P1.11-14 SGLT2 Is a Diagnostic and Therapeutic Target for Early-Stage Lung Adenocarcinoma

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

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The Case for GNMT as a Biomarker and a Therapeutic Target in Pancreatic Cancer

Pharmaceuticals ◽

10.3390/ph14030209 ◽

2021 ◽

Vol 14 (3) ◽

pp. 209

Author(s):

Zachary Heinzman ◽

Connor Schmidt ◽

Marek K. Sliwinski ◽

Nalin C. W. Goonesekere

Keyword(s):

Pancreatic Cancer ◽

Cell Lines ◽

Therapeutic Target ◽

Early Stage ◽

Dependent Manner ◽

Strong Inhibitor ◽

Tissue Samples ◽

Reverse Transcription Pcr ◽

Tumor Stages ◽

Early Tumor

The high mortality rate for pancreatic cancer (PC) is due to the lack of specific symptoms at early tumor stages and a high biological aggressiveness. Reliable biomarkers and new therapeutic targets would help to improve outlook in PC. In this study, we analyzed the expression of GNMT in a panel of pancreatic cancer cell lines and in early-stage paired patient tissue samples (normal and diseased) by quantitative reverse transcription-PCR (qRT-PCR). We also investigated the effect of 1,2,3,4,6-penta-O-galloyl-β-d-glucopyranoside (PGG) as a therapeutic agent for PC. We find that GNMT is markedly downregulated (p < 0.05), in a majority of PC cell lines. Similar results are observed in early-stage patient tissue samples, where GNMT expression can be reduced by a 100-fold or more. We also show that PGG is a strong inhibitor of PC cell proliferation, with an IC50 value of 12 ng/mL, and PGG upregulates GNMT expression in a dose-dependent manner. In conclusion, our data show that GNMT has promise as a biomarker and as a therapeutic target for PC.

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A circular RNAs dataset landscape reveals potential signatures for the detection and prognosis of early-stage lung adenocarcinoma

BMC Cancer ◽

10.1186/s12885-021-08293-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhiying Chen ◽

Jiahui Wei ◽

Min Li ◽

Yongjuan Zhao

Keyword(s):

Functional Analysis ◽

Survival Analysis ◽

Lung Adenocarcinoma ◽

Target Genes ◽

Interaction Analysis ◽

Early Stage ◽

Gene Interaction ◽

Gene Expression Omnibus ◽

Circular Rnas ◽

Cerna Network

Abstract Background This study aimed to identify potential circular ribonucleic acid (circRNA) signatures involved in the pathogenesis of early-stage lung adenocarcinoma (LAC). Methods The circRNA sequencing dataset of early-stage LAC was downloaded from the Gene Expression Omnibus database. First, the differentially expressed circRNAs (DEcircRNAs) between tumour and non-tumour tissues were screened. Then, the corresponding miRNAs and their target genes were predicted. In addition, prognosis-related genes were identified using survival analysis and further used to build a network of competitive endogenous RNAs (ceRNAs; DEcircRNA–miRNA–mRNA). Finally, the functional analysis and drug–gene interaction analysis of mRNAs in the ceRNA network was performed. Results A total of 35 DEcircRNAs (30 up-regulated and 5 down-regulated circRNAs) were identified. Moreover, 135 DEcircRNA–miRNA and 674 miRNA–mRNA pairs were predicted. The survival analysis of these target mRNAs revealed that 60 genes were significantly associated with survival outcomes in early-stage LAC. Of these, high levels of PSMA 5 and low levels of NAMPT, CPT 2 and TNFSF11 exhibited favourable prognoses. In addition, the DEcircRNA–miRNA–mRNA network was constructed, containing 5 miRNA–circRNA (hsa_circ_0092283/hsa-miR-762/hsa-miR-4685-5p; hsa_circ_0070610/hsa-let-7a-2-3p/hsa-miR-3622a-3p; hsa_circ_0062682/hsa-miR-4268) and 60 miRNA–mRNA pairs. Functional analysis of the genes in the ceRNA network showed that they were primarily enriched in the Wnt signalling pathway. Moreover, PSMA 5, NAMPT, CPT 2 and TNFSF11 had strong correlations with different drugs. Conclusion Three circRNAs (hsa_circ_0062682, hsa_circ_0092283 and hsa_circ_0070610) might be potential novel targets for the diagnosis of early-stage LAC.

