Automated Quantitative CD8+ Tumor-Infiltrating Lymphocytes and Tumor Mutation Burden as Independent Biomarkers in Melanoma Patients Receiving Front-Line Anti-PD-1 Immunotherapy

BACKGROUND

CD8+ tumor-infiltrating lymphocyte (TIL) predicts response to anti-PD-(L)1 therapy. However, there remains no standardized method to assess CD8+ TIL in melanoma, and developing a specific, cost-effective, reproducible, and clinically actionable biomarker to anti-PD-(L)1 remains elusive. We report on the development of automatic CD8+ TIL density quantification via whole slide image (WSI) analysis in advanced melanoma patients treated with front-line anti-PD-1 blockade, and correlation immunotherapy response.

METHODS

Seventy-eight patients treated with PD-1 inhibitors in the front-line setting between January 2015 and May 2023 at the University of Pittsburgh Cancer Institute were included. CD8+ TIL density was quantified using an image analysis algorithm on digitized WSI. Targeted next-generation sequencing (NGS) was performed to determine tumor mutation burden (TMB) in a subset of 62 patients. ROC curves were used to determine biomarker cutoffs and response to therapy. Correlation between CD8+ TIL density and TMB cutoffs and response to therapy was studied.

RESULTS

Higher CD8+ TIL density was significantly associated with improved response to front-line anti-PD-1 across all time points measured. CD8+ TIL density ≥222.9 cells/mm2 reliably segregated responders and non-responders to front-line anti-PD-1 therapy regardless of when response was measured. In a multivariate analysis, patients with CD8+ TIL density exceeding cutoff had significantly improved PFS with a trend toward improved OS. Similarly, increasing TMB was associated with improved response to anti-PD-1, and a cutoff of 14.70 Mut/Mb was associated with improved odds of response. The correlation between TMB and CD8+ TIL density was low, suggesting that each represented independent predictive biomarkers of response.

CONCLUSIONS

An automatic digital analysis algorithm provides a standardized method to quantify CD8+ TIL density, which predicts response to front-line anti-PD-1 therapy. CD8+ TIL density and TMB are independent predictors of response to anti-PD-1 blockade.

Implementation of Digital Image Analysis in Assessment of Ki67 Index in Breast Cancer

The clinical utility of the proliferation marker Ki67 in breast cancer treatment and prognosis is an active area of research. Studies have suggested that differences in pre-analytic and analytic factors contribute to low analytical validity of the assay, with scoring methods accounting for a large proportion of this variability. Use of standard scoring methods is limited, in part due to the time intensive nature of such reporting protocols. Therefore, use of digital image analysis tools may help to both standardize reporting and improve workflow. In this study, digital image analysis was utilized to quantify Ki67 indices in 280 breast biopsy and resection specimens during routine clinical practice. The supervised Ki67 indices were then assessed for agreement with a manual count of 500 tumor cells. Agreement was excellent, with an intraclass correlation coefficient of 0.96 for the pathologist-supervised analysis. This study illustrates an example of a rapid, accurate workflow for implementation of digital image analysis in Ki67 scoring in breast cancer.

Pembrolizumab plus azacitidine in patients with chemotherapy refractory metastatic colorectal cancer: a single-arm phase 2 trial and correlative biomarker analysis

Background

DNA mismatch repair proficient (pMMR) metastatic colorectal cancer (mCRC) is not responsive to pembrolizumab monotherapy. DNA methyltransferase inhibitors can promote antitumor immune responses. This clinical trial investigated whether concurrent treatment with azacitidine enhances the antitumor activity of pembrolizumab in mCRC.

Methods

We conducted a phase 2 single-arm trial evaluating activity and tolerability of pembrolizumab plus azacitidine in patients with chemotherapy-refractory mCRC (NCT02260440). Patients received pembrolizumab 200 mg IV on day 1 and azacitidine 100 mg SQ on days 1–5, every 3 weeks. A low fixed dose of azacitidine was chosen in order to reduce the possibility of a direct cytotoxic effect of the drug, since the main focus of this study was to investigate its potential immunomodulatory effect. The primary endpoint of this study was overall response rate (ORR) using RECIST v1.1., and secondary endpoints were progression-free survival (PFS) and overall survival (OS). Tumor tissue was collected pre- and on-treatment for correlative studies.

