Intratumor Heterogeneity of ALK-Rearrangements and Homogeneity of EGFR-Mutations in Mixed Lung Adenocarcinoma

EML4-ALK fusions drive lung adeno-to-squamous transition through JAK-STAT activation

Human lung adenosquamous cell carcinoma (LUAS), containing both adenomatous and squamous pathologies, exhibits strong cancer plasticity. We find that ALK rearrangement is detectable in 5.1-7.5% of human LUAS, and transgenic expression of EML4-ALK drives lung adenocarcinoma (LUAD) formation initially and squamous transition at late stage. We identify club cells as the main cell-of-origin for squamous transition. Through recapitulating lineage transition in organoid system, we identify JAK-STAT signaling, activated by EML4-ALK phase separation, significantly promotes squamous transition. Integrative study with scRNA-seq and immunostaining identify a plastic cell subpopulation in ALK-rearranged human LUAD showing squamous biomarker expression. Moreover, those relapsed ALK-rearranged LUAD show notable upregulation of squamous biomarkers. Consistently, mouse squamous tumors or LUAD with squamous signature display certain resistance to ALK inhibitor, which can be overcome by combined JAK1/2 inhibitor treatment. This study uncovers strong plasticity of ALK-rearranged tumors in orchestrating phenotypic transition and drug resistance and proposes a potentially effective therapeutic strategy.

Spatially Resolved Proteomic and Transcriptomic Profiling of Anaplastic Lymphoma Kinase-Rearranged Pulmonary Adenocarcinomas Reveals Key Players in Inter- and Intratumoral Heterogeneity

Pulmonary adenocarcinomas (pADCs) with an ALK rearrangement are a rare cancer subtype, necessitating comprehensive molecular investigations to unravel their heterogeneity and improve therapeutic strategies. In this pilot study, we employed spatial transcriptomic (NanoString GeoMx) and proteomic profiling to investigate seven treatment-naïve pADCs with an ALK rearrangement. On each FFPE tumor slide, 12 smaller and 2-6 larger histopathologically annotated regions were selected for transcriptomic and proteomic analysis, respectively. The correlation between proteomics and transcriptomics was modest (average Pearson's r = 0.43 at the gene level). Intertumoral heterogeneity was more pronounced than intratumoral heterogeneity, and normal adjacent tissue exhibited distinct molecular characteristics. We identified potential markers and dysregulated pathways associated with tumors, with a varying extent of immune infiltration, as well as with mucin and stroma content. Notably, some markers appeared to be specific to the ALK-driven subset of pADCs. Our data showed that within tumors, elements of the extracellular matrix, including FN1, exhibited substantial variability. Additionally, we mapped the co-localization patterns of tumor microenvironment elements. This study represents the first spatially resolved profiling of ALK-driven pADCs at both the gene and protein expression levels. Our findings may contribute to a better understanding of this cancer type prior to treatment with ALK inhibitors.

The percentage of ALK-positive cells and the efficacy of first-line alectinib in advanced non-small cell lung cancer: is it a novel factor for stratification? (Turkish Oncology Group Study)

INTRODUCTION

Alectinib is an effective second-generation ALK tyrosine kinase inhibitor (TKI) used in the first-line treatment of patients with advanced ALK-positive NSCLC. Recent studies demonstrated that the percentage of ALK-positive tumor cells in patient groups receiving crizotinib might affect outcomes. This study aimed to investigate whether the percentage of ALK-positive cells had a predictive effect in patients with advanced NSCLC who received first-line Alectinib as ALK-TKI.

MATERIALS AND METHODS

This retrospective study included patients with advanced-stage NSCLC who received alectinib as a first-line ALK-TKI and whose percentage of ALK-positive cells was determined by FISH at 27 different centers. Patients who received any ALK-TKI before alectinib were not included in the study. Patients were separated into two groups according to the median (40%) value of the percentage of ALK-positive cells (high-positive group ≥ 40% and low-positive group < 40%). The primary endpoint was PFS, and the secondary endpoints were OS, ORR, and PFS of the subgroups based on different threshold values for the percentage of ALK-positive cells.

