progressing After Immunotherapy.

**Authors:** Caroline Plazy¹, Mohamed Boussena²,³, Linh Nguyen Phuong²,³, Jean Baptiste Assié⁴, Emmanuel Grolleau⁵, Valerie Gounant⁶, Nouha Chaabane⁷, Chantal Decroisette⁸,¹⁶, Eric Dansin⁹, Hélène Babey¹⁰, Catherine Daniel¹¹, Etienne Giroux Leprieur¹², David Planchard¹³, Mariona Riuvadets¹³, Anthony Canellas¹⁴, Youssef Oulkhouir¹⁵, Maurice Pérol¹⁶, Anne-Claire Toffart¹⁷, Sebastien Benzekry²,³ and Elisa Gobbini¹²,¹⁸

## Affiliation:

¹ Biochemistry, Molecular Biology and Environmental Toxicology department, Grenoble-Alpes University Hospital (France)

² COMPUTational Pharmacology and Clinical Oncology Department, Inria Sophia Antipolis - Méditerranée, Faculté de Pharmacie, Marseille (France)

³ Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Marseille, (France)

⁴ Thoracic Oncology Unit, CHU Créteil, Paris, (France)

⁵ Thoracic Cancer Unit, Hospices Civils de Lyon Cancer Institute, Lyon (France)

⁶ Thoracic Oncology Department, CIC 1425 INSERM, Center Bichat, Paris (France)

⁷ Pulmonology Service, AP-HP Paris Center University Hospitals, Cochin Hospital, Paris (France)

⁸ Pulmonology and Thoracic Oncology Unit, CH Annecy-Genevois, Annecy (France)

⁹ Thoracic Oncology Unit, Center Oscar Lambret, Lille (France)

¹⁰ Thoracic Oncology Unit, CHRU Brest, Brest (France)

¹¹ Curie Institut, Oncology Departement, Paris (France)

¹² Department of Respiratory Diseases and Thoracic Oncology, APHP Hôpital Ambroise Pare, Paris (France)

¹³ Department of Medical Oncology, Thoracic Group, Gustave Roussy, Villejuif, France

¹⁴ Pulmonology Unit, APHP Hôpital Tenon and GRC 04 Theranoscan Sorbonne University, Paris (France)

¹⁵ Pulmonology and Thoracic Oncology Unit, CHU Caen (France)

¹⁶ Department of Medical Oncology, Léon Bérard Cancer Center, Lyon (France)

¹⁷ Grenoble-Alpes University Hospital, Thoracic Cancer Unit, Grenoble-Alpes University Hospital (France)

¹⁸ Cancer Research Center of Lyon, UMR Inserm 1052 - CNRS 5286, Lyon (France)

## Abstract

**Background:** Most patients with advanced Non-Small-Cell Lung Cancer (NSCLC) receive an immune checkpoint inhibitor (ICI) as a first-line treatment. Therapeutic options upon progression are limited, making ICI rechallenge a potential strategy. However, identifying patients who may benefit from this approach remains a challenge. This study presents a predictive score based on routine blood tests to identify patients who can specifically respond to rechallenge.

**Methods:** We retrospectively collected data from 252 advanced NSCLC patients who received first-line ICI between 2015 and 2021 across 16 French Oncologic Centers. Patients had received ICI rechallenge (Rechallenge cohort; N = 80) or chemotherapy (Chemotherapy cohort; N = 82) after progression. Results from 17 routine blood tests conducted before the first and second cycles of treatment were collected. Relevant blood tests associated with progression-free survival over rechallenge (rPFS) were identified using Cox regression and validated through a Bootstrap Cox-LASSO machine learning approach. An integrative score was then developed and tested for its ability to stratify patients on rPFS.

**Results:** Patients in the Rechallenge cohort showed a better ECOG PS and a deeper clinical benefit over the first ICI, compared to the Chemotherapy cohort. However, rPFS and PFS over the first chemotherapy post-ICI were similar (3.0 vs. 3.1 months, p = 0.4) justifying the identification of biomarkers for the decision-making. We defined the Rechallenge Benefit Score (RBS) able to identify patients likely to achieve longer clinical benefits from ICI rechallenge (C-index 0.79) regardless of their clinical features. The RBS was computed from routine blood-tests including inflammation-based markers such as the dNLR (derived Neutrophils to Lymphocytes ratio) and the MLR (Monocytes to Lymphocytes Ratio),
but also AST (Aspartate Transferase), ALP (Alkaline Phosphatase), and Albumin. The median rPFS was 6.4 months for patients with a low RBS versus 1.9 months for those with a high RBS (p < 0.0001, HR = 0.31 (CI 0.17 – 0.55)). Interestingly, no difference in PFS was observed between patients with high versus low RBS in the chemotherapy group or in the first ICI cohort.

**Conclusion:** We presented and validated the RBS score able to identify patients who respond to ICI rechallenge using routine blood tests. Since clinical features alone are inadequate for selecting patients, our findings can help in formulating treatment strategies for patients who already received immunotherapy during their history of disease.

**Key Messages:**

* What is already known on this topic:

Previous studies showed that blood-based biomarkers, including inflammation scores, are able to predict clinical benefit from anticancer treatments. However, no information is available about the role of blood-based predictors in defining the treatment strategy for those patients progressing over the first ICI. In those patients, the ICI rechallenge may be an interesting option, requiring the identification of selection criteria.

* What this study adds:

In this retrospective multicenter study, we confirmed that clinical criteria were insufficient to identify patients who could benefit from ICI Rechallenge. We thus identified and validated the RBS, a score based on routine blood tests capable of identifying patients who benefit from Rechallenge. Of note, this score is specifically predictive of response to ICI rechallenge. It was not associated with a benefit from chemotherapy or first-line ICI.

* How this study might affect research, practice or policy:

The RBS is a ready-to-use tool for informing treatment strategy in NSCLC patients who already received a first ICI during their history of disease. RBS is able to identify those patients who are more likely to benefit from ICI rechallenge regardless of their clinical features.

**Key words**

Immune checkpoint inhibitors, Rechallenge, Predictive biomarkers, Inflammation Score, Lung cancer, Machine Learning approach
## Background:

Most advanced Non-Small-Cell lung cancer (NSCLC) patients without oncogenic addiction received Immune checkpoint Inhibitors (ICI) associated or not to a platinum-based chemotherapy as upfront treatment. Their 5-year overall survival (OS) rate ranges from 9% to 30% according to histology and PDL-1 expression (Garassino et al., 2023; Novello et al., 2023; Reck et al., 2021). However, 5-year progression free survival (PFS) rates do not exceed 15% with a median PFS ranging from 6 to 11 months meaning that treatment choices at ICI progression remain a major question. Real-world data demonstrated a median PFS of 2.9 months, and median OS of 8.1 months for different second-line chemotherapy options given after initial progression to chemo-immunotherapy (Auclin et al., 2023).

