Tumor core biopsies adequately represent immune microenvironment of high-grade serous carcinoma

Quantifying intratumoral biomarker heterogeneity in tubo-ovarian high-grade serous carcinoma to optimize clinical translation

Intratumoral heterogeneity (ITH) is spatial, phenotypic, or molecular differences within the same tumor that have important implications for accurate tumor classification and assessment of predictive biomarkers. The Canadian Ovarian Experimental Unified Resource (COEUR) has created a cohort of 437 FFPE tissue specimens from 108 tubo-ovarian high-grade serous carcinoma (HGSC) patients to quantify ITH across the anatomical sites and between primary and recurrence. We quantified the ITH of six clinically used immunohistochemical diagnostic and prognostic biomarkers (WT1, p53, p16, PR, CD8, and Ki67). Markers were stained on tissue microarrays and scored using a continuous or categorical interpretation of staining patterns. Two-way random effect and nested intraclass correlation were used to assess continuous markers, and Gwet's AC1 was used for categorical markers. All biomarkers showed at least substantial agreement over several spatial comparisons, with WT1, p53 and p16 showing almost perfect agreement for most spatial comparisons. Similarly, categorical WT1, p53 and p16 showed almost perfect agreement for temporal comparisons, while the agreement for primary versus recurrence for PR, CD8 and Ki67 was only fair. We provide power calculations to achieve reliability of > 0.60 and recommend testing emerging protein biomarkers to see whether they reach a clinically acceptable benchmark level of ITH.

Hepatocellular Carcinoma Immune Microenvironment Analysis: A Comprehensive Assessment with Computational and Classical Pathology

PURPOSE

The spatial variability and clinical relevance of the tumor immune microenvironment (TIME) are still poorly understood for hepatocellular carcinoma (HCC). In this study, we aim to develop a deep learning (DL)-based image analysis model for the spatial analysis of immune cell biomarkers and microscopically evaluate the distribution of immune infiltration.

EXPERIMENTAL DESIGN

Ninety-two HCC surgical liver resections and 51 matched needle biopsies were histologically classified according to their immunophenotypes: inflamed, immune-excluded, and immune-desert. To characterize the TIME on immunohistochemistry (IHC)-stained slides, we designed a multistage DL algorithm, IHC-TIME, to automatically detect immune cells and their localization in the TIME in tumor-stroma and center-border segments.

RESULTS

Two models were trained to detect and localize the immune cells on IHC-stained slides. The framework models (i.e., immune cell detection models and tumor-stroma segmentation) reached 98% and 91% accuracy, respectively. Patients with inflamed tumors showed better recurrence-free survival than those with immune-excluded or immune-desert tumors. Needle biopsies were found to be 75% accurate in representing the immunophenotypes of the main tumor. Finally, we developed an algorithm that defines immunophenotypes automatically based on the IHC-TIME analysis, achieving an accuracy of 80%.

CONCLUSIONS

Our DL-based tool can accurately analyze and quantify immune cells on IHC-stained slides of HCC. Microscopic classification of the TIME can stratify HCC according to the patient prognosis. Needle biopsies can provide valuable insights for TIME-related prognostic prediction, albeit with specific constraints. The computational pathology tool provides a new way to study the HCC TIME.

Analyzing the Spatial Distribution of Immune Cells in Lung Adenocarcinoma

(1) Background: This study investigates the tumor immune microenvironment, focusing on immune cell distribution in lung adenocarcinoma. (2) Methods: We evaluated fifty cases of lung adenocarcinoma, and suitable areas for further studies were annotated on the histological slides. Two tumor cores per case were obtained, one from the tumor's center and another from its periphery, and introduced into three paraffin receptor blocks for optimized processing efficiency. The 4-micrometer-thick tissue microarray sections were stained for H&E and for CD68, CD163, CD8, CD4, and PD-L1; (3) Results: Our investigation revealed significant correlations between PD-L1 expression in tumor cells and the presence of CD163+ macrophages, between CD4+ cells and CD8+, CD68+, and CD163+ cells, and also between CD8+ T cells and CD163+ cells. Additionally, while we observed some differences in cellular components and densities between the tumor center and periphery, these differences were not statistically significant. However, distinct correlations between PD-L1 and immune cells in these regions were identified, suggesting spatial heterogeneity in the immune landscape. (4) Conclusions: These results emphasize the intricate interactions between immune cells and tumor cells in lung adenocarcinoma. Understanding patient spatial immune profile could improve patient selection for immunotherapy, ensuring that those most likely to benefit are identified.

