Subtype Of Adenocarcinoma Research Articles

Abstract 4627: Immune profiling of immunotherapy and adjuvant chemotherapy pretreatment NSCLC tissues by CODEX

Abstract Non-small cell lung cancer (NSCLC), including adenocarcinoma and squamous cell carcinoma subtypes, is a leading cause of cancer deaths worldwide. Treatment of NSCLC has advanced from chemotherapy modalities to the use of immunotherapy, namely immune checkpoint inhibitors (ICIs) which enhance the adaptive immune response against tumour cells. Thus, while the immune cell composition of tumours and its influence on treatment outcomes is poorly understood, it likely holds the key to effective, personalized treatment regimens. Here we profiled an adjuvant chemotherapy (n=61) as well as a second line immunotherapy (n=42) NSCLC cohort by the Phenocycler CODEX technology (highplex spatial proteomics) to investigate the association between immune composition and patient outcome. We applied a panel of 38 markers to delineate naïve, memory, cytotoxic and hyperactivated T cell states, as well as B cells, Tregs and myeloid lineage innate immune cell types. Our study sought to understand the heterogeneity of tumour-immune composition across patients and investigate the spatial neighbourhoods and clusters that these cells inhabit within TMA cores. We used Phenoplex™ software for tissue segmentation (into classes for tumor, stroma, artifacts, blood vessels, etc.), cellular segmentation, and cellular phenotyping based on thresholds for each marker, and then performed spatial analyses (distances, interactions, neighborhoods) using SpatialMap1 to identify cellular motifs associated with clinical phenotypes. Our study has identified spatial features associated with immune contexture linked to therapy outcome in both chemotherapy and immunotherapy modalities. Taken together, our study demonstrates the utility of spatial proteomics to identify cellular features associated with outcome to therapy in lung cancer. 1) Trevino A, Ivison G, Hamel S, Chiou A (2022). SpatialMap: Analysis of Spatial Biology Data. R package version 0.4.57. Citation Format: James Monkman, Afshin Moradi, Connor O'Leary, Zhenqin Wu, Steven Hamel, David Mason, Fabian Schneider, James Robert Mansfield, Aaron Meyer, Ken O'Byrne, Arutha Kulasinghe. Immune profiling of immunotherapy and adjuvant chemotherapy pretreatment NSCLC tissues by CODEX. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4627.

Abstract 2494: Heterogeneous tumor-driven immune landscapes between lung adenocarcinomas in ever and never-smokers

Abstract To depict the genomic and immune landscapes of lung adenocarcinomas (ADCs) in ever and never-smokers and guide the design of immunotherapy strategies, we applied multiple deconvolution methods that allow the identification of immune cell infiltration and immune pathway enrichment using a whole exome and transcriptome sequencing-based approach in 145 patient-matched lung ADC and non-malignant lung parenchymal tissue pairs from ever and never smokers in our in-house cohort. Results were subsequently validated by an external lung ADC cohort, including 108 never-smokers and 61 ever-smokers. Firstly, we showed that ever-smokers had an increased abundance of neutrophils and macrophage infiltration, an elevated antigen processing and presentation capacity, and activated T-cell-mediated immunity. Secondly, ever-smokers revealed elevated macrophages in acinar ADCs, decreased mast cells in lepidic ADCs, and increased neutrophils in papillary ADCs, suggesting the tumor-driven immune characteristic discrepancies among various pathologic subtypes of ADC. Thirdly, we identified mutational disparities in tumor mutation burdens, subclonal single nucleotide variants, and the predicted and expressed neoantigens between ever and never-smokers. The mutant gene KRAS preferably occurred in ever-smokers, further contributing to the infiltration of macrophages and expression of cytokines. The copy number variations of immune-related genes (NFIB, TNFAIP3) were identified as conferring to the dysregulated immune environment in ever-smokers. Moreover, a higher genomic instability index was observed in ever-smokers (p = 0.008). Fourthly, the neoantigen was significantly increased in ever-smoker lung ADCs without nodal disease. While DNA immunoediting preferably occurred in never-smokers. Notably, in lung ADC patients with nodal disease, ever-smokers exhibited slightly more homogenous mutations (p = 0.094), consistent with the evolutionary bottleneck during cancer progression. Finally, the lower immunoediting and higher microenvironment scores significantly determined the more prolonged progression-free survival in never-smokers (p < 0.050). Our multiscale genomic and immune profiling analyses thus offer valuable knowledge of tumor-driven immune disparity arising from smoking and provide a robust solution to the rational design of immune therapies. Citation Format: Xiaozheng Kang, Xin Wang, Liyan Ji, Bingfa Yan, Xuan Gao, Xuefeng Xia, Xingsheng Hu. Heterogeneous tumor-driven immune landscapes between lung adenocarcinomas in ever and never-smokers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2494.

