Spatial Transcriptome-Wide Profiling of Small Cell Lung Cancer Reveals Intra-Tumoral Molecular and Subtype Heterogeneity.

Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid growth and early metastasis and is susceptible to treatment resistance and recurrence. Understanding the intra-tumoral spatial heterogeneity in SCLC is crucial for improving patient outcomes and clinically relevant subtyping. In this study, a spatial whole transcriptome-wide analysis of 25 SCLC patients at sub-histological resolution using GeoMx Digital Spatial Profiling technology is performed. This analysis deciphered intra-tumoral multi-regional heterogeneity, characterized by distinct molecular profiles, biological functions, immune features, and molecular subtypes within spatially localized histological regions. Connections between different transcript-defined intra-tumoral phenotypes and their impact on patient survival and therapeutic response are also established. Finally, a gene signature, termed ITHtyper, based on the prevalence of intra-tumoral heterogeneity levels, which enables patient risk stratification from bulk RNA-seq profiles is identified. The prognostic value of ITHtyper is rigorously validated in independent multicenter patient cohorts. This study introduces a preliminary tumor-centric, regionally targeted spatial transcriptome resource that sheds light on previously unexplored intra-tumoral spatial heterogeneity in SCLC. These findings hold promise to improve tumor reclassification and facilitate the development of personalized treatments for SCLC patients.

Pathological response and tumor stroma immunogenic features predict long-term survival in non-small cell lung cancer after neoadjuvant chemotherapy.

PURPOSE: Major pathological response (MPR) has become a surrogate endpoint for overall survival (OS) in non-small cell lung cancer (NSCLC) after neoadjuvant therapy, however, the prognostic histologic features and optimal N descriptor after neoadjuvant therapy are poorly defined. METHODS: We retrospectively analyzed data from 368 NSCLC patients who underwent surgery after neoadjuvant chemotherapy (NAC) from January 2010 to December 2020. The percentage of residual viable tumors in the primary tumor, lymph nodes (LN), and inflammation components within the tumor stroma were comprehensively reviewed. The primary endpoint was OS. RESULTS: Of the 368 enrolled patients, 12.0% (44/368) achieved MPR in the primary tumor, which was associated with significantly better OS (HR, 0.36 0.17-0.77, p = 0.008) and DFS (HR = 0.59, 0.36-0.92, p = 0.038). In patients who did not have an MPR, we identified an immune-activated phenotype in primary tumors, characterized by intense tumor-infiltrating lymphocyte or multinucleated giant cell infiltration, that was associated with similar OS and DFS as patients who had MPR. Neoadjuvant pathologic grade (NPG), consisting of MPR and immune-activated phenotype, identified 30.7% (113/368) patients that derived significant OS (HR 0.28, 0.17-0.46, p < 0.001) and DFS (HR 0.44, 0.31-0.61, p < 0.001) benefit from NAC. Moreover, the combination of NPG and the number of positive LN stations (nS) in the multivariate analysis had a higher C-index (0.711 vs. 0.663, p < 0.001) than the ypTNM Stage when examining OS. CONCLUSION: NPG integrated with nS can provide a simple, practical, and robust approach that may allow for better stratification of patients when evaluating neoadjuvant chemotherapy in clinical practice.

Proposed novel grading system for stage I invasive lung adenocarcinoma and a comparison with the 2020 IASLC grading system.

BACKGROUND: Several studies have proposed grading systems for risk stratification of early-stage lung adenocarcinoma based on histological patterns. However, the reproducibility of these systems is poor in clinical practice, indicating the need to develop a new grading system which is easy to apply and has high accuracy in prognostic stratification of patients. METHODS: Patients with stage I invasive nonmucinous lung adenocarcinoma were retrospectively collected from pathology archives between 2009 and 2016. The patients were divided into a training and validation set at a 6:4 ratio. Histological features associated with patient outcomes (overall survival [OS] and progression-free survival [PFS]) identified in the training set were used to construct a new grading system. The newly proposed system was validated using the validation set. Survival differences between subgroups were assessed using the log-rank test. The prognostic performance of the novel grading system was compared with two previously proposed systems using the concordance index. RESULTS: A total of 539 patients were included in this study. Using a multioutcome decision tree model, four pathological factors, including the presence of tumor spread through air space (STAS) and the percentage of lepidic, micropapillary and solid subtype components, were selected for the proposed grading system. Patients were accordingly classified into three groups: low, medium, and high risk. The high-risk group showed a 5-year OS of 52.4% compared to 89.9% and 97.5% in the medium and low-risk groups, respectively. The 5-year PFS of patients in the high-risk group was 38.1% compared to 61.7% and 90.9% in the medium and low-risk groups, respectively. Similar results were observed in the subgroup analysis. Additionally, our proposed grading system provided superior prognostic stratification compared to the other two systems with a higher concordance index. CONCLUSION: The newly proposed grading system based on four pathological factors (presence of STAS, and percentage of lepidic, micropapillary, and solid subtypes) exhibits high accuracy and good reproducibility in the prognostic stratification of stage I lung adenocarcinoma patients.

