Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch.

Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackles this issue would be helpful for diagnoses and subtyping of lung carcinoma. In this study, we developed AI models to classify multinomial patterns of lung carcinoma; ADC, LCNEC, SCC, SCLC, and non-neoplastic lung tissue based on convolutional neural networks (CNN or ConvNet). Four CNNs that were pre-trained using transfer learning and one CNN built from scratch were used to classify patch images from pathology whole-slide images (WSIs). We first evaluated the diagnostic performance of each model in the test sets. The Xception model and the CNN built from scratch both achieved the highest performance with a macro average AUC of 0.90. The CNN built from scratch model obtained a macro average AUC of 0.97 on the dataset of four classes excluding LCNEC, and 0.95 on the dataset of three subtypes of lung carcinomas; NSCLC, SCLC, and non-tumor, respectively. Of particular note is that the relatively simple CNN built from scratch may be an approach for pathological image analysis.

Subtyping non-small cell lung cancer by histology-guided spatial metabolomics.

PURPOSE: Most cancer-related deaths worldwide are associated with lung cancer. Subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (AC) and squamous cell carcinoma (SqCC) is of importance, as therapy regimes differ. However, conventional staining and immunohistochemistry have their limitations. Therefore, a spatial metabolomics approach was aimed to detect differences between subtypes and to discriminate tumor and stroma regions in tissues. METHODS: Fresh-frozen NSCLC tissues (n = 35) were analyzed by matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) of small molecules (< m/z 1000). Measured samples were subsequently stained and histopathologically examined. A differentiation of subtypes and a discrimination of tumor and stroma regions was performed by receiver operating characteristic analysis and machine learning algorithms. RESULTS: Histology-guided spatial metabolomics revealed differences between AC and SqCC and between NSCLC tumor and tumor microenvironment. A diagnostic ability of 0.95 was achieved for the discrimination of AC and SqCC. Metabolomic contrast to the tumor microenvironment was revealed with an area under the curve of 0.96 due to differences in phospholipid profile. Furthermore, the detection of NSCLC with rarely arising mutations of the isocitrate dehydrogenase (IDH) gene was demonstrated through 45 times enhanced oncometabolite levels. CONCLUSION: MALDI-MSI of small molecules can contribute to NSCLC subtyping. Measurements can be performed intraoperatively on a single tissue section to support currently available approaches. Moreover, the technique can be beneficial in screening of IDH-mutants for the characterization of these seldom cases promoting the development of treatment strategies.

High KIF11 expression is associated with poor outcome of NSCLC.

PURPOSE: To clarify the correlation between KIF11 (kinesin family member 11) and clinicopathologic characteristics of non-small cell lung cancer (NSCLC) and identify the prognostic value of KIF11 in patients with NSCLC. METHODS: For investigating the expression of KIF11 in NSCLC, two tissue microarrays (TMAs: one contained 60 paired NSCLC tissues and paratumor tissues, the other contained 140 NSCLC tissues and 10 normal lung tissues) were constructed, stained, and scored. The Cancer Genome Atlas (TCGA) datasets were used to explore the differential expression level of KIF11 between NSCLC and paratumor. Kaplan-Meier survival curves were plotted and multivariate analysis were carried out. RESULTS: The staining of KIF11 mainly distributed throughout the cytoplasm of tumor cells. Its expression was higher in NSCLC than paratumor cells, and similar results were obtained from TCGA datasets. We found that high expression of KIF11 had a significant correlation with lymph node metastases (p = 0.024) and pathologic stage (p = 0.018); that significant difference was not found in any other clinicopathologic characteristic. As univariate and multivariate analysis showed, KIF11 expression was significantly correlated with overall survival time of NSCLC (p = 0.002, p = 0.025, respectively). High KIF11 expression was found to significantly associate with overall survival of stage II-III (p = 0.001) and lung adenocarcinoma (p = 0.036). CONCLUSION: High KIF11 expression predicts poor outcome in NSCLC. KIF11 is expected to be a viable prognostic biomarker for NSCLC.

Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images.

Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry labelling to confirm the diagnosis, which delays molecular analysis and utilises precious sample. Therefore, we tested the capacity of convolutional neural networks (CNNs) to classify NSCLC based on pathologic HES diagnostic biopsies. The model was estimated with a learning cohort of 132 NSCLC patients and validated on an external validation cohort of 65 NSCLC patients. Based on image patches, a CNN using InceptionV3 architecture was trained and optimized to classify NSCLC between squamous and non-squamous subtypes. Accuracies of 0.99, 0.87, 0.85, 0.85 was reached in the training, validation and test sets and in the external validation cohort. At the patient level, the CNN model showed a capacity to predict the tumour histology with accuracy of 0.73 and 0.78 in the learning and external validation cohorts respectively. Selecting tumour area using virtual tissue micro-array improved prediction, with accuracy of 0.82 in the external validation cohort. This study underlines the capacity of CNN to predict NSCLC subtype with good accuracy and to be applied to small pathologic samples without annotation.

Prognostic value of different N1 lymph node zones in pN1M0 non-small cell lung cancer: a systematic review and meta-analysis.

The IASLC lymph node map grouped the lymph node stations into "zones" for prognostic analyses. In the N1 lymph nodes group, N1 nodes are divided into the Hilar/Interlobar zone (N1h) and Peripheral zone (N1p). There is no consensus on the different prognostic values of N1 lymph nodes in N1h and N1p. Therefore, we conducted a systematic review and meta-analysis to assess the survival difference between N1h and N1p in patients of pN1M0 NSCLC. Medline, the Cochrane Library, Embase, and the Web of science were systematically searched to identify relevant studies published up to April 4th, 2020. A retrospective and prospective cohort study comparing N1h versus N1p to the pN1M0 NSCLC was included. Hazard ratios (HRs) for OS were aggregated according to a fixed or random-effect model. Ten publications for 1946 patients of pN1M0 NSCLC were included for the meta-analysis.The 5-year OS was lower for patients with N1h (HR: 1.67, 95% CI 1.44-1.94; P < 0.001). The pooled 5-year OS in N1h and N1p were 40% and 56%, respectively. The patients in pN1M0 NSCLC have different survival according to different N1 lymph node zones involvement: patients with N1p metastasis have a better prognosis than those with N1h metastasis.

A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning.

OBJECTIVE: To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse. METHODS: We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome (n = 163), patients with Yin deficiency syndrome (n = 174), and healthy controls (n = 185) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively. RESULTS: Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t4 and t5. The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806. CONCLUSIONS: It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer.

Association of Lung Adenocarcinoma Subtypes According to the IASLC/ATS/ERS Classification and Programmed Cell Death Ligand 1 (PD-L1) Expression in Tumor Cells.

Background: Programmed cell death-ligand 1 (PD-L1) protein expression is one of the most extensively studied biomarkers in patients with non-small cell lung cancer (NSCLC). However, there is scarce information regarding its association with distinct adenocarcinoma subtypes. This study evaluated the frequency of PD-L1 expression according to the IASLC/ATS/ERS classification and other relevant histological and clinical features. Patients and Methods: PD-L1 expression was assessed by immunohistochemistry (IHC). According to its positivity in tumor cells membrane, we stratified patients in three different tumor proportions score (TPS) cut-off points: a) <1% (negative), b) between 1 and 49%, and c) ≥50%; afterward, we analyzed the association among PD-L1 expression and lung adenocarcinoma (LADC) predominant subtypes, as well as other clinical features. As an exploratory outcome we evaluated if a PD-L1 TPS score ≥15% was useful as a biomarker for determining survival. Results: A total of 240 patients were included to our final analysis. Median age at diagnosis was 65 years (range 23-94 years). A PD-L1 TPS ≥1% was observed in 52.5% of the entire cohort; regarding specific predominant histological patterns, a PD-L1 TPS ≥1 was documented in 31.2% of patients with predominant-lepidic pattern, 46.2% of patients with predominant-acinar pattern, 42.8% of patients with a predominant-papillary pattern, and 68.7% of patients with predominant-solid pattern (p = 0.002). On the other hand, proportion of tumors with PD-L1 TPS ≥50% was not significantly different among adenocarcinoma subtypes. At the univariate survival analysis, a PD-L1 TPS cut-off value of ≥15% was associated with a worse PFS and OS. Conclusion: According to IASLC/ATS/ERS lung adenocarcinoma classification, the predominant-solid pattern is associated with a higher proportion of PD-L1 positive samples, no subtype was identified to be associated with a high (≥50%) TPS PD-L1.

