Precision medicine for cancer with next-generation functional diagnostics

Safety and Activity of the Combination of Ceritinib and Dasatinib in Osteosarcoma

Osteosarcoma (OS) is the second most common cause of cancer-related death in pediatric patients. The insulin-like growth factor (IGF) pathway plays a relevant role in the biology of OS but no IGF targeted therapies have been successful as monotherapy so far. Here, we tested the effect of three IGF specific inhibitors and tested ceritinib as an off-target inhibitor, alone or in combination with dasatinib, on the proliferation of seven primary OS cells. Picropodophyllin, particularly in combination with dasatinib and the combination ceritinib/dasatinib were effective in abrogating the proliferation. The ceritinib/dasatinib combination was applied to the primary cells of a 16-year-old girl with a long history of lung metastases, and was more effective than cabozantinib and olaparib. Therefore, the combination was used to treat the patient. The treatment was well tolerated, with toxicity limited to skin rush and diarrhea. A histopathological evaluation of the tumor after three months of therapy indicated regions of high necrosis and extensive infiltration of macrophages. The extension of the necrosis was proportional to the concentration of dasatinib and ceritinib in the area, as analysed by an ultra performance liquid chromatography–tandem mass spectrometer (UPLC-MS/MS). After the cessation of the therapy, radiological analysis indicated a massive growth of the patient’s liver metastases. In conclusion, these data indicate that the combination of ceritinib/dasatinib is safe and may be used to develop new therapy protocols.

Microfluidics as an Enabling Technology for Personalized Cancer Therapy

Tailoring patient-specific treatments for cancer is necessary in order to achieve optimal results but requires new diagnostic approaches at affordable prices. Microfluidics has immense potential to provide solutions for this, as it enables the processing of samples that are not available in large quantities (e.g., cells from patient biopsies), is cost efficient, provides a high level of automation, and allows the set-up of complex models for cancer studies. In this review, individual solutions in the fields of genetics, circulating tumor cell monitoring, biomarker analysis, phenotypic drug sensitivity tests, and systems providing controlled environments for disease modeling are discussed. An overview on how these early stage achievements can be combined or developed further is showcased, and the required translational steps before microfluidics becomes a routine tool for clinical applications are critically discussed.

Simultaneous kinase inhibition with ibrutinib and BCL2 inhibition with venetoclax offers a therapeutic strategy for acute myeloid leukemia

Acute myeloid leukemia (AML) results from the enhanced proliferation and impaired differentiation of hematopoietic stem and progenitor cells. Using an ex vivo functional screening assay, we identified that the combination of the BTK inhibitor ibrutinib and BCL2 inhibitor venetoclax (IBR + VEN), currently in clinical trials for chronic lymphocytic leukemia (CLL), demonstrated enhanced efficacy on primary AML patient specimens, AML cell lines, and in a mouse xenograft model of AML. Expanded analyses among a large cohort of hematologic malignancies (n = 651 patients) revealed that IBR + VEN sensitivity associated with selected genetic and phenotypic features in both CLL and AML specimens. Among AML samples, 11q23 MLL rearrangements were highly sensitive to IBR + VEN. Analysis of differentially expressed genes with respect to IBR + VEN sensitivity indicated pathways preferentially enriched in patient samples with reduced ex vivo sensitivity, including IL-10 signaling. These findings suggest that IBR + VEN may represent an effective therapeutic option for patients with AML.

Study of pharmacogenomic information in FDA-approved drug labeling to facilitate application of precision medicine

Pharmacogenomics (PGx), studying the relationship between drug response and genetic makeup of an individual, is accelerating advances in precision medicine. The FDA includes PGx information in the labeling of approved drugs to better inform on their safety and effectiveness. We herein present a summary of PGx information found in 261 prescription drug labeling documents by querying the publicly available FDALabel database. A total of 362 drug-biomarker pairs (DBPs) were identified. We profiled DBPs using frequency of the biomarkers and their therapeutic classes. Four categories of applications (indication, safety, dosing and information) were discussed according to information in labeling. This analysis facilitates better understanding, utilization and translation of PGx information in drug labeling among researchers, healthcare professionals and the public.

