Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial

The Progression-Free-Survival Ratio in Molecularly Aided Tumor Trials: A Critical Examination of Current Practice and Suggestions for Alternative Methods

The progression-free-survival ratio is a popular endpoint in oncology trials, which is frequently applied to evaluate the efficacy of molecularly targeted treatments in late-stage patients. Using elementary calculations and simulations, numerous shortcomings of the current methodology are pointed out. As a remedy to these shortcomings, an alternative methodology is proposed, using a marginal Cox model or a marginal accelerated failure time model for clustered time-to-event data. Using comprehensive simulations, it is shown that this methodology outperforms existing methods in settings where the intrapatient correlation is low to moderate. The performance of the model is further demonstrated in a real data example from a molecularly aided tumor trial. Sample size considerations are discussed.

Drug response-based precision therapeutic selection for tamoxifen-resistant triple-positive breast cancer

Breast cancer adaptability to the drug environment reduces the chemotherapeutic response and facilitates acquired drug resistance. Cancer-specific therapeutics can be more effective against advanced-stage cancer than standard chemotherapeutics. To extend the paradigm of cancer-specific therapeutics, clinically relevant acquired tamoxifen-resistant MCF-7 proteome was deconstructed to identify possible druggable targets (N = 150). Twenty-eight drug inhibitors were used against identified druggable targets to suppress non-resistant (NC) and resistant cells (RC). First, selected drugs were screened using growth-inhibitory response against NC and RC. Seven drugs were shortlisted for their time-dependent (10-12 days) cytotoxic effect and further narrowed to three effective drugs (e.g., cisplatin, doxorubicin, and hydroxychloroquine). The growth-suppressive effectiveness of selected drugs was validated in the complex spheroid model (progressive and regressive). In the progressive model, doxorubicin (RC: 83.64 %, NC: 54.81 %), followed by cisplatin (RC: 76.66 %, NC: 68.94 %) and hydroxychloroquine (RC: 68.70 %, NC: 61.78 %) showed a significant growth-suppressive effect. However, in fully grown regressive spheroid, after 4th drug treatment, cisplatin significantly suppressed RC (84.79 %) and NC (40.21 %), while doxorubicin and hydroxychloroquine significantly suppressed only RC (76.09 and 76.34 %). Our in-depth investigation effectively integrated the expression data with the cancer-specific therapeutic investigation. Furthermore, our three-step sequential drug-screening approach unbiasedly identified cisplatin, doxorubicin, and hydroxychloroquine as an efficacious drug to target heterogeneous cancer cell populations. SIGNIFICANCE STATEMENT: Hormonal-positive BC grows slowly, and hormonal-inhibitors effectively suppress the oncogenesis. However, development of drug-resistance not only reduces the drug-response but also increases the chance of BC aggressiveness. Further, alternative chemotherapeutics are widely used to control advanced-stage BC. In contrast, we hypothesized that, compared to standard chemotherapeutics, cancer-specific drugs can be more effective against resistant-cancer. Although cancer-specific treatment identification is an uphill battle, our work shows proteome data can be used for drug selection. We identified multiple druggable targets and, using ex-vivo methods narrowed multiple drugs to disease-condition-specific therapeutics. We consider that our investigation successfully interconnected the expression data with the functional disease-specific therapeutic investigation and selected drugs can be used for effective resistant treatment with higher therapeutic response.

Graph convolution networks model identifies and quantifies gene and cancer specific transcriptome signatures of cancer driver events

BACKGROUND

The identification and drug targeting of cancer causing (driver) genetic alterations has seen immense improvement in recent years, with many new targeted therapies developed. However, identifying, prioritizing, and treating genetic alterations is insufficient for most cancer patients. Current clinical practices rely mainly on DNA level mutational analyses, which in many cases fail to identify treatable driver events. Arguably, signal strength may determine cell fate more than the mutational status that initiated it. The use of transcriptomics, a complex and highly informative representation of cellular and tumor state, had been suggested to enhance diagnostics and treatment successes. A gene-expression based model trained over known genetic alterations could improve identification and quantification of cancer related biological aberrations' signal strength.

METHODS

We present STAMP (Signatures in Transcriptome Associated with Mutated Protein), a Graph Convolution Networks (GCN) based framework for the identification of gene expression signatures related to cancer driver events. STAMP was trained to identify the p53 dysfunction of cancer samples from gene expression, utilizing comprehensive curated graph structures of gene interactions. Predictions were modified for generating a quantitative score to rank the severity of a driver event in each sample. STAMP was then extended to almost 300 tumor type-specific predictive models for important cancer genes/pathways, by training to identify well-established driver events' annotations from the literature.

RESULTS

STAMP achieved very high AUC on unseen data across several tumor types and on an independent cohort. The framework was validated on p53 related genetic and clinical characteristics, including the effect of Variants of Unknown Significance, and showed strong correlation with protein function. For genes and tumor types where targeted therapy is available, STAMP showed correlation with drugs sensitivity (IC50) in an independent cell line database. It managed to stratify drug effect on samples with similar mutational profiles. STAMP was validated for drug-response prediction in clinical patients' cohorts, improving over a state-of-the-art method and suggesting potential biomarkers for cancer treatments.

CONCLUSIONS

The STAMP models provide a learning framework that successfully identifies and quantifies driver events' signal strength, showing utility in portraying the molecular landscape of tumors based on transcriptomics. Importantly, STAMP manifested the ability to improve targeted therapy selection and hence can contribute to better treatment.

Toward Precision in Nutrition Therapy

Precision in critical care nutrition is paramount, as it focuses nutrition interventions on those patients most likely to benefit, or those who might potentially be harmed. Critical care nutrition must therefore be tailored to individual metabolic needs as determined by factors that control the capacity for tissue homeostasis and anabolic responses. This ideally involves the accurate and timely assessment of macronutrient and micronutrient requirements, a careful evaluation of metabolic response mechanisms and the identification of circumstances that might interfere with the productive utilization of dietary substrates. Specific surrogate markers of metabolic response, such as blood glucose levels, urea levels, or nitrogen balance, might be used to evaluate the metabolic readiness for nutrition and to establish the timing, nature, and clinical effectiveness of nutrition interventions. Despite the pressing need to further develop more targeted approaches in critically ill patients, indices of immediate metabolic responses that correlate with favorable clinical outcomes are lacking. In addition, the development of precision approaches might address timely adjustments in protein, energy, or micronutrient supplementation based on evolving clinical conditions. Here, we review why precision tools are needed in critical care nutrition, our progress thus far, as well as promising approaches and technologies by which multidisciplinary healthcare teams can improve quality of care and clinical outcomes by individualizing nutrition interventions.

Applications of Nanopore sequencing in precision cancer medicine

Oxford Nanopore Technologies sequencing, also referred to as Nanopore sequencing, stands at the forefront of a revolution in clinical genetics, offering the potential for rapid, long read, and real-time DNA and RNA sequencing. This technology is currently making sequencing more accessible and affordable. In this comprehensive review, we explore its potential regarding precision cancer diagnostics and treatment. We encompass a critical analysis of clinical cases where Nanopore sequencing was successfully applied to identify point mutations, splice variants, gene fusions, epigenetic modifications, non-coding RNAs, and other pivotal biomarkers that defined subsequent treatment strategies. Additionally, we address the challenges of clinical applications of Nanopore sequencing and discuss the current efforts to overcome them.

