Precision oncology: origins, optimism, and potential

[Translated article] Characteristics, clinical benefit, and reimbursement of new authorisations for oncohaematology drugs in Spain between 2017 and 2020

OBJECTIVE

To describe the authorisations and funding resolutions for new onco-haematological drugs in Spain between 2017 and 2020, as well as the results of their main trials.

METHODS

Observational, cross-sectional, descriptive study conducted between October and December 2022. Onco-haematology drugs approved by the European Medicines Agency between 2017 and 2020 were included, according to EFPIA patients W.A.I.T Indicator 2021 Survey. Authorisation information was obtained from the main study of the European Public Assessment Report. Data were collected on medicines, their authorisation and main study, benefit shown, cost, and status and time to reimbursement.

RESULTS

Forty-one new drugs authorised for 49 indications were identified. More than half (58.5%) were targeted therapies, and 61.2% were for the treatment of solid tumours (61.2%). Most had palliative intent (71.4%) and were indicated in relapsed or refractory disease (55.1%). Of the clinical trials, 57.1% were phase III and 63.3% were randomised. The primary endpoint was overall survival in 16.3%, increasing to 25.8% among randomised clinical trials. Regarding licensed drugs based on response rate, the median response rate was 56.4% [IQI 40-66.3]. In those authorised on the basis of surrogate time-to-event endpoints, the median hazard ratio was 0.54 [IQI 0.38-0.57], and among those using overall survival was 0.71 [IQI 0.59-0.77]. Globally, 22.4% had shown benefit in overall survival, with a median gain of 4 months [IQI 3.6-16.7]. One-third (33.3%) of the indications evaluable according to the European Society for Medical Oncology Magnitude of Clinical Benefit Scale showed substantial clinical benefit. Of the indications, 75.5% were funded, half (48.6%; 36.7% of the total) with restrictions. The median time to funding was 19.5 months [IQI 11.4-29.3].

CONCLUSIONS

Most main clinical trials of new onco-haematology drugs approved in Spain used surrogate primary endpoint and, at the time of authorisation, few had shown to prolong overall survival. More than a third were uncontrolled clinical trials.

Determination of endometrial cancer molecular subtypes using a whole exome-sequencing based single-method approach

AIM

Endometrial cancer (EC) is heterogeneous with respect to epidemiology, clinical course, histopathology and tumor biology. Recently, The Cancer Genome Atlas (TCGA) network has identified four molecular subtypes with distinct clinical courses by an integrated multi-omics approach. These subtypes are of critical importance in the clinical management of EC. However, determination of TCGA molecular subtypes requires a complex methodological approach that is resource intensive and difficult to implement in diagnostic routine procedures. In this context, Talhouk et al. reported the precise determination of modified subtypes based on molecular surrogates obtained by a two-method approach comprising immunohistochemistry and DNA-sequence analysis (Proactive Molecular Risk Classifier for Endometrial Cancer; ProMisE). In this study, we aimed to identify EC molecular subtypes in analogy to TCGA and ProMisE applying an innovative whole exome-sequencing (WES) based single-method approach.

METHODS

WES was performed in a cohort comprising N = 114 EC patients. WES data were analyzed using the oncology treatment decision support software MH Guide (Molecular Health, Heidelberg, Germany) and EC molecular subtypes in analogy to TCGA and ProMisE were determined. Results from both classifications were compared regarding their prognostic values using overall survival and progression-free survival analyses.

RESULTS

Applying a single-method WES-approach, EC molecular subtypes analogue to TCGA and ProMisE were identified in the study cohort. The surrogate marker-analogue classification precisely identified high-risk and low-risk EC, whereas the TCGA-analogue classification failed to obtain significant prognostic values in this regard.

CONCLUSION

Our data demonstrate that determination of EC molecular subtypes analogue to TCGA and ProMisE is feasible by using a single-method WES approach. Within our EC cohort, prognostic implications were only reliably provided by applying the surrogate marker-analogue approach. Designation of molecular subtypes in EC will be increasingly important in routine clinical practice. Thus, the single-method WES approach provides an important simple tool to tailor therapeutic decisions in EC.

Factors for a broad technology assessment of comprehensive genomic profiling in advanced cancer, a systematic review

Comprehensive Genomic Profiling (CGP) allows for the identification of many targets. Reimbursement decision-making is, however, challenging because besides the health benefits of on-label treatments and costs, other factors related to diagnostic and treatment pathways may also play a role. The aim of this study was to identify which other factors are relevant for the technology assessment of CGP and to summarize the available evidence for these factors. After a scoping search and two expert sessions, five factors were identified: feasibility, test journey, wider implications of diagnostic results, organisation of laboratories, and "scientific spillover". Subsequently, a systematic search identified 83 studies collecting mainly evidence for the factors "test journey" and "wider implications of diagnostic results". Its nature was, however, of limited value for decision-making. We recommend the use of comparative strategies, uniformity in outcome definitions, and the inclusion of a comprehensive set of factors in future evidence generation.

Healthcare decision-making for tumour-agnostic therapies in Europe: lessons learned

The tumour-agnostic authorisations of larotrectinib and entrectinib shifted the paradigm for indication setting. European healthcare decision-makers agreed on their therapeutic potential but diverged primarily in identified uncertainties concerning basket trial designs and endpoints, prognostic value of neurotrophic tropomyosin receptor kinase (NTRK) gene fusions, and resistance mechanisms. In addition, assessments of relevant comparators, unmet medical needs (UMNs), and implementation of NTRK-testing strategies diverged. In particular, the tumour-specific reimbursement recommendations and guidelines do not reflect tumour-agnostic thinking. These differences indicate difficulties experienced in these assessments and provide valuable lessons for future disruptive therapies. As we discuss here, early multistakeholder dialogues concerning minimum evidence requirements and involving clinicians are essential.

Meta-Analytic Gene-Clustering Algorithm for Integrating Multi-Omics and Multi-Study Data

Gene pathways and gene-regulatory networks are used to describe the causal relationship between genes, based on biological experiments. However, many genes are still to be studied to define novel pathways. To address this, a gene-clustering algorithm has been used to group correlated genes together, based on the similarity of their gene expression level. The existing methods cluster genes based on only one type of omics data, which ignores the information from other types. A large sample size is required to achieve an accurate clustering structure for thousands of genes, which can be challenging due to the cost of multi-omics data. Meta-analysis has been used to aggregate the data from multiple studies and improve the analysis results. We propose a computationally efficient meta-analytic gene-clustering algorithm that combines multi-omics datasets from multiple studies, using the fixed effects linear models and a modified weighted correlation network analysis framework. The simulation study shows that the proposed method outperforms existing single omic-based clustering approaches when multi-omics data and/or multiple studies are available. A real data example demonstrates that our meta-analytic method outperforms single-study based methods.

Patient eligibility for trials with imaging response assessment at the time of molecular tumor board presentation

PURPOSE

To assess the eligibility of patients with advanced or recurrent solid malignancies presented to a molecular tumor board (MTB) at a large precision oncology center for inclusion in trials with the endpoints objective response rate (ORR) or duration of response (DOR) based on Response Evaluation Criteria in Solid Tumors (RECIST version 1.1).