Multifunctional materials for implantable and wearable photonic healthcare devices

Numerous light-based diagnostic and therapeutic devices are routinely used in the clinic. These devices have a familiar look as items plugged in the wall or placed at patients' bedsides, but recently, many new ideas have been proposed for the realization of implantable or wearable functional devices. Many advances are being fuelled by the development of multifunctional materials for photonic healthcare devices. However, the finite depth of light penetration in the body is still a serious constraint for their clinical applications. In this Review, we discuss the basic concepts and some examples of state-of-the-art implantable and wearable photonic healthcare devices for diagnostic and therapeutic applications. First, we describe emerging multifunctional materials critical to the advent of next-generation implantable and wearable photonic healthcare devices and discuss the path for their clinical translation. Then, we examine implantable photonic healthcare devices in terms of their properties and diagnostic and therapeutic functions. We next describe exemplary cases of noninvasive, wearable photonic healthcare devices across different anatomical applications. Finally, we discuss the future research directions for the field, in particular regarding mobile healthcare and personalized medicine.

Identifying biological markers for improved precision medicine in psychiatry

Mental disorders represent an increasing personal and financial burden and yet treatment development has stagnated in recent decades. Current disease classifications do not reflect psychobiological mechanisms of psychopathology, nor the complex interplay of genetic and environmental factors, likely contributing to this stagnation. Ten years ago, the longitudinal IMAGEN study was designed to comprehensively incorporate neuroimaging, genetics, and environmental factors to investigate the neural basis of reinforcement-related behavior in normal adolescent development and psychopathology. In this article, we describe how insights into the psychobiological mechanisms of clinically relevant symptoms obtained by innovative integrative methodologies applied in IMAGEN have informed our current and future research aims. These aims include the identification of symptom groups that are based on shared psychobiological mechanisms and the development of markers that predict disease course and treatment response in clinical groups. These improvements in precision medicine will be achieved, in part, by employing novel methodological tools that refine the biological systems we target. We will also implement our approach in low- and medium-income countries to understand how distinct environmental, socioeconomic, and cultural conditions influence the development of psychopathology. Together, IMAGEN and related initiatives strive to reduce the burden of mental disorders by developing precision medicine approaches globally.

A normalized drug response metric improves accuracy and consistency of anticancer drug sensitivity quantification in cell-based screening

Accurate quantification of drug effects is crucial for identifying pharmaceutically actionable cancer vulnerabilities. Current cell viability-based measurements often lead to biased response estimates due to varying growth rates and experimental artifacts that explain part of the inconsistency in high-throughput screening results. We developed an improved drug scoring model, normalized drug response (NDR), which makes use of both positive and negative control conditions to account for differences in cell growth rates, and experimental noise to better characterize drug-induced effects. We demonstrate an improved consistency and accuracy of NDR compared to existing metrics in assessing drug responses of cancer cells in various culture models and experimental setups. Notably, NDR reliably captures both toxicity and viability responses, and differentiates a wider spectrum of drug behavior, including lethal, growth-inhibitory and growth-stimulatory modes, based on a single viability readout. The method will therefore substantially reduce the time and resources required in cell-based drug sensitivity screening.

Comprehensive characterization of the phosphoproteome of gastric cancer from endoscopic biopsy specimens

Rationale: Cancer phosphoproteomics can provide insights regarding kinases that can be targeted for therapeutic applications. Monitoring the phosphoproteomics in cancer is expected to play a key role in optimizing treatments with kinase inhibitors. Clinical phosphoproteomics in surgical tissues and patient-derived models has been studied intensively. However, the reported data may not accurately reflect the phosphosignaling status in patients due to the effect of ischemia occurring during surgery or changes in the characteristics of cancer cells when establishing the models. In contrast, endoscopic biopsies have an advantage for clinical phosphoproteomics because they can be rapidly cryo-preserved. We aimed to develop a highly sensitive method for phosphoproteomics in endoscopic biopsies of gastric cancer.

Methods: Three tumor biopsies and three normal gastric biopsies were obtained by endoscopy at one time, and subjected to our optimized phosphoproteomics. Phosphopeptides were enriched with an immobilized metal affinity chromatography, and labeled with Tandem Mass Tag reagent. Quantified phosphosites were compared between the pairs of tumor/normal biopsies within same patient. Cancer-specific activated pathways and kinases were identified by pathway enrichment analysis and kinase-substrate enrichment analysis.

