Prognostic Implications of Molecular Subtypes in Primary Small Cell Lung Cancer and Their Correlation With Cancer Immunity

Clinicopathological characteristics of transcription factor-defined subtypes in bladder small cell carcinoma

BACKGROUND

Small cell carcinoma (SmCC) of the bladder is a rare and aggressive malignancy. Characterizing transcription factor (TF)-defined subtypes may provide insights into its biology and inform targeted therapies. This study investigates lineage-specific TF expression in bladder SmCC, its association with clinicopathological features, and comparisons with prostate SmCC.

METHODS

A retrospective analysis was conducted on 9 cases of bladder SmCC and 6 cases of prostate SmCC diagnosed at a single cancer hospital in Japan. Immunohistochemistry was performed for lineage-specific TFs (ASCL1, NEUROD1, POU2F3, and YAP1) and neuroendocrine and other markers. Statistical comparisons were made using Fisher's exact test and independent samples t-tests.

RESULTS

Combined SmCC morphology, including urothelial carcinoma (UC) (5 cases) and adenocarcinoma (2 cases), was more frequent in bladder SmCC than in prostate SmCC (78% [7 of 9 cases] vs. 17% [1 of 6 cases], p = 0.041). NEUROD1 was more frequently expressed in bladder SmCC than in prostate SmCC (67% [6 of 9 cases] vs. 0% [0 of 6 cases]; p = 0.028). NEUROD1 expression was more frequent in combined SmCC and UC bladder tumors than in other bladder SmCC tumors (100% [5 of 5 cases] vs. 25% [1 of 4 cases], p = 0.048). Conversely, HNF4A expression was absent in all combined SmCC and UC bladder tumors (0 of 5) but present in 75% (3 of 4) of other bladder SmCC tumors (p = 0.048). In 2 cases of bladder SmCC, NEUROD1 and POU2F3 were expressed in a mutually exclusive manner, with neuroendocrine markers expressed only in the NEUROD1-expressing component.

CONCLUSIONS

NEUROD1 is characteristically expressed in bladder SmCC, especially in SmCC combined with UC, suggesting a distinct phenotype from prostate SmCC. These findings highlight the potential for TF-based classification to improve diagnostic accuracy and inform therapeutic strategies.

Comparative profiling of surgically resected primary tumors and their lymph node metastases in small-cell lung cancer

BACKGROUND

Profiling studies in small-cell lung cancer (SCLC) have mainly focused on primary tumors, omitting the potential molecular changes that might occur during lymphatic metastasis formation. Here, we assessed the molecular discordance between primary SCLCs and corresponding lymph node (LN) metastases in the light of subtype distribution and expression of clinically relevant proteins.

METHODS

Comparative profiling of 32 surgically resected primary SCLCs and their LN metastases was achieved by RNA expression analysis and immunohistochemistry (IHC). In addition to subtype markers (ASCL1, NEUROD1, POU2F3, and YAP1), the expression of nine cancer-specific proteins was evaluated.

RESULTS

The selected clinically relevant molecules showed no significant differences in their RNA expression profile when assessing the primary tumors and their corresponding LN metastases. Nevertheless, IHC analyses revealed significantly higher DLL3 expression in the primary tumors than in the LN metastases (P = 0.008). In contrast, NEUROD1 expression was significantly lower in the primary tumors (versus LN metastases, P < 0.001). No statistically significant difference was found by IHC analysis in the case of other clinically relevant proteins. Concerning SCLC molecular subtypes, a change in subtype distribution was detected in 21 cases. Phenotype switching from neuroendocrine (NE) subtypes toward non-NE lesions and from non-NE landscape toward NE subtypes were both detected.

CONCLUSIONS

Although the molecular landscape of SCLC LN metastases largely resembles that of the tumor of origin, key differences exist in terms of DLL3 and NEUROD1 expression, and in subtype distribution. These diagnostic pitfalls should be considered when establishing the tumors' molecular profile for future clinical trials solely based on LN biopsies.

Heterogeneity of molecular subtyping and therapy-related marker expression in primary tumors and paired lymph node metastases of small cell lung cancer

The classification of molecular subtypes and the identification of targetable molecules have been proposed for small cell lung cancer (SCLC) patients. Our aim was to investigate whether the expression of these markers evaluated using lymph node (LN) metastases represents that of primary tumors. We enrolled 46 surgically resected SCLC patients' primary tumors and paired mediastinal LN metastases. The protein expression of subtype-defining markers (ASCL1, NEUROD1, POU2F3, and YAP1) and therapeutic markers (DLL3, MYC, PD-L1, and MHC I) was examined by immunohistochemistry and was correlated with clinicopathological parameters and prognoses. In primary and metastatic tumors, the expression of these markers was 78.3% and 87.0%, 50.0% and 63.0%, 13.0% and 6.5%, 17.4% and 15.2%, 84.8% and 87.0%, 17.4% and 6.5%, 50.0% and 34.8%, and 60.9% and 37.0%, respectively. Positive tumor PD-L1 expression was less present in LN metastases (p = 0.015), and the same was true for MHC I expression (p = 0.036). NEUROD1 and DLL3 expression levels in metastatic tumors were stronger (p < 0.001 and p = 0.002, respectively); conversely, POU2F3, MYC, PD-L1, and MHC I expression levels were weaker (p = 0.018, p = 0.019, p = 0.001, and p < 0.001, respectively). In 15 (32.6%) patients, we observed a change in the molecular subtyping pattern, and a higher number of neuroendocrine (NE)-high phenotype patients were diagnosed when using the LN specimens (91.3% vs. 84.8%). TNM stage and postoperative chemotherapy were independent prognostic factors in surgically resected SCLC patients, and no prognostic differences were found among molecular subtypes. This study highlights the discordance of subtype-specific proteins and therapeutic markers between SCLC primary tumors and LN metastases. Additionally, our findings have therapeutic and prognostic implications and warrant further clinical investigation.