METHODS

Prospective patients with available imaging at the time of presentation in the MTB were included. Imaging data was reviewed for objectifiable measurable disease (MD) according to RECIST v1.1. Additionally, we evaluated the patients with MD for representativeness of the identified measurable lesion(s) in relation to the overall tumor burden.

RESULTS

262 patients with different solid malignancies were included. 177 patients (68%) had MD and 85 (32%) had non-measurable disease (NMD) at the time point of MTB presentation in accordance with RECIST v1.1. MD was not representative of the overall tumor burden in eleven patients (6%). The main reasons for NMD were lesions with longest diameter shorter than 10 mm (22%) and non-measurable peritoneal carcinomatosis (18%). Colorectal cancer and malignant melanoma displayed the highest rates of MD (> 75%). In contrast, gastric cancer, head and neck malignancies, and ovarian carcinoma had the lowest rates of MD (< 55%). In case of MD, the measurable lesions were representative of the overall tumor burden in the vast majority of cases (94%).

CONCLUSION

Approximately one third of cancer patients with advanced solid malignancies are not eligible for treatment response assessment in trials with endpoints ORR or DOR at the time of MTB presentation. The rate of patients eligible for trials with imaging endpoints differs significantly based on the underlying malignancy and should be taken under consideration during the planning of new precision oncology trials.

Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

Characteristics, clinical benefit and reimbursement of new authorisations for oncohaematology drugs in Spain between 2017 and 2020

OBJECTIVE

To describe the authorisations and funding resolutions for new onco-hematological drugs in Spain between 2017 and 2020, as well as the results of their main trials.

METHODS

Observational, cross-sectional, descriptive study conducted between October and December 2022. Onco-hematology drugs approved by the European Medicines Agency between 2017 and 2020 were included, according to EFPIA patients W.A.I.T Indicator 2021 Survey. Authorisation information was obtained from the main study of the European Public Assessment Report (EPAR). Data were collected on medicines, their authorisation and main study, benefit shown, cost, and status and time to reimbursement.

RESULTS

Forty-one new drugs authorised for 49 indications were identified. More than half (58.5%) were targeted therapies, and 61.2% were for the treatment of solid tumors (61.2%). Most had palliative intent (71.4%) and were indicated in relapsed or refractory disease (55.1%). Of the clinical trials, 57.1% were phase III and 63.3% were randomised. The primary endpoint was overall survival in 16.3%, increasing to 25.8% among randomised clinical trials. Regarding licensed drugs based on response rate, the median response rate was 56.4% (IQI 40.0-66.3). In those authorised on the basis of surrogate time-to-event endpoints, the median Hazard Ratio was 0.54 (IQI 0.38-0.57), and among those using overall survival was 0.71 (IQI 0.59-0.77). Globally, 22.4% had shown benefit in overall survival, with a median gain of 4 months (IQI 3.6-16.7). One third (33.3%) of the indications evaluable according to the European Society for Medical Oncology Magnitude of Clinical Benefit Scale showed substantial clinical benefit. Of the indications, 75.5% were funded, half (48.6%; 36.7% of the total) with restrictions. The median time to funding was 19.5 months (IQI 11.4-29.3).

CONCLUSIONS

Most main clinical trials of new onco-haematology drugs approved in Spain used surrogate primary endpoint and, at the time of authorisation, few had shown to prolong overall survival. More than a third were uncontrolled clinical trials.

Precision medicine for prostate cancer: An international perspective

Greater personalization of cancer medicine continues to shape therapy development and patient selection accordingly. The treatment of prostate cancer has evolved considerably since the discovery of androgen deprivation therapy. The comprehensive profiling of the prostate cancer genome has mapped the targetable molecular landscape of the disease and identified opportunities for the implementation of novel and combination therapies. In this review, we provide an overview of the molecular biology of prostate cancer and tools developed to aid prognostication and prediction of therapy benefit. Modern treatment of advanced prostate cancer is reviewed as a paradigm of increasing precision-informed approach to patient care, and must be considered on a global scale with respect to the state of science and care delivery.

Cancer-associated fibroblasts: a versatile mediator in tumor progression, metastasis, and targeted therapy

Tumor microenvironment (TME) has been demonstrated to play a significant role in tumor initiation, progression, and metastasis. Cancer-associated fibroblasts (CAFs) are the major component of TME and exhibit heterogeneous properties in their communication with tumor cells. This heterogeneity of CAFs can be attributed to various origins, including quiescent fibroblasts, mesenchymal stem cells (MSCs), adipocytes, pericytes, endothelial cells, and mesothelial cells. Moreover, single-cell RNA sequencing has identified diverse phenotypes of CAFs, with myofibroblastic CAFs (myCAFs) and inflammatory CAFs (iCAFs) being the most acknowledged, alongside newly discovered subtypes like antigen-presenting CAFs (apCAFs). Due to these heterogeneities, CAFs exert multiple functions in tumorigenesis, cancer stemness, angiogenesis, immunosuppression, metabolism, and metastasis. As a result, targeted therapies aimed at the TME, particularly focusing on CAFs, are rapidly developing, fueling the promising future of advanced tumor-targeted therapy.

Cellular Mass Response to Therapy Correlates With Clinical Response for a Range of Malignancies

PURPOSE

Cancer patients with advanced-stage disease have poor prognosis, typically having limited options for efficacious treatment, and genomics-based therapy guidance continues to benefit only a fraction of patients. Next-generation ex vivo approaches, such as cell mass-based response testing (MRT), offer an alternative precision medicine approach for a broader population of patients with cancer, but validation of clinical feasibility and potential impact remain necessary.

MATERIALS AND METHODS

We evaluated the clinical feasibility and accuracy of using live-cell MRT to predict patient drug sensitivity. Using a unified measurement workflow with a 48-hour result turnaround time, samples were subjected to MRT after treatment with a panel of drugs in vitro. After completion of therapeutic course, clinical response data were correlated with MRT-based predictions of outcome. Specimens were collected from 104 patients with solid (n = 69) and hematologic (n = 35) malignancies, using tissue formats including needle biopsies, malignant fluids, bone marrow aspirates, and blood samples. Of the 81 (78%) specimens qualified for MRT, 41 (51%) patients receiving physician-selected therapies had treatments matched to MRT.

RESULTS

MRT demonstrated high concordance with clinical responses with an odds ratio (OR) of 14.80 (P = .0003 [95% CI, 2.83 to 102.9]). This performance held for both solid and hematologic malignances with ORs of 20.67 (P = .0128 [95% CI, 1.45 to 1,375.57]) and 8.20 (P = .045 [95% CI, 0.77 to 133.56]), respectively. Overall, these results had a predictive accuracy of 80% (P = .0026 [95% CI, 65 to 91]).

CONCLUSION

MRT showed highly significant correlation with clinical response to therapy. Routine clinical use is technically feasible and broadly applicable to a wide range of samples and malignancy types, supporting the need for future validation studies.

A Bioinformatics Toolkit for Next-Generation Sequencing in Clinical Oncology

Next-generation sequencing (NGS) has taken on major importance in clinical oncology practice. With the advent of targeted therapies capable of effectively targeting specific genomic alterations in cancer patients, the development of bioinformatics processes has become crucial. Thus, bioinformatics pipelines play an essential role not only in the detection and in identification of molecular alterations obtained from NGS data but also in the analysis and interpretation of variants, making it possible to transform raw sequencing data into meaningful and clinically useful information. In this review, we aim to examine the multiple steps of a bioinformatics pipeline as used in current clinical practice, and we also provide an updated list of the necessary bioinformatics tools. This resource is intended to assist researchers and clinicians in their genetic data analyses, improving the precision and efficiency of these processes in clinical research and patient care.

