Digitally quantified CD8+ cells: the best candidate marker for an immune cell score in non-small cell lung cancer?

Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition

Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.

Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition

Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.

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.

Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis

BACKGROUND

The TP53 tumor suppressor gene is one of the most mutated genes in lung adenocarcinoma (LUAD) and plays a vital role in regulating the occurrence and progression of cancer. We aimed to elucidate the association between TP53 mutations, response to immunotherapies and the prognosis of LUAD.

METHODS

Genomic, transcriptomic, and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) dataset. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA). Gene set variation analysis (GSVA) were performed to determine the differences in biological pathways. A merged protein-protein interaction (PPI) network was constructed and analyzed. MSIpred was used to analyze the correlation between the expression of the TP53 gene, tumor mutation burden (TMB) and tumor microsatellite instability (MSI). CIBERSORT was used to calculate the abundance of immune cells. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of TP53 mutations in LUAD.

RESULTS

TP53 was the most frequently mutated in LUAD, with a mutational frequency of 48%. GO and KEGG enrichment analysis, GSEA, and GSVA results showed a significant upregulation of several signaling pathways, including PI3K-AKT mTOR (P < 0.05), Notch (P < 0.05), E2F target (NES = 1.8, P < 0.05), and G2M checkpoint (NES = 1.7, P < 0.05). Moreover, we found a significant correlation between T cells, plasma cells, and TP53 mutations (R² < 0.01, P = 0.040). Univariate and multivariate Cox regression analyses revealed that the survival prognosis of LUAD patients was related to TP53 mutations (Hazard Ratio (HR) = 0.72 [95% CI, 0.53 to 0.98], P < 0.05), cancer status (P < 0.05), and treatment outcomes (P < 0.05). Lastly, the Cox regression models showed that TP53 exhibited good power in predicting three- and five-year survival rates.

CONCLUSIONS

TP53 may be an independent predictor of response to immunotherapy in LUAD, and patients with TP53 mutations have higher immunogenicity and immune cell infiltration.

Stromal or intraepithelial tumor-infiltrating lymphocytes: which one has more prognostic significance in cervical cancer?

PURPOSE

To investigate the effect of tumor-infiltrating lymphocytes (TILs) and tumor associated macrophages (TAMs) on treatment results in patients with cervical squamous cell carcinoma who underwent definitive or adjuvant radiotherapy (RT) or chemoradiotherapy (CRT).

METHODS

Pathological specimens were evaluated from 96 cervical cancer patients who were treated with definitive or adjuvant RT/CRT between April 2001 and January 2020. The percentage of intraepithelial TILs (iTILs) and stromal TILs (sTILs) were calculated, and immunohistochemistry was used for identifying lymphocyte lineage with CD4, CD8, and CD20 antibodies and macrophages with CD68 antibody. Prognostic values of TILs/TAMs on oncological outcomes were evaluated.

RESULTS

Thirty patients had early-stage disease and 66 patients had advanced-stage disease. Sixty-three and 33 patients received adjuvant RT and definitive CRT, respectively. Low number of sCD20 positive cells was associated with large tumor size and parametrial invasion. In multivariate analysis, low percentage of sTILs and advanced-stage disease were independent poor prognostic factors for overall survival, disease-free survival (DFS), and distant metastasis-free survival; low number of sCD4 positive cells was also an independent poor prognostic factor for DFS. Low percentage of sTILs and low number of sCD8 positive cells was correlated with high rates of distant metastasis (p = 0.038 and p = 0.025, respectively).

CONCLUSION

sTILs have superior predictive value than iTILs in terms of prognosis. Stromal compartment should be investigated as a routine practice in TIL studies in cervical cancer. Intensifying the treatment in cervical cancer patients with low number of sTILs should be studied in further investigations.

