An ex vivo platform to guide drug combination treatment in relapsed/refractory lymphoma

Functional combinatorial precision medicine for predicting and optimizing soft tissue sarcoma treatments

Soft tissue sarcomas (STS) are rare, heterogeneous tumors with poor survival outcomes, primarily due to reliance on cytotoxic chemotherapy and lack of targeted therapies. Given the uniquely individualized nature of STS, we hypothesized that the ex vivo drug sensitivity platform, quadratic phenotypic optimization platform (QPOP), can predict treatment response and enhance combination therapy design for STS. Using QPOP, we screened 45 primary STS patient samples, and showed improved or concordant patient outcomes that are attributable to QPOP predictions. From a panel of approved and investigational agents, QPOP identified AZD5153 (BET inhibitor) and pazopanib (multi-kinase blocker) as the most effective combination with superior efficacy compared to standard regimens. Validation in a panel of established patient lines and in vivo models supported its synergistic interaction, accompanied by repressed oncogenic MYC and related pathways. These findings provide preliminary clinical evidence for QPOP to predict STS treatment outcomes and guide the development of novel therapeutic strategies for STS patients.

Bone-on-a-Chip Systems for Hematological Cancers

Hematological malignancies originating from blood, bone marrow, and lymph nodes include leukemia, lymphoma, and myeloma, which necessitate the use of a distinct chemotherapeutic approach. Drug resistance frequently complicates their treatment, highlighting the need for predictive tools to guide therapeutic decisions. Conventional 2D/3D cell cultures do not fully encompass in vivo criteria, and translating disease models from mice to humans proves challenging. Organ-on-a-chip technology presents an avenue to surmount genetic disparities between species, offering precise design, concurrent manipulation of various cell types, and extrapolation of data to human physiology. The development of bone-on-a-chip (BoC) systems is crucial for accurately representing the in vivo bone microenvironment, predicting drug responses for hematological cancers, mitigating drug resistance, and facilitating personalized therapeutic interventions. BoC systems for modeling hematological cancers and drug research can encompass intricate designs and integrated platforms for analyzing drug response data to simulate disease scenarios. This review provides a comprehensive examination of BoC systems applicable to modeling hematological cancers and visualizing drug responses within the intricate context of bone. It thoroughly discusses the materials pertinent to BoC systems, suitable in vitro techniques, the predictive capabilities of BoC systems in clinical settings, and their potential for commercialization.

Personalized dose selection platform for patients with solid tumors in the PRECISE CURATE.AI feasibility trial

In oncology, the conventional reliance on the maximum tolerated dose (MTD) strategy for chemotherapy may not optimize treatment outcomes for individual patients. CURATE.AI is an AI-derived platform that utilizes a patient's own, small dataset to dynamically personalize only their own dose recommendations. The primary objective of this feasibility trial was to assess the logistical and scientific feasibility of providing dynamically personalized AI-derived chemotherapy dose recommendations for patients with advanced solid tumors at/for treatment with single-agent capecitabine, capecitabine in combination with oxaliplatin (XELOX), or capecitabine in combination with irinotecan (XELIRI). CURATE.AI demonstrated adaptability to clinically relevant situations encountered by patients often treated with palliative intent of care. High rates of user adherence were demonstrated, which could be in part due to the high engagement of the physicians in selecting data and boundaries for CURATE.AI operations.

Combinatorial functionomics identifies HDAC6-dependent molecular vulnerability of radioresistant head and neck cancer

BACKGROUND

Radiotherapy is the primary treatment modality for most head and neck cancers (HNCs). Despite the addition of chemotherapy to radiotherapy to enhance its tumoricidal effects, almost a third of HNC patients suffer from locoregional relapses. Salvage therapy options for such recurrences are limited and often suboptimal, partly owing to divergent tumor and microenvironmental factors underpinning radioresistance. In this study, we utilized a combinatorial functionomics approach, the Quadratic Phenotypic Optimization Platform (QPOP), to rationally design drug pairings that exploit the molecular fingerprint and vulnerability of established in vitro isogenic radioresistant (RR)-HNC models.

