Biomarker-Driven Oncology Clinical Trials: Key Design Elements, Types, Features, and Practical Considerations

Precision cancer medicine platform trials: Concepts and design of AcSé-ESMART

Precision cancer medicine brought the promise of improving outcomes for patients with cancer. High-throughput molecular profiling of tumors at treatment failure aims to direct a patient to a treatment matched to the tumor profile. In this way, improved outcome has been achieved in a small number of patients whose tumors exhibit unique targetable oncogenic drivers. Most cancers, however, contain multiple genetic alterations belonging to and of various hallmarks of cancer; for most of these alterations, there is limited knowledge on the level of evidence, their hierarchical roles in oncogenicity, and utility as biomarkers for response to targeted treatment(s). We developed a proof-of-concept trial that explores new treatment strategies in a molecularly-enriched tumor-agnostic, pediatric population. The evaluation of novel agents, including first-in-child molecules, alone or in combination, is guided by the available understanding of or hypotheses for the mechanisms of action of the diverse cancer events. Main objectives are: to determine 1) recommended phase 2 doses, 2) activity signals to provide the basis for disease specific development, and 3) to define new predictive biomarkers. Here we outline concepts, rationales and designs applied in the European AcSé-ESMART trial and highlight the feasibility but also complexity and challenges of such innovative platform trials.

Enhancing cancer immunotherapy: Exploring strategies to target the PD-1/PD-L1 axis and analyzing the associated patent, regulatory, and clinical trial landscape

Cancer treatment modalities and their progression is guided by the specifics of cancer, including its type and site of localization. Surgery, radiation, and chemotherapy are the most often used conventional treatments. Conversely, emerging treatment techniques include immunotherapy, hormone therapy, anti-angiogenic therapy, dendritic cell-based immunotherapy, and stem cell therapy. Immune checkpoint inhibitors' anticancer properties have drawn considerable attention in recent studies in the cancer research domain. Programmed Cell Death Protein-1 (PD-1) and its ligand (PD-L1) checkpoint pathway are key regulators of the interactions between activated T-cells and cancer cells, protecting the latter from immune destruction. When the ligand PD-L1 attaches to the receptor PD-1, T-cells are prevented from destroying cells that contain PD-L1, including cancer cells. The PD-1/PD-L1 checkpoint inhibitors block them, boosting the immune response and strengthening the body's defenses against tumors. Recent years have seen incredible progress and tremendous advancement in developing anticancer therapies using PD-1/PD-L1 targeting antibodies. While immune-related adverse effects and low response rates significantly limit these therapies, there is a need for research on methods that raise their efficacy and lower their toxicity. This review discusses various recent innovative nanomedicine strategies such as PLGA nanoparticles, carbon nanotubes and drug loaded liposomes to treat cancer targeting PD-1/PD-L1 axis. The biological implications of PD-1/PD-L1 in cancer treatment and the fundamentals of nanotechnology, focusing on the novel strategies used in nanomedicine, are widely discussed along with the corresponding guidelines, clinical trial status, and the patent landscape of such formulations.

Assessing patient burden and benefit: A decade of cabozantinib clinical trials

Drug development is complex and costly. Clinical trial participants take on risks, making it essential to maximize trial efficiency and maintain participant safety. Identifying periods of excessive burden during drug development can inform trial design, ensure patient benefit and prevent harm. This study aims to examine all published clinical trials for cabozantinib to assess patient benefit and burden over time. We conducted a retrospective cross-sectional review of interventional clinical trials of cabozantinib for solid cancer treatment. We searched PubMed/MEDLINE, Embase, Cochrane (CENTRAL) and ClinicalTrials.gov. We extracted adverse event rates, median progression-free survival (PFS), median overall survival and objective response rate (ORR) for each included trial. We calculated frequencies of trial characteristics, cumulative grade 3-5 adverse event rates and cumulative ORRs. Out of 1735 studies, 54 publications were included that involved 6372 participants and 21 cancers. Of the 54 studies in our sample, 31 (57.41%) were single-arm trials and 23 (42.60%) had negative results. Trials among and within various indications had conflicting results over time. Cumulative risk to participants increased over time, and clinical benefit decreased. The findings suggest that the risk profile of cabozantinib increased from 2011 to 2016 and has remained elevated but stable while benefit has decreased over time. The use of non-randomized and single-arm trials is concerning, and more methodologically rigorous trials are needed. The results of trials for different indications are inconsistent, and empirical administration may reduce the drug's efficacy.