Computer-assisted tumor grading, validation of PD-L1 scoring, and quantification of CD8-positive immune cell density in urothelial carcinoma, a visual guide for pathologists using QuPath

Background

Advances in digital imaging in pathology and the new capacity to scan high-quality images have change the way to practice and research in surgical pathology. QuPath is an open-source pathology software that offers a reproducible way to analyze quantified variables. We aimed to present the functionality of biomarker scoring using QuPath and provide a guide for the validation of pathologic grading using a series of cases of urothelial carcinomas.

Methods

Tissue microarrays of urothelial carcinomas were constructed and scanned. The images stained with HE, CD8 and PD-L1 immunohistochemistry were imported into QuPath and dearrayed. Training images were used to build a grade classifier and applied to all cases. Quantification of CD8 and PD-L1 was undertaken for each core using cytoplasmic and membrane color segmentation and output measurement and compared with pathologists semi-quantitative assessments.

Results

There was a good correlation between tumor grade by the pathologist and by QuPath software (Kappa agreement 0.73). For low-grade carcinomas (by the report and pathologist), the concordance was not as high. Of the 32 low-grade tumors, 22 were correctly classified as low-grade, but 11 (34%) were diagnosed as high-grade, with the high-grade to the low-grade ratio in these misclassified cases ranging from 0.41 to 0.58. The median ratio for bona fide high-grade carcinomas was 0.59. Some of the reasons the authors list as potential mimickers for high-grade cases are fulguration artifact, nuclear hyperchromasia, folded tissues, and inconsistency in staining. The correlation analysis between the software and the pathologist showed that the CD8 marker showed a moderate (r = 0.595) and statistically significant (p < 0.001) correlation. The internal consistency of this parameter showed an index of 0.470. The correlation analysis between the software and the pathologist showed that the PDL1 marker showed a robust (r = 0.834) and significant (p < 0.001) correlation. The internal consistency of this parameter showed a CCI of 0.851.

Conclusions

We were able to demonstrate the utility of QuPath in identifying and scoring tumor cells and IHC quantification of two biomarkers. The protocol we present uses a free open-source platform to help researchers deal with imaging and data processing in the surgical pathology field.

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.

CD8+ T Cell-Associated Gene Signature Correlates With Prognosis Risk and Immunotherapy Response in Patients With Lung Adenocarcinoma

The presence of infiltrating CD8⁺ T lymphocytes in the tumor microenvironment of lung adenocarcinoma (LUAD) is correlated with improved patient prognosis, but underlying regulatory mechanisms remain unknown. To identify biomarkers to improve early diagnosis and treatment of LUAD, we downloaded 13 immune cell line-associated datasets from the GEO database. We identified CD8⁺ T cell-associated genes via weighted correlation network analysis. We constructed molecular subtypes based on CD8⁺ T cell-associated genes and constructed a multi-gene signature. We identified 252 CD8⁺ T cell-associated genes significantly enriched in immune function-related pathways and two molecular subtypes of LUAD (immune cluster 1 [IC1] and IC2) using our CD8⁺ T cell-associated gene signature. Patients with the IC2 subtype had a higher tumor mutation burden and lower immune infiltration scores, whereas those with the IC1 subtype were more sensitive to immune checkpoint inhibitors. Prioritizing the top candidate genes to construct a 10-gene signature, we validated our model using independent GSE and TCGA datasets to confirm its robustness and stable prognostic ability. Our risk model demonstrated good predictive efficacy using the Imvigor210 immunotherapy dataset. Thus, we established a novel and robust CD8⁺ T cell-associated gene signature, which could help assess prognostic risk and immunotherapy response in LUAD patients.

A prognostic classification based on the International Association for the Study of Lung Cancer histologic grading and immunoscore in KRAS-mutant invasive non-mucinous adenocarcinoma

BACKGROUND

Tumor immune cell infiltration is important in the prognosis of patients with lung adenocarcinoma. The aim of this study was to develop a prognostic classification based on the tumor immunoscore.

