Comparing Patient-Derived Xenograft and Computational Response Prediction for Targeted Therapy in Patients of Early-Stage Large Cell Lung Cancer

Differential early diagnosis of benign versus malignant lung cancer using systematic pathway flux analysis of peripheral blood leukocytes

Early diagnosis of lung cancer is critically important to reduce disease severity and improve overall survival. Newer, minimally invasive biopsy procedures often fail to provide adequate specimens for accurate tumor subtyping or staging which is necessary to inform appropriate use of molecular targeted therapies and immune checkpoint inhibitors. Thus newer approaches to diagnosis and staging in early lung cancer are needed. This exploratory pilot study obtained peripheral blood samples from 139 individuals with clinically evident pulmonary nodules (benign and malignant), as well as ten healthy persons. They were divided into three cohorts: original cohort (n = 99), control cohort (n = 10), and validation cohort (n = 40). Average RNAseq sequencing of leukocytes in these samples were conducted. Subsequently, data was integrated into artificial intelligence (AI)-based computational approach with system-wide gene expression technology to develop a rapid, effective, non-invasive immune index for early diagnosis of lung cancer. An immune-related index system, IM-Index, was defined and validated for the diagnostic application. IM-Index was applied to assess the malignancies of pulmonary nodules of 109 participants (original + control cohorts) with high accuracy (AUC: 0.822 [95% CI: 0.75-0.91, p < 0.001]), and to differentiate between phases of cancer immunoediting concept (odds ratio: 1.17 [95% CI: 1.1-1.25, p < 0.001]). The predictive ability of IM-Index was validated in a validation cohort with a AUC: 0.883 (95% CI: 0.73-1.00, p < 0.001). The difference between molecular mechanisms of adenocarcinoma and squamous carcinoma histology was also determined via the IM-Index (OR: 1.2 [95% CI 1.14-1.35, p = 0.019]). In addition, a structural metabolic behavior pattern and signaling property in host immunity were found (bonferroni correction, p = 1.32e - 16). Taken together our findings indicate that this AI-based approach may be used for "Super Early" cancer diagnosis and amend the current immunotherpay for lung cancer.

Prognosis and Survival Analysis of 922,317 Lung Cancer Patients from the US Based on the Most Recent Data from the SEER Database (April 15, 2021)

Background

On April 15, 2021, the Surveillance, Epidemiology, and End Results (SEER) database released the latest lung cancer follow-up data. We selected 922,317 lung cancer patients diagnosed from 2000 to 2017 for survival analysis to provide updated data for lung cancer researchers.

Research Question

This study explored the latest trends of survival time in terms of gender, race, nationality, age, income, address, histological type and primary site.

Study Design and Methods

The SEER database covers 27.8% of the US population. We used life table, Kaplan-Meier, log-rank, Breslow and Tarone-Ware tests to calculate survival rate, time, and curve and to compare differences in survival distribution. We performed univariate and multivariate Cox proportional hazards analyses.

Results

The median survival time of all lung cancer patients diagnosed in 2017 increased by 41.72% compared to 2000. Median survival time of female patients diagnosed in 2017 increased by 70.94% compared to 2000. Median survival time of those diagnosed in 2017 for different primary sites was as follows: right middle lobe was the longest, then left lower lobe, right upper lobe, right lower lobe, and left upper lobe. Lung cancer patients older than 75 years had a significantly shorter median survival time. Patients living in metropolitan areas of 250,000 to 1 million had a longer median survival time. Median survival time in the adenocarcinoma group was significantly greater than other patients. Median survival of Asian and other races diagnosed in 2017 was 97.87% higher than those diagnosed in 2000. Survival rate of lung cancer increased gradually with the year of diagnosis.

Interpretation

The rapid improvement of the prognosis of female and young lung cancer patients contributes to the improvement of the overall prognosis. Primary lung cancer in the right middle lobe has the best prognosis.

Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles

(1) Background: Inter-tumour heterogeneity is one of cancer’s most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. (2) Methods: Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. (3) Results: Combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: paclitaxel (breast cancer), binimetinib (breast cancer) and cetuximab (colorectal cancer). Interestingly, each of these multi-gene ML models identifies some treatment-responsive PDXs not harbouring the best actionable mutation for that case. Thus, ML multi-gene predictors generally have much fewer false negatives than the corresponding single-gene marker. (4) Conclusions: As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if ML algorithms were also applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

Prediction of Radiation Pneumonitis Using Genome-Scale Flux Analysis of RNA-Seq Derived From Peripheral Blood

Purpose: Radiation pneumonitis (RP) frequently occurs during a treatment course of chest radiotherapy, which significantly reduces the clinical outcome and efficacy of radiotherapy. The ability to easily predict RP before radiotherapy would allow this disease to be avoided.

Methods and Materials: This study recruited 48 lung cancer patients requiring chest radiotherapy. For each participant, RNA sequencing (RNA-Seq) was performed on a peripheral blood sample before radiotherapy. The RNA-Seq data was then integrated into a genome-scale flux analysis to develop an RP scoring system for predicting the probability of occurrence of RP. Meanwhile, the clinical information and radiation dosimetric parameters of this cohort were collected for analysis of any statistical associations between these parameters and RP. A non-parametric rank sum test showed no significant difference between the predicted results from the RP score system and the clinically observed occurrence of RP in this cohort.