Intratumoral Immunotherapy: Is It Ready for Prime Time?

PURPOSE OF REVIEW

This review presents the rationale for intratumoral immunotherapy, technical considerations and safety. Clinical results from the latest trials are provided and discussed.

RECENT FINDINGS

Intratumoral immunotherapy is feasible and safe in a wide range of cancer histologies and locations, including lung and liver. Studies mainly focused on multi-metastatic patients, with some positive trials such as T-VEC in melanoma, but evidence of clinical benefit is still lacking. Recent results showed improved outcomes in patients with a low tumor burden. Intratumoral immunotherapy can lower systemic toxicities and boost local and systemic immune responses. Several studies have proven the feasibility, repeatability, and safety of this approach, with some promising results in clinical trials. The clinical benefit might be improved in patients with a low tumor burden. Future clinical trials should focus on adequate timing of treatment delivery during the course of the disease, particularly in the neoadjuvant setting.

Classification of Tumor Immune Microenvironment According to Programmed Death-Ligand 1 Expression and Immune Infiltration Predicts Response to Immunotherapy Plus Chemotherapy in Advanced Patients With NSCLC

INTRODUCTION

According to mechanisms of adaptive immune resistance, tumor immune microenvironment (TIME) is classified into four types: (1) programmed death-ligand 1 (PD-L1)-negative and tumor-infiltrating lymphocyte (TIL)-negative (type I); (2) PD-L1-positive and TIL-positive (type II); (3) PD-L1-negative and TIL-positive (type III); and (4) PD-L1-positive and TIL-negative (type IV). However, the relationship between the TIME classification model and immunotherapy efficacy has not been validated by any large-scale randomized controlled clinical trial among patients with advanced NSCLC.

METHODS

On the basis of RNA-sequencing and immunohistochemistry data from the ORIENT-11 study, we optimized the TIME classification model and evaluated its predictive value for the efficacy of immunotherapy plus chemotherapy.

RESULTS

PD-L1 mRNA expression and immune score calculated by the ESTIMATE method were the strongest predictors for the efficacy of immunotherapy plus chemotherapy. Therefore, they were determined as the optimized definition of the TIME classification system. When compared between combination therapy and chemotherapy alone, only the type II subpopulation with high immune score and high PD-L1 mRNA expression was significantly associated with improved progression-free survival (PFS) (hazard ratio = 0.12, 95% confidence interval: 0.06-0.25, p < 0.001) and overall survival (hazard ratio = 0.27, 95% confidence interval: 0.13-0.55, p < 0.001). In the combination group, the type II subpopulation had a much longer survival time, not even reaching the median PFS or overall survival, but the other three subpopulations were susceptible to having similar PFS. In the chemotherapy group, there was no marked association between survival outcomes and TIME subtypes.

CONCLUSIONS

Only patients with both high PD-L1 expression and high immune infiltration could benefit from chemotherapy plus immunotherapy in first-line treatment of advanced NSCLC. For patients lacking either PD-L1 expression or immune infiltration, chemotherapy alone might be a better treatment option to avoid unnecessary toxicities and financial burdens.

Role of Anti-Cancer Peptides as Immunomodulatory Agents: Potential and Design Strategy

The usage of peptide-based drugs to combat cancer is gaining significance in the pharmaceutical industry. The collateral damage caused to normal cells due to the use of chemotherapy, radiotherapy, etc. has given an impetus to the search for alternative methods of cancer treatment. For a long time, antimicrobial peptides (AMPs) have been shown to display anticancer activity. However, the immunomodulatory activity of anti-cancer peptides has not been researched very extensively. The interconnection of cancer and immune responses is well-known. Hence, a search and design of molecules that can show anti-cancer and immunomodulatory activity can be lead molecules in this field. A large number of anti-cancer peptides show good immunomodulatory activity by inhibiting the pro-inflammatory responses that assist cancer progression. Here, we thoroughly review both the naturally occurring and synthetic anti-cancer peptides that are reported to possess both anti-cancer and immunomodulatory activity. We also assess the structural and biophysical parameters that can be utilized to improve the activity. Both activities are mostly reported by different groups, however, we discuss them together to highlight their interconnection, which can be used in the future to design peptide drugs in the field of cancer therapeutics.

