The Promise of Digital Biopsy for the Prediction of Tumor Molecular Features and Clinical Outcomes Associated With Immunotherapy

Prognostic significance of TNFRSF4 expression and development of a pathomics model to predict expression in hepatocellular carcinoma

Background

TNFRSF4 plays a significant role in cancer progression, especially in hepatocellular carcinoma (HCC). This study aims to investigate the prognostic value of TNFRSF4 expression in patients with HCC and to develop a predictive pathomics model for its expression.

Methods

A cohort of patients with HCC retrieved from the TCGA database was analyzed using RNA-seq analysis to determine TNFRSF4 expression and its impact on overall survival (OS). Additionally, hematoxylin-eosin staining analysis was performed to construct a pathomics model for predicting TNFRSF4 expression. Then, pathway enrichment analysis was conducted, immune checkpoint markers were investigated, and immune cell infiltration was examined to explore the underlying biological mechanism of the pathomics score.

Results

TNFRSF4 expression was significantly higher in tumor tissues than in normal tissues. TNFRSF4 expression also exhibited significant correlations with various clinical variables, including pathologic stage III/IV and R1/R2/RX residual tumor. Furthermore, elevated TNFRSF4 expression was associated with unfavorable OS. Interestingly, in the subgroup analysis, elevated TNFRSF4 expression was identified as a significant risk factor for OS in male patients. The newly developed pathomics model successfully predicted TNFRSF4 expression with good performance and revealed a significant association between high pathomics scores and worse OS. In male patients, high pathomics scores were also associated with a higher risk of mortality. Moreover, pathomics scores were also involved in specific hallmarks, immune-related characteristics, and apoptosis-related genes in HCC, such as epithelial-mesenchymal transition, Tregs, and BAX expression.

Conclusions

Our findings suggest that TNFRSF4 expression and the newly devised pathomics scores hold potential as prognostic markers for OS in patients with HCC. Additionally, gender influenced the association between these markers and patient outcomes.

Exploiting cell death and tumor immunity in cancer therapy: challenges and future directions

Cancer remains a significant global challenge, with escalating incidence rates and a substantial burden on healthcare systems worldwide. Herein, we present an in-depth exploration of the intricate interplay between cancer cell death pathways and tumor immunity within the tumor microenvironment (TME). We begin by elucidating the epidemiological landscape of cancer, highlighting its pervasive impact on premature mortality and the pronounced burden in regions such as Asia and Africa. Our analysis centers on the pivotal concept of immunogenic cell death (ICD), whereby cancer cells succumbing to specific stimuli undergo a transformation that elicits robust anti-tumor immune responses. We scrutinize the mechanisms underpinning ICD induction, emphasizing the release of damage-associated molecular patterns (DAMPs) and tumor-associated antigens (TAAs) as key triggers for dendritic cell (DC) activation and subsequent T cell priming. Moreover, we explore the contributions of non-apoptotic RCD pathways, including necroptosis, ferroptosis, and pyroptosis, to tumor immunity within the TME. Emerging evidence suggests that these alternative cell death modalities possess immunogenic properties and can synergize with conventional treatments to bolster anti-tumor immune responses. Furthermore, we discuss the therapeutic implications of targeting the TME for cancer treatment, highlighting strategies to harness immunogenic cell death and manipulate non-apoptotic cell death pathways for therapeutic benefit. By elucidating the intricate crosstalk between cancer cell death and immune modulation within the TME, this review aims to pave the way for the development of novel cancer therapies that exploit the interplay between cell death mechanisms and tumor immunity and overcome Challenges in the Development and implementation of Novel Therapies.

MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

The purpose of this systematic review and meta-analysis was to assess the quality and diagnostic accuracy of MRI-based radiomics for predicting Ki-67 expression in breast cancer.

MATERIALS AND METHODS

A systematic literature search was performed to find relevant studies published in different databases, including PubMed, Web of Science, and Embase up until March 10, 2023. All papers were independently evaluated for eligibility by two reviewers. Studies that matched research questions and provided sufficient data for quantitative synthesis were included in the systematic review and meta-analysis, respectively. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. The predictive value of MRI-based radiomics for Ki-67 antigen in patients with breast cancer was assessed using pooled sensitivity (SEN), specificity, and area under the curve (AUC). Meta-regression was performed to explore the cause of heterogeneity. Different covariates were used for subgroup analysis.

RESULTS

31 studies were included in the systematic review; among them, 21 reported sufficient data for meta-analysis. 20 training cohorts and five validation cohorts were pooled separately. The pooled sensitivity, specificity, and AUC of MRI-based radiomics for predicting Ki-67 expression in training cohorts were 0.80 [95% CI, 0.73-0.86], 0.82 [95% CI, 0.78-0.86], and 0.88 [95%CI, 0.85-0.91], respectively. The corresponding values for validation cohorts were 0.81 [95% CI, 0.72-0.87], 0.73 [95% CI, 0.62-0.82], and 0.84 [95%CI, 0.80-0.87], respectively. Based on QUADAS-2, some risks of bias were detected for reference standard and flow and timing domains. However, the quality of the included article was acceptable. The mean RQS score of the included articles was close to 6, corresponding to 16.6% of the maximum possible score. Significant heterogeneity was observed in pooled sensitivity and specificity of training cohorts (I2 > 75%). We found that using deep learning radiomic methods, magnetic field strength (3 T vs. 1.5 T), scanner manufacturer, region of interest structure (2D vs. 3D), route of tissue sampling, Ki-67 cut-off, logistic regression for model construction, and LASSO for feature reduction as well as PyRadiomics software for feature extraction had a great impact on heterogeneity according to our joint model analysis. Diagnostic performance in studies that used deep learning-based radiomics and multiple MRI sequences (e.g., DWI+DCE) was slightly higher. In addition, radiomic features derived from DWI sequences performed better than contrast-enhanced sequences in terms of specificity and sensitivity. No publication bias was found based on Deeks' funnel plot. Sensitivity analysis showed that eliminating every study one by one does not impact overall results.