Current status of precision oncology in adult glioblastoma

The concept of precision oncology, the application of targeted drugs based on comprehensive molecular profiling, has revolutionized treatment strategies in oncology. This review summarizes the current status of precision oncology in glioblastoma (GBM), the most common and aggressive primary brain tumor in adults with a median survival below 2 years. Targeted treatments without prior target verification have consistently failed. Patients with BRAF V600E-mutated GBM benefit from BRAF/MEK-inhibition, whereas targeting EGFR alterations was unsuccessful due to poor tumor penetration, tumor cell heterogeneity, and pathway redundancies. Systematic screening for actionable molecular alterations resulted in low rates (< 10%) of targeted treatments. Efficacy was observed in one-third and currently appears to be limited to BRAF-, VEGFR-, and mTOR-directed treatments. Advancing precision oncology for GBM requires consideration of pathways instead of single alterations, new trial concepts enabling rapid and adaptive drug evaluation, a focus on drugs with sufficient bioavailability in the CNS, and the extension of target discovery and validation to the tumor microenvironment, tumor cell networks, and their interaction with immune cells and neurons.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes in the COPDGene and ECLIPSE cohort studies

BACKGROUND

Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. We aimed to define high-risk COPD subtypes using genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics.

METHODS

We defined high-risk groups based on PRS and TRS quantiles by maximising differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups.

FINDINGS

We examined two high-risk omics-defined groups in non-overlapping test sets (n = 1133 NHW COPDGene, n = 299 African American (AA) COPDGene, n = 468 ECLIPSE). We defined "high activity" (low PRS, high TRS) and "severe risk" (high PRS, high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signalling processes compared to a low-risk (low PRS, low TRS) subgroup. "High activity" but not "severe risk" participants had greater prospective FEV1 decline (COPDGene: -51 mL/year; ECLIPSE: -40 mL/year) and proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors.

INTERPRETATION

Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection.

FUNDING

National Institutes of Health.

Omics-Enhanced Nanomedicine for Cancer Therapy

Cancer nanomedicine has emerged as a promising approach to overcome the limitations of conventional cancer therapies, offering enhanced efficacy and safety in cancer management. However, the inherent heterogeneity of tumors presents increasing challenges for the application of cancer nanomedicine in both diagnosis and treatment. This heterogeneity necessitates the integration of advanced and high-throughput analytical techniques to tailor nanomedicine strategies to individual tumor profiles. Omics technologies, encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and more, provide unparalleled insights into the molecular and cellular mechanisms underlying cancer. By dissecting tumor heterogeneity across multiple levels, these technologies offer robust support for the development of personalized and precise cancer nanomedicine strategies. In this review, the principles, techniques, and applications of key omics technologies are summarized. Especially, the synergistic integration of omics and nanomedicine in cancer therapy is explored, focusing on enhanced diagnostic accuracy, optimized therapeutic strategies and the assessment of nanomedicine-mediated biological responses. Moreover, this review addresses current challenges and outlines future directions in the field of omics-enhanced nanomedicine. By offering valuable insights and guidance, this review aims to advance the integration of omics with nanomedicine, ultimately driving improved diagnostic and therapeutic strategies for cancer.

Tumor necrosis factor superfamily signaling: life and death in cancer

Immune checkpoint inhibitors have shaped the landscape of cancer treatment. However, many patients either do not respond or suffer from later progression. Numerous proteins can control immune system activity, including multiple tumor necrosis factor (TNF) superfamily (TNFSF) and TNF receptor superfamily (TNFRSF) members; these proteins play a complex role in regulating cell survival and death, cellular differentiation, and immune system activity. Notably, TNFSF/TNFRSF molecules may display either pro-tumoral or anti-tumoral activity, or even both, depending on tumor type. Therefore, TNF is a prototype of an enigmatic two-faced mediator in oncogenesis. To date, multiple anti-TNF agents have been approved and/or included in guidelines for treating autoimmune disorders and immune-related toxicities after immune checkpoint blockade for cancer. A confirmed role for the TNFSF/TNFRSF members in treating cancer has proven more elusive. In this review, we highlight the cancer-relevant TNFSF/TNFRSF family members, focusing on the death domain-containing and co-stimulation members and their signaling pathways, as well as their complicated role in the life and death of cancer cells.

Unveiling the Digital Evolution of Molecular Tumor Boards

Molecular tumor boards (MTB) are interdisciplinary conferences involving various experts discussing patients with advanced tumors, to derive individualized treatment suggestions based on molecular variants. These discussions involve using heterogeneous internal data, such as patient clinical data, but also external resources such as knowledge databases for annotations and search for relevant clinical studies. This imposes a certain level of complexity that requires huge effort to homogenize the data and use it in a speedy manner to reach the needed treatment. For this purpose, most institutions involving an MTB are heading toward automation and digitalization of the process, hence reducing manual work requiring human intervention and subsequently time in deriving personalized treatment suggestions. The tools are also used to better visualize the patient's data, which allows a refined overview for the board members. In this paper, we present the results of our thorough literature research about MTBs, their process, the most common knowledge bases, and tools used to support this decision-making process.

Prospective study of the real impact of fusion centered genomic assays in patient management in a national collaborative group: the GETHI-XX-16 study

PURPOSE

Precision medicine represents a paradigm shift in oncology. Access to genetic testing and targeted therapies is frequently limited. Assays based on DNA sequencing can miss druggable alterations. We aimed to determine the impact of a free access program to RNA tests in patient management.

METHODS

We designed a multicenter prospective observational study within the Spanish National Group for Translational Oncology and Rare and Orphan Tumors (GETTHI). Eligible patients were adults with solid cancers that had progressed on standard therapies. Tumor samples were analyzed using two RNA sequencing assays (Trailblaze PharosTM and Archer FusionPlex Solid TumorTM). A central committee evaluated the actionability of genetic alterations and reported the findings to attending physicians, who made the final clinical management decisions.

RESULTS

Between November 2016 and April 2019, 395 patients with 41 different tumors across 30 hospitals were included. Molecular analysis revealed actionable genetic alterations in 57 individuals (14.4%). Targeted therapies were advised for 23 and seven received a matched targeted therapy: two lung cancers (EML4-ALK and CD74-ROS1 fusion), three glioblastomas (EGFR point mutations), one oligodendroglioma (FGFR3-TACC3 fusion) and a prostate cancer (SND1-BRAF fusion). The outcomes included two tumor responses, one disease stabilization, one early withdrawal due to toxicity, one progression, and one unknown.

CONCLUSION

Despite the growing knowledge of cancer biology and its translation to drug development, the overall impact of personalized treatments remains low. Access to comprehensive molecular tests covering properly all known actionable alterations and programs for a wide access to targeted therapies seem to be critical steps.

Recurrent hepatocellular carcinoma is associated with the enrichment of MYC targets gene sets, elevated high confidence deleterious mutations and alternative splicing of DDB2 and BRCA1 transcripts

PURPOSE

Recurrence is the main cause of hepatocellular carcinoma (HCC) related deaths. Underlying recurrence biology can be better understood by comparative analysis of the complete set of transcripts between recurrent and non-recurrent HCC. In this study, transcriptomic data (GSE56545) from 21 male patients diagnosed with either recurrent or non-recurrent HCC were reanalyzed to identify deregulated pathways, somatic mutations, fusion transcripts, alternative splicing events, and the immune context in recurrent HCC.

MATERIALS AND METHODS

DESeq2 was used for differential expression analysis, Mutect2 for somatic mutation analysis, Arriba and STAR-Fusion for fusion transcript analysis, and rMATs for alternative splicing analysis.

RESULTS

The results revealed that MYC targets gene sets (Hallmark_MYC_targets_V1 and Hallmark_MYC_targets_V2) were significantly enriched in recurrent HCC. Among the MYC targets, CBX3, NOP56, CDK4, NPM1, MCM5, MCM4 and PA2G4 upregulation was significantly associated with poor survival. Somatic mutation analysis demonstrated that the numbers of high confidence deleterious mutations were significantly increased in recurrent HCC. Alternative splicing-mediated production of non-functional DDB2 and oncogenic BRCA1 D11q were discovered in recurrent HCC. Finally, CD8+ T-cells were significantly decreased in recurrent HCC.

