Our study's retrospective analysis provides critical insights into the predictive markers for the efficacy of targeted combined immunotherapy in advanced HCC patients. We identified four independent risk factors: NLR, tumor diameter, ΔT, and album levels, then we construct a predict model TIPM, which show good predicted performance. Our study may have important implications for personalized treatment.
The Neutrophil-to-Lymphocyte Ratio (NLR) has emerged as a significant biomarker for evaluating the efficacy of immunotherapies in cancer treatment. Elevated NLR values are indicative of a systemic inflammatory response, which has been consistently associated with adverse effects on the tumor microenvironment and a diminished response to immune checkpoint inhibitors (ICIs) across various cancer types. Studies have demonstrated that patients with higher NLR values exhibit significantly poorer overall survival, progression-free survival, and lower rates of response and clinical benefit from ICIs, underscoring the crucial role of systemic inflammation in the modulation of tumor immunotherapy outcomes[15]. This correlation between NLR and treatment efficacy is further substantiated by the observation of heterogeneity in response across different studies, which underscores the importance of adjusting for prognostic factors and suggests a complex interaction between systemic inflammatory markers and cancer prognosis[16].The predictive value of NLR, alongside other biomarkers such as Tumor Mutational Burden (TMB), offers a nuanced approach to patient stratification and treatment planning, advocating for the integration of systemic inflammation assessment in the decision-making process to optimize immunotherapy strategies[15] .
The size of a tumor, as measured by its diameter, is a critical factor in evaluating the biological aggressiveness of cancer and has significant implications for predicting the efficacy of treatment. Larger tumors often indicate a worse prognosis due to their potential for advanced disease progression and reduced responsiveness to therapeutic interventions. This correlation highlights the importance of tumor diameter as a key predictive measure of treatment efficacy, underscoring its role in guiding clinical decisions regarding cancer management. Advancements in imaging technologies, such as magnetic resonance imaging (MRI), have significantly improved the accuracy of tumor size evaluation, making it possible to assess treatment response with greater precision. MRI, in particular, offers detailed insights into tumor morphology, hemodynamics, and metabolism, allowing for early and comprehensive evaluation of the response to neoadjuvant chemotherapy (NAC). Studies have confirmed that changes in tumor volume and diameter, as observed through MRI, are among the most accurate indicators of pathological response following treatment. This is especially true for breast cancer NAC, where reducing tumor volume has been identified as the most reliable indicator of pathological response, followed closely by changes in tumor diameter[17].
Furthermore, the association between baseline tumor size and outcomes in cancer treatment has been substantiated by various studies. For instance, baseline tumor size has been identified as an independent prognostic factor for overall survival in patients with melanoma treated with pembrolizumab, emphasizing its predictive value beyond traditional staging criteria[18] . Similar findings have been observed in non-small cell lung cancer (NSCLC), where baseline tumor size was found to be a predictive and prognostic factor for patients treated with immune checkpoint inhibitors, suggesting that tumor size may significantly influence the effectiveness of immuno-oncological treatments[18].These insights into the role of tumor diameter in predicting treatment efficacy underscore the necessity of incorporating tumor size evaluations into treatment planning and prognosis assessment. As such, tumor diameter serves not only as a marker of cancer's biological behavior but also as a crucial component of personalized treatment strategies aimed at optimizing therapeutic outcomes.
Incorporating the changes in tumor tissue stiffness (ΔT) as an indicator of treatment effectiveness, recent advances in imaging technologies such as Contrast-Enhanced Ultrasound (CEUS) and Ultrasound Elastography have provided valuable insights into assessing the response of tumors to neoadjuvant chemotherapy (NAC). Ultrasound elastography, which measures changes in tissue stiffness, has emerged as a significant method for evaluating NAC efficacy. This technique, through both strain elastography (SE) and shear wave elastography (SWE), offers qualitative and quantitative analyses of tissue softness and stiffness. Studies have shown that SE can predict NAC response with high sensitivity and specificity after just two treatment cycles. Moreover, SWE has been found to significantly associate the relative change of tumor stiffness after two NAC cycles with the pathological response of postoperative specimens, providing a new predictive parameter for judging the efficacy of NAC for breast cancer. The effectiveness of these imaging techniques in evaluating changes in tumor tissue stiffness underscores the complexity of tumor response to treatments. By offering insights into the physical properties of tumors, such as elasticity and stiffness, these methods contribute significantly to our understanding of how tumors respond to therapies. They also highlight the potential for integrating imaging findings with clinical and biological indicators to enhance the precision of treatment effectiveness evaluations[19].