Clinical and Prognostic Significance of CD117 in Non-Small Cell Lung Cancer: A Systemic Meta-Analysis.

The aim of this study was to assess the relationship of cluster of differentiation 117 (CD117) expression with the clinicopathological characteristics and the prognosis in patients with non-small cell lung cancer (NSCLC). No meta-analysis concerning the correlation of CD117 expression with clinical and prognostic values of the patients with NSCLC is reported. A systematic literature search was conducted to achieve eligible studies. The combined odds ratios (ORs) or hazard ratios (HRs: multivariate Cox analysis) with their 95% confidence intervals (CIs) were calculated in this analysis. Final 17 eligible studies with 4,893 NSCLC patients using immunohistochemical detection were included in this meta-analysis. CD117 expression was not correlated with gender (male vs. female), clinical stage (stages 3-4 vs. stages 1-2), tumor grade (grade 3 vs. grades 1-2), T-stage (T-stages 3-4 vs. T-stages 0-2), distal metastasis, and disease-free survival (DFS) of NSCLC (all p values >0.05). CD117 expression was associated with lymph node metastasis (positive vs. negative: OR = 0.74, 95% CI = 0.56-0.97, p = 0.03), histological type (adenocarcinoma (AC) versus squamous cell carcinoma (SCC): OR = 1.74, 95% CI = 1.26-2.39, p = 0.001), and a worse overall survival (OS) (HR = 1.89, 95% CI = 1.22-2.92, p = 0.004). The expression of CD117 was significantly higher in AC than in SCC. CD117 may be an independent prognostic indicator for worse OS in NSCLC.

The impact of reclassifying cancers of unspecified histology on international differences in survival for small cell and non-small cell lung cancer (ICBP SurvMark-2 project).

Survival from lung cancer remains low, yet is the most common cancer diagnosed worldwide. With survival contrasting between the main histological groupings, small-cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), it is important to assess the extent that geographical differences could be from varying proportions of cancers with unspecified histology across countries. Lung cancer cases diagnosed 2010-2014, followed until 31 December 2015 were provided by cancer registries from seven countries for the ICBP SURVMARK-2 project. Multiple imputation was used to reassign cases with unspecified histology into SCLC, NSCLC and other. One-year and three-year age-standardised net survival were estimated by histology, sex, age group and country. In all, 404 617 lung cancer cases were included, of which 47 533 (11.7%) and 262 040 (64.8%) were SCLC and NSCLC. The proportion of unspecified cases varied, from 11.2% (Denmark) to 29.0% (The United Kingdom). After imputation with unspecified histology, survival variations remained: 1-year SCLC survival ranged from 28.0% (New Zealand) to 35.6% (Australia) NSCLC survival from 39.4% (The United Kingdom) to 49.5% (Australia). The largest survival change after imputation was for 1-year NSCLC (4.9 percentage point decrease). Similar variations were observed for 3-year survival. The oldest age group had lowest survival and largest decline after imputation. International variations in SCLC and NSCLC survival are only partially attributable to differences in the distribution of unspecified histology. While it is important that registries and clinicians aim to improve completeness in classifying cancers, it is likely that other factors play a larger role, including underlying risk factors, stage, comorbidity and care management which warrants investigation.

Comparison of Two Proposed Changes to the Current Nodal Classification for Non--small Cell Lung Cancer Based on the Number and Ratio of Metastatic Lymph Nodes.