The analysis of molecular classification of pulmonary neuroendocrine tumors and relationship between YAP1 and efficacy

A novel molecular classification for small cell lung cancer (SCLC) has been established utilizing the transcription factors achaete-scute homologue 1 (ASCL1), neurogenic differentiation factor 1 (NeuroD1), POU class 2 homeobox 3 (POU2F3), and yes-associated protein 1 (YAP1). This classification was predicated on the transcription factors. Conversely, there is a paucity of information regarding the distribution of these markers in other subtypes of pulmonary neuroendocrine tumors (PNET). Clinical and survival data for PNET patients were gathered from January 2008 to December 2020. Immunohistochemical analysis was employed to evaluate the expression. The relationship between YAP1 expression and outcomes in patients with pulmonary large cell neuroendocrine carcinoma (LCNEC) was examined. Data from low-grade PNET patients who had previously undergone immunotherapy were retrospectively gathered and analyzed. The ASCL1 positive rate was markedly elevated in SCLC (7.1% vs. 60%; P < 0.001) and LCNEC patients (7.1% vs. 38.5%; P = 0.034) compared to PC patients. The YAP1-positive rate was elevated in LCNEC compared to SCLC (43.6% vs. 20%, P = 0.028) and pulmonary carcinoid (PC) patients (43.6% vs. 21.4%; P = 0.021). The DLL3-positive rate in SCLC patients was greater than in SCLC and PC patients (37.1% vs. 23.1% vs. 0%; P = 0.028, P = 0.021). A significant level of tumor heterogeneity was noted, with SCLC and LCNEC patients exhibiting markedly higher heterogeneity than PC patients (65.7% vs. 56.3% vs. 21.4%; P = 0.005, P = 0.025). In patients with LCNEC, YAP1 positivity exhibited no correlation with PD-L1 expression (17.1% vs. 45.7%, P = 0.518). Tumor heterogeneity was also noted in transformed SCLC, with no significant differences in the expression levels of transcription factors between transformed and traditional SCLC. In 13 LCNEC patients with a history of ICI application, YAP1 exhibited no significant effect on PFS (P = 0.331) or OS (P = 0.17) in the subgroup analysis of LCNEC patients. Among the 14 patients with low-grade PNET who underwent immunotherapy, the disease control rate was 85.7%. Patients with high-grade PNET have high levels of expression of ASCL1 and DLL3, whereas patients with LCNEC have high levels of expression of YAP1. With regard to the transcription factor level, it was found that patients with SCLC and LCNEC had a much higher degree of tumor heterogeneity than those with PC. In patients with LCNEC who were receiving monotherapy of ICIs or chemotherapy in combination with ICIs, the expression of YAP1 did not appear to have any clear impact on the prognosis. This is due to the limited sample size of the study, which requires additional investigation. When compared to the expression of TFs in regular SCLC, the expression of TFs in converted SCLC is comparable.

Comparison of molecular subtype composition between independent sets of primary and brain metastatic small cell lung carcinoma and matched samples

INTRODUCTION

Recent advances in the subclassification of small cell lung carcinomas (SCLCs) may help to overcome the unmet need for targeted therapies and improve survival. However, limited information is available on how the expression of the subtype markers changes during tumour progression. Our study aimed to compare the expression of these markers in primary and brain metastatic SCLCs.

MATERIALS AND METHODS

Immunohistochemical analysis of the subtype markers was performed on 120 SCLCs (including 10 matched samples) and SCLC xenografts.

RESULTS

Compared to primary SCLCs, there was a significant increase in the proportion of mixed subtypes in brain metastases, with a rate of ASCL1high/NeuroD1high and ASCL1high/NeuroD1high/YAP1high subtypes increasing to 48 % and 18 %, respectively. The subtype of the paired samples matched in only one-third of the cases. Although we did not observe a significant change after chemotherapy, a continuous decrease in ASCL1 expression coupled with an increase in the NeuroD1 expression was detected in the xenografts in a long-term experiment.

DISCUSSION

Our results indicate that the expression of subtype markers frequently changes during disease progression, and subtype analysis of the primary SCLC may not provide accurate information about the characteristics of the recurrent or metastatic tumour. Therefore, repeated sampling and subtyping may be necessary for subtype-specific targeted therapy.

Molecular subtypes, predictive markers and prognosis in small-cell lung carcinoma

AIMS

A new molecular subtype classification was proposed for small-cell lung carcinoma (SCLC). We aimed to further validate the classification in various SCLC patient samples using immunohistochemistry (IHC) to highlight its clinical significance.

METHODS

We analysed the protein expression of four subtype (achaete-scute family BHLH transcription factor 1 (ASCL1), neuronal differentiation 1 (NEUROD1), POU class 2 homeobox 3 (POU2F3) and Yes1-associated transcriptional regulator (YAP1)) and two predictive markers (delta-like ligand 3 (DLL3) and MYC) using IHC in 216 specimens from 195 SCLC patients, including 21 pairs of resected biopsy tumours. Associations among molecular subtypes, clinicopathological features and prognostic implications were also explored.

