Epidermal Growth Factor Receptor Status Research Articles

The impact of human epidermal growth factor receptor-2 (low) status on the efficacy of first line cyclin-dependent kinase 4/6 inhibitors in advanced breast cancer.

The fact that the human epidermal growth factor receptor 2 (HER2)-low group, historically classified as HER2 negative in breast cancer histology, benefited from HER2-targeted treatments similarly to the HER2-positive group indicates that this group has a distinct histology from the HER2-0 group. The effectiveness of cyclin-dependent kinase 4/6 inhibitors, which are the standard first-line treatment for hormone receptor-positive, HER2-negative advanced breast cancer, in this newly defined histological subgroup remains a topic of debate. In our study, we examined the impact of HER2 status on the efficacy of CDK4/6 inhibitors. Our study is a retrospective, multicenter, real-world data analysis. One hundred sixty patients were included in the study. The relationship between HER2 status and other clinical-pathological features, as well as progression-free survival, was examined. Median follow-up was 20.33 ± 0.98 months. The mPFS could not be reached. All patients exhibited positive estrogen receptor expression. Among the patients, 111 (69.4%) were categorized as HER2-0, and 49 (30.6%) as HER2-low. The 24-month progression-free survival rates were similar between HER2-0 and HER2-low patients (60.6% vs 65.3%, hormone receptor: 1.18, CI: 0.67-2.20, P = .554). We established that the mPFS achieved with cyclin-dependent kinase 4/6 inhibitors as a first-line therapy for patients with advanced breast cancer is unaffected by HER2 status.

Association between exposure to organophosphate flame retardants and epidermal growth factor receptor expression in lung cancer patients.

Organophosphate flame retardants (OPFRs) are extensively distributed in our environment, prompting concerns about potential health hazards, including lung injuries resulting from OPFR exposure. The present study recruited 125 lung cancer patients, assessing their exposure to 10 OPFR compounds through urine samples. The final analysis comprised 108 participants after excluding those lacking epidermal growth factor receptor (EGFR) status and those with chronic kidney disease. Demographic and clinical characteristics, as well as urinary OPFR concentrations, were compared based on OPFR detection. Spearman correlation was conducted to explore the relationship between OPFR compounds, while logistic regression was used to identify OPFR compounds associated with EGFR mutation. The study revealed widespread OPFR exposure among lung cancer patients, with an overall detection frequency of 99.07%. Tris(2-butoxyethyl) phosphate (TBEP) exhibited a strong correlation to its metabolite bis(2-butoxyethyl) phosphate (r = 0.88, p < 0.01). Patients with TBEP in their urine had higher percentage of wild-type EGFR and the detection of TBEP was associated with a reduced likelihood of mutant EGFR expression. OPFR exposure was prevalent in lung cancer patients, with TBEP detection identified as a factor with lower EGFR mutation expression. This study contributes to the understanding of OPFR exposure in lung cancer patients and underscores the significance of TBEP in evaluating EGFR mutation in this population.

Correction: Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer

Correction: Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer

A blind validation of a deep-learning solution providing HER2, ER, and PR results from H&amp;E-stained breast cancer specimens.

562 Background: Newly diagnosed breast cancer specimens are routinely tested for oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER2) status. This requires additional immunohistochemistry (IHC) +/- in situ hybridisation (ISH) assessment. This can increase laboratory/pathologist workloads and turnaround times. A computer-based approach could help to alleviate these issues. PANProfiler Breast is a deep-learning solution designed to predict ER/PR status and detect HER2 negative cases from whole slide images (WSIs) of haematoxylin and eosin (H&amp;E)-stained breast cancer tissue. Here we present a blind validation of PANProfiler Breast. Methods: Three cohorts of H&amp;E-stained archival breast cancers (400 cases, 692 WSIs) from St James’s University Hospital, Leeds, UK, were assigned for calibration (200 cases, 344 WSIs) and blind validation (200 cases, 348 WSIs). Slides were scanned at 40x magnification on an Aperio GT450 (Leica, Illinois, USA) scanner. PANProfiler returned “Positive”, “Negative” or “Indeterminate” for ER and PR, and “Negative” or “Indeterminate” for HER2. Results were compared to the ER/PR/HER2 status given in the corresponding pathology report. For calibration and blind validation, the WSI(s) corresponding to the IHC/ISH testing block was used for analysis. One case with missing HER2 status was excluded from HER2 analyses. Confusion matrices enabled analysis of concordance. While a conventional Positive/Negative confusion matrix was used for ER/PR, “Negative” results were used as the reference Positive event for HER2. Results: Case concordance for ER and PR status across the three cohorts ranged from 89.74-92.86% and 85.71-90.91% respectively. Additionally, sensitivity for ER ranged from 89.19-100%, with specificity ranging between 62.50-100%. For PR, sensitivity was between 87.50% and 93.48% and specificity between 75.00% and 83.33%. False negative rates for both ER and PR were between 0-10.81% and 6.52-12.50% respectively. Regarding HER2, sensitivity was relatively low, ranging between 30.77% and 37.04%. However, over 90% concordance was seen across all cohorts for cases PANProfiler predicted as HER2 “Negative”. Specificity and positive predictive value were also over 90% throughout the three cohorts. Additionally, false positive rates were 0.00%, 4.35% and 9.52% for HER2 across these same three cohorts. Test replacement rates varied amongst the cohorts with 70-84% for ER, 55-84% for PR and 22-27% for HER2. Conclusions: This blind validation of PANProfiler Breast shows impressive levels of performance for predicting ER and PR status in breast cancer WSIs. For HER2, the technology demonstrated a striking level of confidence for cases predicted as HER2 “Negative”. Further development and analysis are warranted to increase the number of HER2 negative BC WSIs that PANProfiler is able to identify.

