Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy

Head-to-head comparison of composite and individual biomarkers to predict clinical benefit to PD-1 blockade in non-small cell lung cancer

BACKGROUND

The efficacy of PD-1 blocking agents in advanced NSCLC has shown prolonged effectiveness, but only in a minority of patients. Multiple biomarkers have been explored to predict treatment benefit, yet their combined performance remains inadequately examined. In this study, we assessed the combined predictive performance of multiple biomarkers in NSCLC patients treated with nivolumab.

METHODS

Pretreatment samples from 135 patients receiving nivolumab were used to evaluate the predictive performance of CD8 tumor-infiltrating lymphocytes (TILs), intratumoral (IT) localization of CD8 TILs, PD-1 high expressing TILs (PD1T TILs), CD3 TILs, CD20 B-cells, tertiary lymphoid structures (TLS), PD-L1 tumor proportion score (TPS) and the Tumor Inflammation score (TIS). Patients were randomly assigned to a training (n = 55) and validation cohort (n = 80). The primary outcome measure was Disease Control at 6 months (DC 6m) and the secondary outcome measure was DC at 12 months (DC 12m).

RESULTS

In the validation cohort, the two best performing composite biomarkers (i.e. CD8+IT-CD8 and CD3+IT-CD8) demonstrated similar or lower sensitivity (64% and 83%) and NPV (76% and 85%) compared to individual biomarkers PD-1T TILs and TIS (sensitivity: 72% and 83%, NPV: 86% and 84%) for DC 6m, respectively. Additionally, at 12 months, both selected composite biomarkers (CD8+IT-CD8 and CD8+TIS) demonstrated inferior predictive performance compared to PD-1T TILs and TIS alone. PD-1T TILs and TIS showed high sensitivity (86% and 100%) and NPV (95% and 100%) for DC 12m. PD-1T TILs could more accurately discriminate patients with no long-term benefit, as specificity was substantially higher compared to TIS (74% versus 39%).

CONCLUSION

Composite biomarkers did not show improved predictive performance compared to PD-1T TILs and TIS alone for both the 6- and 12-month endpoints. PD-1T TILs and TIS identified patients with DC 12m with high sensitivity. Patients with no long-term benefit to PD-1 blockade were most accurately identified by PD-1T TILs.

Prediction performance comparison of biomarkers for response to immune checkpoint inhibitors in advanced non-small cell lung cancer

BACKGROUND

The aim of the present study was to compare the predictive accuracy of PD-L1 immunohistochemistry (IHC), tissue or blood tumor mutation burden (tTMB, bTMB), gene expression profile (GEP), driver gene mutation, and combined biomarkers for immunotherapy response of advanced non-small cell lung cancer (NSCLC).

METHODS

In part 1, clinical trials involved with predictive biomarker exploration for immunotherapy in advanced NSCLC were included. The area under the curve (AUC) of the summary receiver operating characteristic (SROC), sensitivity, specificity, likelihood ratio and predictive value of the biomarkers were evaluated. In part 2, public datasets of immune checkpoint inhibitor (ICI)-treated NSCLC involved with biomarkers were curated (N = 871). Odds ratio (OR) of the positive versus negative biomarker group for objective response rate (ORR) was measured.

RESULTS

In part 1, the AUC of combined biomarkers (0.75) was higher than PD-L1 (0.64), tTMB (0.64), bTMB (0.68), GEP (0.67), and driver gene mutation (0.51). Combined biomarkers also had higher specificity, positive likelihood ratio and positive predictive value than single biomarkers. In part 2, the OR of combined biomarkers of PD-L1 plus TMB (PD-L1 cutoff 1%, 0.14; cutoff 50% 0.13) was lower than that of PD-L1 (cutoff 1%, 0.33; cutoff 50% 0.24), tTMB (0.28), bTMB (0.48), EGFR mutation (0.17) and KRAS mutation (0.47), for distinguishing ORR of patients after immunotherapy. Furthermore, positive PD-L1, tTMB-high, wild-type EGFR, and positive PD-L1 plus TMB were associated with prolonged progression-free survival (PFS).

CONCLUSION

Combined biomarkers have superior predictive accuracy than single biomarkers for immunotherapy response of NSCLC. Further investigation is warranted to select optimal biomarkers for various clinical settings.

Digital Pathology for Better Clinical Practice

(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin-eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists' evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy.

Cancer biomarkers: Emerging trends and clinical implications for personalized treatment

The integration of cancer biomarkers into oncology has revolutionized cancer treatment, yielding remarkable advancements in cancer therapeutics and the prognosis of cancer patients. The development of personalized medicine represents a turning point and a new paradigm in cancer management, as biomarkers enable oncologists to tailor treatments based on the unique molecular profile of each patient's tumor. In this review, we discuss the scientific milestones of cancer biomarkers and explore future possibilities to improve the management of patients with solid tumors. This progress is primarily attributed to the biological characterization of cancers, advancements in testing methodologies, elucidation of the immune microenvironment, and the ability to profile circulating tumor fractions. Integrating these insights promises to continually advance the precision oncology field, fostering better patient outcomes.

CD57-positive CD8 + T cells define the response to anti-programmed cell death protein-1 immunotherapy in patients with advanced non-small cell lung cancer

Immune checkpoint inhibitors have transformed the treatment landscape of non-small cell lung cancer (NSCLC). However, accurately identifying patients who will benefit from immunotherapy remains a challenge. This study aimed to discover potential biomarkers for predicting immunotherapy response in NSCLC patients. Single-cell mass cytometry (CyTOF) was utilized to analyze immune cell subsets in peripheral blood mononuclear cells (PBMCs) obtained from NSCLC patients before and 12 weeks after single-agent immunotherapy. The CyTOF findings were subsequently validated using flow cytometry and multiplex immunohistochemistry/immunofluorescence in PBMCs and tumor tissues, respectively. RNA sequencing (RNA-seq) was performed to elucidate the underlying mechanisms. In the CyTOF cohort (n = 20), a high frequency of CD57+CD8+ T cells in PBMCs was associated with durable clinical benefit from immunotherapy in NSCLC patients (p = 0.034). This association was further confirmed in an independent cohort using flow cytometry (n = 27; p < 0.001), with a determined cutoff value of 12.85%. The cutoff value was subsequently validated in another independent cohort (AUC = 0.733). We also confirmed the CyTOF findings in pre-treatment formalin-fixed and paraffin-embedded tissues (n = 90; p < 0.001). RNA-seq analysis revealed 475 differentially expressed genes (DEGs) between CD57+CD8+ T cells and CD57-CD8+ T cells, with functional analysis identifying DEGs significantly enriched in immune-related signaling pathways. This study highlights CD57+CD8+ T cells as a promising biomarker for predicting immunotherapy success in NSCLC patients.

Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review

Anti-programmed cell death ligand 1 (anti-PD-L1)  immunotherapy is an increasingly crucial in cancer treatment. To date, the Federal Drug Administration (FDA) has approved four PD-L1 immunohistochemistry (IHC) staining protocols, commercially available in the form of "kits", facilitating testing for PD-L1 expression. These kits comprise four PD-L1 antibodies on two separate IHC platforms, each utilizing distinct, non-interchangeable scoring systems. Several factors, including tumor heterogeneity and the size of the tissue specimens assessed, can lead to PD-L1 status misclassification, potentially hindering the initiation of therapy. Therefore, the development of more accurate predictive biomarkers to distinguish between responders and non-responders prior to anti-PD-1/PD-L1 therapy warrants further research. Achieving this goal necessitates refining sampling criteria, enhancing current methods of PD-L1 detection, and deepening our understanding of the impact of additional biomarkers. In this article, we review potential solutions to improve the predictive accuracy of PD-L1 assessment in order to more precisely anticipate patients' responses to anti-PD-1/PD-L1 therapy, monitor disease progression and predict clinical outcomes.

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

BACKGROUND

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.

MATERIALS AND METHODS

We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.

RESULTS

A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.

CONCLUSION

AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

Challenges Coexist with Opportunities: Spatial Heterogeneity Expression of PD-L1 in Cancer Therapy

Cancer immunotherapy using anti-programmed death-ligand 1 (PD-L1) antibodies has been used in various clinical applications and achieved certain results. However, such limitations as autoimmunity, tumor hyperprogression, and overall low patient response rate impede its further clinical application. Mounting evidence has revealed that PD-L1 is not only present in tumor cell membrane but also in cytoplasm, exosome, or even nucleus. Among these, the dynamic and spatial heterogeneous expression of PD-L1 in tumors is mainly responsible for the unsatisfactory efficacy of PD-L1 antibodies. Hence, numerous studies focus on inhibiting or degrading PD-L1 to improve immune response, while a comprehensive understanding of the molecular mechanisms underlying spatial heterogeneity of PD-L1 can fundamentally transform the current status of PD-L1 antibodies in clinical development. Herein, the concept of spatial heterogeneous expression of PD-L1 is creatively introduced, encompassing the structure and biological functions of various kinds of PD-L1 (including mPD-L1, cPD-L1, nPD-L1, and exoPD-L1). Then an in-depth analysis of the regulatory mechanisms and potential therapeutic targets of PD-L1 is provided, seeking to offer a solid basis for future investigation. Moreover, the current status of agents is summarized, especially small molecular modulators development directed at these new targets, offering a novel perspective on potential PD-L1 therapeutics strategies.