Adjuvant and neoadjuvant immunotherapies in hepatocellular carcinoma

Liver cancer, specifically hepatocellular carcinoma (HCC), is the sixth most common cancer and the third leading cause of cancer mortality worldwide. The development of effective systemic therapies, particularly those involving immune-checkpoint inhibitors (ICIs), has substantially improved the outcomes of patients with advanced-stage HCC. Approximately 30% of patients are diagnosed with early stage disease and currently receive potentially curative therapies, such as resection, liver transplantation or local ablation, which result in median overall survival durations beyond 60 months. Nonetheless, up to 70% of these patients will have disease recurrence within 5 years of resection or local ablation. To date, the results of randomized clinical trials testing adjuvant therapy in patients with HCC have been negative. This major unmet need has been addressed with the IMbrave 050 trial, demonstrating a recurrence-free survival benefit in patients with a high risk of relapse after resection or local ablation who received adjuvant atezolizumab plus bevacizumab. In parallel, studies testing neoadjuvant ICIs alone or in combination in patients with early stage disease have also reported efficacy. In this Review, we provide a comprehensive overview of the current approaches to manage patients with early stage HCC. We also describe the tumour immune microenvironment and the mechanisms of action of ICIs and cancer vaccines in this setting. Finally, we summarize the available evidence from phase II/III trials of neoadjuvant and adjuvant approaches and discuss emerging clinical trials, identification of biomarkers and clinical trial design considerations for future studies.

Integration of on-treatment modified Glasgow prognostic score (mGPS) to improve imaging-based prediction of outcomes in patients with non-small cell lung cancer on immune checkpoint inhibition

INTRODUCTION

A large number of patients with non-small cell lung cancer (NSCLC) on immune checkpoint inhibition (ICI) achieve stable disease (SD) as the best overall response, which is associated with heterogeneous outcomes. In this context, complementary biomarkers that improve outcome prediction are needed. We have recently demonstrated that measuring the on-treatment modified Glasgow prognostic score (mGPS), which is based on the two serum markers C-reactive protein (CRP) and albumin, can improve outcome prediction complementary to radiological staging in metastatic renal cell carcinoma. However, this concept has not been assessed for patients with NSCLC on ICI.

METHODS

We assessed the prognostic and predictive value of on-treatment mGPS at week six in patients with NSCLC treated with atezolizumab or docetaxel in the phase 3 OAK trial (NCT02008227) comprising n = 750 patients and validated the findings in the phase 2 BIRCH (NCT02031458, n = 560).

RESULTS

On-treatment mGPS assessed at week six demonstrated valuable prognostic information (Hazard Ratio (HR) for mGPS low-risk vs intermediate risk 2.34 (95 % CI 1.76-3.11, p < 0.001) and vs high risk 3.56, (95 % CI 2.57-4.91, p < 0.001) in the atezolizumab-treated subgroup. On-treatment mGPS predicted overall survival more accurately than imaging using RECIST criteria (concordance index: on-treatment mGPS 0.646 (95 % CI 0.615-0.677) vs RECIST 0.606 (95 % CI 0.575-0.637)). On-treatment mGPS provides additional prognostic information to imaging-assessed treatment response at first staging, especially for the patient subgroup with SD. These findings were validated in the BIRCH trial.

