Strategies for clinical implementation of TNM-Immunoscore in resected nonsmall-cell lung cancer

Immature central tumor tertiary lymphoid structures are associated with better prognosis in non-small cell lung cancer

BACKGROUND & AIMS

Tertiary lymphoid structures (TLSs) are predictive biomarkers of favorable clinical outcomes and immunotherapy response in several solid malignancies, including non-small cell lung cancer (NSCLC). However, the relationship between TLSs and NSCLC prognosis has not been eludicated from the aspects of location, density, and maturity. This study aimed to investigate the clinicopathological and prognostic significance of TLSs in NSCLC.

METHODS

A collection of 151 resected pulmonary nodules in patients with NSCLC was retrospectively analyzed. Two experienced pathologists reviewed hematoxylin-eosin (H&E) slides and assessed TLS scores at different anatomic subregions. Then, we analyzed their correlation with clinicopathologic parameters and CD8 staining intensity and assessed multiple clinicopathological factors affecting patient prognosis.

RESULTS

CD8 expression was correlated with total (TLS-CT) (P = 0.000), aggregates (Agg) (TLS-CT) (P = 0.001), follicles (FOL)-I (TLS-CT) (P = 0.025), and TLS(overall) (P = 0.013). TLS scores in the central tumor (CT) and invasion margin (IM) areas were negatively correlated with distant metastasis and Union for International Cancer Control (UICC) stage in NSCLC patients, while TLS score in the CT area was positively correlated with CD8 expression. TLS (overall), Agg (TLS-CT), and FOL-I (TLS-CT) were positively correlated with distant metastasis, UICC stage, and CD8 expression in NSCLC patients. Agg (TLS-IM) was positively correlated with distant metastasis and UICC stage. FOL-I (TLS-IM) was positively correlated with UICC stage. FOL-II (TLS-IM) was positively correlated with distant metastasis (P < 0.05). Multivariate Cox regression analysis showed that unfavorable independent prognostic factors were associated with metastasis status and UICC stage. Independent prognostic factors with protective effects included Agg (TLS-CT), FOL-I (TLS-CT), total (TLS-CT), and overall TLS (P < 0.05).

CONCLUSION

Histological score assessment of H&E sections of Agg (TLS-CT), FOL-I (TLS-CT), total (TLS-CT), and overall TLS levels in NSCLC has prognostic value.

A prediction nomogram for perineural invasion in colorectal cancer patients: a retrospective study

BACKGROUND

Perineural invasion (PNI), as the fifth recognized pathway for the spread and metastasis of colorectal cancer (CRC), has increasingly garnered widespread attention. The preoperative identification of whether colorectal cancer (CRC) patients exhibit PNI can assist clinical practitioners in enhancing preoperative decision-making, including determining the necessity of neoadjuvant therapy and the appropriateness of surgical resection. The primary objective of this study is to construct and validate a preoperative predictive model for assessing the risk of perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC).

MATERIALS AND METHODS

A total of 335 patients diagnosed with colorectal cancer (CRC) at a single medical center were subject to random allocation, with 221 individuals assigned to a training dataset and 114 to a validation dataset, maintaining a ratio of 2:1. Comprehensive preoperative clinical and pathological data were meticulously gathered for analysis. Initial exploration involved conducting univariate logistic regression analysis, with subsequent inclusion of variables demonstrating a significance level of p < 0.05 into the multivariate logistic regression analysis, aiming to ascertain independent predictive factors, all while maintaining a p-value threshold of less than 0.05. From the culmination of these factors, a nomogram was meticulously devised. Rigorous evaluation of this nomogram's precision and reliability encompassed Receiver Operating Characteristic (ROC) curve analysis, calibration curve assessment, and Decision Curve Analysis (DCA). The robustness and accuracy were further fortified through application of the bootstrap method, which entailed 1000 independent dataset samplings to perform discrimination and calibration procedures.

RESULTS

The results of multivariate logistic regression analysis unveiled independent risk factors for perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). These factors included tumor histological differentiation (grade) (OR = 0.15, 95% CI = 0.03-0.74, p = 0.02), primary tumor location (OR = 2.49, 95% CI = 1.21-5.12, p = 0.013), gross tumor type (OR = 0.42, 95% CI = 0.22-0.81, p = 0.01), N staging in CT (OR = 3.44, 95% CI = 1.74-6.80, p < 0.001), carcinoembryonic antigen (CEA) level (OR = 3.13, 95% CI = 1.60-6.13, p = 0.001), and platelet-to-lymphocyte ratio (PLR) (OR = 2.07, 95% CI = 1.08-3.96, p = 0.028).These findings formed the basis for constructing a predictive nomogram, which exhibited an impressive area under the receiver operating characteristic (ROC) curve (AUC) of 0.772 (95% CI, 0.712-0.833). The Hosmer-Lemeshow test confirmed the model's excellent fit (p = 0.47), and the calibration curve demonstrated consistent performance. Furthermore, decision curve analysis (DCA) underscored a substantial net benefit across the risk range of 13% to 85%, reaffirming the nomogram's reliability through rigorous internal validation.

CONCLUSION

We have formulated a highly reliable nomogram that provides valuable assistance to clinical practitioners in preoperatively assessing the likelihood of perineural invasion (PNI) among colorectal cancer (CRC) patients. This tool holds significant potential in offering guidance for treatment strategy formulation.

Validation of the 9th edition of the TNM staging system for non-small cell lung cancer with lobectomy in stage IA-IIIA

OBJECTIVES

The 9th edition of tumour-node-metastasis (TNM) staging for lung cancer was announced by Prof Hisao Asamura at the 2023 World Conference on Lung Cancer in Singapore. The purpose of this study was to externally validate and compare the latest staging of lung cancer.

METHODS

We collected 19 193 patients with stage IA-IIIA non-small cell lung cancer (NSCLC) who underwent lobectomy from the Surveillance, Epidemiology and End Results database. Survival analysis by TNM stages was compared using the Kaplan-Meier method and further analysed using univariable and multivariable Cox regression analyses. Receiver operating characteristic curves were used to assess model accuracy, Akaike information criterion, Bayesian information criterion and consistency index were used to compare the prognostic, predictive ability between the current 8th and 9th edition TNM classification.

RESULTS

The 9th edition of the TNM staging system can better distinguish between IB and IIA patients on the survival curve (P < 0.0001). In both univariable and multivariable regression analysis, the 9th edition of the TNM staging system can differentiate any 2 adjacent staging patients more evenly than the 8th edition. The 9th and the 8th edition TNM staging have similar predictive power and accuracy for the overall survival of patients with NSCLC [TNM 9th vs 8th, area under the curve: 62.4 vs 62.3; Akaike information criterion: 166 182.1 vs 166 131.6; Bayesian information criterion: 166 324.3 vs 166 273.8 and consistency index: 0.650 (0.003) vs 0.651(0.003)].

CONCLUSIONS

Our external validation demonstrates that the 9th edition of TNM staging for NSCLC is reasonable and valid. The 9th edition of TNM staging for NSCLC has near-identical prognostic accuracy to the 8th edition.

Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis

BACKGROUND

Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology.

METHODS

The study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted.

RESULTS

The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12-0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05-0.92, P = 0.037).

CONCLUSIONS

We quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.

Insight into the significance of CD8+ tumor-infiltrating lymphocytes in squamous cell lung cancer

BACKGROUND

Although there are great expectations regarding the use of tumor-infiltrating lymphocytes (TILs) to predict effects of immunotherapies and prognosis, knowledge about TILs remains insufficient for clinical application.

METHODS

We objectively investigated the prognostic significance of tumor-infiltrating CD8 + lymphocytes (CD8 + TILs) in squamous cell lung cancer (SQ-LC). Among patients who underwent surgical resection of SQ-LC in 2011-2017, 100 patients with pathological stage IA3-III were immunohistochemically studied to evaluate CD8 + TILs in the tumor stroma and parenchyma. The impact of CD8 + TILs on relapse-free survival was analyzed using a Kaplan-Meier survival analysis and multivariate analyses using Fine-Gray and Cox proportional hazards models.

RESULTS

The multivariate analysis showed that large and small numbers, but not intermediate numbers, of CD8 + TILs in the tumor stroma may be related to a more favorable prognosis (small vs. intermediate: HR, 0.64; 95% CI: 0.29-1.41, p = 0.27; large vs. intermediate: HR, 0.48; 95% CI: 0.21-1.09, p = 0.08). In contrast, a large number of CD8 + TILs in the tumor parenchyma was associated with a poor prognosis (HR, 2.60; 95% CI: 0.91-7.42, p = 0.075). An exploratory analysis showed a potentially strong association between an extremely large number of CD8 + TILs in the tumor parenchyma and a poor prognosis, even with a large number of CD8 + TILs in the tumor stroma.

CONCLUSION

Our study provided partial but important information on the significance of CD8 + TILs in SQ-LC. To use CD8 + TILs as biomarkers, a better understanding of CD8 + TILs as well as other important components in the tumor microenvironment and the inflammatory phenotypes they form may be needed.

Tumor Heterogeneity Confounds Lymphocyte Metrics in Diagnostic Lung Cancer Biopsies

CONTEXT.—

The immune microenvironment is involved in fundamental aspects of tumorigenesis, and immune scores are now being developed for clinical diagnostics.

OBJECTIVE.—

To evaluate how well small diagnostic biopsies and tissue microarrays (TMAs) reflect immune cell infiltration compared to the whole tumor slide, in tissue from patients with non-small cell lung cancer.

DESIGN.—

A TMA was constructed comprising tissue from surgical resection specimens of 58 patients with non-small cell lung cancer, with available preoperative biopsy material. Whole sections, biopsies, and TMA were stained for the pan-T lymphocyte marker CD3 to determine densities of tumor-infiltrating lymphocytes. Immune cell infiltration was assessed semiquantitatively as well as objectively with a microscopic grid count. For 19 of the cases, RNA sequencing data were available.