BACKGROUND: The current nodal classification is unsatisfactory in distinguishing the prognostically heterogeneous N1 or N2 non-small cell lung cancer (NSCLC). RESEARCH QUESTION: Is the combination of the current N category and the number of metastatic lymph nodes (N-#number) or the combination of the current N category and the ratio of the number of positive to resected lymph nodes (N-#ratio) better than the current N category alone? STUDY DESIGN AND METHODS: We identified 2,162 patients with N1 or N2 NSCLC from the Surveillance, Epidemiology, and End Results database (2004-2016). We classified these patients into three N-#number categories (N-#number-1, N-#number-2a, N-#number-2b) and three N-#ratio categories (N-#ratio-1, N-#ratio-2a, N-#ratio-2b). Lung cancer-specific survival (LCSS) were compared using the Kaplan-Meier method. The prognostic significance of the new nodal classifications was validated across each tumor size category (≤3 cm, 3-5 cm, 5-7cm, >7 cm). Cox proportional hazards regression was used to evaluate the association between each nodal classification and LCSS. RESULTS: The survival curves showed clear differences between each pair of N-#number and N-#ratio categories. A significant tendency toward the deterioration of LCSS from N-#number-1 to N-#number-2b was observed in all tumor size categories. However, the differences between each pair of N-#ratio categories were significant only in tumors from 3 to 7 cm. Although all three nodal classifications were independent prognostic indicators, the N-#number classification provided more accurate prognostic stratifications compared with the N-#ratio classification and the current nodal classification. INTERPRETATION: The N-#number classification followed by the N-#ratio classification might be better prognostic determinants than the current nodal classification in prognostically heterogeneous N1 or N2 NSCLC.

The influence of the number of lymph nodes removed on the accuracy of a newly proposed N descriptor classification in patients with surgically-treated lung cancer.

INTRODUCTION: The International Association for the Study of Lung Cancer has proposed a new classification of N descriptor based on the number of metastatic lymph nodes (LNs) stations, including skip metastasis. The aim of the study was to determine the effect of removed LNs on the adequacy of this new classification. MATERIALS AND METHODS: The material was collected retrospectively based on the database of the Polish Lung Cancer Group, including information on 8016 patients with non-small cell lung cancer operated in 23 thoracic surgery centers in Poland. The material covered the period from January 2005 to September 2015. We divided patients into two groups: ≤6LNs and >6LNs removed. RESULTS: In the whole group, an average of 13.4 nodes and 4.54 nodal stations were removed. 5-year survivals in the >6LNs group vs ≤ 6LNs group were: 62.3% and 55.1% (N0), 44.5% and 35.9% (N1a), 34.1% and 31,7% (N1b), 37.3% and 26.3% (N2a1), 32.4% and 26.7% (N2a2), 29.4% and 29.2% (N2b1), and 22.0% and 23.0% (N2b2), respectively. Comparing these groups, we detected significant differences at N0 (p < 0.001) and N2a1 (p = 0.022). In the ≤6LNs group, the survival curves for N2a1, N2a2, N2b1, and N2b2 overlapped (p > 0.05). In the >6LNs group, the survival curves were significantly different between grades, with survival for N2a1 better than N1b (p = 0.232). CONCLUSION: The proposed classification N descriptor is potentially better at differentiating patients into different stages. The accuracy of the classification depends on the number of lymph nodes removed. Therefore, the extent of lymphadenectomy has a significant impact on the staging of surgically-treated lung cancer.

Which N Descriptor Is More Predictive of Prognosis in Resected Non-small Cell Lung Cancer: The Number of Involved Nodal Stations or the Location-Based Pathological N Stage?