CONCLUSIONS

We highlight the novel concept of integrating on-treatment mGPS for improved outcome prediction in conjunction with radiological imaging for patients with NSCLC on ICI.

Adhesion, metastasis, and inhibition of cancer cells: a comprehensive review

This comprehensive review delves into cancer's complexity, focusing on adhesion, metastasis, and inhibition. It explores the pivotal role of these factors in disease progression and therapeutic strategies. This review covers cancer cell migration, invasion, and colonization of distant organs, emphasizing the significance of cell adhesion and the intricate metastasis process. Inhibition approaches targeting adhesion molecules, such as integrins and cadherins, are discussed. Overall, this review contributes significantly to advancing cancer research and developing targeted therapies, holding promise for improving patient outcomes worldwide. Exploring different inhibition strategies revealed promising therapeutic targets to alleviate adhesion and metastasis of cancer cells. The effectiveness of integrin-blocking antibodies, small molecule inhibitors targeting Focal adhesion kinase (FAK) and the Transforming Growth Factor β (TGF-β) pathway, and combination therapies underscores their potential to disrupt focal adhesions and control epithelial-mesenchymal transition processes. The identification of as FAK, Src, β-catenin and SMAD4 offers valuable starting points for further research and the development of targeted therapies. The complex interrelationships between adhesion and metastatic signaling networks will be relevant to the development of new treatment approaches.

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

Clinical trial considerations for pediatric cancer drug development

Oncology has been one of the most active therapeutic areas in medicinal products development. Despite this fact, few drugs have been approved for use in pediatric cancer patients when compared to the number approved for adults with cancer. This disparity could be attributed to the fact that many oncology drugs have had orphan drug designation and were exempt from Pediatric Research Equity Act (PREA) requirements. On August 18, 2017, the RACE for Children Act, i.e. Research to Accelerate Cures and Equity Act, was signed into law as Title V of the 2017 FDA Reauthorization Act (FDARA) to amend the PREA. Pediatric investigation is now required if the drug or biological product is intended for the treatment of an adult cancer and directed at a molecular target that FDA determines to be "substantially relevant to the growth or progression of a pediatric cancer." This paper discusses the specific considerations in clinical trial designs and statistical methodologies to be implemented in oncology pediatric clinical programs.

[Antibody-drug conjugates directed against NECTIN-4 as a new treatment option for patients with metastatic urothelial carcinoma]

This compact review article highlights the background and importance of nectins in cancer therapy, focusing specifically on the antibody-drug conjugate enfortumab vedotin (EV) as a targeted treatment option for metastatic urothelial carcinoma. The evolving understanding of nectin-4 expression and its impact on EV therapy underscores the need for personalized approaches to ensure optimal patient outcomes. Further investigation into biomarker-guided therapies and prospective clinical trials are critical to refining patient selection and treatment strategies.

Quantitative PET-based biomarkers in lymphoma: getting ready for primetime

The use of functional quantitative biomarkers extracted from routine PET-CT scans to characterize clinical responses in patients with lymphoma is gaining increased attention, and these biomarkers can outperform established clinical risk factors. Total metabolic tumour volume enables individualized estimation of survival outcomes in patients with lymphoma and has shown the potential to predict response to therapy suitable for  risk-adapted treatment approaches in clinical trials. The deployment of machine learning tools in molecular imaging research can assist in recognizing complex patterns and, with image classification, in tumour identification and segmentation of data from PET-CT scans. Initial studies using fully automated approaches to calculate metabolic tumour volume and other PET-based biomarkers have demonstrated appropriate correlation with calculations from experts, warranting further testing in large-scale studies. The extraction of computer-based quantitative tumour characterization through radiomics can provide a comprehensive view of phenotypic heterogeneity that better captures the molecular and functional features of the disease. Additionally, radiomics can be integrated with genomic data to provide more accurate prognostic information. Further improvements in PET-based biomarkers are imminent, although their incorporation into clinical decision-making currently has methodological shortcomings that need to be addressed with confirmatory prospective validation in selected patient populations. In this Review, we discuss the current knowledge, challenges and opportunities in the integration of quantitative PET-based biomarkers in clinical trials and the routine management of patients with lymphoma.

