Debunking the Delusion That Precision Oncology Is an Illusion

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

A role for microfluidic systems in precision medicine

Precision oncology continues to challenge the "one-size-fits-all" dogma. Under the precision oncology banner, cancer patients are screened for molecular tumor alterations that predict treatment response, ideally leading to optimal treatments. Functional assays that directly evaluate treatment efficacy on the patient's cells offer an alternative and complementary tool to improve the accuracy of precision oncology. Unfortunately, traditional Petri dish-based assays overlook much tumor complexity, limiting their potential as predictive functional biomarkers. Here, we review past applications of microfluidic systems for precision medicine and discuss the present and potential future role of functional microfluidic assays as treatment predictors.

Current Advances and Limitations of Deep Learning in Anticancer Drug Sensitivity Prediction

Anticancer drug screening can accelerate drug discovery to save the lives of cancer patients, but cancer heterogeneity makes this screening challenging. The prediction of anticancer drug sensitivity is useful for anticancer drug development and the identification of biomarkers of drug sensitivity. Deep learning, as a branch of machine learning, is an important aspect of in silico research. Its outstanding computational performance means that it has been used for many biomedical purposes, such as medical image interpretation, biological sequence analysis, and drug discovery. Several studies have predicted anticancer drug sensitivity based on deep learning algorithms. The field of deep learning has made progress regarding model performance and multi-omics data integration. However, deep learning is limited by the number of studies performed and data sources available, so it is not perfect as a pre-clinical approach for use in the anticancer drug screening process. Improving the performance of deep learning models is a pressing issue for researchers. In this review, we introduce the research of anticancer drug sensitivity prediction and the use of deep learning in this research area. To provide a reference for future research, we also review some common data sources and machine learning methods. Lastly, we discuss the advantages and disadvantages of deep learning, as well as the limitations and future perspectives regarding this approach.

Transcriptomics and solid tumors: The next frontier in precision cancer medicine

Transcriptomics, which encompasses assessments of alternative splicing and alternative polyadenylation, identification of fusion transcripts, explorations of noncoding RNAs, transcript annotation, and discovery of novel transcripts, is a valuable tool for understanding cancer mechanisms and identifying biomarkers. Recent advances in high-throughput technologies have enabled large-scale gene expression profiling. Importantly, RNA expression profiling of tumor tissue has been successfully used to determine clinically actionable molecular alterations. The WINTHER precision medicine clinical trial was the first prospective trial in diverse solid malignancies that assessed both genomics and transcriptomics to match treatments to specific molecular alterations. The use of transcriptome analysis in WINTHER and other trials increased the number of targetable -omic changes compared to genomic profiling alone. Other applications of transcriptomics involve the evaluation of tumor and circulating noncoding RNAs as predictive and prognostic biomarkers, the improvement of risk stratification by the use of prognostic and predictive multigene assays, the identification of fusion transcripts that drive tumors, and an improved understanding of the impact of DNA changes as some genomic alterations are silenced at the RNA level. Finally, RNA sequencing and gene expression analysis have been incorporated into clinical trials to identify markers predicting response to immunotherapy. Many issues regarding the complexity of the analysis, its reproducibility and variability, and the interpretation of the results still need to be addressed. The integration of transcriptomics with genomics, proteomics, epigenetics, and tumor immune profiling will improve biomarker discovery and our understanding of disease mechanisms and, thereby, accelerate the implementation of precision oncology.

High Expression of the Long Noncoding RNA SNHG15 in Cancer Tissue Samples Predicts an Unfavorable Prognosis of Cancer Patients: A Meta-Analysis

Background

Although the prognostic value of lncRNA small nucleolar RNA host gene 15 (SNHG15) expression in cancers has been evaluated in many studies, the results remain controversial. This meta-analysis aimed to clarify the role of SNHG15 in the prognosis of different cancer patients.

Materials and Methods

Eligible studies were selected from PubMed, PMC, EMBASE, Web of Science, and Cochrane Library according to the inclusion and exclusion criteria (up to December 20, 2019). The primary outcome was overall survival (OS) and recurrence-free survival (RFS). The secondary outcome was other clinicopathological parameters (including advanced TNM stage, lymph node metastasis, distant metastases, and gender). The Cancer Genome Atlas (TCGA) dataset was used to verify the analysis results.

Results

Eleven eligible studies were eventually included, involving 9 different types of cancer and 1,079 patients. The high expression of SNHG15 was indicative of a significantly poor OS of cancer patients (HR = 1.96, 95% CI = 1.55–2.47, P < 0.00001). Subgroup analysis showed that the high expression of SNHG15 was associated with a significantly poor OS of patients with digestive cancer (HR = 1.91, 95% CI = 1.38–2.66, P=0.0001), but not lung cancer (HR = 1.83, 95% CI = 0.89–3.76, P=0.010). The RFS of patients with high expression of SNHG15 was shorter than that of patients with low expression of SNHG15 (HR = 2.03, 95% CI = 1.46–2.83, P < 0.00001). In addition, high SNHG15 expression level was significantly correlated with later TNM stage (OR = 3.05, 95% CI = 2.31–4.02, P < 0.00001), lymphatic metastasis (OR = 3.20, 95% CI = 2.30–4.45, P < 0.00001), and distant metastasis (OR = 5.05, 95% CI = 2.15–11.85, P=0.0002). The TCGA verification results were consistent with those observed in our meta-analysis.

