Precision medicine in oncology drug development: a pharma perspective

Changes in Companion Diagnostic Labelling: Implementation of FDA's April 2020 Guidance for Industry for In Vitro CDx Labeling for Specific Oncology Therapeutic Groups

Advanced understanding of the molecular pathways of oncologic diseases has shifted therapeutic treatment development to focus on mechanism of actions targeting specific genomic alterations. These precision medicines are indicated for patient subsets defined by these specific mutations as determined by diagnostic devices approved by the Food and Drug Administration (FDA). The Intended Use section within the companion diagnostic (CDx) labeling has historically specified the therapeutic products for which they have been clinically validated. In April 2020, the FDA reiterated their position that therapeutic class labeling may be used, if appropriate, instead of named products. Labels for FDA approved in vitro CDxs were reviewed to evaluate the implementation of therapeutic class labeling. A total of 47 devices have been approved as of 2 January 2022, of which 3 labels were found to contain therapeutic class labeling: two devices targeting EGFR mutations for the treatment of non-small cell lung cancer (NSCLC), and one targeting BRAF V600E and BRAF/MEK inhibitor combinations for melanoma. Two devices received therapeutic class labeling upon initial approval, while the third implemented the language though a label revision. A total of 25 different indications were identified across the 47 CDx devices, of which 9 (34.6%) were associated with more than 1 CDx device. Implementation of therapeutic class labeling has been slow following the release of the FDA’s April 2020 guidance; however, the potential to incorporate such language into existing and newly approved CDx labels exists. Precedence and manufacturer experience are expected to drive an increase in therapeutic class labeling.

Tiny Drosophila makes giant strides in cancer research

Cancer burden has been increasing worldwide, making cancer the second leading cause of death in the world. Over the past decades, various experimental models have provided important insights into the nature of cancer. Among them, the fruit fly Drosophila as a whole-animal toolkit has made a decisive contribution to our understanding of fundamental mechanisms of cancer development including loss of cell polarity. In recent years, scalable Drosophila platforms have proven useful also in developing anti-cancer regimens that are effective not only in mammalian models but also in patients. Here, we review studies using Drosophila as a tool to advance cancer study by complementing other traditional research systems.

Methods and resources to access mutation-dependent effects on cancer drug treatment

In clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

Past, Current, and Future Cancer Clinical Research Collaborations: The Case of the European Organisation for Research and Treatment of Cancer

Although collaborations between academic institutions and industry have led to important scientific breakthroughs in the discovery stage of the pharmaceutical research and development process, the role of multistakeholder partnerships in the clinical development of anticancer medicines necessitates further clarification. The benefits associated with such cooperation could be undercut by the conflicting goals and motivations of the actors included. The aim of this review was to identify and characterize past, present, and future stakeholder partnership models in cancer clinical research through the lens of the European Organisation for Research and Treatment of Cancer (EORTC). Based on the analysis of several landmark EORTC trials performed across the span of three decades, four existing models of stakeholder cooperation were delineated and characterized. Additionally, a hypothetical fifth model representing a potential future collaborative framework for cancer clinical research was formulated. These models mainly differ in terms of the nature and responsibilities of the partners included and show that clinical research partnerships in oncology have evolved over time from small‐scale academia‐industry collaborations to complex interdisciplinary cooperation involving many different stakeholders.

A step towards personalizing next line therapy for resected pancreatic and related cancer patients: A single institution's experience

Background:

There is a lack of precision medicine in pancreatic ductal adenocarcinoma (PDA) and related cancers, and outcomes for patients with this diagnosis remain poor despite decades of research investigating this disease. Therefore, it is necessary to explore novel therapeutic options for these patients who may benefit from personalized therapies.

Objective:

Molecular profiling of hepatopancreaticobiliary malignancies at our institution, including but not limited to PDA, was initiated to assess the feasibility of incorporating molecular profiling results into patient oncological therapy planning.

Methods:

All eligible patients from Thomas Jefferson University (TJU) with hepatopancreaticobiliary tumors including PDA, who agreed to molecular testing profiling, were prospectively enrolled in a registry study from December 2014 to September 2017 and their tumor samples were tested to identify molecular markers that can be used to guide therapy options in the future. Next generation sequencing (NGS) and protein expression in tumor samples were tested at CLIA-certified laboratories. Prospective clinicopathologic data were extracted from medical records and compiled in a de-identified fashion.

