ROS1 fusions in resected stage I-III adenocarcinoma: Results from the European Thoracic Oncology Platform Lungscape project.

BACKGROUND: ROS1 fusion is a relatively low prevalence (0.6-2.0%) but targetable driver in lung adenocarcinoma (LUAD). Robust and low-cost tests, such as immunohistochemistry (IHC), are desirable to screen for patients potentially harboring this fusion. The aim was to investigate the prevalence of ROS1 fusions in a clinically annotated European stage I-III LUAD cohort using IHC screening with the in vitro diagnostics (IVD)-marked clone SP384, followed by confirmatory molecular analysis in pre-defined subsets. METHODS: Resected LUADs constructed in tissue microarrays, were immunostained for ROS1 expression using SP384 clone in a ready-to-use kit and Ventana immunostainers. After external quality control, analysis was performed by trained pathologists. Staining intensity of at least 2+ (any percentage of tumor cells) was considered IHC positive (ROS1 IHC + ). Subsequently, ROS1 IHC + cases were 1:1:1 matched with IHC0 and IHC1 + cases and subjected to orthogonal ROS1 FISH and RNA-based testing. RESULTS: The prevalence of positive ROS1 expression (ROS1 IHC + ), defined as IHC 2+/3+, was 4 % (35 of 866 LUADs). Twenty-eight ROS1 IHC + cases were analyzed by FISH/RNA-based testing, with only two harboring a confirmed ROS1 gene fusion, corresponding to a lower limit for the prevalence of ROS1 gene fusion of 0.23 %. They represent a 7 % probability of identifying a fusion among ROS1 IHC + cases. Both confirmed cases were among the only four with sufficient material and H-score ≥ 200, leading to a 50 % probability of identifying a ROS1 gene fusion in cases with an H-score considered strongly positive. All matched ROS1 IHC- (IHC0 and IHC1 + ) cases were also found negative by FISH/RNA-based testing, leading to a 100 % probability of lack of ROS1 fusion for ROS1 IHC- cases. CONCLUSIONS: The prevalence of ROS1 fusion in an LUAD stage I-III European cohort was relatively low. ROS1 IHC using SP384 clone is useful for exclusion of ROS1 gene fusion negative cases.

Disease relapse in relation to lymph node sampling in lung carcinoid patients.

The predictive value of the extent of peri-operative lymph node (LN) sampling in relation to disease relapse in patients with pulmonary carcinoid (PC) is unknown. Furthermore, post-surgery follow-up recommendations rely on institutional retrospective studies with short follow-ups. We aimed to address these shortcomings by examining the relation between LN sampling and relapse in a population-based cohort with long-term follow-up. By combining the Dutch nationwide pathology and cancer registries, all patients with surgically resected PC (2003-2012) were included in this analysis (last update 2020). The extent of surgical LN dissection was scored for the number of LN samples, location (hilar/mediastinal), and completeness of resection according to European Society of Thoracic Surgeons (ESTS) guidelines. Relapse-free interval (RFI) was evaluated using Kaplan Meier and multivariate regression analysis. 662 patients were included. The median follow-up was 87.5 months. Relapse occurred in 10% of patients, mostly liver (51.8%) and locoregional sites (45%). The median RFI was 48.1 months (95% CI 36.8-59.4). Poor prognostic factors were atypical carcinoid, pN1/2, and R1/R2 resection. In 546 patients LN dissection data could be retrieved; at least one N2 LN was examined in 44% and completeness according to ESTS in merely 7%. In 477 cN0 patients, 5.9% had pN1 and 2.5% had pN2 disease. In conclusion, relapse occurred in 10% of PC patients with a median RFI of 48.1 months thereby underscoring the necessity of long-term follow-up. Extended mediastinal LN sampling was rarely performed but systematic nodal evaluation is recommended as it provides prognostic information on distant relapse.

Detection and subtyping of basal cell carcinoma in whole-slide histopathology using weakly-supervised learning.

