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

Histopathological biomarkers in squamous cell carcinoma of the vulva: the prognostic relevance of tumor-infiltrating lymphocytes (TILs)-a retrospective study of 157 cases

BACKGROUND

The prognostic relevance of tumor-infiltrating lymphocytes (TILs) has so far been recognized in several solid tumors like in breast, endometrial and ovarian cancer-nonetheless, the immune contexture of squamous cell carcinomas of the vulva, analyzed by means of stromal (s) and intratumoral (i) TILs, remains yet to be elucidated.

MATERIAL AND METHODS

In this study, we examined the immunooncological biomarkers sTILs and iTILs in 157 vulvectomy specimens with histologically diagnosed vulvar squamous cell carcinoma (VSCC) according to the standardized methodology proposed by the International Immunooncology Biomarkers Working Group in 2017. In a next step, we evaluated the association of infiltrating lymphocytes to traditional histopathological parameters such as infiltration depth and HPV related tumorigenesis. After determining optimal cut-off values using Receiver Operating Characteristic (ROC) curve analysis, we assessed the prognostic relevance of sTILs and iTILs with regard to overall survival, local recurrence, and metastasis using the Log-rank (Mantel-Cox) test and Fisher's exact test.

RESULTS

We propose optimal cut-off values of 5% for iTILs and 20% for sTILs analysis, which identify patients with a distinct superior survival rate (sTILs: p = 0.0137; iTILs: p = 0.0226). Furthermore, a low number of iTILs was associated with a higher risk of local recurrence in our study collective (p = 0.0432).

CONCLUSION

The fast and cost-effective determination of the histological biomarkers iTILs and sTILs yields prognostic relevance in vulvar cancer. A potential integration within the routine diagnostic workflow could be globally feasible, even in resource-poor settings.

Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma

BACKGROUND

Current prognostic and predictive biomarkers for lung adenocarcinoma (LUAD) predominantly rely on unimodal approaches, limiting their characterization ability. There is an urgent need for a comprehensive and accurate biomarker to guide individualized adjuvant therapy decisions.

METHODS

In this retrospective study, data from patients with resectable LUAD (stage I-III) were collected from two hospitals and a publicly available dataset, forming a training dataset (n=223), a validation dataset (n=95), a testing dataset (n=449), and the non-small cell lung cancer (NSCLC) Radiogenomics dataset (n=59). Tumor and peritumor scores were constructed from preoperative CT radiomics features (shape/intensity/texture). An immune score was derived from the density of tumor-infiltrating lymphocytes (TILs) within the cancer epithelium and stroma on hematoxylin and eosin-stained whole-slide images. A clinical score was constructed based on clinicopathological risk factors. A Cox regression model was employed to integrate these scores, thereby constructing a multimodal nomogram to predict disease-free survival (DFS). The adjuvant chemotherapy benefit rate was subsequently calculated based on this nomogram.

RESULTS

The multimodal nomogram outperformed each of the unimodal scores in predicting DFS, with a C-index of 0.769 (vs 0.634-0.731) in the training dataset, 0.730 (vs 0.548-0.713) in the validation dataset, and 0.751 (vs 0.660-0.692) in the testing dataset. It was independently associated with DFS after adjusting for other clinicopathological risk factors (training dataset: HR=3.02, p<0.001; validation dataset: HR=2.33, p<0.001; testing dataset: HR=2.03, p=0.001). The adjuvant chemotherapy benefit rate effectively distinguished between patients benefiting from adjuvant chemotherapy and those from observation alone (interaction p<0.001). Furthermore, the high-/low-risk groups defined by the multimodal nomogram provided refined stratification of candidates for adjuvant chemotherapy identified by current guidelines (p<0.001). Gene set enrichment analyses using the NSCLC Radiogenomics dataset revealed associations between tumor/peritumor scores and pathways involved in epithelial-mesenchymal transition, angiogenesis, IL6-JAK-STAT3 signaling, and reactive oxidative species.

CONCLUSION

The multimodal nomogram, which incorporates tumor and peritumor morphology with anti-tumor immune response, provides superior prognostic accuracy compared with unimodal scores. Its defined adjuvant chemotherapy benefit rates can inform individualized adjuvant therapy decisions.

