The Role of the Immune Metabolic Prognostic Index in Patients with Non-Small Cell Lung Cancer (NSCLC) in Radiological Progression during Treatment with Nivolumab

Development and validation of a prognostic nomogram model in locally advanced NSCLC based on metabolic features of PET/CT and hematological inflammatory indicators

BACKGROUND

We combined the metabolic features of 18F-FDG-PET/CT and hematological inflammatory indicators to establish a predictive model of the outcomes of patients with locally advanced non-small cell lung cancer (LA-NSCLC) receiving concurrent chemoradiotherapy.

RESULTS

A predictive nomogram was developed based on sex, CEA, systemic immune-inflammation index (SII), mean SUV (SUVmean), and total lesion glycolysis (TLG). The nomogram presents nice discrimination that yielded an AUC of 0.76 (95% confidence interval: 0.66-0.86) to predict 1-year PFS, with a sensitivity of 63.6%, a specificity of 83.3%, a positive predictive value of 83.7%, and a negative predictive value of 62.9% in the training set. The calibration curves and DCA suggested that the nomogram had good calibration and fit, as well as promising clinical effectiveness in the training set. In addition, survival analysis indicated that patients in the low-risk group had a significantly longer mPFS than those in the high-risk group (16.8 months versus 8.4 months, P < 0.001). Those results were supported by the results in the internal and external test sets.

CONCLUSIONS

The newly constructed predictive nomogram model presented promising discrimination, calibration, and clinical applicability and can be used as an individualized prognostic tool to facilitate precision treatment in clinical practice.

Prognostic and clinicopathological significance of Systemic Immune-Inflammation Index in cancer patients receiving immune checkpoint inhibitors: a meta-analysis

BACKGROUND

Among malignant neoplasm patients taking immune checkpoint inhibitors (ICIs), it remains unknown how the systemic immune-inflammation index (SII) affects their clinical prognosis. We therefore performed the present meta-analysis by collecting the most recent data, so that SII's prognostic value among ICI-receiving carcinoma patients could be fully clarified.

METHODS

For the prognostic significance evaluation of SII in ICI-receiving carcinoma patients, the combined hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated.

RESULTS

The number of studies enrolled in the present meta-analysis totaled 17, where 1,990 patients were involved. Among the ICI-treated carcinoma patients, a high SII was linked significantly to inferior overall survival (OS) (HR = 2.62, 95% CI = 1.76-3.90), as well as progression-free survival (PFS) (HR = 2.09, 95% CI = 1.48-2.95) (p both <.001). Contrastively, SII was linked insignificantly to the age (OR = 1.08, 95% CI = 0.39-2.98, p = .881), gender (OR = 1.01, 95% CI = 0.59-1.73, p = .959), lymph node (LN) metastasis (OR = 1.41, 95% CI = 0.92-2.17, p = .117), or metastatic site quantity (OR = 1.49, 95% CI = 0.90-2.46, p = .119).

CONCLUSION

There are prominent associations of elevated SII with the poor survival outcomes (both short- and long-terms) among the ICIreceiving carcinoma patients. SII has potential as a reliable and cheap prognostic biomarker in the clinic for carcinoma patients receiving ICIs.

Neutrophil-lymphocyte ratio and platelet-lymphocyte ratio as potential predictive markers of treatment response in cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis

Background

The role of platelet-lymphocyte ratio (PLR) and neutrophil-lymphocyte ratio (NLR) as independent prognostic markers in different tumors is well established. However, there is a limited review of the potential of NLR and PLR as predictors of treatment outcomes from immune checkpoint inhibitors (ICIs).

Objective

To establish a correlation between NLR and PLR and the potential of clinical benefit from ICIs.

Methods

The literature search was performed for studies that reported the association between NLR, PLR, and treatment outcomes among cancer patients treated with ICIs. The outcomes of interest were objective response rate (ORR), disease control rate (DCR), and progressive disease (PD). ORR was the summation of patients who achieved complete response and partial response. DCR included patients who achieved stable disease. PD was the proportion of patients who progressed, relapsed, or discontinued the treatment. Statistical analysis was performed using the STATA 12.0 package. Heterogeneity was determined by the I2 value. Quality assessment was performed using the Newcastle-Ottawa Scale. Egger's test was used to establish publication bias and sensitivity analysis.