CONCLUSION

This meta-analysis showed that MRI-based radiomics has a good diagnostic accuracy in differentiating breast cancer patients with high Ki-67 expression from low-expressing groups. However, the sensitivity and specificity of these methods still do not surpass 90%, restricting them from being used as a supplement to current pathological assessments (e.g., biopsy or surgery) to predict Ki-67 expression accurately.

Research in the application of artificial intelligence to lung cancer diagnosis

The morbidity and mortality rates in lung cancer are high worldwide. Early diagnosis and personalized treatment are important to manage this public health issue. In recent years, artificial intelligence (AI) has played increasingly important roles in early screening, auxiliary diagnosis, and prognostic assessment. AI uses algorithms to extract quantitative feature information from high-volume and high-latitude data and learn existing data to predict disease outcomes. In this review, we describe the current uses of AI in lung cancer-focused pathomics, imageomics, and genomics applications.

The tumor immune microenvironment in pancreatic cancer and its potential in the identification of immunotherapy biomarkers

INTRODUCTION

Pancreatic cancer (PC) has an extremely poor prognosis, even with surgical resection and triplet chemotherapy treatment. Cancer immunotherapy has been recently approved for tumor-agnostic treatment with genome analysis, including in PC. However, it has limited efficacy.

AREAS COVERED

In addition to the low tumor mutation burden, one of the difficulties of immunotherapy in PC is the presence of abundant stromal cells in its microenvironment. Among stromal cells, cancer-associated fibroblasts (CAFs) play a major role in immunotherapy resistance, and CAF-targeted therapies are currently under development, including those in combination with immunotherapies. Meanwhile, microbiomes and tumor-derived exosomes (TDEs) have been shown to alter the behavior of distant receptor cells in PC. This review discusses the role of CAFs, microbiomes, and TDEs in PC tumor immunity.

EXPERT OPINION

Elucidating the mechanisms by which CAFs, microbiomes, and TDEs are involved in the tumorigenesis of PC will be helpful for developing novel immunotherapeutic strategies and identifying companion biomarkers for immunotherapy. Spatial single-cell analysis of the tumor microenvironment will be useful for identifying biomarkers of PC immunity. Furthermore, given the complexity of immune mechanisms, artificial intelligence models will be beneficial for predicting the efficacy of immunotherapy.

Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology

BACKGROUND

The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re-)activate the patient's immune system and direct it against the individual cancer in the most effective way.

RECENT FINDINGS

Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune-oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune-cancer-network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer-assisted development and clinical validation of such digital biomarker.

CONCLUSIONS

The successful implementation of AI-supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into "precision pathology" delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a "precision oncology".

Artificial Intellgence in the Era of Precision Oncological Imaging

Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection, and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients.Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.

Developing and testing a robotic MRI/CT fusion biopsy technique using a purpose-built interventional phantom

BACKGROUND

Magnetic resonance imaging (MRI) can be used to target tumour components in biopsy procedures, while the ability to precisely correlate histology and MRI signal is crucial for imaging biomarker validation. Robotic MRI/computed tomography (CT) fusion biopsy offers the potential for this without in-gantry biopsy, although requires development.

METHODS

Test-retest T1 and T2 relaxation times, attenuation (Hounsfield units, HU), and biopsy core quality were prospectively assessed (January-December 2021) in a range of gelatin, agar, and mixed gelatin/agar solutions of differing concentrations on days 1 and 8 after manufacture. Suitable materials were chosen, and four biopsy phantoms were constructed with twelve spherical 1-3-cm diameter targets visible on MRI, but not on CT. A technical pipeline was developed, and intraoperator and interoperator reliability was tested in four operators performing a total of 96 biopsies. Statistical analysis included T1, T2, and HU repeatability using Bland-Altman analysis, Dice similarity coefficient (DSC), and intraoperator and interoperator reliability.

RESULTS

T1, T2, and HU repeatability had 95% limits-of-agreement of 8.3%, 3.4%, and 17.9%, respectively. The phantom was highly reproducible, with DSC of 0.93 versus 0.92 for scanning the same or two different phantoms, respectively. Hit rate was 100% (96/96 targets), and all operators performed robotic biopsies using a single volumetric acquisition. The fastest procedure time was 32 min for all 12 targets.

CONCLUSIONS

A reproducible biopsy phantom was developed, validated, and used to test robotic MRI/CT-fusion biopsy. The technique was highly accurate, reliable, and achievable in clinically acceptable timescales meaning it is suitable for clinical application.

Bias and inconsistency in the estimation of tumour mutation burden

BACKGROUND

Tumour mutation burden (TMB), defined as the number of somatic mutations per megabase within the sequenced region in the tumour sample, has been used as a biomarker for predicting response to immune therapy. Several studies have been conducted to assess the utility of TMB for various cancer types; however, methods to measure TMB have not been adequately evaluated. In this study, we identified two sources of bias in current methods to calculate TMB.