RESULTS

211 patients were enrolled (48.3% female, 51.7% male) to study. 37% (n = 78) of the patients had received chemotherapy previously. After a median of 19.4 months of follow-up, the median PFS was not reached in the high-positive group (n = 113), but it was 10.8 months in the low-positive group (n = 98) (HR 0.39; 95% CI 0.25-0.60, p < 0.001). The median OS in the high-positive group was not reached, whereas it was 22.8 months in the low-positive group (HR 0.37; 95% CI 0.22-0.63, p < 0.001). ORR was significantly higher in the high-positive group (87.2 vs. 68.5%; p = 0.002). According to the cut-off values of < 20%, 20-39%, 40-59%, and ≥ 60%, the median PFS was 4.5, 17.1, and 26 months, respectively, and could not be reached in the ≥ 60% group.

CONCLUSION

Our study demonstrated that the efficacy of alectinib varies significantly across patient subgroups with different percentages of ALK-positive cells. If these findings are prospectively validated, the percentage of ALK-positive cells may be used as a stratification factor in randomized trials comparing different ALK-TKIs.

Heterogeneous distribution of EGFR mutation in NSCLC: Case report

Background

We herein report the case of a patient with advanced lung adenocarcinoma who presented a heterogeneous distribution of EGFR mutation.

Case report

A 74-year-old Moroccan male former smoker was diagnosed with advanced lung adenocarcinoma, harboring S768I exon 20 substitution mutation confirmed by Real Time PCR and Pyrosequencing, but not detected by direct sequencing despite 70% of tumor cells. The present report describes a case of minor histologic intratumoral heterogeneity with heterogeneous distribution of EGFR mutation.

Conclusion

Both sensitivity and specificity of molecular methods can provide evidence of intratumoral heterogeneity, which may explain the mismatch between the validation of oncology biomarkers and predicting therapeutic response to targeted therapy.

High Homogeneity of Mesothelin Expression in Primary and Metastatic Ovarian Cancer

To study the extent of heterogeneity of mesothelin overexpression in primary ovarian cancers and their peritoneal and lymph node metastases, a tissue microarray (TMA) was constructed from multiple sites of 220 ovarian cancers and analyzed by immunohistochemistry. One tissue core each was taken from up to 18 different tumor blocks per cancer, resulting in a total of 2460 tissue spots from 423 tumor sites (188 primary cancers, 162 peritoneal carcinosis, and 73 lymph node metastases). Positive mesothelin expression was found in 2041 of the 2342 (87%) arrayed tissue spots and in 372 of the 392 (95%) tumor sites that were interpretable for mesothelin immunohistochemistry. Intratumoral heterogeneity was found in 23% of 168 primary cancer sites interpretable for mesothelin and decreased to 12% in 154 peritoneal carcinosis and to 6% in 71 lymph node metastases ( P <0.0001). Heterogeneity between the primary tumor and matched peritoneal carcinosis was found in 16% of 102 cancers with interpretable mesothelin results. In these cancers, the mesothelin status switched from positive in the primary tumor to negative in the peritoneal carcinosis (3 cancers) in or vice versa (2 cancers), or a mixture of positive and negative peritoneal carcinoses was found (11 cancers). No such switch was seen between the mesothelin-interpretable primary tumors and their nodal metastases of 59 cancers, and only 1 mesothelin-positive tumor had a mixture of positive and negative lymph node metastases. In conclusion, mesothelin expression is frequent and highly homogeneous in ovarian cancer.

Practical Considerations for the Use of Circulating Tumor DNA in the Treatment of Patients With Cancer: A Narrative Review

Importance

Personalized medicine based on tumor profiling and identification of actionable genomic alterations is pivotal in cancer management. Although tissue biopsy is still preferred for diagnosis, liquid biopsy of blood-based tumor analytes, such as circulating tumor DNA, is a rapidly emerging technology for tumor profiling.