Variant allele frequency: a decision-making tool in precision oncology?

Precision oncology requires additional predictive biomarkers for targeted therapy selection. Variant allele frequency (VAF), measuring the proportion of variant alleles within a genomic locus, provides insights into tumor clonality in somatic genomic testing, yielding a strong rationale for targeting dominant cancer cell populations. The prognostic and predictive roles of VAF have been evaluated across different studies. Yet, the absence of validated VAF thresholds and a lack of standardization between sequencing assays currently hampers its clinical utility. Therefore, analytical and clinical validation must be further examined. This Review summarizes the evidence regarding the use of VAF as a predictive biomarker and discusses challenges and opportunities for its clinical implementation as a decision-making tool for targeted therapy selection.

Synchronous Double Primary Combined Hepatocellular-cholangiocarcinoma and Cholangiolocarcinoma in a Cirrhotic Liver

Both combined hepatocellular-cholangiocarcinoma (cHCC-CCA) and cholangiolocarcinoma are rare primary liver cancers. cHCC-CCA is believed to originate from transformed hepatocellular carcinoma or liver stem/progenitor cells. Cholangiolocarcinoma is characterized by ductular reaction-like anastomosing cords and glands resembling cholangioles or canals containing hepatocellular carcinoma components and adenocarcinoma cells. According to the 2019 revision of the World Health Organization criteria, a subtype with stem cell features as a subclassification of cHCC-CCA was abolished for lack of conclusive evidence of the stem cell origin theory. That led to the classification of cholangiolocarcinoma with hepatocytic differentiation as cHCC-CCA. Consequently, cholangiolocarcinoma without hepatocytic differentiation is classified as a subtype of small-duct cholangiocarcinoma and is assumed to originate from the bile duct. Herein, we report the first case of double primary cHCC-CCA and cholangiolocarcinoma without hepatocytic differentiation in different hepatic segments of a cirrhotic liver. We believe this case supports the validity of the new World Health Organization criteria because the pathological finding of cHCC-CCA in this case shows the transformation of hepatocellular carcinoma to cholangiocarcinoma. Furthermore, this case may demonstrate that immature ductular cell stemness and mature hepatocyte cell stemness in hepatocarcinogenesis can coexist in the same environment. The results provide valuable insights into the mechanisms of growth, differentiation, and regulation of liver cancers.

Hepatocellular Carcinoma: Past and Present Challenges and Progress in Molecular Classification and Precision Oncology

Hepatocellular carcinoma (HCC) is one of the most common solid tumor malignancies in the world and represents roughly 90% of all primary malignancies of the liver. The most common risk factors for HCC include hepatitis B virus, hepatitis C virus, alcohol, and increasingly, fatty liver. Most HCC is diagnosed at advanced stages, excluding the possibility of curative resection, which leaves systemic therapy as the only treatment option. However, given the extreme mutational diversity and heterogenous nature of HCC, efforts to develop new targeted systemic therapies were largely unsuccessful until recently. HCC pathogenesis is thought to be a multistage process driven by a wide array of nonmutually exclusive driver mutations accompanied by many passenger mutations, with the average tumor possessing approximately 40 genomic aberrations. Over the past two decades, several efforts to categorize HCC prognostically and therapeutically according to different molecular subclassifications with the intent to guide treatment and identify drug targets have emerged, though, no single consensus has been reached. Recent breakthroughs in drug development have greatly expanded treatment options, but the ideal of uniting each patient's unique HCC with a targeted systemic therapy remains elusive.

Whole Person Modeling: a transdisciplinary approach to mental health research

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

Nanopore sequencing technology and its applications

Since the development of Sanger sequencing in 1977, sequencing technology has played a pivotal role in molecular biology research by enabling the interpretation of biological genetic codes. Today, nanopore sequencing is one of the leading third-generation sequencing technologies. With its long reads, portability, and low cost, nanopore sequencing is widely used in various scientific fields including epidemic prevention and control, disease diagnosis, and animal and plant breeding. Despite initial concerns about high error rates, continuous innovation in sequencing platforms and algorithm analysis technology has effectively addressed its accuracy. During the coronavirus disease (COVID-19) pandemic, nanopore sequencing played a critical role in detecting the severe acute respiratory syndrome coronavirus-2 virus genome and containing the pandemic. However, a lack of understanding of this technology may limit its popularization and application. Nanopore sequencing is poised to become the mainstream choice for preventing and controlling COVID-19 and future epidemics while creating value in other fields such as oncology and botany. This work introduces the contributions of nanopore sequencing during the COVID-19 pandemic to promote public understanding and its use in emerging outbreaks worldwide. We discuss its application in microbial detection, cancer genomes, and plant genomes and summarize strategies to improve its accuracy.

Targeted molecular profiling of solid tumours-Indian tertiary cancer centre experience

PURPOSE

Molecular Profiling of solid tumours is extensively used for prognostic, theranostic, and risk prediction. Next generation sequencing (NGS) has emerged as powerful method for molecular profiling. The present study was performed to identify molecular alterations present in solid tumours in Indian tertiary cancer centre.

METHODS

Study included 1140 formalin Fixed paraffin embedded samples. NGS was performed using two targeted gene panels viz. Ampliseq Focus panel and Sophia Solid Tumor Plus Solution. Data was analyzed using Illumina's Local Run Manager and SOPHiA DDM software. Variant interpretation and annotations were done as per AMP/ACMG guidelines.

RESULTS

Total 896 cases were subjected to NGS after excluding cases with suboptimal nucleic acid quality/quantity. DNA alterations were detected in 64.9% and RNA fusions in 6.9% cases. Among detected variants, 86.7% were clinically relevant aberrations. Mutation frequency among different solid tumours was 70.8%, 67.4%, 64.4% in non-small cell lung (NSCLC), lung squamous cell carcinomas and head neck tumours respectively. EGFR, KRAS, BRAF, ALK and ROS1were commonly altered in NSCLC. Gastrointestinal tumours showed mutations in 63.6% with predominant alterations in pancreatic (88.2%), GIST (87.5%), colorectal (78.7%), cholangiocarcinoma (52.9%), neuroendocrine (45.5%), gall bladder (36.7%) and gastric adenocarcinomas (16.7%). The key genes affected were KRAS, NRAS, BRAF and PIK3CA. NGS evaluation identified co-occurring alterations in 37.7% cases otherwise missed by conventional assays. Resistance mutations were detected in progressive lung tumours (39.5%) against EGFR TKIs and ALK/ROS inhibitors.

CONCLUSION

This is the largest Indian study on molecular profiling of solid tumours providing extensive information about mutational signatures using NGS.

