Personalized Cancer Therapeutics Research Articles

A fractional-order model for optimizing combination therapy in heterogeneous lung cancer: integrating immunotherapy and targeted therapy to minimize side effects

This research presents a novel approach to address the complexities of heterogeneous lung cancer dynamics through the development of a Fractional-Order Model. Focusing on the optimization of combination therapy, the model integrates immunotherapy and targeted therapy with the specific aim of minimizing side effects. Notably, our approach incorporates a clever fusion of Proportional-Integral-Derivative (PID) feedback controls alongside the optimization process. Unlike previous studies, our model incorporates essential equations accounting for the interaction between regular and mutated cancer cells, delineates the dynamics between immune cells and mutated cancer cells, enhances immune cell cytotoxic activity, and elucidates the influence of genetic mutations on the spread of cancer cells. This refined model offers a comprehensive understanding of lung cancer progression, providing a valuable tool for the development of personalized and effective treatment strategies. the findings underscore the potential of the optimized treatment strategy in achieving key therapeutic goals, including primary tumor control, metastasis limitation, immune response enhancement, and controlled genetic mutations. The dynamic and adaptive nature of the treatment approach, coupled with economic considerations and memory effects, positions the research at the forefront of advancing precision and personalized cancer therapeutics.

Next-generation chemotherapy treatments based on black hole algorithms: From cancer remission to chronic disease management

ProblemTherapeutic planning strategies have been developed to enhance the effectiveness of cancer drugs. Nevertheless, their performance is highly limited by the inefficient biological representativeness of predictive tumor growth models, which hinders their translation to clinical practice. ObjectiveThis study proposes a disruptive approach to oncology based on nature-inspired control using realistic Black Hole physical laws, in which tumor masses are trapped to experience attraction dynamics on their path to complete remission or to become a chronic disease. This control method is designed to operate independently of individual patient idiosyncrasies, including high tumor heterogeneities and highly uncertain tumor dynamics, making it a promising avenue for advancing beyond the limitations of the traditional survival probabilistic paradigm. DesignHere, we provide a multifaceted study of chemotherapy therapeutic planning that includes: (1) the design of a pioneering controller algorithm based on physical laws found in the Black Holes; (2) investigation of the ability of this controller algorithm to ensure stable equilibrium treatments; and (3) simulation tests concerning tumor volume dynamics using drugs with significantly different pharmacokinetics (Cyclophosphamide and Atezolizumab), tumor volumes (200 mm3 and 12 732 mm3) and modeling characterizations (Gompertzian and Logistic tumor growth models). ResultsOur results highlight the ability of this new astrophysical-inspired control algorithm to perform effective chemotherapy treatments for multiple tumor-treatment scenarios, including tumor resistance to chemotherapy, clinical scenarios modelled by time-dependent parameters, and highly uncertain tumor dynamics. ConclusionsOur findings provide strong evidence that cancer therapy inspired by phenomena found in black holes can emerge as a disruptive paradigm. This opens new high-impacting research directions, exploring synergies between astrophysical-inspired control algorithms and Artificial Intelligence applied to advanced personalized cancer therapeutics.

Prospective of Mitochondrial DNA Variations in Cancer on Genomic Medicine.

Mitochondrial DNA (mtDNA) has emerged as a pivotal component in understanding the etiology and susceptibility of cancer. A recent study by Chen and colleagues delineated the germline genetic effect of mtDNA single-nucleotide polymorphisms (SNP) and haplogroups across pan-cancer risk. They identified a subset of mtSNPs and the corresponding risk score, as well as haplogroups A and M7 alongside their genetic interactions, conferring a protective effect against various cancers. These findings underscored the value of mtDNA variations as biomarkers for cancer etiology and as tools for cancer risk stratification. Future investigations are encouraged to integrate comprehensive omics data of genomics, transcriptomics, proteomics, and metabolomics, etc., from nuclear DNA with mtDNA variations, alongside single-cell and spatial technologies, to unravel the tumor mechanism and identify the drug targets. Moreover, the incorporation of polygenic risk score, that included mtDNA variations with both rare and common frequencies, and liquid biopsy-based biomarkers would enhance the predictive performance of cancer risk assessment and refine the risk stratification of population-based cancer screening. This commentary advocates for the validation across diverse populations to harness the full potential of mitochondrial genomics, and ultimately paves the prospective way for advancements in personalized cancer therapeutics and prevention strategies. See related article by Chen and colleagues, Cancer Epidemiol Biomarkers Prev 2024;33:381-8.

