Category: 3. Business

Taste of Italy 2, a private equity fund managed by DeA Capital Alternative Funds SGR specializing in the agri-food sector, has acquired from the Europe Capital Partners VII fund a majority stake in Fine Food Group, the leading Italian distributor of premium Tex-Mex, American and fusion foodservice products. The transaction included the reinvestment of Fine Food’s founder and CEO, Fabrizio Fasulo, who will continue to lead the company.

DeA Capital was advised by Baker McKenzie with a team led by Partner Paolo Ghiglione, assisted by Counsel Chiara Marinozzi and Associate Giacomo Lamperti; by Partner Carlo de Vito Piscicelli and Senior Associate Edoardo Filiberto Roversi for the banking aspects; by Partners Francesco Pisciotta and Davide Chiesa for the tax aspects; and by Senior Counsel Alessia Raimondo for the labour aspects.

Pavia e Ansaldo advised DeA Capital for the antitrust matters.
Gatti Pavesi Bianchi Ludovici advised Europe Capital Partners VII and the other sellers.

Simmons & Simmons advised Mr. Fabrizio Fasulo, meanwhile Ashurst advised the financing Banks.

Fiserv Small Business Index remains at 148

Year-over-year sales grew +1.5%

MILWAUKEE–(BUSINESS WIRE)–
Fiserv, Inc. (NYSE: FI), a leading global provider of payments and financial services technology, has published the Fiserv Small Business Index for October 2025, with the seasonally adjusted Index holding steady at 148.

Year-over-year sales (+1.5%) and transactions (+1.1%) both grew, but this was the slowest annual sales growth rate since February 2025. Month-over-month sales (+0.1%) and transactions (+0.1%) each saw small increases compared to September 2025. When adjusted for inflation, small business sales (-1.4%) declined year over year, the steepest decline of the previous eight months.

“Consumers continued spending cautiously in October, pulling back significantly at restaurants and across many discretionary categories,” said Prasanna Dhore, Chief Data Officer, Fiserv. “Small retailers, however, did see an uptick in sales – a positive indicator as these businesses move deeper into the holiday shopping season.”

Key Takeaways

Retail Accelerates from September, Year-Over-Year Pace Slows

Small business retail sales grew (+0.7%) month over month and (+0.6%) year over year. Core Retail, which excludes more volatile categories, was stronger, with sales growth (+1.6%) month over month and (+2.6%) year over year. Significant monthly sales gains were in Sporting Goods (+3.0%) and Clothing (+1.9%), two discretionary categories that benefit from early holiday-themed sales activity. Food and Beverage (grocery) sales were up (+1.4%) month over month, buoyed by increased foot traffic and higher average tickets.

Discretionary Spending Stalls, Essential Purchases Deliver Most of the Growth

Discretionary spending rose (+0.2%) year over year, but Essential sales growth maintained a faster pace (+2.5%), widening the gap between Essentials and Discretionary. Compared to September, Discretionary spending was unchanged (+0.0%) while Essentials rose modestly (+0.4%).

Restaurant Sales Slip Across the Board

Restaurants continue to face headwinds, with sales growing slightly (+0.1%) year over year for October, and declining (-0.3%) month over month. Bars and pubs saw a decrease in foot traffic (-0.5%) and sales (-0.1%) compared to September. Full-service restaurants continued to struggle with month-over-month sales (-0.1%) and foot traffic (-0.2%) dropping while average tickets (+0.2%) grew. Limited service (or quick-service) restaurants saw month-over-month sales (-0.6%) and foot traffic (-0.8%) decrease, while average tickets rose (+0.2%).

To access the full Fiserv Small Business Index, visit fiserv.com/FiservSmallBusinessIndex.

About the Fiserv Small Business Index^®

The Fiserv Small Business Index is published during the first week of every month and differentiated by its direct aggregation of consumer spending activity within the U.S. small business ecosystem. Rather than relying on survey or sentiment data, the Fiserv Small Business Index is derived from point-of-sale transaction data, including card, cash, and check transactions in-store and online across approximately 2 million U.S. small businesses, including hundreds of thousands leveraging the Clover point-of-sale and business management platform.

Benchmarked to 2019, the Fiserv Small Business Index provides a numeric value measuring consumer spending, with an accompanying transaction index measuring customer traffic. Through a simple interface, users can access data by region, state, and/or across business types categorized by the North American Industry Classification System (NAICS) Level-6 Classification System. The Fiserv Small Business Index provides visibility into more than 70 industries, allowing users to track sales trends with precision and understand the diverse dynamics shaping the U.S. small business economy.

About Fiserv

Fiserv, Inc. (NYSE: FI), a Fortune 500 company, moves more than money. As a global leader in payments and financial technology, the company helps clients achieve best-in-class results through a commitment to innovation and excellence in areas including account processing and digital banking solutions; card issuer processing and network services; payments; e-commerce; merchant acquiring and processing; and Clover^®, the world’s smartest point-of-sale system and business management platform. Fiserv is a member of the S&P 500^® Index, one of TIME Magazine’s Most Influential Companies™ and one of Fortune^® World’s Most Admired Companies™. Visit fiserv.com and follow on social media for more information and the latest company news.

FI-G

View source version on businesswire.com: https://www.businesswire.com/news/home/20251103731873/en/

For more information contact:

Media Relations:

Chase Wallace

Director, Communications

+1 470-481-2555

chase.wallace@fiserv.com

Source: Fiserv, Inc.

Released November 3, 2025

The head of the US Food and Drug Administration’s drug center abruptly resigned on Sunday after federal officials began reviewing “serious concerns about his personal conduct”, according to a government spokesperson.

Dr George Tidmarsh, who was named to the FDA post in July, was placed on leave on Friday after officials in the Department of Health and Human Services’ (HHS) office of general counsel were notified of the issues, the HHS press secretary, Emily Hilliard, said in an email. Tidmarsh then resigned on Sunday morning.

