Histopathology and genetic susceptibility in COVID-19 pneumonia

The clinical features of COVID-19 range from a mild illness to patients with a very severe illness with acute hypoxemic respiratory failure requiring ventilation and Intensive Care Unit admission. Risk factors for a fatal disease include older age, respiratory disease, diabetes mellitus, obesity and hypertension. Little is known about the mechanisms behind observed episodes of sudden deterioration or the infrequent idiosyncratic clinical demise in otherwise healthy and young subjects. As in other diseases, the answer to some of these questions may in time be provided by genotyping as well careful clinical, serological, radiological and histopathological phenotyping, which enable mechanistic insights into the differences in pathogenesis and underlying immunological and tissue regenerative response patterns. We will aim to provide a brief overview of the existing evidence for such differences in host response and outcome, and generate hypotheses for divergent patterns and avenues for future research, by highlighting similarities and differences in histopathological appearance between COVID19 and influenza as well as previous coronavirus outbreaks, and by discussing predisposition through genetics and underlying disease

PD-L1 immunohistochemistry in non-small-cell lung cancer:unraveling differences in staining concordance and interpretation

Programmed death ligand 1 (PD-L1) immunohistochemistry (IHC) is accepted as a predictive biomarker for the selection of immune checkpoint inhibitors. We evaluated the staining quality and estimation of the tumor proportion score (TPS) in non-small-cell lung cancer during two external quality assessment (EQA) schemes by the European Society of Pathology. Participants received two tissue micro-arrays with three (2017) and four (2018) cases for PD-L1 IHC and a positive tonsil control, for staining by their routine protocol. After the participants returned stained slides to the EQA coordination center, three pathologists assessed each slide and awarded an expert staining score from 1 to 5 points based on the staining concordance. Expert scores significantly (p <0.01) improved between EQA schemes from 3.8 (n = 67) to 4.3 (n = 74) on 5 points. Participants used 32 different protocols: the majority applied the 22C3 (56.7%) (Dako), SP263 (19.1%) (Ventana), and E1L3N (Cell Signaling) (7.1%) clones. Staining artifacts consisted mainly of very weak or weak antigen demonstration (63.0%) or excessive background staining (19.8%). Participants using CE-IVD kits reached a higher score compared with those using laboratory-developed tests (LDTs) (p <0.05), mainly attributed to a better concordance of SP263. The TPS was under- and over-estimated in 20/423 (4.7%) and 24/423 (5.7%) cases, respectively, correlating to a lower expert score. Additional research is needed on the concordance of less common protocols, and on reasons for lower LDT concordance. Laboratories should carefully validate all test methods and regularly verify their performance. EQA participation should focus on both staining concordance and interpretation of PD-L1 IHC

Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer

Aims Immunohistochemical programmed death-ligand 1 (PD-L1) staining to predict responsiveness to immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) has several drawbacks: a robust gold standard is lacking, and there is substantial interobserver and intraobserver variance, with up to 20% discordance around cutoff points. The aim of this study was to develop a new deep learning-based PD-L1 tumour proportion score (TPS) algorithm, trained and validated on a routine diagnostic dataset of digitised PD-L1 (22C3, laboratory-developed test)-stained samples. Methods and results We designed a fully supervised deep learning algorithm for whole-slide PD-L1 assessment, consisting of four sequential convolutional neural networks (CNNs), using aiforia create software. We included 199 whole slide images (WSIs) of 'routine diagnostic' histology samples from stage IV NSCLC patients, and trained the algorithm by using a training set of 60 representative cases. We validated the algorithm by comparing the algorithm TPS with the reference score in a held-out validation set. The algorithm had similar concordance with the reference score (79%) as the pathologists had with one another (75%). The intraclass coefficient was 0.96 and Cohen's kappa coefficient was 0.69 for the algorithm. Around the 1% and 50% cutoff points, concordance was also similar between pathologists and the algorithm. Conclusions We designed a new, deep learning-based PD-L1 TPS algorithm that is similarly able to assess PD-L1 expression in daily routine diagnostic cases as pathologists. Successful validation on routine diagnostic WSIs and detailed visual feedback show that this algorithm meets the requirements for functioning as a 'scoring assistant'.Pathogenesis and treatment of chronic pulmonary disease

Formalin fixation for optimal concordance of programmed death-ligand 1 immunostaining between cytologic and histologic specimens from patients with non-small cell lung cancer

Background Immunohistochemical staining of programmed death-ligand 1 (PD-L1) is used to determine which patients with non-small cell lung cancer (NSCLC) may benefit most from immunotherapy. Therapeutic management of many patients with NSCLC is based on cytology instead of histology. In this study, concordance of PD-L1 immunostaining between cytology cell blocks and their histologic counterparts was analyzed. Furthermore, the effect of various fixatives and fixation times on PD-L1 immunoreactivity was studied. Methods Paired histologic and cytologic samples from 67 patients with NSCLC were collected by performing fine-needle aspiration on pneumonectomy/lobectomy specimens. Formalin-fixed, agar-based or CytoLyt/PreservCyt-fixed Cellient cell blocks were prepared. Sections from cell blocks and tissue blocks were stained with SP263 (standardized assay) and 22C3 (laboratory-developed test) antibodies. PD-L1 scores were compared between histology and cytology. In addition, immunostaining was compared between PD-L1-expressing human cell lines fixed in various fixatives at increasing increments in fixation duration. Results Agar cell blocks and tissue blocks showed substantial agreement (kappa = 0.70 and kappa = 0.67, respectively), whereas fair-to-moderate agreement was found between Cellient cell blocks and histology (kappa = 0.28 and kappa = 0.49, respectively). Cell lines fixed in various alcohol-based fixatives showed less PD-L1 immunoreactivity compared with those fixed in formalin. In contrast to SP263, additional formalin fixation after alcohol fixation resulted in preserved staining intensity using the 22C3 laboratory-developed test and the 22C3 pharmDx assay. Conclusions Performing PD-L1 staining on cytologic specimens fixed in alcohol-based fixatives could result in false-negative immunostaining results, whereas fixation in formalin leads to higher and more histology-concordant PD-L1 immunostaining. The deleterious effect of alcohol fixation could be reversed to some degree by postfixation in formalin