Personalized Cancer Therapeutics Using Photoacoustic Imaging-Guided Sonodynamic Therapy.

Sonodynamic therapy (SDT) is a promising approach for cancer treatment that uses sonosensitizers (SNSs) to generate reactive oxygen species (ROS) in the presence of ultrasound. However, SDT is oxygen-dependent and requires an imaging tool to monitor the tumor microenvironment and guide treatment. Photoacoustic imaging (PAI) is a noninvasive and powerful imaging tool that offers high spatial resolution and deep tissue penetration. PAI can quantitatively assess tumor oxygen saturation (sO2) and guide SDT by monitoring time-dependent sO2 changes in the tumor microenvironment. Here, we discuss recent advances in PAI-guided SDT for cancer therapy. We discuss various exogenous contrast agents and nanomaterial-based SNSs developed for PAI-guided SDT. Additionally, combining SDT with other therapies, including photothermal therapy, can enhance its therapeutic effect. However, the application of nanomaterial-based contrast agents in PAI-guided SDT for cancer therapy remains challenging due to the lack of simple designs, the need for extensive pharmacokinetic studies, and high production costs. Integrated efforts from researchers, clinicians, and industry consortia are necessary for the successful clinical translation of these agents and SDT for personalized cancer therapy. PAI-guided SDT shows the potential to revolutionize cancer therapy and improve patient outcomes, but further research is necessary to realize its full potential.

MP14-05 EXTRACELLULAR VESICLE BIOMARKER SIGNATURE DETECTION PLATFORM FOR GUIDING CANCER DETECTION AND THERAPY

You have accessJournal of UrologyCME1 Apr 2023MP14-05 EXTRACELLULAR VESICLE BIOMARKER SIGNATURE DETECTION PLATFORM FOR GUIDING CANCER DETECTION AND THERAPY Thomas Osinski, Samuel Walker, Karen Doersch, Jonathan Flax, and James McGrath Thomas OsinskiThomas Osinski More articles by this author , Samuel WalkerSamuel Walker More articles by this author , Karen DoerschKaren Doersch More articles by this author , Jonathan FlaxJonathan Flax More articles by this author , and James McGrathJames McGrath More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003234.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Extracellular Vesicles (EVs) have tremendous potential in cancer diagnostics and personalized cancer therapeutics. They are abundant in all biofluids, carry molecular markers of their cell-of-origin and can be readily obtained via noninvasive techniques. Current EV biomarker assessments have several limitiations. Pooled EVs from samples have low detection sensitivity for less abundant biomarkers and preclude colocalization of biomarkers on individual EVs. Currently, expensive specialized devices requiring large biofluid inputs are needed for more detailed EV analysis. Here, we develop an easy to use and inexpensive device that permits optical detection of multiple markers on individual EVs using only 10 µl of serum. This work investigates whether the microfluidic strategy we call ‘catch and display for liquid biopsy [CAD-LB]’ can detect biomarkers on single EVs. We also sought to determine the dynamic range of accurate quantitation using purified EVs and serum and validate detection of immune checkpoint proteins (PD-L1 and/or PD-1), which may help to distinguish immune checkpoint immunotherapy (ICI) responders from non-responders in bladder cancer. METHODS: For this study EVs were derived from: 1. ultracentrifuge purified urinary EVs; 2. EVs from media of 5637 bladder cancer and primary bladder epithelium cell lines and; 3. Serum from patients with advanced urothelial carcinoma or healthy controls. EVs were labeled with the pan-EV marker carboxyfluorescein succinimidyl ester and fluorescently labeled primary antibodies. Small volumes (10 µL) of solutions were captured on our membrane with nanopores in a transparent membrane and imaged using fluorescence microscopy. Detection range was assessed by serial dilutions of EVs. EV surface antigen labeling was assessed by CAD-LB. RESULTS: 1. Counting curves demonstrate a wide dynamic range of CAD-LB (102 – 106 EVs) for purified EVs while serum plateaus at lower inputs (∼104 EVs). 2. CAD-LB detects PD-1 and PD-L1 biomarkers in urothelial carcinoma patients’ serum. 3. CAD-LB detects T cell biomarker CD3 on approximately 1-2% of EVs in healthy control serum. CONCLUSIONS: CAD-LB can detect rare and clinically significant EV associated biomarkers in conditioned medium and patient serum samples. Furthermore, CAD-LB provides single EV characterization with small sample volumes. These qualities make CAD-LB a potentially useful tool to help discover novel markers that could aid in diagnosis and treatment decisions for cancer patients. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e183 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Thomas Osinski More articles by this author Samuel Walker More articles by this author Karen Doersch More articles by this author Jonathan Flax More articles by this author James McGrath More articles by this author Expand All Advertisement PDF downloadLoading ...