Results

Thirty chemotherapy-refractory patients received a median of three cycles of therapy. One patient achieved partial response (PR), and one patient had stable disease (SD) as best confirmed response. The ORR was 3%, median PFS was 1.9 months, and median OS was 6.3 months. The combination regimen was well-tolerated, and 96% of treatment-related adverse events (TRAEs) were grade 1/2. This trial was terminated prior to the accrual target of 40 patients due to lack of clinical efficacy. DNA methylation on-treatment as compared to pre-treatment decreased genome wide in 10 of 15 patients with paired biopsies and was significantly lower in gene promoter regions after treatment. These promoter demethylated genes represented a higher proportion of upregulated genes, including several immune gene sets, endogenous retroviral elements, and cancer-testis antigens. CD8⁺ TIL density trended higher on-treatment compared to pre-treatment. Higher CD8⁺ TIL density at baseline was associated with greater likelihood of benefit from treatment. On-treatment tumor demethylation correlated with the increases in tumor CD8⁺ TIL density.

Conclusions

The combination of pembrolizumab and azacitidine is safe and tolerable with modest clinical activity in the treatment for chemotherapy-refractory mCRC. Correlative studies suggest that tumor DNA demethylation and immunomodulation occurs. An association between tumor DNA demethylation and tumor-immune modulation suggests immune modulation and may result from treatment with azacitidine.

Trial registration ClinicalTrials.gov, NCT02260440. Registered 9 October 2014, https://clinicaltrials.gov/ct2/show/NCT02260440.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01226-y.

Artificial Intelligence-Based Screening for Mycobacteria in Whole-Slide Images of Tissue Samples

OBJECTIVES

This study aimed to develop and validate a deep learning algorithm to screen digitized acid fast-stained (AFS) slides for mycobacteria within tissue sections.

METHODS

A total of 441 whole-slide images (WSIs) of AFS tissue material were used to develop a deep learning algorithm. Regions of interest with possible acid-fast bacilli (AFBs) were displayed in a web-based gallery format alongside corresponding WSIs for pathologist review. Artificial intelligence (AI)-assisted analysis of another 138 AFS slides was compared to manual light microscopy and WSI evaluation without AI support.

RESULTS

Algorithm performance showed an area under the curve of 0.960 at the image patch level. More AI-assisted reviews identified AFBs than manual microscopy or WSI examination (P < .001). Sensitivity, negative predictive value, and accuracy were highest for AI-assisted reviews. AI-assisted reviews also had the highest rate of matching the original sign-out diagnosis, were less time-consuming, and were much easier for pathologists to perform (P < .001).

CONCLUSIONS

This study reports the successful development and clinical validation of an AI-based digital pathology system to screen for AFBs in anatomic pathology material. AI assistance proved to be more sensitive and accurate, took pathologists less time to screen cases, and was easier to use than either manual microscopy or viewing WSIs.

Combined histopathological risk score using TP53 protein expression, CD8+ T cell density and intratumoral budding is an independent predictor of neoadjuvant therapy response in rectal adenocarcinoma

AIMS

Neoadjuvant therapy is the recommended treatment for locally advanced rectal adenocarcinoma; however, there remains significant variability in response to therapy. Tumour protein 53 (TP53) has been associated with therapy response and prognosis with conflicting data. Recently, we demonstrated that immune cell density and intratumoral budding (ITB) are predictive factors in rectal cancer. We investigated the predictive value of TP53 immunohistochemistry with CD8⁺ T cell density and ITB on pretreatment biopsies of rectal adenocarcinoma for response to neoadjuvant therapy.

METHODS AND RESULTS

Pretreatment biopsies of rectal adenocarcinoma from 117 patients with neoadjuvant therapy were analysed for TP53 expression by immunohistochemistry, ITB, CD8⁺ T cell density and mismatch repair protein (MMR) status. Most rectal adenocarcinomas displayed aberrant TP53 expression (86 of 117, 74%). Compared to wild-type TP53, aberrant TP53 expression was associated with proficient MMR status (P = 0.003) and low CD8⁺ T cell density (P = 0.001). Aberrant TP53 was significantly associated with a partial to poor response to neoadjuvant therapy [odds ratio (OR) = 2.42, 95% confidence interval (CI) = 1.04-5.62, P = 0.04]. A combined histopathological risk score (HRS) was created using CD8⁺ T cell density, ITB and TP53 expression. Patients were separated into low (none to one factor) and high (two to three factors) HRS categories. In the multivariable model, patients with a high HRS were 3.25-fold more likely to have a partial or poor response to neoadjuvant therapy (95% CI = 1.48-7.11, P = 0.003).

CONCLUSIONS

Our study demonstrates that aberrant TP53 expression, high ITB and low CD8⁺ T cell density in pretreatment biopsies can help predict response to neoadjuvant therapy. These biomarkers may be helpful in identifying patients at risk for therapy resistance.

Ki-67 proliferation index in neuroendocrine tumors: Can augmented reality microscopy with image analysis improve scoring?

BACKGROUND

The Ki-67 index is important for grading neuroendocrine tumors (NETs) in cytology. However, different counting methods exist. Recently, augmented reality microscopy (ARM) has enabled real-time image analysis using glass slides. The objective of the current study was to compare different traditional Ki-67 scoring methods in cell block material with newer methods such as ARM.