Clinical outcomes of maintenance durvalumab after definitive concurrent chemoradiotherapy in unresectable locally advanced stage III non-small cell lung cancer according to epidermal growth factor receptor and anaplastic lymphoma kinase status: Korean Cancer Study Group LU-22-18.

Clinical outcomes of maintenance durvalumab after definitive concurrent chemoradiotherapy in unresectable locally advanced stage III non-small cell lung cancer according to epidermal growth factor receptor and anaplastic lymphoma kinase status: Korean Cancer Study Group LU-22-18.

Effect of strong programmed cell death ligand-1 (PD-L1) expressions on clinical outcomes in advanced non-small cell lung cancer treated with third-generation epidermal growth factor receptor-tyrosine kinase inhibitors.

e20500 Background: Third-generation tyrosine kinase inhibitors (TKIs) are the standard treatment for advanced epidermal growth factor receptor (EGFR) mutation-positive lung adenocarcinoma. In first/second generation EGFR-TKIs, strong programmed death ligand 1 (PD-L1) expression contributes to primary resistance, significantly affecting patient prognosis. Despite this, the relationship between PD-L1 expression levels and third-generation TKIs remains unclear. Methods: This retrospective cohort study reviewed patients with advanced NSCLC who received third-generation EGFR-TKIs as first-line systemic therapy at the Shandong Cancer Hospital between March 2019 and June 2022. The EGFR status of the patients was assessed using amplification refractory mutation system fluorescence quantitative polymerase chain reaction, and the PD-L1 expression level was evaluated using Dako 22 C3 immunohistochemical staining. The Kaplan–Meier method was used for survival analysis. The log-rank test was used to compare differences in survival curves between the two groups. Results: Overall, 150 patients were included in this study. PD-L1 expression was negative (PD-L1 tumor proportion score &lt; 1%) in 89 cases, weak (1–49%) in 42 cases, and strong (≥50%) in 19 cases. The median follow-up period for the entire cohort was 22.12 months (median progression-free survival [mPFS]: 24.33 months); the median overall survival was not reached. mPFS for patients with negative, weak, and strong PD-L1 expressions was 23.60, 26.12, and 16.60 months, respectively. The mPFS for strong PD-L1 expression was significantly shorter than that for with weak PD-L1 expression but was not associated with negativity, particularly in the 19DEL and 21L858R subgroups. PFS was significantly shorter in patients with strong PD-L1 expression in both subgroups (19DEL and 21L858R) than in those with weak PD-L1 expression. Conclusions: Strong PD-L1 expression in tumor cells influenced the clinical outcomes of patients with advanced NSCLC treated with third-generation EGFR-TKIs. Stronger PD-L1 expression in TKI-treated patients with advanced first-line EGFR-mutated NSCLC was associated with worse PFS.

Phase 1 study of BDTX-1535, an oral 4th generation covalent EGFR inhibitor, in patients with recurrent glioblastoma: Preliminary dose escalation results.