Harnessing TAGAP to improve immunotherapy for lung squamous carcinoma treatment by targeting c-Rel in CD4+ T cells

Revealing the immunosenescence, particularly in CD4+ T cell function in lung squamous carcinoma (LUSC) assists in devising individual treatment strategies. This study identifies differentially expressed genes (DEGs) between ROS1 mutated (ROS1MUT) and wild-type (ROS1WT) LUSC samples from the TCGA database. Using WGCNA, immune-related DEGs (IRGs) were screened. Prognostic signatures derived from IRGs were used to compare immune infiltration, chemotherapy sensitivity, and immune-phenotyping score (IPS) between high- and low-risk subgroups. Hub gene abundance in different cell clusters was analyzed via Sc-seq. TAGAP overexpression or silencing was employed to assess its impact on cytokines production and differentiation of CD4+ T cells, downstream c-Rel expression, and tumor progression. High-risk subgroups exhibited decreased infiltration of natural killer, follicular helper T, and CD8+ T cells, but increased plasma, CD4+ memory resting T, and macrophage M2 cells. These subgroups were more sensitive to Sunitinib and CTLA4 blockade. TAGAP expression was significantly reduced in LUSC. Overexpressing TAGAP enhanced CD4+ T cells to produce cytokines, promoted differentiation into Th1/Th17 cells, inhibited Treg conversion, and suppressed LUSC cell phenotype in vitro. TAGAP overexpression in CD4+ T cells also inhibited LUSC tumor growth and boosted immune infiltration in vivo. TAGAP's effects on CD4+ T cells were partly reversed by c-Rel overexpression, highlighting TAGAP's role in rejuvenating CD4+ T cells and exerting anticancer effects by inhibiting c-Rel. This study elucidates the novel therapeutic potential of targeting TAGAP to modulate CD4+ T cell activity in immunotherapy for LUSC.

Clinico-Pathological Significance of Tumor Infiltrating Immune Cells in Oral Squamous Cell Carcinoma-Hope or Hype?

BACKGROUND

To correlate between immunohistochemical expression of tumor-infiltrating lymphocytes (TILs), tumor-associated macrophages (TAMs), and natural killer (NK) cells with the AJCC 8th edition TNM staging system and other disease-modifying clinico-pathological variables.

METHODS

The representative histology sections of tumor invasive margin (IM) and tumor core (TC) were selected according to the International Immuno-Oncology Biomarker Working Group and were subjected to immunohistochemistry with antibodies for TILs (CD3, CD8, FOXP3), NK Cells (CD57), TAMs (CD68, CD163) and pan-leukocyte marker (CD45). Histo-immuno-density-intensity (HIDI) scoring was calculated as a product of the proportion and intensity of staining. Ordinal-ordinal and continuous-ordinal variables were correlated using Kendall's tau-b (τb), and binary-ordinal variables were correlated using Rank-Biserial (rrb) statistics.

RESULTS

A total of 111 patients were included in the study. None of the clinical and pathological parameters showed a strong correlation with any of the immune infiltrates including TNM staging.

CONCLUSION

We hypothesize an independent activity of tumor immunology in the disease prognosis.

TRIAL REGISTRATION

CTRI/2020/07/026335.

High density of TCF1+ stem-like tumor-infiltrating lymphocytes is associated with favorable disease-specific survival in NSCLC

Introduction

Tumor-infiltrating lymphocytes are both prognostic and predictive biomarkers for immunotherapy response. However, less is known about the survival benefits oftheir subpopulations.

Methods

Using machine learning models, we assessed the clinical association of the CD8+, PD1+, TCF1+ cel l subset by multiplex immunohistochemistry using tissue microarrays in 553 non-small cell lung cancer (NSCLC) patients and its correlation with other immune cell biomarkers.

Results

We observed positive correlations between TCF1 and CD20 (r=0.37), CD3 (r=0.45)and CD4 (r=0.33). Notably, triple positive (CD8+PD1+TCF1+) were rare, only observed in 29 of 553 patients (5%). Our analysis revealed that cells coexpressing TCF1 with either CD8+ or PD1+ were independent prognostic markers of disease-specific survival in multivariable analysis (HR=0.728, p=0.029 for CD8+TCF1+, and HR=0.612, p=0.002 for PD1+TCF1+). To pilot the subtype of abundant CD8-TCF1+ cells, we explored an immune cell infiltrated whole slideimage and found the majority to be CD4+.