“Proteotranscriptomic analysis of advanced colorectal cancer patient derived organoids for drug sensitivity prediction”

BackgroundPatient-derived organoids (PDOs) from advanced colorectal cancer (CRC) patients could be a key platform to predict drug response and discover new biomarkers. We aimed to integrate PDO drug response with multi-omics characterization beyond genomics.MethodsWe generated 29 PDO lines from 22 advanced CRC patients and provided a morphologic, genomic, and transcriptomic characterization. We performed drug sensitivity assays with a panel of both standard and non-standard agents in five long-term cultures, and integrated drug response with a baseline proteomic and transcriptomic characterization by SWATH-MS and RNA-seq analysis, respectively.ResultsPDOs were successfully generated from heavily pre-treated patients, including a paired model of advanced MSI high CRC deriving from pre- and post-chemotherapy liver metastasis. Our PDOs faithfully reproduced genomic and phenotypic features of original tissue. Drug panel testing identified differential response among PDOs, particularly to oxaliplatin and palbociclib. Proteotranscriptomic analyses revealed that oxaliplatin non-responder PDOs present enrichment of the t-RNA aminoacylation process and showed a shift towards oxidative phosphorylation pathway dependence, while an exceptional response to palbociclib was detected in a PDO with activation of MYC and enrichment of chaperonin T-complex protein Ring Complex (TRiC), involved in proteome integrity. Proteotranscriptomic data fusion confirmed these results within a highly integrated network of functional processes involved in differential response to drugs.ConclusionsOur strategy of integrating PDOs drug sensitivity with SWATH-mass spectrometry and RNA-seq allowed us to identify different baseline proteins and gene expression profiles with the potential to predict treatment response/resistance and to help in the development of effective and personalized cancer therapeutics.

Freddie: annotation-independent detection and discovery of transcriptomic alternative splicing isoforms using long-read sequencing.

Alternative splicing (AS) is an important mechanism in the development of many cancers, as novel or aberrant AS patterns play an important role as an independent onco-driver. In addition, cancer-specific AS is potentially an effective target of personalized cancer therapeutics. However, detecting AS events remains a challenging task, especially if these AS events are novel. This is exacerbated by the fact that existing transcriptome annotation databases are far from being comprehensive, especially with regard to cancer-specific AS. Additionally, traditional sequencing technologies are severely limited by the short length of the generated reads, which rarely spans more than a single splice junction site. Given these challenges, transcriptomic long-read (LR) sequencing presents a promising potential for the detection and discovery of AS. We present Freddie, a computational annotation-independent isoform discovery and detection tool. Freddie takes as input transcriptomic LR sequencing of a sample alongside its genomic split alignment and computes a set of isoforms for the given sample. It then partitions the input reads into sets that can be processed independently and in parallel. For each partition, Freddie segments the genomic alignment of the reads into canonical exon segments. The goal of this segmentation is to be able to represent any potential isoform as a subset of these canonical exons. This segmentation is formulated as an optimization problem and is solved with a dynamic programming algorithm. Then, Freddie reconstructs the isoforms by jointly clustering and error-correcting the reads using the canonical segmentation as a succinct representation. The clustering and error-correcting step is formulated as an optimization problem-the Minimum Error Clustering into Isoforms (MErCi) problem-and is solved using integer linear programming (ILP). We compare the performance of Freddie on simulated datasets with other isoform detection tools with varying dependence on annotation databases. We show that Freddie outperforms the other tools in its accuracy, including those given the complete ground truth annotation. We also run Freddie on a transcriptomic LR dataset generated in-house from a prostate cancer cell line with a matched short-read RNA-seq dataset. Freddie results in isoforms with a higher short-read cross-validation rate than the other tested tools. Freddie is open source and available at https://github.com/vpc-ccg/freddie/.