Abstract 979: Development of a methylation-based classifier to identify pancreatic adenocarcinoma subtype

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) demonstrates variable prognosis and response to first-line FOLFIRINOX therapy depending on molecular subtype; patients with classical tumors showed a greater response compared to basal-like tumors. Identifying molecular subtype may inform treatment approaches, but current subtyping schemas rely on tumor biopsies which are invasive and can be technically challenging. Blood based epigenetic assays are commercially available, and identification of subtype-associated epigenetic modifications may provide a path toward blood-based PDAC molecular subtyping. We analyzed methylation levels and developed a subtype classifier using methylation data on PDAC tumors. Using this classifier, we then developed a methylation-specific qPCR assay to subtype PDAC tumors by analyzing methylation at particular sites in tumor DNA which may now be further adapted to more facile liquid biopsy approaches. Methods: Using methylation data (n=150) from TCGA PAAD, we applied a random forest (RF) model followed by a k-Top Scoring Pairs (k-TSP) algorithm to develop a methylation site-based classifier that allows prediction of subtype by comparing methylation levels between paired sites. The classifier was validated on two independent datasets, including ICGC (n=82) and patient-derived xenografts (PDXs) (n=21). We designed methylation-specific qPCR primers for these sites and validated their specificity with fully methylated or fully unmethylated DNA. Using genomic DNA extracted from 10 PDAC PDXs of known molecular subtype, we obtained methylation levels at sites identified by the classifier via a SYBR Green-based qPCR assay. We then used our classifier to predict subtype of the PDX tumors. Results: We validated our RF-kTSP methylation subtype classifier on two independent cohorts and predicted subtype using 20 methylation sites. In ICGC, we found balanced accuracy=0.94, AUROC=0.98, AU-PR=0.89. In PDX PDAC tumors, we found balanced accuracy=1, AUROC=1, AU-PR=1. We designed 14/20 primers for qPCR and confirmed specificity for methylated DNA. Using 14/20 methylation sites, we achieved 80% correct subtype prediction of 10 PDX tumors (5/5 basal-like, 3/5 classical). Conclusion: We have developed a methylation-based classifier that accurately and replicably predicts PDAC subtype. We continue to develop a qPCR assay for PDAC subtype that so far has 80% accuracy using 14/20 probes. We are completing validation of the remaining 6 primers and correlating methylation levels at each site with those obtained from EPIC array methylation analysis. The long-term goal is to apply our complete set of primers in a digital droplet PCR assay that will be amenable for subtyping using circulating tumor DNA (ctDNA). Citation Format: Zachary Schrank, Des Weighill, Hannah Thel, Hannah Trembath, Ashley Morrison, Jen Jen Yeh. Development of a methylation-based classifier to identify pancreatic adenocarcinoma subtype [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 979.

Abstract 6742: Genomic profiling and sites of metastasis in non-small cell lung cancer (NSCLC)

Abstract Introduction: Molecular genotyping of metastatic NSCLC adenocarcinoma subtypes using next generation sequencing (NGS) is currently the standard practice. However, there is limited data on the biological predisposition to site of metastasis in patients with NSCLC based on their molecular profiling. We sought to identify any association between metastatic site and molecular profile in NSCLC patients. Method: This is a retrospective analysis conducted at UTHealth Houston/Memorial Hermann Cancer Center from January 2014 to June 2022. Clinical characteristics, pathology, NGS panel reports, and imaging were retrieved and reviewed. The χ2 test was used for categorical variables. Continuous variables were compared using Kruskal-Wallis one-way analysis of variance. Progression-free survival (PFS) and overall survival (OS) were analyzed using the Kaplan-Meier test. Results: After excluding patients who did not have complete clinical data, a total of 143 patients were included, and their NGS panels analyzed. Median age was 65 years, with an equal number of men (n=71) and women (n=72). The most common histology was adenocarcinoma (81.8%), followed by squamous cell cancer (11.9%) and large cell carcinoma (3.5%). At least one genetic mutation was discovered in 100 patients. Mutations with a targetable drug were found in 86 patients, and many patients had >1 genetic mutation. The most common mutations were TP53 (25.2%), EGFR (24.5%), KRAS/NRAS (20.3%), and CDKN2A/2B (7.7%). Patients with any mutation were significantly more likely to have metastatic disease to the brain (57% vs 37%, p=0.03), but there was no difference in metastatic disease to bone (34% vs 26%, p=0.32). Patients without a discoverable mutation were significantly more likely to have metastatic disease to other sites (e.g., adrenal gland, liver; 91% vs 66%, p=0.002). There was no difference in PFS or OS between those with versus without mutations. Median PFS was significantly longer in patients with EGFR mutation than those with KRAS/NRAS or TP53 mutations (36 vs 16.2 vs 11.9 months, p=0.03). Median OS for patients with EGFR mutation was not reached and was significantly longer than in patients with TP53 (28.7 months) or KRAS/NRAS (26 months) mutations (p=0.003).Patients with PDL-1 >1% or TP53 were significantly more likely to have metastatic disease to organs other than bone or brain (p=0.047 and p=0.023, respectively). There was also no difference between rates of brain and bone metastasis with regards to mutational profile. Conclusion: Metastatic lung cancer patients with discoverable mutations on NGS are more likely to have metastatic disease to the brain. PDL-1 expression and TP53 mutation tend to lead to disease metastatic to organs other than brain or bone. Patients with EGFR mutations, despite having a great propensity to brain metastasis, have significantly better PFS and OS than patients with KRAS/NRAS and TP53 mutations, likely due to targeted therapy options. Citation Format: Kok Hoe Chan, Ji Lin, Arthi Sridhar, Syed H. Jafri. Genomic profiling and sites of metastasis in non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6742.

