Molecular Subtypes of Pulmonary Large-cell Neuroendocrine Carcinoma Predict Chemotherapy Treatment Outcome

Purpose: Previous genomic studies have identified two mutually exclusive molecular subtypes of large-cell neuroendocrine carcinoma (LCNEC): the RB1 mutated (mostly comutated with TP53) and the RB1 wild-type groups. We assessed whether these subtypes have a predictive value on chemotherapy outcome.Experimental Design: Clinical data and tumor specimens were retrospectively obtained from the Netherlands Cancer Registry and Pathology Registry. Panel-consensus pathology revision confirmed the diagnosis of LCNEC in 148 of 232 cases. Next-generation sequencing (NGS) for TP53, RB1, STK11, and KEAP1 genes, as well as IHC for RB1 and P16 was performed on 79 and 109 cases, respectively, and correlated with overall survival (OS) and progression-free survival (PFS), stratifying for non-small cell lung cancer type chemotherapy including platinum + gemcitabine or taxanes (NSCLC-GEM/TAX) and platinum-etoposide (SCLC-PE).Results:RB1 mutation and protein loss were detected in 47% (n = 37) and 72% (n = 78) of the cases, respectively. Patients with RB1 wild-type LCNEC treated with NSCLC-GEM/TAX had a significantly longer OS [9.6; 95% confidence interval (CI), 7.7-11.6 months] than those treated with SCLC-PE [5.8 (5.5-6.1); P = 0.026]. Similar results were obtained for patients expressing RB1 in their tumors (P = 0.001). RB1 staining or P16 loss showed similar results. The same outcome for chemotherapy treatment was observed in LCNEC tumors harboring an RB1 mutation or lost RB1 protein.Conclusions: Patients with LCNEC tumors that carry a wild-type RB1 gene or express the RB1 protein do better with NSCLC-GEM/TAX treatment than with SCLC-PE chemotherapy. However, no difference was observed for RB1 mutated or with lost protein expression. Clin Cancer Res; 24(1); 33-42. ©2017 AACR.

Genetic subtypes of large cell neuroendocrine carcinoma (LCNEC) to predict response to chemotherapy

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Background: To treat LCNEC with non-small cell lung carcinoma type chemotherapy (NSCLC-ct, i.e. gemcitabine/taxanes or pemetrexed) or small cell lung carcinoma type (SCLC-ct, i.e. platinum-etoposide) is subject of debate. Molecular studies have identified two mutually exclusive subtypes in LCNEC, the co-mutated TP53 and RB1and the STK11/ KEAP1 (predominantly RB1 wildtype(wt)) group. We investigated if overall survival (OS) and progression free survival (PFS) correlates with targeted next-generation sequencing (TNGS) results in LCNEC treated with NSCLC-ct or SCLC-ct. Methods: For this population based retrospective cohort study all diagnoses of stage IV ct treated high grade neuroendocrine carcinomas (NEC, not being SCLC) were retrieved from the Netherlands Cancer Registry and Pathology Registry (PALGA) (2003-2012). Panel-consensus pathology revision of original tumor slides was performed on (N = 230) and TNGS for genes TP53, RB1, STK11 and KEAP1 analyzed with a multi-sample variant caller (Needlestack). Results: LCNEC was consensus diagnosed in 146/230 and 77 passed quality control for TNGS. Mean coverage was 2832x, a mutation(mt) in TP53 was present in 87%, RB1mt in 46%, STK11mt in 13% and KEAP1mt in 18% of sequenced LCNEC. RB1 was co-altered with TP53 in 94% of LCNEC; mutually exclusive to STK11mt (100%) but not KEAP1mt (57%). NSCLC-ct or SCLC-ct was specified in 92% of patients and RB1wt LCNEC treated with NSCLC-ct (n = 22) showed a trend to better OS compared to SCLC-ct (n = 13) (8.5 months (95% confidence interval (CI): [6.3-10.6]) vs. 5.8 [5.5-6.1] months, p = 0.055). Due to reported resistance in NECs we analyzed NSCLC-ct without pemetrexed-ct; OS was significantly longer for NSCLC-ct (n = 15) compared to SCLC-ct (9.6 [7.7-11.6] vs. 5.8 [5.5-6.1] months, p = 0.026). PFS of RB1wt NSCLC-ct treated patients was significantly longer than SCLC-ct (p = 0.044), without pemetrexed (p = 0.018). In patients with RB1mt LCNEC OS/PFS was not significantly different for NSCLC-ct vs. SCLC-ct. Conclusions: In LCNEC with RB1wt, NSCLC-ct correlates with a more favorable outcome compared to SCLC-ct. However, RB1mt LCNEC treated with NSCLC-ct do similarly worse as SCLC-ct. Prospective studies should be initiated.

The Use of Immunohistochemistry Improves the Diagnosis of Small Cell Lung Cancer and Its Differential Diagnosis. An International Reproducibility Study in a Demanding Set of Cases

INTRODUCTION

The current WHO classification of lung cancer states that a diagnosis of SCLC can be reliably made on routine histological and cytological grounds but immunohistochemistry (IHC) may be required, particularly (1) in cases in which histologic features are equivocal and (2) in cases in which the pathologist wants to increase confidence in diagnosis. However, reproducibility studies based on hematoxylin and eosin-stained slides alone for SCLC versus large cell neuroendocrine carcinoma (LCNEC) have shown pairwise κ scores ranging from 0.35 to 0.81. This study examines whether judicious use of IHC improves diagnostic reproducibility for SCLC.