Hilliard said the HHS secretary, Robert F Kennedy Jr, “expects the highest ethical standards from all individuals serving under his leadership and remains committed to full transparency”.

The departure came the same day that a drugmaker connected to one of Tidmarsh’s former business associates filed a lawsuit alleging that he made “false and defamatory statements” during his time at the FDA.

The lawsuit, brought by Aurinia Pharmaceuticals, alleges that Tidmarsh used his FDA position to pursue a “longstanding personal vendetta” against the chair of the company’s board of directors, Kevin Tang.

Tang previously served as a board member of several drugmakers where Tidmarsh was an executive, including La Jolla Pharmaceutical, and was involved in his ouster from those leadership positions, according to the lawsuit.

Messages placed to Tidmarsh and his lawyer were not immediately returned late on Sunday.

Tidmarsh founded and led a series of pharmaceutical companies over several decades working in California’s pharmaceutical and biotech industries. Before joining the FDA, he also served as an adjunct professor at Stanford University. He was recruited to join the agency over the summer after meeting with the FDA commissioner, Marty Makary.

Tidmarsh’s ouster is the latest in a string of haphazard leadership changes at the agency, which has been rocked for months by firings, departures and controversial decisions on vaccines, fluoride and other products.

Dr Vinay Prasad, who oversees FDA’s vaccine and biologics center, resigned in July after coming under fire from conservative activists close to Donald Trump, only to rejoin the agency two weeks later at the behest of Kennedy.

The FDA’s drug center, which Tidmarsh oversaw, has lost more than 1,000 staffers over the past year to layoffs or resignations, according to agency figures. The center is the largest division of the FDA and is responsible for the review, safety and quality control of prescription and over-the-counter medicines.

In September, Tidmarsh drew public attention for a highly unusual post on LinkedIn stating that one of Aurinia Pharmaceutical’s products, a kidney drug, had “not been shown to provide a direct clinical benefit for patients”. It is very unusual for an FDA regulator to single out individual companies and products in public comments online.

According to the company’s lawsuit, Aurinia’s stock dropped 20% shortly after the post, wiping out more than $350m in shareholder value.

Tidmarsh later deleted the LinkedIn post and said he had posted it in his personal capacity – not as an FDA official.

Aurinia’s lawsuit also alleges, among other things, that Tidmarsh used his post at the FDA to target a type of thyroid drug made by another company, American Laboratories, where Tang also serves as board chair.

The lawsuit, filed in Maryland’s US district court, seeks compensatory and punitive damages and “to set the record straight”, according to the company.

Zscaler, Inc. (NASDAQ: ZS), the leader in cloud security, today announced it has acquired innovative AI security pioneer SPLX, extending the Zscaler Zero Trust Exchange^TM platform with shift-left AI asset discovery, automated red teaming, and governance, so organizations can secure their AI investments from development through deployment. 

“Today marks an important step in advancing Zscaler’s role as the trusted partner helping organizations securely adopt AI,” said Jay Chaudhry, CEO, Chairman, and Founder of Zscaler. “AI is creating enormous value, but its full potential can only be realized when it can be secured. By combining SPLX’s technology with the intelligence of the Zscaler Zero Trust Exchange and its native data protection that classifies, governs, and prevents loss of sensitive data across prompts, models, and outputs, Zscaler will secure the entire AI lifecycle on one platform. This will strengthen our industry leadership and give customers the confidence to safely embrace AI.”

As AI drives record infrastructure investments projected to exceed $250 billion by end of 2025¹, organizations face a rapidly expanding attack surface and shadow AI sprawl. Continuously evolving models, agents, and large language models (LLMs) require ongoing discovery, risk assessment, and remediation, while AI agents and Model Context Protocol (MCP) servers demand strict guardrails and new techniques to secure data and AI assets across the lifecycle.

SPLX’s innovative technology and deep expertise in AI red teaming, asset management, threat inspection, prompt hardening and governance will expand Zscaler’s current capabilities, creating a new, dedicated and natively integrated layer of AI protection within the Zscaler Zero Trust Exchange platform, that includes: 

• AI Asset Discovery and Risk Assessment: Discovery extends beyond public generative AI applications and public clouds to include AI models, workflows, code repositories and RAGs and MCP servers in both public and private deployments.
   
• Automated AI Red Teaming and Remediation: From development to production, with 5,000+ purpose-built and domain specific attack simulations to find risks and vulnerabilities, and offer remediation in real time. 
   
• AI Runtime Guardrails and Prompt Hardening: Expands Zscaler’s current AI Runtime Guardrails that protect sensitive data and block malicious attacks between AI apps and LLMs, as well as agentic workflows, to include deep visibility within development environments and automate Guardrails for risky AI assets.
   
• AI Governance and Compliance: Risk mitigation and support for organizations to shift from reactive defense to proactive protection for their valuable AI investments, and comply with governance frameworks. 

“Zscaler and SPLX share a vision to confront the vast new attack surface created by rapidly expanding AI infrastructure investments,” said Kristian Kamber, CEO and co-founder of SPLX. “By joining forces, we’ll bring our innovation to one of the most trusted security platforms in the world, securing AI innovation at the speed organizations are adopting it.” 