A Systematic Review of Clinical Trials on the Efficacy and Safety of CRLX101 Cyclodextrin-Based Nanomedicine for Cancer Treatment

CRLX101 is a cyclodextrin-based nanopharmaceutical designed to improve the delivery and efficacy of the anti-cancer drug camptothecin. Cyclodextrins have unique properties that can enhance drug solubility, stability, and bioavailability, making them an attractive option for drug delivery. The use of cyclodextrin-based nanoparticles can potentially reduce toxicity and increase the therapeutic index compared to conventional chemotherapy. CRLX101 has shown promise in preclinical studies, demonstrating enhanced tumor targeting and prolonged drug release. This systematic review followed PRISMA guidelines, assessing the efficacy and toxicity of CRLX101 in cancer treatment using clinical trials. Studies from January 2010 to April 2023 were searched in PubMed, Scopus, Web of Science, and Cochrane Database of Systematic Reviews, using specific search terms. The risk of bias was assessed using ROBINS-I and Cochrane risk-of-bias tools. After screening 6018 articles, 9 were included in the final review. These studies, conducted between 2013 and 2022, focused on patients with advanced or metastatic cancer resistant to standard therapies. CRLX101 was often combined with other therapeutic agents, resulting in improvements such as increased progression-free survival and clinical benefit rates. Toxicity was generally manageable, with common adverse events including fatigue, nausea, and anemia.

The Combination of R848 with Sorafenib Enhances Antitumor Effects by Reprogramming the Tumor Immune Microenvironment and Facilitating Vascular Normalization in Hepatocellular Carcinoma

Novel promising strategies for combination with sorafenib are urgently needed to enhance its clinical benefit and overcome toxicity in hepatocellular carcinoma (HCC). the molecular and immunomodulatory antitumor effects of sorafenib alone and in combination with the new immunotherapeutic agent R848 are presented. Syngeneic HCC mouse model is presented to explore the antitumor effect and safety of three sorafenib doses alone, R848 alone, or their combination in vivo. R848 significantly enhances the sorafenib antitumor activity at a low subclinical dose with no obvious toxic side effects. Furthermore, the combination therapy reprograms the tumor immune microenvironment by increasing antitumor macrophages and neutrophils and preventing immunosuppressive signaling. Combination treatment promotes classical M1 macrophage-to-FTH1high M1 macrophage transition. The close interaction between neutrophils/classical M1 macrophages and dendritic cells promotes tumor antigen presentation to T cells, inducing cytotoxic CD8+ T cell-mediated antitumor immunity. Additionally, low-dose sorafenib, alone or combined with R848, normalizes the tumor vasculature, generating a positive feedback loop to support the antitumor immune environment. Therefore, the combination therapy reprograms the HCC immune microenvironment and normalizes the vasculature, improving the therapeutic benefit of low-dose sorafenib and minimizing toxicity, suggesting a promising novel immunotherapy (R848) and targeted therapy (tyrosine kinase inhibitors) combination strategy for HCC treatment.

Integrating analysis of proteome profile and drug screening identifies therapeutic potential of MET pathway for the treatment of malignant peripheral nerve sheath tumor

BACKGROUND

Malignant peripheral nerve sheath tumor (MPNST) is an aggressive sarcoma with a poor prognosis that requires novel therapeutic agents. Proteome information is useful for identifying new therapeutic candidates because it directly reflects the biological phenotype. Additionally, in vitro drug screening is an effective tool to identify candidate drugs for common cancers. Hence, we attempted to identify novel therapeutic candidates for MPNST by integrating proteomic analysis and drug screening.

METHODS

We performed comprehensive proteomic analysis on 23 MPNST tumor samples using liquid chromatography-tandem mass spectrometry to identify therapeutic targets. We also conducted drug screening of six MPNST cell lines using 214 drugs.

RESULTS

Proteomic analysis revealed that the MET and IGF pathways were significantly enriched in the local recurrence/distant metastasis group of MPNST, whereas drug screening revealed that 24 drugs showed remarkable antitumor effects on the MPNST cell lines. By integrating the results of these two approaches, MET inhibitors, crizotinib and foretinib, were identified as novel therapeutic candidates for the treatment of MPNST.

CONCLUSIONS

We successfully identified novel therapeutic candidates for the treatment of MPNST, namely crizotinib and foretinib, which target the MET pathway. We hope that these candidate drugs will contribute to the treatment of MPNST.

Molecular tumour board at European Institute of Oncology: Report of the first three year activity of an Italian precision oncology experience

BACKGROUND

Precision oncology aims to improve clinical outcomes by personalising treatment options for patients with cancer. Exploiting vulnerabilities identified in a patient's cancer genome requires reliable interpretation of a huge mole of alterations and heterogeneous biomarkers. ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT) allows evidence-based evaluation of genomic findings. Molecular tumour boards (MTBs) convey the required multi-disciplinary expertise to enable ESCAT evaluation and strategical treatment choice.

MATERIALS AND METHOD

We retrospectively reviewed the records of 251 consecutive patients discussed by European Institute of Oncology MTB between June 2019 and June 2022.

RESULTS

One-hundred eighty-eight (74.6%) patients had at least one actionable alteration. After MTB discussion, 76 patients received molecularly matched therapies (MMTs) while 76 patients received standard of care. Patients receiving MMT displayed higher overall response rate (37.3% versus 12.9%), median progression-free survival (mPFS 5.8 months, 95% confidence interval [CI] 4.1-7.5 versus 3.6 months, 95% CI 2.5-4.8, p = 0.041; hazard ratio 0.679, 95% CI 0.467-0.987) and median overall survival (mOS 35.1 months, 95% CI not evaluable versus 8.5 months, 95% CI 3.8-13.2; hazard ratio 0.431, 95% CI 0.250-0.744, p = 0.002). Superiority in OS and PFS persisted in multivariable models. Among 61 pretreated patients receiving MMT, 37.5% of patients had PFS2/PFS1 ratio ≥1.3. Patients with higher actionable targets (ESCAT tier I) had better OS (p = 0.001) and PFS (p = 0.049), while no difference was observed in lower evidence levels.

CONCLUSIONS

Our experience shows that MTBs can yield valuable clinical benefit. Higher actionability ESCAT level appears to be associated with better outcomes for patients receiving MMT.

Hypoxia-Inducible Factor 2 Alpha (HIF2α) Inhibitors: Targeting Genetically Driven Tumor Hypoxia

Tumors driven by deficiency of the VHL gene product, which is involved in degradation of the hypoxia-inducible factor subunit 2 alpha (HIF2α), are natural candidates for targeted inhibition of this pathway. Belzutifan, a highly specific and well-tolerated HIF2α inhibitor, recently received FDA approval for the treatment of nonmetastatic renal cell carcinomas, pancreatic neuroendocrine tumors, and central nervous system hemangioblastomas from patients with von Hippel-Lindau disease, who carry VHL germline mutations. Such approval is a milestone in oncology; however, the full potential, and limitations, of HIF2α inhibition in the clinic are just starting to be explored. Here we briefly recapitulate the molecular rationale for HIF2α blockade in tumors and review available preclinical and clinical data, elaborating on mutations that might be particularly sensitive to this approach. We also outline some emerging mechanisms of intrinsic and acquired resistance to HIF2α inhibitors, including acquired mutations of the gatekeeper pocket of HIF2α and its interacting partner ARNT. Lastly, we propose that the high efficacy of belzutifan observed in tumors with genetically driven hypoxia caused by VHL mutations suggests that a focus on other mutations that similarly lead to HIF2α stabilization, such as those occurring in neuroendocrine tumors with disruptions in the tricarboxylic acid cycle (SDHA/B/C/D, FH, MDH2, IDH2), HIF hydroxylases (EGLN/PHDs), and the HIF2α-encoding gene, EPAS1, are warranted.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Functional temozolomide sensitivity testing of patient-specific glioblastoma stem cell cultures is predictive of clinical outcome

