A multiparametric approach to improve the prediction of response to immunotherapy in patients with metastatic NSCLC

Liquid biopsy for human cancer: cancer screening, monitoring, and treatment

Currently, tumor treatment modalities such as immunotherapy and targeted therapy have more stringent requirements for obtaining tumor growth information and require more accurate and easy-to-operate tumor information detection methods. Compared with traditional tissue biopsy, liquid biopsy is a novel, minimally invasive, real-time detection tool for detecting information directly or indirectly released by tumors in human body fluids, which is more suitable for the requirements of new tumor treatment modalities. Liquid biopsy has not been widely used in clinical practice, and there are fewer reviews of related clinical applications. This review summarizes the clinical applications of liquid biopsy components (e.g., circulating tumor cells, circulating tumor DNA, extracellular vesicles, etc.) in tumorigenesis and progression. This includes the development process and detection techniques of liquid biopsies, early screening of tumors, tumor growth detection, and guiding therapeutic strategies (liquid biopsy-based personalized medicine and prediction of treatment response). Finally, the current challenges and future directions for clinical applications of liquid biopsy are proposed. In sum, this review will inspire more researchers to use liquid biopsy technology to promote the realization of individualized therapy, improve the efficacy of tumor therapy, and provide better therapeutic options for tumor patients.

Evolving insights into the improvement of adoptive T-cell immunotherapy through PD-1/PD-L1 blockade in the clinical spectrum of lung cancer

Undeniably, cancer immunotherapies have expanded the spectrum of cancer treatment, however, some patients do not respond to immunotherapies. This scenario is no different for lung cancer, whose two main types, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), still pose a serious clinical challenge. Adoptive T-cell therapies (ATC), which primarily include cytokine-induced killer (CIK) cell therapy, chimeric antigen receptor T-cell (CAR T-cell) therapy and γδ-T-cell therapy, strengthen the patient's immune system in combating cancer. Combining ATC with immune checkpoint inhibitors (ICI) further enhances the effectiveness of this approach to eradicate cancer. With a particular emphasis on CIK cell therapy, which recently completed 30 years, we highlight the role of the PD-1/PD-L1 axis in NSCLC and SCLC. Besides, we provide insights into the potential synergies of PD-1/PD-L1 inhibitors with adoptive T-cell immunotherapy in reshaping the treatment paradigm for lung cancer.

Advancements and trends in exosome research in lung cancer from a bibliometric analysis (2004-2023)

Background

Lung cancer, characterized by its high morbidity and lethality, necessitates thorough research to enhance our understanding of its pathogenesis and discover novel therapeutic approaches. Recent studies increasingly demonstrate that lung cancer cells can modulate the tumor microenvironment, promoting tumor growth, and metastasis through the release of exosomes. Exosomes are small vesicles secreted by cells and contain a variety of bioactive molecules such as proteins, nucleic acids, and metabolites. This paper presents a comprehensive review of exosome research in lung cancer and its progress through bibliometric analysis.

Methods

Publications related to exosomes in lung cancer patients were systematically searched on the Web of Science Core Collection (WoSCC) database. Bibliometric analysis was performed using VOSviwers, CiteSpace, and the R package "Bibliometrics". Publications were quantitatively analyzed using Microsoft Office Excel 2019. The language of publication was restricted to "English" and the search strategy employed TS=(exosomes or exosomes or exosomes) and TS=(lung cancer). The search period commenced on January 1, 2004, and concluded on November 12, 2023, at noon. The selected literature types included Articles and Reviews.

Results

The study encompassed 1699 papers from 521 journals across 71 countries and 2105 institutions. Analysis revealed a consistent upward trend in lung cancer exosome research over the years, with a notable surge in recent times. This surge indicates a growing interest and depth of inquiry into lung cancer exosomes. Major research institutions in China and the United States, including Nanjing Medical University, Shanghai Jiao Tong University, Chinese Academy Of Sciences, and Utmd Anderson Cancer Center, emerged as crucial research hubs. The annual publication count in this field witnessed a continuous rise, particularly in recent years. Key terms such as lung cancer, non-small cell lung cancer (NSCLC), microvesicles, intercellular communication, exosomal miRNAs, and oncology dominated the research landscape. Fields like cell biology, biochemistry, biotechnology, and oncology exhibited close relation with this research. Clotilde Théry emerged as the most cited author in the field, underlining her significant contributions. These results demonstrate the broad impact of exosome research in lung cancer, with key terms covering not only disease-specific aspects such as lung cancer and NSCLC but also basic biological concepts like microvesicles and intercellular communication. Explorations into exosomal microRNAs and oncology have opened new avenues for lung cancer exosome research. In summary, lung cancer exosome research is poised to continue receiving attention, potentially leading to breakthroughs in treatment and prevention.

