Impact of the Lung Microbiota on Development and Progression of Lung Cancer.

The past several years have provided a more profound understanding of the role of microbial species in the lung. The respiratory tract is a delicate ecosystem of bacteria, fungi, parasites, and viruses. Detecting microbial DNA, pathogen-associated molecular patterns (PAMPs), and metabolites in sputum is poised to revolutionize the early diagnosis of lung cancer. The longitudinal monitoring of the lung microbiome holds the potential to predict treatment response and side effects, enabling more personalized and effective treatment options. However, most studies into the lung microbiota have been observational and have not adequately considered the impact of dietary intake and air pollutants. This gap makes it challenging to establish a direct causal relationship between environmental exposure, changes in the composition of the microbiota, lung carcinogenesis, and tumor progression. A holistic understanding of the lung microbiota that considers both diet and air pollutants may pave the way to improved prevention and management strategies for lung cancer.

Companion Tests and Personalized Cancer Therapy: Reaching a Glass Ceiling.

The use of companion diagnostics has become a standard in precision oncology in the context of ongoing therapeutic innovation. However, certain limitations make their application imperfect in current practice. This position paper underscores the need to broaden the notion of companion testing, considering the potential of emerging technologies, including computational biology, to overcome these limitations. This wave of progress should impact not only our representation of the analytical tool itself but also the nature of the tumoral sample under analysis (liquid biopsies). The complex inter-relationship between companion test guided-personalized therapy, and health agency policies for new drug agreements will also be discussed.

Critical Appraisal and Future Challenges of Artificial Intelligence and Anticancer Drug Development.

The conventional rules for anti-cancer drug development are no longer sufficient given the relatively limited number of patients available for therapeutic trials. It is thus a real challenge to better design trials in the context of new drug approval for anti-cancer treatment. Artificial intelligence (AI)-based in silico trials can incorporate far fewer but more informative patients and could be conducted faster and at a lower cost. AI can be integrated into in silico clinical trials to improve data analysis, modeling and simulation, personalized medicine approaches, trial design optimization, and virtual patient generation. Health authorities are encouraged to thoroughly review the rules for setting up clinical trials, incorporating AI and in silico methodology once they have been appropriately validated. This article also aims to highlight the limits and challenges related to AI and machine learning.

Further knowledge and developments in resistance mechanisms to immune checkpoint inhibitors.

The past decade has witnessed a revolution in cancer treatment, shifting from conventional drugs (chemotherapies) towards targeted molecular therapies and immune-based therapies, in particular immune-checkpoint inhibitors (ICIs). These immunotherapies release the host's immune system against the tumor and have shown unprecedented durable remission for patients with cancers that were thought incurable, such as metastatic melanoma, metastatic renal cell carcinoma (RCC), microsatellite instability (MSI) high colorectal cancer and late stages of non-small cell lung cancer (NSCLC). However, about 80% of the patients fail to respond to these immunotherapies and are therefore left with other less effective and potentially toxic treatments. Identifying and understanding the mechanisms that enable cancerous cells to adapt to and eventually overcome therapy can help circumvent resistance and improve treatment. In this review, we describe the recent discoveries on the onco-immunological processes which govern the tumor microenvironment and their impact on the resistance to PD-1/PD-L1 checkpoint blockade.

Artificial Intelligence and Anticancer Drug Development-Keep a Cool Head.

Artificial intelligence (AI) is progressively spreading through the world of health, particularly in the field of oncology. AI offers new, exciting perspectives in drug development as toxicity and efficacy can be predicted from computer-designed active molecular structures. AI-based in silico clinical trials are still at their inception in oncology but their wider use is eagerly awaited as they should markedly reduce durations and costs. Health authorities cannot neglect this new paradigm in drug development and should take the requisite measures to include AI as a new pillar in conducting clinical research in oncology.

Current challenges and practical aspects of molecular pathology for non-small cell lung cancers.