Serum-free culturing of patient-derived glioblastoma biopsies enrich for glioblastoma stem cells (GSCs) and is recognized as a disease-relevant model system in glioblastoma (GBM). We hypothesized that the temozolomide (TMZ) drug sensitivity of patient-derived GSC cultures correlates to clinical sensitivity patterns and has clinical predictive value in a cohort of GBM patients. To this aim, we established 51 individual GSC cultures from surgical biopsies from both treatment-naïve primary and pretreated recurrent GBM patients. The cultures were evaluated for sensitivity to TMZ over a dosing range achievable in normal clinical practice. Drug efficacy was quantified by the drug sensitivity score. MGMT-methylation status was investigated by pyrosequencing. Correlative, contingency, and survival analyses were performed for associations between experimental and clinical data. We found a heterogeneous response to temozolomide in the GSC culture cohort. There were significant differences in the sensitivity to TMZ between the newly diagnosed and the TMZ-treated recurrent disease (p <0.01). There was a moderate correlation between MGMT-status and sensitivity to TMZ (r=0.459, p=0.0009). The relationship between MGMT status and TMZ efficacy was statistically significant on multivariate analyses (p=0.0051). We found a predictive value of TMZ sensitivity in individual GSC cultures to patient survival (p=0.0089). We conclude that GSC-enriched cultures hold clinical and translational relevance by their ability to reflect the clinical heterogeneity in TMZ-sensitivity, substantiate the association between TMZ-sensitivity and MGMT-promotor methylation status and appear to have a stronger predictive value than MGMT-promotor methylation on clinical responses to TMZ.

Convex Multi-View Clustering Via Robust Low Rank Approximation With Application to Multi-Omic Data

Recent advances in high throughput technologies have made large amounts of biomedical omics data accessible to the scientific community. Single omic data clustering has proved its impact in the biomedical and biological research fields. Multi-omic data clustering and multi-omic data integration techniques have shown improved clustering performance and biological insight. Cancer subtype clustering is an important task in the medical field to be able to identify a suitable treatment procedure and prognosis for cancer patients. State of the art multi-view clustering methods are based on non-convex objectives which only guarantee non-global solutions that are high in computational complexity. Only a few convex multi-view methods are present. However, their models do not take into account the intrinsic manifold structure of the data. In this paper, we introduce a convex graph regularized multi-view clustering method that is robust to outliers. We compare our algorithm to state of the art convex and non-convex multi-view and single view clustering methods, and show its superiority in clustering cancer subtypes on publicly available cancer genomic datasets from the TCGA repository. We also show our method's better ability to potentially discover cancer subtypes compared to other state of the art multi-view methods.

A Multiview Clustering Method With Low-Rank and Sparsity Constraints for Cancer Subtyping

Multiomics data clustering is one of the major challenges in the field of precision medicine. Integration of multiomics data for cancer subtyping can improve the understanding on cancer and reveal systems-level insights. How to integrate multiomics data for accurate cancer subtyping is an interesting and challenging research problem. To capture the global and the local structure of omics data, a novel framework for integrating multiomics data is proposed for cancer subtyping. Multiview clustering with low-rank and sparsity constraints (MVCLRS) can measure the local similarities of samples in each omics data and obtain global consensus structures by integrating the multiomics data. The main insight provided by MVCLRS is that low-rank sparse subspace clustering for the construction of an affinity matrix can best capture the local similarities in omics data. Extensive testing is conducted on 10 real world cancer datasets with multiomics from The Cancer Genome Atlas. Compared with 10 state-of-the-art multiomics clustering algorithms, the MVCLRS performs better in the 10 cancer datasets by providing its clustering results with at least one enriched clinical label in nine of ten cancer subtypes, the most of any method.

Molecular subtypes based on cuproptosis regulators and immune infiltration in kidney renal clear cell carcinoma

Copper toxicity involves the destruction of mitochondrial metabolic enzymes, triggering an unusual mechanism of cell death called cuproptosis, which proposes a novel approach using copper toxicity to treat cancer. However, the biological function of cuproptosis has not been fully elucidated in kidney renal clear cell carcinoma (KIRC). Using the expression profile of 13 cuproptosis regulators, we first identified two molecular subtypes related to cuproptosis defined as “hot tumor” and “cold tumor”, having different levels of biological function, clinical prognosis, and immune cell infiltration. We obtained three gene clusters using the differentially expressed genes between the two cuproptosis-related subtypes, which were associated with different molecular activities and clinical characteristics. Next, we developed and validated a cuproptosis prognostic model that included two genes (FDX1 and DBT). The calculated risk score could divide patients into high- and low-risk groups. The high-risk group had a poorer prognosis, lower level of immune infiltration, higher frequency of gene alterations, and greater levels of FDX1 methylation and limited DBT methylation. The risk score was also an independent predictive factor for overall survival in KIRC. The established nomogram calculating the risk score achieved a high predictive ability for the prognosis of individual patients (area under the curve: 0.860). We then identified small molecular inhibitors as potential treatments and analyzed the sensitivity to chemotherapy of the signature genes. Tumor immune dysfunction and exclusion (TIDE) showed that the high-risk group had a higher level of TIDE, exclusion and dysfunction that was lower than the low-risk group, while the microsatellite instability of the high-risk group was significantly lower. The results of two independent immunotherapy datasets indicated that cuproptosis regulators could influence the response and efficacy of immunotherapy in KIRC. Our study provides new insights for individualized and comprehensive therapy of KIRC.

Emerging Biomaterials Imaging Antitumor Immune Response

Immunotherapy is one of the most promising clinical modalities for the treatment of malignant tumors and has shown excellent therapeutic outcomes in clinical settings. However, it continues to face several challenges, including long treatment cycles, high costs, immune-related adverse events, and low response rates. Thus, it is critical to predict the response rate to immunotherapy by using imaging technology in the preoperative and intraoperative. Here, the latest advances in nanosystem-based biomaterials used for predicting responses to immunotherapy via the imaging of immune cells and signaling molecules in the immune microenvironment are comprehensively summarized. Several imaging methods, such as fluorescence imaging, magnetic resonance imaging, positron emission tomography imaging, ultrasound imaging, and photoacoustic imaging, used in immune predictive imaging, are discussed to show the potential of nanosystems for distinguishing immunotherapy responders from nonresponders. Nanosystem-based biomaterials aided by various imaging technologies are expected to enable the effective prediction and diagnosis in cases of tumors, inflammation, and other public diseases.

A multi-omics approach based on 1H-NMR metabonomics combined with target protein analysis to reveal the mechanism of RIAISs on cervical carcinoma patients

Cervical carcinoma (CC) is the fourth most common cancer in females and radiotherapy is always as the definitive therapy for cervical cancer patients who are not suitable for surgery. Radiation-induced acute intestinal symptoms (RIAISs) occur in 50-80% of cervical cancer patients. Some research shows that RIAISs may relate to inflammatory reaction by radiotherapy but the action mechanism is also not clearly and the details of the molecular mechanism are still urgently needed. In this paper, basing on ¹H-NMR metabonomic and bioinformatics analysis, an integrated multi-omics analysis including metabonomics and bioinformatics was performed. We propose a hypothesis about pathogenic mechanism on RIAISs and proofed it through western-blot. Our results indicated significant dysregulation of metabolic pathways in RIAIS patients. Most importantly, we found that RIAISs were associated p53 and PI3K-AKT pathway.

