Serial changes in liquid biopsy-derived variant allele frequency predict immune checkpoint inhibitor responsiveness in the pan-cancer setting

Technical Advances in Circulating Cell-Free DNA Detection and Analysis for Personalized Medicine in Patients' Care

Circulating cell-free DNA (cfDNA) refers to small fragments of DNA molecules released after programmed cell death and necrosis in several body fluids such as blood, saliva, urine, and cerebrospinal fluid. The discovery of cfDNA has revolutionized the field of non-invasive diagnostics in the oncologic field, in prenatal testing, and in organ transplantation. Despite the potential of cfDNA and the solid results published in the recent literature, several challenges remain, represented by a low abundance, a need for highly sensitive assays, and analytical issues. In this review, the main technical advances in cfDNA analysis are presented and discussed, with a comprehensive examination of the current available methodologies applied in each field. Considering the potential advantages of cfDNA, this biomarker is increasing its consensus among clinicians, as it allows us to monitor patients' conditions in an easy and non-invasive way, offering a more personalized care. Nevertheless, cfDNA analysis is still considered a diagnostic marker to be further validated, and very few centers are implementing its analysis in routine diagnostics. As technical improvements are enhancing the performances of cfDNA analysis, its application will transversally improve patients' quality of life.

Integrated variant allele frequency analysis pipeline and R package: easyVAF

Somatic sequence variants are associated with cancer diagnosis, prognostic stratification, and treatment response. Variant allele frequency (VAF), the percentage of sequence reads with a specific DNA variant over the read depth at that locus, has been used as a metric to quantify mutation rates in these applications. VAF has the potential for feature detection by reflecting changes in tumor clonal composition across treatments or time points. Although there are several packages, including Genome Analysis Toolkit and VarScan, designed for variant calling and rare mutation identification, there is no readily available package for comparing VAFs among and between groups to identify loci of interest. To this end, we have developed the R package easyVAF, which includes parametric and nonparametric tests to compare VAFs among multiple groups. It is accompanied by an interactive R Shiny app. With easyVAF, the investigator has the option between three statistical tests to maximize power while maintaining an acceptable type I error rate. This paper presents our proposed pipeline for VAF analysis, from quality checking to group comparison. We evaluate our method in a wide range of simulated scenarios and show that choosing the appropriate test to limit the type I error rate is critical. For situations where data is sparse, we recommend comparing VAFs with the beta-binomial likelihood ratio test over Fisher's exact test and Pearson's χ2 test.

Unlocking Early Cancer Detection: Exploring Biomarkers, Circulating DNA, and Innovative Technological Approaches

Research and development improvements in early cancer diagnosis have had a significant positive impact on health. In the treatment and prevention of cancer, early detection is essential. In this context, biomarkers are essential because they offer important information on the state of cells at any particular time. Cells go through unique changes when they shift from a healthy condition to a malignant state, changes that appropriate biomarkers may pick up. Recent advancements have been made to identify and characterize circulating cancer-specific mutations in cell-free circulating DNA derived from tumors and tumor cells. A patient's delay between the time they first detect symptoms and the time they contact a doctor has been noted for many cancer forms. The tumor's location and features significantly impact the presentation of symptoms judged appropriate for early diagnosis. Lack of knowledge of the severity of the symptoms may be one cause for this delay. Our review is largely focused on the ongoing developments of early diagnosis in the study of biomarkers, circulating DNA for diagnosis, the biology of early challenges, early symptoms, liquid biopsies, detectable by imaging, established tumor markers, plasma DNA technologies, gender differences, and artificial intelligence (AI) in diagnosis. This review aims to determine and evaluate Indicators for detecting early cancer, assessing medical conditions, and evaluating potential risks. For Individuals with a heightened likelihood of developing cancer or who have already been diagnosed, early identification is crucial for enhancing prognosis and raising the likelihood of effective treatment.

The potential clinical utility of cell-free DNA for gastric cancer patients treated with nivolumab monotherapy

To assess the potential clinical utility of cell-free DNA (cfDNA)-based biomarkers for identifying gastric cancer (GC) patients who benefit from nivolumab. From 31 GC patients treated with nivolumab monotherapy (240 mg/body, Bi-weekly) in 3rd or later line setting, we prospectively collected blood samples at baseline and before the 3rd dose. We compared cfDNA-based molecular findings, including microsatellite instability (MSI) status, to tissue-based biomarkers. We assessed the clinical value of blood tumor mutation burden (bTMB) and copy number alterations (CNA) as well as the cfDNA dynamics. The concordance between deficient-MMR and cfDNA-based MSI-high was 100% (3/3). Patients with bTMB ≥ 6 mut/Mb had significantly better progression-free survival (PFS) and overall survival (OS); however, such significance disappeared when excluding MSI-High cases. The combination of bTMB and CNA positivity identified patients with survival benefit regardless of MSI status (both PFS and OS, P < 0.001), with the best survival in those with bTMB^≥6mut/Mb and CNA^negative. Moreover, patients with decreased bTMB during treatment had a better disease control rate (P = 0.04) and longer PFS (P = 0.04). Our results suggest that a combination of bTMB and CNA may predict nivolumab efficacy for GC patients regardless of MSI status. bTMB dynamics have a potential utility as an on-treatment biomarker.

Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients

BACKGROUND

Identifying predictive non-invasive biomarkers of immunotherapy response is crucial to avoid premature treatment interruptions or ineffective prolongation. Our aim was to develop a non-invasive biomarker for predicting immunotherapy clinical durable benefit, based on the integration of radiomics and clinical data monitored through early anti-PD-1/PD-L1 monoclonal antibodies treatment in patients with advanced non-small cell lung cancer (NSCLC).

METHODS

In this study, 264 patients with pathologically confirmed stage IV NSCLC treated with immunotherapy were retrospectively collected from two institutions. The cohort was randomly divided into a training (n = 221) and an independent test set (n = 43), ensuring the balanced availability of baseline and follow-up data for each patient. Clinical data corresponding to the start of treatment was retrieved from electronic patient records, and blood test variables after the first and third cycles of immunotherapy were also collected. Additionally, traditional radiomics and deep-radiomics features were extracted from the primary tumors of the computed tomography (CT) scans before treatment and during patient follow-up. Random Forest was used to implementing baseline and longitudinal models using clinical and radiomics data separately, and then an ensemble model was built integrating both sources of information.

RESULTS

The integration of longitudinal clinical and deep-radiomics data significantly improved clinical durable benefit prediction at 6 and 9 months after treatment in the independent test set, achieving an area under the receiver operating characteristic curve of 0.824 (95% CI: [0.658,0.953]) and 0.753 (95% CI: [0.549,0.931]). The Kaplan-Meier survival analysis showed that, for both endpoints, the signatures significantly stratified high- and low-risk patients (p-value< 0.05) and were significantly correlated with progression-free survival (PFS6 model: C-index 0.723, p-value = 0.004; PFS9 model: C-index 0.685, p-value = 0.030) and overall survival (PFS6 models: C-index 0.768, p-value = 0.002; PFS9 model: C-index 0.736, p-value = 0.023).

CONCLUSIONS

Integrating multidimensional and longitudinal data improved clinical durable benefit prediction to immunotherapy treatment of advanced non-small cell lung cancer patients. The selection of effective treatment and the appropriate evaluation of clinical benefit are important for better managing cancer patients with prolonged survival and preserving quality of life.

Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer

Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy for many types of solid tumors. However, the majority of patients with cancer will not respond, and predicting response to this therapy is still a challenge. Artificial intelligence (AI) methods can extract meaningful information from complex data, such as image data. In clinical routine, radiology or histopathology images are ubiquitously available. AI has been used to predict the response to immunotherapy from radiology or histopathology images, either directly or indirectly via surrogate markers. While none of these methods are currently used in clinical routine, academic and commercial developments are pointing toward potential clinical adoption in the near future. Here, we summarize the state of the art in AI-based image biomarkers for immunotherapy response based on radiology and histopathology images. We point out limitations, caveats, and pitfalls, including biases, generalizability, and explainability, which are relevant for researchers and health care providers alike, and outline key clinical use cases of this new class of predictive biomarkers.

Perioperative Immune Checkpoint Inhibition in Early-Stage Non-Small Cell Lung Cancer: A Review

Importance

Although cancer-related mortality continues to decline, lung cancer remains the No. 1 cause of cancer deaths in the US. Almost half of the patients with non-small cell lung cancer (NSCLC) are diagnosed with early-stage, local or regional disease and are at high risk of recurrence within 5 years of diagnosis.

Observations

Immune checkpoint inhibitors (ICIs) have improved outcomes for patients with metastatic NSCLC and have recently been tested in multiple clinical trials to determine their efficacy in the neoadjuvant or adjuvant setting for patients with local or regional disease. The landscape for perioperative ICIs in lung cancer is evolving rapidly, with recently reported and soon to mature clinical trials; however, the recent data highlight the potential of ICIs to increase response rates and decrease rates of relapse in early stages of lung cancer. Concurrently, novel applications of cell-free DNA may guide perioperative management strategies.

Conclusions and Relevance

This article reviews the various approaches of incorporating perioperative use of immunotherapeutic agents for the treatment of early stages of NSCLC.