Results

A total of 40 papers that met the inclusion criteria were included in the systematic review. However, only 17 studies were used in the meta-analysis to determine the correlation between NLR, PLR, and treatment response. We found that treatment with ICIs and monitoring of outcomes and adverse events using PLR and NLR parameters have been studied in different tumors. Our analysis showed that low NLR correlated with higher ORR (OR = 0.62 (95% CI 0.47-0.81, p = 0.001) and higher DCR (OR = 0.23, 95% CI 0.14-0.36, p < 0.001). Higher NLR predicted a higher probability of PD (OR = 3.12, 95% CI 1.44, 6.77, p = 0.004). Similarly, low PLR correlated with higher ORR (OR = 0.69, 95% CI 0.5, 0.95, p = 0.025). Generally, patients with low NLR and PLR were more likely to achieve clinical benefit and better response (p-value < 0.001). Meanwhile, patients with high ratios were more likely to progress (p-value < 0.005), although there was significant heterogeneity among studies. There was no significant publication bias observed.

Conclusion

The study showed that high NLR and PLR either at baseline or during treatment is associated with poorer treatment outcome. Therefore, these ratios can be utilized in clinical practice with other markers to determine treatment efficacy from immunotherapy.

Pretreatment absolute lymphocyte count is an independent predictor for survival outcomes for esophageal squamous cell carcinoma patients treated with neoadjuvant chemoradiotherapy and pembrolizumab: An analysis from a prospective cohort

BACKGROUND

The aim of the study was to analyze the relationship between pretreatment inflammatory biomarkers (IBs) and survival outcomes for patients with esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (neo-CRT) and pembrolizumab.

METHODS

Clinical variables and IBs (absolute monocyte count [AMC], absolute lymphocyte count [ALC], platelet count [PLT], neutrophil-to-lymphocyte ratio [NLR], platelet-to-lymphocyte ratio [PLR], lymphocyte-to-monocyte ratio [LMR], pan-immune inflammation value [PIV], systemic immunoinflammatory index [SII], systemic immunoreactivity index [SIRI] and prognostic nutritional index [PNI]) were collected. Univariate and multivariate analysis were performed to identify the independent factors for outcomes of ESCC.

RESULTS

A total of 51 patients were included. Of these, 35 patients achieved pathological complete response (pCR) after neo-CRT and pembrolizumab (pCR: 68.6%). With a median follow-up of 20 months, the two-year PFS and OS of the cohort was 64% and 91%, respectively. Multivariate logistic regression analysis indicated that ALC (overall response [OR] 4.4, p = 0.051) and PLT (OR 6.7, p = 0.023) were two independent predictors for achieving pCR among ESCC treated with neo-CRT and pembrolizumab. Multivariate Cox regression analysis showed that ALC (HR 0.27, p = 0.028) and SIRI (HR 3.13, p = 0.048) were two independent predictors associated with PFS. Kaplan Meier analysis demonstrated that the PFS of ESCC with high baseline ALC was significantly better than those with low ALC (2-year PFS: 77% vs. 47%, p = 0.027), but not for overall survival (2-year OS: 96% vs. 87%, p = 0.46).

CONCLUSIONS

This retrospective analysis based on a prospective cohort for the first time demonstrates that pretreatment ALC is an independent predictor for achieving pCR and favorable outcomes of ESCC treated with neo-CRT and pembrolizumab.

The predictive value of inflammatory biomarkers for major pathological response in non-small cell lung cancer patients receiving neoadjuvant chemoimmunotherapy and its association with the immune-related tumor microenvironment: a multi-center study

BACKGROUND

Inflammatory biomarkers in the peripheral blood have been established as predictors for immunotherapeutic efficacy in advanced non-small cell lung cancer (NSCLC). Whether they can also predict major pathological response (MPR) in neoadjuvant setting remains unclear.

METHODS

In this multi-center retrospective study, 122 and 92 stage I-IIIB NSCLC patients from six hospitals who received neoadjuvant chemoimmunotherapy followed by surgery were included in the discovery and external validation cohort, respectively. Baseline and on-treatment neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR) and systemic immune-inflammation index (SII) were calculated and associated with MPR. Furthermore, resected tumor samples from 37 patients were collected for RNA-sequencing to investigate the immune-related tumor microenvironment.