The molecular mechanism of actions and clinical utilities of tumor infiltrating lymphocytes in gastrointestinal cancers: a comprehensive review and future prospects toward personalized medicine

Gastrointestinal (GI) cancers remain a significant global health burden, accounting for a substantial number of cases and deaths. Regrettably, the inadequacy of dependable biomarkers hinders the precise forecasting of patient prognosis and the selection of appropriate therapeutic sequencing for individuals with GI cancers, leading to suboptimal outcomes for numerous patients. The intricate interplay between tumor-infiltrating lymphocytes (TILs) and the tumor immune microenvironment (TIME) has been shown to be a pivotal determinant of response to anti-cancer therapy and consequential clinical outcomes across a multitude of cancer types. Therefore, the assessment of TILs has garnered global interest as a promising prognostic biomarker in oncology, with the potential to improve clinical decision-making substantially. Moreover, recent discoveries in immunotherapy have progressively changed the landscape of cancer treatment and significantly prolonged the survival of patients with advanced cancers. Nonetheless, the response rate remains constrained within solid tumor sufferers, even when TIL landscapes appear comparable, which calls for the development of our understanding of cellular and molecular cross-talk between TIME and tumor. Hence, this comprehensive review encapsulates the extant literature elucidating the TILs' underlying molecular pathogenesis, prognostic significance, and their relevance in the realm of immunotherapy for patients afflicted by GI tract cancers. Within this review, we demonstrate that the type, density, and spatial distribution of distinct TIL subpopulations carries pivotal implications for the prediction of anti-cancer treatment responses and patient survival. Furthermore, this review underscores the indispensable role of TILs in modulating therapeutic responses within distinct molecular subtypes, such as those characterized by microsatellite stability or programmed cell death ligand-1 expression in GI tract cancers. The review concludes by outlining future directions in TIL-based personalized medicine, including integrating TIL-based approaches into existing treatment regimens and developing novel therapeutic strategies that exploit the unique properties of TILs and their potential as a promising avenue for personalized cancer treatment.

The Mutational, Prognostic, and Therapeutic Landscape of Neuroendocrine Neoplasms

BACKGROUND

Neuroendocrine neoplasms (NENs) represent clinically and genetically heterogeneous malignancies, thus a comprehensive understanding of underlying molecular characteristics, prognostic signatures, and potential therapeutic targets is urgently needed.

METHODS

Next-generation sequencing (NGS) and immunohistochemistry were applied to acquire genomic and immune profiles of NENs from 47 patients.

RESULTS

Difference was distinguished based on differentiation grade and primary localization. Poorly differentiated neuroendocrine carcinomas (NECs) and well-differentiated neuroendocrine tumors (NETs) harbored distinct molecular features; we observed that tumor mutational burden (TMB) and tumor neoantigen burden (TNB) were significantly higher in NECs versus NETs. Notably, we identified a 7-gene panel (MLH3, NACA, NOTCH1, NPAP1, RANBP17, TSC2, and ZFHX4) as a novel prognostic signature in NENs; patients who carried mutations in any of the 7 genes exhibited significantly poorer survival. Furthermore, loss of heterozygosity (LOH) and germline homogeneity in human leukocyte antigen (HLA) are common in NENs, accounting for 39% and 36%, respectively. Notably, HLA LOH was an important prognostic biomarker for a subgroup of NEN patients. Finally, we analyzed clinically actionable targets in NENs, revealing that TMB high (TMB-H) or gene mutations in TP53, KRAS, and HRAS were the most frequently observed therapeutic indicators, which granted eligibility to immune checkpoint blockade (ICB) and targeted therapy.

CONCLUSION

Our study revealed heterogeneity of NENs, and identified novel prognostic signatures and potential therapeutic targets, which directing improvements of clinical management for NEN patients in the foreseeable future.

Potential biomarkers for immunotherapy in non-small-cell lung cancer

For individuals with advanced or metastatic non-small cell lung cancer (NSCLC), the primary treatment is platinum-based doublet chemotherapy. Immune checkpoint inhibitors (ICIs), primarily PD-1/PD-L1 and CTLA-4, have been found to be effective in patients with NSCLC who have no EGFR/ALK mutations. Furthermore, ICIs are considered a standard therapy. The quantity of fresh immunogenic antigens discovered by cytotoxic T cells was measured by PD-L1 expression and tumor mutational burden (TMB), which were the first biomarkers assessed in clinical trials. However, immunotherapy did not have response efficacy markers similar to targeted therapy, highlighting the significance of newly developed biomarkers. This investigation aims to review the research on immunotherapy for NSCLC, focusing primarily on the impact of biomarkers on efficacy prediction to determine whether biomarkers may be utilized to evaluate the effectiveness of immunotherapy.

The germline HLA-A02B62 supertype is associated with a PD-L1-positive tumour immune microenvironment and poor prognosis in stage I lung cancer

Background

Germline HLA class I molecule supertypes are shown to correlate with response to anti-PD-1 therapy. Here, we investigate the significance of germline HLA-A and HLA-B supertypes in tumour microenvironment of non-small-cell lung cancer.

Methods

Totally 278 NSCLC patients were collected retrospectively. HLA genotyping was conducted using next-generation sequencing. The evaluation of tumour-infiltrating lymphocytes was performed by multiplex immunohistochemistry assay. Correlations among HLA supertypes, tumour infiltrating lymphocytes, and clinicopathological characteristics were assessed.

Results

HLA-A03 and HLA-B62 were the supertypes with the highest proportions, at 69.1% and 52.2%, respectively. HLA-A02 or HLA-B62, but not HLA-A03, associated with higher PD-L1+ tumour and stromal cells levels, CD68+ cells, and CD68+PD-L1+ cells. Patients with both HLA-A02 and HLA-B62 supertypes displayed significantly higher PD-L1+ cells, CD68+ cells, and CD8+ cells levels than patients with other supertypes (P = 0.0301, P = 0.0479, P = 0.0192). These cells collectively constitute a hot but immunosuppressive tumour microenvironment. Accordingly, patients with both HLA-A02 and HLA-B62 supertypes had short progression-free survival after surgery (HR = 2.27, P = 0.0373).

Conclusions

The HLA-A02B62 supertype could serve as a possible indicator of poor prognosis in early-stage lung cancer. However, it may also act as a favorable prognostic factor for immunotherapy, given its association with a PD-L1-positive tumour microenvironment.

Comparing and Correcting Spectral Sensitivities between Multispectral Microscopes: A Prerequisite to Clinical Implementation

Multispectral, multiplex immunofluorescence (mIF) microscopy has been used to great effect in research to identify cellular co-expression profiles and spatial relationships within tissue, providing a myriad of diagnostic advantages. As these technologies mature, it is essential that image data from mIF microscopes is reproducible and standardizable across devices. We sought to characterize and correct differences in illumination intensity and spectral sensitivity between three multispectral microscopes. We scanned eight melanoma tissue samples twice on each microscope and calculated their average tissue region flux intensities. We found a baseline average standard deviation of 29.9% across all microscopes, scans, and samples, which was reduced to 13.9% after applying sample-specific corrections accounting for differences in the tissue shown on each slide. We used a basic calibration model to correct sample- and microscope-specific effects on overall brightness and relative brightness as a function of the image layer. We tested the generalizability of the calibration procedure and found that applying corrections to independent validation subsets of the samples reduced the variation to 2.9 ± 0.03%. Variations in the unmixed marker expressions were reduced from 15.8% to 4.4% by correcting the raw images to a single reference microscope. Our findings show that mIF microscopes can be standardized for use in clinical pathology laboratories using a relatively simple correction model.

