Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer

Differentially Expressed Genes Involved in Primary Resistance to Immunotherapy in Patients with Advanced-Stage Pulmonary Cancer

In the last few years, nivolumab has become the standard of care for advanced-stage lung cancer patients. Unfortunately, up to 60% of patients do not respond to this treatment. In our study, we identified variations in gene expression related to primary resistance to immunotherapy. Bronchoscopy biopsies were obtained from advanced non-small cell lung cancer (NSCLC) patients previously characterized as responders or non-responders after nivolumab treatment. Ten tumor biopsies (from three responders and seven non-responders) were analyzed by the differential expression of 760 genes using the NanoString nCounter platform. These genes are known to be involved in the response to anti-PD1/PD-L1 therapy. All the patients were treated with nivolumab. Examining the dysregulated expression of 24 genes made it possible to predict the response to nivolumab treatment. Supervised analysis of the gene expression profile (GEP) revealed that responder patients had significantly higher levels of expression of CXCL11, NT5E, KLRK1, CD3G, GZMA, IDO1, LCK, CXCL9, GNLY, ITGAL, HLA-DRB1, CXCR6, IFNG, CD8A, ITK, B2M, HLA-B, and HLA-A than did non-responder patients. In contrast, PNOC, CD19, TP73, ARG1, FCRL2, and PTGER1 genes had significantly lower expression levels than non-responder patients. These findings were validated as predictive biomarkers in an independent series of 201 patients treated with nivolumab (22 hepatocellular carcinomas, 14 non-squamous cell lung carcinomas, 5 head and neck squamous cell carcinomas, 1 ureter/renal pelvis carcinoma, 120 melanomas, 4 bladder carcinomas, 31 renal cell carcinomas, and 4 squamous cell lung carcinomas). ROC curve analysis showed that the expression levels of ITK, NT5E, ITGAL, and CD8A were the best predictors of response to nivolumab. Further, 13/24 genes showed an adverse impact on overall survival (OS) in an independent, large series of patients with NSCLC (2166 cases). In summary, we found a strong association between the global GEP of advanced NSCLC and the response to nivolumab. The classification of NSCLC patients based on GEP enabled us to identify those patients who genuinely benefited from treatment with immune checkpoint inhibitors (ICIs). We also demonstrated that abnormal expression of most of the markers comprising the genomic signature has an adverse influence on OS, making them significant markers for therapeutic decision-making. Additional prospective studies in larger series of patients are required to confirm the clinical utility of these biomarkers.

Transcriptional analysis of landmark molecular pathways in lung adenocarcinoma results in a clinically relevant classification with potential therapeutic implications

Lung adenocarcinoma (LUAD) is a molecularly heterogeneous disease. In addition to genomic alterations, cancer transcriptional profiling can be helpful to tailor cancer treatment and to estimate each patient's outcome. Transcriptional activity levels of 50 molecular pathways were inferred in 4573 LUAD patients using Gene Set Variation Analysis (GSVA) method. Seven LUAD subtypes were defined and independently validated based on the combined behavior of the studied pathways: AD (adenocarcinoma subtype) 1-7. AD1, AD4, and AD5 subtypes were associated with better overall survival. AD1 and AD4 subtypes were enriched in epidermal growth factor receptor (EGFR) mutations, whereas AD2 and AD6 showed higher tumor protein p53 (TP53) alteration frequencies. AD2 and AD6 subtypes correlated with higher genome instability, proliferation-related pathway expression, and specific sensitivity to chemotherapy, based on data from LUAD cell lines. LUAD subtypes were able to predict immunotherapy response in addition to CD274 (PD-L1) gene expression and tumor mutational burden (TMB). AD2 and AD4 subtypes were associated with potential resistance and response to immunotherapy, respectively. Thus, analysis of transcriptomic data could improve patient stratification beyond genomics and single biomarkers (i.e., PD-L1 and TMB) and may lay the foundation for more personalized treatment avenues, especially in driver-negative LUAD.

Intelligent phenotype-detection and gene expression profile generation with generative adversarial networks

Gene expression analysis is valuable for cancer type classification and identifying diverse cancer phenotypes. The latest high-throughput RNA sequencing devices have enabled access to large volumes of gene expression data. However, we face several challenges, such as data security and privacy, when we develop machine learning-based classifiers for categorizing cancer types with these datasets. To address these issues, we propose IP3G (Intelligent Phenotype-detection and Gene expression profile Generation with Generative adversarial network), a model based on Generative Adversarial Networks. IP3G tackles two major problems: augmenting gene expression data and unsupervised phenotype discovery. By converting gene expression profiles into 2-Dimensional images and leveraging IP3G, we generate new profiles for specific phenotypes. IP3G learns disentangled representations of gene expression patterns and identifies phenotypes without labeled data. We improve the objective function of the GAN used in IP3G by employing the earth mover distance and a novel mutual information function. IP3G outperforms clustering methods like k-Means, DBSCAN, and GMM in unsupervised phenotype discovery, while also surpassing SVM and CNN classification accuracy by up to 6% through gene expression profile augmentation. The source code for the developed IP3G is accessible to the public on GitHub.