Discussion

Overall, these findings suggest that assessment of CD8+, PD1+, TCF1+ could serve as a potential prognostic biomarker in NSCLC.

The Role of Lymphocytic Infiltrates in the Tumor Microenvironment as a Predictive Factor for the Response to Immunotherapy in Solid Tumors: A Single-Center Experience From Romania

The correlation between tumor-infiltrating lymphocytes (TILs) and immunotherapy responses is an evolving field with significant clinical implications. Immunotherapy has revolutionized antineoplastic therapies, offering promising results for patients diagnosed with solid tumors. Integrating biomarkers, refining imaging techniques, and developing non-invasive methods may enhance personalized medicine, optimizing treatment strategies while minimizing adverse effects. In our study, we conducted a retrospective analysis to assess the practicality of utilizing the predictive value of tumor-infiltrating lymphocytes (TILs) in the tumor microenvironment (TME) correlating the response to immunotherapy in patients with solid tumors, comprehensively navigating through currently available data. Continued research efforts and collaboration between scientists and clinicians are essential to unlock the full potential of these biomarkers and advance the field of immunotherapy in solid tumors.

Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms

AIMS

To study programmed death ligand 1 (PD-L1) expression and tumour infiltrating lymphocytes (TILs) in patients with early-stage non-small cell lung carcinoma (NSCLC) with artificial intelligence (AI) algorithms.

METHODS

The study included samples from 50 early-stage NSCLCs. PD-L1 immunohistochemistry (IHC) stained slides (clone SP263) were scored manually and with two different AI tools (PathAI and Navify Digital Pathology) by three pathologists. TILs were digitally assessed on H&E and CD8 IHC stained sections with two different algorithms (PathAI and Navify Digital Pathology, respectively). The agreement between observers and methods for each biomarker was analysed. For PD-L1, the turn-around time (TAT) for manual versus AI-assisted scoring was recorded.

RESULTS

Agreement was higher in tumours with low PD-L1 expression regardless of the approach. Both AI-powered tools identified a significantly higher number of cases equal or above 1% PD-L1 tumour proportion score as compared with manual scoring (p=0.00015), a finding with potential therapeutic implications. Regarding TAT, there were significant differences between manual scoring and AI use (p value <0.0001 for all comparisons). The total TILs density with the PathAI algorithm and the total density of CD8+ cells with the Navify Digital Pathology software were significantly correlated (τ=0.49 (95% CI 0.37, 0.61), p value<0.0001).

CONCLUSIONS

This preliminary study supports the use of AI algorithms for the scoring of PD-L1 and TILs in patients with NSCLC.

Pan-cancer evaluation of tumor-infiltrating lymphocytes and programmed cell death protein ligand-1 in metastatic biopsies and matched primary tumors

Tumor immunological characterization includes evaluation of tumor-infiltrating lymphocytes (TILs) and programmed cell death protein ligand-1 (PD-L1) expression. This study investigated TIL distribution, its prognostic value, and PD-L1 expression in metastatic and matched primary tumors (PTs). Specimens from 550 pan-cancer patients of the SHIVA01 trial (NCT01771458) with available metastatic biopsy and 111 matched PTs were evaluated for TILs and PD-L1. Combined positive score (CPS), tumor proportion score (TPS), and immune cell (IC) score were determined. TILs and PD-L1 were assessed according to PT organ of origin, histological subtype, and metastatic biopsy site. We found that TIL distribution in metastases did not vary according to PT organ of origin, histological subtype, or metastatic biopsy site, with a median of 10% (range: 0-70). TILs were decreased in metastases compared to PT (20% [5-60] versus 10% [0-40], p < 0.0001). CPS varied according to histological subtype (p = 0.02) and biopsy site (p < 0.02). TPS varied according to PT organ of origin (p = 0.003), histological subtype (p = 0.0004), and metastatic biopsy site (p = 0.00004). TPS was higher in metastases than in PT (p < 0.0001). TILs in metastases did not correlate with overall survival. In conclusion, metastases harbored fewer TILs than matched PT, regardless of PT organ of origin, histological subtype, and metastatic biopsy site. PD-L1 expression increased with disease progression. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Rabenosyn-5 suppresses non-small cell lung cancer metastasis via inhibiting CDC42 activity