Conclusion

Publications on lung cancer exosomes show a rising trend year by year, with China and the United States ranking first and second in terms of the number of publications. However, there is insufficient academic learning cooperation and exchanges between the two sides, and Chinese universities account for a large proportion of research institutions in this field. Jing Li is the most productive author, Clotilde Théry is the most co-cited author, and Cancers is the journal with the highest number of publications. The current focus in the field of lung cancer exosomes is on biomarkers, liquid biopsies, immunotherapy, and tumor microenvironment.

Pharmacogenomics of Drugs Used in β-Thalassemia and Sickle-Cell Disease: From Basic Research to Clinical Applications

In this short review we have presented and discussed studies on pharmacogenomics (also termed pharmacogenetics) of the drugs employed in the treatment of β-thalassemia or Sickle-cell disease (SCD). This field of investigation is relevant, since it is expected to help clinicians select the appropriate drug and the correct dosage for each patient. We first discussed the search for DNA polymorphisms associated with a high expression of γ-globin genes and identified this using GWAS studies and CRISPR-based gene editing approaches. We then presented validated DNA polymorphisms associated with a high HbF production (including, but not limited to the HBG2 XmnI polymorphism and those related to the BCL11A, MYB, KLF-1, and LYAR genes). The expression of microRNAs involved in the regulation of γ-globin genes was also presented in the context of pharmacomiRNomics. Then, the pharmacogenomics of validated fetal hemoglobin inducers (hydroxyurea, butyrate and butyrate analogues, thalidomide, and sirolimus), of iron chelators, and of analgesics in the pain management of SCD patients were considered. Finally, we discuss current clinical trials, as well as international research networks focusing on clinical issues related to pharmacogenomics in hematological diseases.

Could the underlying biological basis of prognostic radiomics and deep learning signatures be explored in patients with lung cancer? A systematic review

OBJECTIVES

To summarize the underlying biological correlation of prognostic radiomics and deep learning signatures in patients with lung cancer and evaluate the quality of available studies.

METHODS

This study examined databases including the PubMed, Embase, Web of Science Core Collection, and Cochrane Library, for studies that elaborated on the underlying biological correlation with prognostic radiomics and deep learning signatures based on CT or PET/CT for predicting the prognosis in patients with lung cancer. Information about the patient and radiogenomic analyses was extracted for the included studies. The Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool were used to assess the quality of these studies.

RESULTS

Twelve studies were included with 7,338 patients from 2014 to 2022. All studies except for one were retrospective. Supervised machine learning was adopted in six studies, and the remaining used unsupervised machine learning methods. Gene sequencing and histopathological data were analyzed by 83.33% and 16.67% of the included studies, respectively. Gene set enrichment analysis and correlation analysis were most used to explore the biological meaning of prognostic signatures. The median RQS for supervised learning articles was 13.5 (range 12-19) and 7.0 (range 5-14) for unsupervised learning articles. The studies included in this report were assessed to have high risk of bias overall.

CONCLUSION

The biological basis for the interpretability of data-driven models mainly focused on genomics and histopathological factors, and it may improve the prognosis of lung cancer with more proper biological interpretation in the future.

Review - The impact of pharmacogenetics on the outcome of immune checkpoint inhibitors

The development of immune checkpoint inhibitors (ICIs) has a tremendous effect on the treatment options for multiple types of cancer. Nonetheless, there is a large interpatient variability in response, survival, and the development of immune-related adverse events (irAEs). Pharmacogenetics is the general term for germline genetic variations, which may cause the observed interindividual differences in response or toxicity to treatment. These genetic variations can either be single-nucleotide polymorphisms (SNPs) or structural variants, such as gene deletions, amplifications or rearrangements. For ICIs, pharmacogenetic variation in the human leukocyte antigen molecules has also been studied with regard to treatment outcome. This review presents a summary of the literature regarding the pharmacogenetics of ICI treatment, discusses the most important known genetic variations and offers recommendations on the application of pharmacogenetics for ICI treatment.