Abstract IACC0601: Radiopharmaceuticals and their future in cancer care

Abstract Radiopharmaceutical agents have been an appealing treatment approach since the use of 131I to treat thyroid malignancies. Advances in cancer radiobiology and radiochemistry have brought radiopharmaceuticals forward to the cutting edge of oncology clinical research. The National Cancer Institute (NCI) and its partnering academic and clinical research networks monitor radiopharmaceutical discoveries that might be incorporated into cancer treatment strategies for patients with unmet therapeutic needs. Such endeavors happen through leveraged partnerships between NCI's Cancer Therapy Evaluation Program (CTEP), its Radiation Research Program (RRP), and academic institutions in the Experimental Therapeutics Clinical Trials Network (ETCTN). If innovative discoveries are made, NCI supports clinician scientists to plan new and original radiopharmaceutical phase I and II monotherapy or combination trials. The clinical trial infrastructure needs, such as radiopharmaceutical dosimetry and treatment planning, are now available in government workflow and regulatory oversight. This presentation introduces a modern approach to the clinical development of radiopharmaceutical agents in an era of molecular targets and personalized cancer therapeutics. Citation Format: Charles A. Kunos. Radiopharmaceuticals and their future in cancer care [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr IACC0601.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics.

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Building Personalized Cancer Therapeutics through Multi-Omics Assays and Bacteriophage-Eukaryotic Cell Interactions.

Bacteriophage-eukaryotic cell interaction provides the biological foundation of Phage Display technology, which has been widely adopted in studies involving protein-protein and protein-peptide interactions, and it provides a direct link between the proteins and the DNA encoding them. Phage display has also facilitated the development of new therapeutic agents targeting personalized cancer mutations. Proteins encoded by mutant genes in cancers can be processed and presented on the tumor cell surface by human leukocyte antigen (HLA) molecules, and such mutant peptides are called Neoantigens. Neoantigens are naturally existing tumor markers presented on the cell surface. In clinical settings, the T-cell recognition of neoantigens is the foundation of cancer immunotherapeutics. This year, we utilized phage display to successfully develop the 1st antibody-based neoantigen targeting approach for next-generation personalized cancer therapeutics. In this article, we discussed the strategies for identifying neoantigens, followed by using phage display to create personalized cancer therapeutics—a complete pipeline for personalized cancer treatment.

Chemotherapeutic efficacy of curcumin and resveratrol against cancer: Chemoprevention, chemoprotection, drug synergism and clinical pharmacokinetics.

The frequent inefficiency of conventional cancer therapies due to drug resistance, non-targeted drug delivery, chemotherapy-associated toxic side effects turned the focus to bioactive phytochemicals. In this context, curcumin and resveratrol have emerged as potent chemopreventive and chemoprotective compounds modulating apoptotic and autophagic cell death pathways in cancer in vitro and in vivo. As synergistic agents in combination with clinically established anticancer drugs, the enhanced anticancer activity at reduced chemotherapy-associated toxicity towards normal organs can be explained by improved pharmacokinetics, pharmacodynamics, bioavailability and metabolism. With promising preclinical and clinical applications, the design of drug-loaded nanoparticles, nanocarriers, liposomes and micelles have gained much attention to improve target specificity and drug efficacy. The present review focuses on the molecular modes of chemoprevention, chemoprotection and drug synergism with special emphasis to preclinical and clinical applications, pharmacokinetics, pharmacodynamics and advanced drug delivery methods for the development of next-generation personalized cancer therapeutics.

Abstract PO-17: Microphysiologic model of ALK+ anaplastic large cell lymphoma and vascular interactions predicts drug efficacy in a 3D microfluidic chip