Tumor-Infiltrating Lymphocytes (TILs) in Epithelial Ovarian Cancer: Heterogeneity, Prognostic Impact, and Relationship with Immune Checkpoints

Epithelial ovarian cancers (EOC) are often diagnosed at an advanced stage with carcinomatosis and a poor prognosis. First-line treatment is based on a chemotherapy regimen combining a platinum-based drug and a taxane-based drug along with surgery. More than half of the patients will have concern about a recurrence. To improve the outcomes, new therapeutics are needed, and diverse strategies, such as immunotherapy, are currently being tested in EOC. To better understand the global immune contexture in EOC, several studies have been performed to decipher the landscape of tumor-infiltrating lymphocytes (TILs). CD8+ TILs are usually considered effective antitumor immune effectors that immune checkpoint inhibitors can potentially activate to reject tumor cells. To synthesize the knowledge of TILs in EOC, we conducted a review of studies published in MEDLINE or EMBASE in the last 10 years according to the PRISMA guidelines. The description and role of TILs in EOC prognosis are reviewed from the published data. The links between TILs, DNA repair deficiency, and ICs have been studied. Finally, this review describes the role of TILs in future immunotherapy for EOC.

Practical consideration for successful sequential tumor biopsies in first-in-human trials

In first-in-human (FIH) trials, sequential tumor biopsies, i.e., two consecutive tumor biopsies, the first performed at baseline (pretreatment) and the second during the early treatment period (on-treatment), provide proof of concept in investigational new drugs. We evaluated the success of sequential tumor biopsies in FIH trials, and explored approaches for improved success rates. We retrospectively reviewed the sequential tumor biopsies required in 17 of 52 FIH trials conducted from 2015 to 2020. One hundred and thirty-eight patients were identified. Success of either pretreatment or on-treatment biopsy alone, and of sequential tumor biopsies, was defined as the acquisition of viable tumor cells and as obtaining tumor cells from both biopsy specimens, respectively. The success rates of pretreatment and on-treatment biopsy were 98.6% and 94.2%, respectively, and of sequential tumor biopsies was 70.3%. Adverse events associated with the pretreatment biopsies (33.3% positive; 72.0% negative) and timing of the first imaging assessment (before on-treatment biopsy = 40.0%; after on-treatment biopsy = 82.7%) correlated with successful sequential tumor biopsies. The reasons for unsuccessful sequential tumor biopsies could be categorized into two groups: 1) patient refusal of the on-treatment biopsy (most frequently due to early disease progression); and 2) absence of tumor cells in the pretreatment or on-treatment biopsy specimen. We propose an approach to achieving greater success in sequential tumor biopsies in FIH trials; the first imaging assessment during the study should be scheduled after on-treatment biopsy. (Registration number UMIN000042487, Date of registration November 18, 2020).

Combined PD-1/PD-L1 and tumor-infiltrating immune cells redefined a unique molecular subtype of high-grade serous ovarian carcinoma

Background

High-grade serous ovarian carcinoma is highly heterogeneous, and although many studies have been conducted to identify high-grade serous ovarian carcinoma molecular subtypes that are sensitive to immunotherapy, no precise molecular subtype has been proposed to date. Immune cell infiltration and immune checkpoints are highly correlated with immunotherapy. Here, we investigated immune cell infiltration and immune checkpoint values for prognosis and precise immunotherapy for high-grade serous ovarian carcinoma based on molecular subtype classification.

Results

“High antigen-presenting cells infiltration molecular subtype of high-grade serous ovarian carcinoma” was identified in immune cell infiltration profiles. Each of the three immune cell infiltration clusters (A, B, and C) demonstrated distinct immune cell characterization, with immune cell infiltration cluster C exhibiting high antigen-presenting cell infiltration, improved prognosis, and higher sensitivity to immunotherapy. Programmed death-1/programmed death ligand 1 has a prognostic and predictive role that can help classify molecular subtypes.

Conclusions

Our findings redefined a unique molecular subtype of high-grade serous ovarian carcinoma, suggesting that high-grade serous ovarian carcinoma patients with higher antigen-presenting cell infiltration and programmed death-1/programmed death ligand 1 expression can benefit from precise immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08265-y.

Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibroblast Activation Protein Expression in Patients With Pancreatic Ductal Adenocarcinoma

BACKGROUND

Fibroblast activation protein (FAP) in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment of patients. Accurate preoperative FAP expression can better identify the population benefitting from FAP-targeting drugs.

PURPOSE

To develop and validate a machine learning classifier based on noncontrast MRI for the preoperative prediction of FAP expression in patients with PDAC.

STUDY TYPE

Retrospective cohort study.