Gene-network analysis predicts clinical response to immunotherapy in patients affected by NSCLC

OBJECTIVES

Predictive biomarkers of response to immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC) with controversial results. Recently, gene-network analysis emerged as a new tool to address tumor biology and behavior, representing a potential tool to evaluate response to therapies.

METHODS

Clinical data and genetic profiles of 644 advanced NSCLCs were retrieved from cBioPortal and the Cancer Genome Atlas (TCGA); 243 ICI-treated NSCLCs were used to identify an immunotherapy response signatures via mutated gene network analysis and K-means unsupervised clustering. Signatures predictive values were tested in an external dataset of 242 cases and assessed versus a control group of 159 NSCLCs treated with standard chemotherapy.

RESULTS

At least two mutations in the coding sequence of genes belonging to the chromatin remodelling pathway (A signature), and/or at least two mutations of genes involved in cell-to-cell signalling pathways (B signature), showed positive prediction in ICI-treated advanced NSCLC. Signatures performed best when combined for patients undergoing first-line immunotherapy, and for those receiving combined ICIs.

CONCLUSIONS

Alterations in genes related to chromatin remodelling complexes and cell-to-cell crosstalk may force dysfunctional immune evasion, explaining susceptibility to immunotherapy. Therefore, exploring mutated gene networks could be valuable for determining essential biological interactions, contributing to treatment personalization.

Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment

With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.

Common inherited variants of PDCD1, CD274 and HAVCR2 genes differentially modulate the risk and prognosis of adenocarcinoma and squamous cell carcinoma

BACKGROUND

To investigate the association between single nucleotide polymorphisms (SNPs) of PDCD1, CD274, and HAVCR2 genes with the risk and outcomes of non-small cell lung cancer (NSCLC) subtypes: squamous cell lung cancer (LUSC) and lung adenocarcinoma (LUAD).

METHODS

TaqMan SNP genotyping assays or polymerase chain reaction-restriction fragment length polymorphism methods were used to determine genotypes of: PDCD1: rs36084323, rs7421861, rs11568821, rs2227981, rs10204525; CD274: rs822335, rs10815225, rs17718883, rs2297136, rs4742098, rs4143815; HAVCR2: rs10057302, rs1036199. Among 383 NSCLC patients, 112 were diagnosed with LUAD and 116 with LUSC. The control group consisted of 433 unrelated, cancer-free subjects.

RESULTS

A CC genotype of rs4143815 and GG genotype of rs4742098 were associated with two times higher risk of developing LUSC (CC vs. GG + GC, OR = 2.31; 95% CI = 1.32, 4.06; P = 0.003; GG vs. AA + AG, OR = 2.26; 95% CI = 1.17, 4.36; P = 0.016, respectively). Moreover, rs4143815 was an independent predictor of the age at diagnosis of LUAD. The carriers of C allele were diagnosed 4.81 years later (95% CI = 1.47, 8.15; P = 0.006) than patients with the GG genotype. The rs10057302 CA genotype was an independent predictor of overall survival in LUSC (adjusted HR = 0.13; 95% CI = 0.02, 0.93; P = 0.043). NSCLC carriers of rs11568821 T allele had almost double the risk of death (adjusted HR = 2.05; 95% CI = 1.28, 3.29; P = 0.003) compared to carriers of CC genotype.

CONCLUSIONS

Our results provided additional evidence that SNPs of genes for PD-1, PD-L1 and TIM-3 differentially modulate the risk and prognosis of LUSC and LUAD.

Prognostic Role of Biologically Active Volume of Disease in Patients With Metastatic Lung Adenocarcinoma

BACKGROUND

Number of metastatic sites can identify patient populations with non-small cell lung cancer (NSCLC) that benefit from aggressive therapy. Total volume of disease is also relevant. We evaluated the prognostic impact of biologically active volume of disease (BaVD) on patients with metastatic lung adenocarcinoma.