Abstract Anaplastic large-cell lymphoma (ALCL) initially responds well to anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor (TKI) therapies (e.g., crizotinib); however, resistance appears once the treatment is concluded and persistent cells are not eradicated. ALCL preferentially grows around blood and lymphatic vessel in the lymph node, and our preliminary data have shown that CCL19/21-CCR7 chemokine-receptor signaling axis might be involved in TKI resistance of ALCL cells. To investigate this mechanism, we developed a microphysiologic model of ALCL interacting with a 3D vasculature using a microfluidic chip. Microfluidic technology has the potential to impact cancer diagnosis and therapy by overcoming the limitations of 2D culture. These ex vivo models are able to uncover the continuous interactions and chemokine signaling that exist between cells of the tumor microenvironment (TME) and evaluate the preclinical efficacy of novel and personalized cancer therapeutics. Two ALK+ ALCL cell lines (COST and Karpas299) were knocked out by CRISPR/Cas9 of the CCR7 receptor, transduced with GFP for microscopic visualization. A commercial microfluidic chip with a central gel channel, flanked by two fluidic media channels, was used to develop the model. A collagen hydrogel (2.5 mg/mL) was injected into the central region of the 3D chip, incubated in sterile humidity chambers, and channels were hydrated with RPMI. 50 µL of 3x106 cells/mL human umbilical vein endothelial cells (HUVECs) suspension was injected twice on the fluidic channel, and the chip was rotated twice to create a confluent hollow-lumen 3D macrovessel. Then, 2x105 cells/mL ALCL cells were added inside the 3D macrovessel. Medium was refreshed daily, supplemented +/- 300 nM crizotinib (ALK TKI). Image analysis was performed using a confocal microscope. The model consisted of a well-formed perfusable macrovessel with ALCL cells flowing inside and interacting with HUVECs. ALCL viability was evaluated with a luminescent readout assay after 3 days of interactions with HUVECs. Strikingly, ALCL cells cultivated within the vasculature showed marked resistance to treatment with ALK TKI compared to cells cultivated in the absence of vessels. CCR7 KO cells showed decreased viability and decreased perivascular localization compared to wild-type control cells. The results suggest that the 3D ALCL-vascular microphysiologic model is a feasible platform with great potential to unveil the molecular mechanisms of drug resistance in a complex TME. The presence of macrovessels was sufficient to induce resistance to ALK TKI, which was decreased in CCR7 KO cells. These data indicate that CCL19/21 and possibly other cytokines produced by vessels generate survival signals in ALCL cells treated with ALK TKI. This physiologically relevant ALCL-vascular model offers a tool to genetically dissect the TME contribution to drug resistance, predict more reliably therapeutic vulnerabilities, and recapitulate patient-specific cell-cell interactions. Citation Format: Marco Campisi, Claudia Voena, Ines Mota, Enrico Patrucco, Roger Dale Kamm, Roberto Chiarle. Microphysiologic model of ALK+ anaplastic large cell lymphoma and vascular interactions predicts drug efficacy in a 3D microfluidic chip [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr PO-17.

Advancing precision medicine in clinical oncology: Whole exome paired tumor-normal DNA and RNA sequencing at a single-institution cancer center.

e14006 Background: Next generation sequencing (NGS) allows for reliable, comprehensive and cost-effective identification of clinically actionable genetic and genomic alterations. The increasing adoption of NGS in clinical oncology has increased our ability to identify germline alterations predisposing to cancer development as well as somatic changes enabling prescription of individualized cancer treatment and enhanced clinical trial participation. Here we summarize implementation of an NGS-based precision medicine initiative involving oncology patients from a single institution cancer center. Methods: IRB-approved NGS matched whole exome (WES) germline and solid tumor somatic tumor sequencing together with somatic tumor RNA sequencing (RNA-seq) were performed using germline DNA extracted from peripheral blood lymphocytes and nucleic acids for tumor DNA and RNA sequencing obtained from formalin-fixed, paraffin-embedded tumor specimens. Results of sequencing and analyses were presented to a multi-disciplinary tumor board to establish recommendations for management of germline pathogenic variation, therapeutic drug matching, clinical trials eligibility and molecularly informed patient prognosis. Results: A total of 1,005 patients completed sequencing. Germline and somatic WES exceeded 100X and 250X mean target coverage, respectively; somatic RNA-seq exceeded 200 million mean reads. Patients ranged in age from 17 to 90 years. The study cohort comprised comparable numbers of female (51%) and male (49%) patients. Ethnicities and races were broadly represented with 22% of participants identifying as Hispanic, 14% as Asian, 4% as Black, 55% as Non-Hispanic White and 5% as other. The most common solid tumor histological classification was colorectal (18%), followed by breast (16%), prostate (7%), head and neck (7%), sarcoma (7%), ovarian (5%), melanoma (4%) and lung (3%). Bioinformatic analyses and precision medicine tumor board review established that 12% of patients harbored a germline pathogenic variant and 43% carried clinically actionable genetic/genomic alterations; a majority of patients met molecular requirements for participation in a clinical trial. Conclusions: This study confirms the feasibility and utility of clinical NGS and precision medicine tumor board review in clinical oncology to identify germline genetic pathology, deliver personalized cancer therapeutics, increase clinical trial enrollment and clarify diagnosis and prognosis.