Docetaxel is commonly used as it has historically been shown to prolong OS and improve disease-related symptoms over best supportive care in patients pretreated with platinum-based chemotherapy (Garon et al., 2014; Reck et al., 2014). The LUME-Lung1 and REVEL trials showed that the addition of an antiangiogenic drug (nintedanib or ramucirumab respectively) to docetaxel led to a significant improvement in PFS and OS (5 months and 11 months respectively)(Garon et al., 2014; Reck et al., 2014). New therapeutic agents such as TROP2 directed antibody-drug conjugates (ADC) represent an interesting strategy in this setting. However, results of phase III trials comparing their efficacy to standard docetaxel do not seem convincing due to the limited absolute gain in term of PFS in the intention-to-treat population (Ahn et al., 2023; Reinmuth et al., 2022). Thus, ICI rechallenge could be an interesting option in this setting. However, heterogenous results were pointed out across rechallenge studies with an overall response rate ranging between 0 and 54%, a PFS ranging from 1.5 to 12.9 months and an OS ranging from 6.5 to 23.8 months (Plazy et al., 2022). Better outcomes have been observed in patients with good ECOG PS and longer duration of initial ICI. Moreover, better results can be achieved for those patients who discontinued their first ICI because of the appearance of toxicities or protocol completion. However, identifying patients that are most likely to achieve a clinical benefit from ICI rechallenge according to their clinical characteristics remains challenging.

Inflammation plays a major role in the development and progression of cancers, both locally and systemically. In the literature, several blood-based scores have been described as predictive
biomarkers of response to anti-cancer treatments. The NLR (Neutrophils to Lymphocytes ratio) appears to be the most relevant in NSCLC, since it has been shown to be a prognostic factor for response to both ICI and chemotherapy in a large number of studies (Bersanelli et al., 2020; Bryant et al., 2022; Chen et al., 2023; Cortellini et al., 2022; Liu et al., 2017; Romano Francesco Jacopo et al., 2022; Wang et al., 2016). The dNLR (derived Neutrophils to Lymphocytes ratio), PLR (Platelets to Lymphocytes Ratio) and the LIPI (Lung Immune Prognostic Index) score appeared to be particularly interesting as predictive markers of response to ICI, since they have never been associated with clinical benefit over chemotherapy (Diem et al., 2017; Huai et al., 2023; Lim et al., 2021; MacDonald et al., 2023; Mezquita et al., 2021; Russo et al., 2018, 2020; Veccia et al., 2021; Zhang et al., 2022). Recently, a large study on cancer patients showed that machine learning scores integrating routine blood tests and clinical data could be useful to identify patients who achieve a deeper clinical benefit from immunotherapy event (Yoo et al., 2025).

To note, no studies have been conducted on the predictive value of these blood-based biomarkers in patients rechallenged with ICI. Identifying patients who are more likely to respond to ICI rechallenge by a blood-based test would be extremely useful in the definition of treatment strategy. Here we report a predictive score based on routine blood tests to identify patients specifically responding to ICI rechallenge regardless of their clinical characteristics.

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## Methods:

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### Patient cohort:

This study was conducted on a retrospective multicenter cohort of patients receiving immunotherapy rechallenge during their history of disease whose clinical characteristics and outcome have been already published elsewhere (Rechallenge-cohort) (Gobbini et al., 2020). As per inclusion criteria, patients: (1) had a diagnosis of advanced NSCLC (stage III-IV disease according to the 7th and 8th editions of the tumor, node, metastasis system [TNM] classification) at the first ICI initiation; (2) received at least two lines of anti-PD-1/PD-L1 agents during their disease course; (3) underwent an anti-PD-1/PD-L1 rechallenge after discontinuing the first ICI for at least 12 weeks because of toxicity,
disease progression, or treatment completion; (4) patients harboring a sensible EGFR or ALK alteration had to have received every targeted agent regularly reimbursed before initiating the first ICI. For the present study we selected only patients whose disease progression was documented before the ICI reintroduction. We collected a second cohort of patients who received only one line of ICI during their history of disease at the University Hospital of Grenoble between 2015 and 2021 (Chemotherapy-cohort). As per inclusion criteria, patients: (1) had a diagnosis of advanced NSCLC (stage III-IV disease according to the 7th and 8th editions of the tumor, node, metastasis system [TNM] classification) at the first ICI initiation; (2) received only one line of anti-PD-1/PD-L1 agents during their disease course; (3) received at least one line of chemotherapy after progression on ICI; (4) patients harboring a sensible EGFR or ALK alteration had to have received every target agent regularly reimbursed before initiating the first ICI.

Both cohorts have been declared and approved by CHU Grenoble-Alpes (France, CNIL 2205066 v 0). A non-opposition letter was sent to patients to inform them about the present study.

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## 72 Clinical and biological data:

For both cohorts, we retrospectively collected information about patient and disease characteristics along with treatment received with a focus on first line ICI, ICI rechallenge and first chemotherapy after ICI for those patients not receiving rechallenge during their history of disease. We then collected results of 17 routine care blood tests (Suppl table 1) performed at baseline (+/- 3 weeks) and at the start of the second cycle (C2D1) of first ICI, rechallenge or chemotherapy post-ICI. For each time point, we calculated inflammation scores including NLR, dNLR, MLR and PLR as detailed in Suppl table 1. It was not possible to calculate other inflammation scores such as the LIPI score and mGPS (Modified Glasgow Pronostic Score) because of missing data (mainly LDH results). The kinetic of these inflammation scores over the first cycle of treatment was assessed by calculating the ratio of the baseline value on the value obtained at second cycle.

Unidentified clinical and biological data were centrally collected using case-report forms to the University Hospital of Grenoble, where they were merged and exploited for statistical analysis.
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## 87 Statistical analysis

Progression free survival (PFS) was defined as the duration between new treatment introduction until progression, death or last visit. Rechallenge PFS (rPFS) was defined as the PFS over ICI rechallenge.

Single variables demonstrating a statistically significant association with rPFS were identified by using univariate Cox regression adjusted for confounding factors (age, gender, histological type, smoking status, and ECOG status). Adjusted p-values for controlling the false discovery rate were computed using the Benjamini-Hochberg method (Benjamini & Hochberg, 1995). All variables with an adjusted p-value lower than 0.05 were selected for further analysis.

For multivariable selection, we then employed the machine learning BOLASSO (Bootstrap Cox-LASSO) method performing 100 bootstraps of the dataset and applying a Cox-LASSO selection on each of them (Bach, 2008). Only variables that were selected in more than 50% of the cases were kept for the analysis. These BOLASSO-selected variables were added to those selected by Cox multivariate analysis, when not already present (Assaf et al., 2023; Bach, 2008).

The predictive score was built by multivariable Cox model for rPFS with the previously selected variables. Training this model led to the identification of a score defined as a linear combination of the normalized variables. To validate the predictive performance of the score, we used a leave-one-out cross validation (LOO-CV) approach adapted to small cohorts (Gareth et al., 2013), using the concordance index (C-index) as evaluation metric (Harrell et al., 1982). We considered clinically meaningful a score having a C-Index greater than or equal to 0.7.

In order to define a score threshold able to identify responder patients to ICI Rechallenge, a first cut-off was defined (using the maximally selected rank statistic by the maxstat R function) on a training dataset composed of 50% of the Rechallenge cohort. Results were further validated on the remaining 50% of patients. The final cut-off was determined on the whole Rechallenge cohort to be more generalizable.
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Eventually, the predictive value of the score was assessed for PFS over chemotherapy and first line ICI. All statistical analyses were performed using R v4.4.0.

