Biology of NSCLC: Interplay between Cancer Cells, Radiation and Tumor Immune Microenvironment

Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC

The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteristics. Prognostic subregions of the biomarker space are delineated for each method using synthetic patient data augmentation and compared between models for overall survival concordance. A total of 423 NSCLC patients (46% female; median age [inter quantile range]: 67 [60-73]) treated with definite surgical resection were included in the study. And 219 (52%) patients experienced events during the observation period consisting of a maximum follow-up of 10 years and median follow up 78 months. The random survival forest (RSF) achieved the highest predictive accuracy, with a C-index of 0.84. The resultant biomarker subtypes demonstrate that patients with high PD-L1 expression combined with low CD3 counts experience higher risk of death within five-years of surgical resection.

Stereotactic central/core ablative radiation therapy: results of a phase I study of a novel strategy to treat bulky tumor

Purpose

Bulky tumor remains as a challenge to surgery, chemotherapy and conventional radiation therapy. Hence, in efforts to overcome this challenge, we designed a novel therapeutic paradigm via strategy of Stereotactic Central/Core Ablative Radiation Therapy (SCART).), which is based on the principles of SBRT (stereotactic body radiation therapy and spatially fractionated radiation therapy (SFRT). We intend to safely deliver an ablative dose to the core of the tumor and with a low dose at tumor edge. The purpose of the phase 1 study was to determine dose-limiting toxicities (DLT)s and the Maximum Tolerated Dose (MTD) of SCART.

Methods and materials

We defined a SCART-plan volume inside the tumor, which is proportional to the dimension of tumor. VMAT/Cyberknife technique was adopted. In the current clinical trial; Patients with biopsy proven recurrent or metastatic bulky cancers were enrolled. The five dose levels were 15 Gy X1, 15Gy X3, 18GyX3, 21GyX3 and 24GyX3, while keeping the whole tumor GTV's border dose at 5Gy each fraction. There was no restriction on concurrent systemic chemotherapy agents.

Results

21 patients were enrolled and underwent SCART. All 21 patients have eligible data for study follow-up. Radiotherapy was well tolerated with all treatment completed as scheduled. The dose was escalated for two patients to 24GyX3. No grade 3 or higher toxicity was observed in any of the enrolled patients. The average age of patients was 66 years (range: 14-85) and 13 (62%) patients were male. The median SCART dose was 18Gy (range: 15 - 24). Six out of the 18 patients with data for overall survival (OS) died, and the median time to death was 16.3 months (range: 1 - 25.6). The mean percent change for tumor shrinkage between first visit volumes and post-SCART volumes was 49.5% (SD: 40.89, p-value:0.009).

Conclusion

SCART was safely escalated to 24 GyX 3 fractions, which is the maximum Tolerated Dose (MTD) for SCART. This regimen will be used in future phase II trials.

Prognostic Biomarkers after Radiotherapy for Nonsmall Cell Lung Cancer Based on Bioinformatics Analysis

Radiotherapy is one of the main treatment modalities in nonsmall cell lung cancer (NSCLC). However, tumor radiosensitivity is influenced by intrinsic factors like genetic variations and extrinsic factors like tumor microenvironment. Consequently, we hope to develop novel biomarkers, so as to improve the response rate of radiotherapy and overcome resistance to radiotherapy in NSCLC. We investigate the difference genes of primary NSCLC patients before and after radiotherapy in GSE162945 dataset. Gene Ontology (GO), KEGG, Reactome, and GSEA were employed to represent the essential gene and biological function. It was found that most pathway genes clustered in extracellular matrix and ECM-receptor signal pathway. Additionally, TMT-based proteomics was used to survey the differential proteins present in the supernatant of H460 cells before or after irradiation with 2 Gy of γ-rays. And then we take the intersection between the proteomics of H460 cell and ECM-receptor signal pathway proteins of GSE162945 datasets. The data revealed that fibronectin 1 (FN1) and thrombin reactive protein 1 (THBS1) were upregulated after radiation in both datasets. Subsequently, survival analyses using the GEPIA web server demonstrated that FN1 and THBS1 had significant prognostic values (Logrank test P value < 0.05) for LUAD and LUSC. Our observations from this study suggest that FN1 and THBS1 might have potential to serve as novel biomarkers for predicting NSCLC tumor response to radiotherapy.