RESULTS.—

The semiquantitative comparison of immune cell infiltration between the whole section and the biopsy displayed fair agreement (intraclass correlation coefficient [ICC], 0.29; P = .01; CI, 0.03-0.51). In contrast, the TMA showed substantial agreement compared with the whole slide (ICC, 0.64; P < .001; CI, 0.39-0.79). The grid-based method did not enhance the agreement between the different tissue materials. The comparison of CD3 RNA sequencing data with CD3 cell annotations confirmed the poor representativity of biopsies as well as the stronger correlation for the TMA cores.

CONCLUSIONS.—

Although overall lymphocyte infiltration is relatively well represented on TMAs, the representativity in diagnostic lung cancer biopsies is poor. This finding challenges the concept of using biopsies to establish immune scores as prognostic or predictive biomarkers for diagnostic applications.

Predictive value of blood biomarkers in elderly patients with non-small-cell lung cancer

Aim: Whether GRHL1 can be considered as a potential biomarker for screening non-small-cell lung cancer (NSCLC) is still uncertain. We aimed to investigate the value of circulating blood GRHL1 on detecting NSCLC in an older population. Materials & methods: Diagnostic models from 351 older patients with NSCLC were constructed to assess the predictive value of blood GRHL1 on distinguishing NSCLC. Results: We observed that GRHL1 (odds ratio: 3.25; 95% CI: 1.70-6.91; p < 0.001) maintained a strong relationship with an elevated rate of NSCLC after adequate clinical confounding factors were controlled for. Importantly, serum GRHL1 (area under the curve: 0.725; 95% CI: 0.708-0.863; p < 0.001) had a good predictive value. Conclusion: This is the first time that circulating GRHL1 has been shown to have good value for early detection of NSCLC in an elderly population.

Pre-treatment inflamed tumor immune microenvironment is associated with FOLFIRINOX response in pancreatic cancer

Introduction

Pancreatic adenocarcinoma (PDAC) is an aggressive tumor with limited response to both chemotherapy and immunotherapy. Pre-treatment tumor features within the tumor immune microenvironment (TiME) may influence treatment response. We hypothesized that the pre-treatment TiME composition differs between metastatic and primary lesions and would be associated with response to modified FOLFIRINOX (mFFX) or gemcitabine-based (Gem-based) therapy.

Methods

Using RNAseq data from a cohort of treatment-naïve, advanced PDAC patients in the COMPASS trial, differential gene expression analysis of key immunomodulatory genes in were analyzed based on multiple parameters including tumor site, response to mFFX, and response to Gem-based treatment. The relative proportions of immune cell infiltration were defined using CIBERSORTx and Dirichlet regression.

Results

145 samples were included in the analysis; 83 received mFFX, 62 received Gem-based therapy. Metastatic liver samples had both increased macrophage (1.2 times more, p < 0.05) and increased eosinophil infiltration (1.4 times more, p < 0.05) compared to primary lesion samples. Further analysis of the specific macrophage phenotypes revealed an increased M2 macrophage fraction in the liver samples. The pre-treatment CD8 T-cell, dendritic cell, and neutrophil infiltration of metastatic samples were associated with therapy response to mFFX (p < 0.05), while mast cell infiltration was associated with response to Gem-based therapy (p < 0.05). Multiple immunoinhibitory genes such as ADORA2A, CSF1R, KDR/VEGFR2, LAG3, PDCD1LG2, and TGFB1 and immunostimulatory genes including C10orf54, CXCL12, and TNFSF14/LIGHT were significantly associated with worse survival in patients who received mFFX (p = 0.01). There were no immunomodulatory genes associated with survival in the Gem-based cohort.

Discussion

Our evidence implies that essential differences in the PDAC TiME exist between primary and metastatic tumors and an inflamed pretreatment TiME is associated with mFFX response. Defining components of the PDAC TiME that influence therapy response will provide opportunities for targeted therapeutic strategies that may need to be accounted for in designing personalized therapy to improve outcomes.

Prognostic significance of tumor microenvironment assessed by machine learning algorithm in surgically resected non-small cell lung cancer

BACKGROUND

A methodology to assess the immune microenvironment (IME) of non-small cell lung cancer (NSCLC) has not been established, and the prognostic impact of IME factors is not yet clear.

AIMS

This study aimed to assess the IME factors and evaluate their prognostic values.

METHODS AND RESULTS

We assessed CD8+ tumor-infiltrating lymphocyte (TIL) density, forkhead box protein P3+ (Foxp3+ ) TIL density, and programmed death receptor ligand-1 (PD-L1) tumor proportion score (TPS) using a machine-learning algorithm in whole-slide imaging (WSI). We dichotomized patients according to TIL density or TPS and compared their clinical outcomes. Between September 2014 and September 2015, 165 patients with NSCLC were enrolled in the study. We assessed IME factors in the epithelium, stroma, and their combination. An improvement in disease-free survival (DFS) was observed in the high CD8+ TIL density group in the epithelium, stroma, and the combination of both. Moreover, the group with high PD-L1 TPS in the epithelium showed better DFS than that with low PD-L1 TPS. In the multivariate analysis, the CD8+ TIL density in the combination of epithelium and stroma and PD-L1 TPS in the epithelium were independent prognostic factors (hazard ratio [HR] = 0.43; 95% confidence interval [CI] = 0.26-0.72; p = .001, HR = 0.49; 95% CI = 0.30-0.81; p = .005, respectively).

CONCLUSION

Our approach demonstrated that the IME factors are related to survival in patients with NSCLC. The quantitative assessment of IME factors enables to discriminate patients with high risk of recurrence, who can be the candidates for adjuvant therapy. Assessing the CD8+ TIL density in the combination of epithelium and stroma might be more useful than their individual assessment because it is a simple and time-saving analysis of TILs in WSI.

An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer

BACKGROUND AND OBJECTIVE

In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based on artificial intelligence (AI) and immunohistochemistry (IHC)-stained whole-slide images (WSIs) of colorectal cancer (CRC) patients to quantitatively assess the spatial associations between tumor cells and immune cells. To achieve this, we employ the Morisita-Horn ecological index (Mor-index), which allows for a comprehensive analysis of the spatial distribution patterns between tumor cells and immune cells within the TME.

MATERIALS AND METHODS

In this study, we employed a combination of deep learning technology and traditional computer segmentation methods to accurately segment the tumor nuclei, immune nuclei, and stroma nuclei within the tumor regions of IHC-stained WSIs. The Mor-index was used to assess the spatial association between tumor cells and immune cells in TME of CRC patients by obtaining the results of cell nuclei segmentation. A discovery cohort (N = 432) and validation cohort (N = 137) were used to evaluate the prognostic value of the Mor-index for overall survival (OS).

RESULTS

The efficacy of our method was demonstrated through experiments conducted on two datasets comprising a total of 569 patients. Compared to other studies, our method is not only superior to the QuPath tool but also produces better segmentation results with an accuracy of 0.85. Mor-index was quantified automatically by our method. Survival analysis indicated that the higher Mor-index correlated with better OS in the discovery cohorts (HR for high vs. low 0.49, 95% CI 0.27-0.77, P = 0.0014) and validation cohort (0.21, 0.10-0.46, < 0.0001).

CONCLUSION

This study provided a novel AI-based approach to segmenting various nuclei in the TME. The Mor-index can reflect the immune status of CRC patients and is associated with favorable survival. Thus, Mor-index can potentially make a significant role in aiding clinical prognosis and decision-making.

Immune Checkpoint Inhibitors in "Special" NSCLC Populations: A Viable Approach?

Over the last decade, the therapeutic scenario for advanced non-small-cell lung cancer (NSCLC) has undergone a major paradigm shift. Immune checkpoint inhibitors (ICIs) have shown a meaningful clinical and survival improvement in different settings of the disease. However, the real benefit of this therapeutic approach remains controversial in selected NSCLC subsets, such as those of the elderly with active brain metastases or oncogene-addicted mutations. This is mainly due to the exclusion or underrepresentation of these patient subpopulations in most pivotal phase III studies; this precludes the generalization of ICI efficacy in this context. Moreover, no predictive biomarkers of ICI response exist that can help with patient selection for this therapeutic approach. Here, we critically summarize the current state of ICI efficacy in the most common "special" NSCLC subpopulations.

Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial

BACKGROUND

We aim to implement an immune cell score model in routine clinical practice for resected non-small-cell lung cancer (NSCLC) patients (NCT03299478). Molecular and genomic features associated with immune phenotypes in NSCLC have not been explored in detail.

PATIENTS AND METHODS

We developed a machine learning (ML)-based model to classify tumors into one of three categories: inflamed, altered, and desert, based on the spatial distribution of CD8+ T cells in two prospective (n = 453; TNM-I trial) and retrospective (n = 481) stage I-IIIA NSCLC surgical cohorts. NanoString assays and targeted gene panel sequencing were used to evaluate the association of gene expression and mutations with immune phenotypes.

RESULTS

Among the total of 934 patients, 24.4% of tumors were classified as inflamed, 51.3% as altered, and 24.3% as desert. There were significant associations between ML-derived immune phenotypes and adaptive immunity gene expression signatures. We identified a strong association of the nuclear factor-κB pathway and CD8+ T-cell exclusion through a positive enrichment in the desert phenotype. KEAP1 [odds ratio (OR) 0.27, Q = 0.02] and STK11 (OR 0.39, Q = 0.04) were significantly co-mutated in non-inflamed lung adenocarcinoma (LUAD) compared to the inflamed phenotype. In the retrospective cohort, the inflamed phenotype was an independent prognostic factor for prolonged disease-specific survival and time to recurrence (hazard ratio 0.61, P = 0.01 and 0.65, P = 0.02, respectively).

CONCLUSIONS

ML-based immune phenotyping by spatial distribution of T cells in resected NSCLC is able to identify patients at greater risk of disease recurrence after surgical resection. LUADs with concurrent KEAP1 and STK11 mutations are enriched for altered and desert immune phenotypes.

