Genomics and the History of Precision Oncology

Transforming Health Care Landscapes: The Lever of Radiology Research and Innovation on Emerging Markets Poised for Aggressive Growth

Advances in radiology are crucial not only to the future of the field but to medicine as a whole. Here, we present three emerging areas of medicine that are poised to change how health care is delivered-hospital at home, artificial intelligence, and precision medicine-and illustrate how advances in radiological tools and technologies are helping to fuel the growth of these markets in the United States and across the globe.

TLE4 downregulation identified by WGCNA and machine learning algorithm promotes papillary thyroid carcinoma progression via activating JAK/STAT pathway

Background: Papillary Thyroid Carcinoma (PTC), a common type of thyroid cancer, has a pathogenesis that is not fully understood. This study utilizes a range of public databases, sophisticated bioinformatics tools, and empirical approaches to explore the key genetic components and pathways implicated in PTC, particularly concentrating on the Transducin-Like Enhancer of Split 4 (TLE4) gene. Methods: Public databases such as TCGA and GEO were utilized to conduct differential gene expression analysis in PTC. Hub genes were identified using Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning techniques, including Random Forest, LASSO regression, and SVM-RFE, were employed for biomarker identification. The clinical impact of the TLE4 gene was assessed in terms of diagnostic accuracy, prognostic value, and its functional enrichment analysis in PTC. Additionally, the study focused on understanding the role of TLE4 in the dynamics of immune cell infiltration, gene function enhancement, and behaviors of PTC cells like growth, migration, and invasion. To complement these analyses, in vivo studies were performed using a xenograft mouse model. Results: 244 genes with significant differential expression across various databases were identified. WGCNA indicated a strong link between specific gene modules and PTC. Machine learning analysis brought the TLE4 gene into focus as a key biomarker. Bioinformatics studies verified that TLE4 expression is lower in PTC, linking it to immune cell infiltration and the JAK-STAT signaling pathways. Experimental data revealed that decreased TLE4 expression in PTC cell lines leads to enhanced cell growth, migration, invasion, and activates the JAK/STAT pathway. In contrast, TLE4 overexpression in these cells inhibited tumor growth and metastasis. Conclusions: This study sheds light on TLE4's crucial role in PTC pathogenesis, positioning it as a potential biomarker and target for therapy. The integration of multi-omics data and advanced analytical methods provides a robust framework for understanding PTC at a molecular level, potentially guiding personalized treatment strategies.

A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets. ENLIGHT-DeepPT successfully predicts true responders in five independent patient cohorts involving four different treatments spanning six cancer types, with an overall odds ratio of 2.28 and a 39.5% increased response rate among predicted responders versus the baseline rate. Notably, its prediction accuracy, obtained without any training on the treatment data, is comparable to that achieved by directly predicting the response from the images, which requires specific training on the treatment evaluation cohorts.

Tumor-Agnostic Therapy-The Final Step Forward in the Cure for Human Neoplasms?

Cancer accounted for 10 million deaths in 2020, nearly one in every six deaths annually. Despite advancements, the contemporary clinical management of human neoplasms faces a number of challenges. Surgical removal of tumor tissues is often not possible technically, while radiation and chemotherapy pose the risk of damaging healthy cells, tissues, and organs, presenting complex clinical challenges. These require a paradigm shift in developing new therapeutic modalities moving towards a more personalized and targeted approach. The tumor-agnostic philosophy, one of these new modalities, focuses on characteristic molecular signatures of transformed cells independently of their traditional histopathological classification. These include commonly occurring DNA aberrations in cancer cells, shared metabolic features of their homeostasis or immune evasion measures of the tumor tissues. The first dedicated, FDA-approved tumor-agnostic agent's profound progression-free survival of 78% in mismatch repair-deficient colorectal cancer paved the way for the accelerated FDA approvals of novel tumor-agnostic therapeutic compounds. Here, we review the historical background, current status, and future perspectives of this new era of clinical oncology.

Nanoparticle-Mediated Drug Delivery Systems for Precision Targeting in Oncology

Nanotechnology has emerged as a transformative force in oncology, facilitating advancements in site-specific cancer therapy and personalized oncomedicine. The development of nanomedicines explicitly targeted to cancer cells represents a pivotal breakthrough, allowing the development of precise interventions. These cancer-cell-targeted nanomedicines operate within the intricate milieu of the tumour microenvironment, further enhancing their therapeutic efficacy. This comprehensive review provides a contemporary perspective on precision cancer medicine and underscores the critical role of nanotechnology in advancing site-specific cancer therapy and personalized oncomedicine. It explores the categorization of nanoparticle types, distinguishing between organic and inorganic variants, and examines their significance in the targeted delivery of anticancer drugs. Current insights into the strategies for developing actively targeted nanomedicines across various cancer types are also provided, thus addressing relevant challenges associated with drug delivery barriers. Promising future directions in personalized cancer nanomedicine approaches are delivered, emphasising the imperative for continued optimization of nanocarriers in precision cancer medicine. The discussion underscores translational research's need to enhance cancer patients' outcomes by refining nanocarrier technologies in nanotechnology-driven, site-specific cancer therapy.