Metastasis, the primary cause of death in lung cancer patients, is facilitated by cytoskeleton remodeling, which plays a crucial role in cancer cell migration and invasion. However, the precise regulatory mechanisms of intracellular trafficking proteins involved in cytoskeleton remodeling remain unclear. In this study, we have identified Rabenosyn-5 (Rbsn) as an inhibitor of filopodia formation and lung cancer metastasis. Mechanistically, Rbsn interacts with CDC42 and functions as a GTPase activating protein (GAP), thereby inhibiting CDC42 activity and subsequent filopodia formation. Furthermore, we have discovered that Akt phosphorylates Rbsn at the Thr253 site, and this phosphorylation negates the inhibitory effect of Rbsn on CDC42 activity. Additionally, our analysis reveals that Rbsn expression is significantly downregulated in lung cancer, and this decrease is associated with a worse prognosis. These findings provide strong evidence supporting the role of Rbsn in suppressing lung cancer progression through the inhibition of metastasis.

Prognostic value of tumour-associated regulatory T-cells as a biomarker in non-small cell lung cancer: a systematic review and meta-analysis

BACKGROUND

Tumour, nodes, and metastases (TNM) staging has been deficient in prognosticating in patients suffering from non-small cell lung cancer (NSCLC). To supplement TNM staging, this systematic review and meta-analysis aimed to evaluate the prognostic value of the regulatory T cells (Treg).

METHODS

A keyword search was conducted in MEDLINE and EMBASE for full-text original human studies from any region published in English during the last 12 years. Eligible for inclusion were studies evaluating the prognostic value of the number of Treg cells in NSCLC except case studies, case series, systematic reviews, and meta-analyses. Two reviewers (one reviewer used an automation tool) independently screened the studies and assessed risk-of-bias using the Quality in Prognosis Studies (QUIPS) tool. Meta-analysis was done for studies reporting significant multivariate hazard ratio (HR).

RESULTS

Out of 809 retrievals, 24 studies were included in the final review. The low number of Treg cells was found significantly associated with improved overall survival (pooled log OR, 1.646; 95% CI, 1.349, 1.944; p (2-tailed) < .001; SE, 0.1217), improved recurrence-free survival (HR, 1.99; 95% CI, 1.15, 3.46; p = .01), improved progression-free survival (pooled log OR, 2.231; 95% CI, 0.424, 4.038; p (2-tailed) .034; SE, 0.4200), and worse disease-free survival (pooled log OR, 0.992; 95% CI, 0.820, 1.163; p (2-tailed) .009; SE, 0.0135), especially when identified by forkhead box P3 (FOXP3), in any stage or non-metastatic NSCLC.

CONCLUSION

A low number of Treg cells indicated better survival, suggesting its potential use as a prognostic biomarker in NSCLC.

SYSTEMATIC REVIEW REGISTRATION

The protocol of this review was prospectively registered on PROSPERO on August 28, 2021, and was assigned the registration number CRD42021270598. The protocol can be accessed from PROSPERO website.

From Tumor Macroenvironment to Tumor Microenvironment: The Prognostic Role of the Immune System in Oral and Lung Squamous Cell Carcinoma

BACKGROUND

The interplay between cancer cells and the immune system is crucial in cancer progression and treatment. In this regard, the tumor immune microenvironment and macroenvironment, marked by systemic inflammation markers and TILs, could be considered key prognostic factors in tumors, including oral and lung squamous cell carcinoma.

METHODS

We conducted a retrospective clinical study on patients with Oral Squamous Cell Carcinoma (OSCC) and Lung Squamous Cell Carcinoma (LUSCC), examining stages, comorbidities, treatments, and outcomes. We evaluated the prognostic significance of pre-surgical systemic inflammation markers and tumor microenvironment composition.

RESULTS

Associations were found between systemic inflammation markers-NLR, MLR, and PLR-and tumor microenvironment factors, such as TILs and CD8+ cell prevalence-elevated inflammation markers correlated with advanced stages. Specifically, NLR was prognostic in OSCC, whereas PLR was prognostic in LUSCC. Using a cutoff value, we divided our tumor samples into two prognostic groups. Moreover, TILs levels >15% of tumor stroma correlated with prolonged overall survival in both OSCC and LUSCC, while increased CD8+ expression was linked to extended disease-free survival in LUSCC.