Source: 1) Goldman Sachs, “Technology in 2025: The Cycle Rolls On” February 2, 2025

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Forward-Looking Statements

This press release contains forward-looking statements that are based on our management’s beliefs and assumptions and on information currently available to our management. These forward-looking statements include the expected benefits of the proposed acquisition to Zscaler and its customers and plans regarding SPLX’s capabilities. These forward-looking statements are subject to the safe harbor provisions created by the Private Securities Litigation Reform Act of 1995. A significant number of factors could cause actual results to differ materially from statements made in this press release, including those factors related to Zscaler’s ability to successfully integrate SPLX technology into our cloud platform, the potential impact of the acquisition to the existing SPLX business and the retention of SPLX employees. Additional risks and uncertainties are set forth in our most recent Annual Report on Form 10-K filed with the Securities and Exchange Commission (“SEC”) on September 11, 2025, which is available on our website at ir.zscaler.com and on the SEC’s website at www.sec.gov. Any forward-looking statements in this release are based on the limited information currently available to Zscaler as of the date hereof, which is subject to change, and Zscaler will not necessarily update the information, even if new information becomes available in the future.

Expanded collaboration makes Falcon Next-Gen SIEM the foundation of EY global cyber managed services for security and non-security data, accelerating AI-driven security transformation for clients

AUSTIN, Texas – November 3, 2025 – CrowdStrike (NASDAQ: CRWD) and Ernst & Young LLP (EY US) today announced that EY US has selected CrowdStrike Falcon® Next-Gen SIEM as the foundational platform powering its global cybersecurity managed services. With Falcon Next-Gen SIEM and EY US managed services experience, enterprises worldwide can accelerate the move beyond legacy security information and event management (SIEM) and modernize security operations at scale.

Adversaries are moving at the speed of AI, scaling attacks faster than defenders can respond. Legacy SIEM, built for a different era, is too slow, noisy, and costly to stop today’s threats. Falcon Next-Gen SIEM delivers real-time speed, efficiency, and outcomes legacy platforms can’t match – and will be further strengthened by CrowdStrike’s acquisition of Onum, a real-time data pipeline platform. 

By standardizing its global managed services on Falcon Next-Gen SIEM for security and non-security data, EY will equip clients with AI-powered protection that moves faster and sees more, enabling organizations to replace outdated SIEM with a modern platform that delivers measurable outcomes at scale.

“The agentic era is accelerating everything, and legacy SIEMs simply can’t cope with threat landscape realities as well as enterprise data proliferation,” said Daniel Bernard, chief business officer at CrowdStrike. “By making Falcon Next-Gen SIEM the foundation of EY US global managed services, we’re helping clients modernize faster and achieve outcomes legacy tools could never deliver.”

Key improvements for clients include:

• Accelerated Migration: EY US professionals will help enterprises move from legacy SIEM to Falcon Next-Gen SIEM, achieving substantial efficiencies and up to 150% faster search.
• AI-Powered, Adversary-Driven Protection: Falcon Next-Gen SIEM unifies CrowdStrike first- and third-party platform data with real-time threat intelligence and AI-powered automation, delivering enterprise-wide visibility and supercharging detection and response.
• Next-Generation Operating Model: EY Managed Services helps clients turn data into a competitive edge by reducing operational burden, increasing cost certainty, accelerating AI adoption, and building smarter operating models that lower risk and unlock strategic value.
• Global Security Operations Center (SOC): The experience of EY US in SOC helps clients strengthen cyber and operational resilience by reducing attack surface exposure, securing digital identity, and managing cybersecurity risks with 24/7 advanced defense across 160 countries.
• Unified Visibility and Scale: CrowdStrike consolidates data into a single platform, delivering complete visibility and massive scale without the cost and complexity of legacy solutions.

“Our clients need security that’s faster, simpler and more effective,” said Tapan Shah, EY Global and Americas Cybersecurity Managed Services Leader. “With EY US as the first mover in building our cyber managed services on Falcon Next-Gen SIEM, we see this as more than a technology upgrade – it’s a strategic move to embrace AI security operations. EY US teams bring deep sector and AI experience, delivering high-impact cybersecurity outcomes that improve operations and efficiency across the enterprise.” 

About EY

EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.

Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.

EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multidisciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.

All in to shape the future with confidence.

EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. Information about how EY collects and uses personal data and a description of the rights individuals have under data protection legislation are available via ey.com/privacy. EY member firms do not practice law where prohibited by local laws. For more information about our organization, please visit ey.com.

Ernst & Young LLP is a client-serving member firm of Ernst & Young Global Limited operating in the US.

About CrowdStrike

CrowdStrike (NASDAQ: CRWD), a global cybersecurity leader, has redefined modern security with the world’s most advanced cloud-native platform for protecting critical areas of enterprise risk – endpoints and cloud workloads, identity and data.

Powered by the CrowdStrike Security Cloud and world-class AI, the CrowdStrike Falcon® platform leverages real-time indicators of attack, threat intelligence, evolving adversary tradecraft and enriched telemetry from across the enterprise to deliver hyper-accurate detections, automated protection and remediation, elite threat hunting and prioritized observability of vulnerabilities.

Purpose-built in the cloud with a single lightweight-agent architecture, the Falcon platform delivers rapid and scalable deployment, superior protection and performance, reduced complexity and immediate time-to-value.

CrowdStrike: We stop breaches.

Learn more: https://www.crowdstrike.com/

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Start a free trial today: https://www.crowdstrike.com/trial

© 2025 CrowdStrike, Inc. All rights reserved. CrowdStrike and CrowdStrike Falcon are marks owned by CrowdStrike, Inc. and are registered in the United States and other countries. CrowdStrike owns other trademarks and service marks and may use the brands of third parties to identify their products and services.

Media Contacts

Jake Schuster

CrowdStrike Corporate Communications

press@crowdstrike.com

 

Cannabinoid receptor 1 (CB₁), which is majorly expressed in the central nervous system (CNS) belongs to the class A G-protein coupled receptor (GPCR) family proteins (Hua et al., 2016; Mackie, 2008; Zou and Kumar, 2018; Dutta and Shukla, 2023). GPCRs are expressed in the cellular membrane and help transduce chemical signals from the extracellular to the intracellular direction with the help of the downstream signaling proteins (G-proteins and β-arrestin) (Rosenbaum et al., 2009; Latorraca et al., 2017; Weis and Kobilka, 2018). In addition, GPCRs are the largest family of drug targets due to their substantial involvement in human pathophysiology and druggability (Hauser et al., 2017; Yang et al., 2021). Significant research efforts have been invested in the discovery of drugs targeting CB₁, which helps to maintain homeostasis in neuron signaling and physiological processes (Smith et al., 2010; An et al., 2020).