MATERIALS AND METHODS

Positron emission tomography/computerized tomography (PET/CT) scans from patients with newly diagnosed lung adenocarcinoma prior to starting any therapy were identified. SUV thresholds of 3 and 4 were used to auto-contour all FDG avid areas. Kaplan-Meier analysis and Cox regression were performed to examine influence on OS.

RESULTS

One hundred forty-eight patients were included in the analysis. The median BaVD when using an SUV threshold of 3 was 122.8 mL. The median BaVD when using an SUV threshold of 4 was 46.2 mL When stratified by median BaVD using an SUV of 3, median OS was higher for patients with <=122.8 mL (2.12 years) compared to patients with >122.8 mL (1.46 years) (log-rank P = .001). Similarly, when stratified by median BaVD using an SUV of 4, median OS was higher for patients with <=46.2 mL (1.91 years; 95% CI: 1.65-3.22 years) compared to patients with >46.2 mL (1.48 years; 95% CI: 1.07-1.80 years) (log-rank P = .007). On multivariable analysis, BaVD was significantly associated with OS when using an SUV threshold of 3 (HR: 20.169, P < .001) and 4 (HR: 4.117, P < .001).

CONCLUSION

BaVD is an important prognostic factor in metastatic lung adenocarcinoma and may aid identification of patients with limited disease who may be candidates for more aggressive therapies.

The Detection of Exosomal PD-L1 in Peripheral Blood

Peripheral blood is a source for liquid biopsy, which can meet the requirements of pretreatment disease typing to determine precise targeted therapy and monitoring of posttreatment minimal residual disease monitoring. Compared with ctDNA and CTC, exosomes have a higher concentration, good biostability, biocompatibility, low immunogenicity, and low toxicity in peripheral blood. Tumors generally secrete a large amounts of exosomes, which have potential pathophysiological roles in tumor progression. With the continuous improvement of liquid biopsy technology, many researchers have found that exosomes are the key for tumor PD-L1 to exert its role, which may be the mechanism that leads to PD-L1 and/or PD-1 inhibitor therapy resistance. Namely, tumor-derived exosomes may mediate systemic immunosuppression against PD-1 or PD-L1 inhibitor therapy, endogenous tumor cell-derived exosomal PD-L1, and tumor microenvironment-derived exosomes. Induction of PD-L1 by exosomes may be a crucial mechanisms of exosome-mediated antitumor immune tolerance. This article reviews the relationship between the detection of peripheral blood exosomal PD-L1 and tumor progression and the mechanism of exosomal PD-L1 in tumor immunotherapy.

Short-term dynamics of circulating tumor DNA predicting efficacy of sintilimab plus docetaxel in second-line treatment of advanced NSCLC: biomarker analysis from a single-arm, phase 2 trial

OBJECTIVE

Robust biomarker predicting efficacy of immunotherapy is limited. Circulating tumor DNA (ctDNA) sought to effectively monitor therapeutic response as well as disease progression. This study aims to investigate predictive role of ctDNA short-term dynamic change (6 weeks postimmunotherapy) in a single-arm, phase 2 trial of sintilimab plus docetaxel for previously treated advanced non-small cell lung cancer (NSCLC) patients.

METHODS

A total of 33 patients with advanced NSCLC with disease progression during or after any first-line treatment were prospectively enrolled between 2019 and 2020. Patients received sintilimab (200 mg, day 1, every 3 weeks) plus docetaxel (75 mg/m², day 3, every 3 weeks) for 4-6 cycles, followed by maintenance therapy with sintilimab (200 mg, day 1, every 3 weeks) until disease progression or unacceptable toxic effects. Blood samples were prospectively collected at baseline, and after 2 cycles of treatment (6 weeks post-treatment). All samples were subjected to targeted next-generation sequencing with a panel of 448 cancer-related genes. The landscape of high-frequency genomic profile of baseline and 6th week was described. Major molecular characteristics in preselected genes of interest associated with response to second-line chemoimmunotherapy were analyzed. The curative effects and prognosis of patients were evaluated.