Observations

This review presents a practical overview for clinicians and allied health care professionals for selection of the most appropriate liquid biopsy assay, specifically focusing on circulating tumor DNA and how it may affect patient treatment and case management across multiple tumor types. Multiple factors influence the analytical validity, clinical validity, and clinical utility of testing. This review provides recommendations and practical guidance for best practice. Current methodologies include polymerase chain reaction-based approaches and those that use next-generation sequencing (eg, capture-based profiling, whole exome, or genome sequencing). Factors that may influence utility include sensitivity and specificity, quantity of circulating tumor DNA, detection of a small vs a large panel of genes, and clonal hematopoiesis of indeterminate potential. Currently, liquid biopsy appears useful in patients unable to undergo biopsy or where mutations detected may be more representative of the predominant tumor burden than for tissue-based assays. Other potential applications may include screening, primary diagnosis, residual disease, local recurrence, therapy selection, or early therapy response and resistance monitoring.

Conclusions and Relevance

This review found that liquid biopsy is increasingly being used clinically in advanced lung cancer, and ongoing research is identifying applications of circulating tumor DNA-based testing that complement tissue analysis across a broad range of clinical settings. Circulating tumor DNA technologies are advancing quickly and are demonstrating potential benefits for patients, health care practitioners, health care systems, and researchers, at many stages of the patient oncologic journey.

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma

Objectives

Anaplastic lymphoma kinase (ALK) rearrangement status examination has been widely used in clinic for non-small cell lung cancer (NSCLC) patients in order to find patients that can be treated with targeted ALK inhibitors. This study intended to non-invasively predict the ALK rearrangement status in lung adenocarcinomas by developing a machine learning model that combines PET/CT radiomic features and clinical characteristics.

Methods

Five hundred twenty-six patients of lung adenocarcinoma with PET/CT scan examination were enrolled, including 109 positive and 417 negative patients for ALK rearrangements from February 2016 to March 2019. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images. The maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were further employed to select the most distinguishable radiomic features to construct predictive models. The mRMR is a feature selection method, which selects the features with high correlation to the pathological results (maximum correlation), meanwhile retain the features with minimum correlation between them (minimum redundancy). LASSO is a statistical formula whose main purpose is the feature selection and regularization of data model. LASSO method regularizes model parameters by shrinking the regression coefficients, reducing some of them to zero. The feature selection phase occurs after the shrinkage, where every non-zero value is selected to be used in the model. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the models, and the performance of different models was compared by the DeLong test.

Results

A total of 22 radiomic features were extracted from PET/CT images for constructing the PET/CT radiomic model, and majority of these features used were based on CT features (20 out of 22), only 2 PET features were included (PET percentile 10 and PET difference entropy). Moreover, three clinical features associated with ALK mutation (age, burr and pleural effusion) were also employed to construct a combined model of PET/CT and clinical model. We found that this combined model PET/CT-clinical model has a significant advantage to predict the ALK mutation status in the training group (AUC = 0.87) and the testing group (AUC = 0.88) compared with the clinical model alone in the training group (AUC = 0.76) and the testing group (AUC = 0.74) respectively. However, there is no significant difference between the combined model and PET/CT radiomic model.

Conclusions

This study demonstrated that PET/CT radiomics-based machine learning model has potential to be used as a non-invasive diagnostic method to help diagnose ALK mutation status for lung adenocarcinoma patients in the clinic.

CT texture analysis for prediction of EGFR mutational status and ALK rearrangement in patients with non-small cell lung cancer

PURPOSE

To develop a CT texture-based model able to predict epidermal growth factor receptor (EGFR)-mutated, anaplastic lymphoma kinase (ALK)-rearranged lung adenocarcinomas and distinguish them from wild-type tumors on pre-treatment CT scans.

MATERIALS AND METHODS

Texture analysis was performed using proprietary software TexRAD (TexRAD Ltd, Cambridge, UK) on pre-treatment contrast-enhanced CT scans of 84 patients with metastatic primary lung adenocarcinoma. Textural features were quantified using the filtration-histogram approach with different spatial scale filters on a single 5-mm-thick central slice considered representative of the whole tumor. In order to deal with class imbalance regarding mutational status percentages in our population, the dataset was optimized using the synthetic minority over-sampling technique (SMOTE) and correlations with textural features were investigated using a generalized boosted regression model (GBM) with a nested cross-validation approach (performance averaged over 1000 resampling episodes).