Renal Toxicity From Pemetrexed and Pembrolizumab in the Era of Combination Therapy in Patients With Metastatic Nonsquamous Cell NSCLC

The combination of chemotherapy and immune checkpoint inhibition (ICI) therapy is the current standard of care for most patients who are fit to undergo treatment for metastatic NSCLC. With this combination, renal toxicity was slightly higher than with chemotherapy alone in initial clinical trials. However, in recent realworld data, loss of kidney function is reported to be more frequent. Both chemotherapy and ICI therapy can induce renal impairment, although the mechanism of renal damage is different. Renal injury from chemotherapy is often ascribed to acute tubular injury and necrosis, whereas the main mechanism of injury caused by ICI therapy is acute tubulointerstitial nephritis. In cases of concomitant use of chemotherapy and ICI therapy, distinguishing the cause of renal failure is a challenge. Discriminating between these two causes is of utmost importance, as it would help assess which drug can be safely continued and which drug must be halted. This review aims to describe the underlying mechanisms of the renal adverse effects caused by chemotherapy and ICI therapy, leading to a suggested diagnostic and treatment algorithm on the basis of clinical, laboratory, radiographic, and pathologic parameters. This algorithm could serve as a supportive tool for clinicians to diagnose the underlying cause of acute kidney injury in patients treated with the combination of chemotherapy and immunotherapy

Tumor mutational load, CD8+ T cells, expression of PD-L1 and HLA class I to guide immunotherapy decisions in NSCLC patients

Objectives: A minority of NSCLC patients benefit from anti-PD1 immune checkpoint inhibitors. A rational combination of biomarkers is needed. The objective was to determine the predictive value of tumor mutational load (TML), CD8+ T cell infiltration, HLA class-I and PD-L1 expression in the tumor. Materials and methods: Metastatic NSCLC patients were prospectively included in an immune-monitoring trial (NTR7015) between April 2016-August 2017, retrospectively analyzed in FFPE tissue for TML (NGS: 409 cancer-related-genes) and by IHC staining to score PD-L1, CD8+ T cell infiltration, HLA class-I. PFS (RECISTv1.1) and OS were analyzed by Kaplan–Meier methodology. Results: 30 patients with adenocarcinoma (67%) or squamous cell carcinoma (33%) were included. High TML was associated with better PFS (p = 0.004) and OS (p = 0.025). Interaction analyses revealed that patients with both high TML and high total CD8+ T cell infiltrate (p = 0.023) or no loss of HLA class-I (p = 0.026), patients with high total CD8+ T cell infiltrate and no loss of HLA class-I (p = 0.041) or patients with both high PD-L1 and high TML (p = 0.003) or no loss of HLA class-I (p = 0.032) were significantly associated with better PFS. Unsupervised cluster analysis based on these markers revealed three sub-clusters, of which cluster-1A was overrepresented by patients with progressive disease (15 out of 16), with significant effect on PFS (p = 0.007). Conclusion: This proof-of-concept study suggests that a combination of PD-L1 expression, TML, CD8+ T cell infiltration and HLA class-I functions as a better predictive biomarker for response to anti-PD-1 immunotherapy. Consequently, refinement of this set of biomarkers and validation in a larger set of patients is warranted

Tumor mutational load, CD8(+) T cells, expression of PD-L1 and HLA class I to guide immunotherapy decisions in NSCLC patients

Objectives A minority of NSCLC patients benefit from anti-PD1 immune checkpoint inhibitors. A rational combination of biomarkers is needed. The objective was to determine the predictive value of tumor mutational load (TML), CD8(+) T cell infiltration, HLA class-I and PD-L1 expression in the tumor. Materials and methods Metastatic NSCLC patients were prospectively included in an immune-monitoring trial (NTR7015) between April 2016-August 2017, retrospectively analyzed in FFPE tissue for TML (NGS: 409 cancer-related-genes) and by IHC staining to score PD-L1, CD8(+) T cell infiltration, HLA class-I. PFS (RECISTv1.1) and OS were analyzed by Kaplan-Meier methodology. Results 30 patients with adenocarcinoma (67%) or squamous cell carcinoma (33%) were included. High TML was associated with better PFS (p = 0.004) and OS (p = 0.025). Interaction analyses revealed that patients with both high TML and high total CD8(+) T cell infiltrate (p = 0.023) or no loss of HLA class-I (p = 0.026), patients with high total CD8(+) T cell infiltrate and no loss of HLA class-I (p = 0.041) or patients with both high PD-L1 and high TML (p = 0.003) or no loss of HLA class-I (p = 0.032) were significantly associated with better PFS. Unsupervised cluster analysis based on these markers revealed three sub-clusters, of which cluster-1A was overrepresented by patients with progressive disease (15 out of 16), with significant effect on PFS (p = 0.007). Conclusion This proof-of-concept study suggests that a combination of PD-L1 expression, TML, CD8(+) T cell infiltration and HLA class-I functions as a better predictive biomarker for response to anti-PD-1 immunotherapy. Consequently, refinement of this set of biomarkers and validation in a larger set of patients is warranted.Pathogenesis and treatment of chronic pulmonary disease