MSMFN: An Ultrasound Based Multi-Step Modality Fusion Network for Identifying the Histologic Subtypes of Metastatic Cervical Lymphadenopathy.

Identifying squamous cell carcinoma and adenocarcinoma subtypes of metastatic cervical lymphadenopathy (CLA) is critical for localizing the primary lesion and initiating timely therapy. B-mode ultrasound (BUS), color Doppler flow imaging (CDFI), ultrasound elastography (UE) and dynamic contrast-enhanced ultrasound provide effective tools for identification but synthesis of modality information is a challenge for clinicians. Therefore, based on deep learning, rationally fusing these modalities with clinical information to personalize the classification of metastatic CLA requires new explorations. In this paper, we propose Multi-step Modality Fusion Network (MSMFN) for multi-modal ultrasound fusion to identify histological subtypes of metastatic CLA. MSMFN can mine the unique features of each modality and fuse them in a hierarchical three-step process. Specifically, first, under the guidance of high-level BUS semantic feature maps, information in CDFI and UE is extracted by modality interaction, and the static imaging feature vector is obtained. Then, a self-supervised feature orthogonalization loss is introduced to help learn modality heterogeneity features while maintaining maximal task-consistent category distinguishability of modalities. Finally, six encoded clinical information are utilized to avoid prediction bias and improve prediction ability further. Our three-fold cross-validation experiments demonstrate that our method surpasses clinicians and other multi-modal fusion methods with an accuracy of 80.06%, a true-positive rate of 81.81%, and a true-negative rate of 80.00%. Our network provides a multi-modal ultrasound fusion framework that considers prior clinical knowledge and modality-specific characteristics. Our code will be available at: https://github.com/RichardSunnyMeng/MSMFN.

Recent Advances in the Management of Small Cell Carcinoma of the Uterine Cervix.

Small cell neuroendocrine carcinoma of the cervix (SCNEC) is a rare disease characterized by a higher incidence of lymphatic invasion, metastasis, and recurrence compared to the squamous cell carcinoma and adenocarcinoma subtypes. Furthermore, it is prone to early distant metastasis and has a poor prognosis. Chemotherapy has an important role in the management of cervical SCNEC. The effective treatment schemes for early-stage SCNEC are local treatment with radical surgery and systemic chemotherapy. However no standard treatment regimen exists because of a rare disease. We reviewed previous reports to determine whether etoposide/platinum, which is used for histopathologically similar small cell carcinoma of the lung, is an appropriate initial chemotherapy regimen for SCNEC of the cervix. In this review the Cochrane library sources, ClinicalTrials.gov, Web of Silence, PubMed and search engine of Google scholar were searched for all interventional studies, reviews, case reports and meta-analyses published in 1997-2021. Etoposide/platinum (EP) is the most commonly used regimen and paclitaxel/carboplatin is the second most common, used as a part of multimodality therapy for SCNEC of the cervix in most studies. Cisplatin/vincristine/bleomycin, cisplatin/irinotecan, cisplatin/ifosfamide/etoposide were also reported in concurrence with EP; however no clinical trials are dedicated to SCNEC. Etoposide and platinum tend to have a better prognosis compared to other regimens used for other subtypes of cervical cancer. For recurrent cervical SCNEC, treatment options for patients are very limited. The application of molecular testing for targeted mutations may suggest potential future therapies that may be useful in this disease.

Clinical features and prognosis of pulmonary enteric adenocarcinoma: A retrospective study in China and the SEER database.

Pulmonary enteric adenocarcinoma (PEAC) is a rare subtype of pulmonary adenocarcinoma that lacks effective treatment. The purpose of this research was to investigate the clinical characteristics, treatment, and prognosis of PEAC, as well as the impact of relevant factors on survival, thus providing a reference for the clinical management of patients with this disease. For this study, we gathered clinical data from 26 patients with PEAC in the Affiliated Cancer Hospital of Zhengzhou University from June 2014 to June 2021. We used SEER*Stat software V8.3.5 to download the PEAC patients from the Surveillance, Epidemiology, and End Results (SEER) database. In total, 20 patients were identified. Clinical data, including general information, imaging findings, and treatment protocols, were obtained, together with a follow-up of disease regression. The relevant clinical data were then analyzed. It included 12 males and 14 females out of 26 patients from China, whose mean age was (62.73 ± 11.89) years; 20 were in the lower lung, 11 were stage I-II, and 15 were stage III-IV. Five had EGFR mutations, and four had KRAS mutations. In terms of treatment, patients with stage I-II were primarily treated by surgery, and patients with stage III-IV were treated mostly by chemotherapy. We extended the follow-up date to January 2022. On completion of the follow-up visit, 11 patients died, and the remaining 15 patients survived. The overall survival (OS) of 26 patients was 2.0-76.0 months, while the mean was 53.1 months, and the median OS (mOS) was 38.0 months (95% CI:1.727-74.273). In the case of progression-free survival (PFS) times, it was 2.0-76.0 months, with a mean PFS of 31.0 months and a median PFS (mPFS) of 8.0 months (95% CI:4.333-11.667). The PFS of the 15 patients in stage III-IV was 2.0-17 months, while the mean PFS was 6.5 months and the mPFS was 6.0 months (95% CI:4.512-7.488). Out of the 20 patients identified in the SEER database, the average age was 69.9 years, with 14 males and 6 females. Of these patients, 8 were diagnosed with stage I-II, while the remaining 11 were diagnosed with stage III-IV. 10 underwent surgery, 4 received radiation therapy, and 9 received chemotherapy. The mean OS of the 20 patients was 67.5 months, mOS was 28.0 months (95% CI: 9.664- 46.336). For patients diagnosed with stage III-IV, the mean OS was 14.8 months and mOS was 20 months (95% CI: 4.713-35.287). PEAC is rare, and the prognosis is determined mainly by the stage; patients who undergo surgery in stage I-II have a better prognosis.