RESULTS

Patients with ctDNA clearance at 6th week had decreased tumor volume, while most patients with positive ctDNA at 6th-week experienced an increase in tumor volume. Positive 6th-week ctDNA was associated with significantly shorter progression-free survival (PFS) (91 vs NR days; p<0.0001) and overall survival (47 vs 467 days; p =0.0039). Clearance of clonal mutations and none new clonal formation at 6th week were associated with longer PFS (mPFS 89 vs 266 days, p =0.003). ctDNA clearance at 6th week was an independent risk factor for progression or death (HR=100 (95% CI 4.10 to 2503.00), p=0.005).

CONCLUSION

ctDNA status and ctDNA mutation clearance putatively serve as predictive biomarkers for sintilimab combined with docetaxel chemotherapy in pretreated advanced NSCLC patients.

Molecular Docking and Intracellular Translocation of Extracellular Vesicles for Efficient Drug Delivery

Extracellular vesicles (EVs), including exosomes, mediate intercellular communication by delivering their contents, such as nucleic acids, proteins, and lipids, to distant target cells. EVs play a role in the progression of several diseases. In particular, programmed death-ligand 1 (PD-L1) levels in exosomes are associated with cancer progression. Furthermore, exosomes are being used for new drug-delivery systems by modifying their membrane peptides to promote their intracellular transduction via micropinocytosis. In this review, we aim to show that an efficient drug-delivery system and a useful therapeutic strategy can be established by controlling the molecular docking and intracellular translocation of exosomes. We summarise the mechanisms of molecular docking of exosomes, the biological effects of exosomes transmitted into target cells, and the current state of exosomes as drug delivery systems.

Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review

BACKGROUND

Checkpoint inhibition has radically improved the perspective for patients with metastatic cancer, but predicting who will not respond with high certainty remains difficult. Imaging-derived biomarkers may be able to provide additional insights into the heterogeneity in tumour response between patients. In this systematic review, we aimed to summarise and qualitatively assess the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types.

METHODS

PubMed and Embase were searched from database inception to 29th November 2021. Articles eligible for inclusion described baseline imaging predictive factors, radiomics and/or imaging machine learning models for predicting response and survival in patients with any kind of malignancy treated with checkpoint inhibitors. Risk of bias was assessed using the QUIPS and PROBAST tools and data was extracted.

RESULTS

In total, 119 studies including 15,580 patients were selected. Of these studies, 73 investigated simple imaging factors. 45 studies investigated radiomic features or deep learning models. Predictors of worse survival were (i) higher tumour burden, (ii) presence of liver metastases, (iii) less subcutaneous adipose tissue, (iv) less dense muscle and (v) presence of symptomatic brain metastases. Hazard rate ratios did not exceed 2.00 for any predictor in the larger and higher quality studies. The added value of baseline fluorodeoxyglucose positron emission tomography parameters in predicting response to treatment was limited. Pilot studies of radioactive drug tracer imaging showed promising results. Reports on radiomics were almost unanimously positive, but numerous methodological concerns exist.

CONCLUSIONS

There is well-supported evidence for several imaging biomarkers that can be used in clinical decision making. Further research, however, is needed into biomarkers that can more accurately identify which patients who will not benefit from checkpoint inhibition. Radiomics and radioactive drug labelling appear to be promising approaches for this purpose.

Feasibility of using CT radiomic signatures for predicting CD8-T cell infiltration and PD-L1 expression in renal cell carcinoma

Objectives

To identify computed tomography (CT)-based radiomic signatures of cluster of differentiation 8 (CD8)-T cell infiltration and programmed cell death ligand 1 (PD-L1) expression levels in patients with clear-cell renal cell carcinoma (ccRCC).

Methods

Seventy-eight patients with pathologically confirmed localized ccRCC, preoperative multiphase CT and tumor resection specimens were enrolled in this retrospective study. Regions of interest (ROI) of the ccRCC volume were manually segmented from the CT images and processed using a radiomics panel comprising of 1708 metrics. The extracted metrics were used as inputs to three machine learning classifiers: Random Forest, AdaBoost, and ElasticNet to create radiomic signatures for CD8-T cell infiltration and PD-L1 expression, respectively.