Initial drug discovery efforts, especially the design of synthetic agonists, were based on modifying the scaffolds of phytocannabinoids (e.g. ${\mathrm{\Delta }}^9$-Tetrahydrocannabinol, cannabinol) and endocannabinoids (e.g. Anandamide, 2-arachidonoylglycerol) (Figure 1; Pertwee, 2006; Pertwee and Ross, 2002; Pertwee et al., 2010). The synthetic molecules, which maintain the aromatic, pyran, and cyclohexenyl ring of the most common psychoactive phytocannabinoid ${\mathrm{\Delta }}^9$-THC, are known as classical cannabinoids (Figure 1—figure supplement 1; Razdan, 2009 Madras, 2018; Dutta et al., 2022a). However, the pharmacological potential of these molecules was diminished due to their psychological and physiological side effects (‘tetrad’ side effect) (Moore and Weerts, 2022; Wang et al., 2020; Tummino et al., 2023). One such example of a synthetic cannabinoid is 1,1-Dimethylheptyl-11-hydroxy-tetrahydrocannabinol (commonly known as HU-210), which is a Schedule I controlled substance in the United States (Farinha-Ferreira et al., 2022).

Apart from the canonical structures of synthetic cannabinoids, molecules with diverse scaffolds were also synthesized through structure-activity studies (Wiley et al., 2016; Schoeder et al., 2018; Walsh and Andersen, 2020). However, these molecules also lacked any pharmacological importance due to psychological side effects (Akram et al., 2019; Worob and Wenthur, 2020). Due to the diverse structures and psychological effects, these molecules became unregulated substitutes for traditional illicit substances (Peacock et al., 2019). These synthetic cannabinoids belong to a class of molecules known as NPS as these molecules are not scheduled under the Single Convention on Narcotic Drugs (1961) or the Convention on Psychotropic Substances (1971) (Peacock et al., 2019; Madras, 2016). Synthetic cannabinoids make up the largest category of NPS molecules (Shafi et al., 2020; Alam and Keating, 2020). NPS creates a significant challenge for drug enforcement agencies, as they appeal to drug users seeking ‘legal highs’ to avoid the legal consequences of using traditional drugs and to be undetectable in drug screenings (Worob and Wenthur, 2020).

The molecular structures of NPS synthetic cannabinoids consist of four pharmacophore components: linked, linker, core, and tail groups (Worob and Wenthur, 2020; Potts et al., 2020). The core usually consists of aromatic scaffolds (e.g. indole, indazole, carbazole, benzimidazole) (Figure 1—figure supplement 2; Schoeder et al., 2018). The tail and linker groups are connected to the core. In the tail group, long alkyl chain-like scaffolds are ubiquitous in most NPSs; however, molecules with bulkier cyclic chains (e.g. AB-CHMINACA) are also present (Potts et al., 2020). Frequently encountered scaffolds in linker groups are methanone, ethanone, carboxamide, and carboxylate ester groups (Hill et al., 2018). The linker acts as a bridge between the core and the linked group. In the initial NPS synthetic cannabinoids, the linked group included polyaromatic rings; however, non-cyclic linked groups have also been identified in NPS recently (Schoeder et al., 2018; Potts et al., 2020). Structural diversity in every component, while maintaining high binding affinity and potency for CB₁ make these molecules easier for drug manufacturers and harder to ban by drug enforcement agencies (Banister et al., 2015a; Ametovski et al., 2020; Cannaert et al., 2020; Banister et al., 2015b).

The use of NPS synthetic cannabinoids has been found to cause more physiological side effects than traditional cannabinergic ‘tetrad’ side effects (Tai and Fantegrossi, 2014). These side effects include tachycardia, drowsiness, dizziness, hypertension, seizures, convulsions, nausea, high blood pressure, and chest pain (Tai and Fantegrossi, 2014; Finlay et al., 2019). For instance, Gatch and Forster have shown that the high concentrations of AMB-FUBINACA, the molecule which caused ‘zombie outbreak’ in New York, induced tremors (Gatch and Forster, 2019; Adams et al., 2017). A recent biochemical study has linked these discriminatory effects with the differential signaling of β-arrestin (Finlay et al., 2019). According to Finlay et al., NPS shows higher β-arrestin signaling compared to the classical cannabinoids, which has also been confirmed by other β-arrestin signaling studies (Finlay et al., 2019; Grafinger et al., 2021). However, a mechanistic understanding of these differential downstream signaling effects between NPS and classical cannabinoids is still missing.

Mutagenesis studies have shown that the conserved NPxxY motif of CB₁ have a larger role in downstream β-arrestin signaling than G-protein signaling (Leo et al., 2023; Liao et al., 2023). Recently published MDMB-FUBINACA bound CB₁-β-arrestin-1 complex structure also points out the importance of the unique triad interaction (Y397^7.53-Y294^5.58-T210^3.46) involving NPxxY motif in β-arrestin-1 signaling (Liao et al., 2023). However, structural comparison of the classical cannabinoid (AM841) and NPS (MDMB-FUBINACA) bound active CB₁-G_i complex shows a conformationally similar NPxxY motif (Figure 2; Krishna Kumar et al., 2019; Hua et al., 2020). In light of these experimental observations, it can be inferred that higher β-arrestin signaling stems from higher dynamic propensity of triad interaction formation for NPS-bound CB₁. We hypothesized that distinct orthosteric pocket interactions for NPS and classical cannabinoids cause differential allosteric modulation of intracellular dynamics that facilitate triad interaction.