METHODS

A QPOP-specific protocol was applied to RR-HNC models to rank and compare all possible drug combinations from a 12-drug set comprising standard chemotherapy, small molecule inhibitors and targeted therapies specific to HNC. Drug combination efficacy was evaluated by computing combination index scores, and by measuring apoptotic response. Drug targeting was validated by western blot analyses, and the Comet assay was used to quantify DNA damage. Enhanced histone deacetylase inhibitor (HDACi) efficacy in RR models was further examined by in vivo studies, and genetic and chemical inhibition of major Class I/II HDACs. Regulatory roles of HDAC6/SP1 axis were investigated using immunoprecipitation, gel shift and ChIP-qPCR assays. Comparative transcriptomic analyses were employed to determine the prognostic significance of targeting HDAC6.

RESULTS

We report the therapeutic potential of combining panobinostat (pan-HDAC inhibitor) with AZD7762 (CHK1/2 inhibitor; AstraZeneca) or ionizing radiation (IR) to re-sensitize RR-HNC cells and showed increased DNA damage underlying enhanced synergy. We further refined this RR-specific drug combination and prioritized HDAC6 as a targetable dependency in reversing radioresistance. We provide mechanistic insights into HDAC6-mediated regulation via a crosstalk involving SP1 and oncogenic and repair genes. From two independent patient cohorts, we identified a four-gene signature that may have discriminative ability to predict for radioresistance and amenable to HDAC6 inhibition.

CONCLUSION

We have uncovered HDAC6 as a promising molecular vulnerability that should be explored to treat RR-HNC.

Synergistic combinations of Angelica sinensis for myocardial infarction treatment: network pharmacology and quadratic optimization approach

Background and aim

Angelica sinensis (Oliv.) Diels (Danggui, DG), exhibits potential in myocardial infarction (MI) treatment. However, research on its synergistic combinations for cardioprotective effects has been limited owing to inadequate approaches.

Experimental procedure

We identified certain phenolic acids and phthalein compounds in DG. Network pharmacology analysis and experimental validation revealed the components that protected H9c2 cells and reduced lactate dehydrogenase levels. Subsequently, a combination of computational experimental strategies and a secondary phenotypic optimization platform was employed to identify effective component combinations with synergistic interactions. The Chou-Talalay and Zero Interaction Potency (ZIP) models were utilized to quantify the synergistic relationships. The optimal combination identified, Z-Ligustide and Chlorogenic acid (Z-LIG/CGA), was evaluated for its protective effects on cardiac function and cardiomyocytes apoptosis induced by inflammatory in a mouse model of induced by left anterior descending coronary artery ligation. Flow cytometry was further utilized to detect the polarization ratio of M1/M2 macrophages and the expression of inflammatory cytokines in serum was measured, assessing the inhibition of inflammatory responses and pro-inflammatory signaling factors by Z-LIG/CGA.

Key results

Quadratic surface analysis revealed that the Z-LIG/CGA combination displayed synergistic cardioprotective effects (combination index value <1; ZIP value >10). In vivo, Z-LIG/CGA significantly improved cardiac function and reduced the fibrotic area in mice post-MI, surpassing the results in groups treated with Z-LIG or CGA alone. Compared to the MI group, the Z-LIG/CGA group exhibited decreased ratios of the myocardial cell apoptosis-related proteins BAX/Bcl-2 and Cleaved Caspase-3/Caspase-3 in mice. Further research revealed that Z-LIG/CGA treatment significantly increased IL-1R2 levels, significantly decreased IL-17RA levels, and inhibited the activation of p-STAT1, thereby alleviating cell apoptosis after MI. Additionally, the Z-LIG/CGA combination significantly inhibited the ratio of M1/M2 macrophages and suppressed the expression levels of pro-inflammatory cytokines IL-1β, IL-6, IL-17, and TNF-α in the serum.