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.

Explainable survival analysis with uncertainty using convolution-involved vision transformer.

Image-based precision medicine research is able to help doctors make better decisions on treatments. Among all kinds of medical images, a special form is called Whole Slide Image (WSI), which is used for diagnosing patients with cancer, aiming to enable more accurate survival prediction with its high resolution. However, One unique challenge of the WSI-based prediction models is processing the gigabyte-size or even terabyte-size WSIs, which would make most models computationally infeasible. Although existing models mostly use a pre-selected subset of key patches or patch clusters as input, they might discard some important morphology information, making the prediction inferior. Another challenge is improving the prediction models' explainability, which is crucial to help doctors understand the predictions given by the models and make faithful decisions with high confidence. To address the above two challenges, in this work, we propose a novel explainable survival prediction model based on Vision Transformer. Specifically, we adopt dual-channel convolutional layers to utilize the complete WSIs for more accurate predictions. We also introduce the aleatoric uncertainty into our model to understand its limitation and avoid overconfidence in using the prediction results. Additionally, we present a post-hoc explainable method to identify the most salient patches and distinct morphology features as supporting evidence for predictions. Evaluations of two large cancer datasets show that our proposed model is able to make survival predictions more effectively and has better explainability for cancer diagnosis.

Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging.

BACKGROUND: This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging. METHODS: A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method 1 involved direct measurement of nodule dimensions using a calliper, while Method 2 involved tumour lesion segmentation and voxel volume calculation using the "py-radiomics" package in Python. Agreement between the two methods was assessed using the intraclass correlation coefficient (ICC). Volume doubling time (VDT) and growth rate (GR) were used as evaluation indices for SCLC growth, and growth distribution based on GR and volume measurements were depicted. We collected potential factors related to imaging VDT and performed a differential analysis. Patients were classified into slow-growing and fast-growing groups based on a VDT cut-off point of 60 days, and univariate analysis was used to identify factors influencing VDT. RESULTS: Median VDT calculated by the two methods were 61 days and 71 days, respectively, with strong agreement. All patients had continuously growing tumours, and none had tumours that decreased in size or remained unchanged. Eight patients showed possible growth patterns, with six possibly exhibiting exponential growth and two possibly showing Gompertzian growth. Tumours deeper in the lung grew faster than those adjacent to the pleura. CONCLUSIONS: Peripheral SCLC tumours grow rapidly and continuously without periods of nongrowth or regression. Tumours located deeper in the lung tend to grow faster, but further research is needed to confirm this finding.

Multi-dimensional characterization of immunological profiles in small cell lung cancer uncovers clinically relevant immune subtypes with distinct prognoses and therapeutic vulnerabilities.