RESULTS

In both the discovery and validation cohorts, the on-treatment NLR, dNLR, PLR, and SII levels were significantly lower in the patients with MPR versus non-MPR. On-treatment SII remained an independent predictor of MPR in multivariate logistic regression analysis. The area under the curve (AUC) of on-treatment SII for predicting MPR was 0.75 (95%CI, 0.67-0.84) in the discovery cohort. Moreover, the predictive value was further improved by combining the on-treatment SII and radiological tumor regression data, demonstrating an AUC of 0.82 (95%CI, 0.74-0.90). The predictive accuracy was validated in the external cohort. Compared with the SII-high group, patients with SII-Low were associated with the activated B cell receptor signaling pathway and a higher intratumoral immune cell infiltration level.

CONCLUSIONS

On-treatment SII was independently associated with MPR in NSCLC patients receiving neoadjuvant chemoimmunotherapy. Further prospective studies are warranted.

Prognostic value of inflammatory markers and clinical features for survival in advanced or metastatic esophageal squamous cell carcinoma patients receiving anti-programmed death 1 treatment

Purpose

This study aims to assess the prognostic value of inflammatory markers and clinical features in advanced or metastatic esophageal squamous cell carcinoma (ESCC) patients receiving anti-programmed death 1 (PD-1) treatment.

Methods

Based on receiver operating characteristic curve (ROC) analysis, Youden's indexes were applied to determine the cut-off values for inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocye ratio (dNLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII). Wilcoxon test was conducted to evaluate the changes in above inflammatory markers. Kaplan-Meier method was utilized to estimate progression-free survival (PFS) and overall survival (OS), and the Log-rank test was used to compare the different survival between groups. Univariate and multivariate Cox regression analyses were performed to assess the prognostic value of inflammatory markers and clinical features.

Results

162 advanced or metastatic ESCC patients receiving anti-PD-1 treatment were enrolled in this retrospective study. The cut-off values of NLR, dNLR, MLR, PLR, and SII were 4.748, 2.214, 0.309, 250.505, and 887.895, respectively. NLR, dNLR, PLR, and SII declined significantly among the partial response (PR) (P<0.001, P<0.001, P=0.036, P<0.001), objective response rate (ORR) (P<0.001, P<0.001, P=0.036, P<0.001), and disease control rate (DCR) (P<0.001, P<0.001, P=0.038, P<0.001) groups, respectively. Significant increases were found in NLR (P<0.001), dNLR (P<0.001), MLR (P=0.001), and SII (P=0.024) when anti-PD-1 treatment failed. Multivariate Cox regression analysis indicated that NLR (P<0.001, P=0.002), lymph node metastasis (P=0.013, P=0.001), Eastern Cooperative Oncology Group Performance Status (ECOG PS) (P=0.008, P=0.002), and treatment lines (P=0.037, P=0.048) were significant prognostic indicators of PFS and OS. Additionally, SII (P=0.016) was also significantly related to OS in ESCC patients. The risk score model showed that low risk patients prolonged PFS and OS than those with middle or high risk (P<0.001, P<0.001).

Conclusion

Inflammatory markers can reflect short-term outcomes of anti-PD-1 treatment for ESCC patients. NLR, lymph node metastases, ECOG PS, and treatment lines are significant prognostic indicators for PFS and OS. And the risk score model constructed based on the above factors has favourable prognostic predictive value.

Systemic immune-inflammation index predicts prognosis of cancer immunotherapy: systemic review and meta-analysis

Objective: This meta-analysis was designed to explore the association between the systemic immune-inflammation index (SII) and the therapeutic effect of immune checkpoint inhibitors. Materials & methods: The authors retrieved relevant studies published before May 25, 2022. Hazard ratio (HR) with 95% CI was used to evaluate the relationship between SII and overall survival (OS) and progression-free survival (PFS). Results: 14 articles comprising 2721 patients were included in this study. The pooled results proved that high SII levels were closely related to poor prognosis in cancer patients receiving immune checkpoint inhibitors (OS HR = 2.40; 95% CI: 2.04-2.82; PFS HR = 1.57; 95% CI: 1.33-1.86) and that an SII value of 750 was appropriate as a cut-off value (OS HR = 2.20; 95% CI: 1.83-2.63; PFS HR = 1.54; 95% CI: 1.33-1.80). Conclusion: High SII levels (>750) may be an indicator of worse OS and PFS in cancer patients treated with immune checkpoint inhibitors.