The continuing evolution of treatment options in thoracic oncology requires the pathologist to regularly update diagnostic algorithms for management of tumor samples. It is essential to decide on the best way to use tissue biopsies, cytological samples, as well as liquid biopsies to identify the different mandatory predictive biomarkers of lung cancers in a short turnaround time. However, biological resources and laboratory member workforce are limited and may be not sufficient for the increased complexity of molecular pathological analyses and for complementary translational research development. In this context, the surgical pathologist is the only one who makes the decisions whether or not to send specimens to immunohistochemical and molecular pathology platforms. Moreover, the pathologist can rapidly contact the oncologist to obtain a new tissue biopsy and/or a liquid biopsy if he/she considers that the biological material is not sufficient in quantity or quality for assessment of predictive biomarkers. Inadequate control of algorithms and sampling workflow may lead to false negative, inconclusive, and incomplete findings, resulting in inappropriate choice of therapeutic strategy and potentially poor outcome for patients. International guidelines for lung cancer treatment are based on the results of the expression of different proteins and on genomic alterations. These guidelines have been established taking into consideration the best practices to be set up in clinical and molecular pathology laboratories. This review addresses the current predictive biomarkers and algorithms for use in thoracic oncology molecular pathology as well as the central role of the pathologist, notably in the molecular tumor board and her/his participation in the treatment decision-making. The perspectives in this setting will be discussed.

Checkpoint inhibitors and anti-angiogenic agents: a winning combination.

The combination of immune checkpoint inhibitors and anti-angiogenic agents is a promising new approach in cancer treatment. Immune checkpoint inhibitors block the signals that help cancer cells evade the immune system, while anti-angiogenic agents target the blood vessels that supply the tumour with nutrients and oxygen, limiting its growth. Importantly, this combination triggers synergistic effects based on molecular and cellular mechanisms, leading to better response rates and longer progression-free survival than treatment alone. However, these combinations can also lead to increased side effects and require close monitoring.

Manipulating the gut and tumor microbiota for immune checkpoint inhibitor therapy: from dream to reality.

The past decade has witnessed a revolution in cancer treatment by shifting from conventional therapies to immune checkpoint inhibitors (ICIs). These immunotherapies unleash the host immune system against the tumor and have achieved unprecedented durable remission. However, 80% of patients do not respond. This review discusses how bacteria are unexpected drivers that reprogram tumor immunity. Manipulating the microbiota impacts on tumor development and reprograms the tumor microenvironment (TME) of mice on immunotherapy. We anticipate that harnessing commensals and the tumor microbiome holds promise to identify patients who will benefit from immunotherapy and guide the choice of new ICI combinations to advance treatment efficacy.

Assessment of Different Circulating Tumor Cell Platforms for Uveal Melanoma: Potential Impact for Future Routine Clinical Practice.

Liquid biopsy and circulating tumor cell (CTC) screening has gained interest over the last two decades for detecting almost all solid malignancies. To date, the major limitation in terms of the applicability of CTC screening in daily clinical practice is the lack of reproducibility due to the high number of platforms available that use various technologies (e.g., label-dependent versus label-free detection). Only a few studies have compared different CTC platforms. The aim of this study was to compare the efficiency of four commercially available CTC platforms (Vortex (VTX-1), ClearCell FX, ISET, and Cellsearch) for the detection and identification of uveal melanoma cells (OMM 2.3 cell line). Tumor cells were seeded in RPMI medium and venous blood from healthy donors, and then processed similarly using these four platforms. Melan-A immunochemistry was performed to identify tumor cells, except when the Cellsearch device was used (automated identification). The mean overall recovery rates (with mean recovered cells) were 39.2% (19.92), 22.2% (11.31), 8.9% (4.85), and 1.1% (0.20) for the ISET, Vortex (VTX-1), ClearCell FX, and CellSearch platforms, respectively. Although paramount, the recovery rate is not sufficient to assess a CTC platform. Other parameters, such as the purpose for using a platform (diagnosis, genetics, drug sensitivity, or patient-derived xenograft models), reproducibility, purity, user-friendliness, cost-effectiveness, and ergonomics, should also be considered before they can be used in daily clinical practice and are discussed in this article.