Small-cell lung cancer (SCLC) is generally considered a 'homogenous' disease, with little documented inter-tumor heterogeneity in treatment guidance or prognosis evaluation. The precise identification of clinically relevant molecular subtypes remains incomplete and their translation into clinical practice is limited. In this retrospective cohort study, we comprehensively characterized the immune microenvironment in SCLC by integrating transcriptional and protein profiling of formalin-fixation-and-paraffin-embedded (FFPE) samples from 29 patients. We identified two distinct disease subtypes: immune-enriched (IE-subtype) and immune-deprived (ID-subtype), displaying heterogeneity in immunological, biological, and clinical features. The IE-subtype was characterized by abundant immune infiltrate and elevated levels of interferon-alpha/gamma (IFNα/IFNγ) and inflammatory response, while the ID-subtype featured a complete lack of immune infiltration and a more proliferative phenotype. These two immune subtypes are associated with clinical benefits in SCLC patients treated with adjuvant therapy, with the IE-subtype exhibiting a more favorable response leading to improved survival and reduced disease recurrence risk. Additionally, we identified and validated a personalized prognosticator of immunophenotyping, the CCL5/CXCL9 chemokine index (CCI), using machine learning. The CCI demonstrated superior predictive abilities for prognosis and clinical benefits in SCLC patients, validated in our institute immunohistochemistry cohort and multicenter bulk transcriptomic data cohorts. In conclusion, our study provides a comprehensive and multi-dimensional characterization of the immune architecture of SCLC using clinical FFPE samples and proposes a new immune subtyping conceptual framework enabling risk stratification and the appropriate selection of individualized therapy.

Prognostic significance of YAP1 expression and its association with neuroendocrine markers in resected pulmonary large cell neuroendocrine carcinoma (LCNEC).

BACKGROUND: YAP1 (Yes-associated protein 1), an important effector of the Hippo pathway, acts as an oncogene and is overexpressed in various malignant tumors. However, the function and expression pattern of YAP1 in pulmonary large cell neuroendocrine carcinoma (LCNEC) have not been systematically established. This study aimed to explore the relationship between YAP1 expression and neuroendocrine differentiation markers and their prognostic significance in LCNEC. MATERIALS AND METHODS: YAP1 protein and neuroendocrine markers (INSM1, NeuroD1 and DLL3) expression were examined by immunohistochemical (IHC) staining in 80 resected pulmonary LCNEC cases. The possible association between these markers and clinicopathological features was evaluated and survival analyses were performed. RESULTS: YAP1 was highly expressed in 25% LCNECs (20/80) , especially at a relatively higher T stage (p = 0.015). YAP1 expression was negatively correlated with INSM1 (χ2=11.53, p = 0.001) and DLL3(χ2=8.55, p = 0.004), but not with NeuroD1 (p = 0.482). For survival analyses, YAP1 expression was associated with worse disease-free survival (DFS) and overall survival (OS) (median DFS: 13 months vs. not reached (NR), p = 0.0096; median OS: not reached, NR vs. NR, p = 0.038), and was an unfavorable prognostic factor for DFS (HR:3.285; 95%CI: 1.526-7.071, p = 0.002) and OS (HR: 2.864, 95% CI: 0.932-8.796, p = 0.066). CONCLUSIONS: YAP1 was found to be conversely correlated with neuroendocrine markers and a prognostic factor for worse survival in resected LCNEC patients, and mechanisms need to be further investigated.

Clinical significance of ALDH1A1 expression and its association with E-cadherin and N-cadherin in resected large cell neuroendocrine carcinoma.

BACKGROUND: The roles of cancer stem cells (CSCs) and epithelial-mesenchymal transition (EMT) in solid tumors are well established. However, the interaction between CSCs and EMT in pulmonary large cell neuroendocrine carcinoma (LCNEC) remains unknown. The aim of this study was to investigate the expression and clinical significance of a CSC marker (ALDH1A1) and its correlation with Epithelial-like phenotype marker (E-cadherin) and Mesenchymal-like phenotype marker (N-cadherin) in LCNEC patients. METHODS: Immunohistochemistry (IHC) for ALDH1A1, E-cadherin and N-cadherin expression was conducted on tissue microarrays made from 79 resected LCNEC patient samples. ALDH1A1 protein expression was evaluated by the IHC score, and its correlations with the expression of E-cadherin, N-cadherin and clinicopathological features were determined based on IHC data. Survival analyses were also performed. RESULTS: ALDH1A1 was positively expressed in 75.9% (60/79 cases) of LCNEC patients. No significant difference in clinicopathological variables was observed between the ALDH1A1-negative and ALDH1A1-positive groups. However, ALDH1A1 expression was positively correlated with E-cadherin (Spearman's rho = 0.229, p-value = 0.007), which represents the epithelial-like phenotype, but not with N-cadherin. Patients with expression of ALDH1A1 had significantly longer disease-free survival (DFS) and overall survival (OS) than those who were ALDH1A1 negative (median DFS: 52 vs 12 months, p = 0.028; median OS: not reached; p = 0.027). Multivariate analysis showed that ALDH1A1 was an independent favorable prognostic factor for DFS (p = 0.032, HR: 0.438, 95% CI: 0.206-0.932) and OS (p = 0.025, HR: 0.279, 95% CI: 0.091-0.852) in LCNEC patients. CONCLUSION: This study suggests that ALDH1A1 can act as a favorable independent prognostic factor for LCNEC, which related to the epithelioid phenotype in EMT, and its internal mechanism needs further study.