METHODS

We used simulated data to quantify the two sources of bias and their effect on TMB calculation, we down-sampled sequencing reads from exome sequencing datasets from TCGA to evaluate the consistency in TMB estimation across different sequencing depths. We analyzed data from ten cancer cohorts to investigate the relationship between inferred TMB and sequencing depth.

RESULTS

We found that TMB, estimated by counting the number of somatic mutations above a threshold frequency (typically 0.05), is not robust to sequencing depth. Furthermore, we show that, because only mutations with an observed frequency greater than the threshold are considered, the observed mutant allele frequency provides a biased estimate of the true frequency. This can result in substantial over-estimation of the TMB, when the cancer sample includes a large number of somatic mutations at low frequencies, and exacerbates the lack of robustness of TMB to variation in sequencing depth and tumour purity.

CONCLUSION

Our results demonstrate that care needs to be taken in the estimation of TMB to ensure that results are unbiased and consistent across studies and we suggest that accurate and robust estimation of TMB could be achieved using statistical models that estimate the full mutant allele frequency spectrum.

Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients

Limited previous studies focused on the death and progression risk stratification of colorectal cancer (CRC) lung metastasis patients. The aim of this study is to construct a nomogram model combing machine learning-pathomics, radiomics features, Immunoscore and clinical factors to predict the postoperative outcome of CRC patients with lung metastasis. In this study, a total of 103 CRC patients having metastases limited to lung and undergoing radical lung resection were identified. Patch-level convolutional neural network training in weakly supervised manner was used to perform whole slides histopathological images survival analysis. Synthetic minority oversampling technique and support vector machine classifier were used to identify radiomics features and build predictive signature. The Immunoscore for each patient was calculated from the density of CD3+ and CD8+ cells at the invasive margin and the center of metastatic tumor which were assessed on consecutive sections of automated digital pathology. Finally, pathomics and radiomics signatures were successfully developed to predict the overall survival (OS) and disease free survival (DFS) of patients. The predicted pathomics and radiomics scores are negatively correlated with Immunoscore and they are three independent prognostic factors for OS and DFS prediction. The combined nomogram showed outstanding performance in predicting OS (AUC = 0.860) and DFS (AUC = 0.875). The calibration curve and decision curve analysis demonstrated the considerable clinical usefulness of the combined nomogram. Taken together, the developed nomogram model consisting of machine learning-pathomics signature, radiomics signature, Immunoscore and clinical features could be reliable in predicting postoperative OS and DFS of colorectal lung metastasis patients.

Prognostic and Predictive Factors in Advanced Urothelial Carcinoma Treated with Immune Checkpoint Inhibitors: A Review of the Current Evidence

In recent years, the treatment landscape of urothelial carcinoma has significantly changed due to the introduction of immune checkpoint inhibitors (ICIs), which are the standard of care for second-line treatment and first-line platinum-ineligible patients with advanced disease. Despite the overall survival improvement, only a minority of patients benefit from this immunotherapy. Therefore, there is an unmet need to identify prognostic and predictive biomarkers or models to select patients who will benefit from ICIs, especially in view of novel therapeutic agents. This review describes the prognostic and predictive role, and clinical readiness, of clinical and tumour factors, including new molecular classes, tumour mutational burden, mutational signatures, circulating tumour DNA, programmed death-ligand 1, inflammatory indices and clinical characteristics for patients with urothelial cancer treated with ICIs. A classification of these factors according to the levels of evidence and grades of recommendation currently indicates both a prognostic and predictive value for ctDNA and a prognostic relevance only for concomitant medications and patients' characteristics.

Diagnostic and prognostic biomarkers in oligometastatic non-small cell lung cancer: a literature review

Objective

This review aims to summarize the possibilities of recently discovered molecular diagnostic techniques in lung cancer, by evaluating their impact on diagnosis, monitoring, and prognosis in oligometastatic disease.

Background

Oligometastatic non-small cell lung cancer (OM-NSCLC) is currently defined based on morphological rather than biological features. Major advances in the detection of molecular biomarkers in cell-free tumoral DNA and the models of oncogene addiction make as feasible an early diagnosis and guide the therapeutic decision-making progress to improve the prognosis.

Methods

This narrative review EXAMINES current approaches of diagnosis, monitoring, and prognosis of OM-NSCLC and describes the fast-evolving therapeutic scenario of this disease. We provide an overview of the powerful capability of liquid biopsy techniques applied to blood and fluid and we focus on the technological advancement of circulant biomolecular factors in OM NSCLC pathology, starting from apparently simpler models such as oncogene addicted tumors to evaluate themselves in the light of treatment with immune-checkpoint inhibitors.

Conclusions

A better understanding of spatial and temporal evolution of oligometastatic diseases would contribute to a more accurate diagnosis and tailored treatment. Data from prospective clinical trials in the early stage of disease, coupled with knowledge of genetic characteristics of lung tumors, are warranted. These efforts would lead to improving the possibility to eradicate the residual disease in these low burden tumoral settings, thus enhancing the definitive cure perspectives.