BACKGROUND: The eighth edition of nodal classification for non-small cell lung cancer (NSCLC) is defined only by the anatomical location of metastatic lymph nodes. RESEARCH QUESTION: We sought to evaluate the prognostic significance and discriminatory capability of the number of involved nodal stations (nS) in a large Chinese cohort. STUDY DESIGN AND METHODS: A total of 4,011 patients with NSCLC undergoing surgical resection between 2009 and 2013 were identified. The optimal cutoff values for nS classification were determined with X-tile software. Kaplan-Meier and multivariate Cox analysis were used to examine the prognostic performance of nS classification in comparison with location-based N classification. A decision curve analysis was performed to evaluate the standardized net benefit of nS classification in predicting prognosis. RESULTS: All the patients were classified into four prognostically different subgroups according to the number of involved nodal stations: (1) nS0 (none positive), (2) nS1 (one involved station), (3) nS2 (two involved stations), and (4) nS ≥ 3 (three or more involved stations). The prognoses among all the neighboring categories of nS classification were statistically significantly different in terms of disease-free survival and overall survival. The multivariate Cox analysis demonstrated that nS was an independent prognostic factor of disease-free survival and overall survival. Patients with N1 or N2 stage disease could be divided into three prognostically different subgroups according to nS classification. However, the prognosis was similar between the N1 and N2 subgroups when patients were staged in the same nS category. The decision curve analysis showed that nS classification tended to have a higher predictive capability than location-based N classification. INTERPRETATION: The nS classification could be used to provide a more accurate prognosis for patients with resected NSCLC. The nS is worth taking into consideration when defining nodal category in the forthcoming ninth edition of the staging system.

Association of PARP1-specific polymorphisms and haplotypes with non-small cell lung cancer subtypes.

OBJECTIVE: The carcinogenesis role of PARP1 in lung cancer is still not clear. Analysis at allelic levels cannot fully explain the function of PARP1 on lung cancer. Our study aims to further explore the relation between PARP1 haplotypes and lung cancer. MATERIALS AND METHODS: DNA and RNA were extracted from non-small cell lung cancer (NSCLC) tumor and adjacent normal fresh frozen tissue. Five PARP1-SNPs were genotyped and PARP1-specific SNPs were imputed using IMPUTE and SHAPEIT software. The SNPs were subjected to allelic, haplotype and SNP-SNP interaction analyses. Correlation between SNPs and mRNA/protein expressions were performed. RESULTS: SNP imputation inferred the ungenotyped SNPs and increased the power for association analysis. Tumor tissue samples are more likely to carry rs1805414 (OR = 1.85; 95% CI: 1.12-3.06; P-value: 0.017) and rs1805404 (OR = 2.74; 95%CI 1.19-6.32; P-value: 0.015) compared to normal tissues. Our study is the first study to show that haplotypes comprising of 5 SNPs on PARP1 (rs1136410, rs3219073, rs1805414, rs1805404, rs1805415) is able to differentiate the NSCLC tumor from normal tissues. Interaction between rs3219073, rs1805415, and rs1805414 were significantly associated with the NSCLC tumor with OR ranging from 3.61-6.75; 95%CI from 1.82 to 19.9; P-value<0.001. CONCLUSION: PARP1 haplotypes may serve as a better predictor in lung cancer development and prognosis compared to single alleles.

Distinguishing Non-Small Cell Lung Cancer Subtypes in Fine Needle Aspiration Biopsies by Desorption Electrospray Ionization Mass Spectrometry Imaging.

BACKGROUND: Distinguishing adenocarcinoma and squamous cell carcinoma subtypes of non-small cell lung cancers is critical to patient care. Preoperative minimally-invasive biopsy techniques, such as fine needle aspiration (FNA), are increasingly used for lung cancer diagnosis and subtyping. Yet, histologic distinction of lung cancer subtypes in FNA material can be challenging. Here, we evaluated the usefulness of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to diagnose and differentiate lung cancer subtypes in tissues and FNA samples. METHODS: DESI-MSI was used to analyze 22 normal, 26 adenocarcinoma, and 25 squamous cell carcinoma lung tissues. Mass spectra obtained from the tissue sections were used to generate and validate statistical classifiers for lung cancer diagnosis and subtyping. Classifiers were then tested on DESI-MSI data collected from 16 clinical FNA samples prospectively collected from 8 patients undergoing interventional radiology guided FNA. RESULTS: Various metabolites and lipid species were detected in the mass spectra obtained from lung tissues. The classifiers generated from tissue sections yielded 100% accuracy, 100% sensitivity, and 100% specificity for lung cancer diagnosis, and 73.5% accuracy for lung cancer subtyping for the training set of tissues, per-patient. On the validation set of tissues, 100% accuracy for lung cancer diagnosis and 94.1% accuracy for lung cancer subtyping were achieved. When tested on the FNA samples, 100% diagnostic accuracy and 87.5% accuracy on subtyping were achieved per-slide. CONCLUSIONS: DESI-MSI can be useful as an ancillary technique to conventional cytopathology for diagnosis and subtyping of non-small cell lung cancers.