Plasma-Derived Cell-Free DNA as a Biomarker for Early Detection, Prognostication, and Personalized Treatment of Urothelial Carcinoma

Bladder cancer (BC) is one of the most common malignancies in the United States, with over 80,000 new cases and 16,000 deaths each year. Urothelial carcinoma (UC) is the most common histology and accounts for 90% of cases. BC management is complicated by recurrence rates of over 50% in both muscle-invasive and non-muscle-invasive bladder cancer. As such, the American Urological Association (AUA) recommends that patients undergo close surveillance during and after treatment. This surveillance is in the form of cystoscopy or imaging tests, which can be invasive and costly tests. Considering this, there have been recent pushes to find complements to bladder cancer surveillance. Cell-free DNA (CfDNA), or DNA released from dying cells, and circulating tumor DNA (ctDNA), or mutated DNA released from tumor cells, can be analyzed to detect and characterize the molecular characteristics of tumors. Research has shown promising results for ctDNA use in the BC care realm. A PubMed literature review was performed finding studies discussing cfDNA and ctDNA in BC detection, prognostication, and monitoring for recurrence. Keywords used included bladder cancer, cell-free DNA, circulating tumor DNA, urothelial carcinoma, and liquid biopsy. Studies show that ctDNA can serve as prognostic indicators of both early- and late-stage BC, aid in risk stratification prior to major surgery, assist in detection of disease progression and metastatic relapse, and can assess patients who may respond to immunotherapy. The benefit of ctDNA is not confined to BC, as studies have also suggested its promise as a biomarker for neoadjuvant chemotherapy in upper-tract UC. However, there are some limitations to ctDNA that require improvements in ctDNA-specific detection methods and BC-specific mutations before widespread utilization can be achieved. Further prospective, randomized trials are needed to elucidate the true potential ctDNA has in advancements in BC care.

Saliva as a potential non-invasive liquid biopsy for early and easy diagnosis/prognosis of head and neck cancer

Head and neck squamous cell carcinomas (HNSCCs) are the most devastating diseases in India and southeast Asia. It is a preventable and curable disease if detected early. Tobacco and alcohol consumption are the two major risk-factors but infection of high-risk HPVs are also associated with development of predominantly oral and oropharyngeal carcinomas. Interestingly, unlike cervical cancer, HPV-induced HNSCCs show good prognosis and better survival in contrast, majority of tobacco-associated HPV-ve HNSCCs are highly aggressive with poor clinical outcome. Biomarker analysis in circulatory body-fluids for early cancer diagnosis, prognosis and treatment monitoring are becoming important in clinical practice. Early diagnosis using non-invasive saliva for oral or other diseases plays an important role in successful treatment and better prognosis. Saliva mirrors the body's state of health as it comes into direct contact with oral lesions and needs no trained manpower to collect, making it a suitable bio-fluid of choice for screening. Saliva can be used to detect not only virus, bacteria and other biomarkers but variety of molecular and genetic markers for an early detection, treatment and monitoring cancer and other diseases. The performance of saliva-based diagnostics are reported to be highly (≥95 %) sensitive and specific indicating the test's ability to correctly identify true positive or negative cases. This review focuses on the potentials of saliva in the early detection of not only HPV or other pathogens but also identification of highly reliable gene mutations, oral-microbiomes, metabolites, salivary cytokines, non-coding RNAs and exosomal miRNAs. It also discusses the importance of saliva as a reliable, cost-effective and an easy alternative to invasive procedures.

Expression of Soluble Form of Aurora A as a Predictive Factor for Neoadjuvant Therapy in Breast Cancer Patients: A Single-Center Pilot Study

PURPOSE

To search for new predictive breast cancer biomarkers. We analyzed the serum concentrations of biomarkers involved in carcinogenesis, which can also be targeted by therapy.

METHODS

In a single-center prospective study, the serum levels of Aurora A, thymidine kinase 1, and human epidermal growth factor receptor type 3 (HER3) were determined in 119 women with BC before neoadjuvant treatment using ELISA kits.