CONCLUSIONS

These results indicated that the enrichment of MYC targets gene sets is one of the most critical factors that leads to the development of recurrent HCC. In addition, elevated deleterious mutation numbers and alternative spliced DDB2 and BRCA1 isoforms have been identified as prominent contributors to increasing genomic instability in male patients with recurrent HCC.

Spatial immunogenomic patterns associated with lymph node metastasis in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) with lymph node (LN) metastasis is linked to poor prognosis, yet the underlying mechanisms remain largely undefined. This study aimed to elucidate the immunogenomic landscape associated with LN metastasis in LUAD.

METHODS

We employed broad-panel next-generation sequencing (NGS) on a cohort of 257 surgically treated LUAD patients to delineate the molecular landscape of primary tumors and identify actionable driver-gene alterations. Additionally, we used multiplex immunohistochemistry (mIHC) on a propensity score-matched cohort, which enabled us to profile the immune microenvironment of primary tumors in detail while preserving cellular metaclusters, interactions, and neighborhood functional units. By integrating data from NGS and mIHC, we successfully identified spatial immunogenomic patterns and developed a predictive model for LN metastasis, which was subsequently validated independently.

RESULTS

Our analysis revealed distinct immunogenomic alteration patterns associated with LN metastasis stages. Specifically, we observed increased mutation frequencies in genes such as PIK3CG and ATM in LN metastatic primary tumors. Moreover, LN positive primary tumors exhibited a higher presence of macrophage and regulatory T cell metaclusters, along with their enriched neighborhood units (p < 0.05), compared to LN negative tumors. Furthermore, we developed a novel predictive model for LN metastasis likelihood, designed to inform non-surgical treatment strategies, optimize personalized therapy plans, and potentially improve outcomes for patients who are ineligible for surgery.

CONCLUSIONS

This study offers a comprehensive analysis of the genetic and immune profiles in LUAD primary tumors with LN metastasis, identifying key immunogenomic patterns linked to metastatic progression. The predictive model derived from these insights marks a substantial advancement in personalized treatment, underscoring its potential to improve patient management.

Identification of a central network hub of key prognostic genes based on correlation between transcriptomics and survival in patients with metastatic solid tumors

Background

Dysregulated pathways in cancer may be hub addicted. Identifying these dysregulated networks for targeting might lead to novel therapeutic options.

Objective

Considering the hypothesis that central hubs are associated with increased lethality, identifying key hub targets within central networks could lead to the development of novel drugs with improved efficacy in advanced metastatic solid tumors.

Design

Exploring transcriptomic data (22,000 gene products) from the WINTHER trial (N = 101 patients with various metastatic cancers), in which both tumor and normal organ-matched tissue were available.

Methods

A retrospective in silico analysis of all genes in the transcriptome was conducted to identify genes different in expression between tumor and normal tissues (paired t-test) and to determine their association with survival outcomes using survival analysis (Cox proportional hazard regression algorithm). Based on the biological relevance of the identified genes, hub targets of interest within central networks were then pinpointed. Patients were grouped based on the expression level of these genes (K-mean clustering), and the association of these groups with survival was examined (Cox proportional hazard regression algorithm, Forest plot, and Kaplan-Meier plot).

Results

We identified four key central hub genes-PLOD3, ARHGAP11A, RNF216, and CDCA8, for which high expression in tumor tissue compared to analogous normal tissue had the most significant correlation with worse outcomes. The correlation was independent of tumor or treatment type. The combination of the four genes showed the highest significance and correlation with the poorer outcome: overall survival (hazard ratio (95% confidence interval (CI)) = 10.5 (3.43-31.9) p = 9.12E-07 log-rank test in a Cox proportional hazard regression model). Findings were validated in independent cohorts.

Conclusion

The expression of PLOD3, ARHGAP11A, RNF216, and CDCA8 constitute, when combined, a prognostic tool, agnostic of tumor type and previous treatments. These genes represent potential targets for intercepting central hub networks in various cancers, offering avenues for novel therapeutic interventions.

The Molecular Tumor Board Turns 10: The Age of Complexity

Gene sequencing has brought a titanic of complex data into clinical precision oncology. Deciphering this complexity for practice requires new constructs. In 2014, the Molecular Tumor Board (MTB) was introduced into the literature by a publication in The Oncologist. Ten years later, MTBs have become globally established vehicles that integrate rapidly emerging "omic" information, helping to transform cancer management.

Deciphering resistance mechanisms in cancer: final report of MATCH-R study with a focus on molecular drivers and PDX development

BACKGROUND

Understanding the resistance mechanisms of tumor is crucial for advancing cancer therapies. The prospective MATCH-R trial (NCT02517892), led by Gustave Roussy, aimed to characterize resistance mechanisms to cancer treatments through molecular analysis of fresh tumor biopsies. This report presents the genomic data analysis of the MATCH-R study conducted from 2015 to 2022 and focuses on targeted therapies.

METHODS

The study included resistant metastatic patients (pts) who accepted an image-guided tumor biopsy. After evaluation of tumor content (TC) in frozen tissue biopsies, targeted NGS (10 < TC < 30%) or Whole Exome Sequencing and RNA sequencing (TC > 30%) were performed before and/or after the anticancer therapy. Patient-derived xenografts (PDX) were established by implanting tumor fragments into NOD scid gamma mice and amplified up to five passages.

RESULTS

A total of 1,120 biopsies were collected from 857 pts with the most frequent tumor types being lung (38.8%), digestive (16.3%) and prostate (14.1%) cancer. Molecular targetable driver were identified in 30.9% (n = 265/857) of the patients, with EGFR (41.5%), FGFR2/3 (15.5%), ALK (11.7%), BRAF (6.8%), and KRAS (5.7%) being the most common altered genes. Furthermore, 66.0% (n = 175/265) had a biopsy at progression on targeted therapy. Among resistant cases, 41.1% (n = 72/175) had no identified molecular mechanism, 32.0% (n = 56/175) showed on-target resistance, and 25.1% (n = 44/175) exhibited a by-pass resistance mechanism. Molecular profiling of the 44 patients with by-pass resistance identified 51 variants, with KRAS (13.7%), PIK3CA (11.8%), PTEN (11.8%), NF2 (7.8%), AKT1 (5.9%), and NF1 (5.9%) being the most altered genes. Treatment was tailored for 45% of the patients with a resistance mechanism identified leading to an 11 months median extension of clinical benefit. A total of 341 biopsies were implanted in mice, successfully establishing 136 PDX models achieving a 39.9% success rate. PDX models are available for EGFR (n = 31), FGFR2/3 (n = 26), KRAS (n = 18), ALK (n = 16), BRAF (n = 6) and NTRK (n = 2) driven cancers. These models closely recapitulate the biology of the original tumors in term of molecular alterations and pharmacological status, and served as valuable models to validate overcoming treatment strategies.

CONCLUSION

The MATCH-R study highlights the feasibility of on purpose image guided tumor biopsies and PDX establishment to characterize resistance mechanisms and guide personalized therapies to improve outcomes in pre-treated metastatic patients.

Molecular and functional landscape of malignant serous effusions for precision oncology

Personalized treatment for patients with advanced solid tumors critically depends on the deep characterization of tumor cells from patient biopsies. Here, we comprehensively characterize a pan-cancer cohort of 150 malignant serous effusion (MSE) samples at the cellular, molecular, and functional level. We find that MSE-derived cancer cells retain the genomic and transcriptomic profiles of their corresponding primary tumors, validating their use as a patient-relevant model system for solid tumor biology. Integrative analyses reveal that baseline gene expression patterns relate to global ex vivo drug sensitivity, while high-throughput drug-induced transcriptional changes in MSE samples are indicative of drug mode of action and acquired treatment resistance. A case study exemplifies the added value of multi-modal MSE profiling for patients who lack genetically stratified treatment options. In summary, our study provides a functional multi-omics view on a pan-cancer solid tumor cohort and underlines the feasibility and utility of MSE-based precision oncology.

