Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers and models of immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC). However, evidence for many biomarkers remains inconclusive, and the opaqueness of machine learning models hinders practicality. We aimed to provide compelling evidence for biomarkers and develop a transparent decision tree model. METHODS: We consolidated data from 3,288 ICI-treated patients with NSCLC across real-world multicenter, public cohorts and the Choice-01 trial (ClinicalTrials.gov: NCT03856411). Over 50 features were examined for predicting durable clinical benefits (DCBs) from ICIs. Noteworthy biomarkers were identified to establish a decision tree model. Additionally, we explored the tumor microenvironment and peripheral CD8+ programmed death-1 (PD-1)+ T cell receptor (TCR) profiles. FINDINGS: Multivariate logistic regression analysis identified tumor histology, PD-ligand 1 (PD-L1) expression, tumor mutational burden, line, and regimen of ICI treatment as significant factors. Mutation subtypes of EGFR, KRAS, KEAP1, STK11, and disruptive TP53 mutations were associated with DCB. The decision tree (DT10) model, using the ten clinicopathological and genomic markers, showed superior performance in predicting DCB in the training set (area under the curve [AUC] = 0.82) and consistently outperformed other models in test sets. DT10-predicted-DCB patients manifested longer survival, an enriched inflamed tumor immune phenotype (67%), and higher peripheral TCR diversity, whereas the DT10-predicted-NDB (non-durable benefit) group showed an enriched desert immune phenotype (86%) and higher peripheral TCR clonality. CONCLUSIONS: The model effectively predicted DCB after front-/subsequent-line ICI treatment, with or without chemotherapy, for squamous and non-squamous lung cancer, offering clinicians valuable insights into efficacy prediction using cost-effective variables. FUNDING: This study was supported by the National Key R&D Program of China.

Advances in preclinical assessment of therapeutic targets for bladder cancer precision medicine.

PURPOSE OF REVIEW: Bladder cancer incidence is on the rise, and until recently, there has been little to no change in treatment regimens over the last 40 years. Hence, it is imperative to work on strategies and approaches to untangle the complexity of intra- and inter-tumour heterogeneity of bladder cancer with the aim of improving patient-specific care and treatment outcomes. The focus of this review is therefore to highlight novel targets, advances, and therapy approaches for bladder cancer patients. RECENT FINDINGS: The success of combining an antibody-drug conjugate (ADC) with immunotherapy has been recently hailed as a game changer in treating bladder cancer patients. Hence, interest in other ADCs as a treatment option is also rife. Furthermore, strategies to overcome chemoresistance to standard therapy have been described recently. In addition, other studies showed that targeting genomic alterations (e.g. mutations in FGFR3 , DNA damage repair genes and loss of the Y chromosome) could also be helpful as prognostic and treatment stratification biomarkers. The use of single-cell RNA sequencing approaches has allowed better characterisation of the tumour microenvironment and subsequent identification of novel targets. Functional precision medicine could be another avenue to improve and guide personalized treatment options. SUMMARY: Several novel preclinical targets and treatment options have been described recently. The validation of these advances will lead to the development and implementation of robust personalized treatment regimens for bladder cancer patients.

Comprehensive prognostic assessment in kidney cancer: A multidimensional approach analyzing Fufang Sanling granules, genetic variants, and immune infiltration.

This study delves into the potential therapeutic benefits of Fufang Sanling Granules for kidney cancer, focusing on their active components and the underlying mechanisms of their interaction with cancer-related targets. By constructing a drug-active component-target network based on eight herbs, key active compounds such as kaempferol, quercetin, and linolenic acid were identified, suggesting their pivotal roles in modulating immune responses and cellular signaling pathways relevant to cancer progression. The research further identified 51 central drug-disease genes through comprehensive bioinformatics analyses, implicating their involvement in crucial biological processes and pathways. A novel risk score model, encompassing six genes with significant prognostic value for renal cancer, was established and validated, showcasing its effectiveness in predicting patient outcomes through mutation analysis and survival studies. The model's predictive power was further confirmed by its ability to stratify patients into distinct risk groups with significant survival differences, highlighting its potential as a prognostic tool. Additionally, the study explored the relationship between gene expression within the identified black module and the risk score, uncovering significant associations with the extracellular matrix and immune infiltration patterns. This reveals the complex interplay between the tumor microenvironment and cancer progression. The integration of the risk score with clinical parameters through a nomogram significantly improved the model's predictive accuracy, offering a more comprehensive tool for predicting kidney cancer prognosis. In summary, by combining detailed molecular analyses with clinical insights, this study presents a robust framework for understanding the therapeutic potential of Fufang Sanling Granules in kidney cancer. It not only sheds light on the active components and their interactions with cancer-related genes but also introduces a reliable risk score model, paving the way for personalized treatment strategies and improved patient management in the future.