Artificial intelligence for prediction of response to cancer immunotherapy

Artificial intelligence (AI) indicates the application of machines to imitate intelligent behaviors for solving complex tasks with minimal human intervention, including machine learning and deep learning. The use of AI in medicine improves health-care systems in multiple areas such as diagnostic confirmation, risk stratification, analysis, prognosis prediction, treatment surveillance, and virtual health support, which has considerable potential to revolutionize and reshape medicine. In terms of immunotherapy, AI has been applied to unlock underlying immune signatures to associate with responses to immunotherapy indirectly as well as predict responses to immunotherapy responses directly. The AI-based analysis of high-throughput sequences and medical images can provide useful information for management of cancer immunotherapy considering the excellent abilities in selecting appropriate subjects, improving therapeutic regimens, and predicting individualized prognosis. In present review, we aim to evaluate a broad framework about AI-based computational approaches for prediction of response to cancer immunotherapy on both indirect and direct manners. Furthermore, we summarize our perspectives about challenges and opportunities of further AI applications on cancer immunotherapy relating to clinical practicability.

Combined pembrolizumab and bevacizumab therapy effectively inhibits non-small-cell lung cancer growth and prevents postoperative recurrence and metastasis in humanized mouse model

Antibodies targeting the programmed cell death protein 1/programmed cell death ligand-1 (PD-1/PD-L1) pathway have dramatically changed the treatment landscape of advanced non-small cell lung cancer (NSCLC). However, combination approaches are required to extend this benefit beyond a subset of patients. In addition, it is of equal interest whether these combination therapy can be applied to neoadjuvant therapy of early-stage NSCLC. In this study, we hypothesized that combining immunotherapy with anti-angiogenic therapy may have a synergistic effect in local tumor control and neoadjuvant therapy. To this end, the effect of combination of bevacizumab and pembrolizumab in humanized mouse models was evaluated. Furthermore, we innovatively constructed a neoadjuvant mouse model that can simulate postoperative recurrence and metastasis of NSCLC to perform neoadjuvant study. Tumor growth and changes in the tumor vasculature, along with the frequency and phenotype of tumor-infiltrating lymphocytes, were examined. Additionally, in vivo imaging system (IVIS) was used to observe the effect of neoadjuvant therapy. Results showed that combination therapy could inhibited tumor growth by transforming tumor with low immunoreactivity into inflamed ('hot') tumor, as demonstrated by increased CD8⁺granzyme B⁺ cytotoxic T cell infiltration. Subsequent studies revealed that this process is mediated by vascular normalization and endothelial cell activation. IVIS results showed that neoadjuvant therapy can effectively prevent postoperative recurrence and metastasis. Taken together, these preclinical studies demonstrated that the combination of bevacizumab and pembrolizumab had a synergistic effect in both advanced tumor therapy and neoadjuvant setting and therefore provide a theoretical basis for translating this basic research into clinical applications.

Liquid biopsy: current technology and clinical applications

Liquid biopsies are increasingly used for cancer molecular profiling that enables a precision oncology approach. Circulating extracellular nucleic acids (cell-free DNA; cfDNA), circulating tumor DNA (ctDNA), and circulating tumor cells (CTCs) can be isolated from the blood and other body fluids. This review will focus on current technologies and clinical applications for liquid biopsies. ctDNA/cfDNA has been isolated and analyzed using many techniques, e.g., droplet digital polymerase chain reaction, beads, emulsion, amplification, and magnetics (BEAMing), tagged-amplicon deep sequencing (TAm-Seq), cancer personalized profiling by deep sequencing (CAPP-Seq), whole genome bisulfite sequencing (WGBS-Seq), whole exome sequencing (WES), and whole genome sequencing (WGS). CTCs have been isolated using biomarker-based cell capture, and positive or negative enrichment based on biophysical and other properties. ctDNA/cfDNA and CTCs are being exploited in a variety of clinical applications: differentiating unique immune checkpoint blockade response patterns using serial samples; predicting immune checkpoint blockade response based on baseline liquid biopsy characteristics; predicting response and resistance to targeted therapy and chemotherapy as well as immunotherapy, including CAR-T cells, based on serial sampling; assessing shed DNA from multiple metastatic sites; assessing potentially actionable alterations; analyzing prognosis and tumor burden, including after surgery; interrogating difficult-to biopsy tumors; and detecting cancer at early stages. The latter can be limited by the small amounts of tumor-derived components shed into the circulation; furthermore, cfDNA assessment in all cancers can be confounded by clonal hematopoeisis of indeterminate potential, especially in the elderly. CTCs can be technically more difficult to isolate that cfDNA, but permit functional assays, as well as evaluation of CTC-derived DNA, RNA and proteins, including single-cell analysis. Blood biopsies are less invasive than tissue biopsies and hence amenable to serial collection, which can provide critical molecular information in real time. In conclusion, liquid biopsy is a powerful tool, and remarkable advances in this technology have impacted multiple aspects of precision oncology, from early diagnosis to management of refractory metastatic disease. Future research may focus on fluids beyond blood, such as ascites, effusions, urine, and cerebrospinal fluid, as well as methylation patterns and elements such as exosomes.