Clinical Utility of the 31-Gene Expression Profile Test on the Management of Cutaneous Melanoma by Nurse Practitioners and Physician Assistants

Objective

The 31-gene expression profile (31-GEP) can predict the risk of recurrence and metastasis in cutaneous melanoma (CM). We assessed the viewpoints and use of 31-GEP testing by physician assistants (PAs) and nurse practitioners (NPs) for patients with CM.

Methods

NPs and PAs (n = 369) completed an 18-question online survey about their viewpoints and use of the 31-GEP risk-stratification test.

Results

Most practitioners (n = 334, 90.5%) felt prognostic testing improved patient care and would recommend the 31-GEP to a colleague (n = 333, 90.2%) or a friend or family member (n = 289, 78.3%) who was diagnosed with CM. The 31-GEP test was used by 176 respondents in the preceding 12 months (53%). Among users of the 31-GEP test, 78% stated that the results would impact follow-up schedule and referral, 66% overall treatment decisions, 62% sentinel lymph node biopsy recommendations, and 50% surveillance imaging. In thin tumors (≤ 1 mm), 82% of 31-GEP users and 44% of nonusers stated that the 31-GEP results would impact their treatment plan decisions.

Conclusion

The 31-GEP test significantly impacts treatment plans in CM, particularly for thin and stage I melanomas. Importantly, even nonusers stated that 31-GEP test results would impact treatment plans as well as recommendations to a friend or family member.

Gene expression profiling unveils the temporal dynamics of CIGB-300-regulated transcriptome in AML cell lines

BACKGROUND

Protein kinase CK2 activity is implicated in the pathogenesis of various hematological malignancies like Acute Myeloid Leukemia (AML) that remains challenging concerning treatment. This kinase has emerged as an attractive molecular target in therapeutic. Antitumoral peptide CIGB-300 blocks CK2 phospho-acceptor sites on their substrates but it also binds to CK2α catalytic subunit. Previous proteomic and phosphoproteomic experiments showed molecular and cellular processes with relevance for the peptide action in diverse AML backgrounds but earlier transcriptional level events might also support the CIGB-300 anti-leukemic effect. Here we used a Clariom S HT assay for gene expression profiling to study the molecular events supporting the anti-leukemic effect of CIGB-300 peptide on HL-60 and OCI-AML3 cell lines.

RESULTS

We found 183 and 802 genes appeared significantly modulated in HL-60 cells at 30 min and 3 h of incubation with CIGB-300 for p < 0.01 and FC >  = │1.5│, respectively; while 221 and 332 genes appeared modulated in OCI-AML3 cells. Importantly, functional enrichment analysis evidenced that genes and transcription factors related to apoptosis, cell cycle, leukocyte differentiation, signaling by cytokines/interleukins, and NF-kB, TNF signaling pathways were significantly represented in AML cells transcriptomic profiles. The influence of CIGB-300 on these biological processes and pathways is dependent on the cellular background, in the first place, and treatment duration. Of note, the impact of the peptide on NF-kB signaling was corroborated by the quantification of selected NF-kB target genes, as well as the measurement of p50 binding activity and soluble TNF-α induction. Quantification of CSF1/M-CSF and CDKN1A/P21 by qPCR supports peptide effects on differentiation and cell cycle.

CONCLUSIONS

We explored for the first time the temporal dynamics of the gene expression profile regulated by CIGB-300 which, along with the antiproliferative mechanism, can stimulate immune responses by increasing immunomodulatory cytokines. We provided fresh molecular clues concerning the antiproliferative effect of CIGB-300 in two relevant AML backgrounds.

Systematic analysis of transcriptome signature for improving outcomes in lung adenocarcinoma

PURPOSE

The updated guidelines highlight gene expression-based multigene panel as a critical tool to assess overall survival (OS) and improve treatment for lung adenocarcinoma (LUAD) patients. Nevertheless, genome-wide expression signatures are still limited in real clinical utility because of insufficient data utilization, a lack of critical validation, and inapposite machine learning algorithms.

METHODS

2330 primary LUAD samples were enrolled from 11 independent cohorts. Seventy-six algorithm combinations based on ten machine learning algorithms were applied. A total of 108 published gene expression signatures were collected. Multiple pharmacogenomics databases and resources were utilized to identify precision therapeutic drugs.