Therapies Targeting Immune Cells in Tumor Microenvironment for Non-Small Cell Lung Cancer

The tumor microenvironment (TME) plays critical roles in immune modulation and tumor malignancies in the process of cancer development. Immune cells constitute a significant component of the TME and influence the migration and metastasis of tumor cells. Recently, a number of therapeutic approaches targeting immune cells have proven promising and have already been used to treat different types of cancer. In particular, PD-1 and PD-L1 inhibitors have been used in the first-line setting in non-small cell lung cancer (NSCLC) with PD-L1 expression ≥1%, as approved by the FDA. In this review, we provide an introduction to the immune cells in the TME and their efficacies, and then we discuss current immunotherapies in NSCLC and scientific research progress in this field.

Functional status and spatial architecture of tumor-infiltrating CD8+ T cells are associated with lymph node metastases in non-small cell lung cancer

BACKGROUND

Anti-PD-(L)1 immunotherapy has been recommended for non-small cell lung cancer (NSCLC) patients with lymph node metastases (LNM). However, the exact functional feature and spatial architecture of tumor-infiltrating CD8 + T cells remain unclear in these patients.

METHODS

Tissue microarrays (TMAs) from 279 IA-IIIB NSCLC samples were stained by multiplex immunofluorescence (mIF) for 11 markers (CD8, CD103, PD-1, Tim3, GZMB, CD4, Foxp3, CD31, αSMA, Hif-1α, pan-CK). We evaluated the density of CD8 + T-cell functional subsets, the mean nearest neighbor distance (mNND) between CD8 + T cells and neighboring cells, and the cancer-cell proximity score (CCPS) in invasive margin (IM) as well as tumor center (TC) to investigate their relationships with LNM and prognosis.

RESULTS

The densities of CD8 + T-cell functional subsets, including predysfunctional CD8 + T cells (T_predys) and dysfunctional CD8 + T cells (T_dys), in IM predominated over those in TC (P < 0.001). Multivariate analysis identified that the densities of CD8 + T_predys cells in TC and CD8 + T_dys cells in IM were significantly associated with LNM [OR = 0.51, 95%CI (0.29-0.88), P = 0.015; OR = 5.80, 95%CI (3.19-10.54), P < 0.001; respectively] and recurrence-free survival (RFS) [HR = 0.55, 95%CI (0.34-0.89), P = 0.014; HR = 2.49, 95%CI (1.60-4.13), P = 0.012; respectively], independent of clinicopathological factors. Additionally, shorter mNND between CD8 + T cells and their neighboring immunoregulatory cells indicated a stronger interplay network in the microenvironment of NSCLC patients with LNM and was associated with worse prognosis. Furthermore, analysis of CCPS suggested that cancer microvessels (CMVs) and cancer-associated fibroblasts (CAFs) selectively hindered CD8 + T cells from contacting with cancer cells, and were associated with the dysfunction of CD8 + T cells.

CONCLUSION

Tumor-infiltrating CD8 + T cells were in a more dysfunctional status and in a more immunosuppressive microenvironment in patients with LNM compared with those without LNM.

Impact of microvessel patterns and immune status in NSCLC: a non-angiogenic vasculature is an independent negative prognostic factor in lung adenocarcinoma

Introduction

Non-small cell lung carcinomas (NSCLC) exhibit different microvessel patterns (MVPs). Basal (BA), diffuse (DA) and papillary (PA) patterns show signs of angiogenesis (new blood vessels), while an alveolar pattern indicates that tumors are co-opting existing normal vessels (non-angiogenic alveolar, NAA). NAA tumor growth is known to exist in NSCLC, but little is known about its prognostic impact in different histological subgroups, and about associations between MVPs and immune cell infiltration.

Methods

Detailed patterns of angiogenic and non-angiogenic tumor growth were evaluated by CD34 immunohistochemistry in whole tissue slides from 553 surgically treated patients with NSCLC stage I-IIIB disease. Associations with clinicopathological variables and markers related to tumor immunology-, angiogenesis- and hypoxia/metabolism were explored, and disease-specific survival (DSS) was analyzed according to histological subtypes.

Results

The predominant MVP was angiogenic in 82% of tumors: BA 40%, DA 34%, PA 8%, while a NAA pattern dominated in 18%. A contribution of the NAA pattern >5% (NAA+), i.e., either dominant or minority, was observed in 40.1% of tumors and was associated with poor disease-specific survival (DSS) (p=0.015). When stratified by histology, a significantly decreased DSS for NAA+ was found for adenocarcinomas (LUAD) only (p< 0.003). In multivariate analyses, LUAD NAA+ pattern was a significant independent prognostic factor; HR 2.37 (CI 95%, 1.50-3.73, p< 0.001). The immune cell density (CD3, CD4, CD8, CD45RO, CD204, PD1) added prognostic value in squamous cell carcinoma (LUSC) and LUAD with 0-5% NAA (NAA-), but not in LUAD NAA+. In correlation analyses, there were several significant associations between markers related to tumor metabolism (MCT1, MCT4, GLUT1) and different MVPs.

Conclusion

The NAA+ pattern is an independent poor prognostic factor in LUAD. In NAA+ tumors, several immunological markers add prognostic impact in LUSC but not in LUAD.

T cell effects and mechanisms in immunotherapy of head and neck tumors

Head and neck tumors (HNCs) are a common tumor in otorhinolaryngology head and neck surgery, accounting for 5% of all malignant tumors in the body and are the sixth most common malignant tumor worldwide. In the body, immune cells can recognize, kill, and remove HNCs. T cell-mediated antitumor immune activity is the most important antitumor response in the body. T cells have different effects on tumor cells, among which cytotoxic T cells and helper T cells play a major killing and regulating role. T cells recognize tumor cells, activate themselves, differentiate into effector cells, and activate other mechanisms to induce antitumor effects. In this review, the immune effects and antitumor mechanisms mediated by T cells are systematically described from the perspective of immunology, and the application of new immunotherapy methods related to T cells are discussed, with the objective of providing a theoretical basis for exploring and forming new antitumor treatment strategies. Video Abstract.

Systemic Immune-Inflammation Index May Predict Mortality in Neuroblastoma

INTRODUCTION

Neuroblastomas (NB) are among the most frequent childhood solid tumors. The link between inflammation and cancer is well understood. Many research studies have been conducted to determine the prognostic importance of inflammatory markers in cancer patients. C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) are all potential inflammation indicators. The purpose of this study is to assess the efficacy of NLR and SII as inflammatory indicators in predicting NB patient survival.

MATERIALS AND METHODS

Patients with NB diagnosed between January 1, 2012 and December 31, 2021 were studied retrospectively, and death was documented. By dividing the number of neutrophils by the number of lymphocytes, the NLR was obtained. The SII was calculated by multiplying the NLR by the platelet count.

RESULTS

 46 patients with NB were included in the study with a mean age of 57.58 months (4.14-170.05). When the patients were analyzed based on mortality the NLR and SII values were statistically significantly increased in the dead group (2.71 (1.22-4.1 ) vs. 1.7 (0.16-5.1); p=0.02; and 677.8 (215-1322) vs. 294.6 (69.49-799.1), respectively; p=0.012). Analysis of the receiver operating curve found that 328.49 is the ideal cutoff value for SII to predict mortality with a sensitivity of 83% and a specificity of 68% (area under the receiver operating characteristic curve = 0.814 (95% confidence interval: 0.671-0.956), p=0.005 ). Analyzing the influence of risk factors on survival using Cox regression analysis, SII was discovered as a significant predictor of survival in the study (HR =1.001, 95% CI =1-1.20; p=0.049).

CONCLUSION

 SII may be used to predict the overall survival of NB patients.

Immunotherapy for Metastatic Non-Small Cell Lung Cancer: Therapeutic Advances and Biomarkers

Immunotherapy has revolutionized the treatment paradigm of non-small cell lung cancer and improved patients' prognosis. Immune checkpoint inhibitors have quickly become standard frontline treatment for metastatic non-oncogene addicted disease, either as a single agent or in combination strategies. However, only a few patients have long-term benefits, and most of them do not respond or develop progressive disease during treatment. Thus, the identification of reliable predictive and prognostic biomarkers remains crucial for patient selection and guiding therapeutic choices. In this review, we provide an overview of the current strategies, highlighting the main clinical challenges and novel potential biomarkers.

High Expression of E2F4 Is an Adverse Prognostic Factor and Related to Immune Infiltration in Oral Squamous Cell Carcinoma

Background

We aimed to evaluate the prognostic value of E2F4 expression in oral squamous cell carcinoma (OSCC) and clarify its influence on immune cell infiltration and biological functions.

Methods

The Cancer Genome Atlas (TCGA) database, the STRING database, and related online tools as well as single-sample gene set enrichment analysis (ssGSEA) were used for the analyses in our study.

Results

The E2F4 expression was elevated in OSCC tumor tissue compared with paracancerous tissues. The high expression of E2F4 was closely related to the poorer overall survival, disease-free survival, and progression-free interval of OSCC. In addition, pathway enrichment analyses revealed that the top 49 genes most closely related to E2F4 were strongly associated with the cell cycle. E2F4-related proteins were closely related to the following KEGG pathways: cell cycle, cellular senescence, PI3K-Akt signaling pathway, Wnt signaling pathway, and notch signaling pathway. It was also demonstrated that the E2F4 expression was negatively correlated with immune purity and strongly related to immune cell infiltration in OSCC.

Conclusions

E2F4 can be used as a novel biomarker for the diagnosis and prognosis of OSCC.

Clinical significance of preoperative neutrophil‐lymphocyte ratio and platelet‐lymphocyte ratio in the prognosis of resected early‐stage patients with non‐small cell lung cancer: A meta‐analysis

BACKGROUND

Poor prognosis is linked to peripheral blood levels of preoperative platelet-lymphocyte ratio (PLR) and neutrophil-lymphocyte ratio (NLR) in many advanced cancers. Nevertheless, whether the correlation exists in resected early-stage cases with non-small cell lung cancer (NSCLC) stays controversial. Consequently, we performed a meta-analysis to explore the preoperative NLR and PLR's prognostic significance in early-stage patients with NSCLC undergoing curative surgery.