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Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma

10.21203/rs.3.pex-963/v1 ◽

2021 ◽

Author(s):

Lin Huang ◽

Kun Qian

Keyword(s):

Machine Learning ◽

Lung Adenocarcinoma ◽

Cancer Detection ◽

High Performance ◽

Large Scale ◽

Early Cancer ◽

Early Stage ◽

Early Cancer Detection ◽

Ionization Mass ◽

Efficient Test

Abstract Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 second using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.

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Osimertinib as neoadjuvant treatment for resectable stage II-IIIB EGFR mutant lung adenocarcinoma (NEOS).

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.8524 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 8524-8524

Author(s):

Chao Lyu ◽

Wentao Fang ◽

Haitao Ma ◽

Jia Wang ◽

Wenjie Jiao ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Surgical Resection ◽

Interim Analysis ◽

Kinase Inhibitors ◽

Response Rate ◽

Early Stage ◽

Neoadjuvant Treatment ◽

Complete Response ◽

Stage Ii ◽

Meaningful Improvement

8524 Background: Neoadjuvant treatment has demonstrated efficacy in several types of cancer and is increasingly used for the treatment of early-stage cancers with the potential of cancer downstaging to enhance complete surgical resection and to improve clinical outcomes. Recent evidences have demonstrated that the neoadjuvant use of first/second-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) may provide clinically meaningful improvement in EGFRm non-small cell lung cancer (NSCLC) patients, however, limited data were reported on osimertinib, the third-generation EGFR-TKI, in the neoadjuvant setting. Here we present an interim analysis of osimertinib as neoadjuvant treatment for resectable EGFRm NSCLC. Methods: NEOS is a prospective, multi-center, single-arm study to evaluate the efficacy and safety of osimertinib as neoadjuvant treatment in resectable EGFRm (19del/L858R) lung adenocarcinoma. Eligible patients were treated with osimertinib 80 mg orally per day for six weeks followed by surgery. Assessment of response to neoadjuvant therapy was performed according to RECIST 1.1. The primary endpoint was response rate. Secondary endpoints included safety, R0 surgical resection rate, quality of life, major pathologic response (MPR) rate, pathological complete response (pCR) rate, and N2 downstaging rate. Results: As of Dec. 17, 2020, 18 eligible patients (median age 61 [range 46-73], 27.8% male, 22.2% ECOG PS 1) have been enrolled. Patients with clinical stages IIa, IIb, and IIIa (8th AJCC) accounted for 16.7%, 22.2% and 61.1%, respectively. Half (9/18) of the patients had EGFR exon 21 L858R mutations and the other half (9/18) had EGFR exon 19del mutations. Amongst all 15 patients who completed efficacy assessment after neoadjuvant osimertinib, the response rate (RR) was 73.3% (11/15) and the disease control rate (DCR) was 100% (15/15). R0 surgical resection was performed in 93.3% (14/15) patients. Pathological downstaging occurred in 53.3% (8/15) patients. 42.9% (3/7) of the patients with confirmed N2 lymph nodes experienced downstaging to N0 disease after receiving neoadjuvant osimertinib. One patient was identified with a pCR. Adverse events (AEs) were reported in 66.7% (12/18) of patients, with the most common AE being rash (8/18, 44.4%), oral ulceration (8/18, 44.4%), and diarrhea (5/18, 27.8%). No grade 3-5 AEs or serious AEs were reported. Conclusions: Interim analysis from this study indicated neoadjuvant osimertinib as an effective and feasible treatment in patients with resectable stage II-IIIB EGFRm NSCLC. The trial is ongoing and the final results will be provided in the future. Clinical trial information: ChiCTR1800016948.

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