Personalized Cancer Treatment Research Articles

Impact of lymph node retrieval on prognosis in elderly and non-elderly patients with T3-4/N+ rectal cancer following neoadjuvant therapy: a retrospective cohort study

PurposeThe optimal number of lymph nodes to be resected in patients with rectal cancer who undergo radical surgery after neoadjuvant therapy remains controversial. This study evaluated the prognostic variances between elderly and non-elderly patients and determined the ideal number of lymph nodes to be removed in these patients.MethodsThe Surveillance, Epidemiology, and End Results (SEER) datasets were used to gather information on 7894 patients diagnosed with stage T3-4/N+ rectal cancer who underwent neoadjuvant therapy from 2010 to 2019. Of these patients, 2787 were elderly and 5107 were non-elderly. A total of 152 patients from the Longyan First Affiliated Hospital of Fujian Medical University were used for external validation. Overall survival (OS) and cancer-specific survival (CSS) were evaluated to determine the optimal quantity of lymph nodes for surgical resection.ResultsThe study found significant differences in OS and CSS between elderly and non-elderly patients, both before and after adjustment for confounders (P < 0.001). The removal of 14 lymph nodes may be considered a benchmark for patients with stage T3-4/N+ rectal cancer who undergo radical surgery following neoadjuvant therapy, as this number provides a more accurate foundation for the personalized treatment of rectal cancer. External data validated the differences in OS and CSS and supported the 14 lymph nodes as a new benchmark in these patients.ConclusionFor patients with T3-4/N+ stage rectal cancer who undergo radical surgery following neoadjuvant therapy, the removal of 14 lymph nodes serves as a cutoff point that distinctly separates patients with a favorable prognosis from those with an unfavorable one.

Correlation of distinct circulating cytokine/chemokine profiles with clinical benefits of Pexa-Vec (thymidine kinase-deactivated vaccinia virus plus GM-CSF) and cemiplimab (REGN2810; anti-PD-1) in metastatic or unresectable renal cell carcinoma (mRCC).

e14539 Background: This study explores the transformation of 'cold' tumour microenvironments into 'hot' ones in mRCC, focusing on the potential of Oncolytic Viruses (OVs) for personalized immunotherapy. Recognizing the variability in patient responses, the research underscores the necessity for reliable biomarkers. The objective is to examine the correlation between plasma cytokine/chemokine levels and the efficacy of Pexa-Vec and cemiplimab, proposing that these biomarkers could predict treatment outcomes and contribute to more personalized cancer treatment protocols. Methods: In this study, mRCC patients from the REN026 trial were categorized into three treatment groups: Pexa-Vec (Intratumoral and intravenous) and cemiplimab combination therapy (N=65), intravenous Pexa-Vec and cemiplimab combination therapy (N=54), and cemiplimab monotherapy (N=14). Blood samples were collected at baseline, during treatment, and at end of treatment (EOT) for analysis. Subsequently, the plasma samples underwent analysis using the Human Cytokine 96-Plex Discovery Assay through the Luminex 200 system. The statistical approach included the Mann–Whitney U-test or Wilcoxon signed-rank test, along with a Cox proportional hazards model. The False Discovery Rate (FDR) method was employed to control type I error. Results: During the follow-up period from June 2018 to February 2023, key outcomes were observed as follows (Table): In the Pexa-Vec and cemiplimab cohort, a lower baseline of MCP-3 or an increased rate of change in post-treatment levels of IFNβ, Eotaxin, IL-1α, and sFasL were associated with improved Progression-Free Survival (PFS). Specifically, a low baseline of MCP-3 or an increased level of Eotaxin and sFasL post-treatment correlated with enhanced Overall Survival (OS). In the intravenous Pexa-Vec and cemiplimab group, a rise in post-treatment levels of IFNβ, Eotaxin, IL-17F, and sFasL were indicative of better PFS, with elevated Eotaxin also suggesting improved OS. Within the cemiplimab monotherapy group, an increase in post-treatment IP-10 was linked to favourable PFS. Conclusions: Our findings suggest the potential utility of plasma cytokine and chemokine profiles in clinical benefits of mRCC patient to the combination of Pexa-Vec and cemiplimab, thereby aiding in future personalized treatment approaches.