Results:

Seventy eight (78) patients were enrolled in the study, which included 65/78 patients with PDA (local and metastatic) and out of that subset, 52/65 patients had surgically resected PDA. Therapy recommendations were generated based on molecular and clinicopathologic data for all enrolled patients. NGS uncovered actionable alterations in 25/52 surgically resected PDAs (48%) which could be used to guide therapy options in the future. High expression of three proteins, TS (p ¼ 0.005), ERCC1 (p = 0.001), and PD-1 (p = 0.04), was associated with reduced recurrence-free survival (RFS), while TP53 mutations were correlated with longer RFS (p = 0.01).

Conclusions:

The goal of this study was to implement a stepwise strategy to identify and profile resected PDAs at our institution. Consistent with previous studies, approximately half of patients with resected PDA harbor actionable mutations with possible targeted therapeutic implications. Ongoing studies will determine the clinical value of identifying these mutations in patients with resected PDA.

Intellectual Structure and Evolutionary Trends of Precision Medicine Research: Coword Analysis

BACKGROUND

Precision medicine (PM) is playing a more and more important role in clinical practice. In recent years, the scale of PM research has been growing rapidly. Many reviews have been published to facilitate a better understanding of the status of PM research. However, there is still a lack of research on the intellectual structure in terms of topics.

OBJECTIVE

This study aimed to identify the intellectual structure and evolutionary trends of PM research through the application of various social network analysis and visualization methods.

METHODS

The bibliographies of papers published between 2009 and 2018 were extracted from the Web of Science database. Based on the statistics of keywords in the papers, a coword network was generated and used to calculate network indicators of both the entire network and local networks. Communities were then detected to identify subdirections of PM research. Topological maps of networks, including networks between communities and within each community, were drawn to reveal the correlation structure. An evolutionary graph and a strategic graph were finally produced to reveal research venation and trends in discipline communities.

RESULTS

The results showed that PM research involves extensive themes and, overall, is not balanced. A minority of themes with a high frequency and network indicators, such as Biomarkers, Genomics, Cancer, Therapy, Genetics, Drug, Target Therapy, Pharmacogenomics, Pharmacogenetics, and Molecular, can be considered the core areas of PM research. However, there were five balanced theme directions with distinguished status and tendencies: Cancer, Biomarkers, Genomics, Drug, and Therapy. These were shown to be the main branches that were both focused and well developed. Therapy, though, was shown to be isolated and undeveloped.

CONCLUSIONS

The hotspots, structures, evolutions, and development trends of PM research in the past ten years were revealed using social network analysis and visualization. In general, PM research is unbalanced, but its subdirections are balanced. The clear evolutionary and developmental trend indicates that PM research has matured in recent years. The implications of this study involving PM research will provide reasonable and effective support for researchers, funders, policymakers, and clinicians.

Utility of Integrated Analysis of Pharmacogenomics and Pharmacometabolomics in Early Phase Clinical Trial: A Case Study of a New Molecular Entity

In this report, we present a case study of how pharmacogenomics and pharmacometabolomics can be useful to characterize safety and pharmacokinetic profiles in early phase new drug development clinical trials. During conducting a first-in-human trial for a new molecular entity, we were able to determine the mechanism of dichotomized variability in plasma drug concentrations, which appeared closely related to adverse drug reactions (ADRs) through integrated omics analysis. The pharmacogenomics screening was performed from whole blood samples using the Affymetrix DMET (Drug-Metabolizing Enzymes and Transporters) Plus microarray, and confirmation of genetic variants was performed using real-time polymerase chain reaction. Metabolomics profiling was performed from plasma samples using liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. A GSTM1 null polymorphism was identified in pharmacogenomics test and the drug concentrations was higher in GSTM1 null subjects than GSTM1 functional subjects. The apparent drug clearance was 13-fold lower in GSTM1 null subjects than GSTM1 functional subjects (p < 0.001). By metabolomics analysis, we identified that the study drug was metabolized by cysteinylglycine conjugation in GSTM functional subjects but those not in GSTM1 null subjects. The incidence rate and the severity of ADRs were higher in the GSTM1 null subjects than the GSTM1 functional subjects. Through the integrated omics analysis, we could understand the mechanism of inter-individual variability in drug exposure and in adverse response. In conclusion, integrated multi-omics analysis can be useful for elucidating the various characteristics of new drug candidates in early phase clinical trials.

