Personalized Medicine In Oncology Research Articles

Prognostic value of CDKN2A in head and neck squamous cell carcinoma via pathomics and machine learning.

This study aims to enhance the prognosis prediction of Head and Neck Squamous Cell Carcinoma (HNSCC) by employing artificial intelligence (AI) to analyse CDKN2A gene expression from pathology images, directly correlating with patient outcomes. Our approach introduces a novel AI-driven pathomics framework, delineating a more precise relationship between CDKN2A expression and survival rates compared to previous studies. Utilizing 475 HNSCC cases from the TCGA database, we stratified patients into high-risk and low-risk groups based on CDKN2A expression thresholds. Through pathomics analysis of 271 cases with available slides, we extracted 465 distinctive features to construct a Gradient Boosting Machine (GBM) model. This model was then employed to compute Pathomics scores (PS), predicting CDKN2A expression levels with validation for accuracy and pathway association analysis. Our study demonstrates a significant correlation between higher CDKN2A expression and improved median overall survival (66.73 months for high expression vs. 42.97 months for low expression, p = 0.013), establishing CDKN2A's prognostic value. The pathomic model exhibited exceptional predictive accuracy (training AUC: 0.806; validation AUC: 0.710) and identified a strong link between higher Pathomics scores and cell cycle activation pathways. Validation through tissue microarray corroborated the predictive capacity of our model. Confirming CDKN2A as a crucial prognostic marker in HNSCC, this study advances the existing literature by implementing an AI-driven pathomics analysis for gene expression evaluation. This innovative methodology offers a cost-efficient and non-invasive alternative to traditional diagnostic procedures, potentially revolutionizing personalized medicine in oncology.

Synergistic applications of cyclodextrin-based systems and metal-organic frameworks in transdermal drug delivery for skin cancer therapy.

This review article explores the innovative field of eco-friendly cyclodextrin-based coordination polymers and metal-organic frameworks (MOFs) for transdermal drug delivery in the case of skin cancer therapy. We critically examine the significant advancements in developing these nanocarriers, with a focus on their unique properties such as biocompatibility, targeted drug release, and enhanced skin permeability. These attributes are instrumental in addressing the limitations inherent in traditional skin cancer treatments and represent a paradigm shift towards more effective and patient-friendly therapeutic approaches. Furthermore, we discuss the challenges faced in optimizing the synthesis process for large-scale production while ensuring environmental sustainability. The review also emphasizes the immense potential for clinical applications of these nanocarriers in skin cancer therapy, highlighting their role in facilitating targeted, controlled drug release which minimizes systemic side effects. Future clinical applications could see these nanocarriers being customized to individual patient profiles, potentially revolutionizing personalized medicine in oncology. With further research and clinical trials, these nanocarriers hold the promise of transforming the landscape of skin cancer treatment. With this study, we aim to provide a comprehensive overview of the current state of research in this field and outline future directions for advancing the development and clinical application of these innovative nanocarriers.

A Network-Based Mixed Methods Approach to Analyze Current Perspectives on Personalized Oncological Medicine in Austria.

Personalized medicine (PM) to tailor healthcare (HC) to the individual, is a promising but challenging concept. So far, no study exists investigating stakeholders’ perspectives on PM in oncology in Austria potentially hindering implementation, which was the aim of this study. We performed semi-structured interviews among experts (n = 14) and cancer patients (n = 2) of the Vienna General Hospital and the Medical University of Vienna and analyzed them by a mixed methods network theoretical approach. Study results show a great variety of topics addressed by the interviewees. Clear differences in the topic selection between patients and experts could be observed. Patient-doctor relationship was the most prominent theme among experts, whereas HC systems and public health in PM was more relevant for the patients. Although promising new molecular pathology methods were explicitly mentioned, the experts believed that their practical implementation and the implementation of PM in standard care will take a long time in Austria. A variety of concerns regarding PM were mentioned by the experts, including communication issues and knowledge gaps. Besides important insights into the current situation of PM in Austria, the study has shown that network theory is a powerful tool for analyzing qualitative interview data.