Chemotherapy Assessment in Advanced Multicellular 3D Models of Pancreatic Cancer: Unravelling the Importance of Spatiotemporal Mimicry of the Tumor Microenvironment.

Pancreatic ductal adenocarcinoma (PDAC) is a challenge for global health with very low survival rate and high therapeutic resistance. Hence, advanced preclinical models for treatment screening are of paramount importance. Herein, chemotherapeutic (gemcitabine) assessment on novel (polyurethane) scaffold-based spatially advanced 3D multicellular PDAC models is carried out. Through comprehensive image-based analysis at the protein level, and expression analysis at the mRNA level, the importance of stromal cells is confirmed, primarily activated stellate cells in the chemoresistance of PDAC cells within the models. Furthermore, it is demonstrated that, in addition to the presence of activated stellate cells, the spatial architecture of the scaffolds, i.e., segregation/compartmentalization of the cancer and stromal zones, affect the cellular evolution and is necessary for the development of chemoresistance. These results highlight that, further to multicellularity, mapping the tumor structure/architecture and zonal complexity in 3D cancer models is important for better mimicry of the in vivo therapeutic response.

Filling the gap between risk assessment and molecular determinants of tumor onset.

In the past two decades, a ponderous epidemiological literature has causally linked tumor onset to environmental exposure to carcinogens. As consequence, risk assessment studies have been carried out with the aim to identify both predictive models of estimating cancer risks within exposed populations and establishing rules for minimizing hazard when handling carcinogenic compounds. The central assumption of these works is that neoplastic transformation is directly related to the mutational burden of the cell without providing further mechanistic clues to explain increased cancer onset after carcinogen exposure. Nevertheless, in the last few years, a growing number of studies have implemented the traditional models of cancer etiology, proposing that neoplastic transformation is a complex process in which several parameters and crosstalk between tumor and microenvironmental cells must be taken into account and integrated with mutagenesis. In this conceptual framework, the current strategies of risk assessment that are solely based on the 'mutator model' require an urgent update and revision to keep pace with advances in our understanding of cancer biology. We will approach this topic revising the most recent theories on the biological mechanisms involved in tumor formation in order to envision a roadmap leading to a future regulatory framework for a new, protective policy of risk assessment.

Noninvasive assessment and therapeutic monitoring of drug-resistant colorectal cancer by MR molecular imaging of extradomain-B fibronectin.