Identification of a prognostic index system and tumor immune infiltration characterization for lung adenocarcinoma based on mRNA molecular of pyroptosis

Background and purpose

Pyroptosis is a form of programmed cell death, which plays an important role in tumorigenesis, progression, and regulation of the tumor microenvironment. It can affect lung adenocarcinoma (LUAD) progression. This study aimed to construct a pyroptosis-related mRNA prognostic index (PRMPI) for LUAD and clarify the tumor microenvironment infiltration characterization of LUAD.

Materials and methods

We performed a univariate Cox regression analysis for pyroptosis-related mRNAs in the TCGA cohort. Then, we used LASSO Cox regression to establish a PRMPI. The quantitative real time polymerase chain reaction (qRT-PCR) was used to quantify the relative expression of pyroptosis-related mRNAs. The CPTAC cohort was used to confirm the stability and wide applicability of the PRMPI. The single-sample gene set enrichment analysis (ssGSEA) was performed to assess the tumor microenvironment infiltration characterization.

Results

A total of 36 pyroptosis-related mRNAs were identified. The PRMPI was established based on five pyroptosis-related mRNAs. The expression patterns of these mRNAs were verified in LUAD samples from our medical center by qRT-PCR. High-PRMPI patients had worse overall survival than low-PRMPI patients. The result was validated in the CPTAC cohort. The comprehensive analysis indicated that the high-PRMPI patients exhibited lower immune activity, more aggressive immunophenotype, lower expression of immune checkpoint molecule, higher TP53 mutation rate, and higher tumor stemness than low-PRMPI patients. Low-PRMPI patients may be more sensitive to immunotherapy, while high-PRMPI patients may benefit more from chemotherapy and targeted therapy.

Conclusions

The PRMPI may be a promising biomarker to predict the prognosis, tumor microenvironment infiltration characterization, and the response to adjuvant therapy in LUAD.

Clinically validated model predicts the effect of intratumoral heterogeneity on overall survival for non-small cell lung cancer (NSCLC) patients

BACKGROUND AND OBJECTIVE

Radiation therapy is used in nearly 50% of cancer treatments in the developed world. Currently, radiation treatments are homogenous and fail to take into consideration intratumoral heterogeneity. We demonstrate the importance of considering intratumoral heterogeneity and the development of resistance during fractionated radiotherapy when the same dose of radiation is delivered for all fractions (Fractional Equivalent Dosing FED).

METHODS

A mathematical model was developed with the following parameters: a starting population of 10¹¹ non-small cell lung cancer (NSCLC) tumor cells, 48 h doubling time, and cell death per the linear-quadratic (LQ) model with α and β values derived from RSIα/β, in a previously described gene expression based model that estimates α and β. To incorporate both inter- and intratumor radiation sensitivity, RSIα/β output for each patient sample is assumed to represent an average value in a gamma distribution with the bounds set to -50% and +50% of RSIα/b. Therefore, we assume that within a given tumor there are subpopulations that have varying radiation sensitivity parameters that are distinct from other tumor samples with a different mean RSIα/β. A simulation cohort (SC) comprised of 100 lung cancer patients with available RSIα/β (patient specific α and β values) was used to investigate 60 Gy in 30 fractions with fractionally equivalent dosing (FED). A separate validation cohort (VC) of 57 lung cancer patients treated with radiation with available local control (LC), overall survival (OS), and tumor gene expression was used to clinically validate the model. Cox regression was used to test for significance to predict clinical outcomes as a continuous variable in multivariate analysis (MVA). Finally, the VC was used to compare FED schedules with various altered fractionation schema utilizing a Kruskal-Wallis test. This was examined using the end points of end of treatment log cell count (LCC) and by a parameter described as mean log kill efficiency (LKE) defined as: LCC  =  log10(tumorcellcount) [Formula: see text] RESULTS: Cox regression analysis on LCC for the VC demonstrates that, after incorporation of intratumoral heterogeneity, LCC has a linear correlation with local control (p = 0.002) and overall survival (p = < 0.001). Other suggested treatment schedules labeled as High Intensity Treatment (HIT) with a total 60 Gy delivered over 6 weeks have a lower mean LCC and an increased LKE compared to standard of care 60 Gy delivered in FED in the VC.