Conclusion and implications

We successfully identified a synergistic drug combination, Z-LIG/CGA, which improves MI outcomes by inhibiting cardiomyocyte apoptosis and inflammatory damage through modulating macrophage polarization and regulating the IL-1R2/IL-17RA/STAT1 signaling pathway. This study provides a charming paradigm to explore effective drug combinations in traditional Chinese medicine and a promising treatment for MI.

Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance

The advent of artificial intelligence (AI) has catalyzed a profound transformation in the pharmaceutical industry, ushering in a paradigm shift across various domains, including drug discovery, formulation development, manufacturing, quality control, and post-market surveillance. This comprehensive review examines the multifaceted impact of AI-driven technologies on all stages of the pharmaceutical life cycle. It discusses the application of machine learning algorithms, data analytics, and predictive modeling to accelerate drug discovery processes, optimize formulation development, enhance manufacturing efficiency, ensure stringent quality control measures, and revolutionize post-market surveillance methodologies. By describing the advancements, challenges, and future prospects of harnessing AI in the pharmaceutical landscape, this review offers valuable insights into the evolving dynamics of drug development and regulatory practices in the era of AI-driven innovation.

Recent advances in and applications of ex vivo drug sensitivity analysis for blood cancers

Blood cancers, including leukemia, multiple myeloma, and lymphoma, pose significant challenges owing to their heterogeneous nature and the limitations of traditional treatments. Precision medicine has emerged as a transformative approach that offers tailored therapeutic strategies based on individual patient profiles. Ex vivo drug sensitivity analysis is central to this advancement, which enables testing of patient-derived cancer cells against a panel of therapeutic agents to predict clinical responses. This review provides a comprehensive overview of the latest advancements in ex vivo drug sensitivity analyses and their application in blood cancers. We discuss the development of more comprehensive drug response metrics and the evaluation of drug combinations to identify synergistic interactions. Additionally, we present evaluation of the advanced therapeutics such as antibody-drug conjugates using ex vivo assays. This review describes the critical role of ex vivo drug sensitivity analyses in advancing precision medicine by examining technological innovations and clinical applications. Ultimately, these innovations are paving the way for more effective and individualized treatments, improving patient outcomes, and establishing new standards for the management of blood cancers.

A Combinatorial Functional Precision Medicine Platform for Rapid Therapeutic Response Prediction in AML

BACKGROUND

Despite advances made in targeted biomarker-based therapy for acute myeloid leukemia (AML) treatment, remission is often short and followed by relapse and acquired resistance. Functional precision medicine (FPM) efforts have been shown to improve therapy selection guidance by incorporating comprehensive biological data to tailor individual treatment. However, effectively managing complex biological data, while also ensuring rapid conversion of actionable insights into clinical utility remains challenging.

METHODS

We have evaluated the clinical applicability of quadratic phenotypic optimization platform (QPOP), to predict clinical response to combination therapies in AML and reveal patient-centric insights into combination therapy sensitivities. In this prospective study, 51 primary samples from newly diagnosed (ND) or refractory/relapsed (R/R) AML patients were evaluated by QPOP following ex vivo drug testing.

RESULTS

Individualized drug sensitivity reports were generated in 55/63 (87.3%) patient samples with a median turnaround time of 5 (4-10) days from sample collection to report generation. To evaluate clinical feasibility, QPOP-predicted response was compared to clinical treatment outcomes and indicated concordant results with 83.3% sensitivity and 90.9% specificity and an overall 86.2% accuracy. Serial QPOP analysis in a FLT3-mutant patient sample indicated decreased FLT3 inhibitor (FLT3i) sensitivity, which is concordant with increasing FLT3 allelic burden and drug resistance development. Forkhead box M1 (FOXM1)-AKT signaling was subsequently identified to contribute to resistance to FLT3i.

CONCLUSION

Overall, this study demonstrates the feasibility of applying QPOP as a functional combinatorial precision medicine platform to predict therapeutic sensitivities in AML and provides the basis for prospective clinical trials evaluating ex vivo-guided combination therapy.

N-of-1 medicine

ABSTRACT

The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.