METHODS

Ki-67 immunostained slides from 50 NETs of varying grades were retrieved (39 from the pancreas and 11 metastases). Methods with which to quantify the Ki-67 index in up to 3 hot spots included: 1) "eyeball" estimation (EE); 2) printed image manual counting (PIMC); 3) ARM with live image analysis; and 4) image analysis using whole-slide images (WSI) (field of view [FOV] and the entire slide).

RESULTS

The Ki-67 index obtained using the different methods varied. The pairwise kappa results varied from no agreement for image analysis using digital image analysis WSI (FOV) and histology to near-perfect agreement for ARM and PIMC. Using surgical pathology as the gold standard, the EE method was found to have the highest concordance rate (84.2%), followed by WSI analysis of the entire slide (73.7%) and then both the ARM and PIMC methods (63.2% for both). The PIMC method was the most time-consuming whereas image analysis using WSI (FOV) was the fastest method followed by ARM.

CONCLUSIONS

The Ki-67 index for NETs in cell block material varied by the method used for scoring, which may affect grade. PIMC was the most time-consuming method, and EE had the highest concordance rate. Although real-time automated counting using image analysis demonstrated inaccuracies, ARM streamlined and hastened the task of Ki-67 quantification in NETs.

Quantitative image analysis for CD8 score in lung small biopsies and cytology cell-blocks

INTRODUCTION

Immunotherapy has shown promising results in non-small cell lung cancer (NSCLC), for which tumour-infiltrating cytotoxic (CD8+) T cells play a critical role. We investigated the utility of image analysis (IA) to quantify CD8+ T cells in a series of matched small biopsies and resections of NSCLC.

METHODS

CD8 immunohistochemistry was performed on cell-blocks (CB), core needle biopsies (CNB) and corresponding resections from primary NSCLCs. Slides were digitised using an Aperio AT2 scanner (Leica) and annotated by whole slide image (WSI) or fields of view occupied by tissue spots (TS). Quantitative IA was performed with a customised Aperio algorithm (Leica). CD8 scores (number of T cells with 1-3+ staining/total area) were then compared.

RESULTS

Forty-four cases with CB or CNB material and a corresponding resection were analysed. Average CD8 score was determined in CB (7.67 WSI, 77.67 TS) and/or CNB (47.35 WSI, 325.67 TS), and corresponding resections (190.35 WSI, 336.58 TS). CD8 score concordance was highest (78.6%) for CNBs using WSI annotation. Overall, small biopsies (CB or CNB) correlated with the resection in 71.4% cases using WSI and 63.3% cases using TS annotation. IA performed better for low CD8 scores.

CONCLUSIONS

These findings show that CD8 density in NSCLC can be quantified by IA in small biopsies and cell blocks, achieving the best concordance using WSI scores. Discrepancies were attributed to values near the cut-off and background detection of staining. These data warrant future studies with more cases and follow-up data to further investigate the clinical utility of IA for CD8 analysis in NSCLC.

Critical diagnoses in cytopathology: Experience at a large medical center

BACKGROUND

Critical values have been well established and accepted in laboratory medicine, but to the authors' knowledge are less well accepted in anatomic pathology. Herein, the authors used a reporting process whereby reports with critical diagnoses were coded to ensure that the patient's clinical team was promptly notified electronically of this finding. The aim of the current study was to determine whether this coding mechanism was used appropriately for critical cytopathology diagnoses in the study health care system.

METHODS

A retrospective review of the University of Pittsburgh Medical Center anatomic pathology laboratory information system was performed to identify cytopathology reports in which a critical diagnosis code (MedTrak notification/CoPath Tissue Code TC66; TC66) was used from 2011 through 2016. TC66-coded cytopathology reports between 2015 and 2016 were reviewed further to determine whether this code was used appropriately.

RESULTS

A total of 1687 TC66-coded cytopathology reports were identified. Between 2015 and 2016, a total of 30 of 46 reports (65%) from academic hospitals and 46 of 441 reports (10%) from community hospitals met the critical diagnoses criteria outlined by institutional policy. The remaining TC66-coded cases were predominantly for new diagnoses of malignancy in patients clinically suspected of having cancer.

CONCLUSIONS

Use of a code for critical cytopathology diagnoses was found to be occurring increasingly at the study health care system. Pathologists at the academic and community hospitals in the study institution used this code somewhat differently, reflecting the need to satisfy communication with clinicians in different practice settings. Nevertheless, the authors' experiences with using a code for critical diagnoses not only ensured timely patient care but also proposed a model that could be used by other medical specialties to enhance communication and improve quality of care. Cancer Cytopathol 2017;125:726-30. © 2017 American Cancer Society.