Experience of personalizing cancer treatment in economically-constrained and resource-restricted community oncology practice in India through cost-effective broad spectrum NGS and survival outcomes: An observational study of 3 years.

e15173 Background: NGS based mutational findings are increasingly used in oncology practice to personalize treatment strategies. We elucidate the impact of comprehensive genomic profiling and the new avenues it opens for personalized precision medicine. Methods: This was a retrospective study evaluating patients who underwent a cost-effective (&lt;US $1000), inhouse developed comprehensive NGS test. Descriptive data was tabulated &amp; mutational distribution, therapeutic strategies and survival outcomes were evaluated. Results: A total of 336 patients were evaluated from a community oncology practice over the last 3 years in Western India. The mean age was 59.2 years; with F:M being 1.04:1 and stage distribution being 3.5%, 5.9%, 9.5% and 80.9% for Stage I, II, III and IV respectively. Comprehensive NGS was performed to evaluate SNVs, CNVs, and indels spanning across over 15000 loci for 1080 tumor specific genes wherein 71.7% were tissue-based, 24.4% were liquid biopsy-based and 3.8% were combined tissue-DNA and ctDNA-based analysis. Majority (69.9%) of the patients were pre-treated while 30% were therapy naïve. The most common cancers evaluated were Lung (18.1%), Pancreatobiliary (15.1%), Breast (11.3%), Gynaec (9.5%), and Lower GI (9.5%). Pathogenic mutations were reported in 83.6% of the cohort while in 16.3% no mutations were detected. Most common mutations observed in this retrospective study were TP53 (38.3%), KRAS (16.6%), PIK3CA (9.5%), EGFR (7.4%) and PTEN (5.6%); and the distribution was largely similar in untreated as well as pre-treated cohorts. Furthermore, genomic alterations for each cancer type are described in Table 1. MSI high and TMB high (&gt;10) were reported in 0.5% and 7.4% respectively. PDL1 positive IHC was reported in 3.2% of tissue samples, whereas HRD high (&gt;50) was reported in 2.3% of the cohort of which the majority (11.5%) were from Upper GI tumors. Targetable genomic alterations were detected in 53.8% (181/336) of which 38.1%(69/181) received the suggested targeted therapy. Clinical response was seen as PR in 14.4% and CR in 2.8% of patients. Of 336 patients 4.76% received modified therapeutic regimens which differed from the standard of care, wherein median PFS was 8 mo [CI 95% (5.3-10.7)] while median OS was 55.1 mo [CI 95% (25.8-NA)]. Conclusions: This observational study outlines the potential benefit of personalizing treatment regimens in the management of solid organ cancers, especially in scenarios where standard-of-care regimens may have failed and the need to incorporate broad-spectrum NGS. [Table: see text]

Multimodal Co-attention Fusion Network with Online Data Augmentation for Cancer Subtype Classification.

It is an essential task to accurately diagnose cancer subtypes in computational pathology for personalized cancer treatment. Recent studies have indicated that the combination of multimodal data, such as whole slide images (WSIs) and multi-omics data, could achieve more accurate diagnosis. However, robust cancer diagnosis remains challenging due to the heterogeneity among multimodal data, as well as the performance degradation caused by insufficient multimodal patient data. In this work, we propose a novel multimodal co-attention fusion network (MCFN) with online data augmentation (ODA) for cancer subtype classification. Specifically, a multimodal mutual-guided co-attention (MMC) module is proposed to effectively perform dense multimodal interactions. It enables multimodal data to mutually guide and calibrate each other during the integration process to alleviate inter- and intra-modal heterogeneities. Subsequently, a self-normalizing network (SNN)-Mixer is developed to allow information communication among different omics data and alleviate the high-dimensional small-sample size problem in multi-omics data. Most importantly, to compensate for insufficient multimodal samples for model training, we propose an ODA module in MCFN. The ODA module leverages the multimodal knowledge to guide the data augmentations of WSIs and maximize the data diversity during model training. Extensive experiments are conducted on the public TCGA dataset. The experimental results demonstrate that the proposed MCFN outperforms all the compared algorithms, suggesting its effectiveness.

