Molecular Characterization and Therapeutic Approaches to Small Cell Lung Cancer: Imaging Implications

Comprehensive genomic and spatial immune infiltration analysis of survival outliers in extensive-stage small cell lung cancer receiving first-line chemoimmunotherapy

BACKGROUND

A minority of patients with extensive-stage small cell lung cancer (ES-SCLC) exhibit prolonged survival following first-line chemoimmunotherapy, which warrants the use of reliable biomarkers. Here, we investigated the disparities in genomics and immune cell spatial distribution between long- and short-term survival of patients with ES-SCLC.

METHODS

We retrospectively recruited 11 long-term (>2 years) and 13 short-term (<9 months) ES-SCLC survivors receiving first-line chemoimmunotherapy. The samples were processed using targeted next-generation sequencing (tNGS), programmed death ligand-1 staining, multiplex immunohistochemical staining for immune cells (mIHC), tumor mutation burden (TMB), and chromosomal instability score measurements. The expression of putative genes in SCLC at the bulk and single-cell RNA-sequencing levels, as well as the role of putative genes in pan-cancer immunotherapy cohorts, were analyzed.

RESULTS

At the genomic level, a greater proportion of the smoking signature and higher TMB (>3.1) were associated with favorable survival. At the single-gene and pathway levels, tNGS revealed that MCL1 and STMN1 amplification and alterations in the apoptosis pathway were more common in short-term survivors, whereas alterations in the DLL3, KMT2B, HGF, EPHA3, ADGRB3, lysine deprivation, and HGF-cMET pathways were observed more frequently in long-term survivors. mIHC analysis of immune cells with different spatial distributions revealed that long-term survivors presented increased numbers of M1-like macrophages in all locations and decreased numbers of CD8+ T cells in the tumor stroma. Bulk transcriptomic analysis demonstrated that high levels of STMN1 and DLL3 represented an immune-suppressive tumor immune microenvironment (TIME), whereas HGF indicated an immune-responsive TIME. The expression levels of our putative genes were comparative in both TP53/RB1 mutant-type and TP53/RB1 wild-type. At the single-cell level, STMN1, MCL1, and DLL3 were highly expressed among all molecular subtypes (SCLC-A, SCLC-N, and SCLC-P), with STMN1 being enriched in cell division and G2M checkpoint pathways.

CONCLUSIONS

For ES-SCLC patients receiving first-line chemoimmunotherapy, alterations in DLL3, KMT2B, HGF, EPHA3, and ADGRB3 and a greater proportion of M1-like macrophages infiltration in all locations were predictors of favorable survival, while MCL1 and STMN1 amplification, as well as a greater proportion of CD8+ T cells infiltrating the tumor stroma, predicted worse survival.

KM04416 suppressed lung adenocarcinoma progression by promoting immune infiltration

OBJECTIVES

Lung adenocarcinoma (LUAD) is a malignant tumor originating from the bronchial mucosa or glands of the lung, with the fastest increasing morbidity and mortality. Therefore, the prognosis of lung cancer remains poor. Glycerol-3-phosphate dehydrogenase 2 (GPD2) is a widely existing protein pattern sequence in biology and is closely related to tumor progression. The therapy values of GPD2 inhibitor in LUAD were unclear. Therefore, we aimed to analyze the therapy values of GPD2 inhibitor in LUAD.

MATERIALS AND METHODS

The Cancer Genome Atlas (TCGA)-LUAD database was used to analyze the expression levels of GPD2 in LUAD tissues. The relationship between GPD2 expression and LUAD patient survival was analyzed by Kaplan-Meier method. Moreover, KM04416 as a target inhibitor of GPD2 was used to further investigate the therapy value of GPD2 inhibitor in LUAD cells lines (A549 cell and H1299 cell). The TISIDB website was used to investigate the associations between GPD2 expression and immune cell infiltration in LUAD.

RESULTS

The results showed that GPD2 is overexpressed in LUAD tissues and significantly associated with poor survival. KM04416 can suppress the progression of LUAD cells by targeting GPD2. Low expression of GPD2 is related to high infiltration of immune cells.

CONCLUSIONS

In summary, our present study found that targeting inhibition of GPD2 by KM04416 can suppress LUAD progression via adjusting immune cell infiltration.

Biomarker-driven molecular imaging probes in radiotherapy

Background: Biomarker-driven molecular imaging has emerged as an integral part of cancer precision radiotherapy. The use of molecular imaging probes, including nanoprobes, have been explored in radiotherapy imaging to precisely and noninvasively monitor spatiotemporal distribution of biomarkers, potentially revealing tumor-killing mechanisms and therapy-induced adverse effects during radiation treatment. Methods: We summarized literature reports from preclinical studies and clinical trials, which cover two main parts: 1) Clinically-investigated and emerging imaging biomarkers associated with radiotherapy, and 2) instrumental roles, functions, and activatable mechanisms of molecular imaging probes in the radiotherapy workflow. In addition, reflection and future perspectives are proposed. Results: Numerous imaging biomarkers have been continuously explored in decades, while few of them have been successfully validated for their correlation with radiotherapeutic outcomes and/or radiation-induced toxicities. Meanwhile, activatable molecular imaging probes towards the emerging biomarkers have exhibited to be promising in animal or small-scale human studies for precision radiotherapy. Conclusion: Biomarker-driven molecular imaging probes are essential for precision radiotherapy. Despite very inspiring preliminary results, validation of imaging biomarkers and rational design strategies of probes await robust and extensive investigations. Especially, the correlation between imaging biomarkers and radiotherapeutic outcomes/toxicities should be established through multi-center collaboration involving a large cohort of patients.