METHODS

Patients with KRAS-mutant invasive non-mucinous lung adenocarcinoma who underwent radical surgery were enrolled in the study. Histologic grading was assessed according to the recommendations of the International Association for the Study of Lung Cancer. Programmed death-ligand 1 (PD-L1) and CD8 expression was detected using immunohistochemistry. The number of CD8⁺ tumor-infiltrating lymphocytes (TILs) per high-power field was assessed. A classification based on histological grade and CD8⁺ TIL level was established (Grading-Immunoscore type): low-to-medium grade with high or low infiltration (type A); high-grade, high-infiltration (type B); and high-grade, low-infiltration (type C).

RESULTS

A total of 112 patients participated. In the multivariable analysis, histological grading and level of CD8⁺ TILs were independent prognostic factors for overall survival (OS) and progression-free survival (PFS) (p < 0.001 and p = 0.007, respectively). Patients with type A tumors had the best OS and PFS, whereas those with type C tumors had the worst OS (89.6%, 65.0%, and 29.5% 5-year OS for types A, B, and C, respectively). PD-L1 positivity and high expression rate was highest in type B tumors (tumor proportion score [TPS] ≥ 1%: 29.4%, 73.1%, and 42.9%; TPS ≥50%: 7.8%, 42.3%, and 17.1%, for types A, B, and C, respectively).

CONCLUSIONS

The Grading-Immunoscore classification refines the prognostic grouping of histological grading and might aid in the screening of potential candidates for immunotherapy in patients with KRAS-mutant adenocarcinoma.

Therapeutic Implications of Tumor Microenvironment in Lung Cancer: Focus on Immune Checkpoint Blockade

In the last decade, the treatment of non-small cell lung cancer (NSCLC) has been revolutionized by the introduction of immune checkpoint inhibitors (ICI) directed against programmed death protein 1 (PD-1) and its ligand (PD-L1), or cytotoxic T lymphocyte antigen 4 (CTLA-4). In spite of these improvements, some patients do not achieve any benefit from ICI, and inevitably develop resistance to therapy over time. Tumor microenvironment (TME) might influence response to immunotherapy due to its prominent role in the multiple interactions between neoplastic cells and the immune system. Studies investigating lung cancer from the perspective of TME pointed out a complex scenario where tumor angiogenesis, soluble factors, immune suppressive/regulatory elements and cells composing TME itself participate to tumor growth. In this review, we point out the current state of knowledge involving the relationship between tumor cells and the components of TME in NSCLC as well as their interactions with immunotherapy providing an update on novel predictors of benefit from currently employed ICI or new therapeutic targets of investigational agents. In first place, increasing evidence suggests that TME might represent a promising biomarker of sensitivity to ICI, based on the presence of immune-modulating cells, such as Treg, myeloid derived suppressor cells, and tumor associated macrophages, which are known to induce an immunosuppressive environment, poorly responsive to ICI. Consequently, multiple clinical studies have been designed to influence TME towards a pro-immunogenic state and subsequently improve the activity of ICI. Currently, the mostly employed approach relies on the association of "classic" ICI targeting PD-1/PD-L1 and novel agents directed on molecules, such as LAG-3 and TIM-3. To date, some trials have already shown promising results, while a multitude of prospective studies are ongoing, and their results might significantly influence the future approach to cancer immunotherapy.

The effect of neoadjuvant therapy on PD-L1 expression and CD8+lymphocyte density in non-small cell lung cancer

