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.

Analytical validation of automated multiplex chromogenic immunohistochemistry for diagnostic and predictive purpose in non-small cell lung cancer.

OBJECTIVES: The evaluation of an increasing number of diagnostic and predictive markers is playing a central role in precision thoracic oncology. Multiplex immunohistochemistry (mIHC), alongside next-generation sequencing, is ideally situated for this purpose and maximizes tumor tissue preservation for molecular analyses that use increasingly large panels. However, the standardization and validation of mIHC that supports routine clinical laboratory processes are mandatory. After a previous proof-of-concept study, we now (i) optimized two automated four-plex assays on a commercially available IHC autostainer for use in daily practices worldwide and (ii) evaluated the repeatability and concordance of the assessment of the cell density. PATIENTS AND METHODS: Two four-plex mIHC assays [i) TTF1, p40, PD-L1, CD8; and, ii) ALK, ROS1, BRAFV600E, NTRK] were optimized on the BenchMark ULTRA autostainer (Ventana Medical Systems, Inc.), as determined in comparison to conventional IHC chromogenic assays. Intra-site repeatability was evaluated on serial tumor sections from non-small cell lung carcinomas (NSCLC). The concordance was assessed by linear fit to plots of the percentage staining evaluated on tumor sections from 89 NSCLC patients. RESULTS: Following optimization, an average concordance for a staining rate of 95.4% was achieved between conventional IHC and mIHC across all selected markers. Assessment of intra-site repeatability showed strong concordance for all these markers (average, R2 = 0.96; P-value < 0.001). CONCLUSIONS: Our optimized mIHC assay gave a sensitive and repeatable assessment of two panels of eight diagnostic and predictive biomarkers for NSCLC. The availability of standardized protocols to determine these biomarkers on a widely available IHC platform will expand the number of pathology laboratories able to determine the eligibility of patients with NSCLC for targeted treatment or immunotherapy in a reliable and concordant manner, thus providing a unique sample-sparing tool to characterize limited tissue samples in thoracic oncology.

Association of TRF2 expression and myeloid-derived suppressor cells infiltration with clinical outcome of patients with cutaneous melanoma.

The outcome of patients with cutaneous melanoma has been strongly modified by recent advances obtained with Immune Checkpoint Inhibitors (ICIs). However, despite this breakthrough, durable response to ICIs is limited to a subset of patients. We investigated whether the expression of TRF2, which preserves telomere integrity, and have an effect on tumor immunosurveillance notably by directly recruiting and activating myeloid-derived suppressor cells (MDSCs), could be a prognostic biomarker in patients with relapsed or metastatic melanoma based on different treatment regimens. We evaluated retrospectively the association of TRF2 expressed in melanoma cells in combination with intratumoral CD33+ CD15+ CD14- MDSCs, as detected by immunohistochemistry and quantified by digital analysis, to clinicopathological features and overall survival (OS) among 48 patients treated with ICIs and 77 patients treated with other treatment options. The densities/mm2 of TRF2+ cells (P=.003) and CD33+ cells (P=.004) were individually significantly related to poor OS. In addition, only the combined expression of CD33+/CD15+/CD14- cells/mm2 was significantly correlated to poor OS (P=.017) in the whole study population as well as in patients treated by ICIs (P=.023). There was no significant difference in OS when analyzing the other markers individually or in combination according to the treatment regimen. The pre-treatment assessment of TRF2 expression and CD33+ cells/mm2 along with the density of CD33+/CD15+/CD14- cells/mm2 could assess OS and better predict clinical response of patients with melanoma treated by ICIs.

Establishment of a Collection of Blood-Derived Products from COVID-19 Patients for Translational Research: Experience of the LPCE Biobank (Nice, France).

In only a few months after its inception, the COVID-19 pandemic lead to the death of hundreds of thousands of patients and to the infection of millions of people on most continents, mostly in the United States and in Europe. During this crisis, it was demonstrated that a better understanding of the pathogenicity, virulence, and contagiousness of SARS-CoV-2, all of which were initially underestimated, was urgently needed. The development of diagnostic tests to identify SARS-CoV-2 or to detect anti-SARS-CoV2 antibodies in blood, of vaccines, and of preventive and curative treatments has been relying on intense activity of scientists in academia and industry. It is noteworthy that these scientists depend on the use of high-quality biological samples taken from positive COVID-19 patients in a manner that preserves their integrity. Given this unique and emergent situation, it was necessary to urgently establish biological collections clinically annotated for immediate development of clinical and translational research projects focusing on COVID-19 biological aspects. It is in this very specific context that biobanks must rapidly adapt their infrastructure and/or operational capacity to fulfill new critical needs. We report the establishment of a biobank dedicated to the collection of blood-derived products (plasma, serum, and leukocytes) from COVID-19 patients hospitalized in the Nice Pasteur Hospital (Nice, France).