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## Results:

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### Rechallenged Patients characteristics and outcomes:

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We evaluated 389 patients receiving at least one line of ICI, among whom 144 patients receiving ICI rechallenge during their history of disease. After medical records verification, 252 patients were included into the analysis, 80 of them received an ICI Rechallenge during their course of disease (Rechallenge cohort) and 82 patients received only chemotherapy regimens after progression on ICI (Chemotherapy cohort) (Figure 1). Clinical characteristics of the patients are detailed in Table 1. Patients included in the Rechallenge and Chemotherapy cohorts were comparable on most of clinical characteristics recorded at time of the first ICI introduction, such as age, gender, smoking status, stage of the disease at diagnosis, histological type, mutational status, metastatic sites or ECOG status. However, patients in the Rechallenge cohort received their first ICI in a more advanced setting compared to patients in the Chemotherapy cohort. Patients in the Rechallenge cohort showed a longer duration on treatment over the first ICI and had better objective response (objective response rate [ORR]: 55% vs. 33% and disease control rate [DCR]: 76% vs. 60% [Fisher's Exact Test, p=0.03] in the Rechallenge and Chemotherapy cohorts, respectively). Moreover, a higher percentage of patients discontinued the first ICI due to progression in the Chemotherapy cohort compared to the Rechallenge cohort (85% versus 53% respectively, p < 0.001). Finally, more ECOG PS ≥ 2 patients were observed within the Chemotherapy cohort at the time of the first chemotherapy introduction after ICI progression compared to the rechallenged patients (42% versus 22%, p < 0.001). These results confirmed that patients who were offered the rechallenge strategy in real life were highly selected among those presenting good prognostic factors. Consistently, PFS over the first ICI was significantly longer for patients in the Rechallenge cohort (10 months versus 4.13 months respectively, p < 0.001). Moreover, patients included

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138 in the Rechallenge cohort had a significantly longer median OS from the first ICI than patients included
139 in the Chemotherapy cohort (36 months versus 13 months respectively, p < 0.001).

140 Interestingly, even though OS from rechallenge was significantly longer than OS from first
141 chemotherapy after ICI in the Chemotherapy cohort (12.5 versus 6.3 months, p = 0.006) no difference
142 in term of PFS (3 months versus 3.1 months, p = 0,4) or response rate (ORR/DCR: 20%/41% versus
143 22%/47% over ICI Rechallenge in the Rechallenge cohort and first chemotherapy after ICI in the
144 Chemotherapy cohort respectively) were detected.

145 These results highlighted that clinical characteristics are not sufficient to identify those patients
146 who will benefit from the Rechallenge strategy.

147

148 A blood-based score predicting outcomes over Rechallenge:

149 We used the Rechallenge cohort to identify responding patients thanks to a blood-based score
150 calculated early on Rechallenge introduction. Distribution of the 19 blood-based parameters routinely
151 collected at baseline and at C2D1 of Rechallenge in the study population are detailed in Suppl Table 1.
152 The first rPFS Cox models adjusted for confounding factors identified 6 biomarkers significantly
153 associated with rPFS, namely: baseline dNLR, NLR, MLR, Neutrophils, Albumin and Aspartate
154 aminotransferase (AST) (Supp Table 2). The univariate C-Index values of these biomarkers ranged from
155 0.57 to 0.685, with NLR and dNLR being the highest-ranked variables (C-index: 0.660 and 0.685
156 respectively). The BOLASSO algorithm (see Methods) confirmed the prognostic value of the
157 previously selected variables except for Neutrophils (Suppl Table 3). On the other hand, BOLASSO
158 identified Hemoglobin, ALP and dNLR kinetics (dNLR_KIN) as rPFS determinants. These 3 variables
159 were considered as potential additional candidates. Using LOO-CV for adding these 3 variables to the
160 previous set, we found that, while ALP and dNLR_KIN increased the model C-index, it was not the case
161 for Hemoglobin, which was thus discarded. To avoid redundancy, only dNLR was kept into the model
162 and NLR was discarded. The final set of variables was therefore: dNLR, AST, MLR, Albumin, ALP and
163 dNLR_KIN.

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The multivariable rPFS Cox model integrating these variables achieved a mean C-Index of 0.79 (SD [standard deviation]: 0.03). The predictive performances of the score were validated using a LOO-CV (C-index of 0.72). This model showed a better performance to predict rPFS than each biomarker considered separately (Suppl Fig 1). The score can be calculated as follows:

$$ \text{Rechallenge Benefit Score (RBS):} \\ 0.608*dNLR + 0.0161*AST - 0.656*dNLR_Kin - 0.0673*MLR - 0.0847*ALB - 0.00279*APL. $$

Then, we assessed the score's ability to stratify patients on rPFS. The study cohort was split into a training set (n=40) and a test set (n=40). Survival analysis was conducted on the training set leading to the identification of a cut-off = -2.95 (p < 0.0001, HR = 0.30 (0.14 – 0.66), Figure 2A). Applying this cut-off to the test set, we were able to confirm its good performance (p < 0.001, HR = 0.25 (0.08 – 0.79) Figure 2B). To improve the generalization of our model, we then defined the final cut-off on the entire cohort (Figure 2C). The final Rechallenge Benefit Score (RBS) cut-off was then set up at -2.42. Low RBS (< -2.42) patients were shown to have a longer median rPFS compared to high RBS (> -2.42) patients (6.4 versus 1.9 months, p < 0.0001, HR = 0.31 (0.17 – 0.55), Figure 2C). Interestingly, despite not statistically significant because of limited sample size, we showed that the RBS was able to identify patients more likely to achieve better outcomes over ICI Rechallenge regardless of their clinical prognostic factor related to the first ICI (Suppl Figure 2). Notably, a longer rPFS was shown in Low-RBS patients regardless of the first ICI duration (<3 months or > 3 months) and the reason of first ICI discontinuation (clinical decision, toxicity or disease progression).

To make easier the clinical introduction of this score, we assessed the model with baseline parameters only, without considering biomarkers calculated at C2D1. Even if less performant, this modified model was satisfying with a C-index of 0.74 [SD: 0.03] at Cox multivariate analysis and 0.68 for the LOO-CV.

$$ \text{Modified RBS (mRBS):} 0.333*dNLR + 0,312*MLR + 0.014*AST - 0.08*ALB - 0.0043*APL, \text{ with an optimal cut-off of -2.2.} $$
193 **The RBS specifically predicts ICI Rechallenge efficacy:**

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In order to test whether the RBS was predictive of ICI rechallenge response – in contrast to merely prognostic – we applied the same pipeline of analysis to patients included into the Chemotherapy cohort, with regards to blood-based variables collected during the first 2 cycles of chemotherapy after ICI progression. We found that only a few biomarkers were associated with PFS, namely: GGT, AST and MLR kinetics, with low C-Index values ranging from 0.53 to 0.61 (Suppl Table 4). Consequently, it was not possible to create any blood-based score with a significant clinical impact for predicting chemotherapy benefit (multivariable Cox modeling and subsequent LOO-CV showed a C-index of 0.67 [SD: 0.04] and 0.58 respectively). Notably, applying the RBS cut-off identified from the Rechallenge cohort, no PFS difference was observed in the Chemotherapy cohort (Figure 2D).