DISCUSSION

Systemic inflammation markers and TILs can be valuable prognostic factors of survival, highlighting the immune response's role in OSCC and LUSCC. Despite limited clinical integration of the presented cohorts due to a lack of standardization, we concluded that analyzing tumor immune profiles may offer novel prognostic insights.

CONCLUSIONS

Future integration into cancer classification could improve risk stratification and treatment guidance.

Spatial cell interplay networks of regulatory T cells predict recurrence in patients with operable non-small cell lung cancer

BACKGROUND

The interplay between regulatory T cells (Tregs) and neighboring cells, which is pivotal for anti-tumor immunity and closely linked to patient prognosis, remains to be fully elucidated.

METHODS

Tissue microarrays of 261 operable NSCLC patients were stained by multiplex immunofluorescence (mIF) assay, and the interaction between Tregs and neighboring cells in the tumor microenvironment (TME) was evaluated. Employing various machine learning algorithms, we developed a spatial immune signature to predict the prognosis of NSCLC patients. Additionally, we explored the interplay between programmed death-1/programmed death ligand-1 (PD-1/PD-L1) interactions and their relationship with Tregs.

RESULTS

Survival analysis indicated that the interplay between Tregs and neighboring cells in the invasive margin (IM) and tumor center was associated with recurrence in NSCLC patients. We integrated the intersection of the three algorithms to identify four crucial spatial immune features [P(CD8+Treg to CK) in IM, P(CD8+Treg to CD4) in IM, N(CD4+Treg to CK) in IM, N(CD4+Tcon to CK) in IM] and employed these characteristics to establish SIS, an independent prognosticator of recurrence in NSCLC patients [HR = 2.34, 95% CI (1.53, 3.58), P < 0.001]. Furthermore, analysis of cell interactions demonstrated that a higher number of Tregs contributed to higher PD-L1+ cells surrounded by PD-1+ cells (P < 0.001) with shorter distances (P = 0.004).

CONCLUSION

We dissected the cell interplay network within the TME, uncovering the spatial architecture and intricate interactions between Tregs and neighboring cells, along with their impact on the prognosis of NSCLC patients.

A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method

BACKGROUND

Robust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine.

MATERIAL AND METHODS

We recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection. We quantified the expressions of immune-related proteins (CD8, CD68, CD163, PD-1 and PD-L1) in paired HCC tissues and non-tumor liver tissues from these HCC patients using immunohistochemistry (mIHC) assays. We constructed the HCC prognosis prediction model using five different machine learning methods based on the patients in the discovery cohort, such as Cox proportional hazards (CoxPH).

RESULTS

We identified 19 features that were associated with overall survival of HCC patients in the discovery cohort (p < 0.1), such as immune-related features CD68+ and CD8+ cell infiltration. We constructed five HCC prognosis prediction models using five different machine learning methods. Among the five different machine learning models, the CoxPH model achieved the best performance (area under the curve [AUC], 0.839; C-index, 0.779). According to the risk score from CoxPH model, we divided HCC patients into high-risk group/low-risk group. In both discovery cohort and validation cohort, the patients in low-risk group showed longer overall survival compared with those in high-risk group (p = 1.8 × 10-7 and 3.4 × 10-5, respectively). Moreover, our novel scoring system efficiently predicted the 6, 12, and 18 months survival rate of HCC patients with AUC >0.75 in both discovery cohort and validation cohort. In addition, we found that the scoring system could also distinguish the patients with high/low risks of relapse in both discovery cohort and validation cohort (p = 0.00015 and 0.00012).

CONCLUSION

The novel CoxPH-based risk scoring model on clinical, laboratory-testing and immune-related features showed high prediction efficiencies for overall survival and recurrence of HCCs undergone surgical resection. Our results may be helpful to optimize clinical follow-up or therapeutic interventions.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

In situ analysis of CCR8+ regulatory T cells in lung cancer: suppression of GzmB+ CD8+ T cells and prognostic marker implications

BACKGROUND

CCR8-expressing regulatory T cells (Tregs) are selectively localized within tumors and have gained attention as potent suppressors of anti-tumor immunity. This study focused on CCR8+ Tregs and their interaction with CD8+ T cells in the tumor microenvironment of human lung cancer. We evaluated their spatial distribution impact on CD8+ T cell effector function, specifically granzyme B (GzmB) expression, and clinical outcomes.