To study these distinct dynamic effects, we compared the (un)binding of the classical cannabinoid (HU-210) and NPS (MDMB-FUBINACA) from the receptor binding site. These molecules have nanomolar affinities. Obtaining the initial pathway of ligand unbinding from unbiased sampling will be computationally expensive. Therefore, a well-tempered metadynamics approach was used to sample the unbinding event, where a time-dependent biased potential is deposited for the faster sampling of the metastable minima along the pathway (Barducci et al., 2008). However, a detailed characterization of the unbinding processes is only possible through the thermodynamics and kinetics estimation of intermediate states. Thus, a transition operator-based approach is needed, which helps to estimate the transition timescale between the states and the stationary density of each state. Estimation from these approaches usually depends on the equilibrium between the local states, which can only be maintained by reversible sampling. For high-affinity ligands like MDMB-FUBINACA and HU-210, reversible sampling is expensive as ligands move from high energy unbound states to lower energy bound states irreversibly. Hence, we implemented a transition operator approach named the transition-based reweighting analysis (TRAM) method, which can tackle this lack of local equilibrium between states by combining unbiased and biased approaches (Wu et al., 2016). TRAM has been used in in different simulation studies for estimating thermodynamics and kinetics of processes that have high free energy barriers. For example, TRAM have been utilized for characterization of small molecule and peptide (un)binding processes (Wu et al., 2016; Paul et al., 2017; Ge et al., 2021; Spiriti et al., 2022; Ge and Voelz, 2022), protein dimerization (Meral et al., 2018), ion transportation (Hu et al., 2019). To implement TRAM for our study, extensive sampling of the (un)binding process of both ligands was performed using a combination of umbrella sampling and unbiased simulations from the pathway obtained using metadynamics (see Methods section) (Kästner, 2011). We showed that TRAM can produce consistent kinetic estimation with less unbiased simulation data compared to traditional methods like the Markov state model (Prinz et al., 2011).

Based on estimates of thermodynamics and kinetics, it was observed that both NPS and classical cannabinoids have similar unbinding pathways. However, their unbinding mechanisms differ due to the aromatic tail of the MDMB-FUBINACA compared to the alkyl side chain of HU-210. Furthermore, dynamic interaction calculations reveal a major difference with TM7 between NPS and classical cannabinoid. Specifically, the hydroxyl group in the benzopyran moiety of HU-210 forms much stronger polar interactions with S383^7.39 compared to the carbonyl oxygen of the linker group in MDMB-FUBINACA. MD simulations of other classical cannabinoids and NPS molecules bound to CB₁ also support these significant interaction differences. The ligand binding effect in intracellular signaling was estimated by measuring the probability of triad formation in the intracellular region. NPS-bound CB₁ shows higher probability of forming triad interaction compared to the classical cannabinoids, which supports the experimental observations of high $\beta$-arrestin signaling of NPS-bound receptors. To validate that the triad formation is indeed caused by the binding pocket interaction differences between the two ligands, allosteric strength binding pocket residues and NPxxY motif was estimated with the deep learning technique, Neural relational inference (NRI) (Zhu et al., 2022a). NRI network revealed that binding pocket residues of NPS-bound ensemble have higher allosteric weights for the NPxxY motif compared to classical cannabinoids. These analyses validate our hypothesis that the differential dynamic allosteric control of the NPxxY motif might lead to the $\beta$-arrestin signaling for different ligands. This study provides a foundation for additional computational and experimental research to enhance our understanding of the connection between ligand scaffolds and downstream signaling. This knowledge will assist drug enforcement agencies in proactively banning these molecules and inform policies that can protect individuals from the effects of abuse.

Introduction

Lung cancer is the second most common type of cancer in women and men, but it is the most important cause of cancer-related deaths.¹ Non-small-cell lung cancer (NSCLC) constitutes approximately 85% of all lung cancers.² Currently, immunotherapy is the main treatment modality for NSCLC cancer without driver mutation.³

Nivolumab is a human immunoglobulin G4 (IgG4) antibody targeting programmed death-1 (PD-1) receptors. It is an important treatment agent with demonstrated efficacy independent of PD-L1 score in chemotherapy-resistant NSCLC cancer without ALK, RET, or ROS1 mutations. In daily practice, a validated marker that will benefit from treatment in patients receiving Nivolumab has not yet been clearly established. In the Checkmate 227 and CheckMate 568 studies, ipilimumab, a CTLA-4 antibody, was found to be effective together with nivolumab in patients with advanced stage NSCLC patients with tumor mutation burden independent of PD-L1 score. Tumor Mutational Burden (TMB) is an important biomarker in predicting response to immunotherapy. Studies have shown that patients with a TMB greater than 10 mutations per megabase have the highest predicted objective response rate (ORR), reaching up to 38% to 42%, regardless of PD-L1 expression levels. However, despite its predictive value, TMB is not yet used as a standard test due to its high financial cost and the technical complexity of measurement.^4–6

In the inflammatory tumor microenvironment (TME), innate immune cells such as monocytes and adaptive immune cells (eg T lymphocytes) produce various inflammatory mediators in response to abnormal signals from the tumor, creating an inflammatory response.^7,8 In this process, neutrophils stand out as both effective elements of innate immunity and cellular messengers shaping the adaptive response. They not only suppress the tumor-killing activity of cytotoxic T lymphocytes (CTLs), but also trigger angiogenesis and support tumor progression by secreting numerous growth factors and chemokines, such as TGF-β, VEGF, IL-6, IL-8, IL-12, and matrix metalloproteinases. G-CSF secreted by tumor cells reinforces this tumor-supportive cycle by increasing the number of neutrophils.^9–12 Only 15–60% of patients can achieve the expected response with CTLA-4, PD-1 and PD-L1 inhibitors developed through activation of the immune system.¹³

In this study, we aimed to investigate the prognostic importance of inflammatory and nutritional markers along with clinical parameters on treatment response in metastatic NSCLC patients who received nivolumab treatment.

