Tumour-infiltrating lymphocyte density is associated with favourable outcome in patients with advanced non-small cell lung cancer treated with immunotherapy

Treatment-Related Lymphopenia is Possibly a Marker of Good Prognosis in Nasopharyngeal Carcinoma: a Propensity-Score Matching Analysis

Purpose

The aims of the study were to monitor circulating lymphocyte subset counts before and after therapy for nasopharyngeal carcinoma (NPC), and investigate their relationships with patient outcomes.

Patients and Methods

Subjects comprised patients with TNM stage I-IVA NPC who underwent radiotherapy. Peripheral venous blood samples were collected before and after treatment. Lymphocyte subset counts were analyzed by flow cytometry. Differences between post-treatment and baseline counts were calculated to determine Δ values. Patients were divided into high and low groups, based on median lymphocyte subset counts; propensity score matching was applied to balance groups. Progression-free survival (PFS) and overall survival (OS) were plotted using Kaplan-Meier curves and compared using a Log rank test. Relationships between lymphocyte subset counts and patient survival were subjected to Cox regression analysis.

Results

Patients with NPC (n=746) were enrolled from 2012-2022. Higher CD8+ and total T cell baseline counts were associated with better 5-year PFS (73.7% vs 63.1%, P=0.002 and 73.8% vs 64.1%, P=0.005, respectively). Similarly, higher Δ values of CD4+ and total T cells were associated with higher 5-year PFS (76.2% vs 63.5%, P=0.001; 74.3% vs 65.4%, P=0.010) and OS (89.8% vs 81.6%, P=0.005; 88.6% vs 82.5%, P=0.009). Multivariate Cox regression revealed that CD8+ (hazard ratio (HR) 0.651, P=0.002) and total T (HR 0.600, P<0.001) cells were significantly associated with PFS. CD4+ (HR 0.708, P=0.038) and total T (HR 0.639, P=0.031) cells were independent prognostic factors for OS.

Conclusion

NPC patients with low total or CD8+ T cell counts before treatment had worse prognosis; however, those with more significant decreases in total or CD4+ T cells possibly had better outcomes. T cell counts can be reliable indicators to predict prognosis.

LncRNAs exhibit subtype-specific expression, survival associations, and cancer-promoting effects in breast cancer

Long non-coding RNAs (lncRNAs) play important roles in cancer progression, influencing processes such as invasion, metastasis, and drug resistance. Their reported cell type-dependent expression patterns suggest the potential for specialized functions in specific contexts. In breast cancer, lncRNA expression has been associated with different subtypes, highlighting their relevance in disease heterogeneity. However, our understanding of lncRNA function within breast cancer subtypes remains limited, warranting further investigation. We conducted a comprehensive analysis using the TANRIC dataset derived from the TCGA-BRCA cohort, profiling the expression, patient survival associations and immune cell type correlations of 12,727 lncRNAs across subtypes. Our findings revealed subtype-specific associations of lncRNAs with patient survival, tumor infiltrating lymphocytes and other immune cells. Targeting of lncRNAs exhibiting subtype-specific survival associations and expression in a panel of breast cancer cells demonstrated a selective reduction in cell proliferation within their associated subtype, supporting subtype-specific functions of certain lncRNAs. Characterization of HER2 + -specific lncRNA LINC01269 and TNBC-specific lncRNA AL078604.2 showed nuclear localization and altered expression of hundreds of genes enriched in cancer-promoting processes, including apoptosis, cell proliferation and immune cell regulation. This work emphasizes the importance of considering the heterogeneity of breast cancer subtypes and the need for subtype-specific analyses to fully uncover the relevance and potential impact of lncRNAs. Collectively, these findings demonstrate the contribution of lncRNAs to the distinct molecular, prognostic, and cellular composition of breast cancer subtypes.

Exploring histological predictive biomarkers for immune checkpoint inhibitor therapy response in non-small cell lung cancer

Treatment challenges persist in advanced lung cancer despite the development of therapies beyond the traditional platinum-based chemotherapy. The early 2000s marked a shift to tyrosine kinase inhibitors targeting epidermal growth factor receptor, ushering in personalized genetic-based treatment. A further significant advance was the development of immune checkpoint inhibitors (ICIs), especially for non-small cell lung cancer. These target programmed death-ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen 4, which enhanced the immune response against tumor cells. However, not all patients respond, and immune-related toxicities arise. This review emphasizes identifying biomarkers for ICI response prediction. While PD-L1 is a widely used, validated biomarker, its predictive accuracy is imperfect. Investigating tumor-infiltrating lymphocytes, tertiary lymphoid structure, and emerging biomarkers such as high endothelial venule, Human leukocyte antigen class I, T-cell immunoreceptors with Ig and ITIM domains, and lymphocyte activation gene-3 counts is promising. Understanding and exploring additional predictive biomarkers for ICI response are crucial for enhancing patient stratification and overall care in lung cancer treatment.

Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.

Association of Baseline Tumor-Specific Neoantigens and CD8+ T-Cell Infiltration With Immune-Related Adverse Events Secondary to Immune Checkpoint Inhibitors

PURPOSE

Recent evidence has shown that higher tumor mutational burden strongly correlates with an increased risk of immune-related adverse events (irAEs). By using an integrated multiomics approach, we further studied the association between relevant tumor immune microenvironment (TIME) features and irAEs.

METHODS

Leveraging the US Food and Drug Administration Adverse Event Reporting System, we extracted cases of suspected irAEs to calculate the reporting odds ratios (RORs) of irAEs for cancers treated with immune checkpoint inhibitors (ICIs). TIME features for 32 cancer types were calculated on the basis of the cancer genomic atlas cohorts and indirectly correlated with each cancer's ROR for irAEs. A separate ICI-treated cohort of non-small-cell lung cancer (NSCLC) was used to evaluate the correlation between tissue-based immune markers (CD8+, PD-1/L1+, FOXP3+, tumor-infiltrating lymphocytes [TILs]) and irAE occurrence.

RESULTS

The analysis of 32 cancers and 33 TIME features demonstrated a significant association between irAE RORs and the median number of base insertions and deletions (INDEL), neoantigens (r = 0.72), single-nucleotide variant neoantigens (r = 0.67), and CD8+ T-cell fraction (r = 0.51). A bivariate model using the median number of INDEL neoantigens and CD8 T-cell fraction had the highest accuracy in predicting RORs (adjusted r2 = 0.52, P = .002). Immunoprofile assessment of 156 patients with NSCLC revealed a strong trend for higher baseline median CD8+ T cells within patients' tumors who experienced any grade irAEs. Using machine learning, an expanded ICI-treated NSCLC cohort (n = 378) further showed a treatment duration-independent association of an increased proportion of high TIL (>median) in patients with irAEs (59.7% v 44%, P = .005). This was confirmed by using the Fine-Gray competing risk approach, demonstrating higher baseline TIL density (>median) associated with a higher cumulative incidence of irAEs (P = .028).