Results

Using a cut-off of 80 lymphocytes per high power field, 59 % were classified to CD8 highly infiltrated tumors and 41 % were CD8 non highly infiltrated tumors, respectively. An ElasticNet classifier discriminated between these two groups of CD8-T cells with an AUC of 0.68 (95 % CI, 0.55-0.80). In addition, based on tumor proportion score with a cut-off of > 1 % tumor cells expressing PD-L1, 76 % were PD-L1 positive and 24 % were PD-L1 negative. An Adaboost classifier discriminated between PD-L1 positive and PD-L1 negative tumors with an AUC of 0.8 95 % CI: (0.66, 0.95). 3D radiomics metrics of graylevel co-occurrence matrix (GLCM) and graylevel run-length matrix (GLRLM) metrics drove the performance for CD8-Tcell and PD-L1 classification, respectively.

Conclusions

CT-radiomic signatures can differentiate tumors with high CD8-T cell infiltration with moderate accuracy and positive PD-L1 expression with good accuracy in ccRCC.

Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?

Strong rationale and a growing number of preclinical and clinical studies support combining radiotherapy and immunotherapy to improve patient outcomes. However, several critical questions remain, such as the identification of patients who will benefit from immunotherapy and the identification of the best modalities of treatment to optimize patient response. Imaging biomarkers and radiomics have recently emerged as promising tools for the non-invasive assessment of the whole disease of the patient, allowing comprehensive analysis of the tumor microenvironment, the spatial heterogeneity of the disease and its temporal changes. This review presents the potential applications of medical imaging and the challenges to address, in order to help clinicians choose the optimal modalities of both radiotherapy and immunotherapy, to predict patient’s outcomes and to assess response to these promising combinations.

Extracellular vesicle PD-L1 dynamics predict durable response to immune-checkpoint inhibitors and survival in patients with non-small cell lung cancer

BACKGROUND

Immune-checkpoint inhibitors (ICIs) changed the therapeutic landscape of patients with lung cancer. However, only a subset of them derived clinical benefit and evidenced the need to identify reliable predictive biomarkers. Liquid biopsy is the non-invasive and repeatable analysis of biological material in body fluids and a promising tool for cancer biomarkers discovery. In particular, there is growing evidence that extracellular vesicles (EVs) play an important role in tumor progression and in tumor-immune interactions. Thus, we evaluated whether extracellular vesicle PD-L1 expression could be used as a biomarker for prediction of durable treatment response and survival in patients with non-small cell lung cancer (NSCLC) undergoing treatment with ICIs.

METHODS

Dynamic changes in EV PD-L1 were analyzed in plasma samples collected before and at 9 ± 1 weeks during treatment in a retrospective and a prospective independent cohorts of 33 and 39 patients, respectively.

RESULTS

As a result, an increase in EV PD-L1 was observed in non-responders in comparison to responders and was an independent biomarker for shorter progression-free survival and overall survival. To the contrary, tissue PD-L1 expression, the commonly used biomarker, was not predictive neither for durable response nor survival.

CONCLUSION

These findings indicate that EV PD-L1 dynamics could be used to stratify patients with advanced NSCLC who would experience durable benefit from ICIs.

A Cancer Associated Fibroblasts-Related Six-Gene Panel for Anti-PD-1 Therapy in Melanoma Driven by Weighted Correlation Network Analysis and Supervised Machine Learning

Background

Melanoma is a highly aggressive skin cancer with a poor prognosis and mortality. Immune checkpoint blockade (ICB) therapy (e.g., anti-PD-1 therapy) has opened a new horizon in melanoma treatment, but some patients present a non-responsive state. Cancer-associated fibroblasts (CAFs) make up the majority of stromal cells in the tumor microenvironment (TME) and have an important impact on the response to immunotherapy. There is still a lack of identification of CAFs-related predictors for anti-PD-1 therapy, although the establishment of immunotherapy biomarkers is well underway. This study aims to explore the potential CAFs-related gene panel for predicting the response to anti-PD-1 therapy in melanoma patients and elucidating their potential effect on TME.