Multiplexed Immunohistochemistry for Molecular and Immune Profiling in Lung Cancer-Just About Ready for Prime-Time?

As targeted molecular therapies and immuno-oncology have become pivotal in the management of patients with lung cancer, the essential requirement for high throughput analyses and clinical validation of biomarkers has become even more intense, with response rates maintained in the 20%⁻30% range. Moreover, the list of treatment alternatives, including combination therapies, is rapidly evolving. The molecular profiling and specific tumor-associated immune contexture may be predictive of response or resistance to these therapeutic strategies. Multiplexed immunohistochemistry is an effective and proficient approach to simultaneously identify specific proteins or molecular abnormalities, to determine the spatial distribution and activation state of immune cells, as well as the presence of immunoactive molecular expression. This method is highly advantageous for investigating immune evasion mechanisms and discovering potential biomarkers to assess mechanisms of action and to predict response to a given treatment. This review provides views on the current technological status and evidence for clinical applications of multiplexing and how it could be applied to optimize clinical management of patients with lung cancer.

Using 22C3 Anti-PD-L1 Antibody Concentrate on Biopsy and Cytology Samples from Non-small Cell Lung Cancer Patients.

Pembrolizumab monotherapy has been approved for the first- and second-line treatment of patients with PD-L1-expressing advanced non-small cell lung cancer (NSCLC). Testing for PD-L1 expression with the PD-L1 immunohistochemistry (IHC) 22C3 companion diagnostic assay, which gives a tumor proportion score (TPS), has been validated on tumor tissue. We developed an optimized laboratory-developed test (LDT) that uses the 22C3 antibody (Ab) concentrate on a widely available IHC autostainer for biopsy and cytology specimens. The PD-L1 TPS was evaluated with 120 paired whole-tumor tissue sections and biopsy samples and with 70 paired biopsy and cytology samples (bronchial washes, n = 40; pleural effusions, n = 30). The 22C3 Ab concentrate-based LDT showed a high concordance rate between biopsy (~100%) and cytology (~95%) specimens when compared to PD-L1 IHC expression determined using the PD-L1 IHC 22C3 companion assay at both TPS cut points (≥1%, ≥50%). The optimized LDT presented here, using the 22C3 Ab concentrate to determine the PD-L1 expression in both tumor tissue and in cytology specimens, will expand the ability of laboratories worldwide to assess the eligibility of patients with NSCLC for treatment with pembrolizumab monotherapy in a reliable and reproducible manner.

Automated chromogenic multiplexed immunohistochemistry assay for diagnosis and predictive biomarker testing in non-small cell lung cancer.

OBJECTIVES: The current challenge in the management of non-small cell lung cancer (NSCLC) in pathology laboratories is to combine immunohistochemistry (IHC) and molecular approaches on increasingly smaller biopsies and the need to reserve a fair amount of tumor material for molecular analyses with increasingly larger panels. The latest lung cancer classification, especially in the setting of poorly differentiated tumors, requires an IHC workup to allow for accurate diagnosis and also to preserve as much tissue as possible for molecular testing. Thus, it is recommended to reduce use of the term NSCLC not otherwise specified as much as possible and classify tumors according to their specific histologic subtype. This implies limiting the number of tissue slides despite the existence of specific and sensitive biomarkers (ALK, ROS1, BRAF V600E, PD-L1) and the obligation to distinguish lung adenocarcinoma (TTF-1 positive) from squamous cell carcinoma (p40 positive). MATERIALS AND METHODS: Samples from 18 patients with NSCLC, previously characterized for histologic and genomic/immune features, were included. Two multiplexed IHC assays were developed, for diagnosis and immunophenotyping including TTF1, p40, PD-L1, and pan-Keratin antibodies, and for molecular profiling panel including ALK, ROS1 and BRAF V600E antibodies. RESULTS: We developed two sensitive multiplexed IHC assays to comprehensively characterize major NSCLC histotypes and FDA-cleared predictive biomarkers, without antigenicity loss, steric interference or increased cross-reactivity. The assays rely on standard antigen retrieval and automated staining protocols, limiting the need for validation strategies. CONCLUSION: Our multiplexed IHC approach provides a unique sample-sparing tool to characterize limited tissue samples in lung oncology and making it an alternative method in the clinical setting for therapeutic decision making of advanced NSCLC, provided that validation in a larger population is performed.