A qualitative transcriptional signature for the histological reclassification of lung squamous cell carcinomas and adenocarcinomas

A Simple Method for Robust and Accurate Intrinsic Subtyping of Breast Cancer

Motivation

The PAM50 signature/method is widely used for intrinsic subtyping of breast cancer samples. However, depending on the number and composition of the samples included in a cohort, the method may assign different subtypes to the same sample. This lack of robustness is mainly due to the fact that PAM50 subtracts a reference profile, which is computed using all samples in the cohort, from each sample before classification. In this paper we propose modifications to PAM50 to develop a simple and robust single-sample classifier, called MPAM50, for intrinsic subtyping of breast cancer. Like PAM50, the modified method uses a nearest centroid approach for classification, but the centroids are computed differently, and the distances to the centroids are determined using an alternative method. Additionally, MPAM50 uses unnormalized expression values for classification and does not subtract a reference profile from the samples. In other words, MPAM50 classifies each sample independently, and so avoids the previously mentioned robustness issue.

Results

A training set was employed to find the new MPAM50 centroids. MPAM50 was then tested on 19 independent datasets (obtained using various expression profiling technologies) containing 9637 samples. Overall good agreement was observed between the PAM50- and MPAM50-assigned subtypes with a median accuracy of 0.792, which (we show) is comparable with the median concordance between various implementations of PAM50. Additionally, MPAM50- and PAM50-assigned intrinsic subtypes were found to agree comparably with the reported clinical subtypes. Also, survival analyses indicated that MPAM50 preserves the prognostic value of the intrinsic subtypes. These observations demonstrate that MPAM50 can replace PAM50 without loss of performance. On the other hand, MPAM50 was compared with 2 previously published single-sample classifiers, and with 3 alternative modified PAM50 approaches. The results indicated a superior performance by MPAM50.

Conclusions

MPAM50 is a robust, simple, and accurate single-sample classifier of intrinsic subtypes of breast cancer.

Hierarchical identification of a transcriptional panel for the histological diagnosis of lung neuroendocrine tumors

Background: Lung cancer is a complex disease composed of neuroendocrine (NE) and non-NE tumors. Accurate diagnosis of lung cancer is essential in guiding therapeutic management. Several transcriptional signatures have been reported to distinguish between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) belonging to non-NE tumors. This study aims to identify a transcriptional panel that could distinguish the histological subtypes of NE tumors to complement the morphology-based classification of an individual.

Methods: A public dataset with NE subtypes, including 21 small-cell lung cancer (SCLC), 56 large-cell NE carcinomas (LCNECs), and 24 carcinoids (CARCIs), and non-NE subtypes, including 85 ADC and 61 SCC, was used as a training set. In the training set, consensus clustering was first used to filter out the samples whose expression patterns disagreed with their histological subtypes. Then, a rank-based method was proposed to develop a panel of transcriptional signatures for determining the NE subtype for an individual, based on the within-sample relative gene expression orderings of gene pairs. Twenty-three public datasets with a total of 3,454 samples, which were derived from fresh-frozen, formalin-fixed paraffin-embedded, biopsies, and single cells, were used for validation. Clinical feasibility was tested in 10 SCLC biopsy specimens collected from cancer hospitals via bronchoscopy.

Results: The NEsubtype-panel was composed of three signatures that could distinguish NE from non-NE, CARCI from non-CARCI, and SCLC from LCNEC step by step and ultimately determine the histological subtype for each NE sample. The three signatures achieved high average concordance rates with 97.31%, 98.11%, and 90.63%, respectively, in the 23 public validation datasets. It is worth noting that the 10 clinic-derived SCLC samples diagnosed via immunohistochemical staining were also accurately predicted by the NEsubtype-panel. Furthermore, the subtype-specific gene expression patterns and survival analyses provided evidence for the rationality of the reclassification by the NEsubtype-panel.

Conclusion: The rank-based NEsubtype-panel could accurately distinguish lung NE from non-NE tumors and determine NE subtypes even in clinically challenging samples (such as biopsy). The panel together with our previously reported signature (KRT5-AGR2) for SCC and ADC would be an auxiliary test for the histological diagnosis of lung cancer.