ASCL1 and DLL3 expressions and their clinicopathological implications in surgically resected pure small cell lung cancer: A study of 247 cases from the National Cancer Center of China.

OBJECTIVE: Small cell lung cancer (SCLC) is one of the most aggressive malignancies characterized by neuroendocrine (NE) differentiation. The Delta-like protein 3 (DLL3), as a direct downstream target of ASCL1, is involved in NE differentiation and carcinogenesis of SCLC. This study aims to investigate the relationship between ASCL1 and DLL3 expressions and their clinicopathological implications in SCLC. METHODS: A total of 247 surgically resected pure SCLC samples with limited clinical stage and follow-up data were retrieved in this retrospective study. ASCL1 and DLL3 protein expression was detected by immunohistochemistry staining. The correlations between ASCL1 and DLL3 expressions, as well as their clinicopathological features, were analyzed by χ2 tests. Disease-free survival (DFS) and overall survival (OS) in SCLC patients with ASCL1/DLL3 low and high expressions were compared by the Kaplan-Meier method and log-rank tests. RESULTS: ASCL1 high expression was detected in 105 (42.5%) patients. Its expression was positively correlated with the clinical stage (p = 0.02) and nerve invasion (p = 0.03). DLL3 high expression was observed in 188 (72.8%) patients and was correlated with vascular invasion (p = 0.04). ASCL1 expression was positively associated with DLL3 expression (p = 0.03). In addition, DLL3 expression has a strong correlation with the expression of thyroid transcription factor-1 (TTF1) and conventional NE markers. CONCLUSION: ASCL1 and DLL3 were highly expressed in SCLC tumor samples, and a positive correlation between these two markers was observed. Co-analysis of ASCL1 and DLL3 may identify a distinct SCLC subgroup benefit from targeted therapy. Therefore, ASCL1 and DLL3 could be potential biomarkers served for the selection of related patients.

YAP1 protein expression has variant prognostic significance in small cell lung cancer (SCLC) stratified by histological subtypes.

BACKGROUND: Recently, expression of YAP1, a nuclear effector of an inactivated HIPPO pathway, has been identified as one of four molecular subtypes of SCLC. However, the clinicopathological relevance and prognostic significance of YAP1 expression in SCLC stratified by histological subtypes has not been systematically reported to date. METHODS: Tumor sections and corresponding formalin-fixed paraffin-embedded (FFPE) samples of 297 SCLC patients were retrieved from the pathological specimen repository and were subsequently reviewed by pathologists. Forty-six C-SCLCs (combined SCLCs) (15.5%) and 251P-SCLCs (pure SCLCs) (84.5%) were identified respectively. YAP1 expression was examined by immunohistochemistry (IHC) and assessed semi-quantitatively on tumor tissue array (TMA). Propensity score was used to match C-SCLCs and P-SCLCs in a ratio of 1 to 2 to balance age, gender, tumor stage and treatment methods. Finally, 46C-SCLCs and 92P-SCLCs were included for prognostic analysis. RESULTS: The positive rate of YAP1 expression was significantly higher in C-SCLCs than P-SCLCs before matching (52.2% vs 29.1%, P = 0.004). After matching by propensity score, the prescribed clinical parameters were well balanced between P-SCLCs and C-SCLCs. Expression of YAP1 was associated worse overall survival (OS) (5- year OS%, 39.0% vs. 74.9%, P = 0.013) and was an independent risk factor for OS (HR = 2.93, 95% CI: 1.01-8.51; P = 0.048) exclusively in C-SCLC. Univariate survival analysis in subgroups of different clinical variables also confirmed the prognostic impact of YAP1 was most significant in C-SCLC. But for P-SCLCs, expression of YAP1 showed no prognostic impact. CONCLUSIONS: Expression of YAP1 in small cell components of C-SCLC was significantly higher than that in P-SCLC. Besides, it served as an unfavorable predictor for OS in C-SCLC but not in P-SCLC, which suggested different entities of small cell components with variant YAP1 expression and potential different targetable oncogenic pathway between C-SCLC and P-SCLC.