2068 Background: Epidermal growth factor receptor ( EGFR) gene is the most frequently altered oncogenic driver in glioblastoma (GBM). In-frame deletion alterations (e.g., EGFRvIII) and missense mutations co-occur in the setting of EGFR gene amplification and are characterized as a hallmark of disease pathogenesis in GBM. BDTX-1535 is an oral, highly potent, brain penetrant, selective, irreversible 4th generation tyrosine kinase inhibitor that targets EGFR alterations in GBM and NSCLC. Preliminary results of the Phase 1 dose escalation study (NCT05256290) of patients with recurrent GBM (rGBM) are presented here. Methods: BDTX-1535-101 is a first-in-human study that enrolled patients with either rGBM harboring EGFR alterations following standard of care or patients with locally advanced or metastatic EGFR mutated NSCLC that progressed on prior EGFR TKIs. Using an adaptive Bayesian optimal interval design in the Phase 1 part, patients were enrolled at increasing dose cohorts and were treated daily for 21-day cycles until treatment discontinuation. The primary objective was to determine the BDTX-1535 recommended Phase 2 dose based on the overall safety, PK, pharmacodynamics, and preliminary antitumor activity. Results: Twenty-seven patients with rGBM were enrolled in the Phase 1 cohort that consisted of 54 patients in total including 27 patients with NSCLC. Patients were treated across seven dose levels (15mg – 400mg QD). The mean age of patients with rGBM was 58.7 years (range 41-85) with 96% of patients with previous temozolomide (TMZ) treatment and a median of 2 lines of prior therapy (range 1-4). The most common all-grade treatment-related AEs across all cohorts were rash (78%), diarrhea (41%), fatigue (15%), stomatitis (11%), decreased appetite (11%), nausea (11%) and paronychia (11%). Gr 3 TRAEs ≥ 10% included rash (19%) reported at 300 or 400 mg QD doses. Plasma exposure of BDTX-1535 increased dose proportionally and had a half-life of ~15h, supporting once daily dosing. The maximum tolerated dose (MTD) is 300 mg QD. Among 19 evaluable patients for response based on RANO criteria, 1 confirmed partial response was observed and 8 patients achieved stable disease. Five patients remained on BDTX-1535 with stable disease for an extended period (&gt;5 months) who previously performed poorly on TMZ with short treatment duration, and 1 patient continues on BDTX-1535 after 16 months of treatment. Conclusions: BDTX-1535 was well-tolerated up to 300mg daily, which is the MTD, and promising preliminary clinical activity was observed in patients with rGBM after relapse on standard of care treatment. Given the inability to reconfirm EGFR status at the time of treatment with BDTX-1535 in this Phase 1 trial, further exploration of BDTX-1535 in a “window of opportunity” study is ongoing (NCT06072586). Clinical trial information: NCT05256290 .

The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations

Objectives18F-fluorodeoxyglucose (FDG) PET/CT has been widely used for the differential diagnosis of cancer. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may make it difficult to differentiate between benign and malignant lesions. It is crucial to find reliable quantitative metabolic parameters to further support the diagnosis. This study aims to evaluate the value of the quantitative metabolic parameters derived from dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status.MethodsWe included 147 patients with lung lesions to perform FDG PET/CT dynamic plus static imaging with informed consent. Based on the results of the postoperative pathology, the patients were divided into benign/malignant groups, adenocarcinoma (AC)/squamous carcinoma (SCC) groups, and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Quantitative parameters including K1, k2, k3, and Ki of each lesion were obtained by applying the irreversible two-tissue compartmental modeling using an in-house Matlab software. The SUV analysis was performed based on conventional static scan data. Differences in each metabolic parameter among the group were analyzed. Wilcoxon rank-sum test, independent-samples T-test, and receiver-operating characteristic (ROC) analysis were performed to compare the diagnostic effects among the differentiated groups. P < 0.05 were considered statistically significant for all statistical tests.ResultsIn the malignant group (N = 124), the SUVmax, k2, k3, and Ki were higher than the benign group (N = 23), and all had-better performance in the differential diagnosis (P < 0.05, respectively). In the AC group (N = 88), the SUVmax, k3, and Ki were lower than in the SCC group, and such differences were statistically significant (P < 0.05, respectively). For ROC analysis, Ki with cut-off value of 0.0250 ml/g/min has better diagnostic specificity than SUVmax (AUC = 0.999 vs. 0.70). In AC group, 48 patients further underwent EGFR testing. In the EGFR (+) group (N = 31), the average Ki (0.0279 ± 0.0153 ml/g/min) was lower than EGFR (-) group (N = 17, 0.0405 ± 0.0199 ml/g/min), and the difference was significant (P < 0.05). However, SUVmax and k3 did not show such a difference between EGFR (+) and EGFR (-) groups (P>0.05, respectively). For ROC analysis, the Ki had a cut-off value of 0.0350 ml/g/min when predicting EGFR status, with a sensitivity of 0.710, a specificity of 0.588, and an AUC of 0.674 [0.523–0.802].ConclusionAlthough both techniques were specific, Ki had a greater specificity than SUVmax when the cut-off value was set at 0.0250 ml/g/min for the differential diagnosis of lung cancer. At a cut-off value of 0.0350 ml/g/min, there was a 0.710 sensitivity for EGFR status prediction. If EGFR testing is not available for a patient, dynamic imaging could be a valuable non-invasive screening method.