Types and progress of clinical trial design for breast cancer: a narrative review

Background and Objective

In recent years, the field of breast cancer diagnosis and therapy has witnessed rapid technological advances. Concurrently, the emergence of molecular biology and novel detection methodologies has facilitated the transition of breast cancer management into the precision medicine era. The primary objective of this review is to discuss the transformation in the research and development paradigm for breast cancer therapies and strategies.

Methods

We systematically searched PubMed, EMBASE and Cochrane databases for relevant studies published over the past 20 years using keywords including "breast cancer", "clinical trial", "seamless", "master protocol", "umbrella", "basket", "platform", and "precision medicine". Articles were screened for eligibility and key data extracted. The search was limited to English-language publications.

Key Content and Findings

The review identifies three core innovations in breast cancer trial methodology: (I) in terms of research speed, the traditional three-stage drug development models are being substituted by "seamless designs" as exemplified by the immunotherapy combination study NCT0328056. (II) Addressing research breadth, "master protocols" such as basket trials (IMMU-132-01), umbrella trials (FUTURE), and platform trials (I-SPY 2) have been introduced, allowing the simultaneous assessment of multiple treatments or disease subtypes within a singular framework. (III) Pertaining to research precision, newer designs utilize biomarkers such as "enrichment" (seen in EMBRACA and OlympiA trials) and "marker stratification" (as in the SOLAR-1 trial), enabling the identification of appropriate patient subgroups and the provision of tailored therapy strategies, a stark contrast to traditional histopathology-based evaluations.

Conclusions

Clinical trial design in breast cancer research has been revolutionized, moving towards more efficient and targeted strategies. Despite the presence of ethical, logistical, and data complexities, it is anticipated that ongoing technological and regulatory enhancements will pave the way for even more refined research approaches, subsequently influencing future research, clinical practices, and policymaking in breast cancer care.

Simulation study for evaluating an adaptive-randomisation Bayesian hybrid trial design with enrichment

Background

As we enter the era of precision medicine, the role of adaptive designs, such as response-adaptive randomisation or enrichment designs in drug discovery and development, has become increasingly important to identify the treatment given to a patient based on one or more biomarkers. Tailoring the ventilation supply technique according to the responsiveness of patients to positive end-expiratory pressure is a suitable setting for such a design.

Methods

In the setting of marker-strategy design, we propose a Bayesian response-adaptive randomisation with enrichment design based on group sequential analyses. This design combines the elements of enrichment design and response-adaptive randomisation. Concerning the enrichment strategy, Bayesian treatment-by-subset interaction measures were used to adaptively enrich the patients most likely to benefit from an experimental treatment while controlling the false-positive rate.The operating characteristics of the design were assessed by simulation and compared to those of alternate designs.

Results

The results obtained allowed the detection of the superiority of one treatment over another and the presence of a treatment-by-subgroup interaction while keeping the false-positive rate at approximately 5\% and reducing the average number of included patients. In addition, simulation studies identified that the number of interim analyses and the burn-in period may have an impact on the performance of the scheme.

Conclusion

The proposed design highlights important objectives of precision medicine, such as determining whether the experimental treatment is superior to another and identifying wheter such an efficacy could depend on patient profile.

Pancreatic cancer derived 3D organoids as a clinical tool to evaluate the treatment response

Background and purpose

Pancreatic cancer (PC) is the fourth leading cause of cancer death in both men and women. The standard of care for patients with locally advanced PC of chemotherapy, stereotactic radiotherapy (RT), or chemo-radiation-therapy has shown highly variable and limited success rates. However, three-dimensional (3D) Pancreatic tumor organoids (PTOs) have shown promise to study tumor response to drugs, and emerging treatments under in vitro conditions. We investigated the potential for using 3D organoids to evaluate the precise radiation and drug dose responses of in vivo PC tumors.