How can the personal discovery process evolve personalized medicine? An interview with Laura Towart.

Laura is the Founder and CEO of My Personal Therapeutics, a London-based digital health company offering the most advanced personalized cancer therapeutics. She is also the Founder and former CEO of Celmatix, a leader in diagnostics and predictive analytics for female infertility and women's health. She is a graduate of the Weill Cornell Graduate School of Medical Sciences and Memorial Sloan Kettering Cancer Center's Doctoral program and received a Certificate in Bioinformatics. She holds a BS/BA in Biology/English from The George Washington University. Besides being passionate about personalized medicine, she enjoys exploring the world with her children Julian, Valentina and Delphine.

Nanobiosensors: Point-of-care approaches for cancer diagnostics

Early cancer diagnosis is of prime importance as it paves the way for effective treatment and possible patient survival. The recent advancements in the field of biosensorics have facilitated the development of functionalized nanobiosensors which have the potential to provide a cost-effective, reliable and rapid diagnostic strategy for cancers. These nanoscaled sensing systems utilize electrochemical, optical, mass and calorimetric sensing mechanisms to specifically identify the disease-specific biomarkers. Because of clinical translational utility, the present review aims to describe the recent developments and status of the nanobiosensors as a point-of-care approach for cancer diagnosis. The review also offers important insights into the design, preparation and characterization of these nano-frameworks. In particular, the state-of-art nanobiosensors based on carbon nanostructures, metal nanoparticles, magnetic nanoparticles, silica-based nanomaterials, conducting polymers based nanoparticles and quantum dots, which provide countless opportunities in the field of cancer biosensorics have been summarized. It also showcases the need to perform robust clinical validation of the emerging nanobiosensor strategies that would act as the ultimate point-of-care test for the personalized cancer therapeutics.

3D Printed Absorber for Capturing Chemotherapy Drugs before They Spread through the Body.

Despite efforts to develop increasingly targeted and personalized cancer therapeutics, dosing of drugs in cancer chemotherapy is limited by systemic toxic side effects. We have designed, built, and deployed porous absorbers for capturing chemotherapy drugs from the bloodstream after these drugs have had their effect on a tumor, but before they are released into the body where they can cause hazardous side effects. The support structure of the absorbers was built using 3D printing technology. This structure was coated with a nanostructured block copolymer with outer blocks that anchor the polymer chains to the 3D printed support structure and a middle block that has an affinity for the drug. The middle block is polystyrenesulfonate which binds to doxorubicin, a widely used and effective chemotherapy drug with significant toxic side effects. The absorbers are designed for deployment during chemotherapy using minimally invasive image-guided endovascular surgical procedures. We show that the introduction of the absorbers into the blood of swine models enables the capture of 64 ± 6% of the administered drug (doxorubicin) without any immediate adverse effects. Problems related to blood clots, vein wall dissection, and other biocompatibility issues were not observed. This development represents a significant step forward in minimizing toxic side effects of chemotherapy.

Nanoinformatics Revolutionizes Personalized Cancer Therapy

Designing personalized cancer nanomedicines is a challenging process. The emerging field of nanoinformatics can facilitate this process by enabling computational design of nanocarrier-encapsulated drugs. Recent data show that quantitative structure-nanoparticle assembly calculations predict particle formation and size, and can lead to safer and more effective personalized cancer therapeutics.

Variation in organ-specific PIK3CA and KRAS mutant levels in normal human tissues correlates with mutation prevalence in corresponding carcinomas.