Fucosyltransferase 4 Predicts Patient Outcome in Rectal Cancer through an Immune Microenvironment-Mediated Multi-Mechanism

Colorectal cancer is the most common type of gastrointestinal malignant tumors worldwide. Standardization of the strategy for the precise treatment of this cancer has been a major challenge. Enrichment analysis of six gene groups (colon cancer-specific genes (upregulated and downregulated); rectal cancer-specific genes (upregulated and downregulated); and common genes (upregulated and downregulated)) revealed the common and specific features of colon and rectal cancer, particularly a hyperactive immune response in rectal cancer. Key common genes exhibited a similar expression pattern, but were associated with distinct patient prognosis in colon and rectal cancer. FUT4 was a core regulatory gene in rectal cancer; it can decrease the level of infiltration by M2 macrophages in the tumor immune microenvironment and participate in the positive regulation of the immune system and glycoprotein biosynthetic process, thereby affecting the outcome of patients with rectal cancer. FUT4 co-expression genes can influence patient’s survival time by regulating the cell cycle. Among the regulators of FUT4 co-expression genes, checkpoint kinase 2 (CHEK2) was linked to patient outcome.

Comparison of Cancer Survival Trends in Hungary in the Periods 2001-2005 and 2011-2015 According to a Population-Based Cancer Registry

Background: Assessment of population-based cancer survival may provide the most valuable feedback about the effectiveness of oncological surveillance and treatment. Aims: Based on the database of the Hungarian National Cancer Registry, standardized incidence rates of lung, breast, colorectal, prostate and cervical cancer were compared to standardized mortality data of the Hungarian Central Statistical Office in the period between 2001 and 2015. Then survival analysis was performed on cleansed database. Results: The incidence of colorectal, breast and prostate cancer increased, while standardized rates of lung and cervical cancer declined. The survival of colorectal, breast and prostate cancer showed improvement. Contrarily, lung cancer exhibited a mild decline, while that of cervical cancer did not change significantly. In earlier stages survival was improved among almost every studied tumor type, while in advanced stages improvement was not observed. Comparison of stage distribution revealed that in the 2011-2015 period colorectal, breast and prostate cancer cases were diagnosed at earlier stages, while lung and cervical cancer patients were typically discovered at more advanced stages. Discussion: The outcome of advanced cancer treatments is better in earlier stages, which highlighted the importance of screening network. However, growth of oncological treatment costs with longer patient survival imposes a constantly increasing burden on society.

Global research trends on precision cancer medicine-related rashes (2008-2021): A bibliographic study

Background

Precision cancer medicine-related rashes are a kind of skin and mucous lesions caused by precision therapy. More and more evidences indicated that such events should not be ignored in the course of anti-tumor therapy. Since cancer treatment entered the “Precision Era”, there has been a rapid increase in this field. However, there was few bibliometric studies to provide an overall review of this field. This study aims to evaluate the literature output and trends in researches on precision cancer medicine-related rashes from a global perspective.

Methods

Collected publications on precision cancer medicine-related rashes from the Web of Science Core Collection database, which were limited to articles and reviews in English. Microsoft Excel, VOS viewer and CiteSpace V were used for quantitative and visual analysis.

Results

A total of 1,229 papers were identified. From 2008 to 2021, annual publications increased year by year. The United States published the most papers in this field (44.9%) and ranking first in citation frequency (19,854 times) and H-index (69). The University of Texas system ranks first with 98 papers published. Lacouture M.E and Robert C were the principal investigators. Cancers has the largest number of articles published, with 70 articles. In recent years, there have been research hotspots related to immunotherapy, including ipilimumab, immunotherapy, tumor microenvironment, association, checkpoint inhibitor, and cutaneous adverse event.

Conclusion

Precision cancer medicine-related rashes are a hot research topic in oncology. The number of relevant publications will increase dramatically. “Checkpoint inhibitors”, “skin adverse events”, “associations” and “tumor microenvironment” may become research hotspots in the future.

Defining a Core Data Set for the Economic Evaluation of Precision Oncology

OBJECTIVES

Precision oncology is generating vast amounts of multiomic data to improve human health and accelerate research. Existing clinical study designs and attendant data are unable to provide comparative evidence for economic evaluations. This lack of evidence can cause inconsistent and inappropriate reimbursement. Our study defines a core data set to facilitate economic evaluations of precision oncology.

METHODS

We conducted a literature review of economic evaluations of next-generation sequencing technologies, a common application of precision oncology, published between 2005 and 2018 and indexed in PubMed (MEDLINE). Based on this review, we developed a preliminary core data set for informal expert feedback. We then used a modified-Delphi approach with individuals involved in implementation and evaluation of precision medicine, including 2 survey rounds followed by a final voting conference to refine the data set.

RESULTS

Two authors determined that variation in published data elements was reached after abstraction of 20 economic evaluations. Expert consultation refined the data set to 83 unique data elements, and a multidisciplinary sample of 46 experts participated in the modified-Delphi process. A total of 68 elements (81%) were selected as required, spanning demographics and clinical characteristics, genomic data, cancer treatment, health and quality of life outcomes, and resource use.

CONCLUSIONS

Cost-effectiveness analyses will fail to reflect the real-world impacts of precision oncology without data to accurately characterize patient care trajectories and outcomes. Data collection in accordance with the proposed core data set will promote standardization and enable the generation of decision-grade evidence to inform reimbursement.

Label-Free Delineation of Human Uveal Melanoma Infiltration With Pump–Probe Microscopy

Uveal melanoma (UM) is the most frequent primary intraocular malignancy in adults, characterized by melanin depositions in melanocytes located in the uveal tract in the eyes. Differentiation of melanin species (eumelanin and pheomelanin) is crucial in the diagnosis and management of UM, yet it remains inaccessible for conventional histology. Here, we report that femtosecond time-resolved pump-probe microscopy could provide label-free and chemical-specific detection of melanin species in human UM based on their distinct transient relaxation dynamics at the subpicosecond timescale. The method is capable of delineating the interface between melanoma and paracancerous regions on various tissue conditions, including frozen sections, paraffin sections, and fresh tissues. Moreover, transcriptome sequencing was conducted to confirm the active eumelanin synthesis in UM. Our results may hold potential for sensitive detection of tumor boundaries and biomedical research on melanin metabolism in UM.

Clinical utility of targeted next-generation sequencing assay in IDH-wildtype glioblastoma for therapy decision-making

BACKGROUND

Targeted gene NGS testing is available through many academic institutions and commercial entities and is increasingly incorporated in practice guidelines for glioblastoma (GBM). This single-center retrospective study aimed to evaluate the clinical utility of incorporating NGS results in the management of GBM patients at a clinical trials-focused academic center.

METHODS

We identified 1011 consecutive adult patients with pathologically confirmed GBM (IDHwt or IDHmut) who had somatic tumor sequencing (Oncopanel, ~500 cancer gene panel) at DFCI from 2013-2019. Clinical records of all IDHwt GBM patients were reviewed to capture clinical trial enrollment and off-label targeted therapy use based on NGS results.