The Many Faces of Urothelial Carcinomas: An Update From Pathology to Clinical Approach and Challenges in Practice

Urothelial carcinoma is a heterogeneous disease with histomorphological and genomic variations throughout the same tumor or between tumors from different patients. It has been shown that most of these histologic and genetic differences have prognostic significance and may have a guiding role in determining the appropriate treatment choice for the patient. Therefore, it is crucial for both the pathologist and the clinician to be conscious of these variations and to consider them in patient management. Recently, a consensus molecular classification has been developed and categorized urothelial carcinomas into 6 subclasses. These molecular subclasses seem to be associated with prognosis and/or response to certain therapeutic approaches like chemotherapy or immune checkpoint inhibitory therapy; however, it has not yet been sufficiently validated and has some limitations for routine application. As is well known, there are therapeutic limitations in locally advanced or metastatic urothelial carcinomas, especially those inappropriate for standard therapy with platinum-based chemotherapy regimens. Emerging new therapeutic approaches and testing for appropriate patient selection for those are discussed in this article.

Association between CD8+ tumor-infiltrating lymphocytes and prognosis of non-small cell lung cancer patients treated with PD-1/PD-L1 inhibitors: a systematic review and meta-analysis

BACKGROUND

A meta-analysis method was used to investigate the prognostic value of CD8+ tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC) patients treated with PD-1/PD-L1 inhibitors.

METHODS

A database search of PubMed, Embase, Web of Science and Cochrane Library up until February 7th, 2023. A clinical study on the relationship between CD8+ TILs and PD-1/PD-L1 inhibitors in the therapeutics of NSCLC. RevMan 5.3 and StataMP 17.0 software were used for meta-analysis. The outcome indicators incorporated overall survival (OS), progression-free survival (PFS) and objective response rate (ORR).

RESULTS

Nineteen articles with 1488 patients were included. The analysis results showed that high CD8+ TILs were associated with better OS (HR=0.60, 95% CI: 0.46-0.77; P<0.0001), PFS (HR=0.68, 95% CI: 0.53-0.88; P=0.003) and ORR (OR=2.26, 95% CI: 1.52-3.36; P<0.0001) in NSCLC patients treated with PD-1/PD-L1 inhibitors. Subgroup analysis indicated that patients with high CD8+ TILs had good clinical prognostic benefits whether the location of CD8+ TILs was intratumoral or stromal, and compared with East Asian, high CD8+ TILs in Caucasians showed a better prognosis. High CD8+ TILs in peripheral blood did not improve OS (HR=0.83, 95% CI: 0.69-1.01; P=0.06) and PFS (HR=0.93, 95% CI: 0.61-1.14; P=0.76) in NSCLC patients receiving PD-1/PD-L1 inhibitors.

CONCLUSION

In spite of the location of CD8+ TILs, high densities of CD8+ TILs were predictive of treatment outcomes in NSCLC patients treated with PD-1/PD-L1 inhibitors. However, high CD8+ TILs in peripheral blood had no predictive effect.

Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide

The incidence and mortality rates of lung cancer are high worldwide, where non-small cell lung cancer (NSCLC) accounts for more than 85% of lung cancer cases. Recent non-small cell lung cancer research has been focused on analyzing patient prognosis after surgery and identifying mechanisms in connection with clinical cohort and ribonucleic acid (RNA) sequencing data, including single-cell ribonucleic acid (scRNA) sequencing data. This paper investigates statistical techniques and artificial intelligence (AI) based non-small cell lung cancer transcriptome data analysis methods divided into target and analysis technology groups. The methodologies of transcriptome data were schematically categorized so researchers can easily match analysis methods according to their goals. The most widely known and frequently utilized transcriptome analysis goal is to find essential biomarkers and classify carcinomas and cluster NSCLC subtypes. Transcriptome analysis methods are divided into three major categories: Statistical analysis, machine learning, and deep learning. Specific models and ensemble techniques typically used in NSCLC analysis are summarized in this paper, with the intent to lay a foundation for advanced research by converging and linking the various analysis methods available.

Tissue microarrey: a potential cost-effective approach for mismatch repair testing in colorectal cancer

BACKGROUND

Deficiencies in Mismatch Repair (MMR) proteins are one of the major pathways in the development of colorectal cancer (CRC). MMR status evaluation is recommended in every new CRC patient. However, this is not fully implemented due to high costs. Tissue microarray (TMA) enables allocating tissue cores from few specimens to a single paraffin block. The primary objective of this study was to evaluate the accuracy of TMA MMR immunohistochemistry (IHC) compared to whole slide. The secondary objective was to evaluate and validate automatic digital image analysis software in differentiating pathological and normal TMA cores.

METHODS

Pathological cores were defined if at least one MMR protein was unstained. Tumoral and normal tissue of 11 CRC patients with known MMR status was used to obtain 623 TMA cores. The MMR staining of each core was evaluated by a pathologist and compared to the whole slide result. Digital analysis software by 3DHistech Ltd. was used to identify cell nucleus and quantify nuclear staining in 323 tissue cores. To identifying pathological tissue, cores the cohort was divided into a test (N = 146 cores) and validation sets (N = 177 cores). A staining intensity score (SIS) was developed, and its performance compared to the pathologist review of each core and to the whole slide result.

RESULTS

Compared to the whole slide, the pathologist's assessment had 100% sensitivity (n/N = 112/112) and 100% specificity (n/N = 278/278) with 95% lower limit of 97 and 99% respectively. The area under the receiver operating characteristic (ROC) curve of SIS was 77%. A cutoff of 55 was obtained from the ROC curve. By implementing the cutoff in the validation dataset, the SIS had sensitivity and specificity of 98.2% [90.1-100%] and 58.5% [49.3-67.4%] respectively.

CONCLUSIONS

The MMR status of CRC can be evaluated in TMA tissue cores thus potentially reducing MMR testing costs. The SIS can be used as triage indicator during pathologic review.

TRIAL REGISTRATION

Institutional ethical approval was granted for the performance of this study (Emek Medical Center Ethics ID: EMC-19-0179).

Advances in the Lung Cancer Immunotherapy Approaches

Despite the progress in the comprehension of LC progression, risk, immunologic control, and treatment choices, it is still the primary cause of cancer-related death. LC cells possess a very low and heterogeneous antigenicity, which allows them to passively evade the anticancer defense of the immune system by educating cytotoxic lymphocytes (CTLs), tumor-infiltrating lymphocytes (TILs), regulatory T cells (Treg), immune checkpoint inhibitors (ICIs), and myeloid-derived suppressor cells (MDSCs). Though ICIs are an important candidate in first-line therapy, consolidation therapy, adjuvant therapy, and other combination therapies involving traditional therapies, the need for new predictive immunotherapy biomarkers remains. Furthermore, ICI-induced resistance after an initial response makes it vital to seek and exploit new targets to benefit greatly from immunotherapy. As ICIs, tumor mutation burden (TMB), and microsatellite instability (MSI) are not ideal LC predictive markers, a multi-parameter analysis of the immune system considering tumor, stroma, and beyond can be the future-oriented predictive marker. The optimal patient selection with a proper adjuvant agent in immunotherapy approaches needs to be still revised. Here, we summarize advances in LC immunotherapy approaches with their clinical and preclinical trials considering cancer models and vaccines and the potential of employing immunology to predict immunotherapy effectiveness in cancer patients and address the viewpoints on future directions. We conclude that the field of lung cancer therapeutics can benefit from the use of combination strategies but with comprehension of their limitations and improvements.

PD-1T TILs as a Predictive Biomarker for Clinical Benefit to PD-1 Blockade in Patients with Advanced NSCLC

PURPOSE

Durable clinical benefit to PD-1 blockade in non-small cell lung cancer (NSCLC) is currently limited to a small fraction of patients, underlining the need for predictive biomarkers. We recently identified a tumor-reactive tumor-infiltrating T lymphocyte (TIL) pool, termed PD-1T TILs, with predictive potential in NSCLC. Here, we examined PD-1T TILs as biomarker in NSCLC.

EXPERIMENTAL DESIGN

PD-1T TILs were digitally quantified in 120 baseline samples from advanced NSCLC patients treated with PD-1 blockade. Primary outcome was disease control (DC) at 6 months. Secondary outcomes were DC at 12 months and survival. Exploratory analyses addressed the impact of lesion-specific responses, tissue sample properties, and combination with other biomarkers on the predictive value of PD-1T TILs.

RESULTS

PD-1T TILs as a biomarker reached 77% sensitivity and 67% specificity at 6 months, and 93% and 65% at 12 months, respectively. Particularly, a patient group without clinical benefit was reliably identified, indicated by a high negative predictive value (NPV) (88% at 6 months, 98% at 12 months). High PD-1T TILs related to significantly longer progression-free (HR 0.39, 95% CI, 0.24-0.63, P < 0.0001) and overall survival (HR 0.46, 95% CI, 0.28-0.76, P < 0.01). Predictive performance was increased when lesion-specific responses and samples obtained immediately before treatment were assessed. Notably, the predictive performance of PD-1T TILs was superior to PD-L1 and tertiary lymphoid structures in the same cohort.