RESULTS

ALK rearrangements, EGFR mutations and wild-type tumors were observed in 19, 28 and 37 patients, respectively, in the original dataset. The balanced dataset was composed of 171 observations. Among the 29 original texture variables, 17 were employed for model building. Skewness on unfiltered images and on fine texture was the most important features. EGFR-mutated tumors showed the highest skewness while ALK-rearranged tumors had the lowest values with wild-type tumors showing intermediate values. The average accuracy of the model calculated on the independent nested validation set was 81.76% (95% CI 81.45-82.06).

CONCLUSION

Texture analysis, in particular skewness values, could be promising for noninvasive characterization of lung adenocarcinoma with respect to EGFR and ALK mutations.

Individualized nomogram for predicting ALK rearrangement status in lung adenocarcinoma patients

OBJECTIVES

To develop a nomogram to identify anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients using clinical, CT, PET/CT, and histopathological features.

METHODS

This retrospective study included 399 lung adenocarcinoma patients (129 ALK-rearranged patients and 270 ALK-negative patients) that were randomly divided into a training cohort and an internal validation cohort (4:1 ratio). Clinical factors, radiologist-defined CT features, maximum standard uptake values (SUVmax), and histopathological features were used to construct predictive models with stepwise backward-selection multivariate logistic regression (MLR). The models were then evaluated using the AUC. The integrated model was compared to the clinico-radiological model using the DeLong test to evaluate the role of histopathological features. An associated individualized nomogram was established.

RESULTS

The integrated model reached an AUC of 0.918 (95% CI, 0.886-0.950), sensitivity of 0.774, and specificity of 0.934 in the training cohort and an AUC of 0.857 (95% CI, 0.777-0.937), sensitivity of 0.739, and specificity of 0.810 in the validation cohort. The MLR analysis showed that younger age, never smoker, lymph node enlargement, the presence of cavity, high SUVmax, solid or micropapillary predominant histology subtype, and local invasiveness were strong and independent predictors of ALK rearrangements. The nomogram calculated the risk of harboring ALK mutation for lung adenocarcinoma patients and exhibited a good generalization ability.

CONCLUSION

Our study demonstrates that histopathological features added value to the imaging characteristics-based model. The nomogram with clinical, imaging, and histopathological features can serve as a supplementary non-invasive tool to evaluate the probability of ALK rearrangement in lung adenocarcinoma.

KEY POINTS

• The developed nomogram can accurately predict the probability of lung adenocarcinoma harboring ALK-fused gene. • Pathological analysis is important to predict ALK rearrangement in lung adenocarcinoma. • Lung adenocarcinoma with lepidic predominant growth pattern and TTF-1 negativity is unlikely to have ALK rearrangement.

Unproductive Effects of ALK Gene Amplification and Copy Number Gain in Non-Small-Cell Lung Cancer. ALK Gene Amplification and Copy Gain in NSCLC

Background: The Anaplastic Lymphoma Kinase (ALK) gene is known to be affected by several genetic alterations, such as rearrangement, amplification and point mutation. The main goal of this study was to comprehensively analyze ALK amplification (ALK-A) and ALK gene copy number gain (ALK-CNG) in a large cohort of non-small-cell lung cancer (NSCLC) patients in order to evaluate the effects on mRNA and protein expression. Methods: ALK locus number status was evaluated in 578 NSCLC cases by fluorescence in situ hybridization (FISH). In addition, ALK immunohistochemistry and ALK mRNA in situ hybridization were performed. Results: Out of 578 cases, 17 cases showed ALK-A. In addition, 14 cases presented ALK-CNG and 72 cases presented chromosome 2 polyploidy. None of those carrying ALK-A and -CNG showed either ALK immunohistochemical expression or ALK mRNA expression through in situ hybridization. We observed a high frequency of extra copies of the ALK gene. Conclusions: Our findings demonstrated that ALK-A is not involved in mRNA production and consequently is not involved in protein production; these findings support the hypothesis that ALK-A might not play a role in the pathogenesis of NSCLC, underlining the absence of a specific clinical application.