Evaluation of the InterDosi code in estimating S-values with the digimouse voxelized mouse phantom

This study delves into the realm of internal dosimetry in small animals, focusing on the crucial role of S-values (absorbed dose rate per unit activity). The primary objective is to enhance our understanding of ionizing radiation distribution and optimize treatment strategies involving multidisciplinary approach which integrates dosimetry data, computational modeling, clinical expertise, and patient-specific factors to deliver safe, effective, and personalized cancer treatments, paving the way for more effective and tailored radiotherapeutic interventions and diagnostic cancer research. The research specifically targets the assessment of radiation doses absorbed in target organs and adjacent tissues during targeted radionuclide therapy with iodine-131 and yttrium-90. Executed on the ERSN (Radiations and Nuclear Systems Laboratory) computing grid, the study employed optimized calculations using 20 CPU (Central Processing Unit) cores for each of the 103 SAF (Specific Absorbed Fractions) grid sizes of photons and electrons. The InterDosi code, a user-friendly tool, streamlined S-value estimation within the Digimouse phantom. Nine source organs and the entire murine anatomy were comprehensively evaluated. The G4EMLiverMorePhysics package ensured high-confidence dosimetric coefficient simulations. S-values revealed insights into absorbed dose influences, including organ mass and inter-organ distance. Comparative analyses were conducted with reference data, which involved a simplified S-value calculation. The results aligned well with the reference data, especially with similar SAF grid sizes for electrons and photons. Accuracy improved with larger grid sizes. This study confirms InterDosi's effectiveness, highlighting its adaptability and precision in estimating S-values for various radionuclides, making it a valuable tool for evaluating radiation doses in murine anatomy.

A Synthetic Circuit Empowering Reprogrammed B Cells for Therapeutic Proteins Expression Regulated by Tumor Detection.

Cancer remains a leading cause of death worldwide, but immunotherapies hold promises to cure it by awaking the patient's immune system to provide long-term protection. Cell therapies, involving the infusion of immune cells, either directly or genetically modified, are being developed to recognize and destroy cancer cells. Here, we explored the potential of a new synthetic circuit to reprogram B cells to cure cancers. This circuit consists in a sensor (a membrane-anchored IgG1), a transducer (a fragment of the NR4A1 promoter) and an effector molecule. Upon recognition of its target, this sensor triggers signaling pathways leading to the activation of the transducer and to effector expression (here, a reporter molecule). We showed that this circuit could discriminate tumors expressing the target antigen from those that did not, in a dose dependent manner in vitro. Going further, we replaced the original membrane-anchored sensor by an immunoglobulin expression cassette that can not only be membrane-anchored but also be secreted depending on B-cell maturation status. This allowed concomitant activation of the circuit and secretion of transgenic antibodies directed against the targeted antigen. Of note, these antibodies could correctly bind their target and were recognized by FcR expressed at the surface of immune cells, which should synergically amplify the action of the effector. The potential of reprogrammed B cells remains to be assessed in vivo by implementing a therapeutic effector. In the future, B-cell reprogramming platforms should allow personalized cancer treatment by adapting both the sensor and the therapeutic effectors to patients.

Plasma ctDNA enhances the tissue-based detection of oncodriver mutations in colorectal cancer.

The advent of circulating tumor DNA (ctDNA) technology has provided a convenient and noninvasive means to continuously monitor cancer genomic data, facilitating personalized cancer treatment. This study aimed to evaluate the supplementary benefits of plasma ctDNA alongside traditional tissue-based next-generation sequencing (NGS) in identifying targetable mutations and tumor mutational burden (TMB) in colorectal cancers (CRC). Our study involved 76 CRC patients, collecting both tissue and plasma samples for NGS. We assessed the concordance of gene mutational status between ctDNA and tissue, focusing on actionable genes such as KRAS, NRAS, PIK3CA, BRAF, and ERBB2. Logistic regression analysis was used to explore variables associated with discordance and positive mutation rates. In total, 26 cancer-related genes were identified. The most common variants in tumor tissues and plasma samples were in APC (57.9% vs 19.7%), TP53 (55.3% vs 22.4%) and KRAS (47.4% vs 43.4%). Tissue and ctDNA showed an overall concordance of 73.53% in detecting actionable gene mutations. Notably, plasma ctDNA improved detection for certain genes and gene pools. Variables significantly associated with discordance included gender and peritoneal metastases. TMB analysis revealed a higher detection rate in tissues compared to plasma, but combining both increased detection. Our study highlights the importance of analyzing both tissue and plasma for detecting actionable mutations in CRC, with plasma ctDNA offering added value. Discordance is associated with gender and peritoneal metastases, and TMB analysis can benefit from a combination of tissue and plasma data. This approach provides valuable insights for personalized CRC treatment.