RESULTS

The following clinical data were analyzed: age; TNM; the expression of ER, PGR, HER2, and Ki67; histological grade (G); and the response to neoadjuvant treatment (NAT) in the residual tumor burden classification (RCB). A complete pathological response (pCR) was achieved after NAT in 41 patients (34%). The highest proportion of the patients with a confirmed pCR was found for triple negative breast cancer (TNBC) (62.5%); non-luminal HER2-positive (52.6%) cancer subtypes (p = 0.0003); and in the G3 group (50%; p = 0.0078). The patients with higher levels of Aurora A were more likely to achieve pCR (p = 0.039). In the multivariate analysis, the serum Aurora A levels ≥ 4.75 ng/mL correlated with a higher rate of pCR (OR: 3.5; 95% CI: 1.2-10.1; p = 0.023).

CONCLUSIONS

We showed that in a biologically heterogeneous group of BC patients, the pretreatment serum Aurora A levels were of significant value in predicting the response to NAT.

Prediction of cancer treatment response from histopathology images through imputed transcriptomics

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Our key contribution is showing that ENLIGHT-DeepPT successfully predicts true responders in five independent patients' cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, which needs to be trained and tested on the same cohort. ENLIGHT-DeepPT future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries.

Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation

Post-translational modifications (PTMs) play key roles in regulating cell signaling and physiology in both normal and cancer cells. Advances in mass spectrometry enable high-throughput, accurate, and sensitive measurement of PTM levels to better understand their role, prevalence, and crosstalk. Here, we analyze the largest collection of proteogenomics data from 1,110 patients with PTM profiles across 11 cancer types (10 from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium [CPTAC]). Our study reveals pan-cancer patterns of changes in protein acetylation and phosphorylation involved in hallmark cancer processes. These patterns revealed subsets of tumors, from different cancer types, including those with dysregulated DNA repair driven by phosphorylation, altered metabolic regulation associated with immune response driven by acetylation, affected kinase specificity by crosstalk between acetylation and phosphorylation, and modified histone regulation. Overall, this resource highlights the rich biology governed by PTMs and exposes potential new therapeutic avenues.

Trends in the approval of cancer therapies by the FDA in the twenty-first century

The cancer treatment landscape has changed dramatically since the turn of the century, resulting in substantial improvements in outcomes for patients. This Review summarizes trends in the approval of oncology therapeutic products by the United States Food and Drug Administration (FDA) from January 2000 to October 2022, based on a categorization of these products by their mechanism of action and primary target. Notably, the rate of oncology indication approvals has increased in this time, driven by approvals for targeted therapies, as has the rate of introduction of new therapeutic approaches. Kinase inhibitors are the dominant product class by number of approved products and indications, yet immune checkpoint inhibitors have the second most approvals despite not entering the market until 2011. Other trends include a slight increase in the share of approvals for biomarker-defined populations and the emergence of tumour-site-agnostic approvals. Finally, we consider the implications of the trends for the future of oncology therapeutic product development, including the impact of novel therapeutic approaches and technologies.

Agnostic Approvals in Oncology: Getting the Right Drug to the Right Patient with the Right Genomics

(1) Background: The oncology field has drastically changed with the advent of precision medicine, led by the discovery of druggable genes or immune targets assessed through next-generation sequencing. Biomarker-based treatments are increasingly emerging, and currently, six tissue-agnostic therapies are FDA-approved. (2) Methods: We performed a review of the literature and reported the trials that led to the approval of tissue-agnostic treatments and ongoing clinical trials currently investigating novel biomarker-based approaches. (3) Results: We discussed the approval of agnostic treatments: pembrolizumab and dostarlimab for MMRd/MSI-H, pembrolizumab for TMB-H, larotrectinib and entrectinib for NTRK-fusions, dabrafenib plus trametinib for BRAF V600E mutation, and selpercatinib for RET fusions. In addition, we reported novel clinical trials of biomarker-based approaches, including ALK, HER2, FGFR, and NRG1. (4) Conclusions: Precision medicine is constantly evolving, and with the improvement of diagnostic tools that allow a wider genomic definition of the tumor, tissue-agnostic targeted therapies are a promising treatment strategy tailored to the specific tumor genomic profile, leading to improved survival outcomes.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

BACKGROUND

Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers.

METHODS

We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort.

FINDINGS

Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy.

CONCLUSIONS

ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome.

FUNDING

This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

Programmatic Efforts Increase Adoption of Genomic Precision Medicine in Cancer Care in a Community Cancer Center

PURPOSE

The adoption of precision medicine (PMed) depends on the critical curation of data and interpretation of genomic results. Herein, we sought to study the effect of a coordinated multidisciplinary program to assess results in a community cancer center clinic.