Methods

PTOs were created from mouse pancreatic tumor tissues, and their microenvironment was compared to that of in vivo tumors using immunohistochemical and immunofluorescence staining. The organoids and in vivo PC tumors were treated with fractionated X-ray RT, 3-bromopyruvate (3BP) anti-tumor drug, and combination of 3BP + fractionated RT.

Results

Pancreatic tumor organoids (PTOs) exhibited a similar fibrotic microenvironment and molecular response (as seen by apoptosis biomarker expression) as in vivo tumors. Untreated tumor organoids and in vivo tumor both exhibited proliferative growth of 6 folds the original size after 10 days, whereas no growth was seen for organoids and in vivo tumors treated with 8 (Gray) Gy of fractionated RT. Tumor organoids showed reduced growth rates of 3.2x and 1.8x when treated with 4 and 6 Gy fractionated RT, respectively. Interestingly, combination of 100 µM of 3BP + 4 Gy of RT showed pronounced growth inhibition as compared to 3-BP alone or 4 Gy of radiation alone. Further, positive identification of SOX2, SOX10 and TGFβ indicated presence of cancer stem cells in tumor organoids which might have some role in resistance to therapies in pancreatic cancer.

Conclusions

PTOs produced a similar microenvironment and exhibited similar growth characteristics as in vivo tumors following treatment, indicating their potential for predicting in vivo tumor sensitivity and response to RT and combined chemo-RT treatments.

Systematic Review and Meta-Analysis of Plasma and Urine Biomarkers for CKD Outcomes

BACKGROUND

Sensitive and specific biomarkers are needed to provide better biologic insight into the risk of incident and progressive CKD. However, studies have been limited by sample size and design heterogeneity.

METHODS

In this assessment of the prognostic value of preclinical plasma and urine biomarkers for CKD outcomes, we searched Embase (Ovid), MEDLINE ALL (Ovid), and Scopus up to November 30, 2020, for studies exploring the association between baseline kidney biomarkers and CKD outcomes (incident CKD, CKD progression, or incident ESKD). We used random-effects meta-analysis.

RESULTS

After screening 26,456 abstracts and 352 full-text articles, we included 129 studies in the meta-analysis for the most frequently studied plasma biomarkers (TNFR1, FGF23, TNFR2, KIM-1, suPAR, and others) and urine biomarkers (KIM-1, NGAL, and others). For the most frequently studied plasma biomarkers, pooled RRs for CKD outcomes were 2.17 (95% confidence interval [95% CI], 1.91 to 2.47) for TNFR1 (31 studies); 1.21 (95% CI, 1.15 to 1.28) for FGF-23 (30 studies); 2.07 (95% CI, 1.82 to 2.34) for TNFR2 (23 studies); 1.51 (95% CI, 1.38 to 1.66) for KIM-1 (18 studies); and 1.42 (95% CI, 1.30 to 1.55) for suPAR (12 studies). For the most frequently studied urine biomarkers, pooled RRs were 1.10 (95% CI, 1.05 to 1.16) for KIM-1 (19 studies) and 1.12 (95% CI, 1.06 to 1.19) for NGAL (19 studies).

CONCLUSIONS

Studies of preclinical biomarkers for CKD outcomes have considerable heterogeneity across study cohorts and designs, limiting comparisons of prognostic performance across studies. Plasma TNFR1, FGF23, TNFR2, KIM-1, and suPAR were among the most frequently investigated in the setting of CKD outcomes.