Comprehensive pan-cancer analysis reveals the versatile role of GALNT7 in epigenetic alterations and immune modulation in cancer

Cancer is a leading cause of mortality globally, characterized by intricate molecular alterations, including epigenetic changes such as glycosylation. This study presents a comprehensive pan-cancer analysis of Polypeptide N-Acetylgalactosaminyltransferase 7 (GALNT7), an enzyme involved in mucin-type O-linked protein glycosylation. GALNT7 has previously been linked to various cancers, but a unified analysis across cancer types is lacking.Leveraging data from TCGA, GTEx, and other sources, we scrutinized GALNT7's expression, prognostic relevance, links to immune-related genes, immune cell infiltration, and its involvement in tumor genetic heterogeneity across 33 cancer types. GALNT7 exhibited diverse expression patterns across cancer types, showcasing its potential as an oncogenic factor, with its expression levels linked to both positive and negative prognoses, highlighting the context-specific nature of its role in cancer progression.We delved into the intricate interplay between GALNT7 and immune genes, unveiling positive and negative correlations, underscoring complex interactions in the tumor microenvironment. GALNT7 was found to impact immune cell infiltration, which could have implications for treatment strategies. Additionally, GALNT7 displayed associations with genetic tumor aspects, encompassing genomic instability, DNA repair issues, and genetic mutations, hinting at its pivotal role in shaping the genetic landscape of diverse cancers.Enrichment analysis uncovered potential functions of GALNT7 beyond glycosylation, such as its participation in signaling pathways and its association with various diseases, notably cancer.This comprehensive analysis elucidates the multifaceted role of GALNT7 in cancer biology, underlining its potential as a therapeutic target and biomarker across various cancer types. These findings provide valuable insights for future research and the development of personalized cancer treatment strategies.

Homologous Recombination Repair Gene Mutations in Prostate Cancer: Prevalence and Clinical Value.

Despite advances in our understanding of the molecular landscape of prostate cancer and the development of novel biomarker-driven therapies, the prognosis of patients with metastatic prostate cancer that is resistant to conventional hormonal therapy remains poor. Data suggest that a significant proportion of patients with metastatic castration-resistant prostate cancer (mCRPC) have mutations in homologous recombination repair (HRR) genes and may benefit from poly(ADP-ribose) polymerase (PARP) inhibitors. However, the adoption of HRR gene mutation testing in prostate cancer remains low, meaning there is a missed opportunity to identify patients who may benefit from targeted therapy with PARP inhibition, with or without novel hormonal agents. Here, we review the current knowledge regarding the clinical significance of HRR gene mutations in prostate cancer and discuss the efficacy of PARP inhibition in patients with mCRPC. This comprehensive overview aims to increase the clinical implementation of HRR gene mutation testing and inform future efforts in personalized treatment of prostate cancer.

Pan-cancer analysis of telomere maintenance mechanisms

Telomeres, protective caps at chromosome ends, maintain genomic stability and control cell lifespan. Dysregulated telomere maintenance mechanisms (TMMs) are cancer hallmarks, enabling unchecked cell proliferation. We conducted a pan-cancer evaluation of TMM using RNA sequencing data from The Cancer Genome Atlas for 33 different cancer types and analyzed the activities of telomerase-dependent (TEL) and alternative lengthening of telomeres (ALT) TMM pathways in detail. To further characterize the TMM profiles, we categorized the tumors based on their ALT and TEL TMM pathway activities into five major phenotypes: ALThigh TELlow, ALTlow TELlow, ALTmiddle TELmiddle, ALThigh TELhigh, and ALTlow TELhigh. These phenotypes refer to variations in telomere maintenance strategies, shedding light on the heterogeneous nature of telomere regulation in cancer. Moreover, we investigated the clinical implications of TMM phenotypes by examining their associations with clinical characteristics and patient outcomes. Specific TMM profiles were linked to specific survival patterns, emphasizing the potential of TMM profiling as a prognostic indicator and aiding in personalized cancer treatment strategies. Gene ontology analysis of the TMM phenotypes unveiled enriched biological processes associated with cell cycle regulation (both TEL and ALT), DNA replication (TEL), and chromosome dynamics (ALT) showing that telomere maintenance is tightly intertwined with cellular processes governing proliferation and genomic stability. Overall, our study provides an overview of the complexity of transcriptional regulation of telomere maintenance mechanisms in cancer.