CONCLUSION

Our findings highlight a potential role for TIME features, specifically INDEL neoantigens and baseline-immune infiltration, in enabling optimal irAE risk stratification of patients.

A novel model for predicting prognosis and response to immunotherapy in nasopharyngeal carcinoma patients

Blood-based biomarkers of immune checkpoint inhibitors (ICIs) response in patients with nasopharyngeal carcinoma (NPC) are lacking, so it is necessary to identify biomarkers to select NPC patients who will benefit most or least from ICIs. The absolute values of lymphocyte subpopulations, biochemical indexes, and blood routine tests were determined before ICIs-based treatments in the training cohort (n = 130). Then, the least absolute shrinkage and selection operator (Lasso) Cox regression analysis was developed to construct a prediction model. The performances of the prediction model were compared to TNM stage, treatment, and Epstein-Barr virus (EBV) DNA using the concordance index (C-index). Progression-free survival (PFS) was estimated by Kaplan-Meier (K-M) survival curve. Other 63 patients were used for validation cohort. The novel model composed of histologic subtypes, CD19+ B cells, natural killer (NK) cells, regulatory T cells, red blood cells (RBC), AST/ALT ratio (SLR), apolipoprotein B (Apo B), and lactic dehydrogenase (LDH). The C-index of this model was 0.784 in the training cohort and 0.735 in the validation cohort. K-M survival curve showed patients with high-risk scores had shorter PFS compared to the low-risk groups. For predicting immune therapy responses, the receiver operating characteristic (ROC), decision curve analysis (DCA), net reclassifcation improvement index (NRI) and integrated discrimination improvement index (IDI) of this model showed better predictive ability compared to EBV DNA. In this study, we constructed a novel model for prognostic prediction and immunotherapeutic response prediction in NPC patients, which may provide clinical assistance in selecting those patients who are likely to gain long-lasting clinical benefits to anti-PD-1 therapy.

Prognostic Impact of the Immune-Cell Infiltrate in N1-Positive Non-Small-Cell Lung Cancer

INTRODUCTION

The tumoral immune milieu plays a crucial role for the development of non-small-cell lung cancer (NSCLC) and may influence individual prognosis. We analyzed the predictive role of immune cell infiltrates after curative lung cancer surgery.

MATERIALS AND METHODS

The tumoral immune-cell infiltrate from 174 patients with pN1 NSCLC and adjuvant chemotherapy was characterized using immunofluorescence staining. The density and distribution of specific immune cells in tumor center (TU), invasive front (IF) and normal tissue (NORM) were correlated with clinical parameters and survival data.

RESULTS

Tumor specific survival (TSS) of all patients was 69.9% at 5 years. The density of tumor infiltrating lymphocytes (TIL) was higher in TU and IF than in NORM. High TIL density in TU (low vs. high: 62.0% vs. 86.7%; p = .011) and the presence of cytotoxic T-Lymphocytes (CTLs) in TU and IF were associated with improved TSS (positive vs. negative: 90.6% vs. 64.7% p = .024). High TIL-density correlated with programmed death-ligand 1 expression levels ≥50% (p < .001). Multivariate analysis identified accumulation of TIL (p = .016) and low Treg density (p = .003) in TU as negative prognostic predictors in squamous cell carcinoma (p = .025), whereas M1-like tumor- associated macrophages (p = .019) and high programmed death-ligand 1 status (p = .038) were associated with better survival in adenocarcinoma.

CONCLUSION

The assessment of specific intratumoral immune cells may serve as a prognostic predictor in pN1 NSCLC. However differences were observed related to adenocarcinoma or squamous cell carcinoma histology. Prospective assessment of the immune-cell infiltrate and further clarification of its prognostic relevance could assist patient selection for upcoming perioperative immunotherapies.

Clinical and Molecular Features of Long-term Response to Immune Checkpoint Inhibitors in Patients with Advanced Non-Small Cell Lung Cancer

PURPOSE

We sought to identify features of patients with advanced non-small cell lung cancer (NSCLC) who achieve long-term response (LTR) to immune checkpoint inhibitors (ICI), and how these might differ from features predictive of short-term response (STR).

EXPERIMENTAL DESIGN

We performed a multicenter retrospective analysis of patients with advanced NSCLC treated with ICIs between 2011 and 2022. LTR and STR were defined as response ≥ 24 months and response < 12 months, respectively. Tumor programmed death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), next-generation sequencing (NGS), and whole-exome sequencing (WES) data were analyzed to identify characteristics enriched in patients achieving LTR compared with STR and non-LTR.

RESULTS

Among 3,118 patients, 8% achieved LTR and 7% achieved STR, with 5-year overall survival (OS) of 81% and 18% among LTR and STR patients, respectively. High TMB (≥50th percentile) enriched for LTR compared with STR (P = 0.001) and non-LTR (P < 0.001). Whereas PD-L1 ≥ 50% enriched for LTR compared with non-LTR (P < 0.001), PD-L1 ≥ 50% did not enrich for LTR compared with STR (P = 0.181). Nonsquamous histology (P = 0.040) and increasing depth of response [median best overall response (BOR) -65% vs. -46%, P < 0.001] also associated with LTR compared with STR; no individual genomic alterations were uniquely enriched among LTR patients.

CONCLUSIONS

Among patients with advanced NSCLC treated with ICIs, distinct features including high TMB, nonsquamous histology, and depth of radiographic improvement distinguish patients poised to achieve LTR compared with initial response followed by progression, whereas high PD-L1 does not.

Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction

Immune checkpoint inhibitors (ICIs) have dramatically enhanced the treatment outcomes for diverse malignancies. Yet, only 15-60% of patients respond significantly. Therefore, accurate responder identification and timely ICI administration are critical issues in tumor ICI therapy. Recent rapid developments at the intersection of oncology, immunology, biology, and computer science have provided an abundance of predictive biomarkers for ICI efficacy. These biomarkers can be invasive or non-invasive, depending on the specific sample collection method. Compared with invasive markers, a host of non-invasive markers have been confirmed to have superior availability and accuracy in ICI efficacy prediction. Considering the outstanding advantages of dynamic monitoring of the immunotherapy response and the potential for widespread clinical application, we review the recent research in this field with the aim of contributing to the identification of patients who may derive the greatest benefit from ICI therapy.

Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer

The tumor immune composition influences prognosis and treatment sensitivity in lung cancer. The presence of effective adaptive immune responses is associated with increased clinical benefit after immune checkpoint blockers. Conversely, immunotherapy resistance can occur as a consequence of local T-cell exhaustion/dysfunction and upregulation of immunosuppressive signals and regulatory cells. Consequently, merely measuring the amount of tumor-infiltrating lymphocytes (TILs) may not accurately reflect the complexity of tumor-immune interactions and T-cell functional states and may not be valuable as a treatment-specific biomarker. In this work, we investigate an immune-related biomarker (PhenoTIL) and its value in associating with treatment-specific outcomes in non-small cell lung cancer (NSCLC). PhenoTIL is a novel computational pathology approach that uses machine learning to capture spatial interplay and infer functional features of immune cell niches associated with tumor rejection and patient outcomes. PhenoTIL's advantage is the computational characterization of the tumor immune microenvironment extracted from H&E-stained preparations. Association with clinical outcome and major non-small cell lung cancer (NSCLC) histology variants was studied in baseline tumor specimens from 1,774 lung cancer patients treated with immunotherapy and/or chemotherapy, including the clinical trial Checkmate 057 (NCT01673867).

Prophylactic cranial irradiation-related lymphopenia affects survival in patients with limited-stage small cell lung cancer

Background

The study aimed to identify the relations of the absolute lymphocyte count (ALC) nadir during prophylactic cranial irradiation (PCI) and patient outcomes in limited-stage small cell lung cancer (LS-SCLC).

Methods

We analyzed 268 L S-SCLC patients who underwent PCI from 2012 to 2019. ALC values were collected prior, during, and 3 months post PCI. Kaplan-Meier and Cox regression analyses were performed to assess the relation of ALC to patient prognosis. Two nomograms were developed on the basis of clinical variables for survival prediction.

Results

Compared with the ALC before PCI (1.13 × 10⁹ cells/L), the ALC nadir during PCI was significantly reduced by 0.68 × 10⁹ cells/L (P < 0.001) and raised to 1.02 × 10⁹ cells/L 3 months post PCI. Patients with a low ALC nadir during PCI (<0.68 × 10⁹ cells/L) had inferior progression free survival (PFS) (median PFS: 17.2 m vs. 43.7 m, P = 0.019) and overall survival (OS) (median OS: 29.0 m vs 39.1 m, P = 0.012). Multivariate Cox analysis revealed that age, smoking history, clinical stage, and ALC nadir were independent OS (P = 0.006, P = 0.005, P < 0.001 and P = 0.027, respectively), as well as independent PFS predictors (P = 0.032, P = 0.012, P = 0.012 and P = 0.018, respectively). After internal cross-validation, the corrected concordance indices of the predictive nomograms for PFS and OS were 0.637 and 0.663, respectively.

Conclusion

LS-SCLC patients with a low ALC nadir during PCI likely have worse survival outcomes. Dynamic evaluation of the ALC during PCI is recommended for LS-SCLC patients.

Comparative expression analysis of immune-related markers in surgically resected lung neuroendocrine neoplasms

BACKGROUND

Although immunotherapy has led to a paradigm shift in the treatment of lung cancer, the therapeutic approaches for lung neuroendocrine neoplasms (LNENs) are still limited. Our aim was to explore the immunological landscape and the expression of immune checkpoint markers in LNENs.

METHODS

Surgically removed tumor samples of 26 atypical carcinoid (AC), 30 large cell neuroendocrine carcinoma (LCNEC) and 29 small cell lung cancer (SCLC) patients were included. The immune phenotype of each tumor type was assessed by using a panel of 15 immune-related markers. As these markers are potentially expressed by immune cells and/or tumor cells, they might serve as putative targets for immunotherapy. Expression patterns were measured by immunohistochemistry and correlated with clinicopathological parameters and prognosis.

RESULTS

Unsupervised hierarchical clustering revealed distinct immunologic profiles across tumor types. Specifically, AC tumors were characterized by high tumor cell CD40 expression and low levels of immune infiltrates whereas SCLC samples had a high CD47 and Inducible T Cell Costimulator (ICOS) expression in tumor cells and immune cells, respectively. High CD70 and CD137 expression by tumor cells as well as elevated expression of CD27, Lymphocyte Activation Gene 3 (LAG3), and CD40 by immune cells were characteristic for LCNEC samples. Overall, SCLC and LCNEC tumors had a more immunogenic phenotype than AC samples. High tumor cell CD47 and CD40 expressions were associated with impaired and improved survival outcomes, respectively.

CONCLUSIONS

By providing insights into the widely divergent immunologic profiles of LNENs, our results might serve as a basis for the development of novel immunotherapy-related approaches in these devastating malignancies.

Radiation combined with immune checkpoint inhibitors for unresectable locally advanced non-small cell lung cancer: synergistic mechanisms, current state, challenges, and orientations

Until the advent of immune checkpoint inhibitors (ICIs), definitive radiotherapy (RT) concurrently with chemotherapy was recommended for unresectable, locally advanced non-small cell lung cancer (LA-NSCLC). The trimodality paradigm with consolidation ICIs following definitive concurrent chemoradiotherapy has been the standard of care since the PACIFIC trial. Preclinical evidence has demonstrated the role of RT in the cancer-immune cycle and the synergistic effect of RT combined with ICIs (iRT). However, RT exerts a double-edged effect on immunity and the combination strategy still could be optimized in many areas. In the context of LA-NSCLC, optimized RT modality, choice, timing, and duration of ICIs, care for oncogenic addicted tumors, patient selection, and novel combination strategies require further investigation. Targeting these blind spots, novel approaches are being investigated to cross the borders of PACIFIC. We discussed the development history of iRT and summarized the updated rationale for the synergistic effect. We then summarized the available research data on the efficacy and toxicity of iRT in LA-NSCLC for cross-trial comparisons to eliminate barriers. Progression during and after ICIs consolidation therapy has been regarded as a distinct resistance scenario from primary or secondary resistance to ICIs, the subsequent management of which has also been discussed. Finally, based on unmet needs, we probed into the challenges, strategies, and auspicious orientations to optimize iRT in LA-NSCLC. In this review, we focus on the underlying mechanisms and recent advances of iRT with an emphasis on future challenges and directions that warrant further investigation. Taken together, iRT is a proven and potential strategy in LA-NSCLC, with multiple promising approaches to further improve the efficacy. Video Abstract.