Radiomics Study for Predicting the Expression of PD-L1 and Tumor Mutation Burden in Non-Small Cell Lung Cancer Based on CT Images and Clinicopathological Features

Background

The present study compared the predictive performance of pretreatment computed tomography (CT)-based radiomics signatures and clinicopathological and CT morphological factors for ligand programmed death-ligand 1 (PD-L1) expression level and tumor mutation burden (TMB) status and further explored predictive models in patients with advanced-stage non-small cell lung cancer (NSCLC).

Methods

A total of 120 patients with advanced-stage NSCLC were enrolled in this retrospective study and randomly assigned to a training dataset or validation dataset. Here, 462 radiomics features were extracted from region-of-interest (ROI) segmentation based on pretreatment CT images. The least absolute shrinkage and selection operator (LASSO) and logistic regression were applied to select radiomics features and develop combined models with clinical and morphological factors for PD-L1 expression and TMB status prediction. Ten-fold cross-validation was used to evaluate the accuracy, and the predictive performance of these models was assessed using receiver operating characteristic (ROC) and area under the curve (AUC) analyses.

Results

The PD-L1-positive expression level correlated with differentiation degree (p = 0.005), tumor shape (p = 0.006), and vascular convergence (p = 0.007). Stage (p = 0.023), differentiation degree (p = 0.017), and vacuole sign (p = 0.016) were associated with TMB status. Radiomics signatures showed good performance for predicting PD-L1 and TMB with AUCs of 0.730 and 0.759, respectively. Predictive models that combined radiomics signatures with clinical and morphological factors dramatically improved the predictive efficacy for PD-L1 (AUC = 0.839) and TMB (p = 0.818). The results were verified in the validation datasets.

Conclusions

Quantitative CT-based radiomics features have potential value in the classification of PD-L1 expression levels and TMB status. The combined model further improved the predictive performance and provided sufficient information for the guiding of immunotherapy in clinical practice, and it deserves further analysis.

A review in radiomics: Making personalized medicine a reality via routine imaging

Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.

Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers

Radiomics is an emerging field in radiology that utilizes advanced statistical data characterizing algorithms to evaluate medical imaging and objectively quantify characteristics of a given disease. Due to morphologic heterogeneity and genetic variation intrinsic to neoplasms, radiomics have the potential to provide a unique insight into the underlying tumor and tumor microenvironment. Radiomics has been gaining popularity due to potential applications in disease quantification, predictive modeling, treatment planning, and response assessment - paving way for the advancement of personalized medicine. However, producing a reliable radiomic model requires careful evaluation and construction to be translated into clinical practices that have varying software and/or medical equipment. We aim to review the diagnostic utility of radiomics in otorhinolaryngology, including both cancers of the head and neck as well as the thyroid.

Pathomics in urology

PURPOSE OF REVIEW

Pathomics, the fusion of digitalized pathology and artificial intelligence, is currently changing the landscape of medical pathology and biologic disease classification. In this review, we give an overview of Pathomics and summarize its most relevant applications in urology.

RECENT FINDINGS

There is a steady rise in the number of studies employing Pathomics, and especially deep learning, in urology. In prostate cancer, several algorithms have been developed for the automatic differentiation between benign and malignant lesions and to differentiate Gleason scores. Furthermore, several applications have been developed for the automatic cancer cell detection in urine and for tumor assessment in renal cancer. Despite the explosion in research, Pathomics is not fully ready yet for widespread clinical application.

SUMMARY

In prostate cancer and other urologic pathologies, Pathomics is avidly being researched with commercial applications on the close horizon. Pathomics is set to improve the accuracy, speed, reliability, cost-effectiveness and generalizability of pathology, especially in uro-oncology.

Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology

The field of immuno-oncology has expanded rapidly over the past decade, but key questions remain. How does tumour-immune interaction regulate disease progression? How can we prospectively identify patients who will benefit from immunotherapy? Identifying measurable features of the tumour immune-microenvironment which have prognostic or predictive value will be key to making meaningful gains in these areas. Recent developments in deep learning enable big-data analysis of pathological samples. Digital approaches allow data to be acquired, integrated and analysed far beyond what is possible with conventional techniques, and to do so efficiently and at scale. This has the potential to reshape what can be achieved in terms of volume, precision and reliability of output, enabling data for large cohorts to be summarised and compared. This review examines applications of artificial intelligence (AI) to important questions in immuno-oncology (IO). We discuss general considerations that need to be taken into account before AI can be applied in any clinical setting. We describe AI methods that have been applied to the field of IO to date and present several examples of their use.

Immune checkpoint inhibitor-induced aseptic meningitis and encephalitis: a case-series and narrative review

BACKGROUND

Along with the increasing use of immune checkpoint inhibitors comes a surge in immune-related toxicity. Here, we review the currently available data regarding neurological immune adverse events, and more specifically aseptic meningitis and encephalitis, and present treatment and diagnostic recommendations. Furthermore, we present five cases of immunotherapy-induced aseptic meningitis and encephalitis treated at our institution.

RECENT FINDINGS

Neurological immune-related adverse events, including aseptic meningitis and encephalitis, secondary to checkpoint inhibitors are a rare but complex and clinically relevant entity, comprising a wide range of diseases, most often presenting with symptoms with a wide range of differential diagnoses. Our case-series highlights the challenges of such entities and the importance of properly identifying and managing aseptic meningitis and encephalitis.

