Redefining Clinical Hyperprogression: The Incidence, Clinical Implications, and Risk Factors of Hyperprogression in Non-Small Cell Lung Cancer Treated with Immunotherapy.

INTRODUCTION: Immune checkpoint inhibitors (ICIs) may be associated with hyperprogressive disease (HPD). However, there is currently no standardized definition of HPD, with its risk factors and clinical implications remaining unclear. We investigated HPD in lung cancer patients undergoing immunotherapy, aiming to redefine HPD, identify risk factors, and assess its impact on survival. METHODS: Clinical and radiologic data from 121 non-small cell lung cancer (NSCLC) patients with 136 immunotherapy cases were reviewed retrospectively. Three HPD definitions (Champiat et al., HPDc; Saâda-Bouzid et al., HPDs; and Ferrara et al., HPDf) were employed. Additionally, all new measurable lesions on the post-treatment CT scan were incorporated in measuring the sum of longest diameters (SLD) to define modified HPD (mHPD). RESULTS: Among the 121 patients, 4 (3.3%) had HPDc, 11 (9.1%) had HPDs, and none had HPDf. Adding all new measurable lesions increased HPD incidence by 5%-10% across definitions. Multivariate analysis revealed significantly lower progression-free survival (PFS) and overall survival (OS) for patients with HPDc (HR 5.25, P = .001; HR 3.75, P = .015) and HPDs (HR 3.74, P < .001; HR 3.46, P < .001) compared to those without. Patients with mHPD showed similarly poor survival outcomes as HPD patients. Liver metastasis at diagnosis was associated with HPDs, and a high tumor burden correlated with HPDc. CONCLUSIONS: The incidence and risk factors of HPD varied with different definitions, but mHPD identified more cases with poor outcomes. This comprehensive approach may enhance the identification of at-risk patients and lead to a better understanding of HPD in lung cancer during immunotherapy.

Evaluation of Major Pathologic Response and Pathologic Complete Response as Surrogate End Points for Survival in Randomized Controlled Trials of Neoadjuvant Immune Checkpoint Blockade in Resectable in NSCLC.

INTRODUCTION: Controversy remains as to whether pathologic complete response (pCR) and major pathologic response (MPR) represent surrogate end points for event-free survival (EFS) and overall survival (OS) in neoadjuvant trials for resectable NSCLC. METHODS: A search of PubMed and archives of international conference abstracts was performed from June 2017 through October 31, 2023. Studies incorporating a neoadjuvant arm with immune checkpoint blockade alone or in combination with chemotherapy were included. Those not providing information regarding pCR, MPR, EFS, or OS were excluded. For trial-level surrogacy, log ORs for pCR and MPR and log hazard ratios for EFS and OS were analyzed using a linear regression model weighted by sample size. The regression coefficient and R2 with 95% confidence interval were calculated by the bootstrapping approach. RESULTS: Seven randomized clinical trials were identified for a total of 2385 patients. At the patient level, the R2 of pCR and MPR with 2-year EFS were 0.82 (0.66-0.94) and 0.81 (0.63-0.93), respectively. The OR of 2-year EFS rates by response status was 0.12 (0.07-0.19) and 0.11 (0.05-0.22), respectively. For the 2-year OS, the R2 of pCR and MPR were 0.55 (0.09-0.98) and 0.52 (0.10-0.96), respectively. At the trial level, the R2 for the association of OR for response and HR for EFS was 0.58 (0.00-0.97) and 0.61 (0.00-0.97), respectively. CONCLUSIONS: Our analyses reveal a robust correlation between pCR and MPR with 2-year EFS but not OS. Trial-level surrogacy was moderate but imprecise. More mature follow-up and data to assess the impact of study crossover are needed.

Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types.

BACKGROUND: The inflamed immune phenotype (IIP), defined by enrichment of tumor-infiltrating lymphocytes (TILs) within intratumoral areas, is a promising tumor-agnostic biomarker of response to immune checkpoint inhibitor (ICI) therapy. However, it is challenging to define the IIP in an objective and reproducible manner during manual histopathologic examination. Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types. METHODS: Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. We evaluated the correlation between the IIP and ICI treatment outcomes in terms of objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) in a cohort of 1,806 ICI-treated patients representing over 27 solid tumor types retrospectively collected from multiple institutions. RESULTS: We observed an overall IIP prevalence of 35.2% and significantly more favorable ORRs (26.3% vs 15.8%), PFS (median 5.3 vs 3.1 months, HR 0.68, 95% CI 0.61 to 0.76), and OS (median 25.3 vs 13.6 months, HR 0.66, 95% CI 0.57 to 0.75) after ICI therapy in IIP compared with non-IIP patients, respectively (p<0.001 for all comparisons). On subgroup analysis, the IIP was generally prognostic of favorable PFS across major patient subgroups, with the exception of the microsatellite unstable/mismatch repair deficient subgroup. CONCLUSION: The AI-based IIP may represent a practical, affordable, clinically actionable, and tumor-agnostic biomarker prognostic of ICI therapy response across diverse tumor types.

