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.

BackgroundThe 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.MethodsKi‐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).ResultsThe 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.ConclusionsThe 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.

Despite their association with DNA mismatch repair (MMR) protein deficiency, colonic adenocarcinomas with mucinous, signet ring cell, or medullary differentiation have not been associated with improved survival compared with conventional adenocarcinomas in most studies. Recent studies indicate that increased T-cell infiltration in the tumor microenvironment has a favorable prognostic effect in colonic adenocarcinoma. However, the prognostic effect of tumor-associated T cells has not been evaluated in histologic subtypes of colonic adenocarcinoma. We evaluated CD8-positive T-cell density in 259 patients with colonic adenocarcinoma, including 113 patients with tumors demonstrating mucinous, signet ring cell, or medullary differentiation, using a validated automated quantitative digital image analysis platform and correlated CD8-positive T-cell density with histopathologic variables, MMR status, molecular alterations, and survival. CD8-positive T-cell densities were significantly higher for MMR protein-deficient tumors (P<0.001), BRAF V600E mutant tumors (P=0.004), and tumors with medullary differentiation (P<0.001) but did not correlate with mucinous or signet ring cell histology (P>0.05 for both). In the multivariable model of factors predicting disease-free survival, increased CD8-positive T-cell density was associated with improved survival both in the entire cohort (hazard ratio=0.34, 95% confidence interval, 0.15-0.75, P=0.008) and in an analysis of patients with tumors with mucinous, signet ring cell, or medullary differentiation (hazard ratio=0.06, 95% confidence interval, 0.01-0.54, P=0.01). The prognostic effect of CD8-positive T-cell density was independent of tumor stage, MMR status, KRAS mutation, and BRAF mutation. Venous invasion was the only other variable independently associated with survival in both the entire cohort and in patients with tumors with mucinous, signet ring cell, or medullary differentiation. In summary, our results indicate that the prognostic value of MMR protein deficiency is most likely attributed to increased tumor-associated CD8-positive T cells and that automated quantitative CD8 T-cell analysis is a better biomarker of patient survival, particularly in patients with tumors demonstrating mucinous, signet ring cell, or medullary differentiation.

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 &lt; .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 &lt; .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 adenocarcinomaAims: 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.