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

Detection of ALK fusion transcripts in plasma of non-small cell lung cancer patients using a novel RT-PCR based assay

International audienceBackground: Detection of genomic rearrangements, like anaplastic lymphoma kinase (ALK) fusions, is a pivotal requirement in non-small cell lung cancer (NSCLC) for the initiation of a targeted treatment. While tissue testing remains the gold standard, detection of these alterations using liquid biopsies is an unmet need. To enable the detection of ALK rearrangements from circulating-free RNA (cfRNA) from NSCLC patients, we have evaluated a novel reverse transcription PCR (RT-PCR) based assay.Methods: Sixty-six patients with advanced stage NSCLC were included in the study. ALK status was determined by immunohistochemistry (IHC) and/or FISH on tissue sections. For the detection of ALK rearrangements from 2ml plasma collected in EDTA or Streck BCT DNA tubes, cfRNA was extracted using a prototype cfRNA sample preparation method and tested by a novel multiplex ALK/RET RT-PCR assay (Roche).Results: Of the forty-two patients with an ALK rearrangement, 30 (71%) were included at baseline. In 10 of the baseline patients, an ALK rearrangement was detected by RT-PCR [baseline sensitivity 33.33% (95% CI: 17.29–52.81%)]. All 24 negative ALK IHC/FISH-negative patients were negative using the RT-PCR based assay (specificity =100%).Conclusions: The prototype Roche ALK/RET RT-PCR assay was able to detect ALK fusion transcripts in the plasma of NSCLC patients at baseline as well as at disease progression with limited sensitivity but high specificity. Consequently, this assay could potentially be considered to select patients for an ALK-targeting therapy when tissue samples are lacking

Deep Learning Facilitates Distinguishing Histologic Subtypes of Pulmonary Neuroendocrine Tumors on Digital Whole-Slide Images

The histological distinction of lung neuroendocrine carcinoma, including small cell lung carcinoma (SCLC), large cell neuroendocrine carcinoma (LCNEC) and atypical carcinoid (AC), can be challenging in some cases, while bearing prognostic and therapeutic significance. To assist pathologists with the differentiation of histologic subtyping, we applied a deep learning classifier equipped with a convolutional neural network (CNN) to recognize lung neuroendocrine neoplasms. Slides of primary lung SCLC, LCNEC and AC were obtained from the Laboratory of Clinical and Experimental Pathology (University Hospital Nice, France). Three thoracic pathologists blindly established gold standard diagnoses. The HALO-AI module (Indica Labs, UK) trained with 18,752 image tiles extracted from 60 slides (SCLC = 20, LCNEC = 20, AC = 20 cases) was then tested on 90 slides (SCLC = 26, LCNEC = 22, AC = 13 and combined SCLC with LCNEC = 4 cases; NSCLC = 25 cases) by F1-score and accuracy. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The tumor maps were false colored and displayed side by side to original hematoxylin and eosin slides with superimposed pathologist annotations. The trained HALO-AI yielded a mean F1-score of 0.99 (95% CI, 0.939–0.999) on the testing set. Our CNN model, providing further larger validation, has the potential to work side by side with the pathologist to accurately differentiate between the different lung neuroendocrine carcinoma in challenging cases

Accurate Detection of SARS-CoV-2 by Next-Generation Sequencing in Low Viral Load Specimens

As new SARS-CoV-2 variants emerge, there is an urgent need to increase the efficiency and availability of viral genome sequencing, notably to detect the lineage in samples with a low viral load. SARS-CoV-2 genome next-generation sequencing (NGS) was performed retrospectively in a single center on 175 positive samples from individuals. An automated workflow used the Ion AmpliSeq SARS-CoV-2 Insight Research Assay on the Genexus Sequencer. All samples were collected in the metropolitan area of the city of Nice (France) over a period of 32 weeks (from 19 July 2021 to 11 February 2022). In total, 76% of cases were identified with a low viral load (Ct ≥ 32, and ≤200 copies/µL). The NGS analysis was successful in 91% of cases, among which 57% of cases harbored the Delta variant, and 34% the Omicron BA.1.1 variant. Only 9% of cases had unreadable sequences. There was no significant difference in the viral load in patients infected with the Omicron variant compared to the Delta variant (Ct values, p = 0.0507; copy number, p = 0.252). We show that the NGS analysis of the SARS-CoV-2 genome provides reliable detection of the Delta and Omicron SARS-CoV-2 variants in low viral load samples

Accurate Detection of SARS-CoV-2 by Next-Generation Sequencing in Low Viral Load Specimens

