Surrogate study endpoints in the era of cancer immunotherapy

THOR: a TMB heterogeneity-adaptive optimization model predicts immunotherapy response using clonal genomic features in group-structured data

With the increasing number of indications for immune checkpoint inhibitors in early and advanced cancers, the prospect of a tumor-agnostic biomarker to prioritize patients is compelling. Tumor mutation burden (TMB) is a widely endorsed biomarker that quantifies nonsynonymous mutations within tumor DNA, essential for neoantigen production, which, in turn, correlates with the immune response and guides decision-making. However, the general clinical application of TMB-relying on simple mutational counts targeted at a single endpoint-does not adequately capture the complex clonal structure of tumors nor the multifaceted nature of prognostic indicators. This recognition has spurred the exploration of sophisticated high-dimensional regression techniques. Unfortunately, the limited cohort sizes in immunotherapy trials have hindered the full potential of these advanced methods. Our approach considers patient subgroups as related yet distinct entities, enabling precise tailoring and refinement to address subgroup-specific dynamics. Given the deficiencies and the constraints, we introduce a TMB heterogeneity-optimized regression (THOR). This innovative model enhances the predictive capabilities of TMB by integrating tumor clonality and a diverse spectrum of clinical endpoints, further augmented by fusion techniques across subgroups to facilitate robust data sharing and interpretation. Our simulations validate THOR's superiority in parameter estimation for statistical inference. Clinically, we assess the utility of THOR in a structured cohort of 238 cancer patients undergoing immunotherapy, supplemented by 2212 patients across 19 subgroups from public datasets. The forecast of the responses and comparison of survival hazards demonstrate that THOR significantly enhances patient stratification and prognostic predictions by incorporating complex immunogenetic biology and subgroup-specific dynamics.

Adapting Health Technology Assessment agency standards for surrogate outcomes in early stage cancer trials: what needs to happen?

INTRODUCTION

An avalanche of early stage cancer clinical trials is coming. The majority of these solely use surrogate outcomes that have not been validated against a target outcome of interest (e.g. overall survival). Current HTA guidance on surrogate outcome validation are not methodologically or practically conducive to this scenario.

AREAS COVERED

We provide a high-level overview of methods, approaches, and conceptual thinking for making better use of limited evidence within early stage cancer HTA submissions. We outline regulatory and HTA issues and emphasize how evidence transitions from one to another, what major gaps currently exist, and how these may be bridged. We summarize current methodologies and practices, their pros and cons. We outline how complementary measurements strengthen evaluations and address fallacies and biases of conventional statistical methods for surrogate outcomes validation. The value of real-world data to support some of the necessary validity components is discussed. Lastly, we address the importance of the patient voice for better understanding which surrogate outcomes may appropriately inform HTA.

EXPERT OPINION

Conventional surrogate outcome validation represents a fraught and sub-optimal framework for HTA purposes, particularly for early stage cancer. Tools for optimizing use of limited evidence exist. Education of stakeholders is highly needed.

Tislelizumab plus chemotherapy versus pembrolizumab plus chemotherapy for the first-line treatment of advanced non-small cell lung cancer: systematic review and indirect comparison of randomized trials