Role of Surgical Pathologist for the Detection of Immuno-oncologic Predictive Factors in Non-small Cell Lung Cancers

Until very recently, surgery, chemotherapy, and radiation therapy have been the mainstay of treatment in non-small cell carcinomas (NSCLCs). However, recent advances in molecular immunology have unveiled some of the complexity of the mechanisms regulating cellular immune responses and led to the successful targeting of immune checkpoints in attempts to enhance antitumor T-cell responses. Immune checkpoint molecules such as cytotoxic T-lymphocyte associated protein-4, programmed cell death protein-1, and programmed death ligand (PD-L) 1 have been shown to play central roles in evading cancer immunity. Thus, these molecules have been targeted by inhibitors for the management of cancers forming the basis of immunotherapy. Advanced NSCLC has been the paradigm for the benefits of immunotherapy in any cancer. Treatment decisions are made based on the expression of PD-L1 on the tumor cells and the presence or absence of driver mutations. Patients with high PD-L1 expression (≥50%) and no driver mutations are treated with single-agent immunotherapy whereas, for all other patients with a lower level of PD-L1 expression, a combination of chemotherapy and immunotherapy is preferred. Thus, PD-L1 blockers are the only immunotherapeutic agents approved in advanced NSCLC without any oncogenic driver mutations. PD-L1 immunohistochemistry, however, may not be the best biomarker in view of its dynamic nature in time and space, and the benefits may be seen regardless of PD -L1 expression. Each immunotherapy molecule is prescribed based on the levels of PD-L1 expression as assessed by a Food and Drug Administration-approved companion diagnostic assay. Other biomarkers that have been studied include tumor mutational burden, the T-effector signature, tumor-infiltrating lymphocytes, radiomic assays, inflammation index, presence or absence of immune-related adverse events and specific driver mutations, and gut as well as local microbiome. At the current time, none of these biomarkers are routinely used in the clinical decision-making process for immunotherapy in NSCLC. However, in individual cases, they can be useful adjuncts to conventional therapy. This review describes our current understanding of the role of biomarkers as predictors of response to immune checkpoint molecules. To begin with a brief on cancer immunology in general and in NSCLC, in particular, is discussed. In the end, recent advancements in laboratory techniques for refining biomarker assays are described.

Predictive Biomarkers for Immune-Checkpoint Inhibitor Treatment Response in Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) has one of the highest mortality rates among solid cancers. Late diagnosis and a lack of efficacious treatment options contribute to the dismal prognosis of HCC. Immune checkpoint inhibitor (ICI)-based immunotherapy has presented a new milestone in the treatment of cancer. Immunotherapy has yielded remarkable treatment responses in a range of cancer types including HCC. Based on the therapeutic effect of ICI alone (programmed cell death (PD)-1/programmed death-ligand1 (PD-L)1 antibody), investigators have developed combined ICI therapies including ICI + ICI, ICI + tyrosine kinase inhibitor (TKI), and ICI + locoregional treatment or novel immunotherapy. Although these regimens have demonstrated increasing treatment efficacy with the addition of novel drugs, the development of biomarkers to predict toxicity and treatment response in patients receiving ICI is in urgent need. PD-L1 expression in tumor cells received the most attention in early studies among various predictive biomarkers. However, PD-L1 expression alone has limited utility as a predictive biomarker in HCC. Accordingly, subsequent studies have evaluated the utility of tumor mutational burden (TMB), gene signatures, and multiplex immunohistochemistry (IHC) as predictive biomarkers. In this review, we aim to discuss the current state of immunotherapy for HCC, the results of the predictive biomarker studies, and future direction.

Dissecting tumor lymphocyte infiltration to predict benefit from immune-checkpoint inhibitors in metastatic colorectal cancer: lessons from the AtezoT RIBE study

BACKGROUND

Tumor immune cells influence the efficacy of immune-checkpoint inhibitors (ICIs) and many efforts aim at identifying features of tumor immune microenvironment able to predict benefit from ICIs in proficient mismatch repair (pMMR)/microsatellite stable (MSS) metastatic colorectal cancer (mCRC).

METHODS

We characterized tumor immune cell infiltrate, by assessing tumor-infiltrating lymphocytes (TILs), Immunoscore, Immunoscore-IC, and programmed death ligand-1 (PD-L1) expression in tumor samples of patients with mCRC enrolled in the AtezoTRIBE study, a phase II randomized trial comparing FOLFOXIRI/bevacizumab/atezolizumab to FOLFOXIRI/bevacizumab, with the aim of evaluating the prognostic and predictive value of these features.

RESULTS

Out of 218 patients enrolled, 181 (83%), 77 (35%), 157 (72%) and 162 (74%) specimens were successfully tested for TILs, Immunoscore, Immunoscore-IC and PD-L1 expression, respectively, and 69 (38%), 45 (58%), 50 (32%) and 21 (13%) tumors were classified as TILs-high, Immunoscore-high, Immunoscore-IC-high and PD-L1-high, respectively. A poor agreement was observed between TILs and Immunoscore or Immunoscore-IC (K of Cohen <0.20). In the pMMR population, longer progression-free survival (PFS) was reported for Immunoscore-high and Immunoscore-IC-high groups compared with Immunoscore-low (16.4 vs 12.2 months; HR: 0.55, 95% CI: 0.30 to 0.99; p=0.049) and Immunoscore-IC-low (14.8 vs 11.5 months; HR: 0.55, 95% CI: 0.35 to 0.85; p=0.007), respectively, with a significant interaction effect between treatment arms and Immunoscore-IC (p for interaction: 0.006) and a trend for Immunoscore (p for interaction: 0.13). No PFS difference was shown according to TILs and PD-L1 expression. Consistent results were reported in the overall population.

CONCLUSIONS

The digital evaluation of tumor immune cell infiltrate by means of Immunoscore-IC or Immunoscore identifies the subset of patients with pMMR mCRC achieving more benefit from the addition of the anti-PD-L1 to the upfront treatment. Immunoscore-IC stands as the most promising predictor of benefit from ICIs.

Spatial immunophenotyping of the tumour microenvironment in non-small cell lung cancer

INTRODUCTION

Immune cells in the tumour microenvironment are associated with prognosis and response to therapy. We aimed to comprehensively characterise the spatial immune phenotypes in the mutational and clinicopathological background of non-small cell lung cancer (NSCLC).