Lower albumin levels have been extensively studied and found to reflect poor overall patient health status, impacting outcomes from targeted immunotherapies across various cancer types. Serum albumin serves not only as a marker for general health but also has a prognostic and predictive value in patients undergoing immune checkpoint blockade (ICB) therapy. Studies have shown that high serum albumin levels are associated with better overall survival (OS) and progression-free survival (PFS) in patients treated with ICB across multiple cancer types, including melanoma and non-small cell lung cancer (NSCLC)[20]. Moreover, albumin levels have been linked to the body's inflammatory status and nutritional health, which are crucial in the context of cancer treatment. Specifically, higher albumin levels indicate a lower systemic inflammatory state and better nutritional status, which are conducive to a more favorable response to immunotherapy[20, 21]. The dose-dependent relationship observed between serum albumin levels and clinical responses to ICB therapy further underscores the importance of maintaining optimal albumin levels for enhancing treatment outcomes[21]. In the realm of metastatic NSCLC, hypoalbuminemia has been identified as a biomarker indicative of resistance to first-line treatments, further emphasizing the role of albumin in predicting treatment outcomes[22]. The significant association between pretreatment albumin levels and OS, demonstrated through Kaplan-Meier survival analyses and Cox’s proportional-hazards models, indicates that serum albumin could serve as a critical factor in stratifying patients for ICB therapy[22].
Our study expands upon previous research by further validating the importance of serological and ultrasonography indicators in predicting responses to targeted combined immunotherapy in patients with advanced hepatocellular carcinoma (HCC). Previous studies laid the groundwork by focusing primarily on single predictive factors or analyzing smaller patient cohorts. In contrast, our research takes a more comprehensive approach, incorporating multiple predictive factors and applying these models to a larger cohort of patients. This broader perspective allows for a more nuanced understanding of the complex interplay between various biomarkers and their predictive value in the context of HCC immunotherapy[23, 24].
Additionally, the revolutionizing role of contrast-enhanced ultrasound (CEUS) and serological indicators in early recurrence prediction postoperatively for HCC patients further exemplifies the advancements in surveillance methodologies. These techniques enhance our ability to monitor and predict treatment outcomes, providing valuable insights into the effectiveness of therapeutic interventions[25].Our study not only corroborates the utility of serological and ultrasonography indicators in forecasting immunotherapy responses in advanced HCC but also highlights the evolving landscape of HCC treatment strategies. By integrating these predictive models with current knowledge on the immune microenvironment and the latest advances in immunotherapy, we can better tailor treatments to individual patients, ultimately improving clinical outcomes.
The clinical application potential of predictive models for HCC is underscored by research indicating their utility in guiding clinical decisions, tailoring personalized treatment plans, and optimizing cancer treatments based on comprehensive risk and prognosis assessments. Nomograms, derived from large-scale databases like SEER, have demonstrated strong predictive abilities for all-cause and cancer-specific early death, offering a promising tool for clinicians to enhance survival outcomes for HCC patients. However, limitations such as retrospective study design and the specificity of data sources like the SEER database to the US population suggest the need for further validation and global research to refine these models[26]. Therefore, we developed TIPM, a predictive model suitable for Chinese population characteristics.
Limitations:
The study acknowledges limitations, including a retrospective design that may hinder causal inference and introduce bias, a small sample size impacting generalizability and statistical power, and an absence of prospective validation which could affirm the real-world applicability of predictive models. Additionally, a deeper understanding of the biological mechanisms behind NLR and ΔT is required to improve predictive accuracy. Finally, subgroup analyses are needed to devise more personalized treatment strategies, addressing these concerns will enhance the robustness and applicability of future research outcomes in hepatocellular carcinoma management.