SUMMARY

Checkpoint inhibitor-induced meningoencephalitis warrants prompt investigations and treatment. Properly diagnosing aseptic meningitis, encephalitis, or mixed presentations may guide the treatment decision, as highlighted by our case-series. After rapid exclusion of alternative diagnoses, urgent corticosteroids are the therapeutic backbone but this could change in favour of highly specific cytokine-directed treatment options.

PLAIN LANGUAGE SUMMARY

Aseptic meningitis and encephalitis with immune checkpoint inhibitors: a single centre case-series and review of the literature Over the course of the past decade, checkpoint inhibitors have revolutionized cancer care. With their favourable toxicity profile and potential for durable and deep responses, they have become ubiquitous across the field of oncology. Furthermore, combination checkpoint inhibitors are also gaining ground, with increased efficacy and, unfortunately, immune-related toxicity. While there are guidelines based on extensive clinical experience for frequent adverse events, uncommon entities are less readily identified and treated. Neurological immune-related adverse events secondary to checkpoint inhibitors are a rare but complex entity, comprising a wide range of diseases, most often presenting with aspecific symptoms. In this paper, we discuss a single institution case-series of patients with autoimmune aseptic meningitis and encephalitis, and we perform a narrative literature review on this subject. We conclude with our treatment recommendations based on available evidence.

Three-Dimensional Deep Noninvasive Radiomics for the Prediction of Disease Control in Patients With Metastatic Urothelial Carcinoma treated With Immunotherapy

INTRODUCTION

Immunotherapy is effective in a small percentage of patients with cancer and no reliable predictive biomarkers are currently available. Artificial Intelligence algorithms may automatically quantify radiologic characteristics associated with disease response to medical treatments.

METHODS

We investigated an innovative approach based on a 3-dimensional (3D) deep radiomics pipeline to classify visual features of chest-abdomen computed tomography (CT) scans with the aim of distinguishing disease control from progressive disease to immune checkpoint inhibitors (ICIs). Forty-two consecutive patients with metastatic urothelial cancer had progressed on first-line platinum-based chemotherapy and had baseline CT scans at immunotherapy initiation. The 3D-pipeline included self-learned visual features and a deep self-attention mechanism. According to the outcome to the ICIs, a 3D deep classifier semiautomatically categorized the most discriminative region of interest on the CT scans.

RESULTS

With a median follow-up of 13.3 months (95% CI, 11.1-15.6), the median overall survival was 8.5 months (95% CI, 3.1-13.8). According to disease response to immunotherapy, the median overall survival was 3.6 months (95% CI, 2.0-5.2) for patients with progressive disease; it was not yet reached for those with disease control. The predictive accuracy of the 3D-pipeline was 82.5% (sensitivity 96%; specificity, 60%). The addition of baseline clinical factors increased the accuracy to 92.5% by improving specificity to 87%; the accuracy of other architectures ranged from 72.5% to 90%.

CONCLUSION

Artificial Intelligence by 3D deep radiomics is a potential noninvasive biomarker for the prediction of disease control to ICIs in metastatic urothelial cancer and deserves validation in larger series.

The lung immuno-oncology prognostic score (LIPS-3): a prognostic classification of patients receiving first-line pembrolizumab for PD-L1 ≥ 50% advanced non-small-cell lung cancer

Background

To stratify the prognosis of patients with programmed cell death-ligand 1 (PD-L1) ≥ 50% advanced non-small-cell lung cancer (aNSCLC) treated with first-line immunotherapy.

Methods

Baseline clinical prognostic factors, the neutrophil-to-lymphocyte ratio (NLR), PD-L1 tumour cell expression level, lactate dehydrogenase (LDH) and their combination were investigated by a retrospective analysis of 784 patients divided between statistically powered training (n = 201) and validation (n = 583) cohorts. Cut-offs were explored by receiver operating characteristic (ROC) curves and a risk model built with validated independent factors by multivariate analysis.

Results

NLR < 4 was a significant prognostic factor in both cohorts (P < 0.001). It represented 53% of patients in the validation cohort, with 1-year overall survival (OS) of 76.6% versus 44.8% with NLR > 4, in the validation series. The addition of PD-L1 ≥ 80% (21% of patients) or LDH < 252 U/l (25%) to NLR < 4 did not result in better 1-year OS (of 72.6% and 74.1%, respectively, in the validation cohort). Eastern Cooperative Oncology Group (ECOG) performance status (PS) of 2 [P < 0.001, hazard ratio (HR) 2.04], pretreatment steroids (P < 0.001, HR 1.67) and NLR < 4 (P < 0.001, HR 2.29) resulted in independent prognostic factors. A risk model with these three factors, namely, the lung immuno-oncology prognostic score (LIPS)-3, accurately stratified three OS risk-validated categories of patients: favourable (0 risk factors, 40%, 1-year OS of 78.2% in the whole series), intermediate (1 or 2 risk factors, 54%, 1-year OS 53.8%) and poor (>2 risk factors, 5%, 1-year OS 10.7%) prognosis.

Conclusions

We advocate the use of LIPS-3 as an easy-to-assess and inexpensive adjuvant prognostic tool for patients with PD-L1 ≥ 50% aNSCLC.

•

Immunotherapy/chemoimmunotherapy combinations are currently not superior to immunotherapy alone for high PD-L1 aNSCLC.

•

NLR with a cut-off of 4 was validated as an independent prognostic factor for immunotherapy in high PD-L1 aNSCLC.