CONCLUSIONS

This study established PD-1T TILs as predictive biomarker for clinical benefit to PD-1 blockade in patients with advanced NSCLC. Most importantly, the high NPV demonstrates an accurate identification of a patient group without benefit. See related commentary by Anagnostou and Luke, p. 4835.

Immunotherapy targeting inhibitory checkpoints: The role of NK and other innate lymphoid cells

Monoclonal antibodies that target specific ligand-receptor signaling pathways and act as immune checkpoint inhibitors have been designed to remove the brakes in T cells and restore strong and long-term antitumor-immunity. Of note, many of these inhibitory receptors are also expressed by Innate Lymphoid Cells (ILCs), suggesting that also blockade of inhibitory pathways in innate lymphocytes has a role in the response to the treatment with checkpoint inhibitors. ILCs comprise cytotoxic NK cells and "helper" subsets and are important cellular components in the tumor microenvironment. In addition to killing tumor cells, ILCs release inflammatory cytokines, thus contributing to shape adaptive cell activation in the context of immunotherapy. Therefore, ILCs play both a direct and indirect role in the response to checkpoint blockade. Understanding the impact of ILC-mediated response on the treatment outcome would contribute to enhance immunotherapy efficacy, as still numerous patients resist or relapse.

Maximizing the value of phase III trials in immuno-oncology: A checklist from the Society for Immunotherapy of Cancer (SITC)

The broad activity of agents blocking the programmed cell death protein 1 and its ligand (the PD-(L)1 axis) revolutionized oncology, offering long-term benefit to patients and even curative responses for tumors that were once associated with dismal prognosis. However, only a minority of patients experience durable clinical benefit with immune checkpoint inhibitor monotherapy in most disease settings. Spurred by preclinical and correlative studies to understand mechanisms of non-response to the PD-(L)1 antagonists and by combination studies in animal tumor models, many drug development programs were designed to combine anti-PD-(L)1 with a variety of approved and investigational chemotherapies, tumor-targeted therapies, antiangiogenic therapies, and other immunotherapies. Several immunotherapy combinations improved survival outcomes in a variety of indications including melanoma, lung, kidney, and liver cancer, among others. This immunotherapy renaissance, however, has led to many combinations being advanced to late-stage development without definitive predictive biomarkers, limited phase I and phase II data, or clinical trial designs that are not optimized for demonstrating the unique attributes of immune-related antitumor activity-for example, landmark progression-free survival and overall survival. The decision to activate a study at an individual site is investigator-driven, and generalized frameworks to evaluate the potential for phase III trials in immuno-oncology to yield positive data, particularly to increase the number of curative responses or otherwise advance the field have thus far been lacking. To assist in evaluating the potential value to patients and the immunotherapy field of phase III trials, the Society for Immunotherapy of Cancer (SITC) has developed a checklist for investigators, described in this manuscript. Although the checklist focuses on anti-PD-(L)1-based combinations, it may be applied to any regimen in which immune modulation is an important component of the antitumor effect.

Tumor infiltrating lymphocytes (TILs) as a predictive biomarker of response to checkpoint blockers in solid tumors: a systematic review

Immunotherapy is a standard of care in many solid tumors but many patients derive limited benefit from it. There is increasing interest toward tumor infiltrating lymphocytes (TILs) since their presence may be related with good outcomes from treatment with immune checkpoint blockers. We aimed at systematically reviewing existing evidence about the role of TILs as possible predictors of response to immunotherapy in solid tumors. We reviewed 1193 records published from January 2010 until December 2021. Associations between TILs and outcomes were observed mainly in melanoma and breast cancer. Overall survival and overall response rate for advanced disease and pathological complete response for early-phase tumors were the most commonly assessed endpoints. No definitive conclusion can be drawn on the predictive role of TILs. Additional studies, exploiting data from prospective, randomized clinical trials should further evaluate TILs also with the aim of identifying standard cut-off to differentiate between high and low TILs.

A Randomized Phase II Study of MEDI0680 in Combination with Durvalumab versus Nivolumab Monotherapy in Patients with Advanced or Metastatic Clear-cell Renal Cell Carcinoma

PURPOSE

MEDI0680 is a humanized anti-programmed cell death-1 (PD-1) antibody, and durvalumab is an anti-PD-L1 antibody. Combining treatment using these antibodies may improve efficacy versus blockade of PD-1 alone. This phase II study evaluated antitumor activity and safety of MEDI0680 plus durvalumab versus nivolumab monotherapy in immunotherapy-naïve patients with advanced clear-cell renal cell carcinoma who received at least one prior line of antiangiogenic therapy.

PATIENTS AND METHODS

Patients received either MEDI0680 (20 mg/kg) with durvalumab (750 mg) or nivolumab (240 mg), all intravenous, every 2 weeks. The primary endpoint was investigator-assessed objective response rate (ORR). Secondary endpoints included best overall response, progression-free survival (PFS), safety, overall survival (OS), and immunogenicity. Exploratory endpoints included changes in circulating tumor DNA (ctDNA), baseline tumor mutational burden, and tumor-infiltrated immune cell profiles.

RESULTS

Sixty-three patients were randomized (combination, n = 42; nivolumab, n = 21). ORR was 16.7% [7/42; 95% confidence interval (CI), 7.0-31.4] with combination treatment and 23.8% (5/21; 95% CI, 8.2-47.2) with nivolumab. Median PFS was 3.6 months in both arms; median OS was not reached in either arm. Because of adverse events, 23.8% of patients discontinued MEDI0680 and durvalumab and 14.3% of patients discontinued nivolumab. In the combination arm, reduction in ctDNA fraction was associated with longer PFS. ctDNA mutational analysis did not demonstrate an association with response in either arm. Tumor-infiltrated immune profiles showed an association between immune cell activation and response in the combination arm.

CONCLUSIONS

MEDI0680 combined with durvalumab was safe and tolerable; however, it did not improve efficacy versus nivolumab monotherapy.

Assessing dynamic metabolic heterogeneity in non-small cell lung cancer patients via ultra-high sensitivity total-body [18F]FDG PET/CT imaging: quantitative analysis of [18F]FDG uptake in primary tumors and metastatic lymph nodes

PURPOSE

This study aimed to quantitatively assess [¹⁸F]FDG uptake in primary tumor (PT) and metastatic lymph node (mLN) in newly diagnosed non-small cell lung cancer (NSCLC) using the total-body [¹⁸F]FDG PET/CT and to characterize the dynamic metabolic heterogeneity of NSCLC.

METHODS

The 60-min dynamic total-body [¹⁸F]FDG PET/CT was performed before treatment. The PTs and mLNs were manually delineated. An unsupervised K-means classification method was used to cluster patients based on the imaging features of PTs. The metabolic features, including Patlak-Ki, Patlak-Intercept, SUV_mean, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and textural features, were extracted from PTs and mLNs. The targeted next-generation sequencing of tumor-associated genes was performed. The expression of Ki67, CD3, CD8, CD34, CD68, and CD163 in PTs was determined by immunohistochemistry.

RESULTS

A total of 30 patients with stage IIIA-IV NSCLC were enrolled. Patients were divided into fast dynamic FDG metabolic group (F-DFM) and slow dynamic FDG metabolic group (S-DFM) by the unsupervised K-means classification of PTs. The F-DFM group showed significantly higher Patlak-Ki (P < 0.001) and SUV_mean (P < 0.001) of PTs compared with the S-DFM group, while no significant difference was observed in Patlak-Ki and SUV_mean of mLNs between the two groups. The texture analysis indicated that PTs in the S-DFM group were more heterogeneous in FDG uptake than those in the F-DFM group. Higher T cells (CD3⁺/CD8⁺) and macrophages (CD68⁺/CD163⁺) infiltration in the PTs were observed in the F-DFM group. No significant difference was observed in tumor mutational burden between the two groups.

CONCLUSION

The dynamic total-body [¹⁸F]FDG PET/CT stratified NSCLC patients into the F-DFM and S-DFM groups, based on Patlak-Ki and SUV_mean of PTs. PTs in the F-DFM group seemed to be more homogenous in terms of [¹⁸F]FDG uptake than those in the S-DFM group. The higher infiltrations of T cells and macrophages were observed in the F-DFM group, which suggested a potential benefit from immunotherapy.