Methods

Three gene expression datasets from melanoma patients without anti-PD-1 treatment, in a total of 87 samples, were downloaded from Gene Expression Omnibus (GEO) as the discovery sets (GSE91061) and validation sets (GSE78220 and GSE122220). The CAFs-related module genes were identified from the discovery sets by weighted gene co-expression network analysis (WGCNA). Concurrently, we utilized differential gene analysis on the discovery set to obtain differentially expressed genes (DEGs). Then, CAFs-related key genes were screened with the intersection of CAFs-related module genes and DEGs, succeeded by supervised machine learning-based identification. As a consequence of expression analysis, gene set enrichment analysis, survival analysis, staging analysis, TME analysis, and correlation analysis, the multidimensional systematic characterizations of the key genes were uncovered. The diagnostic performance of the CAFs-related gene panel was assessed by receiver operating characteristic (ROC) curves in the validation sets. Eventually, the CAFs-related gene panel was verified by the expression from the single-cell analysis.

Results

The six-gene panel associated with CAFs were finally identified for predicting the response to anti-PD-1 therapy, including CDK14, SYNPO2, TCF4, GJA1, CPXM1, and TFPI. The multigene panel demonstrated excellent combined diagnostic performance with the area under the curve of ROC reaching 90.5 and 75.4% ~100% in the discovery and validation sets, respectively.

Conclusion

Confirmed by clinical treatment outcomes, the identified CAFs-related genes can be used as a promising biomarker panel for prediction to anti-PD-1 therapy response, which may serve as new immunotherapeutic targets to improve survival outcomes of melanoma patients.

Harnessing Liquid Biopsies to Guide Immune Checkpoint Inhibitor Therapy

Immunotherapy (IO), involving the use of immune checkpoint inhibition, achieves improved response-rates and significant disease-free survival for some cancer patients. Despite these beneficial effects, there is poor predictability of response and substantial rates of innate or acquired resistance, resulting in heterogeneous responses among patients. In addition, patients can develop life-threatening adverse events, and while these generally occur in patients that also show a tumor response, these outcomes are not always congruent. Therefore, predicting a response to IO is of paramount importance. Traditionally, tumor tissue analysis has been used for this purpose. However, minimally invasive liquid biopsies that monitor changes in blood or other bodily fluid markers are emerging as a promising cost-effective alternative. Traditional biomarkers have limitations mainly due to difficulty in repeatedly obtaining tumor tissue confounded also by the spatial and temporal heterogeneity of tumours. Liquid biopsy has the potential to circumvent tumor heterogeneity and to help identifying patients who may respond to IO, to monitor the treatment dynamically, as well as to unravel the mechanisms of relapse. We present here a review of the current status of molecular markers for the prediction and monitoring of IO response, focusing on the detection of these markers in liquid biopsies. With the emerging improvements in the field of liquid biopsy, this approach has the capacity to identify IO-eligible patients and provide clinically relevant information to assist with their ongoing disease management.

Surgical approach does not influence changes in circulating immune cell populations following lung cancer resection

INTRODUCTION

The multimodal management of operable non-small cell lung cancer (NSCLC) continues to evolve rapidly. The immune milieu allowing for immunotherapeutic benefit can be affected by multiple parameters including clinicopathologic and genetic. Surgery induced physiological changes has received attention for modulating and affecting post-operative oncotaxis and immunosuppression. Here, we sought to investigate how surgical stress influences phenotype of peripheral blood mononuclear cells (PBMCs) in patients with NSCLC who underwent lobectomy.

METHODS

Blood was prospectively collected from patients with Stage IA-IIIA NSCLC undergoing lung resection between 2016 and 2018. Samples were obtained pre-operatively, 24 h and 4 weeks after surgery. PBMCs were isolated and subject to high-dimensional flow cytometry, analyzing a total of 115 cell populations with a focus on myeloid cells, T cell activation, and T cell trafficking. We further evaluated how surgical approach influenced post-operative PBMC changes, whether the operation was conducted in an open fashion with thoracotomy, or with minimally invasive Video Assisted Thoracoscopic Surgery (VATS).

RESULTS

A total of 76 patients met the inclusion criteria (Open n = 55, VATS n = 21). Surgical resection coincided with a decrease in T lymphocyte populations, including total CD3+ T cells, CD8+ T cells, and T effector memory cells, as well as an increase in monocytic myeloid-derived suppressor cells (mMDSC). Post-operative changes in PBMC populations were resolved after 4 weeks. Surgical-induced changes in immune populations were equivalent in patients undergoing open thoracotomy and VATS.