Purpose: Pembrolizumab and tislelizumab have demonstrated significant clinical benefits in first-line treatment for advanced NSCLC. However, no head-to-head clinical trial has ever compared the optimal choice. Therefore, we conducted an indirect comparison to explore the optimal choice for advanced NSCLC combined with chemotherapy. Methods: We conducted a systematic review of randomized trials; the clinical outcomes included overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and adverse events (AEs). Indirect comparisons between tislelizumab and pembrolizumab were conducted with the Bucher method. Results: Data were abstracted from 6 randomized trials involving more than 2,000 participants. Direct meta-analysis showed that both treatment regimens improved clinical outcomes compared with chemotherapy alone (PFS: hazard ratio (HR)_{tis+chemo/chemo} 0.55, 95% CI 0.45-0.67; HR_{pem+chemo/chemo} 0.53, 95% CI 0.47-0.60; ORR: relative risk (RR)_{tis+chemo/chemo} 1.50, 95% CI 1.32-1.71; RR_{pem+chemo/chemo} 1.89, 95% CI 1.44-2.48). Regarding safety outcomes, tislelizumab and pembrolizumab have a higher risk in the incidence of grade 3 or higher AEs (RR_{tis+chemo/chemo} 1.12, 95% CI 1.03-1.21; RR_{pem+chemo/chemo} 1.13, 95% CI 1.03-1.24). The indirect comparison showed that there was no significant difference between tislelizumab plus chemotherapy and pembrolizumab plus chemotherapy in terms of PFS (HR: 1.04, 95% CI 0.82-1.31), ORR (RR: 0.79, 95% CI 0.59-1.07), the incidence of grade 3 or higher AEs (RR 0.99, 95% CI 0.87-1.12), and AEs leading to death (RR 0.70, 95% CI 0.23-2.09). In progression-free survival subgroup analysis, the results demonstrate no significant differences in PFS by PD-L1 TPS expression level, age, liver metastasis status, and smoking status between tislelizumab plus chemotherapy and pembrolizumab plus chemotherapy. Conclusion: The efficacy and safety of tislelizumab combination chemotherapy were not substantially different from pembrolizumab combination chemotherapy.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using ¹H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the ¹H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

Machine learning on the road to unlocking microbiota's potential for boosting immune checkpoint therapy

The intestinal microbiota is a complex and diverse ecological community that fulfills multiple functions and substantially impacts human health. Despite its plasticity, unfavorable conditions can cause perturbations leading to so-called dysbiosis, which have been connected to multiple diseases. Unfortunately, understanding the mechanisms underlying the crosstalk between those microorganisms and their host is proving to be difficult. Traditionally used bioinformatic tools have difficulties to fully exploit big data generated for this purpose by modern high throughput screens. Machine Learning (ML) may be a potential means of solving such problems, but it requires diligent application to allow for drawing valid conclusions. This is especially crucial as gaining insight into the mechanistic basis of microbial impact on human health is highly anticipated in numerous fields of study. This includes oncology, where growing amounts of studies implicate the gut ecosystems in both cancerogenesis and antineoplastic treatment outcomes. Based on these reports and first signs of clinical benefits related to microbiota modulation in human trials, hopes are rising for the development of microbiome-derived diagnostics and therapeutics. In this mini-review, we're inspecting analytical approaches used to uncover the role of gut microbiome in immune checkpoint therapy (ICT) with the use of shotgun metagenomic sequencing (SMS) data.

OSPred Tool: A Digital Health Aid for Rapid Predictive Analysis of Correlations Between Early End Points and Overall Survival in Non-Small-Cell Lung Cancer Clinical Trials

Overall survival (OS) is the gold standard end point for establishing clinical benefits in phase III oncology trials. However, these trials are associated with low success rates, largely driven by failure to meet the primary end point. Surrogate end points such as progression-free survival (PFS) are increasingly being used as indicators of biologic drug activity and to inform early go/no-go decisions in oncology drug development. We developed OSPred, a digital health aid that combines actual clinical data and machine intelligence approaches to visualize correlation trends between early (PFS-based) and late (OS) end points and provide support for shared decision making in the drug development pipeline.

The role of fructose 1,6-bisphosphate-mediated glycolysis/gluconeogenesis genes in cancer prognosis

Metabolic reprogramming and elevated glycolysis levels are associated with tumor progression. However, despite cancer cells selectively inhibiting or expressing certain metabolic enzymes, it is unclear whether differences in gene profiles influence patient outcomes. Therefore, identifying the differences in enzyme action may facilitate discovery of gene ontology variations to characterize tumors. Fructose-1,6-bisphosphate (F-1,6-BP) is an important intermediate in glucose metabolism, particularly in cancer. Gluconeogenesis and glycolysis require fructose-1,6-bisphosphonates 1 (FBP1) and fructose-bisphosphate aldolase A (ALDOA), which participate in F-1,6-BP conversion. Increased expression of ALDOA and decreased expression of FBP1 are associated with the progression of various forms of cancer in humans. However, the exact molecular mechanism by which ALDOA and FBP1 are involved in the switching of F-1,6-BP is not yet known. As a result of their pancancer pattern, the relationship between ALDOA and FBP1 in patient prognosis is reversed, particularly in lung adenocarcinoma (LUAD) and liver hepatocellular carcinoma (LIHC). Using The Cancer Genome Atlas (TCGA), we observed that FBP1 expression was low in patients with LUAD and LIHC tumors, which was distinct from ALDOA. A similar trend was observed in the analysis of Cancer Cell Line Encyclopedia (CCLE) datasets. By dissecting downstream networks and possible upstream regulators, using ALDOA and FBP1 as the core, we identified common signatures and interaction events regulated by ALDOA and FBP1. Notably, the identified effectors dominated by ALDOA or FBP1 were distributed in opposite patterns and can be considered independent prognostic indicators for patients with LUAD and LIHC. Therefore, uncovering the effectors between ALDOA and FBP1 will lead to novel therapeutic strategies for cancer patients.