Antineoplastic resistance represents a multifaceted challenge for cancer therapy and diagnostics. Extensive molecular heterogeneity, even within neoplasms of the same type, can elicit distinct outcomes of administering therapeutic pressures, frequently leading to the development of drug-resistant populations. Improved success of oncotherapies merits the exploration of precise molecular imaging technologies that can detect not only anatomical but also molecular changes in tumors and their microenvironment, early on in the treatment regimen. To this end, we developed magnetic resonance molecular imaging (MRMI) strategies to target the extracellular matrix oncoprotein, extradomain-B fibronectin (EDB-FN), for non-invasive assessment and therapeutic monitoring of drug-resistant colorectal cancer (CRC). Methods: Two drug-resistant CRC lines generated from parent DLD-1 and RKO cells by long-term treatment with 5'-FU and 5'-FU plus CB-839 respectively, were characterized for functional and gene expression changes using 3D culture, transwell invasion, qRT-PCR, and western blot assays. Contrast-enhanced MRMI of EDB-FN was performed in athymic nu/nu mice bearing subcutaneous tumor xenografts with 40 µmol/kg dose of macrocyclic ZD2-targeted contrast agent MT218 [ZD2-N3-Gd (HP-DO3A)] on a 3T MRS 3000 scanner. Immunohistochemistry was conducted on patient specimens and xenografts using anti-EDB-FN antibody G4. Results: Analyses of TCGA and GTEx databases revealed poor prognosis of colon cancer patients with higher levels of EDB-FN. Similarly, immunohistochemical staining of patient specimens showed increased EDB-FN expression in primary colon adenocarcinoma and hepatic metastases, but none in normal adjacent tissues. Drug-resistant DLD1-DR and RKO-DR cells were also found to demonstrate enhanced invasive potential and significantly elevated EDB-FN expression over their parent counterparts. MRMI of EDB-FN with 40 µmol/kg dose of MT218 (60% lower than the clinical dose) resulted in robust signal enhancement in the drug-resistant CRC xenografts with 84-120% increase in their contrast-to-noise ratios (CNRs) over the non-resistant counterparts. The feasibility of non-invasive therapeutic monitoring using MRMI of EDB-FN was also evaluated in drug-resistant DLD1-DR tumors treated with a pan-AKT inhibitor MK2206-HCl. The treated drug-resistant tumors failed to respond to therapy, which was accurately detected by MRMI with MT218, demonstrating higher signal enhancement and increased CNRs in the 4-week follow-up scans over the pre-treatment scans. Conclusions: EDB-FN is a promising molecular marker for assessing drug resistance. MRMI of EDB-FN with MT218 at a significantly reduced dose can facilitate effective non-invasive assessment and treatment response monitoring of drug-resistant CRC, highlighting its translational potential for active surveillance and management of CRC and other malignancies.

QSP-IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno-Oncology Applications.

Immunotherapy has shown great potential in the treatment of cancer; however, only a fraction of patients respond to treatment, and many experience autoimmune-related side effects. The pharmaceutical industry has relied on mathematical models to study the behavior of candidate drugs and more recently, complex, whole-body, quantitative systems pharmacology (QSP) models have become increasingly popular for discovery and development. QSP modeling has the potential to discover novel predictive biomarkers as well as test the efficacy of treatment plans and combination therapies through virtual clinical trials. In this work, we present a QSP modeling platform for immuno-oncology (IO) that incorporates detailed mechanisms for important immune interactions. This modular platform allows for the construction of QSP models of IO with varying degrees of complexity based on the research questions. Finally, we demonstrate the use of the platform through two example applications of immune checkpoint therapy.

New insights on sorafenib resistance in liver cancer with correlation of individualized therapy.

Liver cancer is highly malignant and insensitive to cytotoxic chemotherapy and is associated with very poor patient prognosis. In 2007, the small-molecule targeted drug sorafenib was approved for the treatment of advanced liver cancer. In the subsequent ten years, sorafenib has been the only first-line therapeutic targeted drug for advanced hepatocellular carcinoma (HCC). However, a number of clinical studies show that a considerable percentage of patients with liver cancer are insensitive to sorafenib. The number of patients who actually benefit significantly from sorafenib treatment is very limited, and the overall efficacy of sorafenib is far from satisfactory, which has attracted the attention of researchers. Based on previous studies and reports, this article reviews the potential mechanisms of sorafenib resistance (SR) and summarizes the biomarkers and clinicopathological indicators that might be used for predicting sorafenib response and developing personalized therapy.

Assessment of postoperative adjuvant treatment using toceranib phosphate against adenocarcinoma in dogs.

BACKGROUND: Toceranib phosphate (TOC) could be made widely available for treating tumors in dogs if evidence shows that TOC inhibits recurrence after surgery. OBJECTIVES: To investigate how postoperative adjuvant treatment with TOC modulates the tumor microenvironment (TME), by assessing effects on angiogenic activity, tumor-infiltrating regulatory T cells (Tregs), and intratumoral hypoxia. ANIMALS: Ninety-two client-owned dogs were included: 28 with apocrine gland anal sac adenocarcinoma, 24 with small intestinal adenocarcinoma, 22 with lung adenocarcinoma, and 18 with renal cell carcinoma. METHODS: Retrospective, multicenter study comparing time to progression (TTP) between 42 dogs treated by surgery and TOC and 50 dogs treated by surgery alone. Differences were analyzed in the expression of vascular endothelial growth factor receptor-2 (VEGFR2) and the number of Foxp3+ Tregs and hypoxia-inducible factor (HIF)-1α+ cells in tumor tissues sampled at the first and second (recurrence) surgeries. RESULTS: Median TTP for dogs treated by surgery and TOC (360 days) was higher than that for dogs treated by surgery alone (298 days; hazard ratio, 0.82; 95% confidence interval [CI], 0.65-0.96; P = .02). In dogs treated by surgery and TOC, VEGFR2 expression and the number of Tregs and HIF-1α+ cells were significantly lower in tissues sampled at the second surgery than in those sampled after the first surgery. In dogs treated by surgery alone, significant differences were found between samples from the 2 surgeries. CONCLUSIONS AND CLINICAL IMPORTANCE: Toceranib phosphate could prove to be a useful postoperative adjuvant treatment because of its modulation of the TME.