Should evidence of an autolysosomal de-acidification defect in Alzheimer and Parkinson diseases call for caution in prescribing chronic PPI and DMARD?

Nearly fifty million older people suffer from neurodegenerative diseases, including Alzheimer (AD) and Parkinson (PD) disease, a global burden expected to triple by 2050. Such an imminent "neurological pandemic" urges the identification of environmental risk factors that are hopefully avoided to fight the disease. In 2022, strong evidence in mouse models incriminated defective lysosomal acidification and impairment of the autophagy pathway as modifiable risk factors for dementia. To date, the most prescribed lysosomotropic drugs are proton pump inhibitors (PPIs), chloroquine (CQ), and the related hydroxychloroquine (HCQ), which belong to the group of disease-modifying antirheumatic drugs (DMARDs). This commentary aims to open the discussion on the possible mechanisms connecting the long-term prescribing of these drugs to the elderly and the incidence of neurodegenerative diseases.Abbreviations: AD: Alzheimer disease; APP-ßCTF: amyloid beta precursor protein-C-terminal fragment; BACE1: beta-secretase 1; BBB: brain blood barrier; CHX: Ca2+/H+ exchanger; CMI: cognitive mild impairment; CQ: chloroquine; DMARD: disease-modifying antirheumatic drugs; GBA1: glucosylceramidase beta 1; HCQ: hydroxychloroquine; HPLC: high-performance liquid chromatography; LAMP: lysosomal associated membrane protein; MAPK/JNK: mitogen-activated protein kinase; MAPT: microtubule associated protein tau; MCOLN1/TRPML1: mucolipin TRP cation channel 1; NFE2L2/NRF2: NFE2 like bZIP transcription factor 2; NRBF2: nuclear receptor binding factor 2; PANTHOS: poisonous flower; PD: Parkinson disease; PIK3C3: phosphatIdylinositol 3-kinase catalytic subunit type 3; PPI: proton pump inhibitor; PSEN1: presenilin 1, RUBCN: rubicon autophagy regulator; RUBCNL: rubicon like autophagy enhancer; SQSTM1: sequestosome 1; TMEM175: transmembrane protein 175; TPCN2: two pore segment channel 2; VATPase: vacuolar-type H+-translocating ATPase; VPS13C: vacuolar protein sorting ortholog 13 homolog C; VPS35: VPS35 retromer complex component; WDFY3: WD repeat and FYVE domain containing 3; ZFYVE1: zinc finger FYVE-type containing 1.

KRAS and NRAS Translation Is Increased upon MEK Inhibitors-Induced Processing Bodies Dissolution.

Overactivation of the mitogen-activated protein kinase (MAPK) pathway is a critical driver of many human cancers. However, therapies directly targeting this pathway lead to cancer drug resistance. Resistance has been linked to compensatory RAS overexpression, but the mechanisms underlying this response remain unclear. Here, we find that MEK inhibitors (MEKi) are associated with an increased translation of the KRAS and NRAS oncogenes through a mechanism involving dissolution of processing body (P-body) biocondensates. This effect is seen across different cell types and is extremely dynamic since removal of MEKi and ERK reactivation result in reappearance of P-bodies and reduced RAS-dependent signaling. Moreover, we find that P-body scaffold protein levels negatively impact RAS expression. Overall, we describe a new feedback loop mechanism involving biocondensates such as P-bodies in the translational regulation of RAS proteins and MAPK signaling.

Need for a Dedicated Ophthalmic Malignancy Clinico-Biological Biobank: The Nice Ocular MAlignancy (NOMA) Biobank.