Materials and Methods

Patient Characteristics

In this multicenter study, data of metastatic non-small cell lung cancer (NSCLC) patients diagnosed and treated between February 2021 and November 2024 at three oncology centers (Ankara Etlik City Hospital, Nevşehir State Hospital, and Ankara Atatürk Sanatoryum Hospital) were retrospectively analyzed. Patients who had previously received 1 line of platinum-containing chemotherapy (cisplatin or carboplatin) and progressed and received second-line immunotherapy were included. (Figure 1) Patients with pathologically confirmed NSCLC who received at least 2 cycles of nivolumab treatment at the metastatic stage with 240 mg every 2 weeks or 360 mg every 3 weeks were included in the study. Patients who had another concurrent malignancy, had active infection findings, did not have driver mutations (EGFR, ALK, ROS1), and had pre-treatment blood parameters, were over 18 years of age, and had ECOG <2 before nivolumab treatment were included. PD-L1 expression was evaluated by immunohistochemical staining using the VENTANA PD-L1 (SP263) assay (Roche, Switzerland) on the BenchMark ULTRA automated staining platform. All procedures, including antigen retrieval and detection steps, were performed in accordance with the manufacturer’s protocol. The Common Terminology Criteria for Adverse Events Scoring System (CTCAE) v4.0 was used for the definition and evaluation of immune-related adverse events (irAEs). The study received approval from the local ethics board (Approval no: 2500043238 / 28.05.2025, Non-Interventional Ethics Committee of Nevsehir Haci Bektas Veli University).

Figure 1 CONSORT Flow Diagram.

Calculation of Indices

In the metastatic stage, the indices were calculated as follows by taking the complete blood count and biochemistry data obtained from the blood samples collected 7 days or earlier before the start of treatment. CRP levels were quantitatively measured using the immunoturbidimetric method, while albumin levels were determined by spectrophotometric methods.

• Neutrophil-to-Lymphocyte Ratio (NLR) = Neutrophils (10⁹/L) / Lymphocytes (10⁹/L)
• Platelet-to-Lymphocyte Ratio (PLR) = Platelets (10⁹/L) / Lymphocytes (10⁹/L)
• Lymphocyte-to-Monocyte Ratio (LMR) = Lymphocytes (10⁹/L) / Monocytes (10⁹/L)
• Systemic Immune-Inflammation Index (SII) = (Platelets (10⁹/L) × Neutrophils (10⁹/L)) / Lymphocytes (10⁹/L)
• Systemic Inflammation Response Index (SIRI) = (Neutrophils (10⁹/L) × Monocytes (10⁹/L)) / Lymphocytes (10⁹/L)
• Prognostic Nutritional Index (PNI) = Albumin (g/dL) + 0.005 × Lymphocytes (10⁹/L)
• Hemoglobin, Albumin, Lymphocyte, Platelet Score (HALP) = Hemoglobin (g/dL) × Albumin (g/dL) × Lymphocytes (10⁹/L)) / Platelets (10⁹/L)
• Neutrophil-to-Eosinophil Ratio (NER) = Neutrophils (10⁹/L) / Eosinophils (10⁹/L)
• C-Reactive Protein-to-Albumin Ratio (CAR): CRP (mg/L) / Albumin (g/dL).

Statistical Analysis

Statistical analyses were performed using SPSS 24 (SPSS Inc., Chicago, III) and Microsoft^® Excel^® 2019 (Version 2503) 32 bit and R software (R Core Team, 2024). Receiver operating characteristic (ROC) analysis was used to determine the ideal cut-off for the markers and to calculate sensitivity-specificity, and the Youden index method was used.¹⁴ In determining the ideal cut-off, long survival was considered a predictive marker. For variables that did not reach a statistically significant p value in the ROC analysis, the median cut-off value was included in the analyses. Kaplan-Meier survival analysis was used to calculate the estimated median overall survival time. Cox-regression analysis was used to calculate the univariate overall survival Hazard Ratio, and the Forward Stepwise (Likelihood Ratio) method was used for multivariate models. In the univariate Cox regression analysis for PDL1, patients with unknown PDL1 were not included, and 3 categories (< 1 vs 1–49 vs ≥ 50) were compared. All patients were included in the other univariate analyses. The proportional hazards assumption was tested using Schoenfeld residuals. The Schoenfeld residuals test was evaluated using the survival library in the R program (R Core Team, 2024).^15,16 Variables demonstrating a p-value below 0.05 were deemed statistically significant and consequently included in the final model based on the predetermined retention criteria. Situations where the P value was below 0.05 and the Type 1 error level was below 5% were interpreted as statistically significant.

Results

The study included 229 patients with metastatic non-small cell lung cancer who received nivolumab treatment. The median age of the patients was 63 (min: 41, max: 83). 84.3% (n=193) of the patients were male and 56.8% (n=130) of the tumorswere localized to the right lung. 51.1% (n=117) patients had adenocarcinoma, 40.6% had squamous histology and 8.3% (n=19) had “not otherwise specified (nos) histology”. The PDl-1 score of 49 patients (21.4%) was unknown. More than half of them (43.2%, n=122) were metastatic at the time of diagnosis and the most common metastatic site was bone metastasis (31.4%, n=72). Other clinical and pathological data are shown in Table 1.

Table 1 Clinical and Laboratory Characteristics of the Patients (n = 229)

The ideal cut-off values for inflammatory and nutrient markers to predict long survival were calculated separately. The ideal cut-off value for SII was determined as 1224 (AUC: 0.564, P=0.094), for PNI as 45.1 (AUC: 0.591, P=0.018), for HALP score as 2.0 (AUC: 0.576, P=0.046) and for CAR as 8.5 (AUC: 0.598, P=0.010) and the median values for other parameters were included in the analyses. Sensitivity and specificity are given in Table 2 with AUC 95% Confidence interval.