In Vivo Immunofluorescence Localization for Assessment of Therapeutic and Diagnostic Antibody Biodistribution in Cancer Research.

Monoclonal antibodies (mAbs) are important tools in cancer detection, diagnosis, and treatment. They are used to unravel the role of proteins in tumorigenesis, can be directed to cancer biomarkers enabling tumor detection and characterization, and can be used for cancer therapy as mAbs or antibody-drug conjugates to activate immune effector cells, to inhibit signaling pathways, or directly kill cells carrying the specific antigen. Despite clinical advancements in the development and production of novel and highly specific mAbs, diagnostic and therapeutic applications can be impaired by the complexity and heterogeneity of the tumor microenvironment. Thus, for the development of efficient antibody-based therapies and diagnostics, it is crucial to assess the biodistribution and interaction of the antibody-based conjugate with the living tumor microenvironment. Here, we describe In Vivo Immunofluorescence Localization (IVIL) as a new approach to study interactions of antibody-based therapeutics and diagnostics in the in vivo physiological and pathological conditions. In this technique, a therapeutic or diagnostic antigen-specific antibody is intravenously injected in vivo and localized ex vivo with a secondary antibody in isolated tumors. IVIL, therefore, reflects the in vivo biodistribution of antibody-based drugs and targeting agents. Two IVIL applications are described assessing the biodistribution and accessibility of antibody-based contrast agents for molecular imaging of breast cancer. This protocol will allow future users to adapt the IVIL method for their own antibody-based research applications.

Assessment of Heparanase-Mediated Angiogenesis Using Microvascular Endothelial Cells: Identification of λ-Carrageenan Derivative as a Potent Anti Angiogenic Agent.

Heparanase is overexpressed by tumor cells and degrades the extracellular matrix proteoglycans through cleavage of heparan sulfates (HS), allowing pro-angiogenic factor release and thus playing a key role in tumor angiogenesis and metastasis. Here we propose new HS analogs as potent heparanase inhibitors: Heparin as a positive control, Dextran Sulfate, λ-Carrageenan, and modified forms of them obtained by depolymerization associated to glycol splitting (RD-GS). After heparanase activity assessment, 11 kDa RD-GS-λ-Carrageenan emerged as the most effective heparanase inhibitor with an IC50 of 7.32 ng/mL compared to 10.7 ng/mL for the 16 kDa unfractionated heparin. The fractionated polysaccharides were then tested in a heparanase-rich medium-based in vitro model, mimicking tumor microenvironment, to determine their effect on microvascular endothelial cells (HSkMEC) angiogenesis. As a preliminary study, we identified that under hypoxic and nutrient poor conditions, MCF-7 cancer cells released much more mature heparanase in their supernatant than in normal conditions. Then a MatrigelTM assay using HSkMEC cultured under hypoxic conditions in the presence (or not) of this heparanase-rich supernatant was realized. Adding heparanase-rich media strongly enhanced angiogenic network formation with a production of twice more pseudo-vessels than with the control. When sulfated polysaccharides were tested in this angiogenesis assay, RD-GS-λ-Carrageenan was identified as a promising anti-angiogenic agent.

A Computational/Experimental Assessment of Antitumor Activity of Polymer Nanoassemblies for pH-Controlled Drug Delivery to Primary and Metastatic Tumors.