Abstract PO5-27-01: Comparison between the 7th and the 8th edition of TNM Staging System of American joint committee on Cancer(AJCC) for Breast Cancer Patients, diagnosed and treated at King Abdulaziz Medical City

Abstract Breast cancer is a heterogeneous disease with different biological and molecular characteristics that affect local recurrence, metastases, prognoses, and response to available therapies. The 8th edition of the American Joint Committee on Cancer (AJCC) staging system for breast cancer has incorporated the biological and molecular characteristics, tumor grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor status(Her-2)and tumor grade, into the traditional anatomical 7th edition that only considers tumor size, lymph nodes status, and distant metastases. The aim of this study is to compare the 7th to 8th editions of the AJCC staging system for prognostic impact, assess the effect of the new edition on the treatment and treatment outcome Method A retrospective cohort study was performed for all breast cancer patients, diagnosed between (2013 -2019) and underwent upfront surgery at King Abdulaziz Medical City (KAMC).Patients’ clinicopathological, immunohistochemical, surgical, therapeutic, and follow-up data were retrieved. Overall survival (OS) and breast cancer-specific survival (BCSS) were estimated using the Kaplan–Meier method. Univariate analysis using Fisher's exact test was used to compare staging systems' prognostic accuracy. Multivariable analysis using Cox proportional hazards regression analysis was performed to identify factors independently associated with disease outcome Results A total of 169 female patients were included, the 8th AJCC staging system resulted in a reallocation of 108 (63.9%) of the patients to a different stage group. The majority were downstaged(60.9%) while only 3% were upstaged, most of the upstaged patients had a triple-negative subtype (80%). The change in overall stage for the 8th edition was most significant in stage I (an increase by 140.3%), followed by stage II (downstaged by 74.3%) and stage III (downstaged by 49.7%) compared to the 7th edition. Critically 42.8% of all downstaged patients treated with adjuvant chemotherapy were found to have received unnecessary chemotherapy after applying the 8th AJCC staging system as per National Comprehensive Cancer Network (NCCN) clinical practice guidelines recommendations. The 8th AJCC staging system was a better prognostic predictor of disease survival, patients in 8th AJCC stage 3 had a higher tendency to develop metastases and die because of their disease (P-value 0.03, 0.051, respectively). Conclusions: Our comparison revealed that the AJCC 8th edition staging system is superior and more comprehensive than the 7th, in term of prognostic value, and in defining the precise adjuvant therapy Figure 1 Comparison of the 7th AJCC and the 8th AJCC staging system Citation Format: Nafisa Abdelhafiez, Abdulmohsen Alkushi, Lolwah Alriyees, Mohammad Arabi, Faris Alsalamah, Emad Masuadi, Ahmad Omair, Mohammad Alkaiyat, Hussam Shehata. Comparison between the 7th and the 8th edition of TNM Staging System of American joint committee on Cancer(AJCC) for Breast Cancer Patients, diagnosed and treated at King Abdulaziz Medical City [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-27-01.

Abstract PO4-07-04: Breast Cancer HER2 Status Prediction from Hematoxylin-Eosin Stained Images Using Point Cloud Transformer

Abstract Background: Human epidermal growth factor receptor-2 (HER2) status is a key factor in determining the treatment strategy for breast cancer patients. Patients with HER2-positive status are more likely to benefit from HER2-targeted therapy, leading to improved prognosis. In current routine diagnostic practice, pathologists utilize Hematoxylin-Eosin (HE) stained tumor tissues for histopathological assessment. Subsequently, IHC assessment and/or FISH test are performed to evaluate the HER2 status. However, manual assessment results may be affected by tissue usability and observer-subjectivity. Therefore, there is a necessity to predict the HER2 status directly from HE images to minimize time and cost while ensuring enhanced consistency. Methods: We identified 608 HE diagnostic slides with HER2 status from The Cancer Genome Atlas in breast cancer (TCGA-BRCA). It contains 474 HER2-positive slides (IHC 3+, IHC 2+ and FISH positive) and 134 HER2-negative slides (IHC 0, IHC 1+, IHC 2+ and FISH negative). To analyze these slides, we first tiled the HE images into patches with a fixed size of 256 × 256 at 20 × magnification. Then the patch-level feature was derived from a self-supervised pretrained transformer. Meanwhile, artificial intelligence (AI) methods are adopted to predict HER2 status from HE images. To capture the long-distance patch relations within a slide, we represented the patches as distinct points and utilized the Point Cloud Transformer model for HER2 status prediction. Specifically, 1024 patches (points) in each slide were randomly selected and input into the Point Cloud Transformer. This process yielded a slide-level prediction result for the HER2 status. Furthermore, Graph Attention Network (GAT), Graph Sample and Aggregate Network (Graph SAGE), and hierarchical Point Cloud Network (PointNet++) were adopted to compare the effectiveness of HER2 status prediction using Point Cloud Transformer. Of note that Point Cloud Transformer incorporated attention mechanisms for point aggregation compared to PointNet++. Results: The Point Cloud Transformer was trained with 5-fold cross-validation, and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was reported. The performance of point-based models outperformed the graph-based models. And the Point Cloud Transformer achieved the highest AUC of 0.7496 among all AI models. The detailed AUC for each AI model was shown in Table 1. Conclusion: Our study revealed that the HER2 status can be predicted directly from HE images without using IHC images. Furthermore, point-based models have demonstrated the ability to capture long-distance relations among patches, surpassing graph-based models in terms of prediction performance. To further enhance performance, we adopted a better point aggregation method, such as the Point Cloud Transformer, which held promise for further improving the accuracy of predictions in the future. Table 1: AUC of HER2 status prediction from HE images in the TCGA-BRCA dataset Method GAT Graph SAGE PointNet++ Point Cloud Transformer AUC 0.6640 0.6737 0.7201 0.7496 Citation Format: Bao Li, Zhenyu Liu, Yang Du, Jie Tian. Breast Cancer HER2 Status Prediction from Hematoxylin-Eosin Stained Images Using Point Cloud Transformer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-04.