Results: Our protocol enabled the identification of more than 10,000 class 1 phosphosites from endoscopic biopsies. A comparison between samples from cancer tissue and normal mucosa demonstrated differences in the phosphosignaling, including biomarkers of response to DNA damage. Finally, cancer-specific activation of DNA damage response signaling was validated by additional phosphoproteomics of other patients and western blotting of gastric cancer/normal cells.

Conclusion: In summary, our pioneering approach will facilitate more accurate clinical phosphoproteomics in endoscopic biopsies, which can be applied to monitor the activities of therapeutic kinases and, ultimately, can be a useful tool to precision medicine.

Bioinformatics for Cancer Immunotherapy

Our immune system plays a key role in health and disease as it is capable of responding to foreign antigens as well as acquired antigens from cancer cells. Latter are caused by somatic mutations, the so-called neoepitopes, and might be recognized by T cells if they are presented by HLA molecules on the surface of cancer cells. Personalized mutanome vaccines are a class of customized immunotherapies, which is dependent on the detection of individual cancer-specific tumor mutations and neoepitope (i.e., prediction, followed by a rational vaccine design, before on-demand production. The development of next generation sequencing (NGS) technologies and bioinformatic tools allows a large-scale analysis of each parameter involved in this process. Here, we provide an overview of the bioinformatic aspects involved in the design of personalized, neoantigen-based vaccines, including the detection of mutations and the subsequent prediction of potential epitopes, as well as methods for associated biomarker research, such as high-throughput sequencing of T-cell receptors (TCRs), followed by data analysis and the bioinformatics quantification of immune cell infiltration in cancer samples.

In Silico Cell-Type Deconvolution Methods in Cancer Immunotherapy

Several computational methods have been proposed to infer the cellular composition from bulk RNA-seq data of a tumor biopsy sample. Elucidating interactions in the tumor microenvironment can yield unique insights into the status of the immune system. In immuno-oncology, this information can be crucial for deciding whether the immune system of a patient can be stimulated to target the tumor. Here, we shed a light on the working principles, capabilities, and limitations of the most commonly used methods for cell-type deconvolution in immuno-oncology and offer guidelines for method selection.

Conditionally reprogrammed colorectal cancer cells combined with mouse avatars identify synergy between EGFR and MEK or CDK4/6 inhibitors

Preclinical models, including patient-derived xenograft (PDX) and organoid and primary cell culture, are essential for studies of cancer cell biology and facilitate translational research and individualization of therapy. We explored the optimum preclinical model by modifying the conventional conditional reprogramming (CR) system followed by screening effective targeted drug combinations against colorectal cancer (CRC). By modifying the ingredients of the culture medium used in a conventional CR system, a novel individualized CR system (termed i-CR) was established. Tumor samples from CRC patients were collected and PDX models were derived followed by high-throughput i-CR drug screening and validation of the effective targeted drug combinations. The i-CR system selectively expanded tumor cells rather than normal epithelial cells and facilitated high-throughput drug screening when combined with high-content imaging and quantitative analysis of cell proliferation. Using inhibitors targeting multiple signaling pathways identified by high-throughput i-CR drug screening, we discovered that inhibition of the EGFR and MEK or CDK4/6 pathways exerted a synergistic inhibitory effect against CRC, and we noted super-synergistic effects when EGFR, MEK, and CDK4/6 inhibitors were used simultaneously. These data were validated using paired PDX models, which showed marked inhibition of tumor growth. The novel i-CR system combined with PDX models will enable individualization of therapy and drug discovery, and strategies combining EGFR, MEK, and CDK4/6 inhibitors warrant clinical validation.

3D microgroove electrical impedance sensing to examine 3D cell cultures for antineoplastic drug assessment

In recent decades, three-dimensional (3D) cancer cell models have attracted increasing interest in the field of drug screening due to their significant advantages in more accurate simulations of heterogeneous tumor behavior in vivo compared to two-dimensional models. Furthermore, drug sensitivity testing based on 3D cancer cell models can provide more reliable in vivo efficacy prediction. The gold standard fluorescence staining is hard to achieve real-time and label-free viability monitoring in 3D cancer cell models. In this study, a microgroove impedance sensor (MGIS) was specially developed for the dynamic and noninvasive monitoring of 3D cell viability. 3D cancer cells were trapped in microgrooves with gold electrodes on opposite walls for in situ impedance measurement. The change in the number of live cells caused inversely proportional changes to the impedance magnitude of the entire cell/Matrigel construct and reflected the proliferation and apoptosis of the 3D cells. It was confirmed that the 3D cell viability detected by the MGIS was highly consistent with the standard live/dead staining by confocal microscope characterization. Furthermore, the accuracy of the MGIS was validated quantitatively using a 3D lung cancer model and sophisticated drug sensitivity testing. In addition, the parameters of the MGIS in the measurement experiments were optimized in detail using simulations and experimental validation. The results demonstrated that the MGIS coupled with 3D cell culture would be a promising platform to improve the efficiency and accuracy of cell-based anticancer drug screening in vitro.