PURPOSE: Polymer nanoassemblies (PNAs) with drug release fine-tuned to occur in acidic tumor regions (pH < 7) while sparing normal tissues (pH = 7.4) were previously shown to hold promise as nanoparticle drug carriers to effectively suppress tumor growth with reduced systemic toxicity. However, therapeutic benefits of pH-controlled drug delivery remain elusive due to complex interactions between the drug carriers, tumor cells with varying drug sensitivity, and the tumor microenvironment. METHODS: We implement a combined computational and experimental approach to evaluate the in vivo antitumor activity of acid-sensitive PNAs controlling drug release in pH 5 ~ 7.4 at different rates [PNA1 (fastest) > PNA2 > PNA3 (slowest)]. RESULTS: Computational simulations projecting the transport, drug release, and antitumor activity of PNAs in primary and metastatic tumor models of colorectal cancer correspond well with experimental observations in vivo. The simulations also reveal that all PNAs could reach peak drug concentrations in tumors at 11 h post injection, while PNAs with slower drug release (PNA2 and PNA3) reduced tumor size more effectively than fast drug releasing PNA1 (24.5 and 20.3 vs 7.5%, respectively, as fraction of untreated control). CONCLUSION: A combined computational/experimental approach may help to evaluate pH-controlled drug delivery targeting aggressive tumors that have substantial acidity.

Targeting the biophysical properties of the myeloma initiating cell niches: a pharmaceutical synergism analysis using multi-scale agent-based modeling.

Multiple myeloma, the second most common hematological cancer, is currently incurable due to refractory disease relapse and development of multiple drug resistance. We and others recently established the biophysical model that myeloma initiating (stem) cells (MICs) trigger the stiffening of their niches via SDF-1/CXCR4 paracrine; The stiffened niches then promote the colonogenesis of MICs and protect them from drug treatment. In this work we examined in silico the pharmaceutical potential of targeting MIC niche stiffness to facilitate cytotoxic chemotherapies. We first established a multi-scale agent-based model using the Markov Chain Monte Carlo approach to recapitulate the niche stiffness centric, pro-oncogenetic positive feedback loop between MICs and myeloma-associated bone marrow stromal cells (MBMSCs), and investigated the effects of such intercellular chemo-physical communications on myeloma development. Then we used AMD3100 (to interrupt the interactions between MICs and their stroma) and Bortezomib (a recently developed novel therapeutic agent) as representative drugs to examine if the biophysical properties of myeloma niches are drugable. Results showed that our model recaptured the key experimental observation that the MBMSCs were more sensitive to SDF-1 secreted by MICs, and provided stiffer niches for these initiating cells and promoted their proliferation and drug resistance. Drug synergism analysis suggested that AMD3100 treatment undermined the capability of MICs to modulate the bone marrow microenvironment, and thus re-sensitized myeloma to Bortezomib treatments. This work is also the first attempt to virtually visualize in 3D the dynamics of the bone marrow stiffness during myeloma development. In summary, we established a multi-scale model to facilitate the translation of the niche-stiffness centric myeloma model as well as experimental observations to possible clinical applications. We concluded that targeting the biophysical properties of stem cell niches is of high clinical potential since it may re-sensitize tumor initiating cells to chemotherapies and reduce risks of cancer relapse.

Characterization of a novel mouse model of multiple myeloma and its use in preclinical therapeutic assessment.

To aid preclinical development of novel therapeutics for myeloma, an in vivo model which recapitulates the human condition is required. An important feature of such a model is the interaction of myeloma cells with the bone marrow microenvironment, as this interaction modulates tumour activity and protects against drug-induced apoptosis. Therefore NOD/SCIDγc(null) mice were injected intra-tibially with luciferase-tagged myeloma cells. Disease progression was monitored by weekly bioluminescent imaging (BLI) and measurement of paraprotein levels. Results were compared with magnetic resonance imaging (MRI) and histology. Assessment of model suitability for preclinical drug testing was investigated using bortezomib, melphalan and two novel agents. Cells engrafted at week 3, with a significant increase in BLI radiance occurring between weeks 5 and 7. This was accompanied by an increase in paraprotein secretion, MRI-derived tumour volume and CD138 positive cells within the bone marrow. Treatment with known anti-myeloma agents or novel agents significantly attenuated the increase in all disease markers. In addition, intra-tibial implantation of primary patient plasma cells resulted in development of myeloma within bone marrow. In conclusion, using both myeloma cell lines and primary patient cells, we have developed a model which recapitulates human myeloma by ensuring the key interaction of tumour cells with the microenvironment.