Immunotherapy in NSCLC Patients with Brain Metastases

Approximately 40% of unselected non-small cell lung cancer (NSCLC) patients develop brain metastases (BMs) during their disease, with considerable morbidity and mortality. The management of BMs in patients with NSCLC is a clinical challenge and requires a multidisciplinary approach to gain effective intracranial disease control. Over the last decade, immune checkpoint inhibitors (ICIs) have emerged as a game-changer in the treatment landscape of advanced NSCLC, with significant improvements in survival outcomes, although patients with BMs are mostly underrepresented in randomized clinical trials. Moreover, the safety and activity of ICIs and radiotherapy combinations compared with single-agent or sequential modalities is still under evaluation to establish the optimal management of these patients. The aim of this review is to summarize the state-of-the-art of clinical evidence of ICIs intracranial activity and the main challenges of incorporating these agents in the treatment armamentarium of NSCLC patients with BMs.

Artificial Intelligence in Digital Pathology to Advance Cancer Immunotherapy

Immune-checkpoint inhibitors (ICIs) have revolutionized the treatment of many malignancies. For instance, in lung cancer, however, only 20~30% of patients can achieve durable clinical benefits from ICI monotherapy. Histopathologic and molecular features such as histological type, PD-L1 expression, and tumor mutation burden (TMB), play a paramount role in selecting appropriate regimens for cancer treatment in the era of immunotherapy. Unfortunately, none of the existing features are exclusive predictive biomarkers. Thus, there is an imperative need to pinpoint more effective biomarkers to identify patients who may achieve the most benefit from ICIs. The adoption of digital pathology in clinical flow, as being powered by artificial intelligence (AI) especially deep learning, has catalyzed the automated analysis of tissue slides. With the breakthrough of multiplex bioimaging technology, researchers can comprehensively characterize the tumor microenvironment, including the different immune cells' distribution, function, and interaction. Here, we briefly summarize recent AI studies in digital pathology and share our perspective on emerging paradigms and directions to advance the development of immunotherapy biomarkers.

The role of biomarkers in personalized immunotherapy

BACKGROUND

Immune checkpoint inhibitors have revolutionized cancer therapeutic paradigm and substantially improved the survival of patients with advanced malignancies. However, a significant limitation is the wide variability in clinical response.

MAIN TEXT

Several biomarkers have been evaluated in prior and ongoing clinical trials to investigate their prognostic and predictive role of patient response, nonetheless, most have not been comprehensively incorporated into clinical practice. We reviewed published data regarding biomarkers that have been approved by the United States Food and Drug Administration as well as experimental tissue and peripheral blood biomarkers currently under investigation. We further discuss the role of current biomarkers to predict response and response to immune checkpoint inhibitors and the promise of combination biomarker strategies. Finally, we discuss ideal biomarker characteristics, and novel platforms for clinical trial design including enrichment and stratification strategies, all of which are exciting and dynamic to advance the field of precision immuno-oncology.

CONCLUSION

Incorporation and standardization of strategies to guide selection of combination biomarker approaches will facilitate expansion of the clinical benefit of immune checkpoint inhibitor therapy to appropriate subsets of cancer patients.

Concordance, Correlation, and Clinical Impact of Standardized PD-L1 and TIL Scoring in SCCHN

Simple Summary

In patients with relapsed or metastasized squamous cell cancer of the head and neck (R/M SCCHN), the PD-L1 Combined Positive Score (CPS) is currently the only predictive biomarker for treatment with anti-PD-1 agents. However, ambiguous results have been determined regarding the overall response rates of immunotherapeutic agents based on PD-L1 status, which may be partially attributed to spatiotemporal heterogeneity. Furthermore, tumor-infiltrating lymphocytes (TILs) have proven to be of significant prognostic value, yet lack of a standardized method for quantification impedes their integration into the current armamentarium of biomarkers in SCCHN. In this paper, concordance of PD-L1 CPS and stromal TILs was investigated in different paired samples of SCCHN subtypes. The results were then linked to well-known clinicopathological variables and prognosis.

Abstract

Background: The clinical significance of tumor-infiltrating lymphocytes (TILs) and programmed cell death-ligand 1 (PD-L1) expression has been thoroughly researched in squamous cell carcinoma of the head and neck (SCCHN). To address the impact of intra- and intertumoral heterogeneity in these biomarkers, we explored the concordance of PD-L1 combined positive score (CPS) and stromal TILs in different paired tissue sample types, while evaluating their internal relationship and prognostic impact. Methods: A total of 165 tissue blocks from 80 SCCHN patients were reviewed for TILs and PD-L1 CPS. Concordance between paired tissue samples was evaluated, and their association with several clinicopathological variables, overall survival (OS), and disease-free survival (DFS) was determined. Results: Biopsies and paired resection material were severely discordant in 39% and 34% of samples for CPS and TIL count, respectively, of which CPS was underscored in 27% of biopsies. In paired primary tumor–metastatic lesions, the disagreement was lower for CPS (19%) but not for TIL count (44%). PD-L1 CPS was correlated with prolonged OS when calculated from tissue acquirement, while extended OS and DFS were observed for high TIL density. Conclusion: Intertumoral and, especially, intratumoral heterogeneity were confounding factors when determining PD-L1 CPS and TIL count on paired tissue samples, indicating the increasing necessity of assessing both biomarkers on representative tissue material. Although TILs hold valuable prognostic information in SCCHN, the robustness of PD-L1 as a biomarker in SCCHN remains ambiguous.

Patient-Derived Organoids of Colorectal Cancer: A Useful Tool for Personalized Medicine

Colorectal cancer is one of the most important malignancies worldwide, with high incidence and mortality rates. Several studies have been conducted using two-dimensional cultured cell lines; however, these cells do not represent a study model of patient tumors very well. In recent years, advancements in three-dimensional culture methods have facilitated the establishment of patient-derived organoids, which have become indispensable for molecular biology-related studies of colorectal cancer. Patient-derived organoids are useful in both basic science and clinical practice; they can help predict the sensitivity of patients with cancer to chemotherapy and radiotherapy and provide the right treatment to the right patient. Regarding precision medicine, combining gene panel testing and organoid-based screening can increase the effectiveness of medical care. In this study, we review the development of three-dimensional culture methods and present the most recent information on the clinical application of patient-derived organoids. Moreover, we discuss the problems and future prospects of organoid-based personalized medicine.

Predictive Markers for Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer

Immune checkpoint inhibitors (ICIs) have dramatically improved the outcomes of non-small cell lung cancer patients and have increased the possibility of long-term survival. However, few patients benefit from ICIs, and no predictive biomarkers other than tumor programmed cell death ligand 1 (PD-L1) expression have been established. Hence, the identification of biomarkers is an urgent issue. This review outlines the current understanding of predictive markers for the efficacy of ICIs, including PD-L1, tumor mutation burden, DNA mismatch repair deficiency, microsatellite instability, CD8⁺ tumor-infiltrating lymphocytes, human leukocyte antigen class I, tumor/specific genotype, and blood biomarkers such as peripheral T-cell phenotype, neutrophil-to-lymphocyte ratio, interferon-gamma, and interleukin-8. A tremendous number of biomarkers are in development, but individual biomarkers are insufficient. Tissue biomarkers have issues in reproducibility and accuracy because of intratumoral heterogeneity and biopsy invasiveness. Furthermore, blood biomarkers have difficulty in reflecting the tumor microenvironment and therefore tend to be less predictive for the efficacy of ICIs than tissue samples. In addition to individual biomarkers, the development of composite markers, including novel technologies such as machine learning and high-throughput analysis, may make it easier to comprehensively analyze multiple biomarkers.

Tumor-Infiltrating Lymphocytes in Head and Neck Cancer: Ready for Prime Time?

Simple Summary

The immune response has been shown to be a promising indicator to predict the clinical behavior of many cancers, including head and neck cancer. Tumor-infiltrating lymphocytes (TILs) were widely introduced as an important tool to reveal the status of the immune response. This review discusses the significance of TILs in head and neck cancers.

Abstract

The evaluation of tumor-infiltrating lymphocytes (TILs) has received global attention as a promising prognostic cancer biomarker that can aid in clinical decision making. Proof of their significance was first shown in breast cancer, where TILs are now recommended in the classification of breast tumors. Emerging evidence indicates that the significance of TILs extends to other cancer types, including head and neck cancer. In the era of immunotherapy as a treatment choice for head and neck cancer, assessment of TILs and immune checkpoints is of high clinical relevance. The availability of the standardized method from the International Immuno-oncology Biomarker Working Group (IIBWG) is an important cornerstone toward standardized assessment. The aim of the current article is to summarize the accumulated evidence and to establish a clear premise for future research toward the implementation of TILs in the personalized management of head and neck squamous cell carcinoma patients.