RESULTS

The ASCL1, NEUROD1, POU2F3, YAP1, DLL3 and MYC-positive expression rates were 70.3%, 56.9%, 14.9%, 19.0%, 75.4% and 22.6%, respectively. DLL3 expression had positive and negative associations with that of ASCL1 and POU2F3/YAP1, respectively, whereas MYC had the opposite effect. Strong associations of ASCL1 (Ρ=0.8603, p<0.0001), NEUROD1 (Ρ=0.8326, p<0.0001), POU2F3 (Ρ=0.6950, p<0.0001) and YAP1 (Ρ=0.7466, p<0.0001) expressions were detected between paired resected biopsy tumours. In addition to SCLC-A (ASCL1-dominant), SCLC-N (NEUROD1-dominant) and SCLC-P (POU2F3-dominant), unsupervised hierarchical cluster analyses identified a fourth, quadruple-negative SCLC subtype (SCLC-QN) characterised by the low expression of all four subtype-specific proteins, and 55.4% (n=108), 27.2% (n=53), 11.8% (n=23) and 5.6% (n=11) were categorised as SCLC-A, SCLC-N, SCLC-P and SCLC-QN, respectively. Significant enrichment of SCLC-P in the combined SCLC cohort was observed, and adenocarcinoma was more prevalent in SCLC-A, while large-cell neuroendocrine carcinoma was more commonly seen in SCLC-P. No survival difference was found among molecular subtypes.

CONCLUSIONS

Our results provide clinical insights into the diagnostic, prognostic and predictive significance of SCLC molecular subtype classifications.

[Immunotherapy for Extensive-stage Small Cell Lung Cancer: Research Progress and Future Perspectives]

At present, immunotherapy combined with chemotherapy has become the first-line standard of treatment for extensive-stage small cell lung cancer (ES-SCLC). In recent years, immune checkpoint inhibitors (ICIs) have received extensive attention and research in the field of lung cancer. At the same time, there are many challenges and tests in this process, such as the exploration of biomarkers, the exploration of new targets and new models, and the management of special populations. This article reviews the research progress in the field of ES-SCLC immunotherapy, and looks forward to the future development trend and potential direction of this field.
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Basal cell of origin resolves neuroendocrine-tuft lineage plasticity in cancer

Neuroendocrine and tuft cells are rare, chemosensory epithelial lineages defined by expression of ASCL1 and POU2F3 transcription factors, respectively1,2. Neuroendocrine cancers, including small cell lung cancer (SCLC), frequently display tuft-like subsets, a feature linked to poor patient outcomes3-13. The mechanisms driving neuroendocrine-tuft tumour heterogeneity, and the origins of tuft-like cancers are unknown. Using multiple genetically-engineered animal models of SCLC, we demonstrate that a basal cell of origin (but not the accepted neuroendocrine origin) generates neuroendocrine-tuft-like tumours that highly recapitulate human SCLC. Single-cell clonal analyses of basal-derived SCLC further uncovers unexpected transcriptional states and lineage trajectories underlying neuroendocrine-tuft plasticity. Uniquely in basal cells, introduction of genetic alterations enriched in human tuft-like SCLC, including high MYC, PTEN loss, and ASCL1 suppression, cooperate to promote tuft-like tumours. Transcriptomics of 944 human SCLCs reveal a basal-like subset and a tuft-ionocyte-like state that altogether demonstrate remarkable conservation between cancer states and normal basal cell injury response mechanisms14-18. Together, these data suggest that the basal cell is a plausible origin for SCLC and other neuroendocrine-tuft cancers that can explain neuroendocrine-tuft heterogeneity-offering new insights for targeting lineage plasticity.

The tumor immune microenvironment of SCLC is not associated with its molecular subtypes

INTRODUCTION

Small-cell lung carcinoma (SCLC) is a high-grade neuroendocrine carcinoma of poor prognosis. Although immune checkpoint blockers have shown promising results in advanced SCLC, the tumor immune microenvironment (TME) remains poorly understood, with no validated prognostic or predictive biomarkers of efficacy.

METHODS

This retrospective study included surgically samples from 48 SCLC patients between 2009 and 2018. We assessed the TME using two quantitative 7-plex immunofluorescence panels focusing on T and B cells, and compared it to NSCLC (N = 10). Molecular subtypes were determined by assessing the expression of ASCL1, NEUROD1 and YAP1 using immunohistochemistry.

RESULTS

Immune-hot SCLC were defined as those exhibiting the highest immune cell and immune-related marker densities. They were associated with longer overall survival, significantly more frequently detected at early stages, and exhibited high PD-L1 expression in immune cells, but were not associated with molecular subtypes. Compared to NSCLC, SCLC had significantly lower densities of CD20 + cells and higher density of PD1 + cells, with no significant differences in CD4 + , CD8 + and plasma cell densities. In univariate analysis, the highest OS was significantly associated with early stage (p < 0.001), low expression of NEUROD1 (p = 0.047), high PD1 + cell density (p < 0.001) and high PD-L1 immune cell expression (p = 0.04). Only stage and PD1 + cell density emerged as independent prognostic markers.

CONCLUSION

SCLC TME is highly heterogeneous. Immune-hot tumors were associated with OS but not with molecular classification. PD1 expression and PD-L1 expression by immune cells may thus serve as a prognostic marker.

Development of a novel nomogram for patients with SCLC and comparison with other models

BACKGROUND

Though several nomograms have been established to predict the survival probability of patients with small-cell lung cancer (SCLC), none involved enough variables. This study aimed to construct a novel prognostic nomogram and compare its performance with other models.

