Functional Precision Medicine: Putting Drugs on Patient Cancer Cells and Seeing What Happens

Zebrafish Avatar-test forecasts clinical response to chemotherapy in patients with colorectal cancer

Cancer patients often undergo rounds of trial-and-error to find the most effective treatment because there is no test in the clinical practice for predicting therapy response. Here, we conduct a clinical study to validate the zebrafish patient-derived xenograft model (zAvatar) as a fast predictive platform for personalized treatment in colorectal cancer. zAvatars are generated with patient tumor cells, treated exactly with the same therapy as their corresponding patient and analyzed at single-cell resolution. By individually comparing the clinical responses of 55 patients with their zAvatar-test, we develop a decision tree model integrating tumor stage, zAvatar-apoptosis, and zAvatar-metastatic potential. This model accurately forecasts patient progression with 91% accuracy. Importantly, patients with a sensitive zAvatar-test exhibit longer progression-free survival compared to those with a resistant test. We propose the zAvatar-test as a rapid approach to guide clinical decisions, optimizing treatment options and improving the survival of cancer patients.

BH3 profiling as pharmacodynamic biomarker for the activity of BH3 mimetics

Not available.

Translating p53-based therapies for cancer into the clinic

Inactivation of the most important tumour suppressor gene TP53 occurs in most, if not all, human cancers. Loss of functional wild-type p53 is achieved via two main mechanisms: mutation of the gene leading to an absence of tumour suppressor activity and, in some cases, gain-of-oncogenic function; or inhibition of the wild-type p53 protein mediated by overexpression of its negative regulators MDM2 and MDMX. Because of its high potency as a tumour suppressor and the dependence of at least some established tumours on its inactivation, p53 appears to be a highly attractive target for the development of new anticancer drugs. However, p53 is a transcription factor and therefore has long been considered undruggable. Nevertheless, several innovative strategies have been pursued for targeting dysfunctional p53 for cancer treatment. In mutant p53-expressing tumours, the predominant strategy is to restore tumour suppressor function with compounds acting either in a generic manner or otherwise selective for one or a few specific p53 mutations. In addition, approaches to deplete mutant p53 or to target vulnerabilities created by mutant p53 expression are currently under development. In wild-type p53 tumours, the major approach is to protect p53 from the actions of MDM2 and MDMX by targeting these negative regulators with inhibitors. Although the results of at least some clinical trials of MDM2 inhibitors and mutant p53-restoring compounds are promising, none of the agents has yet been approved by the FDA. Alternative strategies, based on a better understanding of p53 biology, the mechanisms of action of compounds and treatment regimens as well as the development of new technologies are gaining interest, such as proteolysis-targeting chimeras for MDM2 degradation. Other approaches are taking advantage of the progress made in immune-based therapies for cancer. In this Review, we present these ongoing clinical trials and emerging approaches to re-evaluate the current state of knowledge of p53-based therapies for cancer.

What is Next in Pediatric B-cell Precursor Acute Lymphoblastic Leukemia

Cure rates now exceed 90% in many contemporary trials for children with B-cell acute lymphoblastic leukemia (ALL). However, treatment remains suboptimal and therapy is toxic for all patients. New treatment options potentially offer the chance to reduce both treatment resistance and toxicity. Here, we review recent advances in ALL diagnostics, chemotherapy, and immunotherapy. In addition to describing recently published results, we also attempt to project the impact of these new developments into the future to imagine what B-ALL therapy may look like in the next few years.

Novel Zebrafish Patient-Derived Tumor Xenograft Methodology for Evaluating Efficacy of Immune-Stimulating BCG Therapy in Urinary Bladder Cancer

BACKGROUND

Bacillus Calmette-Guérin (BCG) immunotherapy is the standard-of-care adjuvant therapy for non-muscle-invasive bladder cancer in patients at considerable risk of disease recurrence. Although its exact mechanism of action is unknown, BCG significantly reduces this risk in responding patients but is mainly associated with toxic side-effects in those facing treatment resistance. Methods that allow the identification of BCG responders are, therefore, urgently needed.