•

The addition of either PD-L1 ≥ 80% or LDH < 252 U/l to NLR < 4 did not result in better prognostic stratification.

•

The LIPS-3 is a validated 3-class prognostic classification based on the NLR, ECOG PS and pretreatment steroids.

•

The LIPS-3 is a routinely assessable adjuvant prognostic tool for high PD-L1 aNSCLC patients.

3D Non-Local Neural Network: A Non-Invasive Biomarker for Immunotherapy Treatment Outcome Prediction. Case-Study: Metastatic Urothelial Carcinoma

Immunotherapy is regarded as one of the most significant breakthroughs in cancer treatment. Unfortunately, only a small percentage of patients respond properly to the treatment. Moreover, to date, there are no efficient bio-markers able to early discriminate the patients eligible for this treatment. In order to help overcome these limitations, an innovative non-invasive deep pipeline, integrating Computed Tomography (CT) imaging, is investigated for the prediction of a response to immunotherapy treatment. We report preliminary results collected as part of a case study in which we validated the implemented method on a clinical dataset of patients affected by Metastatic Urothelial Carcinoma. The proposed pipeline aims to discriminate patients with high chances of response from those with disease progression. Specifically, the authors propose ad-hoc 3D Deep Networks integrating Self-Attention mechanisms in order to estimate the immunotherapy treatment response from CT-scan images and such hemato-chemical data of the patients. The performance evaluation (average accuracy close to 92%) confirms the effectiveness of the proposed approach as an immunotherapy treatment response biomarker.

Systematic review of radiomic biomarkers for predicting immune checkpoint inhibitor treatment outcomes

BACKGROUND

Systemic therapy agents targeting immune checkpoint inhibitors have been approved for use since 2011. This type of therapy aims to trigger a patient's immune response to attack tumor cells, rather than acting against the tumor directly. Radiomics is an automated method of medical image analysis that is now being actively investigated for predictive markers of treatment response in immunotherapy.

OBJECTIVE

To conduct an early systematic review determining the current status of radiomic features as potential predictive markers of immunotherapy response. Provide a detailed critical appraisal of methodological quality of models, as this informs the degree of confidence about current reports of model performance. In addition, to offer some recommendations for future studies that could establish robust evidence for radiomic features as immunotherapy response markers.

METHOD

A PubMed citation search was conducted for publications up to and including April 2020, followed by full-text screening. A total of seven articles meeting the eligibility criteria were examined in detail for study characteristics, model information and methodological quality. The review was conducted in the Cochrane style but has not been prospectively registered. Results are reported following Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) guidelines.

RESULTS

A total of seven studies were examined in detail, comprising non-small cell lung cancer, metastatic melanoma and a diverse assortment of solid tumors. Methodological robustness of reviewed studies varied greatly. Principal shortcomings were lack of prospective registration, and deficiencies in feature selection and dimensionality reduction, model calibration, clinical utility and external validation. A few studies with overall moderate to good methodological quality were identified. These results suggest that current state-of-the-art performance of radiomics in regards to discrimination (area under the curve or concordance index) is in the vicinity of 0.7, but the very small number of studies to date prevents any conclusive remarks to be made. We recommended future improvements in regards to prospective study registration, clinical utility, methodological procedure and data sharing.

CONCLUSIONS

Radiomics has a potentially significant role for predicting immunotherapy response. Additional multi-institutional studies with robust methodological underpinning and repeated external validations are required to establish the (added) value of radiomics within the pantheon of clinical tools for decision-making in immunotherapy.

PyHIST: A Histological Image Segmentation Tool

The development of increasingly sophisticated methods to acquire high-resolution images has led to the generation of large collections of biomedical imaging data, including images of tissues and organs. Many of the current machine learning methods that aim to extract biological knowledge from histopathological images require several data preprocessing stages, creating an overhead before the proper analysis. Here we present PyHIST (https://github.com/manuel-munoz-aguirre/PyHIST), an easy-to-use, open source whole slide histological image tissue segmentation and preprocessing command-line tool aimed at tile generation for machine learning applications. From a given input image, the PyHIST pipeline i) optionally rescales the image to a different resolution, ii) produces a mask for the input image which separates the background from the tissue, and iii) generates individual image tiles with tissue content.

Histopathology images are an essential tool to assess and quantify tissue composition and its relationship to disease. The digitization of slides and the decreasing costs of computation and data storage have fueled the development of new computational methods, especially in the field of machine learning. These methods seek to make use of the histopathological patterns encoded in these slides with the aim of aiding clinicians in healthcare decision-making, as well as researchers in tissue biology. However, in order to prepare digital slides for usage in machine learning applications, researchers usually need to develop custom scripts from scratch in order to reshape the image data in a way that is suitable to train a model, slowing down the development process. With PyHIST, we provide a toolbox for researchers that work in the intersection of machine learning, biology and histology to effortlessly preprocess whole slide images into image tiles in a standardized manner for usage in external applications.