Immune Checkpoint Inhibitors in Triple Negative Breast Cancer: The Search for the Optimal Biomarker

Triple negative breast cancer (TNBC) is a high-risk and aggressive malignancy characterized by the absence of estrogen receptors (ER) and progesterone receptors (PR) on the surface of malignant cells, and by the lack of overexpression of human epidermal growth factor 2 (HER2). It has limited therapeutic options compared to other subtypes of breast cancer. There is now a growing body of evidence on the role of immunotherapy in TNBC, however much of the data from clinical trials is conflicting and thus, challenging for clinicians to integrate the data into clinical practice. Landmark phase III trials using immunotherapy in the early-stage neoadjuvant setting concluded that the addition of immunotherapy to chemotherapy improved the pathologic complete response (pCR) rate compared to chemotherapy with placebo while others found no significant improvement in pCR. Phase III trials have investigated the utility of immunotherapy in previously untreated metastatic TNBC, and these studies have similarly arrived at inconsistent conclusions. Some studies showed no benefit while others demonstrated a clinically significant improvement in overall survival in the PD-L1 positive population. It is not yet clear which biomarkers are most useful, and assays for these biomarkers have not been standardized. Given the often serious and severe side effects of immunotherapy, it is important and necessary to identify predictive biomarkers of response and resistance in order to enhance patient selection. In this review, we will discuss both the challenges of traditional biomarkers and the opportunities of emerging biomarkers for patient selection.

A short-term follow-up CT based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer

To develop a short-term follow-up CT-based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer (NSCLC) and investigate the prognostic value of radiomics features in predicting progression-free survival (PFS) and overall survival (OS). We first retrospectively collected 224 advanced NSCLC patients from two centers, and divided them into a primary cohort and two validation cohorts respectively. Then, we processed CT scans with a series of image preprocessing techniques namely, tumor segmentation, image resampling, feature extraction and normalization. To select the optimal features, we applied the feature ranking with recursive feature elimination method. After resampling the training dataset with a synthetic minority oversampling technique, we applied the support vector machine classifier to build a machine-learning-based classification model to predict response to immunotherapy. Finally, we used Kaplan-Meier (KM) survival analysis method to evaluate prognostic value of rad-score generated by CT-radiomics model. In two validation cohorts, the delta-radiomics model significantly improved the area under receiver operating characteristic curve from 0.64 and 0.52 to 0.82 and 0.87, respectively (P < .05). In sub-group analysis, pre- and delta-radiomics model yielded higher performance for adenocarcinoma (ADC) patients than squamous cell carcinoma (SCC) patients. Through the KM survival analysis, the rad-score of delta-radiomics model had a significant prognostic for PFS and OS in validation cohorts (P < .05). Our results demonstrated that (1) delta-radiomics model could improve the prediction performance, (2) radiomics model performed better on ADC patients than SCC patients, (3) delta-radiomics model had prognostic values in predicting PFS and OS of NSCLC patients.

Biomarkers of response to checkpoint inhibitors beyond PD-L1 in lung cancer

Immunotherapy, including use of checkpoint inhibitors against PD-1, PD-L1, and CTLA-4, forms the backbone of oncologic management for the majority of non-small cell lung carcinoma patients. However, response to these therapies varies widely, from patients who have complete resolution of metastatic disease and long-term remission, to those who rapidly progress and succumb to their cancer despite use of the newest checkpoint inhibitors. While PD-L1 protein expression by immunohistochemistry serves as the principle predictive biomarker for immunotherapy response, neither the sensitivity nor the specificity of this approach is optimal, and clinical PD-L1 testing is plagued by concerns around result reproducibility and confusion born from the proliferation of different companion diagnostic assays. At the same time, insights into tumor and host immune-specific factors that inform both prognosis and response prediction are beginning to define better immunotherapy biomarkers. Beyond immune checkpoint expression status, common themes in analyses of immunotherapy response prediction include cancer neoantigen production, the state of the antigen presentation pathway in both tumor and antigen presenting cells, the admixture of effector and suppressor immune cells in the tumor microenvironment, and the genomic drivers and comutations that can influence the all of these variables. This review will address the state of PD-L1 testing in lung cancer, the role for tumor mutation burden as a predictive biomarker, the evolving status of human leukocyte antigen/major histocompatibility complex expression as a marker of antigen presentation, approaches to tumor immune cell quantitation including by multiplex immunofluorescence, and the importance of tumor genomic profiling to ascertain oncogenic driver (EGFR, ALK, KRAS, MET, etc.) and co-mutation (STK11, KEAP1, SMARCA4) status.

Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab

Assessment of programmed death ligand 1 (PD-L1) expression by immunohistochemistry (IHC) has emerged as an important predictive biomarker across multiple tumor types. However, manual quantitation of PD-L1 positivity can be difficult and leads to substantial inter-observer variability. Although the development of artificial intelligence (AI) algorithms may mitigate some of the challenges associated with manual assessment and improve the accuracy of PD-L1 expression scoring, use of AI-based approaches to oncology biomarker scoring and drug development has been sparse, primarily due to the lack of large-scale clinical validation studies across multiple cohorts and tumor types. We developed AI-powered algorithms to evaluate PD-L1 expression on tumor cells by IHC and compared it with manual IHC scoring in urothelial carcinoma, non-small cell lung cancer, melanoma, and squamous cell carcinoma of the head and neck (prospectively determined during the phase II and III CheckMate clinical trials). 1,746 slides were retrospectively analyzed, the largest investigation of digital pathology algorithms on clinical trial datasets performed to date. AI-powered quantification of PD-L1 expression on tumor cells identified more PD-L1-positive samples compared with manual scoring at cutoffs of ≥1% and ≥5% in most tumor types. Additionally, similar improvements in response and survival were observed in patients identified as PD-L1-positive compared with PD-L1-negative using both AI-powered and manual methods, while improved associations with survival were observed in patients with certain tumor types identified as PD-L1-positive using AI-powered scoring only. Our study demonstrates the potential for implementation of digital pathology-based methods in future clinical practice to identify more patients who would benefit from treatment with immuno-oncology therapy compared with current guidelines using manual assessment.

Digital pathology and artificial intelligence in translational medicine and clinical practice

Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)-based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.

Programmed Death-Ligand 1 Expression in Breast Cancer Patients: Clinicopathological Associations from a Single-Institution Study

PURPOSE

Tumor expression of programmed death-ligand 1 (PD-L1) is associated with evasion of immune response in several types of malignancies and such expression may render patients eligible for PD-L1 inhibitors. The use of immune checkpoint blockade therapy has been recently approved for the treatment of breast cancer. However, PD-L1 expression data are lacking among Jordanian breast cancer patients. In this study, the tumor PD-L1 expression was characterized in breast cancer patients to assess their eligibility for immune checkpoint blockade therapy. The study also aimed to explore the association between tumoral PD-L1 expression and the clinicopathologic characteristics and the prognostic factors in patients with breast cancer.

PATIENTS AND METHODS

Tissue samples were available from 153 female patients with primary invasive breast cancer. Immunohistochemistry was performed on paraffin-embedded tumor sections that were stained with a PD-L1 antibody. Expression of tumor PD-L1 was correlated with demographics, clinicopathologic characteristics, and prognosis.

RESULTS

The mean age at diagnosis was 54.2±12.8 years (median 52, interquartile range 45-65). The percentage of PD-L1-positive tumors was 26.1%. PD-L1 expression on tumor cells significantly and positively correlated with tumor size (rho=0.174, p=0.032). PD-L1 positivity was significantly associated with the grade of carcinoma (p=0.001), HER2-positivity (p=0.015), and lymphovascular invasion (p=0.036). PD-L1 intensity was significantly associated with tumor stage (p=0.046). No significant associations were observed for the PD-L1 expression status or intensity with patient menopausal status, hormone receptor expression, and molecular subtypes. PD-L1 expression significantly correlated with a worse prognosis of breast cancer patients at the time of diagnosis (rho=0.230, p=0.005).

CONCLUSION

Tumor PD-L1 expression was associated with advanced clinicopathologic features and worse prognosis in this cohort of Jordanian breast cancer patients. Future studies are needed to better understand the impact of PD-L1 blockade therapy on treatment outcomes in eligible breast cancer patients in Jordan.

The association between CD8+ tumor-infiltrating lymphocytes and the clinical outcome of cancer immunotherapy: A systematic review and meta-analysis

BACKGROUND

The responses of cancer patients to immune checkpoint inhibitors (ICIs) vary in success. CD8+ tumor infiltrating lymphocytes (TILs) play a key role in killing tumor cells. This study aims to evaluate the prognostic role of CD8+ TILs in cancer patients treated with ICIs.

METHODS

We systematically searched all publications from PubMed, EMBASE, and Cochrane Library until 12 Jul 2021 without any restriction of language or article types. Studies assessing high versus low CD8+ TILs in predicting efficacy and survival of various cancer patients were included. The outcomes included overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). The study protocol is prospectively registered on PROSPERO (registration number CRD42021233654).