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Objectives: To predict the anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients non-invasively with machine learning models that combine clinical, conventional CT and radiomic features.

Methods: This retrospective study included 335 lung adenocarcinoma patients who were randomly divided into a primary cohort (268 patients; 90 ALK-rearranged; and 178 ALK wild-type) and a test cohort (67 patients; 22 ALK-rearranged; and 45 ALK wild-type). One thousand two hundred and eighteen quantitative radiomic features were extracted from the semi-automatically delineated volume of interest (VOI) of the entire tumor using both the original and the pre-processed non-enhanced CT images. Twelve conventional CT features and seven clinical features were also collected. Normalized features were selected using a sequential of the F-test-based method, the density-based spatial clustering of applications with noise (DBSCAN) method, and the recursive feature elimination (RFE) method. Selected features were then used to build three predictive models (radiomic, radiological, and integrated models) for the ALK-rearranged phenotype by a soft voting classifier. Models were evaluated in the test cohort using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity, and the performances of three models were compared using the DeLong test.

Results: Our results showed that the addition of clinical information and conventional CT features significantly enhanced the validation performance of the radiomic model in the primary cohort (AUC = 0.83–0.88, P = 0.01), but not in the test cohort (AUC = 0.80–0.88, P = 0.29). The majority of radiomic features associated with ALK mutations reflected information around and within the high-intensity voxels of lesions. The presence of the cavity and left lower lobe location were new imaging phenotypic patterns in association with ALK-rearranged tumors. Current smoking was strongly correlated with non-ALK-mutated lung adenocarcinoma.

Conclusions: Our study demonstrates that radiomics-derived machine learning models can potentially serve as a non-invasive tool to identify ALK mutation of lung adenocarcinoma.

Heterogeneity of PD-L1 Expression in Lung Mixed Adenocarcinomas and Adenosquamous Carcinomas

Immune checkpoint inhibitors against programmed cell death protein 1/programmed death-ligand 1 (PD-L1) have proven to be remarkably effective in non–small cell lung cancer. PD-L1 represents a predictive biomarker in lung cancer, although its heterogenous expression represents an emerging challenge for accurate biomarker-based patient selection. Lung adenocarcinomas (ADCs) show a high rate of intratumor morphologic heterogeneity that may reflect a heterogenous molecular and immunophenotypic profile. The aim of our study was to analyze the expression of PD-L1 in different intratumor subtypes and/or growth patterns in a series of mixed adenocarcinomas (mADCs) and adenosquamous lung carcinomas (AdSqLCs). As many as 73 mADCs and 6 AdSqLCs were selected. Comprehensive histologic subtyping was performed, and PD-L1 expression was assessed by immunohistochemistry assay using different primary antibodies and automated immunostainers. Overall, PD-L1 expression was observed in 37 of 79 cases (39.2%) (31 mADCs and all AdSqLCs). PD-L1 expression was heterogenous in 22 of 37 PD-L1-positive cases (23.2% mADC and 83% AdSqLC). PD-L1 expression was observed more frequently in ADC with solid pattern. Heterogeneity of PD-L1 expression was significantly related to the presence of micropapillary (P=0.028) and solid (P=0.017) patterns. All PD-L1-positive cases were epidermal growth factor receptor wild-type, 2 cases harbored concomitantly PD-L1 expression and ALK rearrangement. Our data suggest that PD-L1 expression is quite heterogenous in mADCs and AdSqLCs, partly contributing to explaining the discrepant results between biopsy and surgical resections and discordant clinical effectiveness in regard to PD-L1-positive or negative ADC diagnosed on cytology/small biopsy.

Multiplex fluorescence in situ hybridisation to detect anaplastic lymphoma kinase and ROS proto-oncogene 1 receptor tyrosine kinase rearrangements in lung cancer cytological samples

AIMS

Several predictive biomarkers of response to specific inhibitors have become mandatory for the therapeutic choice in non-small-cell lung cancer (NSCLC). In most lung cancer patients, the biological materials available to morphological and molecular diagnosis are exclusively cytological samples and minimum tumour wastage is necessary. Multiplex fluorescence in situ hybridisation (mFISH) to detect simultaneously ALK-rearrangement and ROS1-rearrangement on a single slide could be useful in clinical practice to save cytological samples for further molecular analysis. In this study, we aim to validate diagnostic performance of multiplex ALK/ROS1 fluorescence in situ hybridisation (FISH) approach in lung adenocarcinoma cytological series compared with classic single break apart probes.