METHODS

Seven hundred twenty-two patients were pathologically diagnosed with SCLC in Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University from January 2016 to December 2018. We input Forty-one factors by reviewing the medical records. The nomogram was constructed based on the variables identified by univariate and multivariate analyses in the training set and validated in the validation set. Then we compared the performance of the models in terms of discrimination, calibration, and clinical net benefit.

RESULTS

There were eight variables involved in the nomogram: gender, monocyte (MON), neuron-specific enolase (NSE), cytokeratin 19 fragments (Cyfra211), M stage, radiotherapy (RT), chemotherapy cycles (CT cycles), and prophylactic cranial irradiation (PCI). The calibration curve showed a good correlation between the nomogram prediction and actual observation for overall survival (OS). The area under the curve (AUC) of the nomogram was higher, and the Integrated Brier score (IBS) was lower than other models, indicating a more accurate prediction. Decision curve analysis (DCA) showed a significant improvement in the clinical net benefit compared to the other models.

CONCLUSIONS

We constructed a novel nomogram to predict OS for patients with SCLC using more comprehensive and objective variables. It performed better than existing models and would assist clinicians in individually estimating risk and making a therapeutic regimen.

Molecular subtypes and prognostic factors of lung large cell neuroendocrine carcinoma

Background

Lung large cell neuroendocrine carcinoma (LCNEC) is an aggressive disease with poor prognosis and short-term survival, which lacks effective prognostic indicators. The study aims to investigate the molecular subtypes and prognostic markers of lung LCNEC.

Methods

Patients diagnosed with lung LCNEC at Sun Yat-sen University Cancer Center (SYSUCC) between November 2007 and January 2021 were screened. Baseline clinical data were collected and routine blood indexes including lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammation index (SII) were calculated. Immunohistochemistry (IHC) of ASCL1, NEUROD1, POU2F3, YAP1 were done to perform molecular subtyping, while CD56, Syn, CgA, CD3, CD8, CD20, CD68, and CD163 were also stained on tissue samples. Then prognostic factors of lung LCNEC were explored.

Results

One hundred and fifty-one lung LCNEC patients were identified, 103 of whom had complete clinical information, available routine blood and biochemical indexes were eventually included in the present study. Tumor tissue specimens were available from 64 patients. Positive expression rates of ASCL1, NEUROD1, and YAP1 were 82.8%, 50.0%, and 28.1%, respectively. No POU2F3+ cases were detected. Forty (62.5%) patients co-expressed with two or three markers. High LMR (>3.3) was an independent predictor of favorable prognosis of disease-free survival (DFS) [hazard ratio (HR), 0.391; 95% confidence interval (CI): 0.161-0.948; P=0.04] and overall survival (OS) (HR, 0.201; 95% CI: 0.071-0.574; P=0.003). Notably, high LMR was correlated with higher intra-tumoral CD3+ (P=0.004), CD8+ (P=0.01), and CD68+ (P<0.001) immune cell infiltration compared to low LMR in lung LCNEC.

Conclusions

Our study validated molecular subtypes by IHC in lung LCNEC, and co-expression was found among different subtypes, with no prognostic effect. High blood LMR level was associated with a favorable prognosis in lung LCNEC, which might partly reflect a hot tumor tissue immune microenvironment. Our findings may benefit clinical practice, and further studies are warranted.

Redefining YAP1 in small cell lung cancer: shifting from a dominant subtype marker to a favorable prognostic indicator

Background

Molecular and transcription factor subtyping were recently introduced to identify patients with unique clinical features in small cell lung cancer (SCLC). However, its prognostic relevance is yet to be established. This study aims to investigate the clinical implications and prognostic significance of transcription factor subtyping in SCLC using immunohistochemistry.

Methods

One hundred and ninety consecutive SCLC patients treated with platinum-based chemotherapy at a single institution were retrospectively reviewed. Expression of ASCL1, NeuroD1, POU2F3, and YAP1 was assessed by immunohistochemical staining and applied to determine the transcription factor subtype of each case.

Results

The association among transcription factors was not entirely mutually exclusive. YAP1 expression was the most significant prognostic indicator compared with other transcription factors or their related subtypes. Among patients with limited-stage disease (LD), complete response (CR) rates were 46.2% and 22.4% in the YAP1-positive and YAP1-negative groups, respectively. The median duration of response among patients who achieved CR was 64.8 and 36.4 months in the YAP1-positive and YAP1-negative groups, respectively (P=0.06). Median overall survival (OS) in LD was 35.6 and 16.9 months in the YAP1-positive and YAP1-negative groups, respectively (P=0.03). In extensive-stage disease (ED), the median OS was 11.3 months for the YAP1-positive group and 11 months for the YAP1-negative group (P=0.03).

Conclusions

Positive expression of YAP1 can be associated with durable CR and favorable survival outcomes in patients with SCLC, especially in LD.

Extensive-stage small-cell lung cancer in patients receiving atezolizumab plus carboplatin-etoposide: stratification of outcome based on a composite score that combines gene expression profiling and immune characterization of microenvironment

PURPOSE

Small-cell lung cancer (SCLC) is an aggressive disease with a dismal prognosis. The addition of immune checkpoints inhibitors to standard platinum-based chemotherapy in first-line setting achieves a durable benefit only in a patient subgroup. Thus, the identification of predictive biomarkers is an urgent unmet medical need.

EXPERIMENTAL DESIGN

Tumor samples from naive extensive-stage (ES) SCLC patients receiving atezolizumab plus carboplatin-etoposide were analyzed by gene expression profiling and two 9-color multiplex immunofluorescence panels, to characterize the immune infiltrate and SCLC subtypes. Associations of tissue biomarkers with time-to-treatment failure (TTF), progression-free survival (PFS) and overall survival (OS), were assessed.