METHODS

Fluorescently labelled UM-UC-3 cells and dissociated patient tumor samples were used to establish zebrafish tumor xenograft (ZTX) models. Changes in the relative primary tumor size and cell dissemination to the tail were evaluated via fluorescence microscopy at three days post-implantation. The data were compared to the treatment outcomes of the corresponding patients. Toxicity was evaluated based on gross morphological evaluation of the treated zebrafish larvae.

RESULTS

BCG-induced toxicity was avoided by removing the water-soluble fraction of the BCG formulation prior to use. BCG treatment via co-injection with the tumor cells resulted in significant and dose-dependent primary tumor size regression. Heat-inactivation of BCG decreased this effect, while intravenous BCG injections were ineffective. ZTX models were successfully established for six of six patients based on TUR-B biopsies. In two of these models, significant tumor regression was observed, which, in both cases, corresponded to the treatment response in the patients.

CONCLUSIONS

The observed BCG-related anti-tumor effect indicates that ZTX models might predict the BCG response and thereby improve treatment planning. More experiments and clinical studies are needed, however, to elucidate the BCG mechanism and estimate the predictive value.

Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine

Drug testing in patient biopsy-derived cells can identify potent treatments for patients suffering from relapsed or refractory hematologic cancers. Here we investigate the use of weakly supervised deep learning on cell morphologies (DML) to complement diagnostic marker-based identification of malignant and nonmalignant cells in drug testing. Across 390 biopsies from 289 patients with diverse blood cancers, DML-based drug responses show improved reproducibility and clustering of drugs with the same mode of action. DML does so by adapting to batch effects and by autonomously recognizing disease-associated cell morphologies. In a post hoc analysis of 66 patients, DML-recommended treatments led to improved progression-free survival compared with marker-based recommendations and physician's choice-based treatments. Treatments recommended by both immunofluorescence and DML doubled the fraction of patients achieving exceptional clinical responses. Thus, DML-enhanced ex vivo drug screening is a promising tool in the identification of effective personalized treatments.

SIGNIFICANCE

We have recently demonstrated that image-based drug screening in patient samples identifies effective treatment options for patients with advanced blood cancers. Here we show that using deep learning to identify malignant and nonmalignant cells by morphology improves such screens. The presented workflow is robust, automatable, and compatible with clinical routine. This article is highlighted in the In This Issue feature, p. 476.

Modern treatment approaches to adult acute T-lymphoblastic and myeloid/T-lymphoblastic leukemia: from current standards to precision medicine

PURPOSE OF REVIEW

To review the most recent advancements in the management of adult T-cell acute lymphoblastic leukemia (T-ALL), we summarize insights into molecular diagnostics, immunotherapy, targeted therapy and new techniques of drug sensitivity profiling that may support further therapeutic progress in T-ALL subsets.

RECENT FINDINGS

With current induction/consolidation chemotherapy and/or risk-oriented allogeneic stem cell transplantation programs up to 95% adult T-ALL patients achieve a remission and >50% (up to 80% in adolescents and young adults) are cured. The group of patients who fail upfront therapy, between 25% and 40%, is enriched in high-risk characteristics (unfavorable genetics, persistent minimal residual disease) and represents the ideal setting for the study of molecular mechanisms of disease resistance, and consequently explore novel ways of restoration of drug sensitivity and assess patient/subset-specific patterns of drug vulnerability to targeting agents, immunotherapy and cell therapy.

SUMMARY

The emerging evidence supports the contention that precision medicine may soon allow valuable therapeutic chances to adult patients with high-risk T-ALL. The ongoing challenge is to identify the best way to integrate all these new data into the therapeutic path of newly diagnosed patients, with a view to optimize the individual treatment plan and increase the cure rate.

A Three-Dimensional Printed Inertial Microfluidic Platform for Isolation of Minute Quantities of Vital Mitochondria

We demonstrate a fast and easy-to-use three-dimensional printed microfluidic platform for mitochondria isolation from cell and tissue lysates based on inertial microfluidics. We present and quantify the quality of the isolated mitochondria by measuring the respiration rate under various conditions. We demonstrate that the technology produces vital mitochondria of equal quality to traditional, but more burdensome, differential centrifugation. We anticipate that the availability of improved tools for studies of bioenergetics to the broader biological community will enable these and other links to be explored in more meaningful ways, leading to further understanding of the links between energy, health, and disease.