FINDINGS

Findings: A total of 33 studies consisting of 2559 cancer patients were included. The result showed that high CD8+ TILs were significantly associated with better OS (HR, 0.52; 95% confidence interval: 0.41-0.67; p < 0.001), PFS (HR, 0.52; 95% confidence interval: 0.40-0.67; p < 0.001) and ORR (OR, 4.08; 95% confidence interval: 2.73-6.10; p  < 0.001) in patients treated with ICIs. Subgroup analyses suggested that patients with high CD8+ TILs had a better clinical benefit, regardless of different treatments (ICI mono therapy, or combination therapy), cancer types (NSCLC, melanoma and others), and CD8+ T cells locations (intra-tumor, stroma, and invasive margin). The higher baseline circulating CD8+ T cells from peripheral blood did not contribute to the improved OS (HR, 0.93; 95% confidence interval: 0.67-1.29; p = 0.67) and PFS (HR, 0.89; 95% confidence interval: 0.60-1.32; p = 0.56) compared with the low baseline.

INTERPRETATION

Interpretation: Our results suggested that high intra-tumoral, stromal, or invasive marginal, but not circulating CD8+ T cells, can predict treatment outcomes in patients with ICIs therapy across different cancers, in either single-agent ICIs or combination with other therapies.

FUNDING

Funding: China National Science Foundation (Grant No. 82,022,048, 81,871,893), Key Project of Guangzhou Scientific Research Project (Grant No. 201,804,020,030), High-level university construction project of Guangzhou medical university (Grant No. 20,182,737, 201,721,007, 201,715,907, 2,017,160,107); National key R & D Program (Grant No. 2017YFC0907903 & 2017YFC0112704) and the Guangdong high level hospital construction "reaching peak" plan.

Dose escalation biodistribution, positron emission tomography/computed tomography imaging and dosimetry of a highly specific radionuclide-labeled non-blocking nanobody

BACKGROUND

Immunotherapy is a valuable option for cancer treatment, and the curative effect of anti-PD-1/PD-L1 therapy correlates closely with PD-L1 expression levels. Positron emission tomography (PET) imaging of PD-L1 expression is feasible using ⁶⁸Ga-NOTA-Nb109 nanobody. ⁶⁸Ga-NOTA-Nb109 was generated by radionuclide (⁶⁸Ga) labeling of Nb109 using a NOTA chelator. To facilitate clinical trials, we explored the optimal dose range of ⁶⁸Ga-NOTA-Nb109 in BALB/c A375-hPD-L1 tumor-burdened nude mice and C57-hPD-L1 transgenic MC38-hPD-L1 tumor-burdened mice by administration of a single intravenous dose of ⁶⁸Ga-NOTA-Nb109 and confirmed the dose in cynomolgus monkeys. The biodistribution data of cynomolgus monkey PET images were extrapolated to estimate the radiation dose for the adult male and female using OLINDA2.1 software.

RESULTS

⁶⁸Ga-NOTA-Nb109 was stable in physiologic media and human serum. Ex vivo biodistribution studies showed rapid and specific uptake in A375-hPD-L1 or MC38-hPD-L1 tumors. The estimated ED₅₀ was approximately 5.4 µg in humanized mice. The injected mass (0.3-100 µg in nude mice and approximately 1-100 µg in humanized mice) greatly influenced the general biodistribution, with a better tumor-to-background ratio acquired at lower doses of Nb109 (0.3-10 µg in nude mice and approximately 1 µg in humanized mice), indicating maximum uptake in tumors at administered mass doses below the estimated ED_50. Therefore, a single 15-μg/kg dose was adopted for the PET/CT imaging in the cynomolgus monkey. The highest specific and persistent uptake of the tracer was detected in the spleen, except the levels in the kidney and urine bladder, which was related to metabolism and excretion. The spleen-to-muscle ratio of the tracer exceeded 10 from immediately to 4 h after administration, indicating that the dose was appropriate. The estimated effective dose was calculated to yield a radiation dose of 4.1 mSv to a patient after injecting 185 MBq of ⁶⁸Ga-NOTA-Nb109.

CONCLUSION

⁶⁸Ga-NOTA-Nb109 showed specific accumulation in hPD-L1 xenografts in ex vivo biodistribution studies and monkey PET/CT imaging. The dose escalation distribution data provided a recommended dose range for further use, and the safety of the tracer was confirmed in dosimetry studies.

The Combination of Radiotherapy With Immunotherapy and Potential Predictive Biomarkers for Treatment of Non-Small Cell Lung Cancer Patients

Radiotherapy is an effective local treatment modality of NSCLC. Its capabilities of eliminating tumor cells by inducing double strand DNA (dsDNA) damage and modulating anti-tumor immune response in irradiated and nonirradiated sites have been elucidated. The novel ICIs therapy has brought hope to patients resistant to traditional treatment methods, including radiotherapy. The integration of radiotherapy with immunotherapy has shown improved efficacy to control tumor progression and prolong survival in NSCLC. In this context, biomarkers that help choose the most effective treatment modality for individuals and avoid unnecessary toxicities caused by ineffective treatment are urgently needed. This article summarized the effects of radiation in the tumor immune microenvironment and the mechanisms involved. Outcomes of multiple clinical trials investigating immuno-radiotherapy were also discussed here. Furthermore, we outlined the emerging biomarkers for the efficacy of PD-1/PD-L1 blockades and radiation therapy and discussed their predictive value in NSCLC.

The expression landscape of JAK1 and its potential as a biomarker for prognosis and immune infiltrates in NSCLC

Background

Janus-activated kinase-1 (JAK1) plays a crucial role in many aspects of cell proliferation, differentiation, apoptosis and immune regulation. However, correlations of JAK1 with prognosis and immune infiltration in NSCLC have not been documented.

Methods

We analyzed the relationship between JAK1 expression and NSCLC prognosis and immune infiltration using multiple public databases.

Results

JAK1 expression was significantly decreased in NSCLC compared with that in paired normal tissues. JAK1 overexpression indicated a favourable prognosis in NSCLC. In subgroup analysis, high JAK1 expression was associated with a preferable prognosis in lung adenocarcinoma (OS: HR, 0.74, 95% CI from 0.58 to 0.95, log-rank P = 0.017), not squamous cell carcinoma. In addition, data from Kaplan–Meier plotter revealed that JAK1 overexpression was associated with a preferable prognosis in male and stage N2 patients and patients without distant metastasis. Notably, increased levels of JAK1 expression were associated with an undesirable prognosis in patients with stage 1 (OS: HR, 1.46, 95% CI from 1.06 to 2.00, P = 0.02) and without lymph node metastasis (PFS: HR, 2.18, 95% CI from 1.06 to 4.46, P = 0.029), which suggests that early-stage NSCLC patients with JAK1 overexpression may have a bleak prognosis. Moreover, multiple immune infiltration cells, including NK cells, CD8 + T and CD4 + T cells, B cells, macrophages, neutrophils, and dendritic cells (DCs), in NSCLC were positively correlated with JAK1 expression. Furthermore, diverse immune markers are associated with JAK1 expression.

Conclusions

JAK1 overexpression exhibited superior prognosis and immune infiltration in NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04379-y.

Immunoprofiling: An Encouraging Method for Predictive Factors Examination in Lung Cancer Patients Treated with Immunotherapy

The efficiency of immunotherapy using monoclonal antibodies that inhibit immune checkpoints has been proven in many clinical studies and well documented by numerous registration approaches. To date, PD-L1 expression on tumor and immune cells, tumor mutation burden (TMB), and microsatellite instability (MSI) are the only validated predictive factors used for the qualification of cancer patients for immunotherapy. However, they are not the ideal predictive factors. No response to immunotherapy could be observed in patients with high PD-L1 expression, TMB, or MSI. On the other hand, the effectiveness of this treatment method also may occur in patients without PD-L1 expression or with low TMB and with microsatellite stability. When considering the best predictive factor, we should remember that the effectiveness of immunotherapy relies on an overly complex process depending on many factors. To specifically stimulate lymphocytes, not only should their activity in the tumor microenvironment be unlocked, but above all, they should recognize tumor antigens. The proper functioning of the anticancer immune system requires the proper interaction of many elements of the specific and non-specific responses. For these reasons, a multi-parameter analysis of the immune system at its different activity levels is considered a very future-oriented predictive marker. Such complex immunological analysis is performed using modern molecular biology techniques. Based on the gene expression studies, we can determine the content of individual immune cells within the tumor, its stroma, and beyond. This includes all cell types from active memory cytotoxic T cells, M1 macrophages, to exhausted T cells, regulatory T cells, and M2 macrophages. In this article, we summarize the possibilities of using an immune system analysis to predict immunotherapy efficacy in cancer patients. Moreover, we present the advantages and disadvantages of immunoprofiling as well as a proposed future direction for this new method of immune system analysis in cancer patients who receive immunotherapy.

Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association

Tissue biomarkers have been of increasing utility for scientific research, diagnosing disease, and treatment response prediction. There has been a steady shift away from qualitative assessment toward providing more quantitative scores for these biomarkers. The application of quantitative image analysis has thus become an indispensable tool for in-depth tissue biomarker interrogation in these contexts. This white paper reviews current technologies being employed for quantitative image analysis, their application and pitfalls, regulatory framework demands, and guidelines established for promoting their safe adoption in clinical practice.

The expression of CD30 and its clinico-pathologic significance in peripheral T-cell lymphomas

INTRODUCTION

Recent studies have shown that CD30 expression can be an important feature of peripheral and cutaneous T-cell lymphomas (PTCLs and CTCLs) and CD30 testing has increased in importance with the emergence of CD30-directed therapy.

AREAS COVERED

This article reviews the literature on CD30-related biology, prevalence, and therapy in patients with PTCL or CTCL. We searched the PubMed database from 1 January 2010 to 28 April 2020, using terms 'CD30' ('peripheral T-cell lymphomas' or 'cutaneous T-cell lymphoma') and 'immunohistochemistry' or 'flow cytometry' or 'pathology,' and synonyms including terms for T-cell lymphoma subtypes.

EXPERT OPINION

CD30 is expressed at relatively high rates of prevalence across a broad range of PTCLs and CTCLs. CD30 expression may be critical to the development of a subset of PTCLs and also a biomarker for treatment choice in some subtypes. Large-scale randomized, controlled studies have shown that CD30-directed treatment with brentuximab vedotin is significantly more effective against CD30-expressing PTCL and CTCL than current standard-of-care regimens. However, accurate CD30 evaluation is limited by inconsistencies in detection methodology and expression cutoffs defining CD30-expressing disease. Greater understanding of CD30 testing and reporting will enable more patients with CD30-expressing PTCL and CTCL to be identified and treated appropriately.

Multi-institutional TSA-amplified Multiplexed Immunofluorescence Reproducibility Evaluation (MITRE) Study

Background

Emerging data suggest predictive biomarkers based on the spatial arrangement of cells or coexpression patterns in tissue sections will play an important role in precision immuno-oncology. Multiplexed immunofluorescence (mIF) is ideally suited to such assessments. Standardization and validation of an end-to-end workflow that supports multisite trials and clinical laboratory processes are vital. Six institutions collaborated to: (1) optimize an automated six-plex assay focused on the PD-1/PD-L1 axis, (2) assess intersite and intrasite reproducibility of staining using a locked down image analysis algorithm to measure tumor cell and immune cell (IC) subset densities, %PD-L1 expression on tumor cells (TCs) and ICs, and PD-1/PD-L1 proximity assessments.

Methods

A six-plex mIF panel (PD-L1, PD-1, CD8, CD68, FOXP3, and CK) was rigorously optimized as determined by quantitative equivalence to immunohistochemistry (IHC) chromogenic assays. Serial sections from tonsil and breast carcinoma and non-small cell lung cancer (NSCLC) tissue microarrays (TMAs), TSA-Opal fluorescent detection reagents, and antibodies were distributed to the six sites equipped with a Leica Bond Rx autostainer and a Vectra Polaris multispectral imaging platform. Tissue sections were stained and imaged at each site and delivered to a single site for analysis. Intersite and intrasite reproducibility were assessed by linear fits to plots of cell densities, including %PDL1 expression by TCs and ICs in the breast and NSCLC TMAs.

Results

Comparison of the percent positive cells for each marker between mIF and IHC revealed that enhanced amplification in the mIF assay was required to detect low-level expression of PD-1, PD-L1, FoxP3 and CD68. Following optimization, an average equivalence of 90% was achieved between mIF and IHC across all six assay markers. Intersite and intrasite cell density assessments showed an average concordance of R²=0.75 (slope=0.92) and R²=0.88 (slope=0.93) for breast carcinoma, respectively, and an average concordance of R²=0.72 (slope=0.86) and R²=0.81 (slope=0.68) for NSCLC. Intersite concordance for %PD-L1+ICs had an average R² value of 0.88 and slope of 0.92. Assessments of PD-1/PD-L1 proximity also showed strong concordance (R²=0.82; slope=0.75).

Conclusions

Assay optimization yielded highly sensitive, reproducible mIF characterization of the PD-1/PD-L1 axis across multiple sites. High concordance was observed across sites for measures of density of specific IC subsets, measures of coexpression and proximity with single-cell resolution.

Resistance to durvalumab and durvalumab plus tremelimumab is associated with functional STK11 mutations in non-small-cell lung cancer patients and is reversed by STAT3 knockdown

Mutations in the STK11 (LKB1) gene regulate resistance to PD-1/PD-L1 blockade. This study evaluated this association in patients with nonsquamous non-small-cell lung cancer enrolled in three Phase 1/2 trials. STK11 mutations were associated with resistance to the anti-PD-L1 antibody durvalumab (alone/with the anti-CTLA-4 antibody tremelimumab) independently of KRAS mutational status, highlighting STK11 as a potential driver of resistance to checkpoint blockade. Retrospective assessments of tumor tissue, whole blood and serum revealed a unique immune phenotype in patients with STK11 mutations, with increased expression of markers associated with neutrophils (i.e. CXCL2, IL6), Th17 contexture (i.e. IL17A) and immune checkpoints. Associated changes were observed in the periphery. Reduction of STAT3 in the tumor microenvironment using an antisense oligonucleotide reversed immunotherapy resistance in preclinical STK11 knockout models. These results suggest that STK11 mutations may hinder response to checkpoint blockade through mechanisms including suppressive myeloid cell biology, which could be reversed by STAT3-targeted therapy.

The Genitourinary Pathology Society Update on Classification of Variant Histologies, T1 Substaging, Molecular Taxonomy, and Immunotherapy and PD-L1 Testing Implications of Urothelial Cancers

The Genitourinary Pathology Society (GUPS) undertook a critical review of the recent advances in bladder cancer focusing on important topics of high interest for the practicing surgical pathologist and urologist. This review represents the second of 2 manuscripts ensuing from this effort. Herein, we address the effective reporting of bladder cancer, focusing particularly on newly published data since the last 2016 World Health Organization (WHO) classification. In addition, this review focuses on the importance of reporting bladder cancer with divergent differentiation and variant (subtypes of urothelial carcinoma) histologies and the potential impact on patient care. We provide new recommendations for reporting pT1 staging in diagnostic pathology. Furthermore, we explore molecular evolution and classification, emphasizing aspects that impact the understanding of important concepts relevant to reporting and management of patients.

Multistage-responsive nanovehicle to improve tumor penetration for dual-modality imaging-guided photodynamic-immunotherapy

The exploration of an intelligent multifunctional imaging-guided therapeutic platform is of great significance because of its ideal delivery efficiency and controlled release. In this work, a tumor microenvironment (TME)-responsive nanocarrier (denoted as MB@MSP) is designed for on-demand, sequentially release of a short D-peptide antagonist of programmed cell death-ligand 1 (named as P^DPPA-1) and a photosensitizer methylene blue (MB). Fe₃O₄-Au located in the core of MB@MSP is used as a magnetic resonance imaging and micro-computed tomography imaging contrast agent for noninvasive diagnosis of solid tumors and simultaneous monitoring of drug delivery. The P^DPPA-1 coated on MB@MSP can be shed due to the cleavage of the peptide substrate by matrix metalloproteinase-2 (MMP-2) that is highly expressed in the tumor stroma, and disulfide bonding is further broken when it encounters high levels of glutathione (GSH) in TME, which finally leads to significant size reduction and charge-reversal. These transitions facilitate penetration and uptake of nanocarriers against tumors. Noticeably, the released P^DPPA-1 can block the immune checkpoint to create an environment that favors the activation of cytotoxic T lymphocytes and augment the antitumor immune response elicited by photodynamic therapy, thus significantly improving therapeutic outcomes. Studies of the underlying mechanisms suggest that the designed MMP-2 and GSH-sensitive delivery system not only induce apoptosis of tumor cells but also modulate the immunosuppressive tumor microenvironment to eventually augment the suppression tumor metastasis effect of CD8⁺ cytotoxic T cells. Overall, the visualization of the therapeutic processes with comprehensive information renders MB@MSP an intriguing platform to realize the combined treatment of metastatic tumors.