Cytokeratin 6 identifies basal-like subtypes of pancreatic ductal adenocarcinoma with decreased survival

PurposeRising incidence of pancreatic ductal adenocarcinoma (PDAC) bind with insufficient therapy options showcases a great medical challenge. Further biomarkers are required to identify patients, who will benefit from more aggressive therapy.Methods320 patients were included by the PANCALYZE study group. Cytokeratin 6 (CK6) immunohistochemical staining as a putative marker for the basal-like subtype of PDAC was performed. The correlation between CK6 expression patterns and survival data, as well as various markers of the (inflammatory) tumor microenvironment, were analyzed.ResultsWe divided the study population based on the expression pattern of CK6. Patients with a high CK6 tumor expression had a significantly shorter survival (p = 0.013), confirmed in a multivariate cox regression model. CK6-expression is an independent marker for a decreased overall survival (HR = 1.655, 95% CI 1.158–2.365, p = 0.006). In addition, the CK6-positive tumors showed significantly less plasma cell infiltration and more cancer-associated fibroblasts (CAFs) expressing Periostin and SMA.ConclusionsCK6 could be considered as an independent biomarker for a shorter overall survival. CK6 is a clinically easily accessible biomarker for the identification of the basal-like subtype of PDAC. Therefore, it could be taken into consideration in deciding for the more aggressive therapy regimes. Prospectively, studies addressing the chemosensitive characteristics of this subtype are required.

Relationship Between the Diffusing Capacity of the Lung for Carbon Monoxide (DLCO) and Lung Adenocarcinoma Patterns: New Possible Insights

IntroductionThis study aimed to evaluate a potential relationship between the diffusing capacity of the lung for carbon monoxide (DLCO) and the aggressiveness of lung adenocarcinoma (ADC). MethodsPatients who underwent radical surgery for lung ADC between 2001 and 2018 were retrospectively reviewed. DLCO values were dichotomized into DLCOlow (<80% of predicted) and DLCOnormal (≥80%). Relationships between DLCO and ADC histopathological features, clinical features, as well as with overall survival (OS), were evaluated. ResultsFour-hundred and sixty patients were enrolled, of which 193 (42%) were included in the DLCOlow group. DLCOlow was associated with smoking status, low FEV1, micropapillary and solid ADC, tumour grade 3, high tumour lymphoid infiltrate and presence of tumour desmoplasia. In addition, DLCO values were higher in low-grade ADC and progressively decreased in intermediate and high-grade ADC (p=0.024). After adjusting for clinical variables, at multivariable logistic regression analysis, DLCOlow still showed a significant correlation with high lymphoid infiltrate (p=0.017), presence of desmoplasia (p=0.065), tumour grade 3 (p=0.062), micropapillary and solid ADC subtypes (p=0.008). To exclude the association between non-smokers and well-differentiated ADC, the relationship between DLCO and histopathological ADC patterns was confirmed in the subset of 377 former and current smokers (p=0.021). At univariate analysis, gender, DLCO, FEV1, ADC histotype, tumour grade, stage, pleural invasion, tumour necrosis, tumour desmoplasia, lymphatic and blood invasion were significantly related with OS. At multivariate analysis, only gender (p<0.001), tumour stage (p<0.001) and DLCO (p=0.050) were significantly related with the OS. ConclusionsWe found a relationship between DLCO and ADC patterns as well as with tumour grade, tumour lymphoid infiltrate and desmoplasia, suggesting that lung damage may be associated with tumour aggressiveness.

Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?