Path toward Precision Oncology: Review of Targeted Therapy Studies and Tools to Aid in Defining "Actionability" of a Molecular Lesion and Patient Management Support

Precision medicine trials and targeted therapies have shifted to the forefront of oncology. Although targeted therapies have shown initial promise, implementation across the broad landscape of oncology has many challenges. These limitations include an incomplete understanding of the functional significance of variant alleles as well as the need for clinical research and practice models that are more patient-centered and account for the complexity of individual patient tumors. Furthermore, successful implementation of targeted therapies will also be predicated on efforts to standardize the framework for patient management support. Here, we review current implementations of targeted therapies in precision oncology and discuss how "actionability" is defined for molecular targets in cancer therapeutics. We also comment on the growing need for bioinformatics tools and data platforms to complement advances in precision oncology. Finally, we discuss current frameworks for integrating precision oncology into patient management and propose an integrated model that combines features of molecular tumor boards and decision support systems. Mol Cancer Ther; 16(12); 2645-55. ©2017 AACRSee related article by Pilié et al., p. 2641.

Analysis of mutation, selection, and epistasis: an informed approach to cancer clinical trials

Currently, drug development efforts and clinical trials to test them are often prioritized by targeting genes with high frequencies of somatic variants among tumors. However, differences in oncogenic mutation rate-not necessarily the effect the variant has on tumor growth-contribute enormously to somatic variant frequency. We argue that decoupling the contributions of mutation and cancer lineage selection to the frequency of somatic variants among tumors is critical to understanding-and predicting-the therapeutic potential of different interventions. To provide an indicator of that strength of selection and therapeutic potential, the frequency at which we observe a given variant across patients must be modulated by our expectation given the mutation rate and target size to provide an indicator of that strength of selection and therapeutic potential. Additionally, antagonistic and synergistic epistasis among mutations also impacts the potential therapeutic benefit of targeted drug development. Quantitative approaches should be fostered that use the known genetic architectures of cancer types, decouple mutation rate, and provide rigorous guidance regarding investment in targeted drug development. By integrating evolutionary principles and detailed mechanistic knowledge into those approaches, we can maximize our ability to identify those targeted therapies most likely to yield substantial clinical benefit.

Toward a Tiered Model to Share Clinical Trial Data and Samples in Precision Oncology

The recent revolution in science and technology applied to medical research has left in its wake a trial of biomedical data and human samples; however, its opportunities remain largely unfulfilled due to a number of legal, ethical, financial, strategic, and technical barriers. Precision oncology has been at the vanguard to leverage this potential of "Big data" and samples into meaningful solutions for patients, considering the need for new drug development approaches in this area (due to high costs, late-stage failures, and the molecular diversity of cancer). To harness the potential of the vast quantities of data and samples currently fragmented across databases and biobanks, it is critical to engage all stakeholders and share data and samples across research institutes. Here, we identified two general types of sharing strategies. First, open access models, characterized by the absence of any review panel or decision maker, and second controlled access model where some form of control is exercised by either the donor (i.e., patient), the data provider (i.e., initial organization), or an independent party. Further, we theoretically describe and provide examples of nine different strategies focused on greater sharing of patient data and material. These models provide varying levels of control, access to various data and/or samples, and different types of relationship between the donor, data provider, and data requester. We propose a tiered model to share clinical data and samples that takes into account privacy issues and respects sponsors' legitimate interests. Its implementation would contribute to maximize the value of existing datasets, enabling unraveling the complexity of tumor biology, identify novel biomarkers, and re-direct treatment strategies better, ultimately to help patients with cancer.

A whole-animal platform to advance a clinical kinase inhibitor into new disease space

Synthetic tailoring of approved drugs for new indications is often difficult, as the most appropriate targets may not be readily apparent, and therefore few roadmaps exist to guide chemistry. Here, we report a multidisciplinary approach for accessing novel target and chemical space starting from an FDA-approved kinase inhibitor. By combining chemical and genetic modifier screening with computational modeling, we identify distinct kinases that strongly enhance ('pro-targets') or limit ('anti-targets') whole-animal activity of the clinical kinase inhibitor sorafenib in a Drosophila medullary thyroid carcinoma (MTC) model. We demonstrate that RAF-the original intended sorafenib target-and MKNK kinases function as pharmacological liabilities because of inhibitor-induced transactivation and negative feedback, respectively. Through progressive synthetic refinement, we report a new class of 'tumor calibrated inhibitors' with unique polypharmacology and strongly improved therapeutic index in fly and human MTC xenograft models. This platform provides a rational approach to creating new high-efficacy and low-toxicity drugs.