METHODS

We established a multiplexed fluorescence imaging pipeline to spatially quantify 13 immune cell subsets in 359 NSCLC cases: CD4 effector cells (CD4-Eff), CD4 regulatory cells (CD4-Treg), CD8 effector cells (CD8-Eff), CD8 regulatory cells (CD8-Treg), B-cells, natural killer cells, natural killer T-cells, M1 macrophages (M1), CD163+ myeloid cells (CD163), M2 macrophages (M2), immature dendritic cells (iDCs), mature dendritic cells (mDCs) and plasmacytoid dendritic cells (pDCs).

RESULTS

CD4-Eff cells, CD8-Eff cells and M1 macrophages were the most abundant immune cells invading the tumour cell compartment and indicated a patient group with a favourable prognosis in the cluster analysis. Likewise, single densities of lymphocytic subsets (CD4-Eff, CD4-Treg, CD8-Treg, B-cells and pDCs) were independently associated with longer survival. However, when these immune cells were located close to CD8-Treg cells, the favourable impact was attenuated. In the multivariable Cox regression model, including cell densities and distances, the densities of M1 and CD163 cells and distances between cells (CD8-Treg-B-cells, CD8-Eff-cancer cells and B-cells-CD4-Treg) demonstrated positive prognostic impact, whereas short M2-M1 distances were prognostically unfavourable.

CONCLUSION

We present a unique spatial profile of the in situ immune cell landscape in NSCLC as a publicly available data set. Cell densities and cell distances contribute independently to prognostic information on clinical outcomes, suggesting that spatial information is crucial for diagnostic use.

VISTA expression and patient selection for immune-based anticancer therapy

V-domain Ig suppressor of T-cell activation (VISTA) is a B7 family member that plays key roles in maintaining T cell quiescence and regulation of myeloid cell populations, which together establish it as a novel immunotherapy target for solid tumors. Here we review the growing literature on VISTA expression in relation to various malignancies to better understand the role of VISTA and its interactions with both tumor cells and immune cells expressing other checkpoint molecules within the tumor microenvironment (TME). The biology of VISTA creates several mechanisms to maintain the TME, including supporting the function of myeloid-derived suppressor cells, regulating natural killer cell activation, supporting the survival of regulatory T cells, limiting antigen presentation on antigen-presenting cells and maintaining T cells in a quiescent state. Understanding these mechanisms is an important foundation of rational patient selection for anti-VISTA therapy. We provide a general framework to describe distinct patterns of VISTA expression in correlation with other known predictive immunotherapy biomarkers (programmed cell death ligand 1 and tumor-infiltrating lymphocytes) across solid tumors to facilitate investigation of the most efficacious TMEs for VISTA-targeted treatment as a single agent and/or in combination with anti-programmed death 1/anti-cytotoxic T lymphocyte antigen-4 therapies.

Biomarkers of response to immunotherapy in early stage non-small cell lung cancer

Immunotherapy with immune-checkpoint inhibitors (ICIs) targeting programmed cell death 1 or programmed death-ligand 1 has revolutionised the treatment of advanced non-small cell lung cancer (NSCLC) and has been investigated in early NSCLC, alone or in combination with chemotherapy, anti-CTLA-4 antibodies and radiotherapy. Although more mature data are needed before setting a change of paradigm in early stages, reports of pathological response rates and disease-free survival are promising, especially with neoadjuvant multimodality approaches. Nevertheless, major pathological response rates for neoadjuvant anti-PD-(L)1 monotherapy rarely exceed 40%, and biomarkers for characterising patients who may benefit the most from ICIs are lacking. These biomarkers have a distinct value from the metastatic setting, with highly different tumour biologies. Among the most investigated so far in this context, programmed death-ligand 1 expression and, to a lesser extent, tumour mutational burden seem to correlate better with higher pathological response rates and survival. Epidermal growth factor receptor, Serine/Threonine Kinase 11and Kelch-like ECH-associated protein 1 mutations rise as essential determinations for the treatment selection in early-stage NSCLC. Emerging and promising approaches comprise evaluation of blood-based ratios, microbiota, and baseline intratumoural TCR clonality. Circulating tumour DNA will be of great help in the near future when selecting best candidates for adjuvant ICIs, monitoring the tumour response to the neoadjuvant treatment in order to improve the rates of complete resections in the early stage.

Circulating Biomarkers for Prediction of Immunotherapy Response in NSCLC

Non-small cell lung cancer (NSCLC) constitutes the majority of the lung cancer population and the prognosis is poor. In recent years, immunotherapy has become the standard of care for advanced NSCLC patients as numerous trials demonstrated that immune checkpoint inhibitors (ICI) are more efficacious than conventional chemotherapy. However, only a minority of NSCLC patients benefit from this treatment. Therefore, there is an unmet need for biomarkers that could accurately predict response to immunotherapy. Liquid biopsy allows repeated sampling of blood-based biomarkers in a non-invasive manner for the dynamic monitoring of treatment response. In this review, we summarize the efforts and progress made in the identification of circulating biomarkers that predict immunotherapy benefit for NSCLC patients. We also discuss the challenges with future implementation of circulating biomarkers into clinical practice.

Tumour-infiltrating lymphocytes: from prognosis to treatment selection

Tumour-infiltrating lymphocytes (TILs) are considered crucial in anti-tumour immunity. Accordingly, the presence of TILs contains prognostic and predictive value. In 2011, we performed a systematic review and meta-analysis on the prognostic value of TILs across cancer types. Since then, the advent of immune checkpoint blockade (ICB) has renewed interest in the analysis of TILs. In this review, we first describe how our understanding of the prognostic value of TIL has changed over the last decade. New insights on novel TIL subsets are discussed and give a broader view on the prognostic effect of TILs in cancer. Apart from prognostic value, evidence on the predictive significance of TILs in the immune therapy era are discussed, as well as new techniques, such as machine learning that strive to incorporate these predictive capacities within clinical trials.

Prognostic significance of tumor infiltrating lymphocytes on first-line pembrolizumab efficacy in advanced non-small cell lung cancer

AIM

Tumor-infiltrating lymphocytes (TILs) in the tumor and stroma are expected to accurately predict the efficacy of programmed death-1 (PD-1) blockade therapy. However, little is known about the prognostic significance of TILs in first-line PD-1 therapy. We assessed TILs in patients with advanced or metastatic non-small cell lung cancer (NSCLC) treated with pembrolizumab in the palliative setting.

METHODS

Multiplex immunohistochemistry staining of TILs (CD4, CD8, Foxp3, and PD-1) and immunohistochemical staining of CK and PD-L1 in the tumor and stroma was performed in tumor specimens of 107 NSCLC patients and correlated with clinical outcomes, as a single-center retrospective study. TILs and programmed death ligand-1 (PD-L1) were assessed on biopsies (N = 93) or surgical resections (N = 14) before first-line pembrolizumab.