RESULTS

42 patients were included. Higher expression of exhausted CD8-related genes was independently associated with a longer TTF and PFS while increased density of B lymphocytes correlated with longer TTF and OS. Higher percentage of M2-like macrophages close to tumor cells and of CD8+T cells close to CD4+T lymphocytes correlated with increased risk of TF and longer survival, respectively. A lower risk of TF, disease progression and death was associated with a higher density of ASCL1+tumor cells while the expression of POU2F3 correlated with a shorter survival. A composite score combining the expression of exhausted CD8-related genes, B lymphocyte density, ASCL1 tumor expression and quantification of CD163+macrophages close to tumor cells, was able to stratify patients into high-risk and low-risk groups.

CONCLUSIONS

In conclusion, we identified tissue biomarkers and a combined score that can predict a higher benefit from chemoimmunotherapy in ES-SCLC patients.

Landscape and Treatment Options of Shapeshifting Small Cell Lung Cancer

Small cell lung cancer (SCLC) is a deadly neuroendocrine malignancy, notorious for its rapid tumor growth, early metastasis, and relatively "cold" immune environment. Only standard chemotherapies and a few immune checkpoint inhibitors have been approved for SCLC treatment, revealing an urgent need for novel therapeutic approaches. Moreover, SCLC has been recently recognized as a malignancy with high intratumoral and intertumoral heterogeneity, which explains the modest response rate in some patients and the early relapse. Molecular subtypes defined by the expression of lineage-specific transcription factors (ASCL1, NEUROD1, POU2F3, and, in some studies, YAP1) or immune-related genes display different degrees of neuroendocrine differentiation, immune cell infiltration, and response to treatment. Despite the complexity of this malignancy, a few biomarkers and targets have been identified and many promising drugs are currently undergoing clinical trials. In this review, we integrate the current progress on the genomic landscape of this shapeshifting malignancy, the characteristics and treatment vulnerabilities of each subtype, and promising drugs in clinical phases.

Prognostic significance of immunohistochemical classification utilizing biopsy specimens in patients with extensive-disease small-cell lung cancer treated with first-line chemotherapy and immune checkpoint inhibitors

PURPOSE

Although immune checkpoint inhibitors (ICIs), together with cytotoxic chemotherapy (chemoimmunotherapy), have been adapted for the initial treatment of extensive-disease small-cell lung cancer (ED-SCLC), they have achieved limited success. In ED-SCLC, a subtype of SCLC, the expression of immune-related molecules and clinical data are not well understood in relation to ICI treatment efficiency.

METHODS

We examined lung biopsy specimens from patients diagnosed with ED-SCLC treated with chemoimmunotherapy or chemotherapy. SCLC subtype, expression of HLA class I, and infiltration of CD8-positive cells were examined using immunohistochemistry (IHC). Subsequently, the association between clinical factors, IHC results, and progression-free survival or overall survival was assessed.

RESULTS

Most of the cases showed the achaete-scute homolog 1 (ASCL1) subtype. Among the 75 SCLC cases, 29 expressed high levels of HLA class I, while 46 showed low levels or a negative result; 33 patients were characterized as CD8-high, whereas 42 were CD8-low. In the chemoimmunotherapy cohort, multivariate analysis revealed a correlation between CD8-high and improved survival. Specifically, patients in the CD8-high group of the chemoimmunotherapy cohort experienced enhanced survival compared to those in the chemotherapy cohort, which was attributed to ICI addition. IHC subtype analysis demonstrated a survival advantage in the SCLC-I and SCLC-A groups when ICI was combined with chemotherapy compared to chemotherapy alone.

CONCLUSION

Our study highlights the predictive value of IHC-classified subtypes and CD8-positive cell infiltration in estimating outcomes for patients with ED-SCLC treated with chemoimmunotherapy as a first-line therapy. These findings have practical implications for daily clinical assessments and treatment decisions.

Application of Small Cell Lung Cancer Molecular Subtyping Markers to Small Cell Neuroendocrine Carcinoma of the Cervix: NEUROD1 as a Poor Prognostic Factor

Cervical small cell neuroendocrine carcinoma (CSCNEC) is a rare, aggressive type of cervical cancer. The treatment for CSCNEC follows the chemotherapeutic regimens used for small cell lung cancer (SCLC), with which it shares similar clinical and histologic features. For the first time, we applied neuroendocrine (NE) and SCLC molecular subtyping immunohistochemical markers [achaete-scute homolog 1 (ASCL1), neurogenic differentiation factor 1 (NEUROD1), POU class 2 homeobox 3 (POU2F3), and yes-associated protein 1] in 45 patients with CSCNEC. For the combined NE score, 51.1% of NE-high and 48.9% of NE-low subtypes were identified. The NE-high subtype tended to show worse progression-free survival and overall survival (OS) than the NE-low subtype ( P =0.059 and P =0.07, respectively). Applying the SCLC molecular subtyping, 53.3% of cases were identified as NEUROD1-dominant, 17.8% as ASCL1-dominant, 13.3% as YAP-dominant, and 4.4% as POU2F3-dominant, while 11.1% of cases showed negative expression for all markers; the distribution was different from that of SCLC. The NEUROD1-dominant subtype exhibited the worst OS, while the POU2F3 subtype exhibited the best OS ( P =0.003), similar to SCLC. In addition, the ASCL1-dominant and NEUROD1-dominant subtypes showed high NE scores, while yes-associated protein 1-dominant and POU2F3-dominant subtypes showed low NE scores ( P =0.008). In multivariate analysis, the NEUROD1 expression was further identified as the independent prognostic factor for worse OS, together with the high FIGO stage. CSCNEC was revealed to be a heterogeneous disease with different biological phenotypes and to share some similarities and differences with SCLC. Regarding the ongoing development of tailored treatments based on biomarkers in SCLC, the application of biomarker-driven individualized therapy would improve clinical outcomes in patients with CSCNEC.

Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer

BACKGROUND

The prognosis of SCLC is poor and difficult to predict. The aim of this study was to explore whether a model based on radiomics and clinical features could predict the prognosis of patients with limited-stage small cell lung cancer (LS-SCLC).

METHODS

Simulated positioning CT images and clinical features were retrospectively collected from 200 patients with histological diagnosis of LS-SCLC admitted between 2013 and 2021, which were randomly divided into the training (n = 140) and testing (n = 60) groups. Radiomics features were extracted from simulated positioning CT images, and the t-test and the least absolute shrinkage and selection operator (LASSO) were used to screen radiomics features. We then constructed radiomic score (RadScore) based on the filtered radiomics features. Clinical factors were analyzed using the Kaplan-Meier method. The Cox proportional hazards model was used for further analyses of possible prognostic features and clinical factors to build three models including a radiomic model, a clinical model, and a combined model including clinical factors and RadScore. When a model has prognostic predictive value (AUC > 0.7) in both train and test groups, a nomogram will be created. The performance of three models was evaluated using area under the receiver operating characteristic curve (AUC) and Kaplan-Meier analysis.

RESULTS

A total of 1037 features were extracted from simulated positioning CT images which were contrast enhanced CT of the chest. The combined model showed the best prediction, with very poor AUC for the radiomic model and the clinical model. The combined model of OS included 4 clinical features and RadScore, with AUCs of 0.71 and 0.70 in the training and test groups. The combined model of PFS included 4 clinical features and RadScore, with AUCs of 0.72 and 0.71 in the training and test groups. T stages, ProGRP and smoke status were the independent variables for OS in the combined model, whereas T stages, ProGRP and prophylactic cranial irradiation (PCI) were the independent factors for PFS. There was a statistically significant difference between the low- and high-risk groups in the combined model of OS (training group, p < 0.0001; testing group, p = 0.0269) and PFS (training group, p < 0.0001; testing group, p < 0.0001).

CONCLUSION

Combined models involved RadScore and clinical factors can predict prognosis in LS-SCLC and show better performance than individual radiomics and clinical models.

Clinical utility of immunohistochemical subtyping in patients with small cell lung cancer

OBJECTIVES

Molecular subtyping of small cell lung cancer (SCLC) tumors based on the expression of four transcription factors (ASCL1, NEUROD1, POU2F3, and YAP1) using immunohistochemical (IHC) staining has recently emerged as a proposed approach. This study was aimed to examine this subtyping method in Asian patients with SCLC and investigate its correlation with treatment efficacy.

MATERIALS AND METHODS

Seventy-two tumor samples from patients with SCLC, including de novo cases and those transformed from EGFR-mutant tumors, were analyzed. IHC staining was used to measure the expression of the four transcription factors and conventional SCLC markers. Subtypes were defined based on relative expression levels. The treatment response and outcome of patients receiving immune checkpoint inhibitors and chemotherapy were also reviewed.

RESULTS

ASCL1 was the most common subtype, observed in 55.2 % of the samples, followed by NEUROD1 (26.9 %) and POU2F3 (9 %). No tumor exhibited predominant YAP1 positivity, while 41.8 % of the samples demonstrated positivity for two subtype markers. Approximately 50 % of the patients experienced a subtype switch after disease progression. Patients with the ASCL1/NEUROD1 (SCLC-A/N) subtype had similar progression-free survival (PFS) compared to non-SCLC-A/N patients after treatment with immune checkpoint inhibitors plus chemotherapy. Transformed SCLC patients had significantly worse PFS than de novo SCLC patients after chemoimmunotherapy. (2.1 vs. 5.4 months, P = 0.023) CONCLUSIONS: This study revealed the challenges associated with using IHC alone for molecular subtyping, highlighting the frequent co-expression of subtypes and temporal changes following treatment. Further research is warranted to explore the prognostic and therapeutic implications of IHC subtyping in patients with SCLC.

Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer

Small cell lung cancer (SCLC) is a highly aggressive subtype of lung cancer characterized by rapid tumor growth and early metastasis. Accurate prediction of prognosis and therapeutic response is crucial for optimizing treatment strategies and improving patient outcomes. In this study, we conducted a deep-learning analysis of Hematoxylin and Eosin (H&E) stained histopathological images using contrastive clustering and identified 50 intricate histomorphological phenotype clusters (HPCs) as pathomic features. We identified two of 50 HPCs with significant prognostic value and then integrated them into a pathomics signature (PathoSig) using the Cox regression model. PathoSig showed significant risk stratification for overall survival and disease-free survival and successfully identified patients who may benefit from postoperative or preoperative chemoradiotherapy. The predictive power of PathoSig was validated in independent multicenter cohorts. Furthermore, PathoSig can provide comprehensive prognostic information beyond the current TNM staging system and molecular subtyping. Overall, our study highlights the significant potential of utilizing histopathology images-based deep learning in improving prognostic predictions and evaluating therapeutic response in SCLC. PathoSig represents an effective tool that aids clinicians in making informed decisions and selecting personalized treatment strategies for SCLC patients.