Tumor metabolic and secondary lymphoid organ metabolic markers on 18F-fludeoxyglucose positron emission tomography predict prognosis of immune checkpoint inhibitors in advanced lung cancer

Background

The purpose of this study was to investigate the predictive value of tumor metabolic parameters in combination with secondary lymphoid metabolic parameters on positron emission tomography (PET)/computed tomography (CT) for immune checkpoint inhibitor (ICI) prognosis in advanced lung cancer.

Methods

This study retrospectively included 125 patients who underwent 18F-fludeoxyglucose (FDG) PET/CT before ICI therapy, including 41 patients who underwent a second PET/CT scan during ICI treatment. The measured PET/CT parameters included tumor metabolism parameters [maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), total lesion glycolysis (TLG), and total metabolic tumor volume (TMTV)] and secondary lymphoid organ metabolism parameters [spleen-to-liver SUVmax ratio (SLR) and bone marrow-to-liver SUVmax ratio (BLR)]. The correlation of PET/CT metabolic parameters with early ICI treatment response, progression-free survival (PFS), and overall survival (OS) was analyzed.

Results

Within a median follow-up of 28.7 months, there were 44 responders and 81 non-responders. The median PFS was 8.6 months (95% confidence interval (CI): 5.872–11.328), and the median OS was 20.4 months (95% CI: 15.526–25.274). Pretreatment tumor metabolic parameters were not associated with early treatment responses. The high bone marrow metabolism (BLR &gt;1.03) was significantly associated with a shorter PFS (p = 0.008). Patients with a high TMTV (&gt;168 mL) and high spleen metabolism (SLR &gt;1.08) had poor OS (p = 0.019 and p = 0.018, respectively). Among the 41 patients who underwent a second PET/CT scan, the ΔSUVmax was significantly lower (p = 0.01) and the SLR was significantly higher (p = 0.0086) in the responders. Populations with low-risk characteristics (low TMTV, low SLR, and ΔSLR &gt; 0) had the longest survival times.

Conclusion

High pretreatment TMTV and SLR are associated with poor OS, and increased spleen metabolism after ICI therapy predicts treatment benefit. This indicates that the combination of tumor and spleen metabolic parameters is a valuable prognostic strategy.

PET/CT molecular imaging in the era of immune-checkpoint inhibitors therapy

Cancer immunotherapy, especially immune-checkpoint inhibitors (ICIs), has paved a new way for the treatment of many types of malignancies, particularly advanced-stage cancers. Accumulating evidence suggests that as a molecular imaging modality, positron emission tomography/computed tomography (PET/CT) can play a vital role in the management of ICIs therapy by using different molecular probes and metabolic parameters. In this review, we will provide a comprehensive overview of the clinical data to support the importance of 18F-fluorodeoxyglucose PET/CT (18F-FDG PET/CT) imaging in the treatment of ICIs, including the evaluation of the tumor microenvironment, discovery of immune-related adverse events, evaluation of therapeutic efficacy, and prediction of therapeutic prognosis. We also discuss perspectives on the development direction of 18F-FDG PET/CT imaging, with a particular emphasis on possible challenges in the future. In addition, we summarize the researches on novel PET molecular probes that are expected to potentially promote the precise application of ICIs.

18F FDG PET/CT and Novel Molecular Imaging for Directing Immunotherapy in Cancer

Immunotherapy has transformed the treatment landscape of many cancers, with durable responses in disease previously associated with a poor prognosis. Patient selection remains a challenge, with predictive biomarkers an urgent unmet clinical need. Current predictive biomarkers, including programmed death-ligand 1 (PD-L1) (measured with immunohistochemistry), are imperfect. Promising biomarkers, including tumor mutation burden and tumor infiltrating lymphocyte density, fail to consistently predict response and have yet to translate to routine clinical practice. Heterogeneity of immune response within and between lesions presents a further challenge where fluorine 18 fluorodeoxyglucose PET/CT has a potential role in assessing response, stratifying treatment, and detecting and monitoring immune-related toxicities. Novel radiopharmaceuticals also present a unique opportunity to define the immune tumor microenvironment to better predict which patients may respond to therapy, for example by means of in vivo whole-body PD-L1 and CD8+ T cell expression imaging. In addition, longitudinal molecular imaging may help further define dynamic changes, particularly in cases of immunotherapy resistance, helping to direct a more personalized therapeutic approach. This review highlights current and emerging applications of molecular imaging to stratify, predict, and monitor molecular dynamics and treatment response in areas of clinical need.