RESULTS

We comprehensively developed a robust machine learning-derived genome-wide expression signature (RGS) according to stably OS-associated RNAs (OSRs). RGS was an independent risk element and remained robust and reproducible power by comparing it with general clinical parameters, molecular characteristics, and 108 published signatures. RGS-based stratification possessed different biological behaviors, molecular mechanisms, and immune microenvironment patterns. Integrating multiple databases and previous studies, we identified that alisertib was sensitive to the high-risk group, and RITA was sensitive to the low-risk group.

CONCLUSION

Our study offers an appealing platform to screen dismal prognosis LUAD patients to improve clinical outcomes by optimizing precision therapy.

Refining personalized diagnosis, treatment and exploitation of hypothyroidism related to solid nonthyroid cancer

Hypothyroidism in the setting of cancer is a puzzling entity due to the dual role of the thyroid hormones (TH) in cancer – promoting versus inhibitory – and the complexity of the hypothyroidism itself. The present review provides a comprehensive overview of the personalized approach to hypothyroidism in patients with solid nonthyroid cancer, focusing on current challenges, unmet needs and future perspectives. Major electronic databases were searched from January 2011 until March 2022. The milestones of the refinement of such a personalized approach are prompt diagnosis, proper TH replacement and development of interventions and/or pharmaceutical agents to exploit hypothyroidism or, on the contrary, TH replacement as an anticancer strategy. Further elucidation of the dual role of TH in cancer – especially of the interference of TH signaling with the hallmarks of cancer – is anticipated to inform decision-making and optimize patient selection.

Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma

Background

Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD).

Methods

A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties.

Results

Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III.

Conclusion

We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.

Oligometastatic Disease in Non-Small-Cell Lung Cancer: An Update

Simple Summary

Approximately 7–50% of patients with non-small-cell lung cancer (NSCLC) develop oligometastases, which are new tumors found in another part of the body, arising from cancer cells of the original tumor that have travelled through the body. In recent years, these patients have been increasingly regarded as a distinct group that could benefit from treatment that intends to cure the disease, rather than palliative care, to achieve a better clinical outcome. Various treatment procedures have been developed for treating NSCLC patients with different oligometastatic sites. In addition, the newly proposed uniform definition for oligometastases as well as ongoing trials may lead to increased appropriate patient selection and evaluation of treatment effectiveness. The aim of this review article is to summarize the latest evidence regarding optimal management strategies for NSCLC patients with oligometastases.

Abstract

Oligometastatic non-small-cell lung cancer (NSCLC) is a distinct entity that is different from localized and disseminated diseases. The definition of oligometastatic NSCLC varies across studies in past decades owing to the use of different imaging modalities; however, a uniform definition of oligometastatic NSCLC has been proposed, and this may facilitate trial design and evaluation of certain interventions. Patients with oligometastatic NSCLC are candidates for curative-intent management, in which local ablative treatment, such as surgery or stereotactic radiosurgery, should be instituted to improve clinical outcomes. Although current guidelines recommend that local therapy for thoracic and metastatic lesions should be considered for patients with oligometastatic NSCLC with stable disease after systemic therapy, optimal management strategies for different oligometastatic sites have not been established. Additionally, the development of personalized therapies for individual patients with oligometastatic NSCLC to improve their quality of life and overall survival should also be addressed. Here, we review relevant articles on the management of patients with oligometastatic NSCLC and categorize the disease according to the site of metastases. Ongoing trials are also summarized to determine future directions and expectations for new treatment modalities to improve patient management.

The Intriguing Thyroid Hormones-Lung Cancer Association as Exemplification of the Thyroid Hormones-Cancer Association: Three Decades of Evolving Research

Exemplifying the long-pursued thyroid hormones (TH)-cancer association, the TH-lung cancer association is a compelling, yet elusive, issue. The present narrative review provides background knowledge on the molecular aspects of TH actions, with focus on the contribution of TH to hallmarks of cancer. Then, it provides a comprehensive overview of data pertinent to the TH-lung cancer association garnered over the last three decades and identifies obstacles that need to be overcome to enable harnessing this association in the clinical setting. TH contribute to all hallmarks of cancer through integration of diverse actions, currently classified according to molecular background. Despite the increasingly recognized implication of TH in lung cancer, three pending queries need to be resolved to empower a tailored approach: (1) How to stratify patients with TH-sensitive lung tumors? (2) How is determined whether TH promote or inhibit lung cancer progression? (3) How to mimic the antitumor and/or abrogate the tumor-promoting TH actions in lung cancer? To address these queries, research should prioritize the elucidation of the crosstalk between TH signaling and oncogenic signaling implicated in lung cancer initiation and progression, and the development of efficient, safe, and feasible strategies leveraging this crosstalk in therapeutics.