Large‐scale sequencing efforts have described the mutational complexity of individual cancers and identified mutations prevalent in different cancers. As a complementary approach, allele‐specific competitive blocker PCR (ACB‐PCR) is being used to quantify levels of hotspot cancer driver mutations (CDMs) with high sensitivity, to elucidate the tissue‐specific properties of CDMs, their occurrence as tumor cell subpopulations, and their occurrence in normal tissues. Here we report measurements of PIK3CA H1047R mutant fraction (MF) in normal colonic mucosa, normal lung, colonic adenomas, colonic adenocarcinomas, and lung adenocarcinomas. We report PIK3CA E545K MF measurements in those tissues, as well as in normal breast, normal thyroid, mammary ductal carcinomas, and papillary thyroid carcinomas. We report KRAS G12D and G12V MF measurements in normal colon. These MF measurements were integrated with previously published ACB‐PCR data on KRAS G12D, KRAS G12V, and PIK3CA H1047R. Analysis of these data revealed a correlation between the degree of interindividual variability in these mutations (as log10 MF standard deviation) in normal tissues and the frequencies with which the mutations are detected in carcinomas of the corresponding organs in the COSMIC database. This novel observation has important implications. It suggests that interindividual variability in mutation levels of normal tissues may be used as a metric to identify mutations with critical early roles in tissue‐specific carcinogenesis. Additionally, it raises the possibility that personalized cancer therapeutics, developed to target specifically activated oncogenic products, might be repurposed as prophylactic therapies to reduce the accumulation of cells carrying CDMs and, thereby, reduce future cancer risk. Environ. Mol. Mutagen. 58:466–476, 2017. © 2017 This article is a U.S. Government work and is in the public domain in the USA. Environmental and Molecular Mutagenesis published by Wiley Periodicals, Inc. on behalf of Environmental Mutagen Society

Personalized cancer therapeutics: Updates on drug developments and characterization of biosimilars

Personalized cancer therapeutics: Updates on drug developments and characterization of biosimilars

Abstract 167: Inferring subclonal relationships between multiple metastases from rapid autopsy of a single melanoma patient

Abstract Background: Despite recent advances in personalized cancer therapeutics, little impact has been made on curing advanced melanomas over the last several decades. Patients with metastatic melanoma generally have a poor prognosis; survival is limited and typically the 5-year survival rate is less than 15% in patients with metastatic disease. Dysregulation of BRAF signaling has been shown to be one of these key drivers of the pathogenesis of disease in a large subpopulation (∼40%) of patients. Although targeted therapy with BRAF and MEK inhibitors is the mainstay of therapy for recurrent/metastatic melanomas, this treatment modality provides a temporary respite. Often resistant tumor clones(carrying mutations in oncogenes) emerge, followed by a rapid progression, causing the patients to succumb to the disease. As such, in this study, we aim to understand inter- and intra- tumor heterogeneity that leads to resistance to therapy. Methods: We conducted deep exome sequence analysis (250X coverage) of a collection of 8 metastatic melanoma tumor samples collected by rapid autopsy from a patient who became resistant to Vemurafinib despite harboring sensitizing BRAF p. V600E mutations. We used whole exome sequence analysis to detect somatic mutations and copy number variants in every metastatic tumor. To infer subclonal populations present within each metastasis, we applied a combination of clonal inference tools to determine subclonal structures based on somatic point mutations (PyClone) and copy number alterations (TITAN). Subsequently, we applied clustering algorithms to the inferred populations to construct inter-tumor phylogenies. Results: We found that 89% of somatic point mutations were shared across all metastatic sites and were primarily clonal. Conversely, the cellular prevalence inferred from copy number alterations showed a greater degree of subclonal evolution. We found a clear relationship between the physical locations of tumors and shared subclonal patterns, whereby metastases from lung, brain and omentum metastases clustered distinctly from those in rectum, small intestine, peritoneum, diaphragm and mesentery. To look for shared patterns of subclonal variation underlying metastatic disease and resistance to therapy, we are currently extending our analysis to an additional six patients, each with tissues from 7-9 metastatic sites collected by rapid autopsy. Conclusion: Our analysis of multiple metastatic sites from a single patient by deep exome sequence analysis found most somatic mutations were shared across tumor sites and subclonal evolution appears to be driven primarily by acquisition of copy number alterations. Tumors in near proximity shared greater genomic similarity compare to distant sites. Ongoing analysis of additional patients is required to uncover recurrent mechanisms of resistance to targeted therapies in this disease. Citation Format: Soroush Samadian, Prashant Bavi, Dianne Chadwick, Arnavaz Danesh, Mark Dowar, Tiantian Li, Madura Siva, Michael Roehl, Anthony m. Joshua, Trevor J. Pugh. Inferring subclonal relationships between multiple metastases from rapid autopsy of a single melanoma patient. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 167.