Assessing Immunotherapy with Functional and Molecular Imaging and Radiomics

Immunotherapy is changing the treatment paradigm for cancer and has introduced new challenges in medical imaging. Because not all patients benefit from immunotherapy, pretreatment imaging should be performed to identify not only prognostic factors but also factors that allow prediction of response to immunotherapy. Follow-up studies must allow detection of nonresponders, without confusion of pseudoprogression with real progression to prevent premature discontinuation of treatment that can benefit the patient. Conventional imaging techniques and classic tumor response criteria are limited for the evaluation of the unusual patterns of response that arise from the specific mechanisms of action of immunotherapy, so advanced imaging methods must be developed to overcome these shortcomings. The authors present the fundamentals of the tumor immune microenvironment and immunotherapy and how they influence imaging findings. They also discuss advances in functional and molecular imaging techniques for the assessment of immunotherapy in clinical practice, including their use to characterize immune phenotypes, assess patient prognosis and response to therapy, and evaluate immune-related adverse events. Finally, the development of radiomics and radiogenomics in these therapies and the future role of imaging biomarkers for immunotherapy are discussed. Online supplemental material is available for this article. ^©RSNA, 2020.

Radiomics and "radi-…omics" in cancer immunotherapy: a guide for clinicians

In recent years the concept of precision medicine has become a popular topic particularly in medical oncology. Besides the identification of new molecular prognostic and predictive biomarkers and the development of new targeted and immunotherapeutic drugs, imaging has started to play a central role in this new era. Terms such as "radiomics", "radiogenomics" or "radi…-omics" are becoming increasingly common in the literature and soon they will represent an integral part of clinical practice. The use of artificial intelligence, imaging and "-omics" data can be used to develop models able to predict, for example, the features of the tumor immune microenvironment through imaging, and to monitor the therapeutic response beyond the standard radiological criteria. The aims of this narrative review are to provide a simplified guide for clinicians to these concepts, and to summarize the existing evidence on radiomics and "radi…-omics" in cancer immunotherapy.

Promising predictors of checkpoint inhibitor response in NSCLC

INTRODUCTION

The development of immune-checkpoint inhibitors targeting the programmed death-1 (PD-1) and its ligand (PD-L1) axis has transformed the treatment paradigm in non-small-cell lung cancer, bringing about unprecedented 5-year survival rates. Despite this dramatic improvement, roughly 70% of patients do not derive durable benefit from these treatments, illustrating the need for predictive biomarkers.

AREAS COVERED

In this review, we will discuss what makes a successful biomarker and analyze the role and significance of currently available options, including PD-L1, oncogenic alterations and tumor mutation burden. We then discuss potential biomarkers on the horizon, including the microbiome, tumor infiltrating lymphocytes, neutrophil-to-lymphocyte ratio, gene signatures and the emerging field of multiomics.

EXPERT OPINION

To date, only PD-L1 is clinically validated as a positive predictor of response to immunotherapy, yet the need to refine patient selection has never been stronger, given the indication for checkpoint inhibitors alone or in combination in all non-oncogene driven non-small-cell lung cancer patients receiving front-line therapy. Prospective validation of the above-mentioned potential biomarkers, either alone or in combination, may help to elaborate improved predictive tools.

Monitoring von Immuntherapien

Hintergrund

Immuntherapien spielen in der Behandlung fortgeschrittener onkologischer Erkrankungen eine zunehmende Rolle. Bei einigen Patienten birgt die radiologische Diagnostik durch atypische, immuntherapieinduziete Therapieverläufe neue Herausforderungen.

Ziel der Arbeit

Dieser Beitrag soll einen Überblick über die bildgebenden Methoden des Monitorings von Immuntherapien geben, die assoziierten Phänomene Pseudoprogress und Hyperprogress erörtern sowie die Evaluationskriterien iRECIST vorstellen, welche sich als Evaluationsstandard für klinische Studien anbieten. Zusätzlich werden die radiologisch wichtigsten Nebenwirkungen und ihre bildmorphologischen Charakteristika beschrieben.

Material und Methoden

Für diesen Übersichtsartikel wurden Studienergebnisse und Reviews seit 2009 ausgewertet. Die Literaturrecherche erfolgte mittels PubMed, die Suchbegriffe enthielten „immunotherapy“, „checkpoint inhibitor“, „pseudoprogression“, „iRECIST“ und „immune related adverse events“.

Ergebnisse und Diskussion

Mit einer Inzidenz von bis zu 10 % ist der Pseudoprogress insgesamt selten; aktuell ist die Differenzierung von einem echten Progress nur durch eine Beobachtung des zeitlichen Verlaufs möglich. Die 2017 erschienenen iRECIST-Kriterien enthalten daher die neuen Kategorien unbestätigter (immune unconfirmed progressive disease iUPD) und bestätigter Progress (immune confirmed progressive disease iCPD). Bisher konnte keine evidenzbasierte Empfehlung bezüglich des Zeitintervalls zwischen den Untersuchungen gegeben werden. Als radiologisch wichtigste Nebenwirkungen sind die Hypophysitis und die Pneumonitis zu nennen. Letztere kann sich in verschiedenen Mustern der interstitiellen Pneumonie präsentieren. Die Differenzierung zwischen Pneumonitis, Infektion und Tumorprogress kann diagnostische Schwierigkeiten mit sich bringen.