The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four histopathological subtypes of non-small-cell lung carcinoma (NSCLC). In addition, the results that varied with the radiomics model were compared. We investigated the presence of the batch effects and the impact of feature harmonization on the models' performance. Moreover, the question on how the training dataset composition influenced the selected feature subsets and, consequently, the model's performance was also investigated. Therefore, through combining data from the two publicly available datasets, this study involves a total of 152 squamous cell carcinoma (SCC), 106 large cell carcinoma (LCC), 150 adenocarcinoma (ADC), and 58 no other specified (NOS). Through the matRadiomics tool, which is an example of Image Biomarker Standardization Initiative (IBSI) compliant software, 1781 radiomics features were extracted from each of the malignant lesions that were identified in CT images. After batch analysis and feature harmonization, which were based on the ComBat tool and were integrated in matRadiomics, the datasets (the harmonized and the non-harmonized) were given as an input to a machine learning modeling pipeline. The following steps were articulated: (i) training-set/test-set splitting (80/20); (ii) a Kruskal-Wallis analysis and LASSO linear regression for the feature selection; (iii) model training; (iv) a model validation and hyperparameter optimization; and (v) model testing. Model optimization consisted of a 5-fold cross-validated Bayesian optimization, repeated ten times (inner loop). The whole pipeline was repeated 10 times (outer loop) with six different machine learning classification algorithms. Moreover, the stability of the feature selection was evaluated. Results showed that the batch effects were present even if the voxels were resampled to an isotropic form and whether feature harmonization correctly removed them, even though the models' performances decreased. Moreover, the results showed that a low accuracy (61.41%) was reached when differentiating between the four subtypes, even though a high average area under curve (AUC) was reached (0.831). Further, a NOS subtype was classified as almost completely correct (true positive rate ~90%). The accuracy increased (77.25%) when only the SCC and ADC subtypes were considered, as well as when a high AUC (0.821) was obtained-although harmonization decreased the accuracy to 58%. Moreover, the features that contributed the most to models' performance were those extracted from wavelet decomposed and Laplacian of Gaussian (LoG) filtered images and they belonged to the texture feature class.. In conclusion, we showed that our multicenter data were affected by batch effects, that they could significantly alter the models' performance, and that feature harmonization correctly removed them. Although wavelet features seemed to be the most informative features, an absolute subset could not be identified since it changed depending on the training/testing splitting. Moreover, performance was influenced by the chosen dataset and by the machine learning methods, which could reach a high accuracy in binary classification tasks, but could underperform in multiclass problems. It is, therefore, essential that the scientific community propose a more systematic radiomics approach, focusing on multicenter studies, with clear and solid guidelines to facilitate the translation of radiomics to clinical practice.

MOPA: An integrative multi-omics pathway analysis method for measuring omics activity.

Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mES score reflects the strength of regulatory relations between multi-omics in units of pathways. In addition, MOPA is able to measure how much each omics contribute to mES that may be used to observe what kind of omics are active in a pathway within a sample group (e.g., subtype, gender), which we refer to OCR (Omics Contribution Rate). Using nine different cancer types, 93 clinical features and three types of omics (i.e., gene expression, miRNA and methylation), MOPA was used to search for clinical features that were explainable in context of multi-omics. By evaluating the performance of MOPA, we showed that it yielded higher or at least equal performance compared to previous single and multi-omics pathway analysis tools. We find that the advantage of MOPA is the ability to explain pathways in terms of omics relation using mES and OCR. As one of the results, the TGF-beta signaling pathway was captured as an important pathway that showed distinct mES and OCR values specific to the CMS4 subtype in colon adenocarcinoma. The mES and OCR metrics suggested that the mRNA and miRNA expressions were significantly different from the other subtypes, which was concordant with previous studies. The MOPA software is available at https://github.com/jaeminjj/MOPA.

Distribution and prognostic impact of EGFR and KRAS mutations according to histological subtype and tumor invasion status in pTis-3N0M0 lung adenocarcinoma

BackgroundThe prognostic impact of EGFR mutation as major targetable somatic gene variant on lung adenocarcinoma is controversial. KRAS is another major somatic variant in lung adenocarcinoma, and a therapeutic agent for KRAS G12C became available in clinical settings. These mutations represent clinicopathological features of lung adenocarcinoma and can guide the treatment choice after recurrence. We evaluated the prognostic impact of EGFR and KRAS mutations by considering other clinicopathological recurrence risks in resected pTis-3N0M0 lung adenocarcinoma.MethodsClinicopathological features related to recurrence and genetic status were estimated in consecutive 877 resected cases. Recurrence-free survival (RFS), cumulative recurrence rate (CRR), and overall survival (OS) were compared. Uni- and multivariate analyses for RFS were performed after excluding cases with little or no recurrence risks.ResultsEGFR mutation was more likely to be harbored in female, never-smoker, or patients accompanied by > 5% lepidic component. KRAS mutation was more likely to be harbored in patients with current/ex-smoking history, International Association for the Study of Lung Cancer (IASLC) grade 3, or accompanied lymphatic or vascular invasion. In IASLC grade 2 and 3 patients, EGFR or KRAS mutation cases had significantly worse 5-year RFS than wild type patients (76.9% vs. 85.0%, hazard ratio [HR] = 1.55, 95% confidence interval [CI] = 1.62–6.41, P < 0.001). EGFR or KRAS mutation cases had significantly higher 5-year CRR than wild type patients (17.7% vs. 9.8%, HR = 1.69, 95% CI = 1.44–6.59, P = 0.0038). KRAS mutation cases had higher 5-year CRR than EGFR mutation cases (16.7% vs. 21.4%, HR = 1.62, 95% CI = 0.96–7.19, P = 0.061). There was no significant difference in OS between cohorts. Multivariate analysis revealed that a positive EGFR/KRAS mutation status was risk factor for worse RFS (HR = 2.007, 95% CI = 1.265–3.183, P = 0.003).ConclusionPositive EGFR and KRAS mutation statuses were risk factors for recurrence in resected IASLC grade 2 and 3 patients. KRAS mutations were more likely to be confirmed in cases with an increased risk of recurrence. EGFR and KRAS mutation statuses should be evaluated simultaneously when assessing the risk of recurrence.