Nationwide precision oncology pilot study: KOrean Precision Medicine Networking Group Study of MOlecular profiling-guided therapy based on genomic alterations in advanced solid tumors (KOSMOS) KCSG AL-20-05

BACKGROUND

Next-generation sequencing (NGS) has become widely available but molecular profiling-guided therapy (MGT) had not been well established in the real world due to lack of available therapies and expertise to match treatment. Our study was designed to test the feasibility of a nationwide platform of NGS-guided MGT recommended by a central molecular tumor board (cMTB) for metastatic solid tumors.

PATIENTS AND METHODS

Patients with advanced or metastatic solid tumors with available NGS results and without standard treatment were enrolled. The cMTB interpreted the patients' NGS reports and recommended the following: (i) investigational medicinal products (IMPs) approved in other indications; (ii) alternative treatments; (iii) clinical trials. The primary variables were the proportion of patients with actionable genomic alterations and those receiving MGT as per cMTB recommendations. Others included treatment duration (TD), overall response rate (ORR), disease control rate (DCR), and safety.

RESULTS

From February 2021 to February 2022, 193 cases [99 (51.3%) men; median age 58 years (range 24-88 years); median line of previous treatment 3 (range 0-9)] from 29 sites were enrolled for 60 cMTB sessions. The median time from case submission to cMTB discussion was 7 days (range 2-20 days), and to IMP treatment initiation was 28 days (range 14-90 days). Actionable genetic alterations were found in 145 patients (75.1%). A total of 89 (46.1%) patients received actual dosing of IMPs, and 10 (5.2%) were enrolled in cMTB-recommended clinical trials, achieving an MGT rate of 51.3%. ORR and DCR of IMPs were 10.1% and 72.5%, respectively. The median TD was 3.5 months [95% confidence interval (CI) 2.8-5.5 months], and the 4-month TD rate was 44.9%. The median overall survival of patients who received IMPs was 6.9 months (95% CI 5.2-10.0 months).

CONCLUSION

KOSMOS confirmed the feasibility of MGT recommended by the cMTB, achieving a high MGT match rate and promising effectiveness in heavily pretreated advanced cancer patients.

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.

Unlocking the future of cancer diagnosis - promises and challenges of ctDNA-based liquid biopsies in non-small cell lung cancer

The advent of liquid biopsies has brought significant changes to the diagnosis and monitoring of non-small cell lung cancer (NSCLC), presenting both promise and challenges. Molecularly targeted drugs, capable of enhancing survival rates, are now available to around a quarter of NSCLC patients. However, to ensure their effectiveness, precision diagnosis is essential. Circulating tumor DNA (ctDNA) analysis as the most advanced liquid biopsy modality to date offers a non-invasive method for tracking genomic changes in NSCLC. The potential of ctDNA is particularly rooted in its ability to furnish comprehensive (epi-)genetic insights into the tumor, thereby aiding personalized treatment strategies. One of the key advantages of ctDNA-based liquid biopsies in NSCLC is their ability to capture tumor heterogeneity. This capability ensures a more precise depiction of the tumor's (epi-)genomic landscape compared to conventional tissue biopsies. Consequently, it facilitates the identification of (epi-)genetic alterations, enabling informed treatment decisions, disease progression monitoring, and early detection of resistance-causing mutations for timely therapeutic interventions. Here we review the current state-of-the-art in ctDNA-based liquid biopsy technologies for NSCLC, exploring their potential to revolutionize clinical practice. Key advancements in ctDNA detection methods, including PCR-based assays, next-generation sequencing (NGS), and digital PCR (dPCR), are discussed, along with their respective strengths and limitations. Additionally, the clinical utility of ctDNA analysis in guiding treatment decisions, monitoring treatment response, detecting minimal residual disease, and identifying emerging resistance mechanisms is examined. Liquid biopsy analysis bears the potential of transforming NSCLC management by enabling non-invasive monitoring of Minimal Residual Disease and providing early indicators for response to targeted treatments including immunotherapy. Furthermore, considerations regarding sample collection, processing, and data interpretation are highlighted as crucial factors influencing the reliability and reproducibility of ctDNA-based assays. Addressing these challenges will be essential for the widespread adoption of ctDNA-based liquid biopsies in routine clinical practice, ultimately paving the way toward personalized medicine and improved outcomes for patients with NSCLC.

Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity

Stratification of patients diagnosed with cancer has become a major goal in personalized oncology. One important aspect is the accurate prediction of the response to various drugs. It is expected that the molecular characteristics of the cancer cells contain enough information to retrieve specific signatures, allowing for accurate predictions based solely on these multi-omic data. Ideally, these predictions should be explainable to clinicians, in order to be integrated in the patients care. We propose a machine-learning framework based on ensemble learning to integrate multi-omic data and predict sensitivity to an array of commonly used and experimental compounds, including chemotoxic compounds and targeted kinase inhibitors. We trained a set of classifiers on the different parts of our dataset to produce omic-specific signatures, then trained a random forest classifier on these signatures to predict drug responsiveness. We used the Cancer Cell Line Encyclopedia dataset, comprising multi-omic and drug sensitivity measurements for hundreds of cell lines, to build the predictive models, and validated the results using nested cross-validation. Our results show good performance for several compounds (Area under the Receiver-Operating Curve >79%) across the most frequent cancer types. Furthermore, the simplicity of our approach allows to examine which omic layers have a greater importance in the models and identify new putative markers of drug responsiveness. We propose several models based on small subsets of transcriptional markers with the potential to become useful tools in personalized oncology, paving the way for clinicians to use the molecular characteristics of the tumors to predict sensitivity to therapeutic compounds.

Comprehensive genomic profiling in multiple cancer types: A comparative analysis of the National Biobank Consortium of Taiwan and clinical practice cohorts

BACKGROUND

This retrospective study analyzed tumor tissue profiling data to assess the potential of comprehensive genomic profiling (CGP) for patient care across diverse solid tumors.

MATERIAL AND METHODS

Patients with newly diagnosed or recurrent stage IIIB or IV lung adenocarcinoma with a null immunophenotype and esophageal, gastric, pancreatic, or bile duct cancer between January 2020 and July 2023 at two medical centers in Taiwan were included. One cohort was a part of the National Biobank Consortium of Taiwan project, whereas the other consisted of patients undergoing routine clinical practice. Tumor samples were subjected to CGP using FoundationOne®CDx, with therapeutic implications determined using OncoKB classification.

RESULTS

FoundationOne®CDx testing of 574 patients was successful in 456 (79.4%) patients. Clinically actionable genomic alterations were detected in 21.1% (96/456) of the patients, including 17.5%, 2.9%, and 0.7% of patients with evidence levels 1, 2, and 3, respectively. Lung adenocarcinoma accounted for the largest proportion of samples with at least one actionable gene alteration (63.2%), followed by bile duct (26.9%), gastric (17.6%), esophageal (4.0%), and pancreatic (3.1%) cancers. Based on CGP results, 43 patients (9.4%) received matched targeted therapy. The median overall survival of patients who received matched therapy or not was 26.1 months (95% confidence interval (CI), 16.7-35.5 months) and 10.6 months (95% CI, 8.1-13.1 months; hazard ratio, 0.28, 95% CI, 0.14-0.55, p < 0.001), respectively.

CONCLUSIONS

This study provides comprehensive insights into the genomic profiles of diverse cancers in Taiwan, highlighting the crucial role of CGP in identifying actionable genomic alterations and guiding effective therapeutic strategies in real-world practice.