METHODS

We collected a series of 61 lung adenocarcinoma cytological specimens enriched in tumours harbouring ALK-rearrangement and ROS1-rearrangement. ALK and ROS1 status were previously assessed by classic FISH test using single break apart probes and immunohistochemistry. Study population was composed of 6 ALK-positive, 2 ROS1-positive and 53 ALK/ROS1-wild type. All specimens were analysed by multiplex FISH assay using FlexISH ALK/ROS1 DistinguISH Probe Zytovision.

RESULTS

The dual ALK/ROS1 FISH probe test results were fully concordant with the results of previous single ALK and ROS1 FISH tests on two different slides. 6 ALK-positive and 2 ROS1-positive were confirmed through multiplex FISH test, without false-positive and false-negative results. Multiplex ALK/ROS1 FISH test results agreed with immunohistochemistry assay staining results.

CONCLUSION

Multiplex ALK/ROS1 FISH probe test is a useful tool to detect simultaneously ALK-rearrangement and ROS1-rearrangement on a single slide in cytological specimens with a small amount of biomaterial.

Expression of peptide transporter 1 has a positive correlation in protoporphyrin IX accumulation induced by 5-aminolevulinic acid with photodynamic detection of non-small cell lung cancer and metastatic brain tumor specimens originating from non-small cell lung cancer

BACKGROUND

Recently, 5-aminolevulinic acid (5-ALA)-induced protoporphyrin IX fluorescence was reported to be a useful tool during total surgical resection of high-grade gliomas. However, the labeling efficacy of protoporphyrin IX fluorescence is lower in metastatic brain tumors compared to that in high-grade gliomas, and the mechanism underlying protoporphyrin IX fluorescence in metastatic brain tumors remains unclear. Lung cancer, particularly non-small cell lung cancer (NSCLC), is the most common origin for metastatic brain tumor. Therefore, we investigated the mechanism of protoporphyrin IX fluorescence in NSCLC and associated metastatic brain tumors.

METHODS

Western blotting and quantitative real-time polymerase chain reaction (qRT-PCR) was employed to evaluate the protein and mRNA levels of five transporters and enzymes involved in the porphyrin biosynthesis pathway: peptide transporter 1 (PEPT1), hydroxymethylbilane synthase (HMBS), ferrochelatase (FECH), ATP-binding cassette 2 (ABCG2), and heme oxygenase 1 (HO-1). The correlation between protein, mRNA, and protoporphyrin IX levels in NSCLC cells were evaluated in vitro. Immunohistochemistry was used to determine proteins that played a key role in intraoperative protoporphyrin IX fluorescence in clinical samples from patients with NSCLC and pathologically confirmed metastatic brain tumors.

RESULTS

A significant correlation between PEPT1 expression and protoporphyrin IX accumulation in vitro was identified by western blotting (P = 0.003) and qRT-PCR (P = 0.04). Immunohistochemistry results indicated that there was a significant difference in PEPT1 between the intraoperative protoporphyrin IX fluorescence-positive and protoporphyrin IX fluorescence-negative groups (P = 0.009).

CONCLUSION

Expression of PEPT1 was found to be positively correlated with 5-ALA-induced protoporphyrin IX accumulation detected by photodynamic reaction in metastatic brain tumors originating from NSCLC.

