PD-L1 expression and prediction of response to immune modulators in non-small cell lung cancer; reasons for its fragility and strategies to reduce it

Abstract Introduction Anti-PD-1/PD-L1 immunomodulatory (IM) therapy has revolutionised the treatment of non-small cell lung cancer (NSCLC). The only ‘biomarker’ currently-validated for predicting response of these tumours to IM therapy is the extent of PD-L1 expression as detected by immunohistochemistry (IHC). Despite the overall success of this therapy in patients with NSCLC, PD-L1 expression is an imperfect predictor, some patients with tumours displaying low expression responding strongly, and some with high expression not at all. The thesis considers why PD-L1 expression is an imperfect predictor and how it might be improved. Methods The research described in the first part of this thesis considered the impact of pre-analytical conditions on PD-L1 expression. This examined not only the effect of how tumours are sampled, but the influence of specimen processing and fixation and conditions of storage, the latter employing a novel tissue ageing acceleration chamber and mass-spectrometry. The second part describes examination of heterogeneity of expression in a series of resected NSCLCs in which the primary tumour was accompanied by nodal metastases. Biological and artefactual heterogeneity within and between tumour deposits was assessed at different scales using a novel ‘squares method’ and ‘digital sampling’. The third part describes assessment of the tumour immune environment (TME), specifically interrogation of immune cell populations, employing a combination of techniques including traditional IHC, multiplex IHC, multiplex immunofluorescence and image analysis. The fourth and final part of the work involved an assessment of digital pathology and image analysis with integrated machine-learning algorithms as a tool to improve accuracy and consistency in assessing PD-L1 expression. Results PD-L1 expression is consistent across different types of specimen; loss of its immunogenicity can be reduced by storage in cold and dry conditions, particularly when combined with a desiccant. Approximately 20-25% of resected NSCLCs demonstrated tumoural heterogeneity such that sampling from different sites might produce clinically-relevant differences in PD-L1 expression. This can be minimised, but not reduced entirely, by generous sampling. The TME of NSCLCs can be differentiated by assessing different immune cell populations, but only in specimens containing sufficient tissue and routine, small, diagnostic specimens will prove difficult to analyse in this way. Image analysis and algorithms are potentially powerful tools that can reduce intra- and inter-observer consistency when assessing PD-L1 expression, but require learning and experience for their effective use. Discussion The research described in this thesis confirms that assessment of PD-L1 expression by IHC is a powerful, but imperfect biomarker, and indicates also that its utility can be improved. Accuracy and consistency in its interpretation can be increased by optimising pre-analytical conditions. Tumour heterogeneity is a more complex problem; whilst availability of multiple, generous, good quality samples improves accuracy, the confounding effect of this fundamental fact of the biology of PD-L1 expression cannot be removed entirely. Techniques to interrogate the TME yield powerful data but, at present, most are too expensive, too complicated and require too much tissue to be useful in the routine clinical setting. Image analysis, machine learning and algorithms are becoming established techniques and are clearly of value, but possibly largely in improving confidence in and consistency of interpretation

Chondromyxoid Fibroma of the Rib: A Rare Benign Tumor With Potential for Local Recurrence

Chondromyxoid fibroma (CMF) is a benign cartilaginous tumor that typically occurs in the long bones of young adult males, with the clinical presentation varying from asymptomatic to localized pain, swelling, and movement restriction. We report an unusual presentation of CMF involving a rib, along with a literature review of the management of CMF. Although benign, local recurrence is not uncommon, and malignant transformation has been reported on rare occasions. En bloc surgical excision, with adequate tumor-free resection margins, of radiologically suspected chondromyxoid fibroma is crucial for the treatment and confirmation of diagnosis. A high index of suspicion, adequate treatment, and follow-up are critical for the successful management of these uncommon benign chondroid tumors

Analysis of Immune Checkpoint Drug Targets and Tumor Proteotypes in Non-Small Cell Lung Cancer