Genomic and transcriptomic insights into the precision treatment of pulmonary enteric adenocarcinoma

BackgroundPulmonary enteric adenocarcinoma (PEAC) is a rare subtype of lung adenocarcinoma. More investigations about precision therapy in PEAC were required to improve the prognosis. Methods: Twenty-four patients with PEAC were enrolled in this study. Tumor tissue samples were available from 17 patients for both DNA and RNA based next-generation sequencing, PD-L1 IHC staining and PCR-based microsatellite instability (MSI) analysis. Results: TP53 (70.6%) and KRAS (47.1%) were the most frequently mutated genes in PEAC. For KRAS mutations, the prevalence of G12D (37.5%) and G12V (37.5%) was higher than G12A (12.5%) and G12C (12.5%). Actionable mutations in receptor tyrosine kinase (including one EGFR and two ALK mutations), PI3K/mTOR, RAS/RAF/MEK, homologous recombination repair (HRR) and cell cycle signaling pathways were identified in 94.1% of patients with PEAC. While PD-L1 expression was observed in 17.6% (3/17) patients, no MSI-H patients were identified. Transcriptomic data showed that two patients with positive PD-L1 expression had relatively high immune infiltration. In addition, prolonged survival was obtained with the treatment of osimertinib, ensartinib, and immunotherapy combined with chemotherapy in two EGFR-mutated, one ALK-rearranged, and one PD-L1 expressed patients, respectively. Conclusion: PEAC is a disease of genetic heterogeneity. The administration of EGFR and ALK inhibitors was effective in patients with PEAC. PD-L1 expression and KRAS mutation type may be used as predictive biomarkers for immunotherapy in PEAC. This study provided both theoretical basis and clinical evidence for PEAC.

CD46 targeted 212Pb alpha particle radioimmunotherapy for prostate cancer treatment

We recently identified CD46 as a novel prostate cancer cell surface antigen that shows lineage independent expression in both adenocarcinoma and small cell neuroendocrine subtypes of metastatic castration resistant prostate cancer (mCRPC), discovered an internalizing human monoclonal antibody YS5 that binds to a tumor selective CD46 epitope, and developed a microtubule inhibitor-based antibody drug conjugate that is in a multi-center phase I trial for mCRPC (NCT03575819). Here we report the development of a novel CD46-targeted alpha therapy based on YS5. We conjugated 212Pb, an in vivo generator of alpha-emitting 212Bi and 212Po, to YS5 through the chelator TCMC to create the radioimmunoconjugate, 212Pb-TCMC-YS5. We characterized 212Pb-TCMC-YS5 in vitro and established a safe dose in vivo. We next studied therapeutic efficacy of a single dose of 212Pb-TCMC-YS5 using three prostate cancer small animal models: a subcutaneous mCRPC cell line-derived xenograft (CDX) model (subcu-CDX), an orthotopically grafted mCRPC CDX model (ortho-CDX), and a prostate cancer patient-derived xenograft model (PDX). In all three models, a single dose of 0.74 MBq (20 µCi) 212Pb-TCMC-YS5 was well tolerated and caused potent and sustained inhibition of established tumors, with significant increases of survival in treated animals. A lower dose (0.37 MBq or 10 µCi 212Pb-TCMC-YS5) was also studied on the PDX model, which also showed a significant effect on tumor growth inhibition and prolongation of animal survival. These results demonstrate that 212Pb-TCMC-YS5 has an excellent therapeutic window in preclinical models including PDXs, opening a direct path for clinical translation of this novel CD46-targeted alpha radioimmunotherapy for mCRPC treatment.

Gene Expression Analysis of Immune Regulatory Genes in Circulating Tumour Cells and Peripheral Blood Mononuclear Cells in Patients with Colorectal Carcinoma.

Information regarding genetic alterations of driver cancer genes in circulating tumour cells (CTCs) and their surrounding immune microenvironment nowadays can be employed as a real-time monitoring platform for translational applications such as patient response to therapeutic targets, including immunotherapy. This study aimed to investigate the expression profiling of these genes along with immunotherapeutic target molecules in CTCs and peripheral blood mononuclear cells (PBMCs) in patients with colorectal carcinoma (CRC). Expression of p53, APC, KRAS, c-Myc, and immunotherapeutic target molecules PD-L1, CTLA-4, and CD47 in CTCs and PBMCs were analysed by qPCR. Their expression in high versus low CTC-positive patients with CRC was compared and clinicopathological correlations between these patient groups were analysed. CTCs were detected in 61% (38 of 62) of patients with CRC. The presence of higher numbers of CTCs was significantly correlated with advanced cancer stages (p = 0.045) and the subtypes of adenocarcinoma (conventional vs. mucinous, p = 0.019), while being weakly correlated with tumour size (p = 0.051). Patients with lower numbers of CTCs had higher expression of KRAS. Higher KRAS expression in CTCs was negatively correlated with tumour perforation (p = 0.029), lymph node status (p = 0.037), distant metastasis (p = 0.046) and overall staging (p = 0.004). CTLA-4 was highly expressed in both CTCs and PBMCs. In addition, CTLA-4 expression was positively correlated with KRAS (r = 0.6878, p = 0.002) in the enriched CTC fraction. Dysregulation of KRAS in CTCs might evade the immune system by altering the expression of CTLA-4, providing new insights into the selection of therapeutic targets at the onset of the disease. Monitoring CTCs counts, as well as gene expression profiling of PBMCs, can be helpful in predicting tumour progression, patient outcome and treatment.