The frequency of basal cell carcinoma (BCC) cases is putting an increasing strain on dermatopathologists. BCC is the most common type of skin cancer, and its incidence is increasing rapidly worldwide. AI can play a significant role in reducing the time and effort required for BCC diagnostics and thus improve the overall efficiency of the process. To train such an AI system in a fully-supervised fashion however, would require a large amount of pixel-level annotation by already strained dermatopathologists. Therefore, in this study, our primary objective was to develop a weakly-supervised for the identification of basal cell carcinoma (BCC) and the stratification of BCC into low-risk and high-risk categories within histopathology whole-slide images (WSI). We compared Clustering-constrained Attention Multiple instance learning (CLAM) with StreamingCLAM and hypothesized that the latter would be the superior approach. A total of 5147 images were used to train and validate the models, which were subsequently tested on an internal set of 949 images and an external set of 183 images. The labels for training were automatically extracted from free-text pathology reports using a rule-based approach. All data has been made available through the COBRA dataset. The results showed that both the CLAM and StreamingCLAM models achieved high performance for the detection of BCC, with an area under the ROC curve (AUC) of 0.994 and 0.997, respectively, on the internal test set and 0.983 and 0.993 on the external dataset. Furthermore, the models performed well on risk stratification, with AUC values of 0.912 and 0.931, respectively, on the internal set, and 0.851 and 0.883 on the external set. In every single metric the StreamingCLAM model outperformed the CLAM model or is on par. The performance of both models was comparable to that of two pathologists who scored 240 BCC positive slides. Additionally, in the public test set, StreamingCLAM demonstrated a comparable AUC of 0.958, markedly superior to CLAM's 0.803. This difference was statistically significant and emphasized the strength and better adaptability of the StreamingCLAM approach.

Druggable growth dependencies and tumor evolution analysis in patient-derived organoids of neuroendocrine neoplasms from multiple body sites.

Neuroendocrine neoplasms (NENs) comprise well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). Treatment options for patients with NENs are limited, in part due to lack of accurate models. We establish patient-derived tumor organoids (PDTOs) from pulmonary NETs and derive PDTOs from an understudied subtype of NEC, large cell neuroendocrine carcinoma (LCNEC), arising from multiple body sites. PDTOs maintain the gene expression patterns, intra-tumoral heterogeneity, and evolutionary processes of parental tumors. Through hypothesis-driven drug sensitivity analyses, we identify ASCL1 as a potential biomarker for response of LCNEC to treatment with BCL-2 inhibitors. Additionally, we discover a dependency on EGF in pulmonary NET PDTOs. Consistent with these findings, we find that, in an independent cohort, approximately 50% of pulmonary NETs express EGFR. This study identifies an actionable vulnerability for a subset of pulmonary NETs, emphasizing the utility of these PDTO models.

Identification of protein biomarkers for prediction of response to platinum-based treatment regimens in patients with non-small cell lung cancer.

The majority of patients with resected stage II-IIIA non-small cell lung cancer (NSCLC) are treated with platinum-based adjuvant chemotherapy (ACT) in a one-size-fits-all approach. However, a significant number of patients do not derive clinical benefit, and no predictive patient selection biomarker is currently available. Using mass spectrometry-based proteomics, we have profiled tumour resection material of 2 independent, multi-centre cohorts of in total 67 patients with NSCLC who underwent ACT. Unsupervised cluster analysis of both cohorts revealed a poor response/survival sub-cluster composed of ~ 25% of the patients, that displayed a strong epithelial-mesenchymal transition signature and stromal phenotype. Beyond this stromal sub-population, we identified and validated platinum response prediction biomarker candidates involved in pathways relevant to the mechanism of action of platinum drugs, such as DNA damage repair, as well as less anticipated processes such as those related to the regulation of actin cytoskeleton. Integration with pre-clinical proteomics data supported a role for several of these candidate proteins in platinum response prediction. Validation of one of the candidates (HMGB1) in a third independent patient cohort using immunohistochemistry highlights the potential of translating these proteomics results to clinical practice.

Visual interstitial fibrosis assessment as continuous variable in protocol renal transplant biopsies.