Large-Scale EGFR Mutation Testing in Clinical Practice: Analysis of a Series of 18,920 Non-Small Cell Lung Cancer Cases

We make use of a very large dataset of non-small cell lung cancer specimens to examine the molecular epidemiology of EGFR mutations, particularly with respect to rare and compound mutations, and to non-adenocarcinoma histological subtypes. We also demonstrate the feasibility of large-scale EGFR mutation screening using the full range of specimens encountered in routine practice. We retrospectively reviewed 18,920 unselected EGFR mutation results from our centre between July 2009 and October 2016, using Qiagen's therascreen EGFR RGQ PCR Kit. Mutation rates were correlated with patient demographics and tumour histology. Our testing success rate was 93.9%, with similar success rates using histological and cytological specimens. Rare, potentially-targetable mutations accounted for 9.5% of all mutations detected. We identified a 2.5% mutation rate in tumours diagnosed as squamous cell carcinomas. There was a trend towards increasing EGFR mutation rates with increasing age, and while Del19 was the commonest mutation in the young, L858R predominated in the elderly. We found that EGFR mutation heterogeneity is rare within tumours and between primary and metastatic deposits. Our data demonstrate that large-scale, reflex EGFR mutation testing is feasible and affordable in the context of a publicly-funded health system. Furthermore, we have shown that the use of techniques sensitive only to classical mutations and selection of patients on the grounds of age, sex and histology denies patients access to potentially beneficial TKI therapy.

Efficacy of Pemetrexed-based Chemotherapy in Comparison to Non-Pemetrexed-based Chemotherapy in Advanced, ALK+ Non-Small Cell Lung Cancer

PURPOSE

Previous retrospective studies suggest that anaplastic lymphoma kinase (ALK) mutation-positive (ALK+) non-small cell lung cancer (NSCLC) patients are sensitive to pemetrexed. To determine its efficacy, we retrospectively evaluated clinical outcomes of pemetrexed-based chemotherapy in patients with ALK+ NSCLC.

MATERIALS AND METHODS

We identified 126 patients with advanced, ALK+ NSCLC who received first-line cytotoxic chemotherapy. We compared response, progression-free survival (PFS), and overall survival (OS) rates according to chemotherapy regimens. Furthermore, we evaluated intracranial time to tumor progression (TTP) and proportion of ALK+ cells as prognostic factors.

RESULTS

Forty-eight patients received pemetrexed-based chemotherapy, while 78 received other regimens as first-line treatment. The pemetrexed-based chemotherapy group showed superior overall response (44.7% vs. 14.3%, p<0.001) and disease control (85.1% vs. 62.3%, p=0.008) rates. The pemetrexed-based chemotherapy group also exhibited longer PFS (6.6 months vs. 3.8 months, p<0.001); OS rates were not significantly different. The lack of exposure to second-generation ALK inhibitors and intracranial metastasis on initial diagnosis were independent negative prognostic factors of OS. Intracranial TTP was similar between the treatment groups (32.7 months vs. 35.7 months, p=0.733). Patients who harbored a greater number of ALK+ tumor cells (≥70%) showed prolonged OS on univariate analysis (not reached vs. 44.8 months, p=0.041), but not on multivariate analysis (hazard ratio: 0.19, 95% confidence interval: 0.03-1.42; p=0.106).

CONCLUSION

Pemetrexed-based regimens may prolong PFS in patients with ALK+ NSCLC as a first-line treatment, but are not associated with prolonged OS. Exposure to second-generation ALK inhibitors may improve OS rates in patients with ALK+ NSCLC.

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers. Our classification pipeline includes a basic CNN architecture, Google's Inceptions with three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. Training strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3. We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. On average, our pipeline achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer tissues, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ih-_lab/CNN_Smoothie.

Intratumor and Intertumor Heterogeneity in Melanoma

Melanoma is a cancer that exhibits one of the most aggressive and heterogeneous features. The incidence rate escalates. A high number of clones harboring various mutations contribute to an exceptional level of intratumor heterogeneity of melanoma. It also refers to metastases which may originate from different subclones of primary lesion. Such component of the neoplasm biology is termed intertumor and intratumor heterogeneity. These levels of tumor heterogeneity hinder accurate diagnosis and effective treatment. The increasing number of research on the topic reflects the need for understanding limitation or failure of contemporary therapies. Majority of analyses concentrate on mutations in cancer-related genes. Novel high-throughput techniques reveal even higher degree of variations within a lesion. Consolidation of theories and researches indicates new routes for treatment options such as targets for immunotherapy. The demand for personalized approach in melanoma treatment requires extensive knowledge on intratumor and intertumor heterogeneity on the level of genome, transcriptome/proteome, and epigenome. Thus, achievements in exploration of melanoma variety are described in details. Particularly, the issue of tumor heterogeneity or homogeneity given BRAF mutations is discussed.