Table 2 ROC-Curve Analysis for Long-Term Survival and Derived Optimal Cut-off Values

The estimated median survival time of the patients was calculated as 21.2 months (95% CI: 17.4–25.0 months). The factors affecting survival were evaluated according to Univariate Cox regression analysis. Having brain metastasis at the time of diagnosis (HR: 2.08, 95% CI: 1.26–3.44, p=0.004), detection of liver metastasis (HR: 1.85, 95% CI: 1.13–3.03, p=0.014) and presence of adrenal metastasis (HR: 1.64, 95% CI: 1.01–2.66, p=0.045) were detected as negative prognostic findings. High neutrophil-lymphocyte ratio (NLR), which indirectly indicates inflammation (HR: 2.04, 95% CI: 1.42–2.92, p<0.001), high systemic immune-inflammation index (SII) (HR: 1.96, 95% CI: 1.37–2.79, p<0.001), high C-reactive protein-albumin ratio (CAR) (HR: 1.84, 95% CI: 1.29–2.61, p=0.001), high platelet-lymphocyte ratio (PLR) (HR: 1.60, 95% CI: 1.13–2.26, p=0.009) and high systemic inflammation response index (SIRI) (HR: 1.51, 95% CI: 1.07–2.15, p=0.021) were found to be poor prognostic markers predicting survival. Low prognostic nutritional index (PNI) (HR: 0.48, 95% CI: 0.34–0.69, p<0.001), low hemoglobin-albumin-lymphocyte-platelet score (HALP) (HR: 0.49, 95% CI: 0.35–0.70, p<0.001) and low lymphocyte-monocyte ratio (LMR) (HR: 0.65, 95% CI: 0.46–0.92, p=0.016) was found to be a predictive marker indicating poor prognosis. Clinically, the presence of immune-related adverse events was associated with prolonged overall survival (OS) (HR: 0.63, 95% CI: 0.41–0.97, p=0.034). Other investigated markers were tumor histological type (HR: 1.18, 95% CI: 0.98–1.41, p=0.075), disease stage at diagnosis (HR: 1.38, 95% CI: 0.97–1.97, p=0.076), PLD1 (HR: 0.88, 95% CI: 0.74–1.05, p=0.146), presence of pleural effusion (HR: 1.36, 95% CI: 0.90–2.05, p=0.145), use of hepatitis B prophylaxis (HR: 1.31, 95% CI: 0.83–2.09, p=0.250), presence of contralateral lung metastases (HR: 0.82, 95% CI: 0.55–1.20, p=0.298), tumor localization (HR: 0.84, 95% CI: 0.59–1.18, p=0.318), age group (HR: 1.18, 95% CI: 0.82–1.70, p=0.375), presence of bone metastases (HR: 0.87, 95% CI: 0.61–1.26, p=0.474), presence of extramediastinal lymphadenopathy (HR: 0.84, 95% CI: 0.50–1.43, p=0.527), gender (HR: 1.04, 95% CI: 0.64–1.68, p=0.873) and high neutrophil-eosinophil ratio (HR: 1.00, 95% CI: 0.71–1.42, p=0.986) and no statistically significant difference was found between survival (Table 3, Figure 2).

Table 3 Univariate Cox Proportional-Hazard Analysis for Overall Survival

Figure 2 Hazard ratios for clinical, pathological, and inflammatory markers in patients with metastatic non-small cell lung cancer (NSCLC) receiving nivolumab therapy. (Blue dots represent hazard ratio estimates, and horizontal lines indicate 95% confidence intervals.).

In the multivariate Cox regression analysis, the presence of brain metastasis (HR: 2.84, 95% CI: 1.68–4.79, p<0.001), the presence of adrenal metastasis (HR: 1.64, 95% CI: 1.01–2.67, p=0.046) and low PNI value (HR: 0.44, 95% CI: 0.30–0.63, p<0.001) prognosis showed the characteristic of being a prognostic model (Table 4). The findings obtained from the Cox regression analysis were tested for the proportional hazards assumption using Schoenfeld residuals. It was determined that the p-values for all variables in the model were above 0.05, indicating that the proportional hazards assumption was met. The multivariate model created in Table 4 is statistically significant (χ²= 31.93, p<0.001). To assess the fit of the model, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values were calculated and were obtained as 1183.73 and 1192.35, respectively. These values indicate that the overall fit of the model is acceptable VIF values were examined for multicollinearity, and no significant multicollinearity was detected (VIF<5).

Table 4 Multivariate Cox Proportional-Hazard Analysis for Overall Survival

Discussion

In this study, 229 patients with metastatic non-small cell (NSCLC) who received nivolumab immunotherapy were analyzed. In the study, in univariate analysis, the presence of brain metastasis, liver and adrenal metastasis were associated with poor prognosis. Patients who developed immune-related side effects during treatment had better treatment responses. Except for NER, the investigated inflammation markers (PLR, LMR, SII, SIRI, PNI, HALP, NER, CAR) predicted prognosis. In multivariate analysis, brain metastasis, adrenal metastasis and Prognostic nutritional index (PNI) formed a strong prognostic model.