DISCUSSION

Surgical stress resulted in transient reduction in T cells and T effector memory cells, and increase of mMDSC following resection in NSCLC patients. The immune profile modulation was similar regardless of surgical approach. These findings suggest that surgical approach does not seem to affect mononuclear cell lines obtained from peripheral blood. Thus, the decision regarding surgical approach should be patient centered, rather than based on post-operative treatment response optimization.

Current and Emerging Applications of Droplet Digital PCR in Oncology: An Updated Review

In the era of personalized medicine and targeted therapies for the management of patients with cancer, ultrasensitive detection methods for tumor genotyping, such as next-generation sequencing or droplet digital polymerase chain reaction (ddPCR), play a significant role. In the search for less invasive strategies for diagnosis, prognosis and disease monitoring, the number of publications regarding liquid biopsy approaches using ddPCR has increased substantially in recent years. There is a long list of malignancies in which ddPCR provides a reliable and accurate tool for detection of nucleic acid-based markers derived from cell-free DNA, cell-free RNA, circulating tumor cells, extracellular vesicles or exosomes when isolated from whole blood, plasma and serum, helping to anticipate tumor relapse or unveil intratumor heterogeneity and clonal evolution in response to treatment. This updated review describes recent developments in ddPCR platforms and provides a general overview about the major applications of liquid biopsy in blood, including its utility for molecular response and minimal residual disease monitoring in hematological malignancies or the therapeutic management of patients with colorectal or lung cancer, particularly for the selection and monitoring of treatment with tyrosine kinase inhibitors. Although plasma is the main source of genetic material for tumor genomic profiling, liquid biopsy by ddPCR is being investigated in a wide variety of biologic fluids, such as cerebrospinal fluid, urine, stool, ocular fluids, sputum, saliva, bronchoalveolar lavage, pleural effusion, mucin, peritoneal fluid, fine needle aspirate, bile or pancreatic juice. The present review focuses on these "alternative" sources of genetic material and their analysis by ddPCR in different kinds of cancers.

A deep look into radiomics

Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.

Current Landscape and Future Directions of Biomarkers for Immunotherapy in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common liver cancer and one of the leading causes of cancer-related deaths in the world. Multiple immunotherapeutic approaches have been investigated to date, and immunotherapy has become the new standard of care therapy in HCC. However, the current role of immunotherapy in HCC remains non-curative. Given this context, a high priority for oncology is understanding the biomarkers that predict clinical response to immunotherapy, have the potential to improve patient selection to maximize the clinical benefit, and spare unnecessary toxicity. In this review, we summarize the key predictive and prognostic biomarkers investigated in immunotherapy clinical trials in HCC and the emerging biomarkers to serve as a roadmap for future clinical trials. Biomarkers from tumoral tissues including PDL-1 expression, tissue infiltrating lymphocytes, tumor mutational burden (TMB) and specific immune signatures, and from peripheral blood including neutrophil-to-lymphocytes ratio, platelet-to-lymphocytes ratio, circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and specific cytokines, along with gut microbiota are among the studied biomarkers to date in the HCC era. More integrative approaches, including mathematical biomarkers to predict immunotherapy outcomes, are yet to be studied in HCC.

Exosomes in Lung Cancer: Actors and Heralds of Tumor Development

Lung cancer is a leading cause of cancer-related death worldwide and in most cases, diagnosis is reached when the tumor has already spread and prognosis is quite poor. For that reason, the research for new biomarkers that could improve early diagnosis and its management is essential. Exosomes are microvesicles actively secreted by cells, especially by tumor cells, hauling molecules that mimic molecules of the producing cells. There are multiple methods for exosome isolation and analysis, although not standardized, and cancer exosomes from biological fluids are especially difficult to study. Exosomes' cargo proteins, RNA, and DNA participate in the communication between cells, favoring lung cancer development by delivering signals for growth, metastasis, epithelial mesenchymal transition, angiogenesis, immunosuppression and even drug resistance. Exosome analysis can be useful as a type of liquid biopsy in the diagnosis, prognosis and follow-up of lung cancer. In this review, we will discuss recent advances in the role of exosomes in lung cancer and their utility as liquid biopsy, with special attention to isolating methods.