Pericardial Metastasis From Recurrent Squamous Cell Carcinoma of Hypopharynx in a Living Patient.

Cardiac metastatic disease is a rare finding and is usually diagnosed incidentally postmortem; it has been commonly reported in patients with cancers of lung, esophagus, breast, and melanoma. We present a case of a 62-year-old male with a history of squamous cell carcinoma of the pyriform sinus who presented with shortness of breath for one day. He underwent tumor resection followed by chemotherapy and radiotherapy seven months before this presentation. Computed tomography (CT) of the chest revealed pericardial nodular soft tissue that was consistent with the diagnosis of metastatic carcinomatosis. Further imaging with a transthoracic echocardiogram (TTE) showed a likely metastatic pericardial mass. The patient had presented with shortness of breath three months prior to this admission and TTE had demonstrated pericardial effusion. However, pericardial fluid cytology was negative for malignancy, and the repeat TTE had revealed resolution of the pericardial effusion. On the current admission, CT of the neck demonstrated local recurrence of the tumor in the resection bed with scattered regional lymph nodes enlargement. Thus, we report a case of a recurrent laryngopharyngeal tumor with very rarely reported pericardial metastasis.

Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy.

Immunotherapy has fundamentally changed the landscape of cancer treatment. However, only a subset of patients respond to immunotherapy, and a significant portion experience immune-related adverse events (irAEs). In addition, the predictive ability of current biomarkers such as programmed death-ligand 1 (PD-L1) remains unreliable and establishing better potential candidate markers is of great importance in selecting patients who would benefit from immunotherapy. Here, we focus on the role of serum-based proteomic tests in predicting the response and toxicity of immunotherapy. Serum proteomic signatures refer to unique patterns of proteins which are associated with immune response in patients with cancer. These protein signatures are derived from patient serum samples based on mass spectrometry and act as biomarkers to predict response to immunotherapy. Using machine learning algorithms, serum proteomic tests were developed through training data sets from advanced non-small cell lung cancer (Host Immune Classifier, Primary Immune Response) and malignant melanoma patients (PerspectIV test). The tests effectively stratified patients into groups with good and poor treatment outcomes independent of PD-L1 expression. Here, we review current evidence in the published literature on three liquid biopsy tests that use biomarkers derived from proteomics and machine learning for use in immuno-oncology. We discuss how these tests may inform patient prognosis as well as guide treatment decisions and predict irAE of immunotherapy. Thus, mass spectrometry-based serum proteomics signatures play an important role in predicting clinical outcomes and toxicity.

Artificial Intelligence-based Radiomics in the Era of Immuno-oncology.

The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics-the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends-for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.

Association of HLA class I homozygosity with unfavorable clinical outcomes in patients with non-small cell lung cancer treated with chemo-immunotherapy or immunotherapy as first-line therapy.

BACKGROUND: Homozygosity at HLA-I locus has been reported to be an unfavorable predictive biomarker of second-line or beyond immunotherapy in patients with different types of cancer. The linkage between HLA-I zygosity and survival in NSCLC patients treated with first-line immunotherapy with or without chemotherapy has not been reported. METHODS: Next generation sequencing with HLA genotyping was performed on patients with advanced NSCLC treated with immune checkpoint inhibitors with or without chemotherapy as first-line (N = 29). Progression free survival was compared between HLA-I homozygous (defined as homozygosity in at least one locus A, B, or C) and heterozygous patients. Kaplan-Meier curves were built, and log-rank test was used. RESULTS: Among 29 enrollees, 25 patients (86.2%) were HLA-I heterozygous and four patients (13.8%) were HLA-I homozygous. Treatment response was not available in five patients with HLA-I heterozygosity. Among 20 patients with HLA-I heterozygosity, five patients (20.0%) had partial response, 10 patients (50.0%) had stable disease, two patients (8.0%) had non-complete response/non-progressive disease, and three patients (12.0%) had progressive disease. Among four patients with HLA-I heterozygosity, one patient (25.0%) had partial response, one patient (25.0%) had stable disease, and two patients (50.0%) had progressive disease. The median progression free survival was not reached in heterozygous group and was 2.97 months in homozygous group (Log-rank p = 0.68). CONCLUSIONS: We observed a trend toward an inverse association between HLA-I homozygosity and survival outcomes in patients with NSCLC treated with first-line therapy in conjunction with immunotherapy. Further prospective studies to validate aforementioned relationship are warranted.