METHODS

Relevant studies that validated the link between preoperative NLR or PLR and survival results were found via the proceeding databases: PubMed, Embase, Cochrane Library, and Web of Science. The merged 95% confidence interval (CI) and hazard ratio (HR) was employed to validate the link between the NLR or PLR's index and overall survival (OS) and disease-free survival (DFS) in resected NSCLC cases. We used sensitivity and subgroup analyses to assess the studies' heterogeneity.

RESULTS

An overall of 21 studies were attributed to the meta-analysis. The findings indicated that great preoperative NLR was considerably correlated with poor DFS (HR = 1.58, 95% CI: 1.37-1.82, p < 0.001) and poor OS (HR = 1.51, 95% CI: 1.33-1.72, p < 0.001), respectively. Subgroup analyses were in line with the pooled findings. In aspect of PLR, raised PLR was indicative of inferior DFS (HR = 1.28, 95% CI: 1.04-1.58, p = 0.021) and OS (HR = 1.37, 95% CI: 1.18-1.60, p < 0.001). In the subgroup analyses between PLR and DFS, only subgroups with a sample size <300 (HR = 1.67, 95% CI: 1.15-2.43, p = 0.008) and TNM staging of mixed (I-II) (HR = 1.47, 95% CI: 1.04-2.07, p = 0.028) showed that the link between high PLR and poor DFS was significant.

CONCLUSIONS

Preoperative elevated NLR and PLR may act as prognostic biomarkers in resected early-stage NSCLC cases and are therefore valuable for guiding postoperative adjuvant treatment.

The prognostic value of a 4-factor neoimmunologic score system in non-small cell lung cancer

The role of distinct immune cell types in modulating cancer progression has recently gained attention. The immune context is indicated by the abundance of immune infiltration based on quantified lymphocytes in the core of tumors (CT) and invasive tumor margin (IM). Novel immune biomarkers could potentially complement tumor-node-metastasis (TNM) classification for non-small cell lung cancers (NSCLCs), thereby improving prognostic accuracy. This study evaluated the prognostic value of a newly established immunologic score (neo-IS) in patients with NSCLC. We detected 10 immune biomarkers, including CD45RO, CD3, CD8, CD68, CD163, CD66b, FoxP3, PD-1, PD-L1, and TIM-3, in 350 patients with NSCLC from 2 cohorts using immunohistochemistry (IHC). The 3- and 5-year survival and overall survival (OS) rates were evaluated. An immunologic prediction model specifically for NSCLC patients, the neo-immunologic score (neo-IS_NSCLC ), was constructed using a Cox proportional hazards regression model. In the discovery cohort (n = 250), the establishment of neo-IS_NSCLC was based on 4 immune biomarkers: CD3+_IM , CD8+_CT , FoxP3+_IM , and PD-1+_IM . Significant prognostic differences were found upon comparing low-IS_NSCLC patients and high-IS_NSCLC patients. The OS rate in the high-IS_NSCLC group was significantly longer than that in the low-IS_NSCLC group (67.5 months vs. 51.2 months, p < 0.001). The neo-IS_NSCLC was validated in the validation cohort (n = 100), and the results were confirmed. Multivariate analyses indicated that neo-IS_NSCLC was an independent indicator of prognosis in patients with NSCLC. Finally, we combined neo-IS_NSCLC with clinicopathologic factors to establish a tumor-node-metastasis-immune (TNM-I) staging system for clinical use, which showed better prediction accuracy than the TNM stage.

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.

Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Perceptron Network Classifier Based on Non-contrast MRI

RATIONALE AND OBJECTIVES

Conventional chemotherapy has limited benefit in pancreatic ductal adenocarcinoma (PDAC), necessitating identification of novel therapeutic targets. Radiomics may enable non-invasive prediction of CD20 expression, a hypothesized therapeutic target in PDAC. To develop a machine learning classifier based on noncontrast magnetic resonance imaging for predicting CD20 expression in PDAC.

MATERIALS AND METHODS

Retrospective study was conducted on preoperative noncontrast magnetic resonance imaging of 156 patients with pathologically confirmed PDAC from January 2017 to April 2018. For each patient, 1409 radiomics features were selected using minimum absolute contraction and selective operator logistic regression algorithms. CD20 expression was quantified using immunohistochemistry. A multilayer perceptron network classifier was developed using the training and validation set.

RESULTS

A log-rank test showed that the CD20-high group (22.37 months, 95% CI: 19.10-25.65) had significantly longer survival than the CD20-low group (14.9 months, 95% CI: 10.96-18.84). The predictive model showed good differentiation in training (area under the curve [AUC], 0.79) and validation (AUC, 0.79) sets. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 73.21%, 75.47%, 0.74, 0.76, and 0.73, respectively, for the training set and 69.23%, 80.95%, 0.74, 0.82, and 0.68, respectively, for the validation set.

CONCLUSION

Multilayer perceptron classifier based on noncontrast magnetic resonance imaging scanning can predict the level of CD20 expression in PDAC patients.

Identification of Prognostic Genes and Immune Landscape Signatures Based on Tumor Microenvironment in Lung Adenocarcinoma

Background

Lung adenocarcinoma is the most common lung cancer subtype and accounts for the highest proportion of cancer-related deaths. The tumor microenvironment influences prognostic outcomes in lung adenocarcinoma (LUAD).

Materials and Methods

We used the ESTIMATE algorithm (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) to investigate the role of microenvironment-related genes and stromal cells in lung adenocarcinoma prognosis. This analysis was done on lung adenocarcinoma cases from The Cancer Genome Atlas (TCGA). The cases were divided into high and low groups on the basis of immune and stromal scores, respectively.

Results

There were close correlations between immune scores with prognosis and disease stage. There were 367 differentially expressed genes. Combining the Gene Expression Omnibus (GEO) database, we found 14 prognosis-related genes.

Results

There were close correlations between immune scores with prognosis and disease stage. There were 367 differentially expressed genes. Combining the Gene Expression Omnibus (GEO) database, we found 14 prognosis-related genes. Results. Based on the enrichment levels of the immune cell types, we clustered LUAD into Immunity_H and Immunity_L subtypes. Most of these genes were upregulated in Immunity_H subtype. Finally, using the Human Protein Atlas (HPA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, most of the proteins corresponding to prognostic genes were verified to be differentially expressed between the tumor and normal groups.

Conclusions

The key genes identified in this study are involved in molecular mechanisms of LUAD.

Clinical significance of tumor-infiltrating lymphocytes investigated using routine H&E slides in small cell lung cancer

Background

Tumor-infiltrating lymphocytes (TILs), investigated using routine hematoxylin and eosin (H&E)-stained section slides (H&E-sTILs), provide a robust prognostic biomarker in various types of solid cancer. The purpose of the present study was to investigate the prognostic significance of H&E-sTILs in patients with small cell lung cancer (SCLC).

Methods

The clinical data of patients with SCLC who had been treated in our cancer center between January 2013 and October 2019 were collected and retrospectively reviewed. The H&E-sTILs were re-assessed by two experienced pathologists independently. H&E-sTILs that affected the overall survival (OS), progression free survival (PFS) and brain-metastasis free survival (BMFS) rates were explored using the Kaplan–Meier method, and the log-rank test was used to assess the differences. Multivariate analysis was subsequently performed using the Cox proportion hazards model.

Results

A total of 159 patients with SCLC who fulfilled the inclusion criteria were enrolled in the current study. The OS rates at 1, 2 and 3 years were 59.8, 28.6 and 19.8%, respectively, for the whole group. The 3-year OS, PFS and BMFS rates for the H&E-sTILs(+) and H&E-sTILs(−) groups were 25.1% cf. 5.1% (P = 0.030), 14.0% cf. 4.0% (P = 0.013), and 66.0% cf. 11.4% (P = 0.023), respectively. Multivariate analyses subsequently revealed that H&E-sTILs, clinical M stage, the cycles of chemotherapy and short-term response to thoracic radiotherapy were independent factors affecting OS, whereas H&E-sTILs, clinical N stage, clinical M stage and short-term response to chemotherapy were factors affecting PFS. The H&E-sTILs affected OS, PFS and BMFS simultaneously.

Conclusions

The results of this retrospective study have shown that H&E-sTILs may be considered as a prognostic biomarker affecting the short-term response to treatment, and they are the one and only risk factor for BMFS. However, due to the limitations of the nature of the retrospective design and shortcomings in visually assessing the TILs based on the H&E-stained slides, further prospective studies are required to confirm these conclusions.

A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images

Simple Summary

Tumor tissues sampled from patients contain prognostic and predictive information beyond what is currently being used in clinical practice. Large-scale digitization enables new ways of exploiting this information. The most promising analysis pipelines include deep learning/artificial intelligence (AI). However, to ensure success, AI often requires a time-consuming curation of data. In our approach, we repurposed AI pipelines and training data for cell segmentation and classification to identify tissue-infiltrating lymphocytes (TILs) in lung cancer tissue. We showed that our approach is able to identify TILs and provide prognostic information in an unseen dataset from lung cancer patients. Our methods can be adapted in myriad ways and may help pave the way for the large-scale deployment of digital pathology.

Abstract

Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. The manual quantification of immune cells is inaccurate and time-consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in standard diagnostic hematoxylin and eosin-stained sections (H&E slides) from lung cancer patients. Our approach is to transfer an open-source machine learning method for the segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of the data. Our results show that the resulting TIL quantification correlates to the patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small cell lung cancer (current standard CD8 cells in DAB-stained TMAs HR 0.34, 95% CI 0.17–0.68 vs. TILs in HE WSIs: HoVer-Net PanNuke Aug Model HR 0.30, 95% CI 0.15–0.60 and HoVer-Net MoNuSAC Aug model HR 0.27, 95% CI 0.14–0.53). Our approach bridges the gap between machine learning research, translational clinical research and clinical implementation. However, further validation is warranted before implementation in a clinical setting.

Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification

Background

High immune infiltration is associated with favourable prognosis in patients with non-small-cell lung cancer (NSCLC), but an automated workflow for characterizing immune infiltration, with high validity and reliability, remains to be developed.