Biological clock regulation by the PER gene family: a new perspective on tumor development

The Period (PER) gene family is one of the core components of the circadian clock, with substantial correlations between the PER genes and cancers identified in extensive researches. Abnormal mutations in PER genes can influence cell function, metabolic activity, immunity, and therapy responses, thereby promoting the initiation and development of cancers. This ultimately results in unequal cancers progression and prognosis in patients. This leads to variable cancer progression and prognosis among patients. In-depth studies on the interactions between the PER genes and cancers can reveal novel strategies for cancer detection and treatment. In this review, we aim to provide a comprehensive overview of the latest research on the role of the PER gene family in cancer.

Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.

Synchronous double primary small cell lung cancer and invasive ductal breast carcinoma: a case report

BACKGROUND

Although lung and breast cancers are common malignancies, the occurrence of primary synchronous neoplasms involving these organs has been rarely reported in literature.

CASE PRESENTATION

A 75-year-old female patient presented at a local hospital with a ten-day history of dizziness and slurred speech. A CT contrast-enhanced scan revealed a 4.2 cm mass in the lower lobe of the right lung and a 3.8 cm space-occupying lesion in the right breast. Subsequent breast ultrasound identified a hypoechoic lesion measuring5.41 × 4.75 × 3.06 cm in the right breast, and an ultrasound-guided biopsy confirmed the presence of infiltrating ductal carcinoma of the right breast. The immunohistochemistry analysis of the breast mass revealed positive staining for ER, PR, HER-2, AR and Ki67 in the tumor cells, while negative staining was observed for P63, Calponin, CK5/6 and CK14. MR imaging of the head detected abnormal signals in the right frontal lobe (3.6 cm×2.9 cm in size), left cerebellar hemisphere, and punctate enhancement in the left temporal lobe, indicating potential metastasis. Pathological examination of a lung biopsy specimen confirmed the presence of small cell lung cancer (SCLC). Furthermore, immunohistochemistry analysis of the lung lesions demonstrated positive staining for TTF-1, CK-Pan, Syn, CgA, CD56, P53 (90%) and Ki67 (70%), and negative staining for NapsinA and P40 in the tumor cells. The patient's diagnosis of SCLC with stage cT2bN0M1c IVB and brain metastases (BM), as well as invasive ductal breast carcinoma (IDC), was confirmed based on the aforementioned results. Whereupon we proposed a treatment plan consisting of whole-brain radiation (40 Gy/20fractions), focal radiotherapy (60 Gy/20fractions), and adjuvant concurrent chemotherapy with oral etoposide (50 mg on days 1 to 20).

CONCLUSIONS

To the best of our knowledge, the present case is the first of its kind to describe the synchronous double cancer, consisting of primary SCLC and IDC.

Non-invasive decision support for clinical treatment of non-small cell lung cancer using a multiscale radiomics approach

BACKGROUND

Selecting therapeutic strategies for cancer patients is typically based on key target-molecule biomarkers that play an important role in cancer onset, progression, and prognosis. Thus, there is a pressing need for novel biomarkers that can be utilized longitudinally to guide treatment selection.

METHODS

Using data from 508 non-small cell lung cancer (NSCLC) patients across three institutions, we developed and validated a comprehensive predictive biomarker that distinguishes six genotypes and infiltrative immune phenotypes. These features were analyzed to establish the association between radiological phenotypes and tumor genotypes/immune phenotypes and to create a radiological interpretation of molecular features. In addition, we assessed the sensitivity of the models by evaluating their performance at five different voxel intervals, resulting in improved generalizability of the proposed approach.

FINDINGS

The radiomics model we developed, which integrates clinical factors and multi-regional features, outperformed the conventional model that only uses clinical and intratumoral features. Our combined model showed significant performance for EGFR, KRAS, ALK, TP53, PIK3CA, and ROS1 mutation status with AUCs of 0.866, 0.874, 0.902, 0.850, 0.860, and 0.900, respectively. Additionally, the predictive performance for PD-1/PD-L1 was 0.852. Although the performance of all models decreased to different degrees at five different voxel space resolutions, the performance advantage of the combined model did not change.