AIMS: In current renal transplant pathology practice, interstitial fibrosis is visually assessed in categories according to the Banff classification. As this has a moderate reproducibility, which is little ameliorated by morphometric analysis, we investigated whether visual renal fibrosis assessment is feasible on a continuous scale, i.e. as a percentage of affected area of the cortex. METHODS AND RESULTS: Protocol renal biopsies taken at transplantation (n = 125), three (n = 73) and 12 months (n = 88) after transplantation were visually scored in categories (Banff) and percentages for interstitial fibrosis (ci). Interobserver variation (ICC and weighted κ) was assessed, and morphometric analysis on Sirius red-stained sections was performed. Correlations between the different methods and their association with donor age and eGFR 1 and 5 years post-transplant were analysed using Pearson's or Spearman's rho. Interobserver agreement was equivalent for Banff and %ci (κ = 0.713 versus ICC = 0.792), and for Banff IF/TA and %IF/TA (κ = 0.615 versus ICC = 0.743). Both Banff and %ci were associated with Sirius red morphometry in 3 and 12 months. With all three methods, a significant correlation was found between donor age and fibrosis in the implantation biopsy and between fibrosis in the 12 months' biopsy and eGFR at 1 and 5 years (eGFR at 1 year: Sirius red ρ = 0.487, %ci ρ = 0.393, Banff ρ = 0.413, all P < 0.01, eGFR at 5 years: Sirius red ρ = 0.392, %ci ρ = 0.333, Banff ρ = 0.435, all P < 0.01). CONCLUSION: Interstitial fibrosis assessment on a continuous scale can be used next to scoring in categories according to the Banff classification in protocol renal transplant biopsies.

Development and verification of new monoclonal orthopedia homeobox (OTP) specific antibodies for pulmonary carcinoid diagnostics.

Background: Orthopedia homeobox (OTP) has shown to be a useful prognostic marker to predict outcome in pulmonary carcinoids, which is also supported by the World Health Organization. However, the discontinuation of the initially used polyclonal antibody and absence of a reliable routinely applicable monoclonal OTP antibody hampers implementation in routine diagnostics. Here, new monoclonal antibodies directed against OTP were developed and verified on formalin-fixed paraffin-embedded tissue of pulmonary neuroendocrine tumors (NETs) for clinical diagnostics. Methods: OTP specific monoclonal antibodies were produced from mice immunised with a recombinant human OTP protein fragment. Enzyme-linked immunosorbent assay (ELISA) positive hybridomas were evaluated using immunohistochemistry (IHC). Following epitope-mapping and isotyping, purified monoclonal antibodies were validated for IHC in formalin-fixed paraffin-embedded tissues, the optimal dilution was determined, and results were cross validated with the OTP polyclonal antibody (HPA039365, Atlas Antibodies). Staining protocols were optimized on two automated staining platforms and performance was harmonized using a tissue microarray (TMA). Results: Two clones (CL11222 and CL11225) were selected for purified monoclonal antibody (mAb) production. Intratumor heterogeneity assessment revealed similar performance for both clones. While clone CL11225 displayed a unique epitope compared to those present in the polyclonal antibody, this clone performed most similar to the polyclonal antibody. Cross-platform assessment revealed an excellent agreement for clone CL11225 while clone CL11222 showed somewhat discordant results on Dako. Conclusions: New monoclonal OTP specific antibodies have been developed and verified on different automated immunohistochemical staining platforms. The OTP specific monoclonal antibodies showed excellent agreement with the often-used polyclonal antibody allowing application in routine diagnostics.

Differential Orthopedia Homeobox expression in pulmonary carcinoids is associated with changes in DNA methylation.

Limited number of tumor types have been examined for Orthopedia Homeobox (OTP) expression. In pulmonary carcinoids, loss of expression is a strong indicator of poor prognosis. Here, we investigated OTP expression in 37 different tumor types, and the association between OTP expression and DNA methylation levels in lung neuroendocrine neoplasms. We analyzed publicly available multi-omics data (whole-exome-, whole-genome-, RNA sequencing and Epic 850K-methylation array) of 58 typical carcinoids, 27 atypical carcinoids, 69 large cell neuroendocrine carcinoma and 51 small cell lung cancer patients and TCGA (The Cancer Genome Atlas) data of 33 tumor types. 850K-methylation analysis was cross-validated using targeted pyrosequencing on 35 carcinoids. We report bimodality of OTP expression in carcinoids (OTPhigh vs OTPlow group, likelihood-ratio test P = 1.5 × 10-2 ), with the OTPhigh group specific to pulmonary carcinoids while absent from all other cohorts analyzed. Significantly different DNA methylation levels were observed between OTPhigh and OTPlow carcinoids in 12/34 OTP infinium probes (FDR < 0.05 and ß-value effect size > .2). OTPlow carcinoids harbor high DNA methylation levels as compared to OTPhigh carcinoids. OTPlow carcinoids showed a significantly worse overall survival (log-rank test P = .0052). Gene set enrichment analysis for somatically mutated genes associated with hallmarks of cancer showed robust enrichment of three hallmarks in the OTPlow group, that is, sustaining proliferative signaling, evading growth suppressor and genome instability and mutation. Together our data suggest that high OTP expression is a unique feature of pulmonary carcinoids with a favorable prognosis and that in poor prognostic patients, OTP expression is lost, most likely due to changes in DNA methylation levels.