Methods

We performed a multicentre retrospective study of patients with completely resected NSCLC. We developed an image analysis workflow for automatically evaluating the density of CD3⁺ and CD8⁺ T-cells in the tumour regions on immunohistochemistry (IHC)-stained whole-slide images (WSIs), and proposed an immune scoring system “I-score” based on the automated assessed cell density.

Results

A discovery cohort (n = 145) and a validation cohort (n = 180) were used to assess the prognostic value of the I-score for disease-free survival (DFS). The I-score (two-category) was an independent prognostic factor after adjusting for other clinicopathologic factors. Compared with a low I-score (two-category), a high I-score was associated with significantly superior DFS in the discovery cohort (adjusted hazard ratio [HR], 0.54; 95% confidence interval [CI] 0.33–0.86; P = 0.010) and validation cohort (adjusted HR, 0.57; 95% CI 0.36–0.92; P = 0.022). The I-score improved the prognostic stratification when integrating it into the Cox proportional hazard regression models with other risk factors (discovery cohort, C-index 0.742 vs. 0.728; validation cohort, C-index 0.695 vs. 0.685).

Conclusion

This automated workflow and immune scoring system would advance the clinical application of immune microenvironment evaluation and support the clinical decision making for patients with resected NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03458-9.

Development and validation of a computed tomography-based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer

OBJECTIVES

To date, there are no data on the noninvasive surrogate of intratumoural immune status that could be prognostic of survival outcomes in non-small cell lung cancer (NSCLC). We aimed to develop and validate the immune ecosystem diversity index (iEDI), an imaging biomarker, to indicate the intratumoural immune status in NSCLC. We further investigated the clinical relevance of the biomarker for survival prediction.

METHODS

In this retrospective study, two independent NSCLC cohorts (Resec1, n = 149; Resec2, n = 97) were included to develop and validate the iEDI to classify the intratumoural immune status. Paraffin-embedded resected specimens in Resec1 and Resec2 were stained by immunohistochemistry, and the density percentiles of CD3⁺, CD4⁺, and CD8⁺ T cells to all cells were quantified to estimate intratumoural immune status. Then, EDI features were extracted using preoperative computed tomography to develop an imaging biomarker, called iEDI, to determine the immune status. The prognostic value of iEDI was investigated on NSCLC patients receiving surgical resection (Resec1; Resec2; internal cohort Resec3, n = 419; external cohort Resec4, n = 96; and TCIA cohort Resec5, n = 55).

RESULTS

iEDI successfully classified immune status in Resec1 (AUC 0.771, 95% confidence interval [CI] 0.759-0.783; and 0.770 through internal validation) and Resec2 (0.669, 0.647-0.691). Patients with higher iEDI-score had longer overall survival (OS) in Resec3 (unadjusted hazard ratio 0.335, 95%CI 0.206-0.546, p < 0.001), Resec4 (0.199, 0.040-1.000, p < 0.001), and TCIA (0.303, 0.098-0.944, p = 0.001).

CONCLUSIONS

iEDI is a non-invasive surrogate of intratumoural immune status and prognostic of OS for NSCLC patients receiving surgical resection.

KEY POINTS

• Decoding tumour immune microenvironment enables advanced biomarkers identification. • Immune ecosystem diversity index characterises intratumoural immune status noninvasively. • Immune ecosystem diversity index is prognostic for NSCLC patients.

Clinical Significance of a CD3/CD8-Based Immunoscore in Neuroblastoma Patients Using Digital Pathology

Background

Infiltrating immune cells have been reported as prognostic markers in many cancer types. We aimed to evaluate the prognostic role of tumor-infiltrating lymphocytes, namely CD3+ T cells, CD8+ cytotoxic T cells and memory T cells (CD45RO+), in neuroblastoma.

Patients and Methods

Immunohistochemistry was used to determine the expression of CD3, CD8 and CD45RO in the tumor samples of 244 neuroblastoma patients. We then used digital pathology to calculate the densities of these markers and derived an immunoscore based on such densities.

Results

Densities of CD3+ and CD8+ T cells in tumor were positively associated with the overall survival (OS) and event-free survival (EFS), whereas density of CD45RO+ T cells in tumor was negatively associated with OS but not EFS. An immunoscore with low density of CD3 and CD8 (CD3-CD8-) was indictive of a greater risk of death (hazard ratio 6.39, 95% confidence interval 3.09-13.20) and any event (i.e., relapse at any site, progressive disease, second malignancy, or death) (hazard ratio 4.65, 95% confidence interval 2.73-7.93). Multivariable analysis revealed that the CD3-CD8- immunoscore was an independent prognostic indicator for OS, even after adjusting for other known prognostic indicators.

Conclusions

The new immunoscore based on digital pathology evaluated densities of tumor-infiltrating CD3+ and CD8+ T cells contributes to the prediction of prognosis in neuroblastoma patients.

CSF2RB Is a Unique Biomarker and Correlated With Immune Infiltrates in Lung Adenocarcinoma

Background

The tumor microenvironment plays an important role in the occurrence and development of tumors. However, there are gaps in understanding the molecular and cellular interactions between tumor cells and the immune tumor microenvironment (TME). The aim of this study was to identify a novel gene that played an important role in the tumor microenvironment of lung adenocarcinoma (LUAD).

Methods

The gene expression profile and clinical data for LUAD were downloaded from TCGA database. First, we used the ESTIMATE algorithm to evaluate the immune and stromal scores accordingly. Also, we analyzed differentially expressed immune-related genes (IRGs) in the high and low immune/stromal score groups. Then, we used the protein–protein interaction network (PPI network) and a univariate Cox regression analysis to identify the hub gene. After that, we analyzed the relationship between CSF2RB expression and TNM stage/prognosis. Furthermore, gene set enrichment analysis (GSEA) was used to analyze the pathway regulated by CSF2RB and the Pearson correlation analysis method was used to analyze the correlation between the CSF2RB and immune cells. Finally, we used Western blot, real-time quantitative PCR (RT-qPCR), and immunohistochemistry (IHC) to validate CSF2RB expression in cancer and para-cancerous tissues.

Results

We identified that CSF2RB played an important role in the tumor microenvironment of LUAD. The expression of CSF2RB in tumor tissues was lower than that in normal tissues. Furthermore, the Kaplan–Meier plotter showed that a low CSF2RB expression was associated with poor survival and multivariate COX regression analysis revealed that the CSF2RB gene was an independent risk factor for prognosis, independent of whether patients received chemotherapy or radiotherapy. More importantly, a high expression of CSF2RB was related to early T, N, and clinical stages. GSEA analysis revealed that CSF2RB associated with diverse immune-related pathways, including T-cell receptor signaling pathway, Toll-like receptor signaling pathway, and B-cell receptor signaling pathway. CSF2RB expression levels were also positively related with the levels of infiltrating CD4+ T cells, macrophages, NK cells, and monocytes in LUAD. Finally, tumor tissues from LUAD patients were used for the assessment of CSF2RB expression. It was significantly lower in tumor sites than in adjacent normal tissues, which was consistent with data analysis.

Conclusion

CSF2RB effectively predicted the prognosis of patients with lung adenocarcinoma which could also be a potential target for cancer treatment and prevention. However, further studies are required to elucidate the function and regulatory mechanisms of CSF2RB and to develop some novel treatment strategies.

Different In Situ Immune Patterns between Primary Tumor and Lymph Node in Non-Small-Cell Lung Cancer: Potential Impact on Neoadjuvant Immunotherapy

Background

Neoadjuvant immunotherapy is promising for locally advanced non-small-cell lung cancer (NSCLC). The in situ immune patterns, as a predictor of PD-1/PD-L1 blockade outcomes, of the primary tumor (PT) and metastatic lymph nodes (mLNs) are unknown.

Methods

Multiplex immunofluorescence staining and multispectral imaging were used to evaluate the in situ immune patterns of T cells (CD3+) and cytotoxic T cells (CD8+) in terms of density, location (center of tumor (CT) and invasive margin (IM)), and the PD-L1 expression status of tumor cells and stromal T cells of paired PTs and mLNs in 38 stage III NSCLCs.

Results

The densities of T cells and cytotoxic T cells were correlated between PTs and mLNs at both CT and IM. Higher densities of stromal T cells (S-CD3+) at CT and both S-CD3+ and cytotoxic T cells (S-CD8+) at IM were observed in mLNs compared to PTs, while in tumor compartment, there were no differences in the densities of T cells (T-CD3+) or cytotoxic T cells (T-CD8+). Only the density of stromal PD-L1-positive T cells (S-PD-L1+CD3+) at CT was correlated between PTs and mLNs, while the densities and frequencies of S-PD-L1+CD3+ at CT and IM of mLNs were higher than PTs. Combining positive score discordance of PD-L1 between PTs and mLNs was greater than tumor proportion score. Conclusions. In situ immune patterns of T cells and cytotoxic T cells were different between PTs and mLNs in NSCLC. The heterogeneity of the in situ immune patterns may result in different immune-mediated responses to neoadjuvant immunotherapy in PT and mLNs.

Development and Validation of an Immune-Based Prognostic Risk Score for Patients With Resected Non-Small Cell Lung Cancer

Background

Despite the well-known role of immunoscore, as a prognostic tool, that appeared to be superior to tumor–node–metastasis (TNM) staging system, no prognostic scoring system based on immunohistochemistry (IHC) staining digital image analysis has been established in non-small cell lung cancer (NSCLC). Hence, we aimed to develop and validate an immune-based prognostic risk score (IMPRS) that could markedly improve individualized prediction of postsurgical survival in patients with resected NSCLC.

Methods

In this retrospective study, complete resection of NSCLC (stage I–IIIA) was performed for two independent patient cohorts (discovery cohort, n=168; validation cohort, n=115). Initially, paraffin-embedded resected specimens were stained by immunohistochemistry (IHC) of three immune cell types (CD3+, CD4+, and CD8+ T cells), and a total of 5,580 IHC-immune features were extracted from IHC digital images for each patient by using fully automated pipeline. Then, an IHC-immune signature was constructed with selected features using the LASSO Cox analysis, and the association of signature with patients’ overall survival (OS) was analyzed by Kaplan–Meier method. Finally, IMPRS was established by incorporating IHC-immune signature and independent clinicopathological variables in multivariable Cox regression analysis. Furthermore, an external validation cohort was included to validate this prognostic risk score.