RESULTS

Of the 557 IDHwt GBM patients with sequencing, 182 entered clinical trials at diagnosis (32.7%) and 213 (38.2%) entered after recurrence. Sequencing results for 130 patients (23.3%) were utilized for clinical trial enrollment for either targeted therapy indications (6.9 % upfront and 27.7% at recurrent clinical trials and 3.1% for off-label targeted therapy) or exploratory studies (55.4% upfront and 6.9% recurrent clinical trials). Median overall survival was 20.1 months with no survival difference seen between patients enrolled in clinical trials compared to those who were not, in a posthoc analysis.

CONCLUSIONS

While NGS testing has become essential for improved molecular diagnostics, our study illustrates that targeted gene panels remain underutilized for selecting therapy in GBM-IDHwt. Targeted therapy and clinical trial design remain to be improved to help leverage the potential of NGS in clinical care.

Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer

Precision oncology is an absolute need today due to the emergence of treatment resistance and heterogeneity among cancerous profiles. Target-propelled cancer therapy is one of the treasures of precision oncology which has come together with substantial medical accomplishment. Prostate cancer is one of the most common cancers in males, with tremendous biological heterogeneity in molecular and clinical behavior. The spectrum of molecular abnormalities and varying clinical patterns in prostate cancer suggest substantial heterogeneity among different profiles. To identify novel therapeutic targets and precise biomarkers implicated with prostate cancer, we performed a state-of-the-art bioinformatics study, beginning with analyzing high-throughput genomic datasets from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) suggests a set of five dysregulated hub genes (MAF, STAT6, SOX2, FOXO1, and WNT3A) that played crucial roles in biological pathways associated with prostate cancer progression. We found overexpressed STAT6 and SOX2 and proposed them as candidate biomarkers and potential targets in prostate cancer. Furthermore, the alteration frequencies in STAT6 and SOX2 and their impact on the patients' survival were explored through the cBioPortal platform. The Kaplan-Meier survival analysis suggested that the alterations in the candidate genes were linked to the decreased overall survival of the patients. Altogether, the results signify that STAT6 and SOX2 and their genomic alterations can be explored in therapeutic interventions of prostate cancer for precision oncology, utilizing early diagnosis and target-propelled therapy.

Status and trends of personalized medicine research from 2000 to 2020: a bibliometric analysis

BACKGROUND

Personalized medicine (PM), as a rapidly growing research area, provides treatments, practices, and interventions being adapted to an individual patient based on his own risk of disease. This study aims to analyze the productivity of countries, institutions, and authors in this field, to determine the existing research trends worldwide, and to forecast future research activity for specific countries.

METHODS

Documents published between 2000 and 2020 were retrieved from the Web of Science (WoS) database. Bibliometric analysis was performed to assess the outputs, correlation analysis was applied to analyze the relationship between Gross Domestic Product per capita (GDP-PP) and the number of publications, and an extrapolation method was used for predicting the future productivity trends for certain countries.

RESULTS

A total of 7,772 documents were published globally on PM between 2000 and 2020. The most productive country, journal, and institution are the USA, Personalized Medicine, and Harvard Medical School, respectively. The USA is also first in line in terms of total citations. Netherlands, Denmark, and the USA are listed at the top in terms of the total number of papers and citations, after adjusting for GDP-PP and population size. Also, as predictions suggest, the USA will maintain its predominant role in the PM field in the next 5 years.

CONCLUSIONS

Owing to its both interdisciplinary and multidisciplinary nature, PM bestows researchers' numerous sources to benefit and enables them a field that they can be productive of for the future. Therefore, this field is expected to progress and be the lead area in medicine in the upcoming years.

[Oncology and psychiatry: Towards mutual connections/relationships]

Oncology has been proposed as a model of scientificity, in order to promote scientific approaches to psychiatry. In this article, another type of relation between oncology and psychiatry is explored, which promotes the idea of a mutually enriching dialogue and underlines the contributions of psychiatry to oncology. The ways in which both fields address epistemological and ethical issues in their respective approaches to disease is also examined. We argue that these two disciplines can learn from one another in the common context of chronic conditions, thanks to the potential of big data collection and their biostatistics treatment for the identification of markers - sources of individualization -, as well as thanks to the renewed attention given to the temporal and processual dimension of these diseases, in particular within the framework of "staging" models.

Hepatocellular Carcinoma: How the Gut Microbiota Contributes to Pathogenesis, Diagnosis, and Therapy

The gut microbiota is gaining increasing attention, and the concept of the "gut-liver axis" is gradually being recognized. Leaky gut resulting from injury and/or inflammation can cause the translocation of flora to the liver. Microbiota-associated metabolites and components mediate the activation of a series of signalling pathways, thereby playing an important role in the development of hepatocellular carcinoma (HCC). For this reason, targeting the gut microbiota in the diagnosis, prevention, and treatment of HCC holds great promise. In this review, we summarize the gut microbiota and the mechanisms by which it mediates HCC development, and the characteristic alterations in the gut microbiota during HCC pathogenesis. Furthermore, we propose several strategies to target the gut microbiota for the prevention and treatment of HCC, including antibiotics, probiotics, faecal microbiota transplantation, and immunotherapy.

Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.

Precision patients: Selection practices and moral pathfinding in experimental oncology

This paper addresses selection practices in a Danish phase 1 unit specialised in precision medicine in the field of oncology. Where precision medicine holds the ambition of selecting genetically fit medicine for the patient, we find that precision medicine in the early trial setting is oriented towards selecting clinically and genetically fit patients for available treatment protocols. Investigating how phase 1 oncologists experience and respond to the moral challenges of selecting patients for early clinical trials, we show that inclusion criteria and patient categories are not always transparent to patients. Lack of transparency about inclusion criteria has been interpreted as morally problematic. Yet drawing on social science studies of 'unknowing', we argue that silence and non-transparency in interactions between oncologists and patients are crucial to respect the moral agency of patients at the edge of life and recognise them as belonging to the public of Danish health care. In the discussion, we consider the practice of placing 'unfit' patients on a waiting list for trial participation. Rather than representing an ethical and political problem, we argue, the waiting list can act as a valve enabling oncologists to navigate the scientific and as well as the moral uncertainties in phase 1 oncology.

Detecting molecular subtypes from multi-omics datasets using SUMO

We present a data integration framework that uses non-negative matrix factorization of patient-similarity networks to integrate continuous multi-omics datasets for molecular subtyping. It is demonstrated to have the capability to handle missing data without using imputation and to be consistently among the best in detecting subtypes with differential prognosis and enrichment of clinical associations in a large number of cancers. When applying the approach to data from individuals with lower-grade gliomas, we identify a subtype with a significantly worse prognosis. Tumors assigned to this subtype are hypomethylated genome wide with a gain of AP-1 occupancy in demethylated distal enhancers. The tumors are also enriched for somatic chromosome 7 (chr7) gain, chr10 loss, and other molecular events that have been suggested as diagnostic markers for "IDH wild type, with molecular features of glioblastoma" by the cIMPACT-NOW consortium but have yet to be included in the World Health Organization (WHO) guidelines.