RESULTS

A low number of stromal CD4 TILs were significantly associated with bone metastasis and poor performance status (PS). The median progression-free survival (PFS) and overall survival (OS) were significantly higher in patients with a high number of stromal CD4 TILs (336 days and 731 days, respectively) than in those with low infiltration (204 days and 333 days, respectively). Patients with a high number of intratumoral CD8 TILs (731 days) yielded significantly better OS than those with low infiltration (333 days), but not for PFS. Multivariate analysis confirmed that stromal CD4 TILs were independent predictors of PFS, but not OS. Furthermore, intratumoral CD8 TILs were independent predictors of better OS. In the survival analysis of key subgroups, stromal CD4 TILs were identified as significant predictors of survival in patients with non-adenocarcinomatous histology and PD-L1 ≥ 50%.

CONCLUSION

Stromal CD4 TILs were identified as a significant marker for predicting the PFS after pembrolizumab therapy, especially in patients with non-adenocarcinoma and high PD-L1 expression. In addition, intratumoral CD8 TILs were identified as significant predictors of OS.

Heterogeneity of PD-L1 expression and CD8 lymphocyte infiltration in metastatic colorectal cancer and their prognostic significance

Purpose

In recent years, immune checkpoint inhibitors have become a major therapeutic method for the treatment of metastatic colorectal cancer (mCRC). Growing evidence indicates that tumour-infiltrating lymphocytes (TILs) in the tumour microenvironment are a prerequisite for the effectiveness of PD-1/PD-L1 blockade therapy. In this study, we aimed to compare PD-L1 expression and cluster of differentiation 4 (CD4) and CD8 TIL infiltration in primary tumours and paired metastases.

Patients and methods

Altogether, 111 patients with mCRC who underwent surgery at our hospital were included. PD-L1, CD4, and CD8 expression were detected by immunohistochemistry in a tissue microarray. PD-L1 expression was assessed using the combined positivity score (CPS), and a score ≥1 was judged as positive. The area proportion of TILs with positive staining ≥10% was classified as "high", while <10% was classified as "low".

Results

We observed the discordance of PD-L1 expression between primary tumours and paired metastases in 35/111 (31.5%) patients (κ = 0.137, P = 0.142). This heterogeneity was significantly correlated with discordance of CD8 TIL infiltration between primary tumours and paired metastases (P = 0.003). Compared with corresponding colorectal cancer tumours, lung metastases showed more CD8 TIL infiltration (P = 0.022, median: 8.5% vs. 5.0%), whereas liver metastases exhibited less CD8 TIL infiltration (P = 0.028, median: 3.0% vs. 5.0%). Area proportion of CD4⁺ and CD8⁺ TIL infiltration in lung metastases were all higher than those in liver metastases (P = 0.005, median: 15.0% vs. 9.0%; P = 0.001, median: 8.5% vs. 3.0%). Compared with p MMR (MSI-L/MS-S) subgroup, area proportion of CD8 TIL infiltration in primary tumours and CD4, CD8 TIL infiltration in paired metastases were all higher in d MMR (MSI-H) group (P = 0.026, median: 15.0% vs 5.0%; P = 0.039, median: 15.0% vs 9.0%; P = 0.015, median: 15.0% vs 5.0%). Preoperative chemo/radiotherapy may increase CD8 TIL infiltration in primary tumours (P = 0.045, median: 10.0% vs. 5.0%). CD8 TIL infiltration in primary tumours was an independent predictive factor for overall survival (HR 0.28, 95% CI 0.09-0.93, P = 0.038).

Conclusion

Heterogeneity in PD-L1 expression and CD8 TIL infiltration was found between primary tumours and paired metastases in mCRC. CD8 TIL infiltration in primary tumours could independently forecast the overall survival of patients with mCRC.

Association of Machine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images With Outcomes of Immunotherapy in Patients With NSCLC

Importance

Currently, predictive biomarkers for response to immune checkpoint inhibitor (ICI) therapy in lung cancer are limited. Identifying such biomarkers would be useful to refine patient selection and guide precision therapy.

Objective

To develop a machine-learning (ML)-based tumor-infiltrating lymphocytes (TILs) scoring approach, and to evaluate TIL association with clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC).

Design, Setting, and Participants

This multicenter retrospective discovery-validation cohort study included 685 ICI-treated patients with NSCLC with median follow-up of 38.1 and 43.3 months for the discovery (n = 446) and validation (n = 239) cohorts, respectively. Patients were treated between February 2014 and September 2021. We developed an ML automated method to count tumor, stroma, and TIL cells in whole-slide hematoxylin-eosin-stained images of NSCLC tumors. Tumor mutational burden (TMB) and programmed death ligand-1 (PD-L1) expression were assessed separately, and clinical response to ICI therapy was determined by medical record review. Data analysis was performed from June 2021 to April 2022.

Exposures

All patients received anti-PD-(L)1 monotherapy.

Main Outcomes and Measures

Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were determined by blinded medical record review. The area under curve (AUC) of TIL levels, TMB, and PD-L1 in predicting ICI response were calculated using ORR.

Results

Overall, there were 248 (56%) women in the discovery cohort and 97 (41%) in the validation cohort. In a multivariable analysis, high TIL level (≥250 cells/mm2) was independently associated with ICI response in both the discovery (PFS: HR, 0.71; P = .006; OS: HR, 0.74; P = .03) and validation (PFS: HR = 0.80; P = .01; OS: HR = 0.75; P = .001) cohorts. Survival benefit was seen in both first- and subsequent-line ICI treatments in patients with NSCLC. In the discovery cohort, the combined models of TILs/PD-L1 or TMB/PD-L1 had additional specificity in differentiating ICI responders compared with PD-L1 alone. In the PD-L1 negative (<1%) subgroup, TIL levels had superior classification accuracy for ICI response (AUC = 0.77) compared with TMB (AUC = 0.65).

Conclusions and Relevance

In these cohorts, TIL levels were robustly and independently associated with response to ICI treatment. Patient TIL assessment is relatively easily incorporated into the workflow of pathology laboratories at minimal additional cost, and may enhance precision therapy.

Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma

A high abundance of tumor-infiltrating lymphocytes (TILs) has a positive impact on the prognosis of patients with lung adenocarcinoma (LUAD). We aimed to develop and validate an artificial intelligence-driven pathological scoring system for assessing TILs on H&E-stained whole-slide images of LUAD. Deep learning-based methods were applied to calculate the densities of lymphocytes in cancer epithelium (DLCE) and cancer stroma (DLCS), and a risk score (WELL score) was built through linear weighting of DLCE and DLCS. Association between WELL score and patient outcome was explored in 793 patients with stage I-III LUAD in four cohorts. WELL score was an independent prognostic factor for overall survival and disease-free survival in the discovery cohort and validation cohorts. The prognostic prediction model-integrated WELL score demonstrated better discrimination performance than the clinicopathologic model in the four cohorts. This artificial intelligence-based workflow and scoring system could promote risk stratification for patients with resectable LUAD.

Modified immunoscore improves the prediction of progression-free survival in patients with non-muscle-invasive bladder cancer: A digital pathology study

Tumour-infiltrating lymphocytes (TIL), known to be of prognostic value in various solid tumours, have been in the focus of research in the last years. TIL are often quantified via IMMUNOSCORE ® (IS), a scoring system based on TIL cell densities. Recent studies were able to replicate these findings for muscle-invasive bladder cancer (MIBC), however data regarding non-muscle-invasive bladder cancer (NMIBC) are scarce. This study aimed to evaluate the value of a modified Immunoscore (mIS) as a predictive marker for NMIBC prognosis using tissue-micro-arrays (TMAs). We analysed two TMAs containing 316 samples from 158 patients with NMIBC, stained for CD3, CD8, CD45RO and FOXP3. Stained TIL were captured by digital pathology, cumulated, averaged, and reported as density (stained cells per mm²). The mIS was then constructed based on density of all four immune-cell types. Clinical, pathological and follow-up data were collected retrospectively. Univariable and multivariable cox regression analysis was performed to assess the potential value of mIS as a predictor for progression free survival (PFS) and recurrence-free-survival (RFS). Patients within "European Organisation for Research and Treatment of Cancer" (EORTC) risk groups were further substratified in high mIS and low mIS subgroups. Finally log-rank test was used to compare the different survival curves. The median age in our cohort was 68 years (Interquartile Range (IQR): 60 - 76), and 117 (74%) patients were male. A total of 26 patients (16.5%) were classified as EORTC low risk, 45 (28.5%) as intermediate risk and 87 (55.1%) as high risk. Patients in the EORTC high risk group with low mIS showed a shorter PFS in comparison to high mIS (HR 2.9, CI 0.79 - 11.0, p=0.082). In contrast, no predictive potential regarding PFS was observed in intermediate or low risk groups. Furthermore, mIS was not able to predict RFS in any EORTC risk group. mIS could be utilized to predict prognosis more accurately in high-risk patients with NMIBC by identifying those with higher or lower risk of progression. Therefore, mIS could be used to allocate these highrisk patients to more streamlined follow-up or more aggressive treatment strategies.

Tumor infiltrating lymphocytes (TILs) as a predictive biomarker of response to checkpoint blockers in solid tumors: a systematic review

Immunotherapy is a standard of care in many solid tumors but many patients derive limited benefit from it. There is increasing interest toward tumor infiltrating lymphocytes (TILs) since their presence may be related with good outcomes from treatment with immune checkpoint blockers. We aimed at systematically reviewing existing evidence about the role of TILs as possible predictors of response to immunotherapy in solid tumors. We reviewed 1193 records published from January 2010 until December 2021. Associations between TILs and outcomes were observed mainly in melanoma and breast cancer. Overall survival and overall response rate for advanced disease and pathological complete response for early-phase tumors were the most commonly assessed endpoints. No definitive conclusion can be drawn on the predictive role of TILs. Additional studies, exploiting data from prospective, randomized clinical trials should further evaluate TILs also with the aim of identifying standard cut-off to differentiate between high and low TILs.

The state of the art for artificial intelligence in lung digital pathology

Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Prognostic Significance of Primary Tumor-Infiltrating Lymphocytes in a Contemporary Melanoma Cohort

BACKGROUND

The prognostic impact of tumor-infiltrating lymphocytes (TILs) on outcomes and treatment efficacy for patients with melanoma in the contemporary era remains poorly characterized.

METHODS

Consecutive patients who underwent wide excision and sentinel lymph node biopsy for cutaneous melanoma 1 mm thick or thicker at a single institution were identified (2006-2019). The patients were stratified based on primary tumor TIL status as brisk (bTILs), non-brisk (nbTILs), or absent (aTILs). Associations between patient factors and outcomes were analyzed using multivariable analysis.

RESULTS

Of the 1017 patients evaluated, 846 (83.2 %) had primary TILs [nbTILs (n = 759, 89.7 %) and bTILs (n = 87, 10.3 %)]. In the multivariable analysis, the patients with any type of TILs had higher rates of regression [odds ratio (OR), 1.86; p = 0.016], lower rates of acral lentiginous histology (OR, 0.22; p < 0.001), and lower rates of SLN positivity (OR, 0.64; p = 0.042) than those without TILs. The multivariable analysis found no association between disease-specific survival and bTILs [hazard ratio (HR), 1.04; p = 0.927] or nbTILs (HR, 0.89; p = 0.683). An association was found between bTILs and recurrence-free survival (RFS) advantage [bTILs (HR 0.46; p = 0.047), nbTILs (HR 0.71; p = 0.088)], with 5-year RFS rates of 84 % for bTILs, 71.8 % for nbTILs, and 68.4 % for aTILs (p = 0.044). For the 114 immune checkpoint blockade (ICB)-naïve patients who experienced a recurrence treated with ICB therapy, no association was observed between progression-free survival and bTILs (HR, 0.64; p = 0.482) or nbTILs (HR, 0.58; p = 0.176).

CONCLUSIONS

The prognostic significance of primary TILs in the contemporary melanoma era appears complex. Further studies characterizing the phenotype of TILs and their association with regional metastasis and responsiveness to ICB therapy are warranted.

Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non-Small-Cell Lung Cancer

PURPOSE

Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI).

METHODS

We have developed an artificial intelligence (AI)–powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non–small-cell lung cancer (NSCLC).

RESULTS

Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists (P < .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at < 1%, 1%-49%, and ≥ 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP.

CONCLUSION

The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.

The Prognostic Impact of Combined Tumor-Infiltrating Lymphocytes and Pretreatment Blood Lymphocyte Percentage in Locally Advanced Nasopharyngeal Carcinoma

Purpose

To explore the prognostic impact of combined tumor-infiltrating lymphocytes (TILs) and pretreatment peripheral lymphocyte percentage (LYM%) among patients with locally advanced nasopharyngeal carcinoma (LA-NPC).