Durvalumab-associated vasculitis presenting as 'the blue toe syndrome'.

Durvalumab is a selective, high-affinity human immunoglobulin monoclonal antibody in a class called check point inhibitors, that blocks PD-L1 on tumour cells. Despite clinical success in increasing progression-free survival rates in patients with stage III non-small-cell lung cancer, durvalumab has been associated with immune-related side effects such as pneumonitis and colitis. We present a case of an 84-year-old woman with acral vasculitis presenting as blue toe syndrome, associated with prolonged use of durvalumab. After 1 year of fortnightly durvalumab therapy postchemoradiation therapy, the patient came in with a left blue big toe, and later developed bilateral livedo racemosa. The diagnosis of durvalumab-associated vasculitis was made and treatment with prednisolone was started with clinical improvement.

Revealing the subtyping of non-small cell lung cancer based on genomic evolutionary patterns by multi-region sequencing.

Accurately classifying patients with non-small cell lung cancer (NSCLC) from the perspective of tumor evolution has not been systematically studied to date. Here, we reconstructed phylogenetic relationships of somatic mutations in 100 early NSCLC patients (327 lesions) through reanalyzing the TRACERx data. Based on the genomic evolutionary patterns presented on the phylogenetic trees, we grouped NSCLC patients into three evolutionary subtypes. The phylogenetic trees among three subtypes exhibited distinct branching structures, with one subtype representing branched evolution and another reflecting the early accumulation of genomic variation. However, in the evolutionary pattern of the third subtype, some mutations experienced selective sweeps and were gradually replaced by multiple newly formed subclonal populations. The subtype patients with poor prognosis had higher intra-tumor heterogeneity and subclonal diversity. We combined genomic heterogeneity with clinical phenotypes analysis and found that subclonal expansion results in the progression and deterioration of the tumor. The molecular mechanisms of subtype-specific Early Driver Feature (EDF) genes differed across the evolutionary subtypes, reflecting the characteristics of the subtype itself. In summary, our study provided new insights on the stratification of NSCLC patients based on genomic evolution that can be valuable for us to understand the development of pulmonary tumor profoundly.

Molecular profiling of non-small cell lung cancer.

Lung cancer is generally treated with conventional therapies, including chemotherapy and radiation. These methods, however, are not specific to cancer cells and instead attack every cell present, including normal cells. Personalized therapies provide more efficient treatment options as they target the individual's genetic makeup. The goal of this study was to identify the frequency of causal genetic mutations across a variety of lung cancer subtypes in the earlier stages. 833 samples of non-small cell lung cancer from 799 patients who received resection of their lung cancer, were selected for molecular analysis of six known mutations, including EGFR, KRAS, BRAF, PIK3CA, HER2 and ALK. A SNaPshot assay was used for point mutations and fragment analysis searched for insertions and deletions. ALK was evaluated by IHC +/- FISH. Statistical analysis was performed to determine correlations between molecular and clinical/pathological patient data. None of the tested variants were identified in most (66.15%) of cases. The observed frequencies among the total samples vs. only the adenocarcinoma cases were notable different, with the highest frequency being the KRAS mutation (24.49% vs. 35.55%), followed by EGFR (6.96% vs. 10.23%), PIK3CA (1.20% vs. 0.9%), BRAF (1.08% vs. 1.62%), ALK (0.12% vs. 0.18%), while the lowest was the HER2 mutation (0% for both). The statistical analysis yielded correlations between presence of a mutation with gender, cancer type, vascular invasion and smoking history. The outcome of this study will provide data that helps stratify patient prognosis and supports development of more precise treatments, resulting in improved outcomes for future lung cancer patients.