Liquid profiling for patients with advanced cancer is ready for clinical integration

SummaryMolecular profiling of circulating tumor DNA (ctDNA) to guide treatment decisions has found its way into routine management of patients with advanced cancer. This represents a pivotal advancement in precision oncology, offering a non-invasive and fast-tracked method to detecting clinically relevant biomarkers. With the backing of international oncology guidelines, ctDNA analysis is now a standard approach to consider in molecular diagnostics. Despite the promise of ctDNA in refining treatment strategies through the detection of genomic alterations and treatment-relevant biomarkers with high concordance to tissue biopsies, challenges persist. These include the interpretation of discordances due to tumor heterogeneity, sampling biases, and technical limitations, alongside the differentiation of tumor-derived mutations from clonal hematopoiesis. The current consensus supports the utility of comprehensive genomic profiling (CGP) panels for a broad spectrum of actionable targets, while acknowledging the limitations and advocating for a balanced application of “tissue-first” and “plasma-first” approaches tailored to individual patient scenarios. The essential role of molecular tumor boards (MTBs) is in navigating the complexities of ctDNA data interpretation, thereby ensuring the effective incorporation of liquid biopsy into personalized cancer treatment regimens.

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.

Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale drug response data, facilitating data-driven computational models. Such models can capture complex drug-cell line interactions across various contexts in a fully data-driven manner. However, accurately prioritizing the most effective drugs for each cell line still remains a significant challenge. To address this, we developed multiple neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types. Unlike existing approaches that primarily utilize regression and classification techniques for drug response prediction, we formulated the objective of drug selection and prioritization as a drug ranking problem. In this work, we proposed multiple pairwise and listwise neural ranking methods that learn latent representations of drugs and cell lines and then use those representations to score drugs in each cell line via a learnable scoring function. Specifically, we developed neural pairwise and listwise ranking methods, Pair-PushC and List-One on top of the existing methods, pLETORg and ListNet, respectively. Additionally, we proposed a novel listwise ranking method, List-All, that focuses on all the effective drugs instead of the top effective drug, unlike List-One. We also provide an exhaustive empirical evaluation with state-of-the-art regression and ranking baselines on large-scale data sets across multiple experimental settings. Our results demonstrate that our proposed ranking methods mostly outperform the best baselines with significant improvements of as much as 25.6% in terms of selecting truly effective drugs within the top 20 predicted drugs (i.e., hit@20) across 50% test cell lines. Furthermore, our analyses suggest that the learned latent spaces from our proposed methods demonstrate informative clustering structures and capture relevant underlying biological features. Moreover, our comprehensive evaluation provides a thorough and objective comparison of the performance of different methods (including our proposed ones).

The intricate interplay between cancer stem cells and cell-of-origin of cancer: implications for therapeutic strategies.

Cancer stem cells (CSCs) have emerged as pivotal players in tumorigenesis, disease progression, and resistance to therapies. This comprehensive review delves into the intricate relationship between CSCs and the cell-of-origin in diverse cancer types. Comprehensive review of thematically-relevant literature. We explore the underlying molecular mechanisms that drive the conversion of normal cells into CSCs and the impact of the cell-of-origin on CSC properties, tumor initiation, and therapeutic responses. Moreover, we discuss potential therapeutic interventions targeting CSCs based on their distinct cell-of-origin characteristics. Accruing evidence suggest that the cell-of-origin, the cell type from which the tumor originates, plays a crucial role in determining the properties of CSCs and their contribution to tumor heterogeneity. By providing critical insights into the complex interplay between CSCs and their cellular origins, this article aims to enhance our understanding of cancer biology and pave the way for more effective and personalized cancer treatments.