An 8-Gene Signature for Classifying Major Subtypes of Non-Small-Cell Lung Cancer

Motivation:

The precise diagnosis of the major subtypes, lung adenocarcinoma and lung squamous cell carcinoma, of non-small-cell lung cancer is of practical importance as some treatments are subtype-specific. However, in some cases diagnosis via the commonly-used method, that is staining the specimen using immunohistochemical markers, may be challenging. Hence, having a computational method that complements the diagnosis is desirable. In this paper, we propose a gene signature for this purpose.

Results:

We developed an expression-based method that systematically suggests a huge set of candidate gene signatures and finds the best candidate. By applying this method to a training set, the optimal gene signature was found by considering close to 765 billion candidate signatures. The 8-gene signature found for classifying the 2 aforementioned subtypes comprises TP63, CALML3, KRT5, PKP1, TESC, SPINK1, C9orf152, and KRT7. The signature achieved a high overall prediction accuracy of 0.936 when tested using 34 independent gene expression datasets obtained using different technologies and comprising 2556 adenocarcinoma and 1630 squamous cell carcinoma samples. Additionally, the signature performed well in clinically challenging cases, that is poorly differentiated tumors and specimens obtained from biopsies. In comparison with 2 previously reported signatures, our signature performed better in terms of overall accuracy and especially accuracy of classifying lung squamous cell carcinoma.

Conclusions:

Our signature is easy to use and accurate regardless of the technology used to obtain the gene expression profiles. It performs well even in clinically challenging cases and thus can assist pathologists in diagnosis of the ambiguous cases.

Comprehensive Characterization of Human Lung Large Cell Carcinoma Identifies Transcriptomic Signatures with Potential Implications in Response to Immunotherapy

Lung cancer is the leading cause of cancer mortality worldwide, with non-small cell lung cancer (NSCLC) being the most prevalent histology. While immunotherapy with checkpoint inhibitors has shown outstanding results in NSCLC, the precise identification of responders remains a major challenge. Most studies attempting to overcome this handicap have focused on adenocarcinomas or squamous cell carcinomas. Among NSCLC subtypes, the molecular and immune characteristics of lung large cell carcinoma (LCC), which represents 10% of NSCLC cases, are not well defined. We hypothesized that specific molecular aberrations may impact the immune microenvironment in LCC and, consequently, the response to immunotherapy. To that end, it is particularly relevant to thoroughly describe the molecular genotype-immunophenotype association in LCC-to identify robust predictive biomarkers and improve potential benefits from immunotherapy. We established a cohort of 18 early-stage, clinically annotated, LCC cases. Their molecular and immune features were comprehensively characterized by genomic and immune-targeted sequencing panels along with immunohistochemistry of immune cell populations. Unbiased clustering defined two novel subgroups of LCC. Pro-immunogenic tumors accumulated certain molecular alterations, showed higher immune infiltration and upregulated genes involved in potentiating immune responses when compared to pro-tumorigenic samples, which favored tumoral progression. This classification identified a set of biomarkers that could potentially predict response to immunotherapy. These results could improve patient selection and expand potential benefits from immunotherapy.

Comprehensive molecular characterization of lung tumors implicates AKT and MYC signaling in adenocarcinoma to squamous cell transdifferentiation

BACKGROUND

Lineage plasticity, the ability to transdifferentiate among distinct phenotypic identities, facilitates therapeutic resistance in cancer. In lung adenocarcinomas (LUADs), this phenomenon includes small cell and squamous cell (LUSC) histologic transformation in the context of acquired resistance to targeted inhibition of driver mutations. LUAD-to-LUSC transdifferentiation, occurring in up to 9% of EGFR-mutant patients relapsed on osimertinib, is associated with notably poor prognosis. We hypothesized that multi-parameter profiling of the components of mixed histology (LUAD/LUSC) tumors could provide insight into factors licensing lineage plasticity between these histologies.

METHODS

We performed genomic, epigenomics, transcriptomics and protein analyses of microdissected LUAD and LUSC components from mixed histology tumors, pre-/post-transformation tumors and reference non-transformed LUAD and LUSC samples. We validated our findings through genetic manipulation of preclinical models in vitro and in vivo and performed patient-derived xenograft (PDX) treatments to validate potential therapeutic targets in a LUAD PDX model acquiring LUSC features after osimertinib treatment.