Neutrophil-to-lymphocyte ratio in combination with PD-L1 or lactate dehydrogenase as biomarkers for high PD-L1 non-small cell lung cancer treated with first-line pembrolizumab

The identification of prognostic and predictive biomarkers for high-programmed cell death-ligand 1 (PD-L1) advanced non-small cell lung cancer (aNSCLC) treated with first-line pembrolizumab could support the decision-making about possible combination therapies. To explore the baseline neutrophil-to-lymphocyte ratio (NLR) with the possible addition of PD-L1 tumour proportion score (TPS) level or lactate dehydrogenase (LDH) as possible prognostic biomarkers by a multicenter retrospective exploratory analysis aiming at identifying favourable-risk patients. Baseline NLR was available for all 132 high PD-L1 aNSCLC patients, PD-L1 level and LDH for 81 (61%) and 85 (64%) patients, respectively. NLR, PD-L1 and LDH cut-offs by receiver operating characteristic (ROC) curves were 4.9, 77.5% and 268.5, respectively. Seventy-one patients (54%) had NLR <5; 25 out of 81 NLR <5 (31%) had PD-L1 >80%, 26 out of 85 (31%) NLR <5 and normal LDH (nLDH). Median follow-up was 16.3 months. As compared to NLR >5, significantly better 2-year overall survival (OS) and progression-free survival (PFS) were observed with NLR <5 [62% vs. 41%, P=0.005, hazard ratio (HR) 0.45, and median of 12.0 vs. 5.7 months, P=0.01, HR 0.56, respectively], NLR <5 + PD-L1 >80% (81%, P=0.006, HR 0.20 and median of 14.7, P=0.03, HR 0.44, respectively), and NLR <5 + nLDH (74%, P=0.009, HR 0.25 and median of 14.7, P=0.02, HR 0.40, respectively). NLR <5 and NLR <5 + nLDH significantly associated with PD (P=0.008 and P=0.025, respectively) but not response rate (RR) (P=0.09 and P=0.07, respectively); NLR <5 + PD-L1 >80% both RR (P=0.03) and PD (P=0.02). NLR <5 ± PD-L1 >80% or nLDH could represent easy-to-assess tools to identify high PD-L1 aNSCLC patients with favourable outcome following first-line pembrolizumab monotherapy.

A Validated T Cell Radiomics Score Is Associated With Clinical Outcomes Following Multisite SBRT and Pembrolizumab

PURPOSE

Combining immune checkpoint blockade (ICB) with stereotactic body radiation therapy (SBRT) may improve the local response to radiation and the systemic response to immunotherapy. However, no prognostic markers exist to identify patients likely to benefit from combined therapy. The degree of T cell-mediated immunity, which can be quantified with radiomics using computed tomography (CT) imaging, is predictive of immunotherapy response. Herein we investigated whether a validated T cell radiomics score (RS) is correlated with clinical outcomes after multisite SBRT and pembrolizumab (SBRT + P).

METHODS AND MATERIALS

The RS was quantified for 68 patients with metastatic treatment-refractory adult solid tumors who received SBRT (30-50 Gy, 3-5 fractions) and pembrolizumab ≤7 days after SBRT. RS was calculated using 8 variables, including 5 radiomics features extracted from pretreatment CT scans. At a prespecified cutoff of the 25th percentile, we assessed the association between RS and clinical outcomes. The Kaplan-Meier method was used to estimate survival outcomes. The prognostic effect of RS was assessed via logistic regression or Cox proportional hazards models. In an exploratory analysis, RS was also analyzed as a continuous variable.

RESULTS

One hundred thirty-nine tumors were analyzed. At the 25th percentile cutoff, high-RS patients were more likely to exhibit irradiated tumor responses to SBRT + P (odds ratio [OR] 10.2; 95% confidence interval [CI], 1.76-59.17; P = .012). High-RS was associated with improved TMC compared with low-RS tumors (hazard ratio [HR] 0.18; 95% CI, 0.04-0.74; P = .018). Furthermore, high-RS patients had improved PFS (HR 0.47, 95% CI, 0.26-0.85; P = .013) and OS (HR 0.39, 95% CI, 0.20-0.75; P = .005). As a continuous variable, higher RS was associated with improved PFS (HR 0.12, 95% CI, 0.03-0.51; P = .004) but did not reach statistical significance for TMC (HR 0.36, 95% CI, 0.02-7.02; P = .502) or OS (HR 0.28, 95% CI, 0.05-1.55; P = .144).

CONCLUSIONS

We demonstrated the clinical validity of RS (at the 25th percentile cutoff) as a prognostic biomarker in patients treated with SBRT + P. Future validation of the prognostic value of RS in larger similarly treated patient cohorts is warranted.

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence

The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has resulted in long-term survival in advanced cancer patient subgroups. However, the majority of patients do not benefit from single-agent ICI and therefore new combination strategies are eagerly necessitated. In addition to conventional chemotherapy, kinase inhibitors as well as tumor-specific vaccinations are extensively investigated in combination with ICI to augment therapy responses. An unprecedented clinical outcome with chimeric antigen receptor (CAR-)T cell therapy has led to the approval for relapsed/refractory diffuse large B cell lymphoma and B cell acute lymphoblastic leukemia whereas response rates in solid tumors are unsatisfactory. Immune-checkpoints negatively impact CAR-T cell therapy in hematologic and solid malignancies and as a consequence provide a therapeutic target to overcome resistance. Established biomarkers such as programmed death ligand 1 (PD-L1) and tumor mutational burden (TMB) help to select patients who will benefit most from ICI, however, biomarker negativity does not exclude responses. Investigating alterations in the antigen presenting pathway as well as radiomics have the potential to determine tumor immunogenicity and response to ICI. Within this review we summarize the literature about specific combination partners for ICI and the applicability of artificial intelligence to predict ICI therapy responses.