Electrophysiology-based stratification of pancreatic tumorigenicity by label-free single-cell impedance cytometry

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer lacking specific biomarkers that can be correlated to disease onset, promotion and progression. To assess whether tumor cell electrophysiology may serve as a marker for PDAC tumorigenicity, we use multi-frequency impedance cytometry at high throughput (∼350 cells/s) to measure the electrical phenotype of single PDAC tumor cells from xenografts, which are derived from primary pancreatic tumors versus those from liver metastases of different patients. A novel phase contrast metric based on variations in the high and low frequency impedance phase responses that is related to electrophysiology of the cell interior is found to be systematically altered as a function of tumorigenicity. PDAC cells of higher tumorigenicity exhibited lowered interior conductivity and enhanced permittivity, which is validated by the dielectrophoresis on the respective cell types. Using genetic analysis, we suggest the role of dysregulated Na⁺ transport and removal of Ca²⁺ ions from the cytoplasm on key oncogenic KRAS-driven processes that may be responsible for lowering of the interior cell conductivity. We envision that impedance cytometry can serve as a tool to quantify phenotypic heterogeneity for rapidly stratifying tumorigenicity. It can also aid in protocols for dielectrophoretic isolation of cells with a particular phenotype for prognostic studies on patient survival and to tailor therapy selection to specific patients.

Primer on Hereditary Cancer Predisposition Genes Included Within Somatic Next-Generation Sequencing Panels

PURPOSE

It has been estimated that 5% to 10% of cancers are due to hereditary causes. Recent data sets indicate that the incidence of hereditary cancer may be as high as 17.5% in patients with cancer, and a notable subset is missed if screening is solely by family history and current syndrome-based testing guidelines. Identification of germline variants has implications for both patients and their families. There is currently no comprehensive overview of cancer susceptibility genes or inclusion of these genes in commercially available somatic testing. We aimed to summarize genes linked to hereditary cancer and the somatic and germline panels that include such genes.

METHODS

Germline predisposition genes were chosen if commercially available for testing. Penetrance was defined as low, moderate, or high according to whether the gene conferred a 0% to 20%, 20% to 50%, or 50% to 100% lifetime risk of developing the cancer or, when percentages were not available, was estimated on the basis of existing literature descriptions.

RESULTS

We identified a total of 89 genes linked to hereditary cancer predisposition, and we summarized these genes alphabetically and by organ system. We considered four germline and six somatic commercially available panel tests and quantified the coverage of germline genes across them. Comparison between the number of genes that had germline importance and the number of genes included in somatic testing showed that many but not all germline genes are tested by frequently used somatic panels.

CONCLUSION

The inclusion of cancer-predisposing genes in somatic variant testing panels makes incidental germline findings likely. Although somatic testing can be used to screen for germline variants, this strategy is inadequate for comprehensive screening. Access to genetic counseling is essential for interpretation of germline implications of somatic testing and implementation of appropriate screening and follow-up.

Comprehensive molecular profiling of extrahepatic cholangiocarcinoma in Chinese population and potential targets for clinical practice

Background

Cholangiocarcinoma (CCA) is a diverse group of malignancies arising from the intra- or extrahepatic biliary epithelium and characterized by its late diagnosis and fatal outcome. Extrahepatic cholangiocarcinoma (ECC) accounts for 90% of CCA. However, little is known about the comprehensive genomic alterations of ECC in Chinese population for providing clinical managements especially targeted therapy.

Methods

Comprehensive genomic profiling (CGP) was performed with next generation sequencing panel on paraffin-embedded tumor from a cohort of 80 Chinese ECC patients.

Results

The most frequently altered genes were TP53 (68%), KRAS (46%), SMAD4 (22%), ARID1A (20%) and CDKN2A (19%). Mutual exclusivity was observed between multiple genes including ARID1A:TP53, KRAS:LRP1B and NF2:TP53. Genetic alterations with potential therapeutic implications were identified in 43% of patients. The top three actionable alterations include CDKN2A (n=11), BRAF (n=5) and ERBB2 (n=4). Potentially actionable alterations were mainly enriched in the G1-S transition, homologous recombination repair, MAPK/ERK pathway.