The Utility of the Cachexia Index and the Modified Glasgow Score in Young Patients With Breast Cancer.

Background Breast cancer is the most common cancer in women. Body composition and inflammatory markers are increasingly important for predicting cancer prognosis. The Cancer Cachexia Index (CXI) and the modified Glasgow Prognostic Score (GPS) are two new markers evaluating prognosis in cancer. In this study, we evaluated the utility of the CXI and the modified GPS in young patients with breast cancer. Methods Eighty patients diagnosed between 2012 and 2023 were included in the study. The following information was recorded: patient features, pathological subtype, estrogen receptor and human epidermal growth factor receptor-2 (HER-2) status, disease stage, therapies, disease recurrence, and last control or death date. The CXI and the modified GPS were calculated using clinical data, includingskeletal muscle index, albumin, C-reactive protein, andneutrophil-to-lymphocyte ratio. Results There were no differences in overall survival with respect to the CXI in the study population (p=0.96). Only stage 4 patients showed statistically significant survival differences according to the CXI (p=0.046). Although the median survival time was not reached for the modified GPS groups, there was a statistical overall survival difference favoring the negative group (p=0.017). No significant differences were observed in disease-free survival due to the CXI (p=0.128). In multivariate analysis, no factors, including the modified GPS and the CXI, influenced overall survival. There was a significant effect of the modified GPS and body mass index on recurrence (p=0.037; p=0.034). The CXI had a non-significant marginal p-value (p=0.074). Conclusion Our study showed that the modified GPS may be related to disease-free survival and overall survival, whereas the CXI has a more prominent prognostic effect on overall survival in advanced-stage breast cancers. In early-stage and young patients, optimization of risk scores is lacking.

Abstract 7499: Circulating tumor cell enumeration and protein expression by CapioCyte and Allegro Plus imaging

Abstract Radiation or chemoradiation followed by surgical resection is the current standard of therapy for many diseases. These treatments are increasingly supplemented with monoclonal antibodies, such as epidermal growth factor receptor (EGFR)-targeting and programmed death ligand 1 (PD-L1) checkpoint-blocking antibodies. The use of these therapies is typically dependent on solid tissue biopsy to confirm elevated expression of these markers in the solid tumor, which carries complications and may only be sampled at diagnosis. In contrast, a liquid biopsy may detect these markers with minimal invasiveness and at multiple points throughout treatment and recovery. Here, we describe the development of an automated and integrated workflow for circulating tumor cell (CTC) analysis. We previously demonstrated that the CapioCyte method is a highly sensitive approach for enrichment of CTCs using a combination of biomimetic rolling on recombinant E-selectin and antibody-mediated capture on a nanostructured polymer surface within a flow chamber for capture. Here we report a fully automated CapioCyte instrument for CTC purification and immunofluorescence staining using epithelial, white blood cell and nuclear makers to identify and distinguish CTCs from WBCs while also quantifying EGFR or PD-L1 presence on respective CTCs. The immunostaining conditions were developed and validated for high analytical sensitivity and specificity using cell lines and health donor white blood cells with known biomarker status. The final assay conditions were transferred to the CapioCyte for automated immunostaining. CapioCyte enriched and stained CTC samples are paired with the BioView Allegro Plus platform, an imaging and analysis solution using machine learning and customized algorithms for automated imaging and CTC candidate identification based on high-resolution Z-stack images. The reported comprehensive and automated workflow was used to enumerate CTCs in patients with non-small cell lung carcinoma (NSCLC) in a feasibility cohort. Median CTC counts from NSCLC patients was 7.5 CTC/ml (n=26, mean 51.7, range 0.3 - 406.2). Studies are on-going to expand the cohort including testing blood from head and neck cancer patients and respective EGFR and PD-L1 status. The combined and automated CapioCyte-Allegro Plus workflow shows great potential for routine quantification of CTCs and EGFR and PD-L1 status. Citation Format: Ju-heon Lim, Chassidy Johnson, Tal Kaplan, Michael J. Poellmann, Yuval Harari, Elad Kfir, Andrew Z. Wang, Seungpyo Hong. Circulating tumor cell enumeration and protein expression by CapioCyte and Allegro Plus imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7499.