CONCLUSIONS

We validated multiscale radiomic signatures across tumor genotypes and immunophenotypes in a multi-institutional cohort. This imaging-based biomarker offers a non-invasive approach to select patients with NSCLC who are sensitive to targeted therapies or immunotherapy, which is promising for developing personalized treatment strategies during therapy.

Multisequence MRI-based radiomics nomogram for early prediction of osimertinib resistance in patients with non-small cell lung cancer brain metastases

Background

Osimertinib resistance is a major problem in the course of targeted therapy for non-small cell lung cancer (NSCLC) patients. To develop and validate a multisequence MRI-based radiomics nomogram for early prediction of osimertinib resistance in NSCLC with brain metastases (BM).

Methods

Pretreatment brain MRI of 251 NSCLC patients proven with BM were retrospectively enrolled from two centers (training cohort: 196 patients; testing cohort: 55 patients). According to the gene test result of osimertinib resistance, patients were labeled as resistance and non-resistance groups (training cohort: 65 versus 131 patients; testing cohort: 25 versus 30 patients). Radiomics features were extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences separately and radiomics score (rad-score) were built from the four sequences. Then a multisequence MRI-based nomogram was developed and the predictive ability was evaluated by ROC curves and calibration curves.

Results

The rad-scores of the four sequences has significant differences between resistance and non-resistance groups in both training and testing cohorts. The nomogram achieved the highest predictive ability with area under the curve (AUC) of 0.989 (95 % confidence interval, 0.976-1.000) and 0.923 (95 % confidence interval, 0.851-0.995) in the training and testing cohort respectively. The calibration curves showed excellent concordance between the predicted and actual probability of osimertinib resistance using the radiomics nomogram.

Conclusions

The multisequence MRI-based radiomics nomogram can be used as a noninvasive auxiliary tool to identify candidates who were resistant to osimertinib, which could guide clinical therapy for NSCLC patients with BM.

Clinical insights into small cell lung cancer: Tumor heterogeneity, diagnosis, therapy, and future directions

Small cell lung cancer (SCLC) is characterized by rapid growth and high metastatic capacity. It has strong epidemiologic and biologic links to tobacco carcinogens. Although the majority of SCLCs exhibit neuroendocrine features, an important subset of tumors lacks these properties. Genomic profiling of SCLC reveals genetic instability, almost universal inactivation of the tumor suppressor genes TP53 and RB1, and a high mutation burden. Because of early metastasis, only a small fraction of patients are amenable to curative-intent lung resection, and these individuals require adjuvant platinum-etoposide chemotherapy. Therefore, the vast majority of patients are currently being treated with chemoradiation with or without immunotherapy. In patients with disease confined to the chest, standard therapy includes thoracic radiotherapy and concurrent platinum-etoposide chemotherapy. Patients with metastatic (extensive-stage) disease are treated with a combination of platinum-etoposide chemotherapy plus immunotherapy with an anti-programmed death-ligand 1 monoclonal antibody. Although SCLC is initially very responsive to platinum-based chemotherapy, these responses are transient because of the development of drug resistance. In recent years, the authors have witnessed an accelerating pace of biologic insights into the disease, leading to the redefinition of the SCLC classification scheme. This emerging knowledge of SCLC molecular subtypes has the potential to define unique therapeutic vulnerabilities. Synthesizing these new discoveries with the current knowledge of SCLC biology and clinical management may lead to unprecedented advances in SCLC patient care. Here, the authors present an overview of multimodal clinical approaches in SCLC, with a special focus on illuminating how recent advancements in SCLC research could accelerate clinical development.

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

Purpose

Exploring a non-invasive method to accurately differentiate peripheral small cell lung cancer (PSCLC) and peripheral lung adenocarcinoma (PADC) could improve clinical decision-making and prognosis.

Methods

This retrospective study reviewed the clinicopathological and imaging data of lung cancer patients between October 2017 and March 2022. A total of 240 patients were enrolled in this study, including 80 cases diagnosed with PSCLC and 160 with PADC. All patients were randomized in a seven-to-three ratio into the training and validation datasets (170 vs. 70, respectively). The least absolute shrinkage and selection operator regression was employed to generate radiomics features and univariate analysis, followed by multivariate logistic regression to select significant clinical and radiographic factors to generate four models: clinical, radiomics, clinical-radiographic, and clinical-radiographic-radiomics (comprehensive). The Delong test was to compare areas under the receiver operating characteristic curves (AUCs) in the models.

Results

Five clinical-radiographic features and twenty-three selected radiomics features differed significantly in the identification of PSCLC and PADC. The clinical, radiomics, clinical-radiographic and comprehensive models demonstrated AUCs of 0.8960, 0.8356, 0.9396, and 0.9671 in the validation set, with the comprehensive model having better discernment than the clinical model (P=0.036), the radiomics model (P=0.006) and the clinical-radiographic model (P=0.049).

Conclusions

The proposed model combining clinical data, radiographic characteristics and radiomics features could accurately distinguish PSCLC from PADC, thus providing a potential non-invasive method to help clinicians improve treatment decisions.