Clinical Application of Targeted Deep Sequencing in Solid-Cancer Patients and Utility for Biomarker-Selected Clinical Trials

Molecular profiling of actionable mutations in refractory cancer patients has the potential to enable "precision medicine," wherein individualized therapies are guided based on genomic profiling. The molecular-screening program was intended to route participants to different candidate drugs in trials based on clinical-sequencing reports. In this screening program, we used a custom target-enrichment panel consisting of cancer-related genes to interrogate single-nucleotide variants, insertions and deletions, copy number variants, and a subset of gene fusions. From August 2014 through April 2015, 654 patients consented to participate in the program at Samsung Medical Center. Of these patients, 588 passed the quality control process for the 381-gene cancer-panel test, and 418 patients were included in the final analysis as being eligible for any anticancer treatment (127 gastric cancer, 122 colorectal cancer, 62 pancreatic/biliary tract cancer, 67 sarcoma/other cancer, and 40 genitourinary cancer patients). Of the 418 patients, 55 (12%) harbored a biomarker that guided them to a biomarker-selected clinical trial, and 184 (44%) patients harbored at least one genomic alteration that was potentially targetable. This study demonstrated that the panel-based sequencing program resulted in an increased rate of trial enrollment of metastatic cancer patients into biomarker-selected clinical trials. Given the expanding list of biomarker-selected trials, the guidance percentage to matched trials is anticipated to increase.

IMPLICATIONS FOR PRACTICE

This study demonstrated that the panel-based sequencing program resulted in an increased rate of trial enrollment of metastatic cancer patients into biomarker-selected clinical trials. Given the expanding list of biomarker-selected trials, the guidance percentage to matched trials is anticipated to increase.

Hippo pathway mediates resistance to cytotoxic drugs

Chemotherapy is widely used for cancer treatment, but its effectiveness is limited by drug resistance. Here, we report a mechanism by which cell density activates the Hippo pathway, which in turn inactivates YAP, leading to changes in the regulation of genes that control the intracellular concentrations of gemcitabine and several other US Food and Drug Administration (FDA)-approved oncology drugs. Hippo inactivation sensitizes a diverse panel of cell lines and human tumors to gemcitabine in 3D spheroid, mouse xenografts, and patient-derived xenograft models. Nuclear YAP enhances gemcitabine effectiveness by down-regulating multidrug transporters as well by converting gemcitabine to a less active form, both leading to its increased intracellular availability. Cancer cell lines carrying genetic aberrations that impair the Hippo signaling pathway showed heightened sensitivity to gemcitabine. These findings suggest that "switching off" of the Hippo-YAP pathway could help to prevent or reverse resistance to some cancer therapies.

Molecular Triage Trials in Colorectal Cancer

Advances in the understanding of genomic alterations in cancer, and the various therapies targeted to these alterations have permitted the design of trials directed to bringing this science to the clinic, with the ultimate goal of tailoring therapy to the individual. There is a high need for advances in targeted therapy in colorectal cancer, a disease in which only 2 classes of targeted therapies are approved for use in colorectal cancer, despite the majority of colorectal cancers containing a potentially targetable mutation. Here we outline the key elements to the design of these clinical trials and summarize the current active molecular triage trials in colorectal cancer.

Defining actionable mutations for oncology therapeutic development

Genomic profiling of tumours in patients in clinical trials enables rapid testing of multiple hypotheses to confirm which genomic events determine likely responder groups for targeted agents. A key challenge of this new capability is defining which specific genomic events should be classified as 'actionable' (that is, potentially responsive to a targeted therapy), especially when looking for early indications of patient subgroups likely to be responsive to new drugs. This Opinion article discusses some of the different approaches being taken in early clinical development to define actionable mutations, and describes our strategy to address this challenge in early-stage exploratory clinical trials.