Finally, we aimed to test whether the RBS was specifically predictive of ICI Rechallenge or ICI response in general. We applied the same pipeline using blood-based variables measured during the first exposure to ICI pooling the Chemotherapy and Rechallenge Cohort. Interestingly, several variables were identified as associated with PFS (ALP, MLR, NLR, dNLR, Hemoglobin, Albumin, Neutrophils) but with low predictive performances (C-Index ranging from 0.54 to 0.59) (Suppl Table 5). Once again, no clinically meaningful score could be extrapolated (multivariate Cox model and LOO-CV method showed a C-index of 0.61 [SD: 0.04] and 0.58 respectively). When applying the RBS cut-off identified from the Rechallenge cohort, no PFS difference was found during the ICI first line.

213 **Discussion:**

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Immunotherapy rechallenge represents a widely adopted strategy in selected NSCLC patients. We recently reviewed the main clinical factors associated with a better clinical benefit from this strategy according to the literature. Although most studies are retrospective and with a limited sample size, we could highlight the same recurrent results. ICI rechallenge seemed to lead to interesting results in patients with good ECOG PS, longer duration of initial ICI, discontinuation reason of initial ICI other
than progression, and those who received an ICI sequence different from the switch between anti-PD1 and anti-PDL1 (Plazy et al., 2022). Conversely, PDL1 expression, radiological response to the initial ICI, use of intercurrent chemotherapy or radiotherapy between the two ICI courses, and duration of treatment-off period were more debated across the selected studies, preventing a firm conclusion. No randomized trials currently exist comparing ICI rechallenge to chemotherapy and the selection bias cannot be resolved. In our study, we included both patients rechallenged with ICI during their disease history and patients who received chemotherapy regimens only once progressing on first ICI (according to the investigator choice). Looking at their clinical characteristics, it is clear that patients candidates for ICI rechallenge are highly selected in clinical practice. Indeed, consistently with the literature data, the rechallenge group was enriched in patients in good general condition (ECOG PS 0-1) and benefited from a deeper benefit from the first ICI. However, despite this imbalance obviously translating into a better OS for the rechallenged patients, no difference was detected in terms of PFS or response rate when comparing ICI rechallenge and chemotherapy after progression on first ICI. These results raised questions about the efficacy of rechallenge in all comers, even when selected according to their clinical characteristics and their response to previous treatments. Considering long-term side effects of ICI and financial costs, identifying those patients more likely to achieve long response over ICI rechallenge seems necessary into our clinical practice.

We thus developed the RBS tool based on blood variables routinely tested before the instauration of a new treatment in cancer patients, able to identify those patients who could really benefit from ICI rechallenge.

This RBS included the inflammation scores dNLR and MLR that have been previously described as associated with a better response to ICI in NSCLC (Criscitiello et al., 2020; Huangfu et al., 2024; Mezquita et al., 2021; Sekine et al., 2018). However, even if the dNLR has never been investigated in patients receiving chemotherapy, the MLR demonstrated a prognostic impact in chemotherapy-treated NSCLC (Lin et al., 2014; Song et al., 2016). This suggested that inflammation score may have a more prognostic value rather than predictive. In our study the dNLR and MLR demonstrated interesting performance in identifying patients responding to ICI rechallenge (univariable C-index values of 0.69 and 0.67 respectively). However, when integrated within the RBS model that considers other blood-
based parameters (namely AST, ALP and Albumin), they acquired a meaningful performance (C-index of 0.79) with a potential clinically significant impact in our practice. Indeed, patients with low RBS showed four times longer PFS than the others, and the score maintained its clinical utility even in those patients who had achieved a major clinical benefit over first-line immunotherapy. Interestingly, the RBS model was not able to identify responders within chemotherapy-treated patients or among those who were exposed for the first time to immunotherapy. These results supported the predictive value of the RBS model specifically for patients receiving ICI rechallenge being able to identify responders regardless of their clinical characteristics. They are consistent with literature data suggesting that machine learning approaches integrating clinical data and blood tests are useful to identify patients who benefit from immunotherapy (Yoo et al., 2025). Notably, while the predictive performance for overall survival (OS) was substantial (median AUC(t) = 0,763), the ability to predict durable response was more moderate, with AUC values ranging from 0.57 to 0.71 in the lung cancer cohort. Similar observations were found when integrating multi-modal data (Captier et al., 2025). This could be due to the prognostic rather than predictive value of some markers. Therefore, we chose here to focus on prediction of PFS rather than OS. In fact, PFS reflects the contribution of the treatment under investigation, while OS can be affected by many other factors such as subsequent lines and age of the patient, limiting the interpretation of the resulting findings.

Of note, the RBS model considers 5 blood-based parameters measured at baseline before the introduction of a new treatment, but also a variable called “dNLR-KIN” representing the ratio between the inflammation score dNLR measured at baseline and after one cycle of immunotherapy. Even thus the model showed a greater performance when including the dNLR-KIN, we proposed a modified RBS (mRBS) calculated without this kinetic parameter, that maintained a clinically significant performance (C-index 0.74). Although the RBS model exhibited a significantly better performance, the mRBS model remains a valid alternative option by using baseline data only. Details about online calculators for RBS and mRBS are provided in Supplementary data.
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Despite these interesting results, our study has several limitations. Selection bias related to retrospective data collections should be considered especially when comparing outcomes of the rechallenge and chemotherapy cohorts. Moreover, the lack of LDH (Lactate Dehydrogenase) among blood tests routinely tested in most of the patients included in our study, prevented us to integrate the LIPI score into our analysis (Hopkins et al., 2021; Kazandjian et al., 2019; Mezquita et al., 2018). This score has been previously demonstrated as able to predict ICI benefit even though some data showed that it may also have a role in chemotherapy-treated patients suggesting a more prognostic value (Mezquita et al., 2018). Here, we demonstrated for the first time the predictive value of a blood-based tool in different settings (ICI rechallenge, chemotherapy, first-line ICI) and we demonstrated its value specifically for ICI rechallenge patients. The robust statistical methods employed – including the training / validation cohort approach - allowed generalization of our results (Benzekry et al., 2024).

In conclusion, RBS model is a ready-to-use tool able to specifically identify those patients who are more likely to respond to ICI Rechallenge, being a useful tool to integrate into clinical practice.
**Authors' Disclosures of Potential Conflicts of Interest**

EG: speaker bureau for Merck Sharp & Dohme, Bristol-Myers Squibb, Regeneron, Sanofi, Janssen.
Spouse of an AstraZeneca employee. ACT: Boards and consulting for Roche, Astra Zeneca, BMS, Janssen, MSD, Pfizer, Sanofi, Takeda and support for attending meetings and/or travel from Astra Zeneca, Pfizer, MSD, Roche, Takeda. CD: Boards and consulting for Astra Zeneca, Janssen, Roche, Takeda, Novocure, Amgen, MSD, BMS, Danone, Pfizer. Congresses: Pfizer, Novocure, Takeda. VG: Boards and consulting for Regeneron, BMS, Astra Zeneca, Takeda, Amgen, and support for attending meetings and/or travel from Takeda, Pfizer and Regeneron.