POPULATION

Altogether, 129 patients with pathology-confirmed PDAC undergoing MR scan and surgical resection; 90 patients in a training cohort, and 39 patients in a validation cohort. FIELD STRENGTH/SEQUENCE/3T: Breath-hold single-shot fast-spin echo T2-weighted sequence and unenhanced and noncontrast T1-weighted fat-suppressed sequences.

ASSESSMENT

FAP expression was quantified using immunohistochemistry. For each patient, 1409 radiomics features were extracted from T1- and T2-weighted images and reduced using the least absolute shrinkage and selection operator logistic regression algorithm. A multilayer perceptron (MLP) network classifier was developed using the training and validation set. The MLP network classifier performance was determined by its discriminative ability, calibration, and clinical utility.

STATISTICAL TESTS

Kaplan-Meier estimates, student's t-test, the Kruskal-Wallis H test, and the chi-square test, univariable regression analysis, receiver operating characteristic curve, and decision curve analysis were used.

RESULTS

A log-rank test showed that the survival of patients with low FAP expression (24.43 months) was significantly longer (P < 0.05) than that in the FAP-high group (13.50 months). The prediction model showed good discrimination in the training set (area under the curve [AUC], 0.84) and the validation set (AUC, 0.77). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 75.00%, 79.41%, 0.77, 0.86, and 0.66, respectively, whereas those for the validation set were 85.00%, 63.16%, 0.74, 0.71, and 0.80, respectively.

DATA CONCLUSIONS

The MLP network classifier based on noncontrast MRI can accurately predict FAP expression in patients with PDAC.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response

Tumors are not only aggregates of malignant cells but also well-organized complex ecosystems. The immunological components within tumors, termed the tumor immune microenvironment (TIME), have long been shown to be strongly related to tumor development, recurrence and metastasis. However, conventional studies that underestimate the potential value of the spatial architecture of the TIME are unable to completely elucidate its complexity. As innovative high-flux and high-dimensional technologies emerge, researchers can more feasibly and accurately detect and depict the spatial architecture of the TIME. These findings have improved our understanding of the complexity and role of the TIME in tumor biology. In this review, we first epitomized some representative emerging technologies in the study of the spatial architecture of the TIME and categorized the description methods used to characterize these structures. Then, we determined the functions of the spatial architecture of the TIME in tumor biology and the effects of the gradient of extracellular nonspecific chemicals (ENSCs) on the TIME. We also discussed the potential clinical value of our understanding of the spatial architectures of the TIME, as well as current limitations and future prospects in this novel field. This review will bring spatial architectures of the TIME, an emerging dimension of tumor ecosystem research, to the attention of more researchers and promote its application in tumor research and clinical practice.

Identification of a Prognostic Model Based on Immune-Related Genes of Lung Squamous Cell Carcinoma

Immune-related genes (IRGs) play considerable roles in tumor immune microenvironment (IME). This research aimed to discover the differentially expressed immune-related genes (DEIRGs) based on the Cox predictive model to predict survival for lung squamous cell carcinoma (LUSC) through bioinformatics analysis. First of all, the differentially expressed genes (DEGs) were acquired based on The Cancer Genome Atlas (TCGA) using the limma R package, the DEIRGs were obtained from the ImmPort database, whereas the differentially expressed transcription factors (DETFs) were acquired from the Cistrome database. Thereafter, a TFs-mediated IRGs network was constructed to identify the candidate mechanisms for those DEIRGs in LUSC at molecular level. Moreover, Gene Ontology (GO), together with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, was conducted for exploring those functional enrichments for DEIRGs. Besides, univariate as well as multivariate Cox regression analysis was conducted for establishing a prediction model for DEIRGs biomarkers. In addition, the relationship between the prognostic model and immunocytes was further explored through immunocyte correlation analysis. In total, 3,599 DEGs, 223 DEIRGs, and 46 DETFs were obtained from LUSC tissues and adjacent non-carcinoma tissues. According to multivariate Cox regression analysis, 10 DEIRGs (including CALCB, GCGR, HTR3A, AMH, VGF, SEMA3B, NRTN, ENG, ACVRL1, and NR4A1) were retrieved to establish a prognostic model for LUSC. Immunocyte infiltration analysis showed that dendritic cells and neutrophils were positively correlated with IRGs, which possibly exerted an important part within the IME of LUSC. Our study identifies a prognostic model based on IRGs, which is then used to predict LUSC prognosis and analyze immunocyte infiltration. This may provide a novel insight for exploring the potential IRGs in the IME of LUSC.