A Phase II, Open-Label, Randomized Trial of Durvalumab With Olaparib or Cediranib in Patients With Mismatch Repair-Proficient Colorectal or Pancreatic Cancer

BACKGROUND

The use of immunotherapy in mismatch repair proficient colorectal cancer (pMMR-CRC) or pancreatic adenocarcinoma (PDAC) is associated with limited efficacy. DAPPER (NCT03851614) is a phase 2, basket study randomizing patients with pMMR CRC or PDAC to durvalumab with olaparib (durvalumab + olaparib) or durvalumab with cediranib (durvalumab + cediranib).

METHODS

PDAC or pMMR-CRC patients were randomized to either durvalumab+olaparib (arm A), or durvalumab + cediranib (arm B). Co-primary endpoints included pharmacodynamic immune changes in the tumor microenvironment (TME) and safety. Objective response rate, progression-free survival (PFS) and overall survival (OS) were determined. Paired tumor samples were analyzed by multiplexed immunohistochemistry and RNA-sequencing.

RESULTS

A total of 31 metastatic pMMR-CRC patients were randomized to arm A (n = 16) or B (n = 15). In 28 evaluable patients, 3 patients had stable disease (SD) (2 patients treated with durvalumab + olaparib and 1 patient treated with durvalumab + cediranib) while 25 had progressive disease (PD). Among patients with PDAC (n = 19), 9 patients were randomized to arm A and 10 patients were randomized to arm B. In 18 evaluable patients, 1 patient had a partial response (unconfirmed) with durvalumab + cediranib, 1 patient had SD with durvalumab + olaparib while 16 had PD. Safety profile was manageable and no grade 4-5 treatment-related adverse events were observed in either arm A or B. No significant changes were observed for CD3+/CD8+ immune infiltration in on-treatment biopsies as compared to baseline for pMMR-CRC and PDAC independent of treatment arms. Increased tumor-infiltrating lymphocytes at baseline, low baseline CD68+ cells and different immune gene expression signatures at baseline were associated with outcomes.

CONCLUSIONS

In patients with pMMR-CRC or PDAC, durvalumab + olaparib and durvalumab + cediranib showed limited antitumor activity. Different immune components of the TME were associated with treatment outcomes.

High aggressiveness of papillary thyroid cancer: from clinical evidence to regulatory cellular networks

The global incidence of thyroid cancer has increased over recent decades. Papillary thyroid cancer (PTC) is the most common type of thyroid cancer and accounts for nearly 90% of all cases. Typically, PTC has a good prognosis. However, some PTC variants exhibit more aggressive behaviour, which significantly increases the risk of postoperative recurrence. Over the past decade, the high metastatic potential of PTC has drawn the attention of many researchers and these studies have provided useful molecular markers for improved diagnosis, risk stratification and clinical approaches. The aim of this review is to discuss the progress in epidemiology, metastatic features, risk factors and molecular mechanisms associated with PTC aggressiveness. We present a detailed picture showing that epithelial-to-mesenchymal transition, cancer metabolic reprogramming, alterations in important signalling pathways, epigenetic aberrations and the tumour microenvironment are crucial drivers of PTC metastasis. Further research is needed to more fully elucidate the pathogenesis and biological behaviour underlying the aggressiveness of PTC.

Advances in Personalized Oncology

Advances in next-generation sequencing (NGS) have catalyzed a paradigm shift in cancer treatment, steering the focus from conventional, organ-specific protocols to precision medicine. Emerging targeted therapies offer a cutting-edge approach to cancer treatment, while companion diagnostics play an essential role in aligning therapeutic choices with specific molecular changes identified through NGS. Despite these advances, interpreting the clinical implications of a rapidly expanding catalog of genetic mutations remains a challenge. The selection of therapies in the presence of multiple mutations requires careful clinical judgment, supported by quality-centric genomic testing that emphasizes actionable mutations. Molecular tumor boards can play an increasing role in assimilating genomic data into clinical trials, thereby refining personalized treatment approaches and improving patient outcomes.

Molecular Tumor Board for Unicorns: Outcomes for rare and ultra-rare cancers using an N-of-One personalized treatment strategy

Treatment of rare/ultra-rare tumors is an unmet need due to a lack of standardized therapies and clinical trials. We developed the Molecular Tumor Board (MTB), a multidisciplinary team that integrates molecular profiling to generate personalized, N-of-One treatments for advanced cancers. This study evaluates 112 patients with rare/ultra-rare tumors who presented to the MTB and were evaluable for clinical therapeutic outcome. Overall, 46/112 patients (41%) received a treatment regimen with a high degree of matching between tumor molecular alterations and drugs given (reflected by a high Matching Score (≥50%)). Patients with a high versus low Matching Score experienced significantly longer progression-free survival (p = 0.005) and overall survival (p = 0.047), and higher rates of clinical benefit (stable disease ≥6 months, partial response, or complete response) (54% vs. 32% p = 0.027). The MTB facilitated personalized N-of-One matching of drugs to tumor molecular alterations, which was associated with improved clinical outcomes in patients with rare/ultra-rare cancers.

Comprehensive genetic profiling and molecularly guided treatment for patients with primary CNS tumors

Despite major advances in molecular profiling and classification of primary brain tumors, personalized treatment remains limited for most patients. Here, we explored the feasibility of individual molecular profiling and the efficacy of biomarker-guided therapy for adult patients with primary brain cancers in the real-world setting within the molecular tumor board Freiburg, Germany. We analyzed genetic profiles, personalized treatment recommendations, and clinical outcomes of 102 patients with 21 brain tumor types. Alterations in the cell cycle, BRAF, and mTOR pathways most frequently led to personalized treatment recommendations. Molecularly informed therapies were recommended in 71% and implemented in 32% of patients with completed molecular diagnostics. The disease control rate following targeted treatment was 50% and the overall response rate was 30%, with a progression-free survival 2/1 ratio of at least 1.3 in 31% of patients. This study highlights the efficacy of molecularly guided treatment and the need for biomarker-stratified trials in brain cancers.

Addressing the knowledge gap in the genomic landscape and tailored therapeutic approaches to adolescent and young adult cancers

BACKGROUND

Adolescents and young adults (AYAs) represent a small proportion of patients with cancer. The genomic profiles of AYA patients with cancer are not well-studied, and outcomes of genome-matched therapies remain largely unknown.

PATIENTS AND METHODS

We investigated differences between Japanese AYA and older adult (OA) patients in genomic alterations, therapeutic evidence levels, and genome-matched therapy usage by cancer type. We also assessed treatment outcomes.

RESULTS

AYA patients accounted for 8.3% of 876 cases. Microsatellite instability-high and/or tumor mutation burden was less common in AYA patients (1.4% versus 7.7% in OA; P = 0.05). However, BRCA1 alterations were more common in AYA patients with breast cancer (27.3% versus 1.7% in OA; P = 0.01), as were MYC alterations in AYA patients with colorectal cancer (23.5% versus 5.8% in OA; P = 0.02) and sarcoma (31.3% versus 3.4% in OA; P = 0.01). Genome-matched therapy use was similar between groups, with overall survival tending to improve in both. However, in AYA patients, the small number of patients prevented statistical significance. Comprehensive genomic profiling-guided genome-matched therapy yielded encouraging results, with progression-free survival of 9.0 months in AYA versus 3.7 months in OA patients (P = 0.59).