Systematic literature review of trials assessing recommended systemic treatments in hepatocellular carcinoma

AIM

To identify and evaluate the similarity of all trials assessing recommended treatments for advanced hepatocellular carcinoma.

MATERIALS & METHODS

Single arm and randomized trials from any phase and published any time up to February 2021 were systematically searched.

RESULTS

From 5677 records reviewed, 50 trials were included in the review, and 24 for assessed for similarity. In the first-line (1L) setting, several trials assessing sorafenib were noted for enrolling patients with more severe disease and/or performance status than other 1L trials; trials within the second-line (2L) setting were generally similar. Median survival was <2 years in all trial arms.

CONCLUSIONS

Trials assessing recommended treatments are largely similar and appropriate for quantitative comparisons of several efficacy and safety outcomes.

Correlation Between Early Endpoints and Overall Survival in Non-Small-Cell Lung Cancer: A Trial-Level Meta-Analysis

Early endpoints, such as progression-free survival (PFS), are increasingly used as surrogates for overall survival (OS) to accelerate approval of novel oncology agents. Compiling trial-level data from randomized controlled trials (RCTs) could help to develop a predictive framework to ascertain correlation trends between treatment effects for early and late endpoints. Through trial-level correlation and random-effects meta-regression analysis, we assessed the relationship between hazard ratio (HR) OS and (1) HR PFS and (2) odds ratio (OR) PFS at 4 and 6 months, stratified according to the mechanism of action of the investigational product. Using multiple source databases, we compiled a data set including 81 phase II-IV RCTs (35 drugs and 156 observations) of patients with non-small-cell lung cancer. Low-to-moderate correlations were generally observed between treatment effects for early endpoints (based on PFS) and HR OS across trials of agents with different mechanisms of action. Moderate correlations were seen between treatment effects for HR PFS and HR OS across all trials, and in the programmed cell death-1/programmed cell death ligand-1 and epidermal growth factor receptor trial subsets. Although these results constitute an important step, caution is advised, as there are some limitations to our evaluation, and an additional patient-level analysis would be needed to establish true surrogacy.

The Prognostic Role of High Blood Cholesterol in Advanced Cancer Patients Treated With Immune Checkpoint Inhibitors

Immune checkpoint inhibitors (ICI) have improved survival in numerous types of cancer. However, a great number of unselected patients still do not respond to ICI. Moreover, there is a need to identify biomarkers that could predict the prognosis of immunotherapy-treated patients. The aim of our study is to evaluate the prognostic value of baseline plasmatic cholesterol levels in metastatic cancer patients treated with immunotherapy. We retrospectively enrolled advanced cancer patients consecutively treated with ICI at our center between October 2013 and October 2018 to correlate the blood cholesterol level before treatment with overall survival (OS, primary endpoint). The secondary endpoints were the correlation between baseline cholesterol and progression-free survival (PFS), objective response rate, and toxicity (immune-related adverse events). Among 187 patients with availability of baseline plasmatic cholesterol, 58 had cholesterol levels >200 mg/dL. The median age was 70 years. Primary tumors were as follows: non-small cell lung cancer (70.0%), melanoma (15.0%), renal cell carcinoma (9.1%), urothelial cancer (4.6%), head-neck carcinoma (0.9%), and others (0.4%). The median follow-up was 21.3 months. Both OS and PFS were better in patients with high plasmatic cholesterol levels: the median OS was 19.4 versus 5.5 months (P=0.001) and the median PFS was 6.1 versus 2.4 months (P=0.002). The multivariate analysis confirmed the prognostic role of hypercholesterolemia in terms of OS, but not PFS. Hypercholesterolemia was associated with better outcomes in ICI-treated cancer patients and, as an expression of low-grade inflammation state, it could identify tumors more likely to be responsive to immunotherapy.