METHODS

In a retrospective review from July 2018 to July 2021, we analyzed the implementation of a multidisciplinary PMed program in a tertiary referral community cancer center. Germline genetics test results have been reviewed since 2017.

RESULTS

A total of 3,131 tumor samples were analyzed by large panel somatic genomic testing through commercial laboratories during the study period. The number of reviewed cases rose from 661 in the first year to 1,532 in year 3. Additional recommendations beyond what was reported by the commercial laboratory were made in 42.9% of cases. Referrals to the hereditary cancer program for germline testing increased by 32% from the 2017 baseline. Process improvement efforts reduced the rate of DNA quantity nonsufficient for testing to 3.3% compared with a national average of 4.89%. The average time from receipt of orders to issuing of a report of the somatic panel was 15.5 days, compared with 19.1 days for other institutions using the same laboratory. The PMed team has been critical in support of clinical research by assisting in trial procurement and feasibility assessment to the identification of patients for clinical trials.

CONCLUSION

The use of somatic genomic testing is increasing at our cancer center. Education and in-depth analysis of the data are valued by cancer physicians. The development and implementation of a PMed program has demonstrated improved physicians' understanding of molecular testing, resulting in improved outcomes for patients.

Inhibition of cytoplasmic EZH2 induces antitumor activity through stabilization of the DLC1 tumor suppressor protein

mRNA expression of the DLC1 tumor suppressor gene is downregulated in many lung cancers and their derived cell lines, with DLC1 protein levels being low or absent. Although the role of increased EZH2 methyltransferase in cancer is usually attributed to its histone methylation, we unexpectedly observed that post-translational destabilization of DLC1 protein is common and attributable to its methylation by cytoplasmic EZH2, leading to CUL-4A ubiquitin-dependent proteasomal degradation of DLC1. Furthermore, siRNA knockdown of KRAS in several lines increases DLC1 protein, associated with a drastic reduction in cytoplasmic EZH2. Pharmacologic inhibition of EZH2, CUL-4A, or the proteasome can increase the steady-state level of DLC1 protein, whose tumor suppressor activity is further increased by AKT and/or SRC kinase inhibitors, which reverse the direct phosphorylation of DLC1 by these kinases. These rational drug combinations induce potent tumor growth inhibition, with markers of apoptosis and senescence, that is highly dependent on DLC1 protein.

The next horizon in precision oncology: Proteogenomics to inform cancer diagnosis and treatment

When it comes to precision oncology, proteogenomics may provide better prospects to the clinical characterization of tumors, help make a more accurate diagnosis of cancer, and improve treatment for patients with cancer. This perspective describes the significant contributions of The Cancer Genome Atlas and the Clinical Proteomic Tumor Analysis Consortium to precision oncology and makes the case that proteogenomics needs to be fully integrated into clinical trials and patient care in order for precision oncology to deliver the right cancer treatment to the right patient at the right dose and at the right time.

Druggable genome and precision medicine in cancer: current challenges

The past decades have seen tremendous developments with respect to "specific" therapeutics that target key signaling molecules to conquer cancer. The key advancements with multiomics technologies, especially genomics, have allowed physicians and molecular oncologists to design "tailor-made" solutions to the specific oncogenes that are deregulated in individual patients, a strategy which has turned out to be successful though the patients quickly develop resistance. The swift integration of multidisciplinary approaches has led to the development of "next generation" therapeutics and, with synergistic therapeutic regimes combined with immune checkpoint inhibitors to reactivate the dampened immune response, has provided the much-needed promise for cancer patients. Despite these advances, a large portion of the druggable genome remains understudied, and the role of druggable genome in the immune system needs further attention. Establishment of patient-derived organoid models has fastened the preclinical validation of novel therapeutics for swift clinical translation. We summarized the current advances and challenges and also stress the importance of biobanking and collection of longitudinal data sets with structured clinical information, as well as the critical role these "high content data sets" will play in designing new therapeutic regimes in a tailor-made fashion.

Loose Regulatory Standards Portend a New Era of Imprecision Oncology

Precision oncology has revolutionized the therapeutic landscape of oncology and is a goal for cancer drug development. However, lenient drug approvals by the United States Food and Drug Administration under the auspices of precision oncology are setting up this therapeutic approach to fail. In this commentary, I review two recent FDA drug approvals (pembrolizumab for tumor mutation burden-high solid tumors and olaparib for castration-resistant prostate cancer with deleterious homologous recombination repair mutations) where the FDA indication is broader than the studied population. I explain how these broad approvals stray from principles of precision oncology and can cause harm to patients.

Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.