Patients and Methods

TILs and pretreatment LYM% were retrospectively assessed in 253 LA-NPC patients who underwent chemoradiation therapy between January 2012 and December 2017. According to TILs and LYM% status, the patients were divided into three groups: high-risk group (HRG) (TILs–LYM% score = 0), middle-risk group (MRG) (TILs–LYM% score = 1), and low-risk group (LRG) (TILs–LYM% score = 2). The relationship between TILs level and LYM%, and also the associations of TILs–LYM% status with clinicopathological factors and survival, were evaluated.

Results

As a continuous variable, LYM% was significantly higher in TILs-high group. High TILs or high LYM% alone was significantly related to better 3-year disease-free survival (DFS), overall survival (OS), distant metastasis-free survival (DMFS) and locoregional relapse-free survival (LRRFS), respectively. Kaplan–Meier analysis and log-rank tests also revealed significant decreases in DFS, OS, DMFS, and LRRFS among LA-NPC patients with TILs–LYM% score of 0, 1, and 2 (all P <0.05). Further multivariate analyses showed that TILs–LYM% score was an independent factor affecting survival of the patients, and HRG (TILs–LYM% score = 0) had increased hazard ratios (HRs) for disease (HR = 6.89, P <0.001), death (HR = 8.08, P = 0.008), distant metastasis (HR = 7.66, P = 0.001), and local relapse (HR = 5.18, P = 0.013) compared with LRG (TILs–LYM% score = 2). In receiver operating characteristics (ROC) analyses, TILs–LYM% score had a higher area under the ROC curve (AUC) for the prediction of DFS than did TILs or LYM% alone.

Conclusions

A positive correlation was found between TILs level and pretreatment blood lymphocyte percentage. Moreover, TILs–LYM% score can be considered as a novel independent prognostic indicator of survival outcome among patients with LA-NPC.

Chordin-like 1 is a novel prognostic biomarker and correlative with immune cell infiltration in lung adenocarcinoma

Chordin-like 1 (CHRDL1), an inhibitor of bone morphogenetic proteins(BMPs), has been recently reported to participate in the progression of numerous tumors, however, its role in lung adenocarcinoma (LUAD) remains unclear. Our study aimed to demonstrate relationship between CHRDL1 and LUAD based on data from The Cancer Genome Atlas (TCGA). Among them, CHRDL1 expression revealed promising power for distinguishing LUAD tissues form normal sample. Low CHRDL1 was correlated with poor clinicopathologic features, including high T stage (OR=0.45, P<0.001), high N stage (OR=0.57, P<0.003), bad treatment effect (OR=0.64, P=0.047), positive tumor status (OR=0.63, P=0.018), and TP53 mutation (OR=0.49, P<0.001). The survival curve illustrated that low CHRDL1 was significantly correlative with a poor overall survival (HR=0.60, P<0.001). At multivariate Cox regression analysis, CHRDL1 remained independently correlative with overall survival. GSEA identified that the CHRDL1 expression was related to cell cycle and immunoregulation. Immune infiltration analysis suggested that CHRDL1 was significantly correlative with 7 kinds of immune cells. Immunohistochemical validation showed that CHRDL1 was abnormally elevated and negatively correlated with Th2 cells in LUAD tissues. In conclusion, CHRDL1 might become a novel prognostic biomarker and therapy target in LUAD. Moreover, CHRDL1 may improve the effectiveness of immunotherapy by regulating immune infiltration.

The Combination of Radiotherapy With Immunotherapy and Potential Predictive Biomarkers for Treatment of Non-Small Cell Lung Cancer Patients

Radiotherapy is an effective local treatment modality of NSCLC. Its capabilities of eliminating tumor cells by inducing double strand DNA (dsDNA) damage and modulating anti-tumor immune response in irradiated and nonirradiated sites have been elucidated. The novel ICIs therapy has brought hope to patients resistant to traditional treatment methods, including radiotherapy. The integration of radiotherapy with immunotherapy has shown improved efficacy to control tumor progression and prolong survival in NSCLC. In this context, biomarkers that help choose the most effective treatment modality for individuals and avoid unnecessary toxicities caused by ineffective treatment are urgently needed. This article summarized the effects of radiation in the tumor immune microenvironment and the mechanisms involved. Outcomes of multiple clinical trials investigating immuno-radiotherapy were also discussed here. Furthermore, we outlined the emerging biomarkers for the efficacy of PD-1/PD-L1 blockades and radiation therapy and discussed their predictive value in NSCLC.

Prognostic value of inflammatory response biomarkers using peripheral blood and [18F]-FDG PET/CT in advanced NSCLC patients treated with first-line chemo- or immunotherapy

OBJECTIVES

We aimed to compare the prognostic value of inflammatory biomarkers extracted from pretreatment peripheral blood and [18F]-FDG PET for estimating outcomes in non-small cell lung cancer (NSCLC) patients treated with first-line immunotherapy (IT) or chemotherapy (CT).

MATERIALS AND METHODS

In this retrospective multicenter study, we evaluated 111 patients with advanced NSCLC who underwent baseline [18F]-FDG PET/CT before IT or CT between 2016 and 2019. Several blood inflammatory indices were evaluated: derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP) and systemic immune-inflammation index (SII). FDG-PET inflammatory parameters were extracted from lymphoid tissues (BLR and SLR: bone marrow or spleen-to-Liver SUVmax ratios). Association with survival and relationships between parameters were evaluated using Cox prediction models and Spearman's correlation respectively.

RESULTS

Overall, 90 patients were included (IT:CT) (51:39pts). Median PFS was 8.6:6.6 months and median OS was not reached:21.2 months. In the IT cohort, high dNLR (>3), high SII (≥1,270) and high SLR (0.77) were independent statistically significant prognostic factors for one-year progression-free survival (1y-PFS) and two-year overall survival (2y-OS) on multivariable analysis. In the CT cohort, high BLR (≥0.80) and high dNLR (>3) were associated with shorter 1y-PFS (HR 2.2, 95% CI 1.0-4.9) and 2y-OS (HR 3.4, 95CI 1.1-10.3) respectively, on multivariable analysis. Finally, BLR significantly but moderately correlated with most blood-based inflammatory indices (CRP, PLR and SII) while SLR was only associated with CRP (p < 0.01 for all).

CONCLUSION

In advanced NSCLC patients undergoing first-line IT or CT, pretreatment blood and inflammatory factors evaluating the spleen or bone marrow on [18F]-FDG PET/CT provided prognostic information for 1y-PFS and 2y-OS. These biomarkers should be further evaluated for potential clinical application.