Clinical insights into small cell lung cancer: Tumor heterogeneity, diagnosis, therapy, and future directions

Small cell lung cancer (SCLC) is characterized by rapid growth and high metastatic capacity. It has strong epidemiologic and biologic links to tobacco carcinogens. Although the majority of SCLCs exhibit neuroendocrine features, an important subset of tumors lacks these properties. Genomic profiling of SCLC reveals genetic instability, almost universal inactivation of the tumor suppressor genes TP53 and RB1, and a high mutation burden. Because of early metastasis, only a small fraction of patients are amenable to curative-intent lung resection, and these individuals require adjuvant platinum-etoposide chemotherapy. Therefore, the vast majority of patients are currently being treated with chemoradiation with or without immunotherapy. In patients with disease confined to the chest, standard therapy includes thoracic radiotherapy and concurrent platinum-etoposide chemotherapy. Patients with metastatic (extensive-stage) disease are treated with a combination of platinum-etoposide chemotherapy plus immunotherapy with an anti-programmed death-ligand 1 monoclonal antibody. Although SCLC is initially very responsive to platinum-based chemotherapy, these responses are transient because of the development of drug resistance. In recent years, the authors have witnessed an accelerating pace of biologic insights into the disease, leading to the redefinition of the SCLC classification scheme. This emerging knowledge of SCLC molecular subtypes has the potential to define unique therapeutic vulnerabilities. Synthesizing these new discoveries with the current knowledge of SCLC biology and clinical management may lead to unprecedented advances in SCLC patient care. Here, the authors present an overview of multimodal clinical approaches in SCLC, with a special focus on illuminating how recent advancements in SCLC research could accelerate clinical development.

Molecular subtype expression and genomic profiling differ between surgically resected pure and combined small cell lung carcinoma

A new molecular subtype classification method has been proposed for small cell lung carcinoma (SCLC). However, little is known about the differences between the pure (P-SCLC) and combined subtypes (C-SCLC). We aimed to compare the molecular subtype expression and genomic profiling in terms of clinical relevance between the two groups. 154 surgically resected SCLCs were analyzed for protein expression of four subtypes (ASCL1, NEUROD1, POU2F3, and YAP1) and two predictive markers (DLL3 and MYC) by immunohistochemistry (IHC). We also performed whole exome sequencing of 60 samples to examine genomic profiles. A total of 113 patients with P-SCLC and 41 with C-SCLC were included. In P-SCLC and C-SCLC, the expression of these markers was 78.8% and 41.5%, 98.2% and 97.6%, 42.5% and 51.2%, 38.9% and 85.4%, 85.0% and 68.3%, and 24.8% and 34.1%, respectively. ASCL1 and DLL3 were highly expressed in P-SCLC (p = 0.000 and p = 0.021, respectively), and YAP1 expression was significantly enriched in C-SCLC (p = 0.000). NGS results, including 45 P-SCLCs and 15 C-SCLCs, indicated that EGFR gene mutations were mostly observed in C-SCLCs (p = 0.000). C-SCLC showed higher CNA burden and wGII than P-SCLC (p < 0.01 and p < 0.05); conversely, P-SCLC had higher TMB burden and SDI (p < 0.05 and p < 0.05). YAP1 expression was associated with poor prognosis in P-SCLC but with favorable prognosis in C-SCLC. P-SCLC and C-SCLC are heterogeneous diseases characterized by different molecular subtype expressions and genomic profiles. Our data provide a basis for adopting histological subtype-based treatments, and further prospective studies are required to confirm our conclusions.

PD-1 and PD-L1 expression in rare lung tumors

Background: Our knowledge is still limited about the characteristics and treatment of rare lung tumors. The aim of our study was to determine programmed cell death ligand-1 (PD-L1) and programmed cell death-1 (PD-1) expression in rare pulmonary tumors to assess the potential role of immunotherapy. Methods: 66 pathologically confirmed rare lung tumors including 26 mucoepidermoid carcinomas (MECs), 27 adenoid cystic carcinomas (ACCs), and 13 tracheobronchial papillomas (TBPs) were collected retrospectively. Immunohistochemical (IHC) staining was performed on formalin fixed paraffin embedded (FFPE) tumor tissues, and PD-L1 expression on tumor cells (TCs) and immune cells (ICs), and PD-1 expression on ICs were determined. The cut off value for positive immunostaining was set at 1% for all markers. Results: PD-L1 expression on TCs was observed in two cases of MEC (7.7%), one case of ACC (3.7%), and was absent in TBP samples. PD-L1 expression on ICs could be demonstrated in nine cases of MEC (34.6%), four cases of ACC (14.8%), and was absent in TBPs. All PD-L1 TC positive tumors were also PD-L1 IC positive. Higher expression level than 5% of PD-L1 TC and/or IC was observed only in one ACC and in two MEC patients. Among them, strong PD-L1 immunopositivity of >50% on TCs and of >10% on ICs could be demonstrated in one MEC sample. PD-L1 expression of ≥1% on ICs was significantly more common in MEC, than in TBP (p < 0.001). In MEC ≥1% PD-L1 TC or IC expressions were significantly more common in patients aged 55 or older, than in younger patients (p = 0.046, and p = 0.01, respectively). PD-1 expression on ICs was found in five cases of MEC (19.2%), four cases of ACC (14.8%), and in two cases of TBP (15.4%). Only one MEC case showed a higher than 5% expression level of PD-1 on ICs. Conclusion: This retrospective study comprehensively demonstrated the rare expression of PD-L1 and PD-1 in pulmonary MEC, ACC, and TBP. However, we found very strong PD-L1 immunopositivity on both TCs and ICs in one MEC sample, which warrants further investigations in a larger cohort.