Clinicopathological features and prognostic analysis of 247 small cell lung cancer with limited-stage after surgery.

The objective of this study was to analyze the clinical and pathological characteristics of patients with small cell lung cancer (SCLC) after curative surgery and to explore prognostic factors for disease-free survival (DFS) and overall survival (OS). Clinical data of 247 patients were collected, and clinicopathological features were retrieved, including gender, age, smoking history, tumor location, and distant metastasis. Histopathological features were also reviewed by three pathologists, including primary tumor (T), lymph node metastasis (N), pleural invasion, bronchial invasion, nerve invasion, spread through air spaces (STAS), tumor thrombosis, major cell shape (round Vs. spindle), tumor necrosis, stromal fibrosis, and tumor-infiltrating lymphocytes (TILs). Immunohistochemical staining of neuroendocrine markers (CD56, synapsin, chromogranin A) was also reviewed. All patients were followed up for recurrence, distant metastasis, and survival. Kaplan-Meier curves and log-rank tests were applied for survival analysis. The median DFS was 98 months, and the 1-year, 3-year, and 5-year DFS rates were 70.9%, 54.4%, and 52.2%, respectively. The median OS was not reached, and the 1-year, 3-year, and 5-year survival rates were 94.2%, 72.3%, and 65.4%, respectively. Univariate analysis revealed clinicopathological features with DFS (gender, smoking history, primary tumor, regional lymph node metastasis, major cell shape, and TILs) and OS (age, primary tumor, regional lymph node metastasis, distant metastasis, nerve invasion, major cell shape, and TILs). Multivariate analysis revealed DFS-related factors (smoking history, regional lymph node metastasis and major cell shape) and OS-related factors (age, primary tumor, distant metastasis in the brain, liver, bone, nerve invasion, and TILs). Age more than 65 years, smoking, advanced stage (T and N), distant metastasis, nerve invasion, major cell shape as spindle and TILs >30% were negatively correlated with survival. Neuroendocrine immunostaining markers showed no correlation with survival. Of interest, spindle cell type and TILs >30% are revealed as independent negative prognostic factors, and further molecular mechanisms need to be explored.

Clinicopathological features and prognostic implications of ASCL1 expression in surgically resected small cell lung cancer.

BACKGROUND: Small cell lung cancer (SCLC) is one of the most aggressive lung cancers. Treatment of SCLC has remained unchanged during the past decades. Preclinical studies have revealed ASCL1 as a transcription regulator in the neuroendocrine (NE) differentiation and carcinogenesis of SCLC. However, there are few studies on correlation of ASCL1 expression and clinicopathological factors in resected SCLCs. Here, we aimed to analyze the ASCL1 expression of SCLC and investigate its associations with clinicopathological factors and survival. METHODS: A total of 247 surgically resected pure SCLC specimens were included in this retrospective study, all of which were processed using tissue microarrays for immunohistochemistry analysis of ASCL1. A total of 48 of 247 cases were tested by NanoString for mRNA expression analysis on 50 SCLC related genes. Statistical analysis was performed using R studio and SPSS software. RESULTS: NE scores of 48 pure SCLC specimens were calculated by analyzing 50 preselected genes. A significant correlation between NE score with both ASCL1 mRNA expression and ASCL1 protein expression were observed. For the entire cohort of 247 patients, ASCL1 was highly expressed in 42.5% of pure SCLC patients according to IHC results. Significant differences were observed between ASCL1 high and low expression groups in variables including staging, lymph node metastasis, nerve invasion and overall survival. CONCLUSIONS: In limited staged pure SCLC, ASCL1 expression was positively correlated with NE signature, pTNM stage, nerve invasion and OS. ASCL1 may therefore serve as a potential biomarker to predict prognosis as well as in the selection of patients for therapies targeting ASCL1-regulated downstream molecules.