**Ethics approval and consent to participate**

The study was conducted according to the principles of the Declaration of Helsinki and in compliance with the European Regulation (EU) 2016/679, repealing Directive 95/46/EC about General Data Protection Regulation. The patients were fully informed in understandable terms about the study's objectives and the nature of the information collected. Likewise, they were informed about their right to object at any time to their data exploration. Study cohort declaration number: CNIL 2205066 v 0 (France).

**Funding**

This study did not require any funding

**Author Contributions**

E.G. and C.P. conceived of the aim of the study and collected clinical and biological data. E.G. and S.B. supervised the findings of this work driving step-by-step statistical analysis. C.P. and S.B. developed the statistical plan and performed the computations. C.P. and E.G. wrote the manuscript. All authors discussed the results and contributed to the final manuscript.

**Acknowledgment:**

We would like to thank Jordan Sacareau for its support and assistance to the project.

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<table><thead><tr><th></th><th colspan="2">Rechallenge Cohorte<br>n = 80</th><th colspan="2">Chemotherapy Cohorte<br>n = 82</th><th>p-value</th></tr><tr><th></th><th>N</th><th>%</th><th>N</th><th>%</th><th></th></tr></thead><tbody><tr><td>Age (med, [range])</td><td colspan="2">63 [40, 82]</td><td colspan="2">64,8 [29, 83]</td><td>NS</td></tr><tr><td>Gender (Male / Female)</td><td colspan="2">56 / 24</td><td colspan="2">70 / 30</td><td>NS</td></tr><tr><td>Smoking habits</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Never</td><td>4</td><td>5</td><td>7</td><td>9</td><td rowspan="3">NS</td></tr><tr><td>Former</td><td>42</td><td>53</td><td>45</td><td>55</td></tr><tr><td>Current</td><td>30</td><td>38</td><td>26</td><td>32</td></tr><tr><td>Stage at diagnosis</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>II</td><td>9</td><td>11</td><td>6</td><td>7</td><td rowspan="3">NS</td></tr><tr><td>III</td><td>12</td><td>15</td><td>10</td><td>12</td></tr><tr><td>IV</td><td>56</td><td>70</td><td>52</td><td>63</td></tr><tr><td>Histological type</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Squamous</td><td>25</td><td>31</td><td>18</td><td>22</td><td rowspan="2">NS</td></tr><tr><td>Non-Squamous</td><td>51</td><td>64</td><td>60</td><td>73</td></tr><tr><td>Mutations</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>EGFR</td><td>1</td><td>1</td><td>2</td><td>2</td><td rowspan="4">NS</td></tr><tr><td>ALK</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>KRAS</td><td>24</td><td>30</td><td>31</td><td>38</td></tr><tr><td>BRAF</td><td>5</td><td>6</td><td>2</td><td>2</td></tr><tr><td>ECOG statuts</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>0-1</td><td>64</td><td>80</td><td>60</td><td>73</td><td rowspan="4">NS</td></tr><tr><td>2</td><td>10</td><td>13</td><td>14</td><td>17</td></tr><tr><td>3</td><td>2</td><td>3</td><td>2</td><td>2</td></tr><tr><td>NA</td><td>4</td><td>5</td><td>6</td><td>7</td></tr><tr><td>Metastasis</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Brain</td><td>15</td><td>19</td><td>26</td><td>32</td><td rowspan="5">NS</td></tr><tr><td>Pulmonary</td><td>58</td><td>73</td><td>68</td><td>83</td></tr><tr><td>Bone</td><td>23</td><td>29</td><td>34</td><td>41</td></tr><tr><td>Adrenals</td><td>18</td><td>23</td><td>22</td><td>27</td></tr><tr><td>Other</td><td>18</td><td>23</td><td>33</td><td>40</td></tr><tr><td>First ICI - Line</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1</td><td>16</td><td>20</td><td>27</td><td>33</td><td rowspan="3">0.01</td></tr><tr><td>2</td><td>39</td><td>49</td><td>41</td><td>51</td></tr><tr><td>3 and more</td><td>24</td><td>30</td><td>10</td><td>13</td></tr><tr><td>First ICI - Type</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Anti PD1</td><td>Nivolumab</td><td>56</td><td>70</td><td>36</td><td>44</td><td rowspan="4">&lt; 0.01</td></tr><tr><td></td><td>Pembrolizumab</td><td>16</td><td>20</td><td>42</td><td>51</td></tr><tr><td>Anti PD-L1</td><td>Atezolizumab</td><td>4</td><td>5</td><td>0</td><td>0</td></tr><tr><td></td><td>Durvalumab</td><td>4</td><td>5</td><td>0</td><td>0</td></tr><tr><td>First ICI - Duration (med, Q1-Q3s)</td><td colspan="2">5.7 (2.4 - 12.8)</td><td colspan="2">4.02 (2.2 - 7.2)</td><td>0.02</td></tr><tr><td>First ICI - Discontinuation reason</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Disease Progression</td><td>42</td><td>53</td><td>70</td><td>85</td><td rowspan="3">&lt; 0.01</td></tr><tr><td>Toxicity</td><td>26</td><td>33</td><td>6</td><td>7</td></tr><tr><td>Protocol completion</td><td>11</td><td>14</td><td>1</td><td>1</td></tr><tr><td>First ICI - Best response</td><td colspan="2"></td><td colspan="2"></td><td></td></tr></tbody></table>
<table><caption>Table 1: Clinical characteristics of patients.</caption><tbody><tr><td>Complete response</td><td>4</td><td>5</td><td>1</td><td>1</td><td></td></tr><tr><td>Partial response</td><td>40</td><td>50</td><td>26</td><td>32</td><td rowspan="3">0.02</td></tr><tr><td>Stable disease</td><td>17</td><td>21</td><td>22</td><td>27</td></tr><tr><td>Disease progression</td><td>14</td><td>18</td><td>28</td><td>34</td></tr><tr><td colspan="6">Number of subsequent lines of treatment</td></tr><tr><td>1</td><td>34</td><td>43</td><td>50</td><td>61</td><td rowspan="3">&lt; 0.01</td></tr><tr><td>2</td><td>29</td><td>36</td><td>32</td><td>39</td></tr><tr><td>≥ 3</td><td>17</td><td>21</td><td>0</td><td>0</td></tr><tr><td colspan="6">Rechallenge or CT - ECOG before initiation</td></tr><tr><td>0-1</td><td>59</td><td>74</td><td>34</td><td>41</td><td rowspan="4">&lt; 0.01</td></tr><tr><td>2</td><td>13</td><td>16</td><td>26</td><td>32</td></tr><tr><td>≥ 3</td><td>5</td><td>6</td><td>8</td><td>10</td></tr><tr><td>NA</td><td>3</td><td>4</td><td>14</td><td>17</td></tr><tr><td colspan="6">Rechallenge or CT - Discontinuation reason</td></tr><tr><td>Disease Progression</td><td>48</td><td>60</td><td>59</td><td>72</td><td rowspan="4">&lt; 0.01</td></tr><tr><td>Toxicity</td><td>10</td><td>13</td><td>6</td><td>7</td></tr><tr><td>Medical Decision</td><td>0</td><td>0</td><td>8</td><td>10</td></tr><tr><td>On going</td><td>18</td><td>23</td><td>6</td><td>7</td></tr><tr><td colspan="6">Rechallenge or CT - Best response</td></tr><tr><td>Complete response</td><td>1</td><td>1</td><td>0</td><td>0</td><td rowspan="5">NS</td></tr><tr><td>Partial response</td><td>15</td><td>19</td><td>18</td><td>22</td></tr><tr><td>Stable disease</td><td>17</td><td>21</td><td>21</td><td>26</td></tr><tr><td>Progression</td><td>36</td><td>45</td><td>37</td><td>45</td></tr><tr><td>NA</td><td>10</td><td>13</td><td>5</td><td>6</td></tr></tbody></table>

