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

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.