Long Noncoding RNAs: New Regulators of Resistance to Systemic Therapies for Gastric Cancer

Gastric cancer (GC) is the second leading cause of cancer mortality and the fourth most commonly diagnosed malignant disease, with approximately 951,000 new cases diagnosed and approximately 723,000 cases of mortality each year. The highest mortality rate of GC is in East Asia, and the lowest is in North America. A large number of studies have demonstrated that GC patients are characterized by higher morbidity, metastasis rates, and mortality and lower early diagnosis rates, radical resection rates, and 5-year survival rates. All cases of GC can be divided into two important stages, namely, early- and advanced-stage GC, and the stage mainly determines the treatment strategy for and the therapeutic effect in GC patients. Patients with early-stage GC undergo radical surgery followed by chemotherapy, and the 5-year survival rate can be as high as 90%. However, patients with advanced-stage GC cannot undergo radical surgery because they are at risk for metastasis; therefore, they can choose only radiotherapy or chemotherapy and have a poor prognosis. Based on the lack of specific clinical manifestations and detection methods, most GC patients (>70%) are diagnosed in the advanced stage; therefore, continued efforts toward developing treatments have been focused on advanced-stage GC patients and include molecular targeted therapy, immunotherapy, and small molecular therapy. Nevertheless, in recent years, accumulating evidence has indicated that small molecules, especially long noncoding RNAs (lncRNAs), are involved in the occurrence, development, and progression of GC, and their abundantly dysregulated expression has been identified in GC tissues and cell lines. Therefore, lncRNAs are considered easily detectable molecules and ideal biomarkers or target-specific agents for the future diagnosis or treatment of GC. In this review, we primarily discuss the status of GC, the role of lncRNAs in GC, and the emerging systemic treatments for GC.

Metabolomics to Assess Response to Immune Checkpoint Inhibitors in Patients with Non-Small-Cell Lung Cancer

Simple Summary

Recently, immunotherapy has presented new opportunities for clinical development in the treatment of non-small cell lung cancer (NSCLC). Although effective in sustaining overall survival in several clinical trials, not all the NSCLC patients respond to these treatments. Thus, a better patient selection, as well as the identification of predictive biomarkers of treatment efficacy, are of paramount importance. In this work, metabolomics was used with the aim of identifying responder with respect to non-responder subjects. We show that the metabolomic fingerprint of serum samples, collected before therapy, acts as a predictive biomarker to treatment response. Prospective identification of subjects that will benefit from immunotherapy could improve patient stratification, thus optimizing the treatment and avoiding unsuccessful strategies.

Abstract

In the treatment of advanced non-small cell lung cancer (NSCLC), immune checkpoint inhibitors have shown remarkable results. However, not all patients with NSCLC respond to this drug treatment or receive durable benefits. Thus, patient stratification and selection, as well as the identification of predictive biomarkers, represent pivotal aspects to address. In this framework, metabolomics can be used to support the discrimination between responders and non-responders. Here, metabolomics was used to analyze the sera samples from 50 patients with NSCL treated with immune checkpoint inhibitors. All the samples were collected before the beginning of the treatment and were analyzed by NMR spectroscopy and multivariate statistical analyses. Significantly, we show that the metabolomic fingerprint of serum acts as a predictive “collective” biomarker to immune checkpoint inhibitors response, being able to predict individual therapy outcome with > 80% accuracy. Metabolomics represents a potential strategy for the real-time selection and monitoring of patients treated with immunotherapy. The prospective identification of responders and non-responders could improve NSCLC treatment and patient stratification, thus avoiding ineffective therapeutic strategies.