NA = Not Available, NS = Not Significant, ICI= Immune Checkpoint Inhibitors, PD-L1= Programmed Death Ligand-1
<figure><img src="image_3.png" /><figcaption>Figure 1: Study flow chart</figcaption></figure>
A

<figure><img src="image_4.png" /></figure>

B

<figure><img src="image_5.png" /></figure>

C

<figure><img src="image_6.png" /></figure>

D

<figure><img src="image_7.png" /><figcaption>Figure 2: rPFS analysis according to the RBS predictor in the Rechallenge and Chemotherapy cohort</figcaption></figure>

Survival analysis performed on the rPFS according to the RBS status (High: > -2.95; Low: < -2.95) in the training (A) and validation (B) set from the Rechallenge cohort (median of rPFS on training set = 5.9 months and 1.9 months for Low and High score respectively, logrank test p-value < 0.0001, HR = 0.3 (95% CI, 0.14 – 0.66), and median of rPFS on the test set = 5 months and 1.4 months for Low and High score respectively, logrank test p-value < 0.001, HR = 0.25 (95% CI, 0.08 – 0.79)). Survival analysis performed on the rPFS according to the RBS status (High: > -2.42; Low: < -2.42) in the entire Rechallenge cohort (C), median of rPFS = 6.4 months and 1.9 months for Low and High score respectively, p < 0.0001, HR = 0.31 (0.17 – 0.55). Results of the survival analysis performed on PFS over chemotherapy post-ICI progression according to the RBS status, were presented in D (High: > -2.42; Low: < -2.42), median of PFS = 4.3 months and 3 months for Low and High score respectively, p = 0.15, HR = 0.68 (0.42 – 1.11).
<table><thead><tr><th rowspan="2">Variable</th><th rowspan="2">Formula</th><th colspan="2">Rechallenge Cohort<br>n = 80</th><th>Chemotherapy cohort<br>n = 82</th><th>First ICI (all)<br>n = 252</th></tr><tr><th>Med (Q1-Q3)</th><th>Mean [SD]</th><th>Med (Q1-Q3)</th><th>Med (Q1-Q3)</th></tr></thead><tbody><tr><td>Leucocytes</td><td>/</td><td>7.8 (6.4 - 9.7)</td><td>8.1 [2.3]</td><td>9.4 (7 - 12.4)</td><td>8.3 (6.3 - 10.5)</td></tr><tr><td>Lymphocytes</td><td>/</td><td>1.4 (0.97 - 1.9)</td><td>1.5 [0.7]</td><td>1.2 (0.88 - 1.9)</td><td>1.6 (1.1 - 2.1)</td></tr><tr><td>Monocytes</td><td>/</td><td>0.8 (0.6 - 0.9)</td><td>0.8 [0.3]</td><td>0.79 (0.6 - 1.1)</td><td>0.75 (0.6 - 0.98)</td></tr><tr><td>Neutrophils</td><td>/</td><td>5.2 (3.9 - 6.3)</td><td>5.4 [1.9]</td><td>6.6 (4.8 - 9)</td><td>5.3 (3.8 - 7)</td></tr><tr><td>Hemoglobin</td><td>/</td><td>124.5 (113 - 135.3)</td><td>124 [16.7]</td><td>119.5 (101.3 - 131)</td><td>119 (106 - 133)</td></tr><tr><td>Platelets</td><td>/</td><td>281.5 (201.5 - 385.3)</td><td>292.6 [108.5]</td><td>223 (189 - 301.5)</td><td>298 (234 - 397.5)</td></tr><tr><td>Albumin</td><td>/</td><td>37 (33.5 - 40)</td><td>36.2 [5]</td><td>33.2 (28.3 - 39.2)</td><td>36 (31 - 39.5)</td></tr><tr><td>Aspartate aminotransferase (AST)</td><td>/</td><td>21 (16 - 25)</td><td>25 [24.5]</td><td>21 (16 - 28)</td><td>21.5 (16 - 29)</td></tr><tr><td>Alanine aminotransferase (ALT)</td><td>/</td><td>17 (13 - 23)</td><td>21.7 [20.8]</td><td>18 (11 - 23)</td><td>19 (14 - 27)</td></tr><tr><td>Alkaline Phosphatase (ALP)</td><td>/</td><td>79 (66 - 91)</td><td>87.8 [35.7]</td><td>109 (27 - 145)</td><td>96 (73 - 121)</td></tr><tr><td>Gamma Glutamyl Transferase (GGT)</td><td>/</td><td>39 (23.5 - 68)</td><td>56.6 [53.7]</td><td>54.5 (29.8 - 106)</td><td>44 (28 - 97.8)</td></tr><tr><td>Neutrophils to Lymphocytes Ratio (NLR)</td><td>Neutrophils / Lymphocytes</td><td>3.5 (2.3 - 6.6)</td><td>4.8 [3.5]</td><td>5.23 (3.7 - 8.7)</td><td>3.23 (2.1 - 5.3)</td></tr><tr><td>Derived Neutrophils to Lymphocytes Ratio (dNLR)</td><td>Neutrophil count / (Leucocytes - Neutrophil count)</td><td>1.8 (1.5 - 2.7)</td><td>2.2 [1.1]</td><td>2.6 (2.1 - 4)</td><td>1.9 (1.4 - 2.7)</td></tr><tr><td>Monocytes to Lymphocytes Ratio (MLR)</td><td>Monocytes / Lymphocytes</td><td>0.55 (0.4 - 0.8)</td><td>0.69 [0.5]</td><td>0.67 (0.4 - 1)</td><td>0.47 (0.35 - 0.67)</td></tr><tr><td>Platelets to Lymphocytes Ratio (PLR)</td><td>Platelets / Lymphocytes</td><td>207.5 (132.4 - 310.2)</td><td>244.7 [146]</td><td>242.2 (170.7 - 390.5)</td><td>188.4 (132.3 - 264.7)</td></tr><tr><td>NLR Kinetics</td><td rowspan="4">Baseline Value / Value at Second cycle</td><td>0.98 (0.76 - 1.29)</td><td>1.16 [0.8]</td><td>1.26 (0.91 - 1.71)</td><td>0.93 (0.7 - 1.23)</td></tr><tr><td>dNLR Kinetics</td><td>0.99 (0.81 - 1.20)</td><td>1.13 [0.73]</td><td>1 (0.79 - 1.44)</td><td>0.96 (0.79 - 1.18)</td></tr><tr><td>MLR Kinetics</td><td>0.99 (0.78 - 1.21)</td><td>1.19 [0.99]</td><td>2.02 (1.24 - 2.86)</td><td>0.92 (0.78 - 1.19)</td></tr><tr><td>PLR Kinetics</td><td>1 (0.75 - 1.25)</td><td>1.09 [0.49]</td><td>0.92 (0.77 - 1.23)</td><td>0.87 (0.7 - 1.1)</td></tr></tbody></table>