The clinical characteristics of pancreatic colloid carcinoma and the development and validation of its cancer-specific survival prediction nomogram.

Pancreatic colloid carcinoma (CC) is a subtype of pancreatic ductal adenocarcinoma (DAC) with low incidence but high malignancy. Unfortunately, there is no consensus regarding the clinical features and prognostic factors associated with CC, and the prognosis is unpredictable. We aimed to assess the clinicopathological characteristics of this rare disease and develop a nomogram for predicting cancer-specific survival (CSS) in CC. We gathered comprehensive clinicopathological data from the Surveillance, Epidemiology, and End Results (SEER) database on 17,617 patients with DAC and 561 individuals with CC. Kaplan-Meier was used to plot each survival curve. Subsequently, we split the 561 patients with CC in a 7:3 split ratio between an internal training cohort (n=393) and an external validation cohort (n=168). The independent prognostic factors for CC patients in the training cohort were discovered using univariate and multivariate Cox regression analyses, and a nomogram was created. We assessed the nomogram's performance by using the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). The median for follow-up of CC patients was 15 months (range: 1-163 months), and the 1-, 3-, and 5-year CSS were 58.4%, 30.2% and 22.6%. For CC patients in the training cohort, age [hazard ratio (HR) =1.29; 95% confidence interval (CI): 1.00-1.65], sex (HR =0.64; 95% CI: 0.51-0.81), T3 stage (HR =2.21; 95% CI: 1.26-3.88), T4 stage (HR =2.76; 95% CI: 1.47-5.18), N1 stage (HR =1.29; 95% CI: 1.02-1.63), M1 stage (HR =1.60; 95% CI: 1.17-2.18), surgery (HR =0.30; 95% CI: 0.22-0.42), and radiotherapy (HR =0.76; 95% CI: 0.58-1.01) were the main predictors of the nomogram. The C-indexes of the training cohort and the validation cohort were 0.734 and 0.732, respectively. The 1-, 3-, and 5-year AUC values of the nomogram were predicted to be 0.827, 0.816, and 0.831 in the training cohort, 0.801, 0.841, and 0.835 in the validation cohort, respectively. Based on several clinical features, we established the first predictive model of CC. This nomogram could be used to guide treatment decisions in patients with CC.

A Preliminary Study on Microbiota Characteristics of Bronchoalveolar Lavage Fluid in Patients with Pulmonary Nodules Based on Metagenomic Next-Generation Sequencing

The characteristics and roles of microbes in the occurrence and development of pulmonary nodules are still unclear. We retrospectively analyzed the microbial mNGS results of BALF from 229 patients with pulmonary nodules before surgery, and performed a comparative analysis of lung flora between lung cancer and benign nodules according to postoperative pathology. The analysis also focused on investigating the characteristics of lung microbiota in lung adenocarcinomas with varying histopathology. There were differences in lung microbiota between lung cancer and benign lung nodules. Bacterial diversity was lower in lung cancer than in benign lung nodules. Four species (Porphyromonas somerae, Corynebacterium accolens, Burkholderia cenocepacia and Streptococcus mitis) were enriched in lung cancer compared with the benign lung nodules. The areas under the ROC curves of these four species were all greater than 0.6, and the AUC of Streptococcus mitis was 0.702, which had the highest diagnostic value for differentiating lung cancer from benign lung diseases. The significantly enriched microbiota varied with the different pathological subtypes of lung adenocarcinoma. Streptococcus mitis, Burkholderia oklahomensis and Burkholderia latens displayed a trend of increasing from the benign lung disease group to the AIS group, MIA group and IAC group, whereas Lactobacillus plantarum showed a downward trend. Changes in the abundance of lung microbiota are closely related to the development of infiltrating adenocarcinoma. Our findings provide new insights into the relationship between the changes in lung microbiota and the development of lung cancer.

Comparison of treatment strategies for resectable locally advanced primary mucinous adenocarcinoma of the lung.