[18F]FMISO-PET imaging reveals the role of hypoxia severity in checkpoint blockade response

ContextHypoxia within the tumor microenvironment is a critical factor influencing the efficacy of immunotherapy, including immune checkpoint inhibition. Insufficient oxygen supply, characteristic of hypoxia, has been recognized as a central determinant in the progression of various cancers. The reemergence of evofosfamide, a hypoxia-activated prodrug, as a potential treatment strategy has sparked interest in addressing the role of hypoxia in immunotherapy response. This investigation sought to understand the kinetics and heterogeneity of tumor hypoxia and their implications in affecting responses to immunotherapeutic interventions with and without evofosfamide. PurposeThis study aimed to investigate the influence of hypoxia on immune checkpoint inhibition, evofosfamide monotherapy, and their combination on colorectal cancer (CRC). Employing positron emission tomography (PET) imaging, we developed novel analytical methods to quantify and characterize tumor hypoxia severity and distribution. ProceduresMurine CRC models were longitudinally imaged with [18F]-fluoromisonidazole (FMISO)-PET to quantify tumor hypoxia during checkpoint blockade (anti-CTLA-4 + and anti-PD1 +/− evofosfamide). Metrics including maximum tumor [18F]FMISO uptake (FMISOmax) and mean tumor [18F]FMISO uptake (FMISOmean) were quantified and compared with normal muscle tissue (average muscle FMISO uptake (mAvg) and muscle standard deviation (mSD)). Histogram distributions were used to evaluate heterogeneity of tumor hypoxia. FindingsSevere hypoxia significantly impeded immunotherapy effectiveness consistent with an immunosuppressive microenvironment. Hypoxia-specific PET imaging revealed a striking degree of spatial heterogeneity in tumor hypoxia, with some regions exhibiting significantly more severe hypoxia than others. The study identified FMISOmax as a robust predictor of immunotherapy response, emphasizing the impact of localized severe hypoxia on tumor volume control during therapy. Interestingly, evofosfamide did not directly reduce hypoxia but markedly improved the response to immunotherapy, uncovering an alternative mechanism for its efficacy. ConclusionsThese results enhance our comprehension of the interplay between hypoxia and immune checkpoint inhibition within the tumor microenvironment, offering crucial insights for the development of personalized cancer treatment strategies. Non-invasive hypoxia quantification through molecular imaging evaluating hypoxia severity may be an effective tool in guiding treatment planning, predicting therapy response, and ultimately improving patient outcomes across diverse cancer types and tumor microenvironments. It sets the stage for the translation of these findings into clinical practice, facilitating the optimization of immunotherapy regimens by addressing tumor hypoxia and thereby enhancing the efficacy of cancer treatments.

Revolutionizing personalized cancer treatment: the synergy of next-generation sequencing and CRISPR/Cas9.

In the context of cancer heterogeneity, the synergistic action of next-generation sequencing (NGS) and CRISPR/Cas9 plays a promising role in the personalized treatment of cancer. NGS enables high-throughput genomic profiling of tumors and pinpoints specific mutations that primarily lead to cancer. Oncologists use this information obtained from NGS in the form of DNA profiling or RNA analysis to tailor precision strategies based on an individual's unique molecular signature. Furthermore, the CRISPR technique enables precise editing of cancer-specific mutations, allowing targeted gene modifications. Harnessing the potential insights of NGS and CRISPR/Cas9 heralds a remarkable frontier in cancer therapeutics with unprecedented precision, effectivenessand minimal off-target effects.

Creation and Validation of Patient-Derived Cancer Model Using Peritoneal and Pleural Effusion in Patients with Advanced Ovarian Cancer: An Early Experience.

Background: The application of personalized cancer treatment based on genetic information and surgical samples has begun in the field of cancer medicine. However, a biopsy may be painful for patients with advanced diseases that do not qualify for surgical resection. Patient-derived xenografts (PDXs) are cancer models in which patient samples are transplanted into immunodeficient mice. PDXs are expected to be useful for personalized medicine. The aim of this study was to establish a PDX from body fluid (PDX-BF), such as peritoneal and pleural effusion samples, to provide personalized medicine without surgery. Methods: PDXs-BF were created from patients with ovarian cancer who had positive cytology findings based on peritoneal and pleural effusion samples. PDXs were also prepared from each primary tumor. The pathological findings based on immunohistochemistry were compared between the primary tumor, PDX, and PDX-BF. Further, genomic profiles and gene expression were evaluated using DNA and RNA sequencing to compare primary tumors, PDXs, and PDX-BF. Results: Among the 15 patients, PDX-BF was established for 8 patients (5 high-grade serous carcinoma, 1 carcinosarcoma, 1 low-grade serous carcinoma, and 1 clear cell carcinoma); the success rate was 53%. Histologically, PDXs-BF have features similar to those of primary tumors and PDXs. In particular, PDXs-BF had similar gene mutations and expression patterns to primary tumors and PDXs. Conclusions: PDX-BF reproduced primary tumors in terms of pathological features and genomic profiles, including gene mutation and expression. Thus, PDX-BF may be a potential alternative to surgical resection for patients with advanced disease.