Supplementary Table 1: Blood-based variables

Distribution of blood-based variables, inflammation scores and their score kinetics across the first cycles of treatment (Rechallenge, Chemotherapy or First ICI – Immune Checkpoint Inhibitor). SD = Standard Deviation.
Cox Regression on PFS – Rechallenge Cohort

<table><thead><tr><th>Label</th><th>Na</th><th>N</th><th>C Index</th><th>HR uv</th><th>p uv</th><th>signif uv</th><th>HR mv</th><th>p mv</th><th>p adjustable</th><th>signif mv</th></tr></thead><tbody><tr><td>dNLR</td><td>1.6%</td><td>60</td><td>0.685</td><td>1.5 (1.15 - 1.94)</td><td>2.48e-03</td><td>**</td><td>1.88 (1.29 - 2.73)</td><td>1.02e-03</td><td>7.94e-03</td><td>**</td></tr><tr><td>NLR</td><td>3.3%</td><td>59</td><td>0.66</td><td>1.53 (1.13 - 2.07)</td><td>5.88e-03</td><td>**</td><td>1.89 (1.31 - 2.73)</td><td>6.78e-04</td><td>7.94e-03</td><td>***</td></tr><tr><td>Neutrophils</td><td>0.0%</td><td>61</td><td>0.643</td><td>1.63 (1.14 - 2.34)</td><td>7.06e-03</td><td>**</td><td>1.55 (1.02 - 2.36)</td><td>0.0422</td><td>0.0733</td><td>*</td></tr><tr><td>MLR</td><td>8.2%</td><td>56</td><td>0.672</td><td>1.48 (1.1 - 2)</td><td>0.0102</td><td>*</td><td>2 (1.33 - 3.02)</td><td>8.44e-04</td><td>7.94e-03</td><td>***</td></tr><tr><td>Albumin</td><td>27.9%</td><td>44</td><td>0.628</td><td>0.56 (0.358 - 0.874)</td><td>0.0108</td><td>*</td><td>0.454 (0.264 - 0.78)</td><td>4.22e-03</td><td>0.0127</td><td>**</td></tr><tr><td>AST</td><td>6.6%</td><td>57</td><td>0.568</td><td>1.4 (1.05 - 1.86)</td><td>0.0228</td><td>*</td><td>1.57 (1.1 - 2.24)</td><td>0.0126</td><td>0.0245</td><td>*</td></tr><tr><td>ALT</td><td>6.6%</td><td>57</td><td>0.513</td><td>1.45 (1.05 - 2.02)</td><td>0.0259</td><td>*</td><td>1.49 (0.994 - 2.23)</td><td>0.0535</td><td>0.0859</td><td>n.s.</td></tr></tbody></table>

Supplementary Table 2: Variables selection by Cox regression analysis on rPFS.

Multivariate cox regression analysis is adjusted on confounding factors (Age, Genre, ECOG at rechallenge, Histology, Treatment line of first ICI). Only variable showing a statistically significant impact on PFS at least at the univariate analysis are detailed. uv = univariate, mv = multivariate, * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, p adjustable = adjusted p-value for multiple hypothesis testing using the Benjamini-Hochberg correction, HR = hazard ratio (95% confidence interval). dNLR = derived Neutrophils to Lymphocytes Ratio, NLR = Neutrophils to Lymphocytes Ratio, MLR = Monocytes to Lymphocytes Ratio, AST = Aspartate aminotransferase, ALT = Alanine aminotransferase).

<table><thead><tr><th>Feature</th><th>Frequency</th></tr></thead><tbody><tr><td>AST</td><td>0.78</td></tr><tr><td>MLR</td><td>0.74</td></tr><tr><td>Hemoglobin</td><td>0.7</td></tr><tr><td>ALP</td><td>0.62</td></tr><tr><td>dNLR</td><td>0.62</td></tr><tr><td>ALB</td><td>0.56</td></tr><tr><td>dNLR Kinetics</td><td>0.52</td></tr><tr><td>NLR</td><td>0.52</td></tr></tbody></table>

Supplementary Table 3: Variables selection by BOLASSO method on rPFS.

Main biomarkers of interest identified by BOLASSO Machine Learning approach, i.e. variables with LASSO frequency of selection (over the 100 bootstraps) greater than 0.5. Abbreviation: AST = Aspartate aminotransferase, MLR = Monocytes to Lymphocytes Ratio, ALP = Alkaline Phosphatase, dNLR = derived Neutrophils to Lymphocytes Ratio, ALB = Albumin, dNLR = derived Neutrophils to Lymphocytes Ratio).
<table><caption>Cox Regression on PFS - Chemotherapy Cohort</caption><thead><tr><th>Label</th><th>Na</th><th>N</th><th>C Index</th><th>HR uv</th><th>p uv</th><th>signif uv</th><th>HR mv</th><th>p mv</th><th>p adjustable</th><th>signif mv</th></tr></thead><tbody><tr><td>NLR Kinetics</td><td>16.7%</td><td>65</td><td>0.592</td><td>1.67 (1.27 - 2.2)</td><td>2.54e-04</td><td>***</td><td>1.41 (0.98 - 2.02)</td><td>0.0639</td><td>0.355</td><td>n.s.</td></tr><tr><td>GGT</td><td>20.5%</td><td>62</td><td>0.608</td><td>1.69 (1.26 - 2.26)</td><td>5.02e-04</td><td>***</td><td>1.69 (1.09 - 2.6)</td><td>0.0179</td><td>0.274</td><td>*</td></tr><tr><td>MLR Kinetics</td><td>16.7%</td><td>65</td><td>0.542</td><td>2.75 (1.53 - 4.94)</td><td>7.17e-04</td><td>***</td><td>3.54 (1.34 - 9.35)</td><td>0.0107</td><td>0.267</td><td>*</td></tr><tr><td>MLR</td><td>2.6%</td><td>76</td><td>0.625</td><td>1.6 (1.22 - 2.1)</td><td>7.57e-04</td><td>***</td><td>1.4 (0.935 - 2.09)</td><td>0.103</td><td>0.366</td><td>n.s.</td></tr><tr><td>dNLR Kinetics</td><td>16.7%</td><td>65</td><td>0.548</td><td>1.52 (1.16 - 1.97)</td><td>2.03e-03</td><td>**</td><td>1.31 (0.961 - 1.78)</td><td>0.0875</td><td>0.356</td><td>n.s.</td></tr><tr><td>AST</td><td>16.7%</td><td>65</td><td>0.531</td><td>1.68 (1.19 - 2.37)</td><td>3.36e-03</td><td>**</td><td>1.61 (1.07 - 2.41)</td><td>0.0219</td><td>0.274</td><td>*</td></tr><tr><td>ALP</td><td>16.7%</td><td>65</td><td>0.599</td><td>1.34 (1.06 - 1.71)</td><td>0.0163</td><td>*</td><td>1.08 (0.736 - 1.59)</td><td>0.69</td><td>0.884</td><td>n.s.</td></tr><tr><td>NLR</td><td>2.6%</td><td>76</td><td>0.612</td><td>1.32 (1.04 - 1.66)</td><td>0.0214</td><td>*</td><td>1.11 (0.818 - 1.5)</td><td>0.509</td><td>0.847</td><td>n.s.</td></tr></tbody></table>