CONCLUSION

We find that LCC is a clinically relevant metric that is correlated with local control and overall survival in NSCLC. We conclude that 60 Gy delivered over 6 weeks with altered HIT fractionation leads to an enhancement in tumor control compared to FED when intratumoral heterogeneity is considered.

Exploring the optimal dose of low ionizing radiation to enhance immune function: a rabbit model

Primary liver cancer is one of the most common malignant tumors in China. Currently, immunotherapy for liver cancer is a research hotspot. Experimental studies and epidemiological investigations have confirmed the antineoplastic activity of low ionizing radiation. The aim of this study was to explore the optimal dose of low ionizing radiation to enhance immune function. Twenty-five New Zealand rabbits were randomly divided into five groups (n = 5 each): experimental group 1 (25 mGy), experimental group 2 (50 mGy), experimental group 3 (75 mGy), experimental group 4 (100 mGy), and the control group (0 mGy). VX-2 tumor tissue was injected into rabbits using a high-frequency B-ultrasound probe (3.5 MHz). Rabbits were irradiated, and on day 4 after irradiation, blood was collected from each rabbit. Blood chemistry, interleukin (IL)-4, interferon (IFN)-γ, immunoglobulin (Ig)G, and IgM levels were assessed. On day 15 after irradiation, macrophage phagocytic function was assessed. The rabbits were sacrificed, and the spleen was removed and weighed to calculate its spleen index. Each parameter was highest in the experimental group 3 (75 mGy). Thus, we suspect the optimal low ionizing radiation dose to improve immune function may be 75 mGy.

Prognostic Value of Combing Primary Tumor and Nodal Glycolytic-Volumetric Parameters of 18F-FDG PET in Patients with Non-Small Cell Lung Cancer and Regional Lymph Node Metastasis

We investigated whether the combination of primary tumor and nodal ¹⁸F-FDG PET parameters predict survival outcomes in patients with nodal metastatic non-small cell lung cancer (NSCLC) without distant metastasis. We retrospectively extracted pre-treatment ¹⁸F-FDG PET parameters from 89 nodal-positive NSCLC patients (stage IIB–IIIC). The Cox proportional hazard model was used to identify independent prognosticators of overall survival (OS) and progression-free survival (PFS). We devised survival stratification models based on the independent prognosticators and compared the model to the American Joint Committee on Cancer (AJCC) staging system using Harrell’s concordance index (c-index). Our results demonstrated that total TLG (the combination of primary tumor and nodal total lesion glycolysis) and age were independent risk factors for unfavorable OS (p < 0.001 and p = 0.001) and PFS (both p < 0.001), while the Eastern Cooperative Oncology Group scale independently predicted poor OS (p = 0.022). Our models based on the independent prognosticators outperformed the AJCC staging system (c-index = 0.732 versus 0.544 for OS and c-index = 0.672 versus 0.521 for PFS, both p < 0.001). Our results indicate that incorporating total TLG with clinical factors may refine risk stratification in nodal metastatic NSCLC patients and may facilitate tailored therapeutic strategies in this patient group.

The Role of Tumor-Derived Exosomes in the Abscopal Effect and Immunotherapy

Exosomes are microvesicles that can be secreted by various cells and carry a variety of contents; thus, they play multiple biological functions. For instance, the tumor-derived exosomes (TEXs) have been proven to have the effect of immunostimulatory in addition to immunosuppression, making TEXs attractive in clinical immunotherapy and targeted therapy for cancer patients. In addition, TEXs as biomarkers have important clinical diagnostic and prognostic value. Recently, TEXs have been recognized to play important roles in the abscopal effect (AbE), a newly discovered mechanism by which the distant tumors are effectively targeted and repressed during immunotherapy and radiotherapy. Therefore, TEXs has demonstrated great clinical potential in the diagnosis, prognosis and treatment of cancer patients in the future. This review summarizes and discusses the role of TEXs in clinical therapy and their role in AbE in recent studies.