Conclusions

This is the largest data set of ECC cases providing a comprehensive view on genetic alterations in Chinese population which differs significantly from a US cohort, and indicates the potential clinical implications for targeted therapies.

Paradigms for Precision Medicine in Epichaperome Cancer Therapy

Alterations in protein-protein interaction networks are at the core of malignant transformation but have yet to be translated into appropriate diagnostic tools. We make use of the kinetic selectivity properties of an imaging probe to visualize and measure the epichaperome, a pathologic protein-protein interaction network. We are able to assay and image epichaperome networks in cancer and their engagement by inhibitor in patients' tumors at single-lesion resolution in real time, and demonstrate that quantitative evaluation at the level of individual tumors can be used to optimize dose and schedule selection. We thus provide preclinical and clinical evidence in the use of this theranostic platform for precision medicine targeting of the aberrant properties of protein networks.

Major challenges in accurate mutation detection of multifocal lung adenocarcinoma by next-generation sequencing

Background: Many patients with advanced non-small cell lung cancer manifested with metastasis, and molecular heterogeneity may exhibit between primary and metastatic tumors. We sought to investigate the clinical detection strategy of primary and metastatic tumors in Chinese patients with NSCLC.Methods: Here, 77 paired tumors of Chinese patients with lung adenocarcinoma were analyzed, and 1836 mutation in hotspot regions of 22 genes were identified by next-generation sequencing. The expression of ALK in these paired tumors was also detected by immunohistochemistry.Results: The results showed that the concordance rate in multiple pulmonary nodules, primary-LN metastasis pairs and primary-distant metastasis pairs was 67.7%, 94.1% and 86.7%, respectively. In multiple pulmonary nodules, the concordance rate was 100% when the pathologic diagnosis was intrapulmonary metastasis, whereas the concordance rate was 23.1% when the pathologic diagnosis was multiple primary tumors. TP53 and CTNNB1 mutations were detected as the recurrent alterations in LN metastasis. Moreover, the concordance of ALK status was observed in these pairs.Conclusions: Our data suggested that hotspot mutations and ALK status in the primary-metastasis pairs had a high concordance in lung adenocarcinoma. Clinical detection of one lesion may be enough to identify the key alterations except that patients are diagnosed with multiple primary tumors or have disease progression after benefiting from target therapy.

Statins enhance efficacy of venetoclax in blood cancers

Statins have shown promise as anticancer agents in experimental and epidemiologic research. However, any benefit that they provide is likely context-dependent, for example, applicable only to certain cancers or in combination with specific anticancer drugs. We report that inhibition of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) using statins enhances the proapoptotic activity of the B cell lymphoma-2 (BCL2) inhibitor venetoclax (ABT-199) in primary leukemia and lymphoma cells but not in normal human peripheral blood mononuclear cells. By blocking mevalonate production, HMGCR inhibition suppressed protein geranylgeranylation, resulting in up-regulation of proapoptotic protein p53 up-regulated modulator of apoptosis (PUMA). In support of these findings, dynamic BH3 profiling confirmed that statins primed cells for apoptosis. Furthermore, in retrospective analyses of three clinical studies of chronic lymphocytic leukemia, background statin use was associated with enhanced response to venetoclax, as demonstrated by more frequent complete responses. Together, this work provides mechanistic justification and clinical evidence to warrant prospective clinical investigation of this combination in hematologic malignancies.

Cancer metabolism gets physical

Patient-derived culture models enable assessment of drug sensitivity and can connect personalized genomics with therapeutic options. However, their clinical translation is constrained by limited fidelity. We outline how the physical microenvironment regulates cell metabolism and describe how engineered culture systems could enhance the predictive power for precision medicine.