Stakeholder Perspectives on Navigating Evidentiary and Decision Uncertainty in Precision Oncology

(1) Background: Precision oncology has the potential to improve patient health and wellbeing through targeted prevention and treatment. Owing to uncertain clinical and economic outcomes, reimbursement has been limited. The objective of this pan-Canadian qualitative study was to investigate barriers to precision oncology implementation from the perspectives of health system stakeholders. (2) Methods: We conducted 32 semi-structured interviews with health technology decision makers (n = 14) and clinicians (n = 18) experienced with precision oncology. Participants were recruited using a purposive sampling technique. Interviews were analyzed using thematic analysis. Recruitment continued until two qualitative analysts reached agreement that thematic saturation was reached. (3) Results: While cautiously optimistic about the potential for enhanced therapeutic alignment, participants identified multiple decisional challenges under conditions of evidentiary uncertainty. Decision makers voiced concern over resource requirements alongside small benefitting patient populations and limited evidence supporting patient and health system impacts. Clinicians were comparatively tolerant of evidentiary uncertainty guiding clinical decision-making practices. Clinicians applied a broader definition of patient benefit, focusing on the ability to assist patients making informed clinical decisions. (4) Conclusions: Sustainable precision oncology must balance demand with evidence demonstrating benefit. We show that clinicians and decision makers vary in their tolerance for evolving knowledge, suggesting a need to establish evidentiary standards supporting precision oncology reimbursement decisions.

Imaging and dosimetry for alpha-particle emitter radiopharmaceutical therapy: improving radiopharmaceutical therapy by looking into the black box

Radiopharmaceutical therapy using α-particle emitting radionuclides (αRPT) is a novel treatment modality that delivers highly potent alpha-particles to cancer cells or their environment. We review the advantages and challenges of imaging and dosimetry in implementing αRPT for cancer patients.

The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis

INTRODUCTION

Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver malignancies and is currently the fourth most common cause of cancer-related death worldwide. Due to varying underlying etiologies, the prognosis of HCC differs greatly among patients. It is important to develop ways to help stratify patients upon initial diagnosis to provide optimal treatment modalities and follow-up plans. The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC.

METHODS

The Cancer Genome Atlas (TCGA-LIHC) was analyzed for differentially expressed genes. Clinicopathological data were obtained from cBioPortal. ANN analysis of the 75 most significant genes predicting disease-free survival (DFS) was performed. Next, CTA results were used for creation of the scoring system. Cox regression was performed to identify the prognostic value of the scoring system.

RESULTS

363 patients diagnosed with HCC were analyzed in this study. ANN provided 15 genes with normalized importance >50%. CTA resulted in a set of three genes (NRM, STAG3, and SNHG20). Patients were then divided in to 4 groups based on the CTA tree cutoff values. The Kaplan-Meier analysis showed significantly reduced DFS in groups 1, 2, and 3 (median DFS: 29.7 months, 16.1 months, and 11.7 months, p < 0.01) compared to group 0 (median not reached). Similar results were observed when overall survival (OS) was analyzed. On multivariate Cox regression, higher scores were associated with significantly shorter DFS (1 point: HR 2.57 (1.38-4.80), 2 points: 3.91 (2.11-7.24), and 3 points: 5.09 (2.70-9.58), p < 0.01).

CONCLUSION

Long-term outcomes of patients with HCC can be predicted using a simplified scoring system based on tumor mRNA gene expression levels. This tool could assist clinicians and researchers in identifying patients at increased risks for recurrence to tailor specific treatment and follow-up strategies for individual patients.

A sociology of precision-in-practice: The affective and temporal complexities of everyday clinical care

The idea of 'precision medicine', which has gained increasing traction since the early 2000s, is now ubiquitous in health and medicine. Though varied in its implementation across fields, precision medicine has raised hopes of revolutionary treatments and has spurred the proliferation of novel therapeutics, the alteration of professional trajectories and various reconfigurations of health/care. Nowhere is the promise of precision medicine more apparent, nor further institutionalised, than in the field of oncology. While the transformative potential of precision medicine is widely taken for granted, there remains scant attention to how it is being experienced at the coalface of care. Here, drawing on the perspectives of 54 cancer care professionals gleaned through eight focus group discussions in two hospitals in Australia, we explore clinicians' experiences of the day-to-day dynamics of precision-in-practice. We illustrate some of the affective and temporal complexities, analysed here under the rubrics of enchantment, acceleration and distraction that are emerging alongside the uptake of precision medicine in the field of oncology. We argue that these complexities, and their dis/continuities with earlier iterations of cancer care, demonstrate the need for sociological analyses of precision medicine as it is being implemented in practice and its varied effects on 'routine' care.

Precision diagnostics in lymphomas - Recent developments and future directions

Genetics is an integral part of the clinical diagnostics of lymphomas that improves disease subclassification and patient risk-stratification. With the introduction of high-throughput sequencing technologies, a rapid, in-depth portrayal of the genomic landscape in major lymphoma entities was achieved. Whilst a few lymphoma entities were characterized by a predominant gene mutation (e.g. Waldenström's macroglobulinemia and hairy cell leukemia), the vast majority demonstrated a very diverse genetic landscape with a high number of recurrent gene mutations (e.g. chronic lymphocytic leukemia and diffuse large B cell lymphoma), indeed reflecting the great clinical heterogeneity among lymphomas. These studies have allowed better understanding of the ontogeny and evolution of different lymphomas, while also identifying new genetic markers that can complement lymphoma diagnostics and improve prognostication. However, despite these efforts, there is still a limited number of gene mutations with predictive impact that can guide treatment selection. In this review, we will highlight clinically relevant diagnostic, prognostic and predictive markers in lymphomas that are used today in routine diagnostics. We will also discuss how comprehensive genomic characterization using broad sequencing panels, allowing for the simultaneous detection of different types of genetic aberrations, may aid future development of precision diagnostics in lymphomas. This may in turn pave the way for the implementation of tailored precision therapy strategies at the individual patient level.

Personalization of medical treatments in oncology: time for rethinking the disease concept to improve individual outcomes

The agenda of pharmacology discovery in the field of personalized oncology was dictated by the search of molecular targets assumed to deterministically drive tumor development. In this perspective, genes play a fundamental "causal" role while cells simply act as causal proxies, i.e., an intermediate between the molecular input and the organismal output. However, the ceaseless genomic change occurring across time within the same primary and metastatic tumor has broken the hope of a personalized treatment based only upon genomic fingerprint. Indeed, current models are unable in capturing the unfathomable complexity behind the outbreak of a disease, as they discard the contribution of non-genetic factors, environment constraints, and the interplay among different tiers of organization. Herein, we posit that a comprehensive personalized model should view at the disease as a "historical" process, in which different spatially and timely distributed factors interact with each other across multiple levels of organization, which collectively interact with a dynamic gene-expression pattern. Given that a disease is a dynamic, non-linear process - and not a static-stable condition - treatments should be tailored according to the "timing-frame" of each condition. This approach can help in detecting those critical transitions through which the system can access different attractors leading ultimately to diverse outcomes - from a pre-disease state to an overt illness or, alternatively, to recovery. Identification of such tipping points can substantiate the predictive and the preventive ambition of the Predictive, Preventive and Personalized Medicine (PPPM/3PM). However, an unusual effort is required to conjugate multi-omics approaches, data collection, and network analysis reconstruction (eventually involving innovative Artificial Intelligent tools) to recognize the critical phases and the relevant targets, which could help in patient stratification and therapy personalization.