All Together Now: Standardization of Nomenclature for Neuroendocrine Neoplasms across Multiple Organs

Neuroendocrine neoplasms (NENs) span virtually all organ systems and exhibit a broad spectrum of behavior, from indolent to highly aggressive. Historically, nomenclature and grading practices have varied widely across, and even within, organ systems. However, certain core features are recapitulated across anatomic sites, including characteristic morphology and the crucial role of proliferative activity in prognostication. A recent emphasis on unifying themes has driven an increasingly standardized approach to NEN classification, as delineated in the World Health Organization's Classification of Tumours series. Here, we review recent developments in NEN classification, with a focus on NENs of the pancreas and lungs.

Cuproptosis-related lncRNA signatures: Predicting prognosis and evaluating the tumor immune microenvironment in lung adenocarcinoma

Background

Cuproptosis, a unique kind of cell death, has implications for cancer therapy, particularly lung adenocarcinoma (LUAD). Long non-coding RNAs (lncRNAs) have been demonstrated to influence cancer cell activity by binding to a wide variety of targets, including DNA, RNA, and proteins.

Methods

Cuproptosis-related lncRNAs (CRlncRNAs) were utilized to build a risk model that classified patients into high-and low-risk groups. Based on the CRlncRNAs in the model, Consensus clustering analysis was used to classify LUAD patients into different subtypes. Next, we explored the differences in overall survival (OS), the tumor immune microenvironment (TIME), and the mutation landscape between different risk groups and molecular subtypes. Finally, the functions of LINC00592 were verified through in vitro experiments.

Results

Patients in various risk categories and molecular subtypes showed statistically significant variations in terms of OS, immune cell infiltration, pathway activity, and mutation patterns. Cell experiments revealed that LINC00592 knockdown significantly reduced LUAD cell proliferation, invasion, and migration ability.

Conclusion

The development of a trustworthy prediction model based on CRlncRNAs may significantly aid in the assessment of patient prognosis, molecular features, and therapeutic modalities and may eventually be used in clinical applications.

The Importance of the Immune System and Molecular Cell Signaling Pathways in the Pathogenesis and Progression of Lung Cancer

Lung cancer is a disease that in recent years has become one of the greatest threats to modern society. Every year there are more and more new cases and the percentage of deaths caused by this type of cancer increases. Despite many studies, scientists are still looking for answers regarding the mechanisms of lung cancer development and progression, with particular emphasis on the role of the immune system. The aim of this literature review was to present the importance of disorders of the immune system and the accompanying changes at the level of cell signaling in the pathogenesis of lung cancer. The collected results showed that in the process of immunopathogenesis of almost all subtypes of lung cancer, changes in the tumor microenvironment, deregulation of immune checkpoints and abnormalities in cell signaling pathways are involved, which contribute to the multistage and multifaceted carcinogenesis of this type of cancer. We, therefore, suggest that in future studies, researchers should focus on a detailed analysis of tumor microenvironmental immune checkpoints, and to validate their validity, perform genetic polymorphism analyses in a wide range of patients and healthy individuals to determine the genetic susceptibility to lung cancer development. In addition, further research related to the analysis of the tumor microenvironment; immune system disorders, with a particular emphasis on immunological checkpoints and genetic differences may contribute to the development of new personalized therapies that improve the prognosis of patients.

Expression patterns and prognostic relevance of subtype-specific transcription factors in surgically resected small cell lung cancer: an international multicenter study

The tissue distribution and prognostic relevance of subtype‐specific proteins (ASCL1, NEUROD1, POU2F3, YAP1) present an evolving area of research in small‐cell lung cancer (SCLC). The expression of subtype‐specific transcription factors and P53 and RB1 proteins were measured by immunohistochemistry (IHC) in 386 surgically resected SCLC samples. Correlations between subtype‐specific proteins and in vitro efficacy of various therapeutic agents were investigated by proteomics and cell viability assays in 26 human SCLC cell lines. Besides SCLC‐A (ASCL1‐dominant), SCLC‐AN (combined ASCL1/NEUROD1), SCLC‐N (NEUROD1‐dominant), and SCLC‐P (POU2F3‐dominant), IHC and cluster analyses identified a quadruple‐negative SCLC subtype (SCLC‐QN). No unique YAP1‐subtype was found. The highest overall survival rates were associated with non‐neuroendocrine subtypes (SCLC‐P and SCLC‐QN) and the lowest with neuroendocrine subtypes (SCLC‐A, SCLC‐N, SCLC‐AN). In univariate analyses, high ASCL1 expression was associated with poor prognosis and high POU2F3 expression with good prognosis. Notably, high ASCL1 expression influenced survival outcomes independently of other variables in a multivariate model. High POU2F3 and YAP1 protein abundances correlated with sensitivity and resistance to standard‐of‐care chemotherapeutics, respectively. Specific correlation patterns were also found between the efficacy of targeted agents and subtype‐specific protein abundances. In conclusion, we investigated the clinicopathological relevance of SCLC molecular subtypes in a large cohort of surgically resected specimens. Differential IHC expression of ASCL1, NEUROD1, and POU2F3 defines SCLC subtypes. No YAP1‐subtype can be distinguished by IHC. High POU2F3 expression is associated with improved survival in a univariate analysis, whereas elevated ASCL1 expression is an independent negative prognosticator. Proteomic and cell viability assays of human SCLC cell lines revealed distinct vulnerability profiles defined by transcription regulators. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.