Results

Eight key IHC-immune features were selected for the construction of IHC-immune signature, which showed significant associations with OS in all cohorts [discovery: hazard ratio (HR)=11.518, 95%CI, 5.444–24.368; validation: HR=2.664, 95%CI, 1.029–6.896]. Multivariate analyses revealed IHC-immune signature as an independent prognostic factor, and age, T stage, and N stage were also identified and entered into IMPRS (all p&lt;0.001). IMPRS had good discrimination ability for predicting OS (C-index, 0.869; 95%CI, 0.861–0.877), confirmed using external validation cohort (0.731, 0.717–0.745). Interestingly, IMPRS had better prognostic value than clinicopathological-based model and TNM staging system termed as C-index (clinicopathological-based model: 0.674; TNM staging: 0.646, all p&lt;0.05). More importantly, decision curve analysis showed that IMPRS had adequate performance for predicting OS in resected NSCLC patients.

Conclusions

Our findings indicate that the IMPRS that we constructed can provide more accurate prognosis for individual prediction of OS for patients with resected NSCLC, which can help in guiding personalized therapy and improving outcomes for patients.

Molecular Pathology of Lung Cancer

This overview of the molecular pathology of lung cancer includes a review of the most salient molecular alterations of the genome, transcriptome, and the epigenome. The insights provided by the growing use of next-generation sequencing (NGS) in lung cancer will be discussed, and interrelated concepts such as intertumor heterogeneity, intratumor heterogeneity, tumor mutational burden, and the advent of liquid biopsy will be explored. Moreover, this work describes how the evolving field of molecular pathology refines the understanding of different histologic phenotypes of non-small-cell lung cancer (NSCLC) and the underlying biology of small-cell lung cancer. This review will provide an appreciation for how ongoing scientific findings and technologic advances in molecular pathology are crucial for development of biomarkers, therapeutic agents, clinical trials, and ultimately improved patient care.

An Immune Feature-Based, Three-Gene Scoring System for Prognostic Prediction of Head-and-Neck Squamous Cell Carcinoma

Head-and-neck squamous cell carcinoma (HNSCC) is characterized by a high frequency of neck lymph node metastasis (LNM), a key prognostic factor. Therefore, identifying the biological processes during LNM of HNSCC has significant clinical implications for risk stratification. This study performed Gene Ontology enrichment analysis of differentially expressed genes between tumors with LNM and those without LNM and identified the involvement of immune response in the lymphatic metastasis of HNSCC. We further identified greater infiltrations of CD8⁺ T cells in tumors than in adjacent normal tissues through immunochemistry in the patient cohort (n = 62), indicating the involvement of CD8⁺ T cells in the antitumor immunity. Hierarchical clustering analysis was conducted to initially identify the candidate genes relevant to lymphocyte-mediated antitumor response. The candidate genes were applied to construct a LASSO Cox regression analysis model. Three genes were eventually screened out as progression-related differentially expressed candidates in HNSCC and a risk scoring system was established based on LASSO Cox regression model to predict the outcome in patients with HNSCC. The score was calculated using the formula: 0.0636 × CXCL11 - 0.4619 × CXCR3 + 0.2398 × CCR5. Patients with high scores had significantly worse overall survival than those with low scores (p < 0.001). The risk score showed good performance in characterizing tumor-infiltrating lymphocytes and provided a theoretical basis for stratifying patients receiving immune therapies. Additionally, a nomogram including the risk score, age, and TNM stage was constructed. The prediction model displayed marginally better discrimination ability and higher agreement in predicting the survival of patients with HNSCC compared with the TNM stage.

Tumour-infiltrating lymphocytes in oropharyngeal cancer: a validation study according to the criteria of the International Immuno-Oncology Biomarker Working Group

Background

The evaluation of immune response can aid in prediction of cancer behaviour. Here, we assessed the prognostic significance of tumour-infiltrating lymphocytes (TILs) in oropharyngeal squamous cell carcinoma (OPSCC).

Methods

A total of 182 patients treated for OPSCC were included in this study. Assessment of TILs was conducted on tumour sections stained with standard haematoxylin and eosin (HE) staining. We used the scoring criteria proposed by the International Immuno-Oncology Biomarker Working Group.

Results

The multivariable analysis showed that TILs associated with disease-specific survival with a hazard ratio (HR) of 2.13 (95% CI 1.14–3.96; P = 0.017). Similarly, TILs associated significantly with overall survival with HR of 1.87 (95% CI 1.11–3.13; P = 0.018). In a sub-analysis of HPV-positive and HPV-negative cases separately, TILs showed a significant prognostic value in both groups (P < 0.05).

Conclusion

The evaluation of TILs as proposed by the International Immuno-Oncology Biomarker Working Group is a simple and promising method in prediction of survival of OPSCC. It is easily applicable and after further validation can be implemented in the routine pathological report as a basic immune parameter.

AMPD1 Is Associated With the Immune Response and Serves as a Prognostic Marker in HER2-Positive Breast Cancer

Background

Although immunotherapy has been used in the treatment of metastatic triple negative breast cancer (TNBC), its therapeutic influence on human epidermal growth factor receptor 2 (HER2)-positive subtype remains controversial. It is therefore imperative to find biomarkers that can predict the immune response in HER2+ BC.

Methods

ESTIMATE was utilized to compute the ImmuneScore and StromalScore from data obtained from TCGA database, and differentially expressed genes (DEGs) were identified. In addition, univariate Cox regression was used to assess candidate genes such as AMPD1, CD33, and CCR5. Gene set enrichment analysis (GSEA) was used to further understand AMPD1-associated pathways. Moreover, immunohistochemical analyses were performed to further reveal the relationship among AMPD1, CD4 and CD8 genes.

Results

The expression of AMPD1 was markedly associated with disease outcome and tumor-infiltrating immune cells (TICs). In addition, AMPD1 was associated with lymph node status, age and the expression of PD-L1 and PD-L2. High AMPD1 expression was linked to longer overall survival (OS). Upregulated expression of AMPD1 correlated with the enrichment of immune-related signaling pathways. In addition, immunohistochemical analyses demonstrated a co-expression profile among AMPD1, CD4 and CD8 genes.

Conclusions

Taken together, our data demonstrated that AMPD1 might serve as a novel biomarker for predicting the immune response and disease outcome in HER2+ BC.

Identification of Prognostic Model Based on Immune-Related LncRNAs in Stage I-III Non-Small Cell Lung Cancer

Background

Long non-coding RNAs (lncRNAs) participate in the regulation of immune response and carcinogenesis, shaping tumor immune microenvironment, which could be utilized in the construction of prognostic signatures for non-small cell lung cancer (NSCLC) as supplements.

Methods

Data of patients with stage I-III NSCLC was downloaded from online databases. The least absolute shrinkage and selection operator was used to construct a lncRNA-based prognostic model. Differences in tumor immune microenvironments and pathways were explored for high-risk and low-risk groups, stratified by the model. We explored the potential association between the model and immunotherapy by the tumor immune dysfunction and exclusion algorithm.

Results

Our study extracted 15 immune-related lncRNAs to construct a prognostic model. Survival analysis suggested better survival probability in low-risk group in training and validation cohorts. The combination of tumor, node, and metastasis staging systems with immune-related lncRNA signatures presented higher prognostic efficacy than tumor, node, and metastasis staging systems. Single sample gene set enrichment analysis showed higher infiltration abundance in the low-risk group, including B cells (p<0.001), activated CD8+ T cells (p<0.01), CD4+ T cells (p<0.001), activated dendritic cells (p<0.01), and CD56+ Natural killer cells (p<0.01). Low-risk patients had significantly higher immune scores and estimated scores from the ESTIMATE algorithm. The predicted proportion of responders to immunotherapy was higher in the low-risk group. Critical pathways in the model were enriched in immune response and cytoskeleton.

Conclusions

Our immune-related lncRNA model could describe the immune contexture of tumor microenvironments and facilitate clinical therapeutic strategies by improving the prognostic efficacy of traditional tumor staging systems.

E-Cadherin Expression and Blunted Interferon Response in Blastic Plasmacytoid Dendritic Cell Neoplasm

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is an aggressive neoplasm derived from plasmacytoid dendritic cells (pDCs). In this study, we investigated by immunohistochemical analysis the expression of E-cadherin (EC) on pDCs in reactive lymph nodes and tonsils, bone marrow, and in BPDCN. We compared the expression of EC in BPDCN to that in leukemia cutis (LC) and cutaneous lupus erythematosus (CLE), the latter typically featuring pDC activation. In BPDCN, we also assessed the immunomodulatory activity of malignant pDCs through the expression of several type I interferon (IFN-I) signaling effectors and downstream targets, PD-L1/CD274, and determined the extent of tumor infiltration by CD8-expressing T cells. In reactive lymph nodes and tonsils, pDCs expressed EC, whereas no reactivity was observed in bone marrow pDCs. BPDCN showed EC expression in the malignant pDCs in the vast majority of cutaneous (31/33 cases, 94%), nodal, and spleen localizations (3/3 cases, 100%), whereas it was more variable in the bone marrow (5/13, 38,5%), where tumor cells expressed EC similarly to the skin counterpart in 4 cases and differently in other 4. Notably, EC was undetectable in LC (n=30) and in juxta-epidermal pDCs in CLE (n=31). Contrary to CLE showing robust expression of IFN-I-induced proteins MX1 and ISG5 in 20/23 cases (87%), and STAT1 phosphorylation, BPDCN biopsies showed inconsistent levels of these proteins in most cases (85%). Expression of IFN-I-induced genes, IFI27, IFIT1, ISG15, RSAD2, and SIGLEC1, was also significantly (P<0.05) lower in BPDCN as compared with CLE. In BPDCN, a significantly blunted IFN-I response correlated with a poor CD8+T-cell infiltration and the lack of PD-L1/CD274 expression by the tumor cells. This study identifies EC as a novel pDC marker of diagnostic relevance in BPDCN. The results propose a scenario whereby malignant pDCs through EC-driven signaling promote the blunting of IFN-I signaling and, thereby, the establishment of a poorly immunogenic tumor microenvironment.