CONCLUSION

Our study suggests that tailored therapeutic approaches can benefit cancer patients regardless of age.

mTORC1-Driven Protein Translation Correlates with Clinical Benefit of Capivasertib within a Genetically Preselected Cohort of PIK3CA-Altered Tumors

Capivasertib is a potent selective inhibitor of AKT. It was recently FDA approved in combination with fulvestrant to treat HR+, HER2-negative breast cancers with certain genetic alteration(s) activating the PI3K pathway. In phase I trials, heavily pretreated patients with tumors selected for activating PI3K pathway mutations treated with capivasertib monotherapy demonstrated objective response rates of <30%. We investigated the proteomic profile associated with capivasertib response in genetically preselected patients and cancer cell lines. We analyzed samples from 16 PIK3CA-mutated patient tumors collected prior to capivasertib monotherapy in the phase I trial. PI3K pathway proteins were precisely quantified with immuno-Matrix-Assisted Laser Desorption/Ionization-mass spectrometry (iMALDI-MS). Global proteomic profiles were also obtained. Patients were classified according to response to capivasertib monotherapy: "clinical benefit (CB)" (≥12 weeks without progression, n = 7) or "no clinical benefit (NCB)" (progression in <12 weeks, n = 9). Proteins that differed between the patient groups were subsequently quantified in AKT1- or PIK3CA-altered breast cancer cell lines with varying capivasertib sensitivity. The measured concentrations of AKT1 and AKT2 varied among the PIK3CA-mutated tumors but did not differ between the CB and NCB groups. However, analysis of the global proteome data showed that translational activity was higher in tumors of the NCB vs. CB group. When reproducibly quantified by validated LC-MRM-MS assays, the same proteins of interest similarly distinguished between capivasertib-sensitive versus -resistant cell lines. The results provide further evidence that increased mTORC1-driven translation functions as a mechanism of resistance to capivasertib monotherapy. Protein concentrations may offer additional insights for patient selection for capivasertib, even among genetically preselected patients.

SIGNIFICANCE

Capivasertib's first-in-class FDA approval demonstrates its promise, yet there remains an opportunity to optimize its use. Our results provide new evidence that proteomics can stratify genetically preselected patients on clinical benefit. Characterization of the same profile in cell lines furnishes additional validation. Among PIK3CA-altered tumors, increased mTORC1-driven translation appears to confer intrinsic resistance. Assessing mTORC1 activation could therefore prove a useful complement to the existing genetic selection strategy for capivasertib.

Unlocking the potential of Molecular Tumor Boards: from cutting-edge data interpretation to innovative clinical pathways

The emerging era of precision medicine is characterized by an increasing availability of targeted anticancer therapies and by the parallel development of techniques to obtain more refined molecular data, whose interpretation may not always be straightforward. Molecular tumor boards gather various professional figures, in order to leverage the analysis of molecular data and provide prognostic and predictive insights for clinicians. In addition to healthcare development, they could also become a tool to promote knowledge and research spreading. A growing body of evidence on the application of molecular tumor boards to clinical practice is forming and positive signals are emerging, although a certain degree of heterogeneity exists. This work analyzes molecular tumor boards' potential workflows, figures involved, data sources, sample matrices and eligible patients, as well as available evidence and learning examples. The emerging concept of multi-institutional, disease-specific molecular tumor boards is also considered by presenting two ongoing nationwide experiences.

Identification and Validation of JAM-A as a Novel Prognostic and Immune Factor in Human Tumors

Junctional adhesion molecule-A (JAM-A), also known as F11 receptor (F11R), is a transmembrane glycoprotein that is involved in various biological processes, including cancer initiation and progression. However, the functional characteristics and significance of JAM-A in pan-cancer remain unexplored. In this study, we used multiple databases to gain a comprehensive understanding of JAM-A in human cancers. JAM-A was widely expressed in various tissues, mainly located on the microtubules and cell junctions. Aberrant expression of JAM-A was detected in multiple cancers at both mRNA and protein levels, which can be correlated with poorer prognosis and may be attributed to genetic alterations and down-regulated DNA methylation. JAM-A expression was also associated with immune infiltration and may affect immunotherapy responses in several cancers. Functional enrichment analysis indicated that JAM-A participated in tight junction and cancer-related pathways. In vitro experiments verified that JAM-A knockdown suppressed the proliferation and migration abilities of breast cancer cells and liver cancer cells. Overall, our study suggests that JAM-A is a pan-cancer regulator and a potential biomarker for predicting prognosis and immune-therapeutic responses for different tumors.

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

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

The expression patterns of different cell types and their interactions in the tumor microenvironment are predictive of breast cancer patient response to neoadjuvant chemotherapy

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While the TME's impact on immunotherapy has been extensively studied, its role in chemotherapy response remains less explored. To address this, we developed DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning), a generic computational framework leveraging cellular deconvolution of bulk transcriptomics to associate the gene expression of individual cell types in the TME with clinical response. Employing DECODEM to analyze the gene expression of breast cancer (BC) patients treated with neoadjuvant chemotherapy, we find that the gene expression of specific immune cells (myeloid, plasmablasts, B-cells) and stromal cells (endothelial, normal epithelial, CAFs) are highly predictive of chemotherapy response, going beyond that of the malignant cells. These findings are further tested and validated in a single-cell cohort of triple negative breast cancer. To investigate the possible role of immune cell-cell interactions (CCIs) in mediating chemotherapy response, we extended DECODEM to DECODEMi to identify such CCIs, validated in single-cell data. Our findings highlight the importance of active pre-treatment immune infiltration for chemotherapy success. The tools developed here are made publicly available and are applicable for studying the role of the TME in mediating response from readily available bulk tumor expression in a wide range of cancer treatments and indications.

Identification of barriers to implementation of precision oncology in patients with rare cancers

Established treatment options for rare cancers are limited by the small number of patients. The current comprehensive genomic profiling (CGP) testing might not fully exploit opportunities for precision oncology in patients with rare cancers. Therefore, we aimed to explore the factors associated with CGP testing utility in rare cancers and identify barriers to implementing precision oncology. Patients who underwent CGP testing at our institution between September 2019 and June 2021 were enrolled in this retrospective study. Based on their results, the patients received molecularly targeted drugs or immune checkpoint inhibitors. Univariate and multivariate analyses evaluated the association between patient characteristics and the proportion of patients receiving molecularly targeted drugs. Overall, 790 patients underwent CGP testing. Among them, 333 patients with rare cancers were identified, of whom 278 (83.5%) had actionable genomic alterations, 127 (38.1%) had druggable genomic alterations, and 25 (7.5%) received genomically matched therapy. The proportion of patients receiving molecularly targeted drugs was significantly higher among those with treatment options with evidence levels A-D (8.7%) than those without treatment options with evidence levels A-D (2.9%). A potential barrier to CGP testing utility in rare cancers is the limited number of molecularly targeted drugs with clinical evidence. We propose that CGP testing be performed in patients with rare cancers who have treatment options with evidence levels A-D to maximize CGP testing utility in real-world practice.

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.

[The collaborative project "Personalized medicine for oncology" (PM4Onco) as part of the Medical Informatics Initiative (MII)]

The collaborative project Personalized Medicine for Oncology (PM4Onco) was launched in 2023 as part of the National Decade against Cancer (NKD) and is executed within the Medical Informatics Initiative (MII). Its aim is to establish a sustainable infrastructure for the integration and use of data from clinical and biomedical research and therefore combines the experience and preliminary work of all four consortia of the MII and the leading oncology centers in Germany. The data provided by PM4Onco will be prepared in a suitable form to support decision making in molecular tumor boards. This concept and infrastructure will be extended to 23 participating partner sites and thus improve access to targeted therapies based on clinical information and analysis of molecular genetic alterations in tumors at different stages of the disease. This will help to improve the treatment and prognosis of tumor diseases.Clinical cancer registries are involved in the project to improve data quality through standardized documentation routines. Clinical experts advise on the expansion of the core datasets for personalized medicine (PM). Information on quality of life and treatment outcomes reported by patients in questionnaires, which is rarely collected outside of clinical trials, will make a significant contribution. Patient representatives are involved from the onset to ensure that the important perspective of patients is taken into account in the decision-making process. PM4Onco thus creates an alliance between the MII, oncological centers of excellence, clinical cancer registries, young scientists, patients, and citizens to strengthen and advance PM in cancer therapy.

Omics-based molecular classifications empowering in precision oncology

BACKGROUND

In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine.