Tumor cell-intrinsic epigenetic SETpoint of cancer-associated fibroblasts

Cancer-associated fibroblasts (CAFs) exhibit spatial and functional diversity. Here, Niu etal. unveil SETD2's function in lipid metabolism and CAF heterogeneity in pancreatic ductal adenocarcinoma. SETD2 deficiency boosts oxidative phosphorylation activity, prompting lipid-laden CAF formation through BMP2 signaling, offering promising therapeutic avenues in personalized cancer treatment.

Engineering Microorganisms for Cancer Immunotherapy.

Cancer immunotherapy presents a promising approach to fight against cancer by utilizing the immune system. Recently, engineered microorganisms have emerged as a potential strategy in cancer immunotherapy. These microorganisms, including bacteria and viruses, can be designed and modified using synthetic biology and genetic engineering techniques to target cancer cells and modulate the immune system. This review delves into various microorganism-based therapies for cancer immunotherapy, encompassing strategies for enhancing efficacy while ensuring safety and ethical considerations. The development of these therapies holds immense potential in offering innovative personalized treatments for cancer.

CyTOF analysis revealed platelet heterogeneity in breast cancer patients received T-DM1 treatment

T-DM1 (Trastuzumab Emtansine) belongs to class of Antibody-Drug Conjugates (ADC), where cytotoxic drugs are conjugated with the antibody Trastuzumab to specifically target HER2-positive cancer cells. Platelets, as vital components of the blood system, intricately influence the immune response to tumors through complex mechanisms. In our study, we examined platelet surface proteins in the plasma of patients before and after T-DM1 treatment, categorizing them based on treatment response. We identified a subgroup of platelets with elevated expression of CD63 and CD9 exclusively in patients with favorable treatment responses, while this subgroup was absent in patients with poor responses. Another noteworthy discovery was the elevated expression of CD36 in the platelet subgroups of patients exhibiting inadequate responses to treatment. These findings suggest that the expression of these platelet surface proteins may be correlated with the prognosis of T-DM1 treatment. These indicators offer valuable insights for predicting the therapeutic response to T-DM1 and may become important references in future clinical practice, contributing to a better understanding of the impact of ADC therapies and optimizing personalized cancer treatment strategies.

Estimating the Prognostic Value of the NTRK Fusion Biomarker for Comparative Effectiveness Research in The Netherlands.

We evaluated the prognostic value of the neurotrophic tyrosine receptor kinase (NTRK) gene fusions by comparing the survival of patients with NTRK+ tumours with patients without NTRK+ tumours. We used genomic and clinical registry data from the Center for Personalized Cancer Treatment (CPCT-02) study containing a cohort of cancer patients who were treated in Dutch clinical practice between 2012 and 2020. We performed a propensity score matching analysis, where NTRK+ patients were matched to NTRK- patients in a 1:4 ratio. We subsequently analysed the survival of the matched sample of NTRK+ and NTRK- patients using the Kaplan-Meier method and Cox regression, and performed an analysis of credibility to evaluate theplausibility of our result. Among 3556 patients from the CPCT-02 study with known tumour location, 24 NTRK+ patients were identified. NTRK+ patients were distributedacross nine different tumour types: bone/soft tissue, breast, colorectal, head and neck, lung, pancreas, prostate, skin and urinary tract. NTRK fusions involving the NTRK3 gene (46%) and NTRK1 gene (33%) were most common. The survival analysis rendered a hazard ratio (HR) of 1.44 (95% CI 0.81-2.55) for NTRK+ patients. Using the point estimates of three prior studies on the prognostic value of NTRK fusions, our finding that the HR is > 1 was deemed plausible. NTRK+ patients may have an increased risk of death compared with NTRK- patients. When using historic control data to assess the comparative effectiveness of TRK inhibitors, the prognostic value of the NTRK fusion biomarker should therefore be accounted for.