Conclusion

High expression of the long noncoding RNA SNHG15 in cancer tissue samples predicts an unfavorable prognosis for cancer patients. LncRNA SNHG15 can be used as an adverse prognostic biomarker for cancer patients.

Shifting, overlapping and expanding use of "precision oncology" terminology: a retrospective literature analysis

IMPORTANCE

The terms "personalized oncology" and "precision oncology" have increased in usage and have generated considerable traction in terms of public attention and research funding. To our knowledge, no prior study has as thoroughly documented the use of the "precision oncology" terminology over the last decade.

OBJECTIVE

To determine how the use of the terms "personalized oncology" and "precision oncology" have changed over time.

DESIGN

A retrospective literature analysis using two databases (PubMed and Scopus) over 10 years was performed. Manuscripts using either term "personalized oncology" or "precision oncology" were collected. Manuscripts published in 2011, 2013, 2015, 2017 and through 30 June 2019 were pulled for text analysis. Common reasons for exclusion were if the search term appeared in the institution name only, the search term appeared only in keyword or publication title, or the search term was used to justify the relevance or application of research with no clear definition.

SETTING

Manuscripts published and catalogued in PubMed or Scopus.

RESULTS

In our study, we analysed 399 unique manuscripts published over the last decade. Over time, the terminology has shifted from "personalized oncology" to "precision oncology". Targeted therapy, molecular biomarker-guided tumour profiling and next generation sequencing (ie, "omics-guided tumor profiling") are the three most common definitions of the term. While these definitions are somewhat overlapping in concept, over the decade we observed an increase in the number of distinct interpretations of "precision oncology", ranging from structural biology to clinical practice.

CONCLUSIONS AND RELEVANCE

We have observed that the phrase "precision oncology" is shifting, overlapping and expanding in definition. This all-encompassing approach to defining "precision oncology" ironically renders the term imprecise. Our analysis highlights the inherent challenges in defining novel movements in medicine.

"My Research Is Their Business, but I'm Not Their Business": Patient and Clinician Perspectives on Commercialization of Precision Oncology Data

BACKGROUND

Genetic sequencing and precision oncology have supported clinical breakthroughs but depend upon access to vast arrays of research specimens and data. One way for academic medical centers to fund such infrastructure and research is "commercialization" of access to specimens and data to industry. Here we explore patient and clinician perspectives regarding cancer specimen and data commercialization with the goal of improving such processes in the future.

MATERIALS AND METHODS

This qualitative analysis was embedded within a prospective precision oncology sequencing study of adults with head and neck cancer. Via semistructured dyadic interviews with patients with cancer and their doctors, we assessed understanding and concerns regarding potential commercialization, opinions regarding investment of profits, and perspectives regarding the return of information directly to participants from industry.

RESULTS

Several patient- and clinician-participants did not understand that the consent form already permitted commercialization of patient genetic data and expressed concerns regarding who would profit from the data, how profits would be used, and privacy and access. Patients were generally more comfortable with commercialization than clinicians. Many patients and clinicians were comfortable with investing profits back into research, but clinicians were more interested in investment in head and neck cancer research specifically. Patients generally supported potential return-of-results from a private entity, but their clinicians were more skeptical.

CONCLUSION

Our results illustrate the limitations of mandatory disclosures in the informed consent process. The voices of both patients and their doctors are critical to mitigate violations of privacy and a degradation of trust as stakeholders negotiate the terms of academic and commercial engagement.

IMPLICATIONS FOR PRACTICE

Further education is needed regarding how and why specimens and data in precision oncology research may be commercialized for both patients and providers alike. This process will require increased transparency, comprehension, and engagement of involved stakeholders.

The Status and Impact of Clinical Tumor Genome Sequencing

Since the discovery that DNA alterations initiate tumorigenesis, scientists and clinicians have been exploring ways to counter these changes with targeted therapeutics. The sequencing of tumor DNA was initially limited to highly actionable hot spots-areas of the genome that are frequently altered and have an approved matched therapy in a specific tumor type. Large-scale genome sequencing programs quickly developed technological improvements that enabled the deployment of whole-exome and whole-genome sequencing technologies at scale for pristine sample materials in research environments. However, the turning point for precision medicine in oncology was the innovations in clinical laboratories that improved turnaround time, depth of coverage, and the ability to reliably sequence archived, clinically available samples. Today, tumor genome sequencing no longer suffers from significant technical or financial hurdles, and the next opportunity for improvement lies in the optimal utilization of the technologies and data for many different tumor types.

Clinical potential of mass spectrometry-based proteogenomics

Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.