CONCLUSION

This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

Bridging the Divide: A Review on the Implementation of Personalized Cancer Medicine

The shift towards personalized cancer medicine (PCM) represents a significant transformation in cancer care, emphasizing tailored treatments based on the genetic understanding of cancer at the cellular level. This review draws on recent literature to explore key factors influencing PCM implementation, highlighting the role of innovative leadership, interdisciplinary collaboration, and coordinated funding and regulatory strategies. Success in PCM relies on overcoming challenges such as integrating diverse medical disciplines, securing sustainable investment for shared infrastructures, and navigating complex regulatory landscapes. Effective leadership is crucial for fostering a culture of innovation and teamwork, essential for translating complex biological insights into personalized treatment strategies. The transition to PCM necessitates not only organizational adaptation but also the development of new professional roles and training programs, underscoring the need for a multidisciplinary approach and the importance of team science in overcoming the limitations of traditional medical paradigms. The conclusion underscores that PCM's success hinges on creating collaborative environments that support innovation, adaptability, and shared vision among all stakeholders involved in cancer care.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes

Rationale

Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear.

Objectives

Define high-risk COPD subtypes using both genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics.

Methods

We defined high-risk groups based on PRS and TRS quantiles by maximizing differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups.

Measurements and Main Results

We examined two high-risk omics-defined groups in non-overlapping test sets (n=1,133 NHW COPDGene, n=299 African American (AA) COPDGene, n=468 ECLIPSE). We defined "High activity" (low PRS/high TRS) and "severe risk" (high PRS/high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signaling processes compared to a low-risk (low PRS, low TRS) reference subgroup. "High activity" but not "severe risk" participants had greater prospective FEV 1 decline (COPDGene: -51 mL/year; ECLIPSE: - 40 mL/year) and their proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors.

Conclusions

Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection.

Pragmatic nationwide master observational trial based on genomic alterations in advanced solid tumors: KOrean Precision Medicine Networking Group Study of MOlecular profiling guided therapy based on genomic alterations in advanced Solid tumors (KOSMOS)-II study protocol KCSG AL-22-09

BACKGROUND

Next-generation sequencing (NGS) has been introduced to many Korean institutions to support molecular diagnostics in cancer since 2017, when it became eligible for reimbursement by the National Health Insurance Service. However, the uptake of molecularly guided treatment (MGT) based on NGS results has been limited because of stringent regulations regarding prescriptions outside of approved indications, a lack of clinical trial opportunities, and limited access to molecular tumor boards (MTB) at most institutions. The KOSMOS-II study was designed to demonstrate the feasibility and effectiveness of MGT, informed by MTBs, using a nationwide precision medicine platform.

METHODS

The KOSMOS-II trial is a large-scale nationwide master observational study. It involves a framework for screening patients with metastatic solid tumors for actionable genetic alterations based on local NGS testing. It recommends MGT through a remote and centralized MTB meeting held biweekly. MGT can include one of the following options: Tier 1, the therapeutic use of investigational drugs targeting genetic alterations such as ALK, EGFR, ERBB2, BRAF, FH, ROS1, and RET, or those with high tumor mutational burden; Tier 2, comprising drugs with approved indications or those permitted for treatment outside of the indications approved by the Health Insurance Review and Assessment Service of Korea; Tier 3, involving clinical trials matching the genetic alterations recommended by the MTB. Given the anticipated proportion of patients receiving MGT in the range of 50% ± 3.25%, this study aims to enroll 1,000 patients. Patients must have progressed to one or more lines of therapy and undergone NGS before enrollment.

DISCUSSION

This pragmatic master protocol provides a mass-screening platform for rare genetic alterations and high-quality real-world data. Collateral clinical trials, translational studies, and clinico-genomic databases will contribute to generating evidence for drug repositioning and the development of new biomarkers.

TRIAL REGISTRATION

NCT05525858.

[Personalized medicine in oncology]

Due to the considerable technological progress in molecular and genetic diagnostics as well as increasing insights into the molecular pathogenesis of diseases, there has been a fundamental paradigm shift in the past two decades from a "one-size-fits-all approach" to personalized, molecularly informed treatment strategies. Personalized medicine or precision medicine focuses on the genetic, physiological, molecular, and biochemical differences between individuals and considers their effects on the development, prevention, and treatment of diseases. As a pioneer of personalized medicine, the field of oncology is particularly noteworthy, where personalized diagnostics and treatment have led to lasting change in the treatment of cancer patients in recent years. In this article, the significant change towards personalized treatment concepts, especially in the field of personalized oncology, will be discussed and examined in more detail.

[Molecular pathological analysis through the ages]

BACKGROUND

Molecular pathological examinations of tumor samples encompass a wide range of diagnostic analyses. Especially in recent years, numerous new biomarkers have come to the forefront-the analysis of which is crucial for therapy decisions.

OBJECTIVES

Within the field of molecular pathology, the demands of next generation sequencing (NGS)-based requirements have experienced massive growth in recent years. To meet this demand, methods are constantly being adapted and further developed. The following sections aim to illuminate how this trend arises and which analyses are gaining importance.

METHODS

The article provides an overview of the essential nucleic acid-based analysis techniques in the field of massive parallel sequencing. Terms such as DNA- and RNA-based techniques, as well as the associated analysis methods, are described, particularly with regard to their use in routine molecular pathological diagnostics.

RESULTS

The breadth of genomic sequencing has been steadily growing in recent years, particularly due to the increasing relevance of personalized medicine, along with the rising approvals of targeted therapeutics. This necessitates, among other things, the analysis of new biomarkers. The diagnostics as part of interdisciplinary molecular tumor boards (MTB) are now based on large gene panels (> 1 megabase). Furthermore, through the "Modellvorhaben Genomsequenzierung" § 64e, whole exome or whole genome sequencing has been made available for oncological patients. Given these developments, it is evident that future analyses will require the integration of additional omics fields, such as whole transcriptome analysis, epigenomics, and proteomics.

CONCLUSION

The challenges of personalized medicine along with the necessity of simultaneously assessing numerous new biomarkers require the implementation and execution of new techniques in molecular pathology whose complexity is steadily increasing.

NGS-Guided Precision Oncology in Breast Cancer and Gynecological Tumors-A Retrospective Molecular Tumor Board Analysis

Background: Precision oncology treatments are being applied more commonly in breast and gynecological oncology through the implementation of Molecular Tumor Boards (MTBs), but real-world clinical outcome data remain limited. Methods: A retrospective analysis was conducted in patients with breast cancer (BC) and gynecological malignancies referred to our center's MTB from 2018 to 2023. The analysis covered patient characteristics, next-generation sequencing (NGS) results, MTB recommendations, therapy received, and clinical outcomes. Results: Sixty-three patients (77.8%) had metastatic disease, and forty-four patients (54.3%) had previously undergone three or more lines of systemic treatment. Personalized treatment recommendations were provided to 50 patients (63.3%), while 29 (36.7%) had no actionable target. Ultimately, 23 patients (29.1%) underwent molecular-matched treatment (MMT). Commonly altered genes in patients with pan-gyn tumors (BC and gynecological malignancies) included TP53 (n = 42/81, 51.9%), PIK3CA (n = 18/81, 22.2%), BRCA1/2 (n = 10/81, 12.3%), and ARID1A (n = 9/81, 11.1%). Patients treated with MMT showed significantly prolonged progression-free survival (median PFS 5.5 vs. 3.5 months, p = 0.0014). Of all patients who underwent molecular profiling, 13.6% experienced a major clinical benefit (PFSr ≥ 1.3 and PR/SD ≥ 6 months) through precision oncology. Conclusions: NGS-guided precision oncology demonstrated improved clinical outcomes in a subgroup of patients with gynecological and breast cancers.