The Effect of Inflammatory Markers on the Survival of Advanced Gastric Cancer Patients Who Underwent Anti-Programmed Death 1 Therapy

Purpose

This clinical study sought to determine whether the levels of inflammatory markers predicted the survival of advanced gastric cancer (AGC) patients who underwent anti-programmed death 1 (PD-1) therapy.

Methods

Using AGC patient plasma samples and baseline characteristics, we investigated the specific value of inflammatory markers in AGC from a clinical perspective in immunotherapy.

Results

One hundred and six patients with AGC who underwent anti-PD-1 therapy were enrolled in this study between 20 July 2019 and 16 March 2021. A significant decrease in NLR, dNLR, and SII was noticed among the PR (P=0.023; P=0.036; P=0.001), SD (P=0.048; P=0.022; P=0.023), ORR (P=0.021; P=0.032; P=0.001), and DCR (P=0.003; P=0.001; P<0.001) groups after anti-PD-1 therapy. Additionally, a significant decline of PLR was also observed in PR (P=0.010), ORR (P=0.007), and DCR (P=0.005) after anti-PD-1 therapy. Only MLR levels increased significantly at the time of anti-PD-1 immunotherapy the failure compared to baseline (P=0.039). And statistically significant elevations in NLR (P=0.001), MLR (P=0.020), dNLR (P=0.002), and SII (P=0.019) were found in failure of anti-PD-1 treatment compared to optimal efficacy in AGC patients. In first-line treatment, the number of metastatic sites (P=0.001) was an independent prognostic factor for PFS, and peritoneal metastases (P=0.004) and platelet-to-lymphocyte ratio (PLR) level (P=0.014) were independent prognostic predictors of OS according to Cox regression analysis. In second-line or posterior treatment, the number of metastatic sites (P=0.007), ECOG (P=0.011), and PLR level (P=0.033) were independent prognostic factors for PFS in AGC patients, and the number of metastatic sites (P=0.003), differentiation (P=0.030), and NLR level (P<0.001) were independent prognostic factors for OS according to Cox regression analysis.

Conclusions

NLR, PLR, MLR, dNLR, and SII can reflect the short-term efficacy of immunotherapy in patients who underwent anti-PD-1 therapy with AGC. PLR is an independent prognostic factor for OS in AGC patients receiving first-line immunotherapy and PFS in those receiving second-line or posterior immunotherapy. And NLR was an independent prognostic factor for OS in AGC patients receiving second-line or posterior immunotherapy. The number of metastatic sites was significantly associated with the prognosis of AGC patients who received immunotherapy.

Systemic Inflammation Index and Tumor Glycolytic Heterogeneity Help Risk Stratify Patients with Advanced Epidermal Growth Factor Receptor-Mutated Lung Adenocarcinoma Treated with Tyrosine Kinase Inhibitor Therapy

Simple Summary

Patients with advanced epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma have been known to respond to first-line tyrosine kinase inhibitor (TKI) treatment. However, a subgroup of patients are non-responsive to the treatment, with poor survival outcomes, and those who are initially responsive may still experience resistance. A reliable prognostic tool may provide a valuable direction for tailoring individual treatment strategies in this clinical setting. With this aim, the present study explores the prognostic power of the combination of the systemic inflammation index (portrayed by hematological markers) and tumor glycolytic heterogeneity (characterized by ¹⁸F-fluorodeoxyglucose positron emission tomography images). The model integrating these two biomarkers could be used to improve risk stratification, and the subsequent personalized management strategy in patients with advanced EGFR-mutated lung adenocarcinoma.