A functional personalised oncology approach against metastatic colorectal cancer in matched patient derived organoids

Globally, colorectal cancer (CRC) is the third most frequently occurring cancer. Progression on to an advanced metastatic malignancy (metCRC) is often indicative of poor prognosis, as the 5-year survival rates of patients decline rapidly. Despite the availability of many systemic therapies for the management of metCRC, the long-term efficacies of these regimens are often hindered by the emergence of treatment resistance due to intratumoral and intertumoral heterogeneity. Furthermore, not all systemic therapies have associated biomarkers that can accurately predict patient responses. Hence, a functional personalised oncology (FPO) approach can enable the identification of patient-specific combinatorial vulnerabilities and synergistic combinations as effective treatment strategies. To this end, we established a panel of CRC patient-derived organoids (PDOs) as clinically relevant biological systems, of which three pairs of matched metCRC PDOs were derived from the primary sites (ptCRC) and metastatic lesions (mCRC). Histological and genomic characterisation of these PDOs demonstrated the preservation of histopathological and genetic features found in the parental tumours. Subsequent application of the phenotypic-analytical drug combination interrogation platform, Quadratic Phenotypic Optimisation Platform, in these pairs of PDOs identified patient-specific drug sensitivity profiles to epigenetic-based combination therapies. Most notably, matched PDOs from one patient exhibited differential sensitivity patterns to the rationally designed drug combinations despite being genetically similar. These findings collectively highlight the limitations of current genomic-driven precision medicine in guiding treatment strategies for metCRC patients. Instead, it suggests that epigenomic profiling and application of FPO could complement the identification of novel combinatorial vulnerabilities to target synchronous ptCRC and mCRC.

Targeting the DNA damage response in hematological malignancies

Deregulation of the DNA damage response (DDR) plays a critical role in the pathogenesis and progression of many cancers. The dependency of certain cancers on DDR pathways has enabled exploitation of such through synthetically lethal relationships e.g., Poly ADP-Ribose Polymerase (PARP) inhibitors for BRCA deficient ovarian cancers. Though lagging behind that of solid cancers, DDR inhibitors (DDRi) are being clinically developed for haematological cancers. Furthermore, a high proliferative index characterize many such cancers, suggesting a rationale for combinatorial strategies targeting DDR and replicative stress. In this review, we summarize pre-clinical and clinical data on DDR inhibition in haematological malignancies and highlight distinct haematological cancer subtypes with activity of DDR agents as single agents or in combination with chemotherapeutics and targeted agents. We aim to provide a framework to guide the design of future clinical trials involving haematological cancers for this important class of drugs.

Robust scoring of selective drug responses for patient-tailored therapy selection

Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient's responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.

The drug efficacy testing in 3D cultures platform identifies effective drugs for ovarian cancer patients

Most patients with advanced ovarian cancer (OC) relapse and progress despite systemic therapy, pointing to the need for improved and tailored therapy options. Functional precision medicine can help to identify effective therapies for individual patients in a clinically relevant timeframe. Here, we present a scalable functional precision medicine platform: DET3Ct (Drug Efficacy Testing in 3D Cultures), where the response of patient cells to drugs and drug combinations are quantified with live-cell imaging. We demonstrate the delivery of individual drug sensitivity profiles in 20 samples from 16 patients with ovarian cancer in both 2D and 3D culture formats, achieving over 90% success rate in providing results six days after operation. In this cohort all patients received carboplatin. The carboplatin sensitivity scores were significantly different for patients with a progression free interval (PFI) less than or equal to 12 months and those with more than 12 months (p < 0.05). We find that the 3D culture format better retains proliferation and characteristics of the in vivo setting. Using the DET3Ct platform we evaluate 27 tailored combinations with results available 10 days after operation. Notably, carboplatin and A-1331852 (Bcl-xL inhibitor) showed an additive effect in four of eight OC samples tested, while afatinib and A-1331852 led to synergy in five of seven OC models. In conclusion, our 3D DET3Ct platform can rapidly define potential, clinically relevant data on efficacy of existing drugs in OC for precision medicine purposes, as well as provide insights on emerging drugs and drug combinations that warrant testing in clinical trials.