PD-L1 expression is the routine clinical biomarker for the selection of patients to receive immunotherapy in non-small cell lung cancer (NSCLC). However, the application and best timing of immunotherapy in the resectable setting is still under investigation. We aimed to study the effect of chemotherapy on PD-L1 expression and tumor infiltrating lymphocytes (TILs), which is to date still poorly understood. Our retrospective, single-centre neoadjuvant cohort comprised 96 consecutive patients with NSCLC resected 2000-2016 after neoadjuvant therapy, including paired diagnostic chemo-naïve specimens in 53 cases. A biologically matched surgical cohort of 114 primary resected cases was included. PD-L1 expression, CD8 + TILs density and tertiary lymphoid structures were assessed on whole slides and correlated with clinico-pathological characteristics and survival. Seven/53 and 12/53 cases had lower respectively higher PD-L1 expressions after neoadjuvant therapy. Most cases (n = 34) showed no changes in PD-L1 expression, the majority of these harboring PD-L1 < 1% in both samples (21/34 [61.8%]). Although CD8 + TILs density was significantly higher after chemotherapy (p = 0.031) in resections compared to diagnostic biopsies, this might be due to sampling and statistical bias. No difference in PD-L1 expression or CD8 + TILs density was detected when comparing the neoadjuvant and surgical cohort. In univariable analyses, higher CD8 + TILs density, higher numbers of tertiary lymphoid structures but not PD-L1 expression were significantly associated with longer survival. Increased PD-L1 expression after neoadjuvant chemotherapy was not significantly associated with shorter 5-year survival, but the number of cases was very low. In multivariable analysis, only pT category and age remained independent prognostic factors. In summary, PD-L1 expression was mostly unchanged after neoadjuvant chemotherapy compared to diagnostic biopsies. The sample size of cases with changed PD-L1 expression was too small to draw conclusions on any prognostic value.

Canonical NF-κB Promotes Lung Epithelial Cell Tumour Growth by Downregulating the Metastasis Suppressor CD82 and Enhancing Epithelial-to-Mesenchymal Cell Transition

Simple Summary

Canonical NF-κB signalling pathway acts as a tumour promoter in several types of cancer including non-small cell lung cancer (NSCLC), but the mechanism(s) by which it contributes to NSCLC is still under investigation. We show here that NF-κB RelA/p65 is required for the tumour growth of human NSCLC cells grown in vivo as xenografts in immune-compromised mice. RNA-seq transcriptome profile analysis identified the metastasis suppressor CD82/KAI1/TSPAN27 as a canonical NF-κB target. Loss of CD82 correlated with malignancy. RelA/p65 stimulates cell migration and epithelial-to-mesenchymal cell transition (EMT), mediated, in part, by CD82/KAI1, through integrin-mediated signalling, thus, identifying a mechanism mediating NF-κB RelA/p65 lung tumour promoting function.

Abstract

Background: The development of non-small cell lung cancer (NSCLC) involves the progressive accumulation of genetic and epigenetic changes. These include somatic oncogenic KRAS and EGFR mutations and inactivating TP53 tumour suppressor mutations, leading to activation of canonical NF-κB. However, the mechanism(s) by which canonical NF-κB contributes to NSCLC is still under investigation. Methods: Human NSCLC cells were used to knock-down RelA/p65 (RelA/p65^KD) and investigate its impact on cell growth, and its mechanism of action by employing RNA-seq analysis, qPCR, immunoblotting, immunohistochemistry, immunofluorescence and functional assays. Results: RelA/p65^KD reduced the proliferation and tumour growth of human NSCLC cells grown in vivo as xenografts in immune-compromised mice. RNA-seq analysis identified canonical NF-κB targets mediating its tumour promoting function. RelA/p65^KD resulted in the upregulation of the metastasis suppressor CD82/KAI1/TSPAN27 and downregulation of the proto-oncogene ROS1, and LGR6 involved in Wnt/β-catenin signalling. Immunohistochemical and bioinformatics analysis of human NSCLC samples showed that CD82 loss correlated with malignancy. RelA/p65^KD suppressed cell migration and epithelial-to-mesenchymal cell transition (EMT), mediated, in part, by CD82/KAI1, through integrin-mediated signalling involving the mitogenic ERK, Akt1 and Rac1 proteins. Conclusions: Canonical NF-κB signalling promotes NSCLC, in part, by downregulating the metastasis suppressor CD82/KAI1 which inhibits cell migration, EMT and tumour growth.