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ObjectiveIBD therapies and treatments are evolving to deeper levels of remission. Molecular measures of disease may augment current endpoints including the potential for less invasive assessments.DesignTranscriptome analysis on 712 endoscopically defined inflamed (Inf) and 1778 non-inflamed (Non-Inf) intestinal biopsies (n=498 Crohn’s disease, n=421 UC and 243 controls) in the Mount Sinai Crohn’s and Colitis Registry were used to identify genes differentially expressed between Inf and Non-Inf biopsies and to generate a molecular inflammation score (bMIS) via gene set variance analysis. A circulating MIS (cirMIS) score, reflecting intestinal molecular inflammation, was generated using blood transcriptome data. bMIS/cirMIS was validated as indicators of intestinal inflammation in four independent IBD cohorts.ResultsbMIS/cirMIS was strongly associated with clinical, endoscopic and histological disease activity indices. Patients with the same histologic score of inflammation had variable bMIS scores, indicating that bMIS describes a deeper range of inflammation. In available clinical trial data sets, both scores were responsive to IBD treatment. Despite similar baseline endoscopic and histologic activity, UC patients with lower baseline bMIS levels were more likely treatment responders compared with those with higher levels. Finally, among patients with UC in endoscopic and histologic remission, those with lower bMIS levels were less likely to have a disease flare over time.ConclusionTranscriptionally based scores provide an alternative objective and deeper quantification of intestinal inflammation, which could augment current clinical assessments used for disease monitoring and have potential for predicting therapeutic response and patients at higher risk of disease flares.

Targeting the α4β7-MAdCAM-1 axis with vedolizumab (VDZ) is a front-line therapeutic paradigm in ulcerative colitis (UC). However, mechanism(s) of action (MOA) of VDZ remain relatively undefined. Here, we examined three distinct cohorts of patients with UC (n=83, n=60, and n=21), to determine the effect of VDZ on the mucosal and peripheral immune system. Transcriptomic studies with protein level validation were used to study drug MOA using conventional and transgenic murine models. We found a significant decrease in colonic and ileal naive B and T cells and circulating gut-homing plasmablasts (β7+) in VDZ-treated patients, pointing to gut-associated lymphoid tissue (GALT) targeting by VDZ. Murine Peyer's patches (PP) demonstrated a significant loss cellularity associated with reduction in follicular B cells, including a unique population of epithelium-associated B cells, following anti-α4β7 antibody (mAb) administration. Photoconvertible (KikGR) mice unequivocally demonstrated impaired cellular entry into PPs in anti-α4β7 mAb treated mice. In VDZ-treated, but not anti-tumor necrosis factor-treated UC patients, lymphoid aggregate size was significantly reduced in treatment responders compared to non-responders, with an independent validation cohort further confirming these data. GALT targeting represents a novel MOA of α4β7-targeted therapies, with major implications for this therapeutic paradigm in UC, and for the development of new therapeutic strategies.

Realization of precision medicine ideas requires significant research effort to be able to spot subtle differences in complex diseases at the molecular level to develop personalized therapies. It is especially important in many cases of highly heterogeneous cancers. Precision diagnostics and therapeutics of such diseases demands interrogation of vast amounts of biological knowledge coupled with novel analytic methodologies. For instance, pathway-based approaches can shed light on the way tumorigenesis takes place in individual patient cases and pinpoint to novel drug targets. However, comprehensive analysis of hundreds of pathways and thousands of genes creates a combinatorial explosion, that is challenging for medical practitioners to handle at the point of care. Here we extend our previous work on mapping clinical omics data to curated Resource Description Framework (RDF) knowledge bases to derive influence diagrams of interrelationships of biomarker proteins, diseases and signal transduction pathways for personalized theranostics. We present RDF Sketch Maps-a computational method to reduce knowledge complexity for precision medicine analytics. The method of RDF Sketch Maps is inspired by the way a sketch artist conveys only important visual information and discards other unnecessary details. In our case, we compute and retain only so-called RDF Edges-places with highly important diagnostic and therapeutic information. To do this we utilize 35 maps of human signal transduction pathways by transforming 300 KEGG maps into highly processable RDF knowledge base. We have demonstrated potential clinical utility of RDF Sketch Maps in hematopoietic cancers, including analysis of pathways associated with Hairy Cell Leukemia (HCL) and Chronic Myeloid Leukemia (CML) where we achieved up to 20-fold reduction in the number of biological entities to be analyzed, while retaining most likely important entities. In experiments with pathways associated with HCL a generated RDF Sketch Map of the top 30% paths retained important information about signaling cascades leading to activation of proto-oncogene BRAF, which is usually associated with a different cancer, melanoma. Recent reports of successful treatments of HCL patients by the BRAF-targeted drug vemurafenib support the validity of the RDF Sketch Maps findings. We therefore believe that RDF Sketch Maps will be invaluable for hypothesis generation for precision diagnostics and therapeutics as well as drug repurposing studies.

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Breast cancer (BC) is the leading cause of death among female patients with cancer. Patients with triple-negative breast cancer (TNBC) have the lowest survival rate. TNBC has substantial heterogeneity within the BC population. This study utilized our novel patient stratification and drug repositioning method to find subgroups of BC patients that share common genetic profiles and that may respond similarly to the recommended drugs. After further examination of the discovered patient subgroups, we identified five homogeneous druggable TNBC subgroups. A drug repositioning algorithm was then applied to find the drugs with a high potential for each subgroup. Most of the top drugs for these subgroups were chemotherapy used for various types of cancer, including BC. After analyzing the biological mechanisms targeted by these drugs, ferroptosis was the common cell death mechanism induced by the top drugs in the subgroups with neoplasm subdivision and race as clinical variables. In contrast, the antioxidative effect on cancer cells was the common targeted mechanism in the subgroup of patients with an age less than 50. Literature reviews were used to validate our findings, which could provide invaluable insights to streamline the drug repositioning process and could be further studied in a wet lab setting and in clinical trials.

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. There is a crucial need to develop data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Exploratory mining mimicking the trial recruitment process as well as network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The method represents a human-in-the-loop framework, where medical experts use data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. To examine the validity of our method, we implemented our method on individual cancer types and on pan-cancer data to consider the inter- and intra-heterogeneity within a cancer type and among cancer types. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups showed that most of these drugs are FDA-approved drugs for cancer, and others are non-cancer related, but have the potential to be repurposed for cancer. We have discovered novel cancer-related mechanisms that these drugs can target in different cancer types to reduce cancer treatment costs and improve patient survival. Further wet lab experiments to validate these findings are required prior to initiating clinical trials using these repurposed therapies.

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