Genetically informed therapy for lymphoma: the discomfiting benefit of lumping splits

Zhang et al. report a randomized phase 2 trial for diffuse large B cell lymphoma (DLBCL) that compared standard of care (R-CHOP) to R-CHOP combined with one of 5 agents matched to an individual lymphoma's genetics. Overall, the matching strategy significantly outperformed R-CHOP, laying the foundation for a paradigm-shifting phase 3 trial.

Resistance to vincristine in DLBCL by disruption of p53-induced cell cycle arrest and apoptosis mediated by KIF18B and USP28

The frontline therapy R-CHOP for patients with diffuse large B-cell lymphoma (DLBCL) has remained unchanged for two decades despite numerous Phase III clinical trials investigating new alternatives. Multiple large studies have uncovered genetic subtypes of DLBCL enabling a targeted approach. To further pave the way for precision oncology, we perform genome-wide CRISPR screening to uncover the cellular response to one of the components of R-CHOP, vincristine, in the DLBCL cell line SU-DHL-5. We discover important pathways and subnetworks using gene-set enrichment analysis and protein-protein interaction networks and identify genes related to mitotic spindle organization that are essential during vincristine treatment. The inhibition of KIF18A, a mediator of chromosome alignment, using the small molecule inhibitor BTB-1 causes complete cell death in a synergistic manner when administered together with vincristine. We also identify the genes KIF18B and USP28 of which CRISPR/Cas9-directed knockout induces vincristine resistance across two DLBCL cell lines. Mechanistic studies show that lack of KIF18B or USP28 counteracts a vincristine-induced p53 response suggesting that resistance to vincristine has origin in the mitotic surveillance pathway (USP28-53BP1-p53). Collectively, our CRISPR screening data uncover potential drug targets and mechanisms behind vincristine resistance, which may support the development of future drug regimens.

Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment

Recently, a wide spectrum of artificial intelligence (AI)-based applications in the broader categories of digital pathology, biomarker development, and treatment have been explored. In the domain of digital pathology, these have included novel analytical strategies for realizing new information derived from standard histology to guide treatment selection and biomarker development to predict treatment selection and response. In therapeutics, these have included AI-driven drug target discovery, drug design and repurposing, combination regimen optimization, modulated dosing, and beyond. Given the continued advances that are emerging, it is important to develop workflows that seamlessly combine the various segments of AI innovation to comprehensively augment the diagnostic and interventional arsenal of the clinical oncology community. To overcome challenges that remain with regard to the ideation, validation, and deployment of AI in clinical oncology, recommendations toward bringing this workflow to fruition are also provided from clinical, engineering, implementation, and health care economics considerations. Ultimately, this work proposes frameworks that can potentially integrate these domains toward the sustainable adoption of practice-changing AI by the clinical oncology community to drive improved patient outcomes.

Tumor-Associated Macrophages and Related Myelomonocytic Cells in the Tumor Microenvironment of Multiple Myeloma

Multiple myeloma (MM) is the second-most common hematologic malignancy and remains incurable despite potent plasma cell directed therapeutics. The tumor microenvironment (TME) is a key player in the pathogenesis and progression of MM and is an active focus of research with a view to targeting immune dysregulation. Tumor-associated macrophages (TAM), myeloid derived suppressor cells (MDSC), and dendritic cells (DC) are known to drive progression and treatment resistance in many cancers. They have also been shown to promote MM progression and immune suppression in vitro, and there is growing evidence of their impact on clinical outcomes. The heterogeneity and functional characteristics of myelomonocytic cells in MM are being unraveled through high-dimensional immune profiling techniques. We are also beginning to understand how they may affect and be modulated by current and future MM therapeutics. In this review, we provide an overview of the biology and clinical relevance of TAMs, MDSCs, and DCs in the MM TME. We also highlight key areas to be addressed in future research as well as our perspectives on how the myelomonocytic compartment of the TME may influence therapeutic strategies of the future.