New therapeutics targeting immune checkpoint proteins have significantly advanced treatment of non-small cell lung cancer (NSCLC), but protein level quantitation of drug targets presents a critical problem. We used multiplexed, targeted mass spectrometry (MS) to quantify immunotherapy target proteins PD-1, PD-L1, PD-L2, IDO1, LAG3, TIM3, ICOSLG, VISTA, GITR, and CD40 in formalin-fixed, paraffin-embedded (FFPE) NSCLC specimens. Immunohistochemistry (IHC) and MS measurements for PD-L1 were weakly correlated, but IHC did not distinguish protein abundance differences detected by MS. PD-L2 abundance exceeded PD-L1 in over half the specimens and the drug target proteins all displayed different abundance patterns. mRNA correlated with protein abundance only for PD-1, PD-L1, and IDO1 and tumor mutation burden did not predict abundance of any protein targets. Global proteome analyses identified distinct proteotypes associated with high PD-L1-expressing and high IDO1-expressing NSCLC. MS quantification of multiple drug targets and tissue proteotypes can improve clinical evaluation of immunotherapies for NSCLC

Machine-learning-based image analysis algorithms improve interpathologist concordance when scoring PD-L1 expression in non-small-cell lung cancer.

Programmed death ligand 1 (PD-L1) expression on tumour cells is the only predictive biomarker of response to immuno-modulatory therapy for patients with non-small-cell lung cancer (NSCLC). Accuracy of this biomarker is hampered by its challenging interpretation. Here we explore if the use of machine-learning derived image analysis tools can improve interpathologist concordance of assessing PD-L1 expression in NSCLC.Five pathologists who routinely score PD-L1 at a major regional referral hospital for thoracic surgery participated. 13 NSCLC small diagnostic biopsies were stained for PD-L1 (SP263 clone) and digitally scanned. Each pathologist independently scored each case with and without the Roche uPath PD-L1 (SP263) image analysis NSCLC algorithm with a wash-out interim period of 6 weeks.A consistent improvement in interpathologist concordance was seen when using the image analysis tool compared with scoring without: (Fleiss' kappa 0.886 vs 0.613 (p<0.0001) and intraclass coefficient correlation 0.954 vs 0.837 (p<0.001)). Five cases (38%) were classified into clinically relevant different categories (negative/weak/strong) by multiple pathologists when not using the image analysis algorithm, whereas only two cases (15%) were classified differently when using the image analysis algorithm.The use of the image analysis algorithm improved the concordance of assessing PD-L1 expression between pathologists. Critically, there was a marked improvement in the placement of cases into more consistent clinical groupings. This small study is evidence that the use of image analysis tools may improve consistency in assessing tumours for PD-L1 expression and may therefore result in more consistent prediction to targeted treatment options

Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC) Scoring and Survival Analysis on PD-L1 Stained Tissue Images.

We report the ability of two deep learning-based decision systems to stratify non-small cell lung cancer (NSCLC) patients treated with checkpoint inhibitor therapy into two distinct survival groups. Both systems analyze functional and morphological properties of epithelial regions in digital histopathology whole slide images stained with the SP263 PD-L1 antibody. The first system learns to replicate the pathologist assessment of the Tumor Cell (TC) score with a cut-point for positivity at 25% for patient stratification. The second system is free from assumptions related to TC scoring and directly learns patient stratification from the overall survival time and event information. Both systems are built on a novel unpaired domain adaptation deep learning solution for epithelial region segmentation. This approach significantly reduces the need for large pixel-precise manually annotated datasets while superseding serial sectioning or re-staining of slides to obtain ground truth by cytokeratin staining. The capacity of the first system to replicate the TC scoring by pathologists is evaluated on 703 unseen cases, with an addition of 97 cases from an independent cohort. Our results show Lin's concordance values of 0.93 and 0.96 against pathologist scoring, respectively. The ability of the first and second system to stratify anti-PD-L1 treated patients is evaluated on 151 clinical samples. Both systems show similar stratification powers (first system: HR = 0.539, p = 0.004 and second system: HR = 0.525, p = 0.003) compared to TC scoring by pathologists (HR = 0.574, p = 0.01)