Panoptic View of Prognostic Models for Personalized Breast Cancer Management

The efforts to personalize treatment for patients with breast cancer have led to a focus on the deeper characterization of genotypic and phenotypic heterogeneity among breast cancers. Traditional pathology utilizes microscopy to profile the morphologic features and organizational architecture of tumor tissue for predicting the course of disease, and is the first-line set of guiding tools for customizing treatment decision-making. Currently, clinicians use this information, combined with the disease stage, to predict patient prognosis to some extent. However, tumoral heterogeneity stubbornly persists among patient subgroups delineated by these clinicopathologic characteristics, as currently used methodologies in diagnostic pathology lack the capability to discern deeper genotypic and subtler phenotypic differences among individual patients. Recent advancements in molecular pathology, however, are poised to change this by joining forces with multiple-omics technologies (genomics, transcriptomics, epigenomics, proteomics, and metabolomics) that provide a wealth of data about the precise molecular complement of each patient's tumor. In addition, these technologies inform the drivers of disease aggressiveness, the determinants of therapeutic response, and new treatment targets in the individual patient. The tumor architecture information can be integrated with the knowledge of the detailed mutational, transcriptional, and proteomic phenotypes of cancer cells within individual tumors to derive a new level of biologic insight that enables powerful, data-driven patient stratification and customization of treatment for each patient, at each stage of the disease. This review summarizes the prognostic and predictive insights provided by commercially available gene expression-based tests and other multivariate or clinical -omics-based prognostic/predictive models currently under development, and proposes a more inclusive multiplatform approach to tackling the challenging heterogeneity of breast cancer to individualize its management. "The future is already here-it's just not very evenly distributed."-William Ford Gibson.

Advances in PET Diagnostics for Guiding Targeted Cancer Therapy and Studying In Vivo Cancer Biology

Purpose of the Review

We present an overview of recent advances in positron emission tomography (PET) diagnostics as applied to the study of cancer, specifically as a tool to study in vivo cancer biology and to direct targeted cancer therapy. The review is directed to translational and clinical cancer investigators who may not be familiar with these applications of PET cancer diagnostics, but whose research might benefit from these advancing tools.

Recent Findings

We highlight recent advances in 3 areas: (1) the translation of PET imaging cancer biomarkers to clinical trials; (2) methods for measuring cancer metabolism in vivo in patients; and (3) advances in PET instrumentation, including total-body PET, that enable new methodologies. We emphasize approaches that have been translated to human studies.

Summary

PET imaging methodology enables unique in vivo cancer diagnostics that go beyond cancer detection and staging, providing an improved ability to guide cancer treatment and an increased understanding of in vivo human cancer biology.

Effects of culture method on response to EGFR therapy in head and neck squamous cell carcinoma cells

The EGFR pathway plays a critical role in head and neck squamous cell carcinoma (HNSCC). Targeted therapies against the EGFR are utilized as a treatment for HNSCCC. However, patient response is heterogeneous and molecular biomarkers are lacking to predict patient response. Therefore, functional assays where drug response is directly evaluated in tumor cells are an interesting alternative. Previous studies have shown that experimental conditions modify the drug response observed in functional assays. Thus, in this work the influence of the culture environment on response to Cetuximab (EGFR monoclonal antibody) and AZD8055 (mTOR inhibitor) was evaluated. HNSCC UM-SCC-1 and UM-SCC-47 cells were cultured in 2D monoculture and compared with: 2D co-culture with cancer-associated fibroblasts (CAF); 3D culture in collagen hydrogels; and 3D culture in tumor spheroids. The results showed UM-SCC-1 drug response significantly changed in the different culture environments; leading to an increase in drug resistance in the CAF co-culture and the 3D spheroids. Conversely, UM-SCC-47 exhibited a more constant drug response across culture conditions. In conclusion, this work highlights the importance of culture conditions that modulate response to EGFR pathway inhibition.

Bicistronic transfer of CDKN2A and p53 culminates in collaborative killing of human lung cancer cells in vitro and in vivo

Cancer therapies that target a single protein or pathway may be limited by their specificity, thus missing key players that control cellular proliferation and contributing to the failure of the treatment. We propose that approaches to cancer therapy that hit multiple targets would limit the chances of escape. To this end, we have developed a bicistronic adenoviral vector encoding both the CDKN2A and p53 tumor suppressor genes. The bicistronic vector, AdCDKN2A-I-p53, supports the translation of both gene products from a single transcript, assuring that all transduced cells will express both proteins. We show that combined, but not single, gene transfer results in markedly reduced proliferation and increased cell death correlated with reduced levels of phosphorylated pRB, induction of CDKN1A and caspase 3 activity, yet avoiding the induction of senescence. Using isogenic cell lines, we show that these effects were not impeded by the presence of mutant p53. In a mouse model of in situ gene therapy, a single intratumoral treatment with the bicistronic vector conferred markedly inhibited tumor progression while the treatment with either CDKN2A or p53 alone only partially controlled tumor growth. Histologic analysis revealed widespread transduction, yet reduced proliferation and increased cell death was associated only with the simultaneous transfer of CDKN2A and p53. We propose that restoration of two of the most frequently altered genes in human cancer, mediated by AdCDKN2A-I-p53, is beneficial since multiple targets are reached, thus increasing the efficacy of the treatment.