Blood transfusions may adversely affect survival outcomes of patients with lung cancer: a systematic review and meta-analysis.

BACKGROUND: Despite common use in clinical practice, the impact of blood transfusions on prognosis among patients with lung cancer remains unclear. The purpose of the current study is to perform an updated systematic review and meta-analysis to evaluate the influence of blood transfusions on survival outcomes of lung cancer patients. METHODS: We searched PubMed, Embase, Cochrane Library, and Ovid MEDLINE for publications illustrating the association between blood transfusions and prognosis among people with lung cancer from inception to November 2019. Overall survival (OS) and disease-free survival (DFS) were the outcomes of interest. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were computed using the random-effects model. Study heterogeneity was evaluated with the I2 test. Publication bias was explored via funnel plot and trim-and-fill analyses. RESULTS: We included 23 cohort studies with 12,175 patients (3,027 cases and 9,148 controls) for meta-analysis. Among these records, 22 studies investigated the effect of perioperative transfusions, while one examined that of transfusions during chemotherapy. Two studies suggested the possible dose-dependent effect in accordance with the number of transfused units. In pooled analyses, blood transfusions deleteriously influenced both OS (HR=1.35, 95% CI: 1.14-1.61, P<0.001, I2=0%) and DFS (HR=1.46, 95% CI: 1.15-1.86, P=0.001, I2=0%) of people with lung cancer. No evidence of significant publication bias was detected in funnel plot and trim-and-fill analyses (OS: HR=1.26, 95% CI: 1.07-1.49, P=0.006; DFS: HR=1.35, 95% CI: 1.08-1.69, P=0.008). CONCLUSIONS: Blood transfusions were associated with decreased survival of patients with lung cancer.

Overcoming acquired resistance to PD-1 inhibitor with the addition of metformin in small cell lung cancer (SCLC).

Metformin has been widely used as the treatment of type II diabetes mellitus for its anti-hyperglycemic effect. In recent years, it has also been extensively studied for its anti-cancer effect as it diminishes immune exhaustion of CD8 + tumor-infiltrating lymphocytes (TILs). It decreases apoptosis of CD8 + TILs, thereby enhancing T cell-mediated immune response to tumor cells. Here, we present a unique case of a patient with small cell lung cancer (SCLC) who exhibited an overall partial response as per Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST 1.1) since starting metformin in combination with nivolumab therapy. Our patient had been treated with nivolumab monotherapy for 2 years until she had progression of disease. After she was started on metformin along with nivolumab therapy, she has shown a significant durable response for over 6 months. Many patients develop resistance to immunotherapy such as antibodies against cytotoxic T lymphocyte-associated protein 4 (CTLA-4), programmed cell death 1 (PD-1), and programmed cell death ligand 1 (PD-L1). Tumor hypoxia is one of the resistance factors. Signals activated by hypoxic environments in tumors are associated with decreased sensitivity to the PD-1 blockade. Metformin inhibits oxygen consumption in tumor cells in vitro and in vivo, reducing intratumoral hypoxia. These data suggest that metformin can improve susceptibility to anti-PD-1 treatment. To the best of our knowledge, our case is the first reported example demonstrating a proof-of-concept that metformin can contribute to overcoming acquired resistance to PD-1 inhibitors.

Regional variation in states' naloxone accessibility laws in association with opioid overdose death rates-Observational study (STROBE compliant).

Though overall death from opioid overdose are increasing in the United States, the death rate in some states and population groups is stabilizing or even decreasing. Several states have enacted a Naloxone Accessibility Laws to increase naloxone availability as an opioid antidote. The extent to which these laws permit layperson distribution and possession varies. The aim of this study is to investigate differences in provisions of Naloxone Accessibility Laws by states mainly in the Northeast and West regions, and the impact of naloxone availability on the rates of drug overdose deaths.This cross-sectional study was based on the National Vital Statistics System multiple cause-of-death mortality files. The average changes in drug overdose death rates between 2013 and 2017 in relevant states of the Northeast and West regions were compared according to availability of naloxone to laypersons.Seven states in the Northeast region and 10 states in the Western region allowed layperson distribution of naloxone. Layperson possession of naloxone was allowed in 3 states each in the Northeast and the Western regions. The average drug overdose death rates increased in many states in the both regions regardless of legalization of layperson naloxone distribution. The average death rates of 3 states that legalized layperson possession in the West region decreased (-0.33 per 100,000 person); however, in states in the West region that did not allow layperson possession and states in the Northeast region regardless of layperson possession increased between 2013 and 2017.The provision to legalize layperson possession of naloxone was associated with decreased average opioid overdose death rates in 3 states of the West region.