The Genomic Landscape of SMARCA4 Alterations and Associations with Outcomes in Patients with Lung Cancer.

PURPOSE: SMARCA4 mutations are among the most common recurrent alterations in non-small cell lung cancer (NSCLC), but the relationship to other genomic abnormalities and clinical impact has not been established. EXPERIMENTAL DESIGN: To characterize SMARCA4 alterations in NSCLC, we analyzed the genomic, protein expression, and clinical outcome data of patients with SMARCA4 alterations treated at Memorial Sloan Kettering. RESULTS: In 4,813 tumors from patients with NSCLC, we identified 8% (n = 407) of patients with SMARCA4-mutant lung cancer. We describe two categories of SMARCA4 mutations: class 1 mutations (truncating mutations, fusions, and homozygous deletion) and class 2 mutations (missense mutations). Protein expression loss was associated with class 1 mutation (81% vs. 0%, P < 0.001). Both classes of mutation co-occurred more frequently with KRAS, STK11, and KEAP1 mutations compared with SMARCA4 wild-type tumors (P < 0.001). In patients with metastatic NSCLC, SMARCA4 alterations were associated with shorter overall survival, with class 1 alterations associated with shortest survival times (P < 0.001). Conversely, we found that treatment with immune checkpoint inhibitors (ICI) was associated with improved outcomes in patients with SMARCA4-mutant tumors (P = 0.01), with class 1 mutations having the best response to ICIs (P = 0.027). CONCLUSIONS: SMARCA4 alterations can be divided into two clinically relevant genomic classes associated with differential protein expression as well as distinct prognostic and treatment implications. Both classes co-occur with KEAP1, STK11, and KRAS mutations, but individually represent independent predictors of poor prognosis. Despite association with poor outcomes, SMARCA4-mutant lung cancers may be more sensitive to immunotherapy.

Lung cancer cytology and small biopsy specimens: diagnosis, predictive biomarker testing, acquisition, triage, and management.

In the 21st century, there has been a dramatic shift in the management of advanced-stage lung carcinoma, and this has coincided with an increasing use of minimally invasive tissue acquisition methods. Both have had significant downstream effects on cytology and small biopsy specimens. Current treatments require morphologic, immunohistochemical, and/or genotypical subtyping of non-small cell lung carcinoma. To meet these objectives, standardized classification of cytology and small specimen diagnoses, immunohistochemical algorithms, and predictive biomarker testing guidelines have been developed. This review provides an overview of current classification, biomarker testing, methods of small specimen acquisition and triage, clinical management strategies, and emerging technologies.

Classification Model to Estimate MIB-1 (Ki 67) Proliferation Index in NSCLC Patients Evaluated With 18F-FDG-PET/CT.

BACKGROUND/AIM: Proliferation biomarkers such as MIB-1 are strong predictors of clinical outcome and response to therapy in patients with non-small-cell lung cancer, but they require histological examination. In this work, we present a classification model to predict MIB-1 expression based on clinical parameters from positron emission tomography. PATIENTS AND METHODS: We retrospectively evaluated 78 patients with histology-proven non-small-cell lung cancer (NSCLC) who underwent 18F-FDG-PET/CT for clinical examination. We stratified the population into a low and high proliferation group using MIB-1=25% as cut-off value. We built a predictive model based on binary classification trees to estimate the group label from the maximum standardized uptake value (SUVmax) and lesion diameter. RESULTS: The proposed model showed ability to predict the correct proliferation group with overall accuracy >82% (78% and 86% for the low- and high-proliferation group, respectively). CONCLUSION: Our results indicate that radiotracer activity evaluated via SUVmax and lesion diameter are correlated with tumour proliferation index MIB-1.