Navigating uncharted territory: a case report and literature review on the remarkable response to personalized crizotinib containing combinational therapy in a pazopanib refractory patient with novel alterations

This paper presents a patient with a novel Ig-like-III domain fibroblast growth factor receptor (FGFR2) alteration (W290_P307>C) along with CDKN2A/B alterations and a cadherin 1 (CDH1) alteration. Initial responsiveness to pazopanib monotherapy was encouraging, yet progression occurred after 7.5 months. Following progression, the molecular tumor board recommended a combination therapy approach comprising pazopanib, crizotinib, and palbociclib to target all of the changed pathways at the same time. Pazopanib was chosen to specifically target the FGFR2 alteration, while crizotinib was selected due to its potential synthetic lethality with the CDH1 alteration. In addition, the CDK4/6 inhibitor palbociclib was administered to address the CDKN2A/B alterations. The patient exhibited a remarkable and sustained response to this innovative combination. This case not only underscores the potential of tyrosine kinase inhibitors, exemplified by pazopanib, as a viable alternative for patients without access to pan-FGFR inhibitors, but it also emphasizes their efficacy beyond commonly detected point mutations and rearrangements. Notably, the outstanding response to combination therapy, including crizotinib, in a patient with a CDH1 alteration, further substantiates the preclinical evidence of synthetic lethality between crizotinib and CDH1 alterations. To our knowledge, this represents the first clinical evidence demonstrating the efficacy of crizotinib in a patient with a CDH1 alteration. Through careful dosage adjustments and consideration of individualized genomic information, this case exemplifies the power of personalized medicine in achieving favorable treatment outcomes.

DNA-based molecular classifiers for the profiling of gene expression signatures

Although gene expression signatures offer tremendous potential in diseases diagnostic and prognostic, but massive gene expression signatures caused challenges for experimental detection and computational analysis in clinical setting. Here, we introduce a universal DNA-based molecular classifier for profiling gene expression signatures and generating immediate diagnostic outcomes. The molecular classifier begins with feature transformation, a modular and programmable strategy was used to capture relative relationships of low-concentration RNAs and convert them to general coding inputs. Then, competitive inhibition of the DNA catalytic reaction enables strict weight assignment for different inputs according to their importance, followed by summation, annihilation and reporting to accurately implement the mathematical model of the classifier. We validated the entire workflow by utilizing miRNA expression levels for the diagnosis of hepatocellular carcinoma (HCC) in clinical samples with an accuracy 85.7%. The results demonstrate the molecular classifier provides a universal solution to explore the correlation between gene expression patterns and disease diagnostics, monitoring, and prognosis, and supports personalized healthcare in primary care.

Transcriptomic analysis of GITR and GITR ligand reveals cancer immune heterogeneity with implications for GITR targeting

Glucocorticoid-induced tumor necrosis factor related protein (GITR) is a transmembrane protein expressed mostly on CD25+CD4+ regulatory T-cells (Tregs) and upregulated on all T-cells upon activation. It is a T-cell co-stimulatory receptor and has demonstrated promising anti-tumor activity in pre-clinical studies. To date, however, the efficacy of GITR agonism has been discouraging in clinical trials. This study explores GITR and GITR ligand (GITR-L) ribonucleic acid (RNA) expression in solid tumors in an attempt to delineate causes for variable responses to GITR agonists. RNA expression levels of 514 patients with a variety of cancer types were normalized to internal housekeeping gene profiles and ranked as percentiles. 99/514 patients (19.3%) had high GITR expression (defined as ≥ 75th percentile). Breast and lung cancer had the highest proportion of patients with high GITR expression (39% and 35%, respectively). The expression of concomitant high GITR and low-moderate GITR-L expression (defined as <75th percentile) was present in 31% and 30% of patients with breast and lung cancer respectively. High GITR expression also showed a significant independent association with high RNA expression of other immune modulator proteins, namely, PD-L1 immunohistochemistry (IHC) ≥1 (odds ratio (OR) 2.15, P=0.008), CTLA4 (OR=2.17, P=0.05) and OX40 high RNA expression (OR=2.64, P=0.001). Overall, these results suggest that breast and lung cancer have a high proportion of patients with a GITR and GITR-L RNA expression profile that merits further investigation in GITR agonism studies. The association of high GITR expression with high CTLA4 and OX40 RNA expression, as well as positive PD-L1 IHC, provides a rationale for a combination approach targeting these specific immune modulator proteins in patients whose tumors show such co-expression.

Survey of Danish Head and Neck Cancer Patients' Positions on Personalized Medicine, Gene Tests, and Personalized Follow-Up

The field of personalized medicine (PM) has grown rapidly because of the "omics revolution", but PM may be difficult for patients to comprehend. This study sought to explore head and neck cancer (HNC) patients' positions and knowledge of PM, gene tests, and follow-up and to compare HNC patients' positions to a sample from a national Danish questionnaire. To do this, patients with prior HNC were invited to participate in a questionnaire. Initial interviews revealed a heterogenic understanding of PM between patients. A total of 226 patients were included in the survey and 177 patients with complete data were included for analysis. Most patients were more positive than negative towards gene tests and gene research (83% and 93%, respectively), but 72% had little or no knowledge of the subject. Almost all patients, 98%, were satisfied with their follow-up. Significantly more patients with HNC were positive towards gene research compared to a sample from a national Danish questionnaire (p < 0.001). Patients with HNC were positive towards gene tests and PM, but patients may not understand or comprehend the information given, and it is important to inform and educate patients and health professionals to establish common ground in PM.

Agnostic Administration of Targeted Anticancer Drugs: Looking for a Balance between Hype and Caution

Many tumors have well-defined vulnerabilities, thus potentially allowing highly specific and effective treatment. There is a spectrum of actionable genetic alterations which are shared across various tumor types and, therefore, can be targeted by a given drug irrespective of tumor histology. Several agnostic drug-target matches have already been approved for clinical use, e.g., immune therapy for tumors with microsatellite instability (MSI) and/or high tumor mutation burden (TMB), NTRK1-3 and RET inhibitors for cancers carrying rearrangements in these kinases, and dabrafenib plus trametinib for BRAF V600E mutated malignancies. Multiple lines of evidence suggest that this histology-independent approach is also reasonable for tumors carrying ALK and ROS1 translocations, biallelic BRCA1/2 inactivation and/or homologous recombination deficiency (HRD), strong HER2 amplification/overexpression coupled with the absence of other MAPK pathway-activating mutations, etc. On the other hand, some well-known targets are not agnostic: for example, PD-L1 expression is predictive for the efficacy of PD-L1/PD1 inhibitors only in some but not all cancer types. Unfortunately, the individual probability of finding a druggable target in a given tumor is relatively low, even with the use of comprehensive next-generation sequencing (NGS) assays. Nevertheless, the rapidly growing utilization of NGS will significantly increase the number of patients with highly unusual or exceptionally rare tumor-target combinations. Clinical trials may provide only a framework for treatment attitudes, while the decisions for individual patients usually require case-by-case consideration of the probability of deriving benefit from agnostic versus standard therapy, drug availability, associated costs, and other circumstances. The existing format of data dissemination may not be optimal for agnostic cancer medicine, as conventional scientific journals are understandably biased towards the publication of positive findings and usually discourage the submission of case reports. Despite all the limitations and concerns, histology-independent drug-target matching is certainly feasible and, therefore, will be increasingly utilized in the future.

Precision Endpoints for Contemporary Precision Oncology Trials

SUMMARY

Traditional endpoints such as progression-free survival and overall survival do not fully capture the pharmacologic and pharmacodynamic effects of a therapeutic intervention. Incorporating mechanism-driven biomarkers and validated surrogate proximal endpoints can provide orthogonal readouts of anti-tumor activity and delineate the relative contribution of treatment components on an individual level, highlighting the limitation of solely relying on aggregated readouts from clinical trials to facilitate go/no-go decisions for precision therapies.