Challenges and the Evolving Landscape of Assessing Blood-Based PD-L1 Expression as a Biomarker for Anti-PD-(L)1 Immunotherapy

While promising, PD-L1 expression on tumor tissues as assessed by immunohistochemistry has been shown to be an imperfect biomarker that only applies to a limited number of cancers, whereas many patients with PD-L1-negative tumors still respond to anti-PD-(L)1 immunotherapy. Recent studies using patient blood samples to assess immunotherapeutic responsiveness suggests a promising approach to the identification of novel and/or improved biomarkers for anti-PD-(L)1 immunotherapy. In this review, we discuss the advances in our evolving understanding of the regulation and function of PD-L1 expression, which is the foundation for developing blood-based PD-L1 as a biomarker for anti-PD-(L)1 immunotherapy. We further discuss current knowledge and clinical study results for biomarker identification using PD-L1 expression on tumor and immune cells, exosomes, and soluble forms of PD-L1 in the peripheral blood. Finally, we discuss key challenges for the successful development of the potential use of blood-based PD-L1 as a biomarker for anti-PD-(L)1 immunotherapy.

Pan-cancer prognostic genetic mutations and clinicopathological factors associated with survival outcomes: a systematic review

Cancer is a leading cause of death, accounting for almost 10 million deaths annually worldwide. Personalised therapies harnessing genetic and clinical information may improve survival outcomes and reduce the side effects of treatments. The aim of this study is to appraise published evidence on clinicopathological factors and genetic mutations (single nucleotide polymorphisms [SNPs]) associated with prognosis across 11 cancer types: lung, colorectal, breast, prostate, melanoma, renal, glioma, bladder, leukaemia, endometrial, ovarian. A systematic literature search of PubMed/MEDLINE and Europe PMC was conducted from database inception to July 1, 2021. 2497 publications from PubMed/MEDLINE and 288 preprints from Europe PMC were included. Subsequent reference and citation search was conducted and a further 39 articles added. 2824 articles were reviewed by title/abstract and 247 articles were selected for systematic review. Majority of the articles were retrospective cohort studies focusing on one cancer type, 8 articles were on pan-cancer level and 6 articles were reviews. Studies analysing clinicopathological factors included 908,567 patients and identified 238 factors, including age, gender, stage, grade, size, site, subtype, invasion, lymph nodes. Genetic studies included 210,802 patients and identified 440 gene mutations associated with cancer survival, including genes TP53, BRCA1, BRCA2, BRAF, KRAS, BIRC5. We generated a comprehensive knowledge base of biomarkers that can be used to tailor treatment according to patients' unique genetic and clinical characteristics. Our pan-cancer investigation uncovers the biomarker landscape and their combined influence that may help guide health practitioners and researchers across the continuum of cancer care from drug development to long-term survivorship.

Personalized Risk-Based Screening Design for Comparative Two-Arm Group Sequential Clinical Trials

Personalized medicine has been emerging to take into account individual variability in genes and environment. In the era of personalized medicine, it is critical to incorporate the patients’ characteristics and improve the clinical benefit for patients. The patients’ characteristics are incorporated in adaptive randomization to identify patients who are expected to get more benefit from the treatment and optimize the treatment allocation. However, it is challenging to control potential selection bias from using observed efficacy data and the effect of prognostic covariates in adaptive randomization. This paper proposes a personalized risk-based screening design using Bayesian covariate-adjusted response-adaptive randomization that compares the experimental screening method to a standard screening method based on indicators of having a disease. Personalized risk-based allocation probability is built for adaptive randomization, and Bayesian adaptive decision rules are calibrated to preserve error rates. A simulation study shows that the proposed design controls error rates and yields a much smaller number of failures and a larger number of patients allocated to a better intervention compared to existing randomized controlled trial designs. Therefore, the proposed design performs well for randomized controlled clinical trials under personalized medicine.