Primary pure mucinous adenocarcinoma (PMA) is a rare type of lung cancer with unique clinical and prognostic features. Previous studies have shown that PMA have more early-stage cancer compared with other adenocarcinoma (ADC) subtypes. The clinicopathological features and optimal treatment strategies of resectable locally advanced mucinous adenocarcinoma lack evidence and require further study. In this study, we collected information from patients with stage III-N2 PMA who underwent radical surgery between 2004 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) database. The clinicopathological parameters, treatments, overall survival (OS), and cancer-specific survival (CSS) were evaluated. Of 242,699 eligible lung adenocarcinoma patients, 124 with PMA and 3405 with other ADCs of stage III-N2 received radical surgery were identified. Compared with other ADCs, PMA tended to appear more in the lower lobes, with higher degree of differentiation, less early T stage, and more positive lymph nodes numbers. Patients with PMA had significantly worse survival than other ADCs (OS=45.0 vs. 57.1 months, p= 0.005, CSS=51.8 vs. 65.5months, p= 0.017). We explored the benefit population of postoperative radiotherapy (PORT) and found that the population with ≤7 positive lymph nodes could benefit from PORT, and OS was significantly improved (41.2 vs. 69.3 months, p= 0.034). For patients with >7 positive lymph nodes, PORT did not provide a survival benefit, while chemotherapy improved OS (10.9 vs. 23.3 months, p= 0.041). Multivariate analysis showed that race, tumor location, number of positive lymph nodes, and PORT were independent prognostic factors in patients with postoperative III-N2 lung PMA. The prognosis of patients with resectable III-N2 primary lung PMA was significantly worse than that of other ADCs, and PORT was an independent prognostic factor. Patients with ≤7 positive lymph nodes could benefit from PORT and those with >7 positive lymph nodes could benefit from chemotherapy.

Comparison of the radiomics-based predictive models using machine learning and nomogram for epidermal growth factor receptor mutation status and subtypes in lung adenocarcinoma.

The purpose of this study is to develop the predictive models for epidermal growth factor receptor (EGFR) mutation status and subtypes [exon 21-point mutation (L858R) and exon 19 deletion mutation (19Del)] and evaluate their clinical usefulness. Total 172 patients with lung adenocarcinoma were retrospectively analyzed. The analysis of variance and the least absolute shrinkage were used for feature selection from plain computed tomography images. Then, radiomic score (rad-score) was calculated for the training and test cohorts. Two machine learning (ML) models with 5-fold were applied to construct the predictive models with rad-score, clinical features, and the combination of rad-score and clinical features. The nomogram was developed using rad-score and clinical features. The prediction performance was evaluated by the area under the receiver operating characteristic curve (AUC). Finally, decision curve analysis (DCA) was performed using the best ML and nomogram models. In the test cohorts, the AUC of the best ML and the nomogram model were 0.73 (95% confidence interval, 0.59-0.87) and 0.79 (0.65-0.92) in the EGFR mutation groups, 0.83 (0.67-0.99) and 0.85 (0.72-0.97) in the L858R mutation groups, as well as 0.77 (0.58-0.97) and 0.77 (0.60-0.95) in the 19Del groups. The DCA showed that the nomogram models have comparable results with ML models. We constructed two predictive models for EGFR mutation status and subtypes. The nomogram models had comparable results to the ML models. Because the superiority of the performance of ML and nomogram models varied depending on the prediction groups, appropriate model selection is necessary.

Identification of four metabolic subtypes and key prognostic markers in lung adenocarcinoma based on glycolytic and glutaminolytic pathways

BackgroundGlucose and glutamine are the main energy sources for tumor cells. Whether glycolysis and glutaminolysis play a critical role in driving the molecular subtypes of lung adenocarcinoma (LUAD) is unknown. This study attempts to identify LUAD metabolic subtypes with different characteristics and key genes based on gene transcription profiling data related to glycolysis and glutaminolysis, and to construct prognostic models to facilitate patient outcome prediction.MethodsLUAD related data were obtained from the Cancer Genome Atlas and Gene Expression Omnibus, including TCGA-LUAD, GSE42127, GSE68465, GSE72094, GSE29013, GSE31210, GSE30219, GSE37745, GSE50081. Unsupervised consensus clustering was used for the identification of LUAD subtypes. Differential expression analysis, weighted gene co-expression network analysis (WGCNA) and CytoNCA App in Cytoscape 3.9.0 were used for the screening of key genes. The Cox proportional hazards model was used for the construction of the prognostic risk model. Finally, qPCR analysis, immunohistochemistry and immunofluorescence colocalization were used to validate the core genes of the model.ResultThis study identified four distinct characterized LUAD metabolic subtypes, glycolytic, glutaminolytic, mixed and quiescent types. The glycolytic type had a worse prognosis than the glutaminolytic type. Nine genes (CXCL8, CNR1, AGER, ALB, S100A7, SLC2A1, TH, SPP1, LEP) were identified as hub genes driving the glycolytic/glutaminolytic LUAD. In addition, the risk assessment model constructed based on three genes (SPP1, SLC2A1 and AGER) had good predictive performance and could be validated in multiple independent external LUAD cohorts. These three genes were differentially expressed in LUAD and lung normal tissues, and might be potential prognostic markers for LUAD.ConclusionLUAD can be classified into four different characteristic metabolic subtypes based on the glycolysis- and glutaminolysis-related genes. Nine genes (CXCL8, CNR1, AGER, ALB, S100A7, SLC2A1, TH, SPP1, LEP) may play an important role in the subtype-intrinsic drive. This metabolic subtype classification, provides new biological insights into the previously established LUAD subtypes.