Matching-adjusted indirect comparison: entrectinib versus crizotinib in ROS1 fusion-positive non-small cell lung cancer

Aim: To perform indirect treatment comparisons of entrectinib versus alternative ROS1 fusion-positive non-small cell lung cancer treatments. Methods: Relevant studies with crizotinib and chemotherapy as comparators of interest identified by systematic literature review were selected for matching-adjusted indirect comparison by feasibility assessment. Matching was based on known prognostic/predictive factors and scenario analyses were used for unreported confounders in comparator trials. Results: Entrectinib yielded significantly better responses versus crizotinib in all scenarios (odds ratio [OR]: 2.43-2.74). Overall survival (hazard ratio: 0.47-0.61) and adverse event-related discontinuation (OR: 0.79-0.90) favored entrectinib. Progression-free survival was similar across treatments, except in one scenario. Conclusion: These results suggested improved outcomes with entrectinib versus crizotinib/chemotherapy and may help to make better informed treatment decisions.

Immune checkpoint inhibitors for head and neck squamous cell carcinoma: Current landscape and future directions

BACKGROUND

Immune checkpoint inhibitors (ICIs) can reinvigorate T cells and activate the immune system to eliminate cancer cells. Head and neck squamous cell carcinoma (HNSCC) is a malignancy with a poor prognosis. The roles of ICIs for HNSCC treatments are emerging.

METHOD

We reviewed the study results of Programmed-Death 1 (PD-1) and PD-ligand-1 (PD-L1) monoclonal antibodies for HNSCC. The ongoing trials of anti-PD-1 and anti-PD-L1 were also reviewed.

RESULTS

Nivolumab showed a significant overall survival benefit in platinum-refractory HNSCC patients. For platinum-sensitive or first-line patients, pembrolizumab monotherapy (patients with PD-L1 Combined Positive Score ≥ 20) or pembrolizumab-platinum-fluorouracil improved overall survival vs the EXTREME (cetuximab-platinum-fluorouracil). Many HNSCC studies have combined anti-PD1/PD-L1 therapy with various anticancer agents or radiotherapy to improve treatment efficacy.

CONCLUSION

ICIs demonstrate their efficacies for R/M HNSCC patients. The incorporation of ICIs showed a great impact on the treatment landscape of HNSCC.

Master protocols in immuno-oncology: do novel drugs deserve novel designs?

The rapid rise to fame of immuno-oncology (IO) drugs has generated unprecedented interest in the industry, patients and doctors, and has had a major impact in the treatment of most cancers. An interesting aspect in the clinical development of many IO agents is the increasing reliance on nonconventional trial design, including the so-called 'master protocols' that incorporate various adaptive features and often heavily rely on biomarkers to select patient populations most likely to benefit. These novel designs promise to maximize the clinical benefit that can be reaped from clinical research, but are not without costs. Their acceptance as solid evidence basis for use outside of the research context requires profound cultural changes by multiple stakeholders, including regulatory bodies, decision-makers, statisticians, researchers, doctors and, most importantly, patients. Here we review characteristics of recent and ongoing trials testing IO drugs with unconventional design, and we highlight trends and critical aspects.

The emerging use of immune checkpoint blockade in the adjuvant setting for solid tumors: a review

The use of immune checkpoint inhibitors has been approved in the advanced and metastatic setting for many types of solid tumors. Nonetheless, their role in the adjuvant setting is limited to the treatment of surgically resected melanoma. Ipilimumab was the first immune checkpoint inhibitor approved for this indication, followed by nivolumab and pembrolizumab. Many ongoing trials are evaluating these molecules in the postoperative setting, alone or in combination with other therapies. Preliminary results are promising regarding the treatment of other cutaneous tumors, lung cancers, head and neck squamous cell carcinomas, bladder cancer and renal cell carcinomas. Some data assessing their use for the adjuvant treatment of esophageal, colorectal, ovarian cancer and other solid tumors are similarly emerging.

The Statistical Evaluation of Treatment and Outcomes in Head and Neck Squamous Cell Carcinoma Clinical Trials

The purpose of this article is two-fold: to help statisticians confronted with the design, implementation and analysis of clinical trials and new to the field of head and neck cancer; but also to sensitize research physicians with the role, the tasks and the challenges faced by the medical statisticians. These two purposes altogether will hopefully encourage and enable fluid communication between the research physician and the medical statistician and the understanding of each other's field and concerns. In particular, the methodological challenges resulting from the heterogeneity of the head and neck cancer, the complexity of the treatments and the associated comorbidities are presented with examples borrowed from medical literature and from the practical experience of the authors in this field.