Prognostic impact of tumor-infiltrating lymphocytes in non-small cell lung carcinomas

INTRODUCTION

Tumor-infiltrating lymphocytes represent a pivotal component of the host anti-tumor response. Thus, they considerably influence the evolution of cancers including non-small cell lung carcinomas. Even if, this important role is consensual, many discordant results are published in the literature about the prognostic role of the different populations of tumor-infiltrating lymphocytes. The aim of our work was to evaluate the prognostic impact of CD8+, CD4+, and forkhead box protein P3+ lymphocytes in the tumor microenvironment of non-small cell lung carcinomas.

METHODS

We conducted a retrospective descriptive study, which included non-small cell lung carcinomas diagnosed in the department of pathology and followed in the medical oncology department of the same hospital between 2011 and 2015. Tumor-infiltrating lymphocytes were analyzed by the immunohistochemical method for forkhead box protein P3, CD4, and CD8. Intratumoral and stromal-labeled lymphocytes were quantified by manual counting at high magnification (×400). Forkhead box protein P3+/CD8+, forkhead box protein P3+/CD4+, and CD8+/CD4+ ratios were subsequently calculated. The prognostic value of tumor-infiltrating lymphocytes was assessed in respect of overall survival, recurrence-free survival, and relapse-free survival.

RESULTS

Thirty-nine patients were included. The mean age of patients was 59.6 years. A complete surgical resection (p = 0.009), and a CD8/CD4 ratio (p = 0.008) were prognostic factors for overall survival. Complete surgical resection (p = 0.003), the forkhead box protein P3/CD8 (p = 0.005), and forkhead box protein P3/CD4 (p = 0.037) ratios were prognostic factors for recurrence-free survival. The CD8+ tumor-infiltrating lymphocytes rate (p = 0.037) was a prognostic factor for relapse-free survival with a threshold of 67.8/high power field. Microscopic subtype (p = 0.037) was a prognostic factor for relapse-free survival when only adenocarcinoma and squamous cell carcinoma were considered. In multivariate analysis, age (p = 0.004) and a CD8/CD4 ratio (p = 0.016) were independent predictors of overall survival.

CONCLUSION

Despite the limitations of our study, our results confirm the prognostic value of tumor-infiltrating lymphocytes in non-small cell lung carcinomas and the importance of the combined quantification of their different subpopulations.

Activin-A impedes the establishment of CD4+ T cell exhaustion and enhances anti-tumor immunity in the lung

Background

Although tumor-infiltrating T cells represent a favorable prognostic marker for cancer patients, the majority of these cells are rendered with an exhausted phenotype. Hence, there is an unmet need to identify factors which can reverse this dysfunctional profile and restore their anti-tumorigenic potential. Activin-A is a pleiotropic cytokine, exerting a broad range of pro- or anti-inflammatory functions in different disease contexts, including allergic and autoimmune disorders and cancer. Given that activin-A exhibits a profound effect on CD4⁺ T cells in the airways and is elevated in lung cancer patients, we hypothesized that activin-A can effectively regulate anti-tumor immunity in lung cancer.

Methods

To evaluate the effects of activin-A in the context of lung cancer, we utilized the OVA-expressing Lewis Lung Carcinoma mouse model as well as the B16F10 melanoma model of pulmonary metastases. The therapeutic potential of activin-A-treated lung tumor-infiltrating CD4⁺ T cells was evaluated in adoptive transfer experiments, using CD4^−/−-tumor bearing mice as recipients. In a reverse approach, we disrupted activin-A signaling on CD4⁺ T cells using an inducible model of CD4⁺ T cell-specific knockout of activin-A type I receptor. RNA-Sequencing analysis was performed to assess the transcriptional signature of these cells and the molecular mechanisms which mediate activin-A’s function. In a translational approach, we validated activin-A’s anti-tumorigenic properties using primary human tumor-infiltrating CD4⁺ T cells from lung cancer patients.

Results

Administration of activin-A in lung tumor-bearing mice attenuated disease progression, an effect associated with heightened ratio of infiltrating effector to regulatory CD4⁺ T cells. Therapeutic transfer of lung tumor-infiltrating activin-A-treated CD4⁺ T cells, delayed tumor progression in CD4^−/− recipients and enhanced T cell-mediated immunity. CD4⁺ T cells genetically unresponsive to activin-A, failed to elicit effective anti-tumor properties and displayed an exhausted molecular signature governed by the transcription factors Tox and Tox2. Of translational importance, treatment of activin-A on tumor-infiltrating CD4⁺ T cells from lung cancer patients augmented their immunostimulatory capacity towards autologous CD4⁺ and CD8⁺ T cells.

Conclusions

In this study, we introduce activin-A as a novel immunomodulatory factor in the lung tumor microenvironment, which bestows exhausted CD4⁺ T cells with effector properties.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-02092-5.

Digital Image Analysis for Estimating Stromal CD8+ Tumor-Infiltrating Lymphocytes in Lung Adenocarcinoma

Background:

Stromal CD8+ tumor-infiltrating lymphocytes (TILs) are an important prognostic and predictive indicator in non-small cell lung cancer (NSCLC). In this study, we aimed to develop and test the feasibility of a digital image analysis (DIA) workflow for estimating stromal CD8+ TIL density.

Methods:

A DIA workflow developed in a software platform (QuPath) was applied to a specified region of interest (ROI) within the stromal compartment of dual PD-L1/CD8 immunostained slides from 50 lung adenocarcinoma patients. A random tree classifier was trained from 25 training cases and applied to 25 test cases. The DIA-estimated CD8+ TIL densities were compared to manual estimates of three pathologists, who independently quantitated the percentage of CD8+ TILs from predefined ROIs in QuPath.

Results:

The average estimated total stromal cell count per case was 520 (range: 282–816) by QuPath and 551 (range: 265–744) by pathologists. The DIA-estimated CD8+ TIL density (mean = 16.9%) was comparable to pathologists' manual estimates (mean = 15.9%). A paired t-test showed no statistically significant difference between DIA and pathologist estimates of CD8+ TIL density among both training (n = 25, P = 0.55) and test (n = 25, P = 0.34) cases. There was an almost perfect agreement between QuPath and each pathologist's estimates of CD8+ TIL density (κ = 0.85–0.86).

Conclusions:

These findings demonstrate the feasibility of applying a DIA workflow for estimating stromal CD8+ TIL density in NSCLC. DIA has the potential to provide an efficient and standardized approach for estimating stromal CD8+ TIL density.

Intravascular emboli relates to immunosuppressive tumor microenvironment and predicts prognosis in stage III colorectal cancer

BACKGROUND

Stage III colorectal cancer (CRC) patients experience varying degrees of prognosis even if receiving standard therapeutic regimes. Intravascular emboli (IVE), a type of vascular invasion, impacts the clinical outcome in CRC. In this study, we confirmed the role of IVE in predicting the prognosis of stage III CRC patients and characterized the tumor microenvironment (TME) of CRC with IVE.

METHODS

Data from 220 consecutive patients (cohort 1) with stage III CRC undergoing radical surgery was collected retrospectively between January 2009 to December 2014. According to the presence of IVE, which was confirmed by two independent pathologists, patients were classified into two groups. Univariate and multivariate Cox regression analyses were performed to evaluate the relation of IVE presence to patients' prognosis. The association between IVE and clinicopathological factors was also analyzed. Furthermore, differentially expressed genes (DEGs) and gene set enrichment analyses (GSEA) were performed to describe features of the TME based on microarray data consisting of 6 patients. Tumor tissues from a separate cohort of 73 patients with stage III CRC (cohort 2) collected between June 2014 and December 2015 were used to analyze tumor-infiltrating lymphocyte (TIL) by immunohistochemistry (IHC) staining.

RESULTS

IVE was observed in 126 (57.3%) patients and could serve as an unfavorable independent prognostic predictor (P < 0.001) as well as lymph node metastasis (P < 0.05) and tumor location (P < 0.05). Additionally, patients with IVE had a higher neutrophil percentage (P = 0.002) and lower lymphocyte percentage (P = 0.002) relative to those without IVE. CRC with IVE had a significantly different profile of DEGs compared to CRC without IVE, and GSEA showed chronic inflammatory and immunosuppressive TME may promote IVE development. In cohort 2, tumors with IVE had fewer CD3⁺ TILs in the stromal region, as well as fewer CD8⁺ TILs in both stromal and tumoral regions relative to those without IVE.

CONCLUSION

IVE, which was related closely to a chronic inflammatory and immunosuppressive TME, forecasted a worse prognosis of stage III CRC patients and may be taken into consideration when a therapeutic strategy is decided upon.

The emergence of tumor-infiltrating lymphocytes in nasopharyngeal carcinoma: Predictive value and immunotherapy implications

The clinical study of nasopharyngeal carcinoma (NPC) often reveals a large number of lymphocytes infiltrating the primary tumor site. As an important part of the tumor microenvironment, tumor-infiltrating lymphocytes (TILs) do not exist alone but as a complex multicellular population with high heterogeneity. TILs play an extremely significant role in the occurrence, development, invasion and metastasis of NPC. The latest research shows that they participate in tumorigenesis and treatment, and the composition, quantity, functional status and distribution of TILs subsets have good predictive value for the prognosis of NPC patients. TILs are an independent prognostic factor for TNM stage and significantly correlated with better prognosis. Additionally, adoptive immunotherapy using anti-tumor TILs has achieved good results in a variety of solid tumors including NPC. This review evaluates recent clinical and preclinical studies of NPC, summarizes the role of TILs in promoting and inhibiting tumor growth, evaluates the predictive value of TILs, and explores the potential benefits of TILs-based immunotherapy in the treatment of NPC.