While immunotherapy treatments for lung cancer are rapidly advancing, it is still unclear in which patients the treatment will be effective. Although the PDL-1 score is the most basic marker in clinical studies, immunotherapy treatments can be effective in patients with negative PDL-1 scores, while immunotherapy is not effective in some groups with high PDL1 scores.^17,18 With the investigation of tumor mutation burden and microsatellite instability, MSI was detected positive in only 0.33% of small cell lung cancers.¹⁹ TMB is a high-cost test and is not a routinely recommended test.²⁰ Therefore, a cheap, easily applicable marker is needed to predict immunotherapy response. In the study conducted by Lin et al, PDL-1 score did not statistically predict treatment response in patients receiving nivolumab.²¹ Phase 3 study data for nivolumab demonstrated that treatment efficacy was independent of PD-L1 levels. When we excluded the subgroup with unknown PD-L1 levels from our study analysis, there was no association between PD-L1 levels and survival (p=0.146), and these results were consistent with the literature.²²

Immune-Related Adverse Events (irAEs) are the definition of side effects caused by autoimmune or inflammatory toxins that develop due to excessive activation of the immune system in patients treated with immune checkpoint inhibitors.²³ irAEs are seen when the immune system is not limited to tumor cells but also invades normal tissues. In the study conducted by Ebi et al, it was seen to be associated with good prognosis in patients receiving ipilimumab together with nivolumab.²⁴ Zhou et al. The meta-analysis revealed that immunotherapy responses were better in patients who developed irAEs.²⁵ In our study, patients who developed irAEs had better survival, consistent with the results of the mentioned studies.

For a clearer understanding of immunotherapy biomarkers, it would be helpful to focus on neoantigen formation and presentation, the tumor microenvironment, changes in certain gene signaling pathways, MHC molecules, and T-cell receptors.²⁶ Among these pathways, the IL-6/STAT3 axis has been implicated in mediating resistance to checkpoint blockade by intrinsically impairing CD8⁺ T-cell differentiation and function, with elevated circulating IL-6 levels correlating with poor responses to anti-PD-L1 therapy.^12,27,28 Although it is technically not possible to measure these markers directly, indirectly looking at inflammation markers in the blood seems to be an easily accessible and inexpensive method. Recent studies suggest that increased lymphocytes and decreased neutrophils are associated with better prognosis in NSCLC patients treated with nivolumab. Russo et al. In their study, they demonstrated that increased neutrophil levels are poor prognostic in NSCLC patients.²⁹ The same study demonstrated that increased NLR and increased PLR are poor prognostic in nivolumab treatment.²⁹ Cao et al. In a pooled analysis of 14 retrospective studies, increased NLR was found to be a poor prognostic marker and determined the ideal cut-off value as 5.³⁰ In our study, the median cut-off was 4.3, and patients above this cut-off were considered to have poor nivolumab responses.

As a result of the immune system response that occurs together with inflammation, an increase in C-reactive protein (CRP), neutrophils, and a decrease in albumin and lymphocytes in the blood are expected results.³¹ The C-reactive protein/albumin ratio (CAR), which reflects high CRP and low albumin scores, has been suggested to be prognostic in lung cancer. Dai et al found that patients with high CAR scores had shorter OS and PFS.³² Prognostic nutritional index (PNI) is a score that evaluates serum albumin level and lymphocyte count together, and Wang et al found a 57% decrease in the risk of death in patients with low prognostic index compared to those with high prognostic index (HR:0.43).³³ HALP Score, which evaluates the combination of Hemoglobin, Albumin, Lymphocyte, Platelet, is an immune-nutritional index defined in recent years. Akgül et al found that patients with low HALP scores in lung cancer patients treated with nivolumab in the second line had a poor prognosis.³⁴ Our study is consistent with the literature, and when each was evaluated independently, high CAR (HR:1.84), low PNI (HR:0.48), and low HALP (HR:0.49) were found to be associated with poor prognosis in patients receiving nivolumab.

There were some limitations regarding our study. The retrospective nature of our study design was an important limitation. Other limitations were the small percentage of patients with brain metastases (11.4%) and the high percentage of patients with unknown pdl1 (21.4%). In addition, the inability to calculate the ideal cut-off using Roc-Curve analysis for hematological parameters that were significant in the survival analysis was a statistical weakness of our study. The evaluation of immunotherapy results in brain metastatic patients, which is an exclusion criterion in most clinical studies, and the data of a homogeneous center make the study strong.

Conclusion

In conclusion, when evaluated separately in our study, NSCLC patients treated with nivolumab had poor response to treatment in liver, brain and adrenal metastatic patients and markers (NLR, PLR, LMR, SII, SIRI, PNI, HALP, CAR) which are indirect indicators of inflammation were prognostic on their own, and PNI formed a prognostic model with brain metastasis and adrenal metastasis among these markers. Prospective data are needed in further studies.

Abbreviations

ANC, Absolute Neutrophil Count; AUC, Area Under the Curve; CAR, C-Reactive Protein-to-Albumin Ratio; CI, Confidence Interval; CRP, C-Reactive Protein; CTLA-4, Cytotoxic T-Lymphocyte Antigen-4; ECG, Eastern Cooperative Oncology Group; HALP, Hemoglobin, Albumin, Lymphocyte, Platelet Score; HR, Hazard Ratio; irAEs, Immune-related Adverse Events; LMR, Lymphocyte-to-Monocyte Ratio; NER, Neutrophil-to-Eosinophil Ratio; NLR, Neutrophil-to-Lymphocyte Ratio; NOS, Not Otherwise Specified; NSCLC, Non-Small Cell Lung Cancer; PD-1, Programmed Death-1; PD-L1, Programmed Death Ligand-1; PLR, Platelet-to-Lymphocyte Ratio; PNI, Prognostic Nutritional Index; ROC, Receiver Operating Characteristic; SCC, Squamous Cell Carcinoma; SII, Systemic Immune-Inflammation Index; SIRI, Systemic Inflammation Response Index; TMB, Tumor Mutational Burden; VEGF, Vascular Endothelial Growth Factor.

Ethics Approval and Consent to Participate

This study was reviewed and approved by the Ethics Committee of Nevsehir Haci Bektas Veli University.All participants provided written informed consent prior to treatment. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki.

Disclosure

The authors declare that they have no conflicts of interest related to this work.

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