AOJ-1 - Precision Medicine: the Singapore Story

The last few years has seen the rapidly development of newer, faster and more cost effective methods being developed to better understand the unique characteristics of each patient's disease (tumor). This has not only led to better understanding but also increased use of targeted agents with a higher probability of efficacy than our 'blind' approach used in the past. However there still remain challenges in this approach as many of the mutations found may just be noise and not drivers of the particular cancer. In this talk I hope to discuss some new technologies being used in my clinical practice in Singapore and also new technologies that are being developed here that may further refine the way we practice personalised medicine in oncology. I will also describe regional collaborative efforts that have been successful and can serve as great examples to how we can work together to develop this field in Asia.

Early Prediction of Disease Progression in Small Cell Lung Cancer: Toward Model-Based Personalized Medicine in Oncology.

Predictive biomarkers can play a key role in individualized disease monitoring. Unfortunately, the use of biomarkers in clinical settings has thus far been limited. We have previously shown that mechanism-based pharmacokinetic/pharmacodynamic modeling enables integration of nonvalidated biomarker data to provide predictive model-based biomarkers for response classification. The biomarker model we developed incorporates an underlying latent variable (disease) representing (unobserved) tumor size dynamics, which is assumed to drive biomarker production and to be influenced by exposure to treatment. Here, we show that by integrating CT scan data, the population model can be expanded to include patient outcome. Moreover, we show that in conjunction with routine medical monitoring data, the population model can support accurate individual predictions of outcome. Our combined model predicts that a change in disease of 29.2% (relative standard error 20%) between two consecutive CT scans (i.e., 6-8 weeks) gives a probability of disease progression of 50%. We apply this framework to an external dataset containing biomarker data from 22 small cell lung cancer patients (four patients progressing during follow-up). Using only data up until the end of treatment (a total of 137 lactate dehydrogenase and 77 neuron-specific enolase observations), the statistical framework prospectively identified 75% of the individuals as having a predictable outcome in follow-up visits. This included two of the four patients who eventually progressed. In all identified individuals, the model-predicted outcomes matched the observed outcomes. This framework allows at risk patients to be identified early and therapeutic intervention/monitoring to be adjusted individually, which may improve overall patient survival.

Personalized medicine in oncology: ethical implications for the delivery of healthcare.

While personalized medicine brings benefits for the treatment of cancer, there are still key ethical issues at stake in developing personalized medicine in oncology. We propose an ethical analysis of personalized medicine in oncology that highlights the particularities of cancer care, critically assesses the scientific advances behind personalized medicine in oncology and emphasizes fairness in resource allocation in the delivery of personalized healthcare. This allows for a broader understanding of the real impacts on both recipients and the healthcare system.

Working towards personalization of Medicine: Genomics in 2014.

Working towards personalization of Medicine: Genomics in 2014.

Editorial (Thematic Issue: Personalized Medicine in Oncology, the Potential Role of Nuclear Medicine Imaging)

Editorial (Thematic Issue: Personalized Medicine in Oncology, the Potential Role of Nuclear Medicine Imaging)