LncRNA Biomarkers of Inflammation and Cancer

Long noncoding RNAs (lncRNAs) are promising candidates as biomarkers of inflammation and cancer. LncRNAs have several properties that make them well-suited as molecular markers of disease: (1) many lncRNAs are expressed in a tissue-specific manner, (2) distinct lncRNAs are upregulated based on different inflammatory or oncogenic stimuli, (3) lncRNAs released from cells are packaged and protected in extracellular vesicles, and (4) circulating lncRNAs in the blood are detectable using various RNA sequencing approaches. Here we focus on the potential for lncRNA biomarkers to detect inflammation and cancer, highlighting key biological, technological, and analytical considerations that will help advance the development of lncRNA-based liquid biopsies.

Management of Oligoprogression in Patients with Metastatic NSCLC Harboring ALK Rearrangements

Simple Summary

The growing efficacy and availability of new targeted systemic therapies have markedly improved the prognosis of metastatic lung cancer patients harboring ALK rearrangements. The use of effective targeted therapies capable of maintaining a prolonged control of disease, for as long as possible, is paramount to ensure the best survival outcomes. In this regard, in cases of oligoprogression, “beyond progression” systemic treatment added to local ablative therapies is considered a feasible option in an attempt to improve the quality and quantity of patients’ lives, even if based on retrospective data. Certainly, treatment of ALK rearranged lung cancer patients with oligoprogressive disease must be individualized and based on multidisciplinary decisions. Above all, when further molecular targeted therapies are available, options must always be evaluated, especially in case of cerebral progression. In this review, we provide an updated and comprehensive overview of the main treatment strategies in cases of ALK rearranged oligoprogression.

Abstract

Personalized treatment based on driver molecular alterations, such as ALK rearrangement, has revolutionized the therapeutic management of advanced oncogene-addicted NSCLC patients. Multiple effective ALK tyrosine kinase inhibitors (TKIs), with the amelioration of the activity at central nervous system level, are now available, leading to substantial prognosis improvement. The exposure to TKIs triggers resistance mechanisms and the sequential administration of other TKIs and chemotherapy is, for the most part, not targeted. In this context, extending the benefit deriving from precision medicine is paramount, above all, when disease progression occurs in a limited number of sites. Retrospective data indicate that, in oligoprogressive disease, targeted therapy beyond progression combined with definitive local treatment of the progressing site(s) is an effective alternative. In these cases, a multidisciplinary approach becomes essential for an integrated treatment strategy, depending on the site of disease progression, in order to improve not only survival, but also quality of life. In this review we provide an updated and comprehensive overview of the main treatment strategies in cases of ALK rearranged oligoprogression, including systemic treatment as well as local therapy, and report a real-world clinical story, with the final aim of identifying the most promising management for this subset of patients.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

Nationwide evaluation of mutation-tailored anti-EGFR therapy selection in patients with colorectal cancer in daily clinical practice

For a nationwide real-word data study on the application of predictive mutation testing of patients with colorectal cancer (CRC) for anti-epidermal growth factor receptor (EGFR) therapy stratification, pathology data were collected from the Dutch Pathology Registry from October 2017 until June 2019 (N=4060) and linked with the Netherlands Cancer Registry. Mutation testing rates increased from 24% at diagnosis of stage IV disease to 60% after 20-23 months of follow-up (p<0.001). Application of anti-EGFR therapy in KRAS/NRAS wild-type patients was mainly observed from the third treatment line onwards (65% vs 17% in first/second treatment line (p<0.001)). The national average KRAS/NRAS/BRAF mutation rate was 63.9%, being similar for next-generation sequencing (NGS)-based approaches and single gene tests (64.4% vs 61.2%, p=ns). NGS-based approaches detected more additional potential biomarkers, for example, ERBB2 amplifications (p<0.05). Therefore, single gene tests are suitable to stratify patients with mCRC for anti-EGFR therapy, but NGS is superior enabling upfront identification of therapy resistance or facilitate enrolment into clinical trials.

