Bone tissue, by definition, is an organic–inorganic nanocomposite, where metabolically active cells are embedded within a matrix that is heavily calcified on the nanoscale. Currently, there are no strategies that replicate these definitive characteristics of bone tissue. Here we describe a biomimetic approach where a supersaturated calcium and phosphate medium is used in combination with a non-collagenous protein analog to direct the deposition of nanoscale apatite, both in the intra- and extrafibrillar spaces of collagen embedded with osteoprogenitor, vascular, and neural cells. This process enables engineering of bone models replicating the key hallmarks of the bone cellular and extracellular microenvironment, including its protein-guided biomineralization, nanostructure, vasculature, innervation, inherent osteoinductive properties (without exogenous supplements), and cell-homing effects on bone-targeting diseases, such as prostate cancer. Ultimately, this approach enables fabrication of bone-like tissue models with high levels of biomimicry that may have broad implications for disease modeling, drug discovery, and regenerative engineering.

The Chinese natural product, berberine, has biological properties that support its potential efficacy as a colon cancer prevention agent. Its longstanding use in China to treat gastrointestinal tract and rheumatologic disorders is generally regarded as safe, supporting initial investigations in an at-risk population, such as individuals with ulcerative colitis. However, the safety of berberine in this population is not established. Individuals living in China with biopsy-proven ulcerative colitis, ≤grade 2 dysplasia, and with a ulcerative colitis disease activity index (UCDAI) score ≤1 on mesalamine, were randomized 3:1 in a double-blind phase I trial to berberine 900 mg/day or placebo for 3 months, with the primary objective of assessing safety. Blood samples and biopsies of the colorectum, from prespecified locations, were collected prior to and following therapy. Secondary endpoints included changes in UCDAI score, and in tissue and plasma markers of inflammation. Of toxicities at least possibly related, one episode of grade 3 elevation in transaminases and one episode of grade 1 nausea were observed among 12 individuals on berberine, and none were observed among 4 on placebo. The mean plasma berberine concentration was 3.5 nmol/L after berberine treatment, significantly higher than 0.5 nmol/L with placebo. Berberine significantly decreased the Geboes grade in colonic tissue, but had a nonsignificant effect on other tissue or blood biomarkers related to cell growth and inflammation. The combination of berberine and mesalamine is well tolerated in Chinese with ulcerative colitis and may enhance mesalamine's anti-inflammatory effects in colonic tissue.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

Clinical trials are fundamental for advances in cancer treatment. The traditional framework of phase 1 to 3 trials is designed for incremental advances between regimens. However, our ability to understand and treat cancer has evolved with the increase in drugs targeting an expanding array of therapeutic targets, the development of progressively comprehensive data sets, and emerging computational analytics, all of which are reshaping our treatment strategies. A more robust linkage between drugs and underlying cancer biology is blurring historical lines that define trials on the basis of cancer type. The complexity of the molecular basis of cancer, coupled with manifold variations in clinical status, is driving the individually tailored use of combinations of precision targeted drugs. This approach is spawning a new era of clinical trial types. Although most care is delivered in a community setting, large centers support real‐time multi‐omic analytics and their integrated interpretation by using machine learning in the context of real‐world data sets. Coupling the analytic capabilities of large centers to the tailored delivery of therapy in the community is forging a paradigm that is optimizing service for patients. Understanding the importance of these evolving trends across the health care spectrum will affect our treatment of cancer in the future and is the focus of this review.

Molecular heterogeneity in metastatic breast cancer presents multiple clinical challenges in accurately characterizing and treating the disease. Current diagnostic approaches offer limited ability to assess heterogeneity that exists among multiple metastatic lesions throughout the treatment course. We developed a precision oncology platform that combines serial biopsies, multi-omic analysis, longitudinal patient monitoring, and molecular tumor boards, with the goal of improving cancer management through enhanced understanding of the entire cancer ecosystem within each patient. We describe this integrative approach using comprehensive analytics generated from serial-biopsied lesions in a metastatic breast cancer patient. The serial biopsies identified remarkable heterogeneity among metastatic lesions that presented clinically as discordance in receptor status and genomic alterations with mixed treatment response. Based on our study, we highlight clinical scenarios, such as rapid progression or mixed response, that indicate consideration for repeat biopsies to evaluate intermetastatic heterogeneity (IMH), with the objective of refining targeted therapy. We present a framework for understanding the clinical significance of heterogeneity in breast cancer between metastatic lesions utilizing multi-omic analyses of serial biopsies and its implication for effective personalized treatment.

In order to establish the workflows required to implement a real-time process involving multi-omic analysis of patient samples to support precision-guided therapeutic intervention, a tissue acquisition and analysis trial was implemented. This report describes our findings to date, including the frequency with which mutational testing led to precision-guided therapy and outcome for those patients. Eligible patients presenting to Oregon Health and Science University Knight Cancer Institute were enrolled on the study. Patients with biopsy proven metastatic or locally advanced unresectable prostate cancer, breast cancer, pancreatic adenocarcinoma, or refractory acute myelogenous leukemia receiving standard of care therapy were eligible. Metastatic site biopsies were collected and analyzed using the Knight Diagnostic Lab GeneTrails comprehensive solid tumor panel (124 genes). CLIA certified genomic information was made available to the treating physician. Between 1/26/2017 and 5/30/2018, 38 patients were enrolled, with 28 successfully undergoing biopsy. Of these, 25 samples yielded sufficient tumor for analysis. The median biopsy cellularity and number of cores collected were 70% (15–90%) and 5 (2–20), respectively. No procedure-related complications occurred. GeneTrails analysis revealed that 22 of 25 (88%) tumor samples harbored at least one potentially actionable mutation, and 18 (72%) samples harbored 2 or more potentially actionable mutations. The most common genetic alterations identified involved: DNA damage repair genes, cell cycle regulating genes, PIK3CA/Akt/mTOR pathway, and FGF gene family. To date, CLIA certified genomic results were used by treating physicians for precision-guided therapy in 5 (23%) patients. We report the feasibility of real-time tissue acquisition and analysis to support a successful translational oncology platform. The workflow will provide the foundation to improve access and accrual to biomarker driven precision oncology trials.