Results: The results of the univariant analysis suggested that the tumor stage, exposure dose, and bilateral lung dose of V5 and V20 were significantly associated with the occurrence of RP. The results of the multivariant analysis suggested that the exposure doses of V5 and V20 were independent risk factors associated with RP and a level of RP ≥ 2, respectively. Thus, our results indicate that our RP scoring system could be applied to accurately predict the risk of RP before radiotherapy because the scores were highly consistent with the clinically observed occurrence of RP.

Conclusion: Compared with the standard statistical methods, this genome-scale flux-based scoring system is more accurate, straightforward, and economical, and could therefore be of great significance when making clinical decisions for chest radiotherapy.

DRAP: a toolbox for drug response analysis and visualization tailored for preclinical drug testing on patient-derived xenograft models

Background

One of the key reasons for the high failure rate of new agents and low therapeutic benefit of approved treatments is the lack of preclinical models that mirror the biology of human tumors. At present, the optimal cancer model for drug response study to date is patient-derived xenograft (PDX) models. PDX recaptures both inter- and intra-tumor heterogeneity inherent in human cancer, which represent a valuable platform for preclinical drug testing and personalized medicine applications. Building efficient drug response analysis tools is critical but far from adequate for the PDX platform.

Results

In this work, we first classified the emerging PDX preclinical trial designs into four patterns based on the number of tumors, arms, and animal repeats in every arm. Then we developed an R package, DRAP, which implements Drug Response Analyses on PDX platform separately for the four patterns, involving data visualization, data analysis and conclusion presentation. The data analysis module offers statistical analysis methods to assess difference of tumor volume between arms, tumor growth inhibition (TGI) rate calculation to quantify drug response, and drug response level analysis to label the drug response at animal level. In the end, we applied DRAP in two case studies through which the functions and usage of DRAP were illustrated.

Conclusion

DRAP is the first integrated toolbox for drug response analysis and visualization tailored for PDX platform. It would greatly promote the application of PDXs in drug development and personalized cancer treatments.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-1785-7) contains supplementary material, which is available to authorized users.

Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction

The relationship between metabolism and methylation is considered to be an important aspect of cancer development and drug efficacy. However, it remains poorly defined how to apply this aspect to improve preclinical disease characterization and clinical treatment outcome. Using available molecular information from Kyoto Encyclopedia of Genes and Genomes (KEGG) and literature, we constructed a large-scale knowledge-based metabolic in silico model. For the purpose of model validation, we applied data from the Cancer Cell Line Encyclopedia (CCLE) to investigate computationally the impact of metabolism on chemotherapy efficacy. In our model, different metabolic components such as MAT2A, ATP6V0E1, NNMT involved in methionine cycle correlate with biologically measured chemotherapy outcome (IC50) that are in agreement with findings of independent studies. These proteins are potentially also involved in cellular methylation processes. In addition, several components such as 3,4-dihydoxymandelate, PAPSS2, UPP1 from metabolic pathways involved in the production of purine and pyrimidine correlate with IC50. This study clearly demonstrates that complex computational approaches can reflect findings of biological experiments. This demonstrates their high potential to grasp complex issues within systems medicine such as response prediction, biomarker identification using available data resources.

[Advances in Patient Derived Tumor Xenograft (PDTX) Model from Lung Cancer]

当前随着肿瘤分子生物学及基因组学的发展，人们已经认识到同一瘤种在不同个体间其生物学特征、分子分型以及对药物干预的反应性都存在巨大的异质性，这种个体化差异是导致肿瘤治疗过程中同病同治而不同效的重要原因，因此为了实现真正的肿瘤个体化精准治疗，肿瘤研究领域提出了一个新的概念即人源肿瘤组织异种移植模型（patient derived tumor xenograft, PDTX）；该模型可以真实地反映患者肿瘤组织的生物学特性以及药物疗效，是研究个体化治疗、药物耐药以及新药研发的重要手段，已被运用包括肺癌在内多个瘤种的临床诊治过程中。本文就当前肺癌PDTX模型的研究进展进行综述。

Tissue-directed Implantation Using Ultrasound Visualization for Development of Biologically Relevant Metastatic Tumor Xenografts

BACKGROUND

Advances in cancer therapeutics depend on reliable in vivo model systems. To develop biologically relevant xenografts, ultrasound was utilized for tissue-directed implantation of neuroblastoma (NB) cell line and patient-derived tumors in the adrenal gland, and for renal subcapsular engraftment of Ewing's sarcoma (ES).

MATERIALS AND METHODS

NB xenografts were established by direct adrenal injection of luciferase-transfected NB cell lines (IMR32, SH-SY5Y, SK-N-BE2) or NB patient-derived tumor cells (UMNBL001, UMNBL002). ES xenografts were established by renal subcapsular injection of TC32, A673, CHLA-25, or A4573 cells. Progression was monitored by in vivo imaging.

RESULTS

Tumors progressed to local disease with metastasis evident by 5 weeks. Metastatic sites included cortical bone, lung, liver, and lymph nodes. Xenografted tumors retained immunochemical features of the original cancer.

CONCLUSION

Human NB adrenal xenografts, including two patient-derived orthotopic, and ES renal subcapsular xenografts were established by ultrasound without open surgery. Tissue-directed implantation is an effective technique for developing metastatic preclinical models.