Revealing the role of SPP1+ macrophages in glioma prognosis and therapeutic targeting by investigating tumor-associated macrophage landscape in grade 2 and 3 gliomas

BackgroundGlioma is a highly heterogeneous brain tumor categorized into World Health Organization (WHO) grades 1–4 based on its malignancy. The suppressive immune microenvironment of glioma contributes significantly to unfavourable patient outcomes. However, the cellular composition and their complex interplays within the glioma environment remain poorly understood, and reliable prognostic markers remain elusive. Therefore, in-depth exploration of the tumor microenvironment (TME) and identification of predictive markers are crucial for improving the clinical management of glioma patients.ResultsOur analysis of single-cell RNA-sequencing data from glioma samples unveiled the immunosuppressive role of tumor-associated macrophages (TAMs), mediated through intricate interactions with tumor cells and lymphocytes. We also discovered the heterogeneity within TAMs, among which a group of suppressive TAMs named TAM-SPP1 demonstrated a significant association with Epidermal Growth Factor Receptor (EGFR) amplification, impaired T cell response and unfavourable patient survival outcomes. Furthermore, by leveraging genomic and transcriptomic data from The Cancer Genome Atlas (TCGA) dataset, two distinct molecular subtypes with a different constitution of TAMs, EGFR status and clinical outcomes were identified. Exploiting the molecular differences between these two subtypes, we developed a four-gene-based prognostic model. This model displayed strong associations with an elevated level of suppressive TAMs and could be used to predict anti-tumor immune response and prognosis in glioma patients.ConclusionOur findings illuminated the molecular and cellular mechanisms that shape the immunosuppressive microenvironment in gliomas, providing novel insights into potential therapeutic targets. Furthermore, the developed prognostic model holds promise for predicting immunotherapy response and assisting in more precise risk stratification for glioma patients.Graphical abstract

Evaluation of the clinical significance of long non-coding RNA MALAT1 genetic variants in human lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most frequent histological subtype of lung cancer, which is the most common malignant tumor and the main cause of cancer-related mortality globally. Recent reports revealed that long non-coding RNA (lncRNA) of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) plays a crucial role in tumorigenesis and metastasis development in lung cancer. However, the contribution of MALAT1 genetic variants to the development of LUAD is unclear, especially in epidermal growth factor receptor (EGFR) mutation status. In this study, 272 LADC patients with different EGFR status were recruited to dissect the allelic discrimination of the MALAT1 polymorphisms at rs3200401, rs619586, and rs1194338. The findings of the study showed that MALAT1 polymorphisms rs3200401, rs619586, and rs1194338 were not associated to LUAD susceptibility; however, rs3200401 polymorphisms was significantly correlated to EGFR wild-type status and tumor stages in LUAD patients in dominant model (p=0.016). Further analyses using the datasets from The Cancer Genome Atlas (TCGA) revealed that lower MALAT1 mRNA levels were associated with the advanced stage, and lymph node metastasis in LADC patients. In conclusion, our results showed that MALAT1 rs3200401 polymorphisms dramatically raised the probability of LUAD development.

Improved Prediction of Epidermal Growth Factor Receptor Status by Combined Radiomics of Primary Nonsmall-Cell Lung Cancer and Distant Metastasis.

This study aimed to investigate radiomics based on primary nonsmall-cell lung cancer (NSCLC) and distant metastases to predict epidermal growth factor receptor (EGFR) mutation status. A total of 290 patients (mean age, 58.21 ± 9.28) diagnosed with brain (BM, n = 150) or spinal bone metastasis (SM, n = 140) from primary NSCLC were enrolled as a primary cohort. An external validation cohort, consisting of 69 patients (mean age, 59.87 ± 7.23; BM, n = 36; SM, n = 33), was enrolled from another center. Thoracic computed tomography-based features were extracted from the primary tumor and peritumoral area and selected using the least absolute shrinkage and selection operator regression to build a radiomic signature (RS-primary). Contrast-enhanced magnetic resonance imaging-based features were calculated and selected from the BM and SM to build RS-BM and RS-SM, respectively. The RS-BM-Com and RS-SM-Com were developed by integrating the most important features from the primary tumor, BM, and SM. Six computed tomography-based features showed high association with EGFR mutation status: 3 from intratumoral and 3 from peritumoral areas. By combination of features from primary tumor and metastases, the developed RS-BM-Com and RS-SM-Com performed well with areas under curve in the training (RS-BM-Com vs RS-BM, 0.936 vs 0.885, P = 0.177; RS-SM-Com vs RS-SM, 0.929 vs 0.843, P = 0.003), internal validation (RS-BM-Com vs RS-BM, 0.920 vs 0.858, P = 0.492; RS-SM-Com vs RS-SM, 0.896 vs 0.859, P = 0.379), and external validation (RS-BM-Com vs RS-BM, 0.882 vs 0.805, P = 0.263; RS-SM-Com vs RS-SM, 0.865 vs 0.816, P = 0.312) cohorts. This study indicates that the accuracy of detecting EGFR mutations significantly enhanced in the presence of metastases in primary NSCLC. The established radiomic signatures from this approach may be useful as new predictors for patients with distant metastases.