METHODS

A total of 81 patients with lung squamous cell carcinoma (LSCC) who underwent radical surgical resection without preoperative treatment were enrolled. Histological analyses were performed, utilizing an automated image analysis system for double-stained immunohistochemistry assays of CCR8/Foxp3 and GzmB/CD8. We investigated the association of CCR8+ Tregs and GzmB+ CD8+ T cells in tumor tissues and further evaluated the prognostic impact of their distribution profiles.

RESULTS

Histological evaluation using the region of interest (ROI) protocol showed that GzmB expression levels in CD8+ T cells were decreased in areas with high infiltration of CCR8+ Tregs, suggesting a suppressive effect of CCR8+ Tregs on T cell cytotoxicity in the local tumor microenvironment. Analysis of the association with clinical outcomes showed that patients with more CCR8+ Tregs and lower GzmB expression, represented by a low GzmB/CCR8 ratio, had worse progression-free survival.

CONCLUSIONS

Our data suggest that local CCR8+ Treg accumulation is associated with reduced CD8+ T cell cytotoxic activity and poor prognosis in LSCC patients, highlighting the biological role and clinical significance of CCR8+ Tregs in the tumor microenvironment. The GzmB/CCR8 ratio may be a useful prognostic factor for future clinical applications in LSCC.

Prognostic implications of STK11 with different mutation status and its relationship with tumor-infiltrating immune cells in non-small cell lung cancer

Background

Mutations in STK11 (STK11Mut) gene may present a negative impact on survival in Non-small Cell Lung Cancer (NSCLC) patients, however, its relationship with immune related genes remains unclear. This study is to unveil whether overexpressed- and mutated-STK11 impact survival in NSCLC and to explore whether immune related genes (IRGs) are involved in STK11 mutations.

Methods

188 NSCLC patients with intact formalin-fixed paraffin-embedded (FFPE) tissue available for detecting STK11 protein expression were included in the analysis. After immunohistochemical detection of STK11 protein, patients were divided into high STK11 expression group (STK11High) and low STK11 expression group (STK11Low), and then Kaplan-Meier survival analysis and COX proportional hazards model were used to compare the overall survival (OS) and progression-free survival (PFS) of the two groups of patients. In addition, the mutation data from the TCGA database was used to categorize the NSCLC population, namely STK11 Mutated (STK11Mut) and wild-type (STK11Wt) subgroups. The difference in OS between STK11Mut and STK11Wt was compared. Finally, bioinformatics analysis was used to compare the differences in IRGs expression between STK11Mut and STK11Wt populations.

Results

The median follow-up time was 51.0 months (range 3.0 - 120.0 months) for real-life cohort. At the end of follow-up, 64.36% (121/188) of patients experienced recurrence or metastasis. 64.89% (122/188) of patients ended up in cancer-related death. High expression of STK11 was a significant protective factor for NSCLC patients, both in terms of PFS [HR=0.42, 95% CI= (0.29-0.61), P<0.001] and OS [HR=0.36, 95% CI= (0.25, 0.53), P<0.001], which was consistent with the finding in TCGA cohorts [HR=0.76, 95%CI= (0.65, 0.88), P<0.001 HR=0.76, 95%CI= (0.65, 0.88), P<0.001]. In TCGA cohort, STK11 mutation was a significant risk factor for NSCLC in both lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) histology in terms of OS [HR=6.81, 95%CI= (2.16, 21.53), P<0.001; HR=1.50, 95%CI= (1.00, 2.26), P=0.051, respectively]. Furthermore, 7 IRGs, namely CALCA, BMP6, S100P, THPO, CGA, PCSK1 and MUC5AC, were found significantly overexpressed in STK11-mutated NSCLC in both LUSC and LUAD histology.

Conclusions

Low STK11 expression at protein level and presence of STK11 mutation were associated with poor prognosis in NSCLC, and mutated STK11 might probably alter the expression IRGs profiling.

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.

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.

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.

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.

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.

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.