Systemic inflammation markers and cancer incidence in the UK Biobank

Systemic inflammation markers have been linked to increased cancer risk and mortality in a number of studies. However, few studies have estimated pre-diagnostic associations of systemic inflammation markers and cancer risk. Such markers could serve as biomarkers of cancer risk and aid in earlier identification of the disease. This study estimated associations between pre-diagnostic systemic inflammation markers and cancer risk in the prospective UK Biobank cohort of approximately 440,000 participants recruited between 2006 and 2010. We assessed associations between four immune-related markers based on blood cell counts: systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and risk for 17 cancer sites by estimating hazard ratios (HR) using flexible parametric survival models. We observed positive associations with risk for seven out of 17 cancers with SII, NLR, PLR, and negative associations with LMR. The strongest associations were observed for SII for colorectal and lung cancer risk, with associations increasing in magnitude for cases diagnosed within one year of recruitment. For instance, the HR for colorectal cancer per standard deviation increment in SII was estimated at 1.09 (95% CI 1.02-1.16) in blood drawn five years prior to diagnosis and 1.50 (95% CI 1.24-1.80) in blood drawn one month prior to diagnosis. We observed associations between systemic inflammation markers and risk for several cancers. The increase in risk the last year prior to diagnosis may reflect a systemic immune response to an already present, yet clinically undetected cancer. Blood cell ratios could serve as biomarkers of cancer incidence risk with potential for early identification of disease in the last year prior to clinical diagnosis.

An immune-based risk-stratification system for predicting prognosis in pulmonary sarcomatoid carcinoma (PSC)

Pulmonary sarcomatoid carcinoma (PSC) is an uncommon subtype of lung cancer, and immune checkpoint blockade promises in clinical benefit. However, virtually nothing is known about the expression of common immune checkpoints in PSC. Here, we performed immunohistochemistry (IHC) to detect nine immune-related proteins in 97 PSC patients. Based on the univariable Cox regression, random forests were used to establish risk models for OS and DFS. Moreover, we used the GSEA, CIBERSORT, and ImmuCellAI to analyze the enriched pathways and microenvironment. Univariable analysis revealed that CD4 (P = 0.008), programmed cell death protein 1 (PD-1; P = 0.003), galectin-9 (Gal-9) on tumor cells (TCs; P = 0.021) were independent for DFS, while CD4 (P = 0.020), PD-1 (P = 0.004), Gal-9 (P = 0.033), and HLA on TILs (P = 0.031) were significant for OS. Meanwhile, the expression level of CD8 played a marginable role in DFS (P = 0.061), limited by the number of patients. The combination of Gal-9 on TC with CD4 and PD-1 on TILs demonstrated the most accurate prediction for DFS (AUC: 0.636-0.791, F1-score: 0.635–0.799), and a dramatic improvement to TNM-stage (P < 0.001 for F1-score of 1-y, 3-y, and 5-yDFS). A similar finding was also observed in the predictive ability of CD4 for OS (AUC: 0.602-0.678, F1-score: 0.635–0.679). CD4 was negatively associated with the infiltration of neutrophils (P = 0.015). PDCD1 (coding gene of PD-1) was positively correlated to the number of exhausted T cells (Texs; P = 0.020) and induced regulatory T cells (iTregs; P = 0.021), and LGALS9 (coding gene of Gal-9) was positively related to the level of dendritic cells (DCs; P = 0.021). Further, a higher combinational level of CD4, PDCD1 on TILs, and LAGLS9 on TCs were proved to be infiltrated with more M1-type macrophages (P < 0.05). We confirmed the expression status of nine immune-related proteins and established a TNM-Immune system for OS and DFS in PSC to assist clinical risk-stratification.

Tumor-infiltrating plasma cells are the promising prognosis marker for esophageal squamous cell carcinoma

BACKGROUND

There is an urgent need to improve the clinical and basic research of esophageal cancer. The purpose of this study was to explore the prognostic value of tumor-infiltrating plasma cells (TIP) on overall survival (OS) of patients with esophageal squamous cell carcinoma (ESCC).

METHODS

Three independent cohorts, which include 116 consecutive cases who received radical resection of ESCC in our institution (set to be discovery set), 179 cases from public GEO database (validation GEO set) and 95 cases from TCGA (validation TCGA set), with a total of 390 cases were retrospectively enrolled in this study.

RESULTS

TIP was detected by immunohistochemical staining of CD138 in the paraffin-embedded specimen after surgery in the discovery set and was validated by using an established computational algorithm in the GEO and TCGA sets. Kaplan-Meier survival analysis showed high TIP was coincidently and significantly associated with favorable OS of ESCC in discovery set (p = 0.004) and validation GEO set (p = 0.002), showed a trend of better survival in validation TCGA set (p = 0.256 for 5-year OS, p = 0.034 for 15-month OS). Univariate and multivariate Cox regression analysis, together with survival analysis of the interaction between TIP and other variables, confirmed TIP to be a significant and independent prognostic factor for OS of ESCC. The incorporation of TIP into the TNM staging system could improve the accuracy of prognosis prediction for ESCC.

CONCLUSION

This study revealed that high TIP in ESCC was associated with positive regulation of adaptive immunity and anti-tumor activity.

Improving Risk Stratification of Early Oral Tongue Cancer with TNM-Immune (TNM-I) Staging System

Simple Summary

Some patients with early-stage oral tongue cancer suffer from poor survival. The currently used classification requires further improvement to better predict the prognosis. Immune-related parameters (such as assessment of infiltrating lymphocytes) can be used as a modifier for the classification and that can aid in improving the prognostication. We included 290 cases of early-stage oral tongue cancer in this study. Lymphocytes were scored and divided as low or high and incorporated in the traditional tumor-node-metastasis (TNM) classification to form our proposed TNM-Immune staging system. The TNM-Immune staging system allowed for a significant distinction between T1 and T2. The TNM-Immune staging system showed a powerful ability to identify cases with poor survival. TNM-Immune staging forms a step towards a more personalized classification of early-stage oral tongue cancer.

Abstract

Although patients with early-stage oral tongue squamous cell carcinoma (OTSCC) show better survival than those with advanced disease, there is still a number of early-stage cases who will suffer from recurrence, cancer-related mortality and worse overall survival. Incorporation of an immune descriptive factor in the staging system can aid in improving risk assessment of early OTSCC. A total of 290 cases of early-stage OTSCC re-classified according to the American Joint Committee on Cancer (AJCC 8) staging were included in this study. Scores of tumor-infiltrating lymphocytes (TILs) were divided as low or high and incorporated in TNM AJCC 8 to form our proposed TNM-Immune system. Using AJCC 8, there were no significant differences in survival between T1 and T2 tumors (p > 0.05). Our proposed TNM-Immune staging system allowed for significant discrimination in risk between tumors of T1N0M0-Immune vs. T2N0M0-Immune. The latter associated with a worse overall survival with hazard ratio (HR) of 2.87 (95% CI 1.92–4.28; p < 0.001); HR of 2.41 (95% CI 1.26–4.60; p = 0.008) for disease-specific survival; and HR of 1.97 (95% CI 1.13–3.43; p = 0.017) for disease-free survival. The TNM-Immune staging system showed a powerful ability to identify cases with worse survival. The immune response is an important player which can be assessed by evaluating TILs, and it can be implemented in the staging criteria of early OTSCC. TNM-Immune staging forms a step towards a more personalized classification of early OTSCC.

Impact of PD-L1 and PD-1 Expression on the Prognostic Significance of CD8+ Tumor-Infiltrating Lymphocytes in Non-Small Cell Lung Cancer

The immune infiltrate within tumors has proved to be very powerful in the prognostic stratification of patients and much attention is also being paid towards its predictive value. In this work we therefore aimed at clarifying the significance and impact of PD-L1 and PD-1 expression on the prognostic value of CD8⁺ tumor infiltrating lymphocytes (TILs) in a cohort of consecutive patients with primary resected non-small cell lung cancer (NSCLC). Tissue microarrays (TMA) were built using one representative formalin fixed paraffin embedded block for every case, with 5 cores for each block. TMA sections were stained with PD-L1 (clone SP263), PD-1 (clone NAT105) and CD8 (clone SP57). Number of CD8⁺ cells per mm² were automatically counted; median, 25^th and 75^th percentiles of CD8⁺ cells were used as threshold for statistical clinical outcome analysis and evaluated in patients subgroups defined by expression of PD-L1 and PD-1 within tumors. We found an overall strong prognostic value of CD8⁺ cells in our cohort of 314 resected NSCLC, especially in PD-L1 negative tumors lacking PD-1⁺ TILs, and demonstrated that in PD-L1 positive tumors a higher density of CD8⁺ lymphocytes is necessary to improve the prognosis. Our data strengthen the concept of the importance of the assessment and quantification of the immune contexture in cancer and, similarly to what has been carried on in colorectal cancer, promote the efforts for the establishment of an Immunoscore for NSCLC for prognostic and possibly predictive purposes.

[Progress in Single-cell RNA Sequencing of Lung Adenocarcinoma]

肺腺癌（lung adenocarcinoma, LUAD）是临床上肺癌最常见的亚型，是癌症相关死亡最主要的原因之一。过去十几年中，随着薄层计算机断层扫描（computed tomography, CT）广泛用于常规肺癌筛查，影像学上表现为小结节的LUAD发病率显著增高，其发生发展机制复杂，个体预后差异显著。尽管近年来针对LUAD的靶向和免疫疗法取得了重大进展，但肿瘤细胞的耐药性始终未得到有效解决，从而限制了患者获益。随着人类基因组计划的完成，以测序为基本手段的基因组学及转录组学进入临床和科研人员的视野。单细胞测序作为近年来受到高度关注的新型测序手段，与二代测序相比，其能在单细胞水平上对细胞群体进行特异性分析，揭示出每种细胞类型独特的变化，在单细胞水平上对许多异质基质细胞和癌细胞进行较精准地评估，从而揭示了分子成分的复杂性以及与非恶性组织中相应成分的区别。综上，通过单细胞测序深入了解LUAD发生发展机制和肿瘤微环境（tumor microenvironment, TME）的异质性及其耐药性形成机制，从而发现新的治疗靶点是临床医生和基础科学家迫切的需求。本文综合论述了单细胞测序在LUAD中的具体应用和研究进展。