METHODS

Nineteen lung pathologists studied interactive digital images of 79 tumors, predominantly neuroendocrine lung tumors. Images of resection and biopsy specimens were used to make diagnoses solely on the basis of morphologic features (level 1), morphologic features along with requested IHC staining results (level 2), and all available IHC staining results (level 3).

RESULTS

For the 19 pathologists reading all 79 cases, the rate of agreement for level 1 was 64.7%, and it increased to 73.2% and 77.5% in levels 2 and 3, respectively. With IHC, κ scores for four tumor categories (SCLC, LCNEC, carcinoid tumors, and other) increased in resection samples from 0.43 to 0.60 and in biopsy specimens from 0.43 to 0.64.

CONCLUSIONS

Diagnosis using hematoxylin and eosin staining alone showeds moderate agreement among pathologists in tumors with neuroendocrine morphology, but agreement improved to good in most cases with the judicious use of IHC, especially in the diagnosis of SCLC. An approach for IHC in the differential diagnosis of SCLC is provided.

Treatment of metastatic renal cell carcinoma with CAIX CAR-engineered T cells: clinical evaluation and management of on-target toxicity

Autologous T cells genetically modified to express a chimeric antibody receptor (CAR) against carboxy-anhydrase-IX (CAIX) were administered to 12 patients with CAIX-expressing metastatic renal cell carcinoma (RCC). Patients were treated in three cohorts with a maximum of 10 infusions of a total of 0.2 to 2.1 × 10(9) CAR T cells. CTC grade 2-4 liver enzyme disturbances occurred at the lowest CAR T cell doses, necessitating cessation of treatment in four out of eight patients in cohorts 1 and 2. Examination of liver biopsies revealed CAIX expression on bile duct epithelium with infiltration of T cells, including CAR T cells. Subsequently four patients were pre-treated with CAIX monoclonal antibody (mAb) G250 to prevent CAR-specific toxicity and showed no liver toxicities and indications for enhanced peripheral T cell persistence. No clinical responses were recorded. This report shows that CAIX-targeting CAR T cells exerted antigen-specific effects in vivo and induced liver toxicity at the lowest dose of 0.2 × 10(9) T cells applied, illustrating the potency of receptor-modified T cells. We provide in-patient proof that the observed "on-target" toxicity is antigen-directed and can be prevented by blocking antigenic sites in off-tumor organs and allowing higher T cell doses.

Semi-automatic identification of punching areas for tissue microarray building: the tubular breast cancer pilot study

BACKGROUND

Tissue MicroArray technology aims to perform immunohistochemical staining on hundreds of different tissue samples simultaneously. It allows faster analysis, considerably reducing costs incurred in staining. A time consuming phase of the methodology is the selection of tissue areas within paraffin blocks: no utilities have been developed for the identification of areas to be punched from the donor block and assembled in the recipient block.

RESULTS

The presented work supports, in the specific case of a primary subtype of breast cancer (tubular breast cancer), the semi-automatic discrimination and localization between normal and pathological regions within the tissues. The diagnosis is performed by analysing specific morphological features of the sample such as the absence of a double layer of cells around the lumen and the decay of a regular glands-and-lobules structure. These features are analysed using an algorithm which performs the extraction of morphological parameters from images and compares them to experimentally validated threshold values. Results are satisfactory since in most of the cases the automatic diagnosis matches the response of the pathologists. In particular, on a total of 1296 sub-images showing normal and pathological areas of breast specimens, algorithm accuracy, sensitivity and specificity are respectively 89%, 84% and 94%.

CONCLUSIONS

The proposed work is a first attempt to demonstrate that automation in the Tissue MicroArray field is feasible and it can represent an important tool for scientists to cope with this high-throughput technique.

Gene expression-based classification of non-small cell lung carcinomas and survival prediction

Background

Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy.

Methodology and Principal Findings

A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts.

Conclusions

The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.

Improving the specificity of fluorescence bronchoscopy for the analysis of neoplastic lesions of the bronchial tree by combination with optical spectroscopy: preliminary communication

Detection of malignancies of the bronchial tree in an early stage, such as carcinoma in situ (CIS), augments the cure rate considerably. It has been shown that the sensitivity of autofluorescence bronchoscopy is better than white light bronchoscopy for the detection of CIS and dysplastic lesions. Autofluorescence bronchoscopy is, however, characterized by a low specificity with a high rate of false positive findings. In the present paper we propose to combine autofluorescence bronchoscopy with optical spectroscopy to improve the specificity of autofluorescence imaging, while maintaining the high sensitivity. Standard autofluorescence bronchoscopy was used to find suspect lesions in the upper bronchial tree, and these lesions were subsequently characterized spectroscopically using a custom made fiberoptic probe. Autofluorescence spectra of the lesions as well as reflectance spectra were measured. We will show in this preliminary report that the addition of either of these spectroscopic techniques decreases the rate of false positives findings, with the best results obtained when both spectroscopic modalities are combined.