The Price of Hope: Personalized Medicine in 2014

In 1994, shortly after taking my first faculty position, I was invited to give Medical Grand Rounds at my new institution. The title of my talk was “Novel Therapeutic Applications of Cancer Biology.” Today, the title would be “Personalized Medicine in Oncology.” My Kodachrome slides covered numerous biologically rational molecular targets and their proposed treatments, including RAS (farnesyl transferase inhibitors), MYC (retinoic acid—my own research interest at the time), P53 (gene therapy), BCL2 (antisense oligonucleotides), and growth factor receptors (receptor antagonists), only the last of which has proven to have any clinical utility. At that time, the notion of targeting cancer cells based on their molecular derangements was being evaluated mainly in preclinical studies and early-phase clinical trials. Much has changed in the past 20 years. Today, most people don’t know what a Kodachrome slide is, and, more importantly, several molecularly targeted anticancer agents have demonstrated dramatic clinical activity in molecularly defined patient populations. The “poster child” for targeted therapy is imatinib, which has kept many patients with chronic myelogenous leukemia (CML) in remission for well over a decade. But most cancers are not as molecularly homogeneous as chronic-phase CML. The results achieved with targeted agents in solid tumors have been far more modest, because of widespread tumor cell heterogeneity and energetic exploitation of alternative growth and survival pathways leading to the relatively rapid emergence of resistance. Nonetheless, we have learned that defining patient subsets based on predictive biomarkers allows us to target specific treatments to those who will benefit most, while avoiding undue toxicity to those who will not benefit. In thoracic oncology, we are finally starting to understand how to use epidermal growth factor receptor (EGFR) inhibitors most effectively, thanks to the commitment of thousands of patients and many investigators who participated in relevant clinical trials over the past decade. Being enticed by the marvels of molecular technology and the promise of personalized medicine is easy, particularly when confronted by the limitations of current therapy. I recently received an e-mail from the husband of a woman with advanced non–small cell lung cancer (NSCLC) harboring wild-type EGFR, ALK, and ROS1. The plaintive tone was all too familiar. He had done his online research and, to his credit, was turning to the science of molecular tumor profiling rather than to laetrile. For only $40,000, the e-mail said, he could ensure that his wife would get “the best and most appropriate care.” For him, this was the price of hope. But separating the hope from the hype is important, as is clarifying the line between research and clinical reality. A rapidly growing number of commercial vendors offer molecular tumor profiling services that purportedly guide personalized therapy. However, the number of predictive biomarkers and targeted therapies proven to benefit patients with solid tumors remains relatively small, a fact that is not clearly evident from current news stories or most oncology journals. The promise of personalized therapy is not yet commensurate with the hype it has generated. But our patients only hear the hype, from both the lay press and direct-to-consumer advertising. The FDA is to be applauded for the recent warning regarding the need to validate the benefits of direct-to-consumer testing. As academic oncologists, we could also present a more balanced reality when interacting with the press. The impact of this hype extends beyond our patients. Many of the calls I receive from community oncologists reflect a growing confusion about the prevailing standard Gregory P. Kalemkerian, MD

Personalized Medicine in Screening for Malignant Disease: A Review of Methods and Applications

Personalized medicine (PM) is currently a hot topic in the professional world. It is often called the medicine of the future and has already achieved resounding success in the area of targeted therapy. Nevertheless, integration of the concepts of PM into routine clinical practice is slow. This review is intended to give an overview of current and potential applications of PM in oncology. PM could soon play a decisive role, especially in screening. The relevance of PM in screening was examined in the case of four common cancers (colorectal cancer, lung cancer, breast cancer, and prostate cancer). A literature search was performed. This showed that biomarkers in particular play a crucial role in screening. In summary, it can be emphasized that there are already numerous known promising biomarkers in malignant disease. This results in several possibilities for individualizing and revolutionizing screening.

A Structural Analysis of Glycosaminoglycans from Lethal and Nonlethal Breast Cancer Tissues: Toward a Novel Class of Theragnostics for Personalized Medicine in Oncology?

Cancer is one of the leading noncommunicable diseases that vastly impacts both developed and developing countries. Truly innovative diagnostics that inform disease susceptibility, prognosis, and/or response to treatment (theragnostics) are seriously needed for global public health and personalized medicine for patients with cancer. This study examined the structure and content of glycosaminoglycans (GAGs) in lethal and nonlethal breast cancer tissues from six patients. The glycosaminoglycan content isolated from tissue containing lethal cancer tumors was approximately twice that of other tissues. Molecular weight analysis showed that glycosaminoglycans from cancerous tissue had a longer weight average chain length by an average of five disaccharide units, an increase of approximately 15%. Dissacharide analysis found differences in sulfation patterns between cancerous and normal tissues, as well as sulfation differences in GAG chains isolated from patients with lethal and nonlethal cancer. Specifically, cancerous tissue showed an increase in sulfation at the "6S" position of CS chains and an increase in the levels of the HS disaccharide NSCS. Patients with lethal cancer showed a decrease in HS sulfation, with lower levels of "6S" and higher levels of the unsulfated "0S" disaccharide. Although these findings come from a limited sample size, they indicate that structural changes in GAGs exist between cancerous and noncancerous tissues and between tissues from patients with highly metastatic cancer and cancer that was successfully treated by chemotherapy. Based on these findings, we hypothesize that (1) there are putative changes in the body's construction of GAGs as tissue becomes cancerous; (2) there may be innate structural person-to-person variations in GAG composition that facilitate the metastasis of tumors in some patients when they develop cancer.