Systematic review of combinations of targeted or immunotherapy in advanced solid tumors

With rapid advances in our understanding of cancer, there is an expanding number of potential novel combination therapies, including novel-novel combinations. Identifying which combinations are appropriate and in which subpopulations are among the most difficult questions in medical research. We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review of trials of novel-novel combination therapies involving immunotherapies or molecular targeted therapies in advanced solid tumors. A MEDLINE search was conducted using a modified Cochrane Highly Sensitive Search Strategy for published clinical trials between July 1, 2017, and June 30, 2020, in the top-ranked medical and oncology journals. Trials were evaluated according to a criterion adapted from previously published Food and Drug Administration guidance and other key considerations in designing trials of combinations. This included the presence of a strong biological rationale, the use of a new established or emerging predictive biomarker prospectively incorporated into the clinical trial design, appropriate comparator arms of monotherapy or supportive external data sources and a primary endpoint demonstrating a clinically meaningful benefit. Of 32 identified trials, there were 11 (34%) trials of the novel-novel combination of anti-programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and anti-cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) therapy, and 10 (31%) trials of anti-PD-1/PD-L1 and anti-vascular endothelial growth factor (VEGF) combination therapy. 20 (62.5%) trials were phase II trials, while 12 (37.5%) were phase III trials. Most (72%) trials lacked significant preclinical evidence supporting the development of the combination in the given indication. A majority of trials (69%) were conducted in biomarker unselected populations or used pre-existing biomarkers within the given indication for patient selection. Most studies (66%) were considered to have appropriate comparator arms or had supportive external data sources such as prior studies of monotherapy. All studies were evaluated as selecting a clinically meaningful primary endpoint. In conclusion, designing trials to evaluate novel-novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design. Designing trials to evaluate novel-novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design.

E. coli nitroreductase NfsA is a reporter gene for non-invasive PET imaging in cancer gene therapy applications

The use of reporter genes to non-invasively image molecular processes inside cells has significant translational potential, particularly in the context of systemically administered gene therapy vectors and adoptively administered cells such as immune or stem cell based therapies. Bacterial nitroreductase enzymes possess ideal properties for reporter gene imaging applications, being of non-human origin and possessing the ability to metabolize a range of clinically relevant nitro(hetero)cyclic substrates. Methods: A library of eleven Escherichia coli nitroreductase candidates were screened for the ability to efficiently metabolize 2-nitroimidazole based positron emission tomography (PET) probes originally developed as radiotracers for hypoxic cell imaging. Several complementary methods were utilized to detect formation of cell-entrapped metabolites, including various in vitro and in vivo models to establish the capacity of the 2-nitroimidazole PET agent EF5 to quantify expression of a nitroreductase candidate. Proof-of-principle PET imaging studies were successfully conducted using ¹⁸F-HX4. Results: Recombinant enzyme kinetics, bacterial SOS reporter assays, anti-proliferative assays and flow cytometry approaches collectively identified the major oxygen-insensitive nitroreductase NfsA from E. coli (NfsA_Ec) as the most promising nitroreductase reporter gene. Cells expressing NfsA_Ec were demonstrably labelled with the imaging agent EF5 in a manner that was quantitatively superior to hypoxia, in monolayers (2D), multicellular layers (3D), and in human tumor xenograft models. EF5 retention correlated with NfsA_Ec positive cell density over a range of EF5 concentrations in 3D in vitro models and in xenografts in vivo and was predictive of in vivo anti-tumor activity of the cytotoxic prodrug PR-104. Following PET imaging with ¹⁸F-HX4, a significantly higher tumor-to-blood ratio was observed in two xenograft models for NfsA_Ec expressing tumors compared to the parental tumors thereof, providing verification of this reporter gene imaging approach. Conclusion: This study establishes that the bacterial nitroreductase NfsA_Ec can be utilized as an imaging capable reporter gene, with the ability to metabolize and trap 2-nitroimidazole PET imaging agents for non-invasive imaging of gene expression.