As new SARS-CoV-2 variants emerge, there is an urgent need to increase the efficiency and availability of viral genome sequencing, notably to detect the lineage in samples with a low viral load. SARS-CoV-2 genome next-generation sequencing (NGS) was performed retrospectively in a single center on 175 positive samples from individuals. An automated workflow used the Ion AmpliSeq SARS-CoV-2 Insight Research Assay on the Genexus Sequencer. All samples were collected in the metropolitan area of the city of Nice (France) over a period of 32 weeks (from 19 July 2021 to 11 February 2022). In total, 76% of cases were identified with a low viral load (Ct ≥ 32, and ≤200 copies/µL). The NGS analysis was successful in 91% of cases, among which 57% of cases harbored the Delta variant, and 34% the Omicron BA.1.1 variant. Only 9% of cases had unreadable sequences. There was no significant difference in the viral load in patients infected with the Omicron variant compared to the Delta variant (Ct values, p = 0.0507; copy number, p = 0.252). We show that the NGS analysis of the SARS-CoV-2 genome provides reliable detection of the Delta and Omicron SARS-CoV-2 variants in low viral load samples

Identification of a circulating immunological signature predictive of response to immune checkpoint inhibitors in patients with advanced non‐small cell lung cancer

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Integrating artificial intelligence into lung cancer screening: a randomised controlled trial protocol

Introduction Lung cancer (LC) is the most common cause of cancer-related deaths worldwide. Its early detection can be achieved with a CT scan. Two large randomised trials proved the efficacy of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk populations. The decrease in specific mortality is 20%–25%.Nonetheless, implementing LCS on a large scale faces obstacles due to the low number of thoracic radiologists and CT scans available for the eligible population and the high frequency of false-positive screening results and the long period of indeterminacy of nodules that can reach up to 24 months, which is a source of prolonged anxiety and multiple costly examinations with possible side effects.Deep learning, an artificial intelligence solution has shown promising results in retrospective trials detecting lung nodules and characterising them. However, until now no prospective studies have demonstrated their importance in a real-life setting.Methods and analysis This open-label randomised controlled study focuses on LCS for patients aged 50–80 years, who smoked more than 20 pack-years, whether active or quit smoking less than 15 years ago. Its objective is to determine whether assisting a multidisciplinary team (MDT) with a 3D convolutional network-based analysis of screening chest CT scans accelerates the definitive classification of nodules into malignant or benign. 2722 patients will be included with the aim to demonstrate a 3-month reduction in the delay between lung nodule detection and its definitive classification into benign or malignant.Ethics and dissemination The sponsor of this study is the University Hospital of Nice. The study was approved for France by the ethical committee CPP (Comités de Protection des Personnes) Sud-Ouest et outre-mer III (No. 2022-A01543-40) and the Agence Nationale du Medicament et des produits de Santé (Ministry of Health) in December 2023. The findings of the trial will be disseminated through peer-reviewed journals and national and international conference presentations.Trial registration number NCT05704920

Prospective Multicenter Validation of the Detection of ALK Rearrangements of Circulating Tumor Cells for Noninvasive Longitudinal Management of Patients With Advanced NSCLC

International audienceIntroduction: Patients with advanced-stage NSCLC whose tumors harbor an ALK gene rearrangement benefit from treatment with multiple ALK inhibitors (ALKi). Approximately 30% of tumor biopsy samples contain insufficient tissue for successful ALK molecular characterization. This study evaluated the added value of analyzing circulating tumor cells (CTCs) as a surrogate to ALK tissue analysis and as a function of the response to ALKi.Methods: We conducted a multicenter, prospective observational study (NCT02372448) of 203 patients with stage IIIB/IV NSCLC across nine French centers, of whom 81 were ALK positive (immunohistochemistry or fluorescence in situ hybridization [FISH]) and 122 ALK negative on paraffin-embedded tissue specimens. Blood samples were collected at baseline and at 6 and 12 weeks after ALKi initiation or at disease progression. ALK gene rearrangement was evaluated with CTCs using immunocytochemistry and FISH analysis after enrichment using a filtration method.Results: At baseline, there was a high concordance between the detection of an ALK rearrangement in the tumor tissue and in CTCs as determined by immunocytochemistry (sensitivity, 94.4%; specificity 89.4%). The performance was lower for the FISH analysis (sensitivity, 35.6%; specificity, 56.9%). No significant association between the baseline levels or the dynamic change of CTCs and overall survival (hazard ratio = 0.59, 95% confidence interval: 0.24-1.5, p = 0.244) or progression-free survival (hazard ratio = 0.84, 95% confidence interval: 0.44-1.6, p = 0.591) was observed in the patients with ALK-positive NSCLC.Conclusions: CTCs can be used as a complementary tool to a tissue biopsy for the detection of ALK rearrangements. Longitudinal analyses of CTCs revealed promise for real-time patient monitoring and improved delivery of molecularly guided therapy in this population

Efficacy and Safety of Rovalpituzumab Tesirine Compared With Topotecan as Second-Line Therapy in DLL3-High SCLC: Results From the Phase 3 TAHOE Study

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