In-depth molecular analysis of combined and co-primary pulmonary large cell neuroendocrine carcinoma and adenocarcinoma.

Up to 14% of large cell neuroendocrine carcinomas (LCNECs) are diagnosed in continuity with nonsmall cell lung carcinoma. In addition to these combined lesions, 1% to 7% of lung tumors present as co-primary tumors with multiple synchronous lesions. We evaluated molecular and clinicopathological characteristics of combined and co-primary LCNEC-adenocarcinoma (ADC) tumors. Ten patients with LCNEC-ADC (combined) and five patients with multiple synchronous ipsilateral LCNEC and ADC tumors (co-primary) were included. DNA was isolated from distinct tumor parts, and 65 cancer genes were analyzed by next generation sequencing. Immunohistochemistry was performed including neuroendocrine markers, pRb, Ascl1 and Rest. Pure ADC (N = 37) and LCNEC (N = 17) cases were used for reference. At least 1 shared mutation, indicating tumor clonality, was found in LCNEC- and ADC-parts of 10/10 combined tumors but only in 1/5 co-primary tumors. A range of identical mutations was observed in both parts of combined tumors: 8/10 contained ADC-related (EGFR/KRAS/STK11 and/or KEAP1), 4/10 RB1 and 9/10 TP53 mutations. Loss of pRb IHC was observed in 6/10 LCNEC- and 4/10 ADC-parts. The number and intensity of expression of Ascl1 and neuroendocrine markers increased from pure ADC (low) to combined ADC (intermediate) and combined and pure LCNEC (high). The opposite was true for Rest expression. In conclusion, all combined LCNEC-ADC tumors were clonally related indicating a common origin. A relatively high frequency of pRb inactivation was observed in both LCNEC- and ADC-parts, suggesting an underlying role in LCNEC-ADC development. Furthermore, neuroendocrine differentiation might be modulated by Ascl1(+) and Rest(-) expression.

Clinical-Pathologic Challenges in the Classification of Pulmonary Neuroendocrine Neoplasms and Targets on the Horizon for Future Clinical Practice.

Diagnosing a pulmonary neuroendocrine neoplasm (NEN) may be difficult, challenging clinical decision making. In this review, the following key clinical and pathologic issues and informative molecular markers are being discussed: (1) What is the preferred outcome parameter for curatively resected low-grade NENs (carcinoid), for example, overall survival or recurrence-free interval? (2) Does the WHO classification combined with a Ki-67 proliferation index and molecular markers, such as OTP and CD44, offer improved prognostication in low-grade NENs? (3) What is the value of a typical versus atypical carcinoid diagnosis on a biopsy specimen in local and metastatic disease? Diagnosis is difficult in biopsy specimens and recent observations of an increased mitotic rate in metastatic carcinoid from typical to atypical and high-grade NEN can further complicate diagnosis. (4) What is the (ir)relevance of morphologically separating large cell neuroendocrine carcinoma (LCNEC) SCLC and the value of molecular markers (RB1 gene and pRb protein or transcription factors NEUROD1, ASCL1, POU2F3, or YAP1 [NAPY]) to predict systemic treatment outcome? (5) Are additional diagnostic criteria required to accurately separate LCNEC from NSCLC in biopsy specimens? Neuroendocrine morphology can be absent owing to limited sample size leading to missed LCNEC diagnoses. Evaluation of genomic studies on LCNEC and marker studies have identified that a combination of napsin A and neuroendocrine markers could be helpful. Hence, to improve clinical practice, we should consider to adjust our NEN classification incorporating prognostic and predictive markers applicable on biopsy specimens to inform a treatment outcome-driven classification.