Implementing a comprehensive translational oncology platform: from molecular testing to actionability

BACKGROUND

In order to establish the workflows required to implement a real-time process involving multi-omic analysis of patient samples to support precision-guided therapeutic intervention, a tissue acquisition and analysis trial was implemented. This report describes our findings to date, including the frequency with which mutational testing led to precision-guided therapy and outcome for those patients.

METHODS

Eligible patients presenting to Oregon Health and Science University Knight Cancer Institute were enrolled on the study. Patients with biopsy proven metastatic or locally advanced unresectable prostate cancer, breast cancer, pancreatic adenocarcinoma, or refractory acute myelogenous leukemia receiving standard of care therapy were eligible. Metastatic site biopsies were collected and analyzed using the Knight Diagnostic Lab GeneTrails comprehensive solid tumor panel (124 genes). CLIA certified genomic information was made available to the treating physician.

RESULTS

Between 1/26/2017 and 5/30/2018, 38 patients were enrolled, with 28 successfully undergoing biopsy. Of these, 25 samples yielded sufficient tumor for analysis. The median biopsy cellularity and number of cores collected were 70% (15-90%) and 5 (2-20), respectively. No procedure-related complications occurred. GeneTrails analysis revealed that 22 of 25 (88%) tumor samples harbored at least one potentially actionable mutation, and 18 (72%) samples harbored 2 or more potentially actionable mutations. The most common genetic alterations identified involved: DNA damage repair genes, cell cycle regulating genes, PIK3CA/Akt/mTOR pathway, and FGF gene family. To date, CLIA certified genomic results were used by treating physicians for precision-guided therapy in 5 (23%) patients.

CONCLUSION

We report the feasibility of real-time tissue acquisition and analysis to support a successful translational oncology platform. The workflow will provide the foundation to improve access and accrual to biomarker driven precision oncology trials.

Assessing the Value of Next-Generation Sequencing Tests in a Dynamic Environment

Next-generation sequencing (NGS)-based technology has lowered the cost of cancer testing for genomic alterations and is now commercially available from a growing number of diagnostic laboratories. However, laboratories vary in the methodologies underlying their tests, the types and numbers of genomic alterations covered by the test, and the clinical annotation of the sequencing findings. Determining the value of NGS tests is dependent on whether it is used to support clinical trials or as a part of routine clinical care at a time when both the investigational drug pipeline and the list of U.S. Food and Drug Administration-approved or Compendium-listed therapeutics is in a high state of flux. Reimbursement policy for NGS testing by the Centers for Medicare & Medicaid is evolving as the value of NGS testing becomes more clearly defined for specific clinical situations. Patient care and clinical decisions-making are dependent on the oncologist's knowledge of when NGS testing has value. Here, we review principles and practice for NGS testing in this dynamic confluence of technology, cancer biology, and health care policy.

Value-based genomics

Advancements in next-generation sequencing have greatly enhanced the development of biomarker-driven cancer therapies. The affordability and availability of next-generation sequencers have allowed for the commercialization of next-generation sequencing platforms that have found widespread use for clinical-decision making and research purposes. Despite the greater availability of tumor molecular profiling by next-generation sequencing at our doorsteps, the achievement of value-based care, or improving patient outcomes while reducing overall costs or risks, in the era of precision oncology remains a looming challenge. In this review, we highlight available data through a pre-established and conceptualized framework for evaluating value-based medicine to assess the cost (efficiency), clinical benefit (effectiveness), and toxicity (safety) of genomic profiling in cancer care. We also provide perspectives on future directions of next-generation sequencing from targeted panels to whole-exome or whole-genome sequencing and describe potential strategies needed to attain value-based genomics.

Evolving paradigms for new agent development in pediatric oncology

PURPOSE OF REVIEW

To discuss considerations of new paradigms for clinical drug development in pediatric oncology that incorporate our expanding knowledge and complexity of molecular alterations associated with cancer; advances in cancer immunology and cellular therapy; the increasing number of new anticancer drugs, therapeutic approaches, and potential combinations; and recent initiatives by regulatory agencies to improve access to safe and effective therapies.

RECENT FINDINGS

Cancer in children and adolescents is a rare event with significant long-term impact on individuals and society. Using multimodality therapy, stratified by patient and disease characteristics, the cure rate for childhood cancer exceeds 80%. Cancer genomics has transformed anticancer drug development. Understanding the genetic basis of pediatric cancers and the use of genomics for risk stratification has changed the focus of drug development from cytotoxic drugs to targeted therapeutic approaches. Advances in cancer immunology, immune checkpoint blockade, and cellular therapy offer novel approaches to harness T cells to treat cancer. To improve the outcome for children and adolescents with cancer and accelerate drug development, understanding drug and target interactions in preclinical models of pediatric cancer should be coupled with efficient clinical trial designs that incorporate biomarker selection, assessment of toxicity and drug exposure, and improved measures of response.

SUMMARY

Clinical trials for children and adolescents with cancer evaluate cytotoxic drugs, molecularly target drugs, immunotherapy as well as combination therapies. The framework for oncology clinical trials will continually adapt to improve efficiency of trials and evaluate new therapeutic approaches.