Ophthalmic malignancies include various rare neoplasms involving the conjunctiva, the uvea, or the periocular area. These tumors are characterized by their scarcity as well as their histological, and sometimes genetic, diversity. Uveal melanoma (UM) is the most common primary intraocular malignancy. UM raises three main challenges highlighting the specificity of ophthalmic malignancies. First, UM is a very rare malignancy with an estimated incidence of 6 cases per million inhabitants. Second, tissue biopsy is not routinely recommended due to the risk of extraocular dissemination. Third, UM is an aggressive cancer because it is estimated that about 50% of patients will experience metastatic spread without any curative treatment available at this stage. These challenges better explain the two main objectives in the creation of a dedicated UM biobank. First, collecting UM samples is essential due to tissue scarcity. Second, large-scale translational research programs based on stored human samples will help to better determine UM pathogenesis with the aim of identifying new biomarkers, allowing for early diagnosis and new targeted treatment modalities. Other periocular malignancies, such as conjunctival melanomas or orbital malignancies, also raise specific concerns. In this context, the number of biobanks worldwide dedicated to ocular malignancies is very limited. The aims of this article were (i) to describe the specific challenges raised by a dedicated ocular malignancy biobank, (ii) to report our experience in setting up such a biobank, and (iii) to discuss future perspectives in this field.

Host genetic variability and determinants of severe COVID-19.

Since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak, convergent studies have provided evidence that host genetic background may contribute to the development of severe coronavirus disease (COVID-19). Here, we summarize how some genetic variations, such as in SARS-CoV-2 receptor angiotensin-converting enzyme 2 or interferon signaling pathway, may help to understand why some individuals can develop severe COVID-19.

Checkpoint inhibitors in a marriage: consented or arranged?

There is currently a strong development of therapeutic combinations with checkpoint inhibitors (CPIs). The most promising combinations with CPIs concern anti-angiogenic agents and BRAF/MEK inhibitors. The timing of the initiation of the combination should be particularly well investigated for chemotherapy. Combinations between CPIs raise questions about risk/benefit ratio and overall clinical activity.

Daily Practice Assessment of KRAS Status in NSCLC Patients: A New Challenge for the Thoracic Pathologist Is Right around the Corner.

KRAS mutations are among the most frequent genomic alterations identified in non-squamous non-small cell lung carcinomas (NS-NSCLC), notably in lung adenocarcinomas. In most cases, these mutations are mutually exclusive, with different genomic alterations currently known to be sensitive to therapies targeting EGFR, ALK, BRAF, ROS1, and NTRK. Recently, several promising clinical trials targeting KRAS mutations, particularly for KRAS G12C-mutated NSCLC, have established new hope for better treatment of patients. In parallel, other studies have shown that NSCLC harboring co-mutations in KRAS and STK11 or KEAP1 have demonstrated primary resistance to immune checkpoint inhibitors. Thus, the assessment of the KRAS status in advanced-stage NS-NSCLC has become essential to setting up an optimal therapeutic strategy in these patients. This stimulated the development of new algorithms for the management of NSCLC samples in pathology laboratories and conditioned reorganization of optimal health care of lung cancer patients by the thoracic pathologists. This review addresses the recent data concerning the detection of KRAS mutations in NSCLC and focuses on the new challenges facing pathologists in daily practice for KRAS status assessment.

Autophagopathies: from autophagy gene polymorphisms to precision medicine for human diseases.