The therapeutic potential of natural metabolites in targeting endocrine-independent HER-2-negative breast cancer.

Breast cancer (BC) is a heterogenous disease, with prognosis and treatment options depending on Estrogen, Progesterone receptor, and Human Epidermal Growth Factor Receptor-2 (HER-2) status. HER-2 negative, endocrine-independent BC presents a significant clinical challenge with limited treatment options. To date, promising strategies like immune checkpoint inhibitors have not yielded breakthroughs in patient prognosis. Despite being considered archaic, agents derived from natural sources, mainly plants, remain backbone of current treatment. In this context, we critically analyze novel naturally-derived drug candidates, elucidate their intricate mechanisms of action, and evaluate their pre-clinical in vitro and in vivo activity in endocrine-independent HER-2 negative BC. Since pre-clinical research success often does not directly correlate with drug approval, we focus on ongoing clinical trials to uncover current trends. Finally, we demonstrate the potential of combining cutting-edge technologies, such as antibody-drug conjugates or nanomedicine, with naturally-derived agents, offering new opportunities that utilize both traditional cytotoxic agents and new metabolites.

EfficientNet-Based System for Detecting EGFR-Mutant Status and Predicting Prognosis of Tyrosine Kinase Inhibitors in Patients with NSCLC

We aimed to develop and validate a deep learning-based system using pre-therapy computed tomography (CT) images to detect epidermal growth factor receptor (EGFR)-mutant status in patients with non-small cell lung cancer (NSCLC) and predict the prognosis of advanced-stage patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors (TKI). This retrospective, multicenter study included 485 patients with NSCLC from four hospitals. Of them, 339 patients from three centers were included in the training dataset to develop an EfficientNetV2-L-based model (EME) for predicting EGFR-mutant status, and the remaining patients were assigned to an independent test dataset. EME semantic features were extracted to construct an EME-prognostic model to stratify the prognosis of EGFR-mutant NSCLC patients receiving EGFR-TKI. A comparison of EME and radiomics was conducted. Additionally, we included patients from The Cancer Genome Atlas lung adenocarcinoma dataset with both CT images and RNA sequencing data to explore the biological associations between EME score and EGFR-related biological processes. EME obtained an area under the curve (AUC) of 0.907 (95% CI 0.840–0.926) on the test dataset, superior to the radiomics model (P = 0.007). The EME and radiomics fusion model showed better (AUC, 0.941) but not significantly increased performance (P = 0.895) compared with EME. In prognostic stratification, the EME-prognostic model achieved the best performance (C-index, 0.711). Moreover, the EME-prognostic score showed strong associations with biological pathways related to EGFR expression and EGFR-TKI efficacy. EME demonstrated a non-invasive and biologically interpretable approach to predict EGFR status, stratify survival prognosis, and correlate biological pathways in patients with NSCLC.

Correlation analysis of EGFR gene mutation abundance and the efficacy of targeted therapy with osimertinib in nonsmall cell lung cancer-a case control study.

In nonsmall cell lung cancer (NSCLC), epidermal growth factor receptor (EGFR) mutation is the primary cancer-causing mutation. But whether the practical effectiveness of EGFR tyrosine kinase inhibitors (TKIs) can be influenced by plasma EGFR mutation abundance when treating patients with advanced NSCLC remains unanswered. Therefore, this research was intended to reveal the connection between plasma EGFR mutation abundance and clinical outcomes in osimertinib-treated patients with advanced NSCLC. A total of 120 patients with advanced NSCLC were retrospectively analyzed, and 56 patients with EGFR-mutation-positive NSCLC receiving osimertinib first-line therapy were eventually screened and included. The baseline status and abundance of plasma EGFR in patients with NSCLC were detected by cSMART, and the ratio of 0.1 was the critical value. Imaging examinations were performed every 8-12 weeks for the assessment of tumor response. The relationship between baseline EGFR mutation abundance and clinical outcomes of TKI therapy was analyzed. The objective response rates (ORR) of EGFR-mutant patients in the high-/low-abundance groups were 69.2% and 40.0%, respectively. The high abundance group had an obviously higher ORR than the low abundance group (P = 0.029). A much longer median progression-free survival (mPFS) was demonstrated in patients with high mutation abundance than in patients with low abundance (11.2 months vs 7.1 months, P = 0.0133). As for the median overall survival (mOS), it showed the same trend as mPFS in patients from different groups (15.5 vs 10.7 months, P = 0.0028). The role of plasma mutation abundance as an independent prognostic factor for both PFS (hazard ratios [HR]: 0.30, P = 0.006) and OS (HR: 0.35, P = 0.004) was demonstrated by multivariate Cox regression analysis. There is a close connection between plasma EGFR mutation abundance and survival benefit in patients with NSCLC, which can be used for predicting the efficacy of EGFR-TKI targeted therapy. Our study is expected to provide a research basis for screening patients to whom the EGFR-TKI therapy is beneficial.