RESULTS

Our data suggest that LUSC transdifferentiation is primarily driven by transcriptional reprogramming rather than mutational events. We observed consistent relative upregulation of PI3K/AKT, MYC and PRC2 pathway genes. Concurrent activation of PI3K/AKT and MYC induced squamous features in EGFR-mutant LUAD preclinical models. Pharmacologic inhibition of EZH1/2 in combination with osimertinib prevented relapse with squamous-features in an EGFR-mutant patient-derived xenograft model, and inhibition of EZH1/2 or PI3K/AKT signaling re-sensitized resistant squamous-like tumors to osimertinib.

CONCLUSIONS

Our findings provide the first comprehensive molecular characterization of LUSC transdifferentiation, suggesting putative drivers and potential therapeutic targets to constrain or prevent lineage plasticity.

The value of AGR2 and KRT5 as an immunomarker combination in distinguishing lung squamous cell carcinoma from adenocarcinoma

With the advancement of tumor subtype-specific treatments, precise histopathologic distinction between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is of significant clinical importance. Nevertheless, the current markers are insufficiently precise in poorly differentiated tissue. This study aimed to establish a histology-specific immunomarker combination to subclassify non-small cell lung cancer (NSCLC) specimens. Based on previous work, we assessed the differential expression of anterior gradient 2 (AGR2) and keratin 5 (KRT5) in ADC and SCC by analyzing public datasets and postoperative specimens. Subsequently, we established a train set (n = 188) and a validation set (n = 42) comprised of NSCLC surgical specimens for training and verifying the subtype-identification capabilities of the two biomarkers separately and in combination, and contrasted the diagnostic utility of AGR2-KRT5 with that of the classic immunomarker combination, TTF1-P40. Differential expression of the two genes was statistically significant in ADC and SCC samples, both at the mRNA and protein levels. The specificity and sensitivity of AGR2 to detect ADC in the training set were 97.0% and 94.4%, while the sensitivity and specificity of KRT5 to determine SCC were 93.9% and 98.9%, respectively. The accuracies of AGR2-KRT5 in ADC, SCC, and across all samples were 93.3%, 92.0% and 92.6% respectively. In the validation cohort, the predictive accuracy of AGR2-KRT5 was up to 100% for ADC and 86.7% for SCC. Compared with TTF1-P40 in ADC samples, AGR2-KRT5 had 8.4% higher accuracy. In summary, the AGR2-KRT5 immunomarker combination reliably distinguished SCC from ADC, and was more accurate than TTF1-P40 in ADC.

Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data

Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms.

Use of four genes in exosomes as biomarkers for the identification of lung adenocarcinoma and lung squamous cell carcinoma

The determination of biomarkers in the blood specific for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is crucial for the selection of effective treatment strategies and the prediction of prognosis. The purpose of the present study was to analyze the differentially expressed genes (DEGs) in LUSC and LUAD from The Cancer Genome Atlas (TCGA) database. In order to identify the potential biomarkers for non-small cell lung cancer (NSCLC) for clinical diagnosis, bioinformatics was used to analyze the DEGs of two subtypes of NSCLC, LUAD and LUSC. Exosomes were isolated from the serum of patients with LUAD or LUSC and identified using transmission electron microscopy, nanoparticle tracking analysis and western blot analysis. A total of four differential exosomal mRNAs were selected for validation with serum samples from 70 patients with NSCLC via reverse transcription-quantitative polymerase chain reaction. Receiver operating characteristic curves were established to evaluate the clinical diagnostic value of four DEGs for patients with LUAD and LUSC. The analysis based on TCGA data revealed the DEGs in LUSC and LUAD: A total of 1,619 genes were differentially expressed in patients with LUSC and LUAD. DEGs analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that inflammation-related signaling pathways, such as complement pathways, and multiple autoimmune diseases, such as systemic lupus erythematosus and asthma were mainly enriched in LUAD. The cell cycle, Hippo signaling pathway, Rap1 signaling pathway and Wnt signaling pathway were the main signaling pathways enriched in LUSC. The combination of tumor protein P63 (TP63), keratin 5 (KRT5), CEA cell adhesion molecule 6 (CEACAM6) and surfactant protein B (SFTPB) improved the specificity and sensitivity in the diagnosis of different lung cancer subtypes. Exosomal TP63, KRT5, CEACAM6 and SFTPB mRNAs can thus be used as biomarkers to differentiate between LUSC and LUAD, and may provide a novel strategy for their differential diagnosis and treatment.