**Supplementary Table 4: Variables selection by Cox regression analysis on PFS over Chemotherapy**

Results of Cox regression analysis for significant biomarkers impacting PFS in Chemotherapy cohort. Multivariate cox regression analysis is adjusted on confounding factors (Age, Genre, ECOG at rechallenge, Histology, Treatment line of first ICI). uv = univariate, mv = multivariate, * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, p adjustable = adjusted p-value for multiple hypothesis testing using the Benjamini-Hochberg correction, HR = hazard ratio (95% confidence interval). NLR = Neutrophils to Lymphocytes Ratio, GGT = Gamma Glutamyl Transferase, MLR = Monocytes to Lymphocytes Ratio, dNLR = derived Neutrophils to Lymphocytes Ratio, AST = Aspartate aminotransferase, ALP = Alkaline Phosphatase).

<table><caption>Cox Regression on PFS - ICI First Line Cohort</caption><thead><tr><th>Label</th><th>Na</th><th>N</th><th>C Index</th><th>HR uv</th><th>p uv</th><th>signif uv</th><th>HR mv</th><th>p mv</th><th>p adjustable</th><th>signif mv</th></tr></thead><tbody><tr><td>ALP</td><td>6.7%</td><td>235</td><td>0.549</td><td>1.26 (1.12 - 1.41)</td><td>9.00e-05</td><td>****</td><td>1.27 (1.09 - 1.49)</td><td>2.07e-03</td><td>0.032</td><td>**</td></tr><tr><td>MLR</td><td>2.8%</td><td>245</td><td>0.541</td><td>1.25 (1.06 - 1.48)</td><td>6.84e-03</td><td>**</td><td>1.23 (1.01 - 1.49)</td><td>0.0354</td><td>0.0984</td><td>*</td></tr><tr><td>NLR</td><td>2.4%</td><td>246</td><td>0.55</td><td>1.24 (1.06 - 1.45)</td><td>6.99e-03</td><td>**</td><td>1.27 (1.05 - 1.54)</td><td>0.0161</td><td>0.0804</td><td>*</td></tr><tr><td>dNLR</td><td>0.0%</td><td>252</td><td>0.542</td><td>1.2 (1.04 - 1.39)</td><td>0.0141</td><td>*</td><td>1.24 (1.03 - 1.48)</td><td>0.0238</td><td>0.0804</td><td>*</td></tr><tr><td>Hemoglobin</td><td>0.8%</td><td>250</td><td>0.573</td><td>0.856 (0.754 - 0.972)</td><td>0.0166</td><td>*</td><td>0.831 (0.716 - 0.964)</td><td>0.0146</td><td>0.0804</td><td>*</td></tr><tr><td>Albumine</td><td>21.0%</td><td>199</td><td>0.587</td><td>0.843 (0.73 - 0.974)</td><td>0.0203</td><td>*</td><td>0.796 (0.656 - 0.966)</td><td>0.0206</td><td>0.0804</td><td>*</td></tr><tr><td>PLR Kinetics</td><td>23.0%</td><td>194</td><td>0.557</td><td>0.797 (0.655 - 0.97)</td><td>0.0233</td><td>*</td><td>0.892 (0.725 - 1.1)</td><td>0.278</td><td>0.364</td><td>n.s.</td></tr><tr><td>Neutrophiles</td><td>0.0%</td><td>252</td><td>0.541</td><td>1.15 (1.01 - 1.32)</td><td>0.0364</td><td>*</td><td>1.21 (1.02 - 1.43)</td><td>0.0257</td><td>0.0804</td><td>*</td></tr><tr><td>GGT</td><td>6.3%</td><td>236</td><td>0.539</td><td>1.15 (1.01 - 1.31)</td><td>0.0378</td><td>*</td><td>1.09 (0.93 - 1.27)</td><td>0.291</td><td>0.364</td><td>n.s.</td></tr></tbody></table>

**Supplementary Table 5: Variables selection by Cox regression analysis on PFS over ICI in the entire study cohort**

Results of Cox regression analysis for significant biomarkers on PFS in ICI First Line cohort. Multivariate cox regression analysis is adjusted on confounding factors (Age, Genre, ECOG at rechallenge, Histology, Treatment line of first ICI). uv = univariate, mv = multivariate, * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, p adjustable = adjusted p-value for multiple hypothesis testing using the Benjamini-Hochberg correction, HR = hazard ratio (95% confidence interval). ALP = Alkaline Phosphatase, MLR = Monocytes to Lymphocytes Ratio, NLR = Neutrophils to Lymphocytes Ratio, dNLR = derived Neutrophils to Lymphocytes Ratio, PLR = Platelets to Lymphocytes Ratio, GGT = Gamma Glutamyl Transferase).
<figure><img src="image_8.png" /><figcaption>Supplementary Figure 1: rPFS analysis according to blood-test variables selected in the final predictor</figcaption></figure>

Survival analysis on PFS of rechallenged patients according to the values obtained for each biomarker included in the combined score, namely: dNLR (A), MLR (B), kinetics of dNLR (C), Albumin (D), ALP (E) and AST (F). The cut-offs have been determined by using the maximally selected rank statistics by the maxstat R function. Abbreviation: dNLR = derived Neutrophils to Lymphocytes Ratio, MLR = Monocytes to Lymphocytes Ratio, AST = Aspartate aminotransferase, ALP = Alkaline Phosphatase).
A

<figure><img src="image_9.png" /></figure>

B

<figure><img src="image_10.png" /></figure>

C

<figure><img src="image_11.png" /></figure>

D

<figure><img src="image_12.png" /><figcaption>**Supplementary Figure 2: rPFS according to clinical prognostic factors**</figcaption></figure>

Survival analysis conducted on PFS over Rechallenge according to clinical parameters used into clinical practice to select patients most likely benefit from this strategy: duration of the first ICI treatment (A) and reason of first ICI discontinuation (B) regardless the RBS value. Analysis of PFS over Rechallenge according to the RBS status (High or Low) in each group of patients stratified according to the first ICI duration (C) and the reason of first ICI withdrawal (D). CD = clinical decision; DP = Disease progression.

**Supplementary formula:**

RBS calculator: https://compo.inria.fr/RBS/ (to be adapted before submission)

mRBS calculator: (to be adapted before submission)