Resected EGFR-mutated non-small-cell lung cancers: incidence and outcomes in a European population (GFPC Exerpos Study).

Few epidemiological data are available on surgically treated Caucasian patients with non-small-cell lung cancers (NSCLCs) harboring epidermal growth factor receptor (EGFR) mutations. The main objective of this study was to describe, in the real-world setting, these patients' incidence, clinical, and tumoral characteristics. The participating centers included all consecutive localized non-squamous NSCLC patients undergoing surgery between January 2018 and December 2019 in France. EGFR status was determined retrospectively when not available before surgery. The study includes 1391 no squamous NSCLC patients from 16 centers; EGFR status was determined before surgery in 692 (49.7%) of the cases and conducted as part of the study for 699 (50.3%); 171 (12.3%) were EGFR mutated; median age: 70 (range: 36-88) years; female: 59.6%; never smokers: 75.7%; non-squamous histology 97.7%, programmed death ligand-1 expression 0%/1-49%/⩾50 in 60.5%/25.7%/13.8%, respectively. Surgery was predominantly lobectomy (81%) or segmentectomy (14.9%), with systematic lymph node dissection in 95.9%. Resection completeness was R0 for 97%. Post-surgery staging was as follows: IA: 52%, IB: 16%, IIA: 4%, IIB: 10%, IIIA: 16%, and IIIB: 0.05%; EGFR mutation exon was Del19/exon 21 (L858R)/20/18 in 37.4%/36.8%/14%, and 6.4% of cases, respectively; 31 (18%) patients received adjuvant treatment (chemotherapy: 93%, EGFR tyrosine kinase inhibitor: 0%, radiotherapy: 20%). After a median follow-up of 31 (95% confidence interval: 29.6-33.1) months, 45 (26%) patients relapsed: 11/45 (24%) locally and 34 (76%) with metastatic progression. Median disease-free survival (DFS) and overall survival were not reached and 3-year DFS was 60%. This real-world analysis provides the incidence and outcomes of resected EGFR-mutated NSCLCs in a European patient cohort.

End-to-end Prediction of EGFR Mutation Status with Denseformer.

Accurate genotyping of the epidermal growth factor receptor (EGFR) is critical for the treatment planning of lung adenocarcinoma. Currently, clinical identification of EGFR genotyping highly relies on biopsy and sequence testing which is invasive and complicated. Recent advancements in the integration of computed tomography (CT) imagery with deep learning techniques have yielded a non-invasive and straightforward way for identifying EGFR profiles. However, there are still many limitations for further exploration: 1) most of these methods still require physicians to annotate tumor boundaries, which are time-consuming and prone to subjective errors; 2) most of the existing methods are simply borrowed from computer vision field which does not sufficiently exploit the multi-level features for final prediction. To solve these problems, we propose a Denseformer framework to identify EGFR mutation status in a real end-to-end fashion directly from 3D lung CT images. Specifically, we take the 3D whole-lung CT images as the input of the neural network model without manually labeling the lung nodules. This is inspired by the medical report that the mutational status of EGFR is associated not only with the local tumor nodules but also with the microenvironment surrounded by the whole lung. Besides, we design a novel Denseformer network to fully explore the distinctive information across the different level features. The Denseformer is a novel network architecture that combines the advantages of both convolutional neural network (CNN) and Transformer. Denseformer directly learns from the 3D whole-lung CT images, which preserves the spatial location information in the CT images. To further improve the model performance, we designed a combined Transformer module. This module employs the Transformer Encoder to globally integrate the information of different levels and layers and use them as the basis for the final prediction. The proposed model has been tested on a lung adenocarcinoma dataset collected at the Affiliated Hospital of Zunyi Medical University. Extensive experiments demonstrated the proposed method can effectively extract meaningful features from 3D CT images to make accurate predictions. Compared with other state-of-the-art methods, Denseformer achieves the best performance among current methods using deep learning to predict EGFR mutation status based on a single modality of CT images.