Lung Carcinoma Predictive Biomarker Testing by Immunoperoxidase Stains in Cytology and Small Biopsy Specimens: Advantages and Limitations

CONTEXT

- In the burgeoning era of molecular genomics, immunoperoxidase (IPOX) testing grows increasingly relevant as an efficient and effective molecular screening tool. Patients with lung carcinoma may especially benefit from the use of IPOX because most lung carcinomas are inoperable at diagnosis and only diagnosed by small tissue biopsy or fine-needle sampling. When such small specimens are at times inadequate for molecular testing, positive IPOX results still provide actionable information.

OBJECTIVE

- To describe the benefits and pitfalls of IPOX in the detection of biomarkers in lung carcinoma cytology specimens and small biopsies by summarizing the currently available commercial antibodies, preanalytic variables, and analytic considerations.

DATA SOURCES

- PubMed.

CONCLUSIONS

- Commercial antibodies exist for IPOX detection of aberrant protein expression due to EGFR L858R mutation, EGFR E746_A750 deletion, ALK rearrangement, ROS1 rearrangement, and BRAF V600E mutation, as well as PD-L1 expression in tumor cells. Automated IPOX protocols for ALK and PD-L1 detection were recently approved by the Food and Drug Administration as companion diagnostics for targeted therapies, but consistent interpretive criteria remain to be elucidated, and such protocols do not yet exist for other biomarkers. The inclusion of cytology specimens in clinical trials would expand patients' access to testing and treatment, yet there is a scarcity of clinical trial data regarding the application of IPOX to cytology, which can be attributed to trial designers' lack of familiarity with the advantages and limitations of cytology. The content of this review may be used to inform clinical trial design and advance IPOX validation studies.

Mutational status of synchronous and metachronous tumor samples in patients with metastatic non-small-cell lung cancer

BACKGROUNDS

Despite reported discordance between the mutational status of primary lung cancers and their metastases, metastatic sites are rarely biopsied and targeted therapy is guided by genetic biomarkers detected in the primary tumor. This situation is mostly explained by the apparent stability of EGFR-activating mutations. Given the dramatic increase in the range of candidate drugs and high rates of drug resistance, rebiopsy or liquid biopsy may become widespread. The purpose of this study was to test genetic biomarkers used in clinical practice (EGFR, ALK) and candidate biomarkers identified by the French National Cancer Institute (KRAS, BRAF, PIK3CA, HER2) in patients with metastatic non-small-cell lung cancer for whom two tumor samples were available.

METHODS

A retrospective study identified 88 tumor samples collected synchronously or metachronously, from the same or two different sites, in 44 patients. Mutation analysis used SNaPshot (EGFR, KRAS, BRAF missense mutations), pyrosequencing (EGFR and PIK3CA missense mutations), sizing assays (EGFR and HER2 indels) and IHC and/or FISH (ALK rearrangements).

RESULTS

About half the patients (52%) harbored at least one mutation. Five patients had an activating mutation of EGFR in both the primary tumor and the metastasis. The T790M resistance mutation was detected in metastases in 3 patients with acquired resistance to EGFR tyrosine kinase inhibitors. FISH showed discordance in ALK status between a small biopsy sample and the surgical specimen. KRAS mutations were observed in 36% of samples, six patients (14%) having discordant genotypes; all discordances concerned sampling from different sites. Two patients (5%) showed PI3KCA mutations. One metastasis harbored both PI3KCA and KRAS mutations, while the synchronously sampled primary tumor was mutation free. No mutations were detected in BRAF and HER2.

CONCLUSIONS

This study highlighted noteworthy intra-individual discordance in KRAS mutational status, whereas EGFR status was stable. Intratumoral heterogeneity for ALK rearrangement suggests a limitation of single-biopsy analysis for therapeutic strategy with crizotinib.