Efficient, Adaptive Clinical Validation of Predictive Biomarkers in Cancer Therapeutic Development

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.

The optimal design of clinical trials with potential biomarker effects: A novel computational approach

As a future trend of healthcare, personalized medicine tailors medical treatments to individual patients. It requires to identify a subset of patients with the best response to treatment. The subset can be defined by a biomarker (eg, expression of a gene) and its cutoff value. Topics on subset identification have received massive attention. There are over two million hits by keyword searches on Google Scholar. However, designing clinical trials that utilize the discovered uncertain subsets/biomarkers is not trivial and rarely discussed in the literature. This leads to a gap between research results and real-world drug development. To fill in this gap, we formulate the problem of clinical trial design into an optimization problem involving high-dimensional integration, and propose a novel computational solution based on Monte Carlo and smoothing methods. Our method utilizes the modern techniques of general purpose computing on graphics processing units for large-scale parallel computing. Compared to a published method in three-dimensional problems, our approach is more accurate and 133 times faster. This advantage increases when dimensionality increases. Our method is scalable to higher dimensional problems since the precision bound of our estimated study power is a finite number not affected by dimensionality. To design clinical trials incorporating the potential biomarkers, users can use our software "DesignCTPB". This software can be found on Github and will be available as an R package on CRAN. Although our research is motivated by the design of clinical trials, the method can be used widely to solve other optimization problems involving high-dimensional integration.

Lectin-based biosensors as analytical tools for clinical oncology

The review focus on the use of lectin-based biosensors in the oncology field, and ponders the potentialities of using these devices as analytical tools to monitor the levels of cancer glycobiomarkers in biological fluids, helping in the diagnosis, prognosis and treatment assessment. Several examples of lectin-based biosensors directed for cancer biomarkers are described and discussed, and their potential application in the clinic is considered, taking into account their analytical features, advantages and performance in sample analysis. Technical and practical aspects in the construction process, which are specific for lectin biosensors, are debated, as well as the requirements in sample collection and processing, and biosensor validation. Today's challenges for real implementation of these devices in the clinic are presented, along with the future trends in the field.

Modulation of Cytotoxicity by Transcription-Coupled Nucleotide Excision Repair Is Independent of the Requirement for Bioactivation of Acylfulvene

Bioactivation as well as DNA repair affects the susceptibility of cancer cells to the action of DNA-alkylating chemotherapeutic drugs. However, information is limited with regard to the relative contributions of these processes to the biological outcome of metabolically activated DNA alkylating agents. We evaluated the influence of cellular bioactivation capacity and DNA repair on cytotoxicity of the DNA alkylating agent acylfulvene (AF). We compared the cytotoxicity and RNA synthesis inhibition by AF and its synthetic activated analogue iso-M0 in a panel of fibroblast cell lines with deficiencies in transcription-coupled (TC-NER) or global genome nucleotide excision repair (GG-NER). We related these data to the inherent bioactivation capacity of each cell type on the basis of mRNA levels. We demonstrated that specific inactivation of TC-NER by siRNA had the largest positive impact on AF activity in a cancer cell line. These findings establish that transcription-coupled DNA repair reduces cellular sensitivity to AF, independent of the requirement for bioactivation.

HEXIM1 as a Robust Pharmacodynamic Marker for Monitoring Target Engagement of BET Family Bromodomain Inhibitors in Tumors and Surrogate Tissues

An increasing number of BET family protein inhibitors have recently entered clinical trials. It has been reported that attempts of monitoring target engagement of the BET bromodomain inhibitor OTX015 using literature-described putative pharmacodynamic markers, such as c-Myc, BRD2, etc., failed to detect pharmacodynamic marker responses in AML patients treated at active dose and those with clinical responses. Here, we report the identification and characterization of HEXIM1 and other genes as robust pharmacodynamic markers for BET inhibitors. Global gene expression profiling studies were carried out using cancer cells and surrogate tissues, such as whole blood and skin, to identify genes that are modulated by BET family proteins. Candidate markers were further characterized for concentration- and time-dependent responses to the BET inhibitor ABBV-075 in vitro and in vivo HEXIM1 was found to be the only gene that exhibited robust and consistent modulation by BET inhibitors across multiple cancer indications and surrogate tissues. Markers such as SERPINI1, ZCCHC24, and ZMYND8 were modulated by ABBV-075 and other BET inhibitors across cancer cell lines and xenograft tumors but not in blood and skin. Significant downregulation of c-Myc, a well-publicized target of BET inhibitors, was largely restricted to hematologic cancer cell lines. Incorporating well-characterized pharmacodynamic markers, such as HEXIM1 and other genes described here, can provide a better understanding of potential efficacy and toxicity associated with inhibiting BET family proteins and informs early clinical decisions on BET inhibitor development programs. Mol Cancer Ther; 16(2); 388-96. ©2016 AACR.