At a time when complex diseases affect globally 280 million people and claim 14 million lives every year, there is an urgent need to rapidly increase our knowledge into their underlying etiologies. Though critical in identifying the people at risk, the causal environmental factors (microbiome and/or pollutants) and the affected pathophysiological mechanisms are not well understood. Herein, we consider the variations of autophagy-related (ATG) genes at the heart of mechanisms of increased susceptibility to environmental stress. A comprehensive autophagy genomic resource is presented with 263 single nucleotide polymorphisms (SNPs) for 69 autophagy-related genes associated with 117 autoimmune, inflammatory, infectious, cardiovascular, neurological, respiratory, and endocrine diseases. We thus propose the term 'autophagopathies' to group together a class of complex human diseases the etiology of which lies in a genetic defect of the autophagy machinery, whether directly related or not to an abnormal flux in autophagy, LC3-associated phagocytosis, or any associated trafficking. The future of precision medicine for common diseases will lie in our ability to exploit these ATG SNP x environment relationships to develop new polygenetic risk scores, new management guidelines, and optimal therapies for afflicted patients.Abbreviations: ATG, autophagy-related; ALS-FTD, amyotrophic lateral sclerosis-frontotemporal dementia; ccRCC, clear cell renal cell carcinoma; CD, Crohn disease; COPD, chronic obstructive pulmonary disease; eQTL, expression quantitative trait loci; HCC, hepatocellular carcinoma; HNSCC, head and neck squamous cell carcinoma; GTEx, genotype-tissue expression; GWAS, genome-wide association studies; LAP, LC3-associated phagocytosis; LC3-II, phosphatidylethanolamine conjugated form of LC3; LD, linkage disequilibrium; LUAD, lung adenocarcinoma; MAF, minor allele frequency; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; NSCLC, non-small cell lung cancer; OS, overall survival; PtdIns3K CIII, class III phosphatidylinositol 3 kinase; PtdIns3P, phosphatidylinositol-3-phosphate; SLE, systemic lupus erythematosus; SNPs, single-nucleotide polymorphisms; mQTL, methylation quantitative trait loci; ULK, unc-51 like autophagy activating kinase; UTRs, untranslated regions; WHO, World Health Organization.

COVID-19 vaccination and cancer immunotherapy: should they stick together?

The combination of COVID-19 vaccination with immunotherapy by checkpoint inhibitors in cancer patients could intensify immunological stimulation with potential reciprocal benefits. Here, we examine more closely the possible adverse events that can arise in each treatment modality. Our conclusion is that caution should be exercised when combining both treatments.

Evaluation of Sample Pooling for SARS-CoV-2 Detection in Nasopharyngeal Swab and Saliva Samples with the Idylla SARS-CoV-2 Test.

Due to increased demand for testing, as well as restricted supply chain resources, testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues to face many hurdles. Pooling several samples has been proposed as an alternative approach to address these issues. We investigated the feasibility of pooling nasopharyngeal swab (NPS) or saliva samples for SARS-CoV-2 testing with a commercial assay (Idylla SARS-CoV-2 test; Biocartis). We evaluated the 10-pool and 20-pool approaches for 149 subjects, with 30 positive samples and 119 negative samples. The 10-pool approach had sensitivity of 78.95% (95% confidence interval [CI], 54.43% to 93.95%) and specificity of 100% (95% CI, 71.51% to 100%), whereas the 20-pool approach had sensitivity of 55.56% (95% CI, 21.20% to 86.30%) and specificity of 100% (95% CI, 25% to 100%). No significant difference was observed between the results obtained with pooled NPS and saliva samples. Given the rapidity, full automation, and practical advantages of the Idylla SARS-CoV-2 assay, pooling of 10 samples has the potential to significantly increase testing capacity for both NPS and saliva samples, with good sensitivity. IMPORTANCE To control outbreaks of coronavirus disease 2019 (COVID-19) and to avoid reagent shortages, testing strategies must be adapted and maintained for the foreseeable future. We analyzed the feasibility of pooling NPS and saliva samples for SARS-CoV-2 testing with the Idylla SARS-CoV-2 test, and we found that sensitivity was dependent on the pool size. The SARS-CoV-2 testing capacity with both NPS and saliva samples could be significantly expanded by pooling 10 samples; however, pooling 20 samples resulted in lower sensitivity.

New technologies for improved relevance in miRNA research.

After a number of years of research in the field of miRNA, the robustness and biological relevance of many published articles is increasingly being questioned. We propose the use of new RNA-seq approaches, genome editing technologies, and updated public databases to improve the quality, reliability, and relevance of published data.