Abstract

Tyrosine kinase inhibitors (TKIs) are the first-line treatment for patients with advanced epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma. Over half of patients failed to achieve prolonged survival benefits from TKI therapy. Awareness of a reliable prognostic tool may provide a valuable direction for tailoring individual treatments. We explored the prognostic power of the combination of systemic inflammation markers and tumor glycolytic heterogeneity to stratify patients in this clinical setting. One hundred and five patients with advanced EGFR-mutated lung adenocarcinoma treated with TKIs were retrospectively analyzed. Hematological variables as inflammation-induced biomarkers were collected, including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and systemic inflammation index (SII). First-order entropy, as a marker of heterogeneity within the primary lung tumor, was obtained by analyzing ¹⁸F-fluorodeoxyglucose positron emission tomography images. In a univariate Cox regression analysis, sex, smoking status, NLR, LMR, PLR, SII, and entropy were associated with progression-free survival (PFS) and overall survival (OS). After adjusting for confounders in the multivariate analysis, smoking status, SII, and entropy, remained independent prognostic factors for PFS and OS. Integrating SII and entropy with smoking status represented a valuable prognostic scoring tool for improving the risk stratification of patients. The integrative model achieved a Harrell’s C-index of 0.687 and 0.721 in predicting PFS and OS, respectively, outperforming the traditional TNM staging system (0.527 for PFS and 0.539 for OS, both p < 0.001). This risk-scoring model may be clinically helpful in tailoring treatment strategies for patients with advanced EGFR-mutated lung adenocarcinoma.

Advances in Lung Cancer Imaging and Therapy

This series of eight papers (five original articles, two reviews and one meta-analysis) is presented by international leaders covering various aspects of lung cancer management, starting with diagnostic imaging and analyzing the novel perspectives of therapy [...]

Immunotherapy Monitoring with Immune Checkpoint Inhibitors Based on [18F]FDG PET/CT in Metastatic Melanomas and Lung Cancer

Immunotherapy with checkpoint inhibitors has prompted a major change not only in cancer treatment but also in medical imaging. In parallel with the implementation of new drugs modulating the immune system, new response criteria have been developed, aiming to overcome clinical drawbacks related to the new, unusual, patterns of response characterizing both solid tumors and lymphoma during the course of immunotherapy. The acknowledgement of pseudo-progression, hyper-progression, immune-dissociated response and so forth, has become mandatory for all imagers dealing with this clinical scenario. A long list of acronyms, i.e., irRC, iRECIST, irRECIST, imRECIST, PECRIT, PERCIMT, imPERCIST, iPERCIST, depicts the enormous effort made by radiology and nuclear medicine physicians in the last decade to optimize imaging parameters for better prediction of clinical benefit in immunotherapy regimens. Quite frequently, a combination of clinical-laboratory data with imaging findings has been tested, proving the ability to stratify patients into various risk groups. The next steps necessarily require a large scale validation of the most robust criteria, as well as the clinical implementation of immune-targeting tracers for immuno-PET or the exploitation of radiomics and artificial intelligence as complementary tools during the course of immunotherapy administration. For the present review article, a summary of PET/CT role for immunotherapy monitoring will be provided. By scrolling into various cancer types and applied response criteria, the reader will obtain necessary information for better understanding the potentials and limitations of the modality in the clinical setting.

NSCLC Biomarkers to Predict Response to Immunotherapy with Checkpoint Inhibitors (ICI): From the Cells to In Vivo Images

Lung cancer remains the leading cause of cancer-related death, and it is usually diagnosed in advanced stages (stage III or IV). Recently, the availability of targeted strategies and of immunotherapy with checkpoint inhibitors (ICI) has favorably changed patient prognosis. Treatment outcome is closely related to tumor biology and interaction with the tumor immune microenvironment (TME). While the response in molecular targeted therapies relies on the presence of specific genetic alterations in tumor cells, accurate ICI biomarkers of response are lacking, and clinical outcome likely depends on multiple factors that are both host and tumor-related. This paper is an overview of the ongoing research on predictive factors both from in vitro/ex vivo analysis (ranging from conventional pathology to molecular biology) and in vivo analysis, where molecular imaging is showing an exponential growth and use due to technological advancements and to the new bioinformatics approaches applied to image analyses that allow the recovery of specific features in specific tumor subclones.