Development, Implementation and Assessment of Molecular Diagnostics by Next Generation Sequencing in Personalized Treatment of Cancer: Experience of a Public Reference Healthcare Hospital

The establishment of precision medicine in cancer patients requires the study of several biomarkers. Single-gene testing approaches are limited by sample availability and turnaround time. Next generation sequencing (NGS) provides an alternative for detecting genetic alterations in several genes with low sample requirements. Here we show the implementation to routine diagnostics of a NGS assay under International Organization for Standardization (UNE-EN ISO 15189:2013) accreditation. For this purpose, 106 non-small cell lung cancer (NSCLC) and 102 metastatic colorectal cancer (mCRC) specimens were selected for NGS analysis with Oncomine Solid Tumor (ThermoFisher). In NSCLC the most prevalently mutated gene was TP53 (49%), followed by KRAS (31%) and EGFR (13%); in mCRC, TP53 (50%), KRAS (48%) and PIK3CA (16%) were the most frequently mutated genes. Moreover, NGS identified actionable genetic alterations in 58% of NSCLC patients, and 49% of mCRC patients did not harbor primary resistance mechanisms to anti-EGFR treatment. Validation with conventional approaches showed an overall agreement >90%. Turnaround time and cost analysis revealed that NGS implementation is feasible in the public healthcare context. Therefore, NGS is a multiplexed molecular diagnostic tool able to overcome the limitations of current molecular diagnosis in advanced cancer, allowing an improved and economically sustainable molecular profiling.

Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia

Drug susceptibility (also called chemosensitivity) is an important criterion for developing a therapeutic strategy for various cancer types such as breast cancer and leukemia. Recently, functional assays such as high-content screening together with genomic analysis have been shown to be effective for predicting drug susceptibility, but their clinical applicability is poor since they are time-consuming (several days long), labor-intensive, and costly. Here we present a highly simple, rapid, and cost-effective liquid biopsy for ex vivo drug-susceptibility testing of leukemia. The method is based on an extreme-throughput (>1 million cells per second), label-free, whole-blood imaging flow cytometer with a deep convolutional autoencoder, enabling image-based identification of the drug susceptibility of every single white blood cell in whole blood within 24 hours by simply flowing a drug-treated whole blood sample as little as 500 μL into the imaging flow cytometer without labeling. Our results show that the method accurately evaluates the drug susceptibility of white blood cells from untreated patients with acute lymphoblastic leukemia. Our method holds promise for affordable precision medicine.

Patient-Derived Xenografts and Matched Cell Lines Identify Pharmacogenomic Vulnerabilities in Colorectal Cancer

PURPOSE

Patient-derived xenograft (PDX) models accurately recapitulate the tumor of origin in terms of histopathology, genomic landscape, and therapeutic response, but some limitations due to costs associated with their maintenance and restricted amenability for large-scale screenings still exist. To overcome these issues, we established a platform of 2D cell lines (xeno-cell lines, XL), derived from PDXs of colorectal cancer with matched patient germline gDNA available.

EXPERIMENTAL DESIGN

Whole-exome and transcriptome sequencing analyses were performed. Biomarkers of response and resistance to anti-HER therapy were annotated. Dependency on the WRN helicase gene was assessed in MSS, MSI-H, and MSI-like XLs using a reverse genetics functional approach.

RESULTS

XLs recapitulated the entire spectrum of colorectal cancer transcriptional subtypes. Exome and RNA-seq analyses delineated several molecular biomarkers of response and resistance to EGFR and HER2 blockade. Genotype-driven responses observed in vitro in XLs were confirmed in vivo in the matched PDXs. MSI-H models were dependent upon WRN gene expression, while loss of WRN did not affect MSS XLs growth. Interestingly, one MSS XL with transcriptional MSI-like traits was sensitive to WRN depletion.

CONCLUSIONS

The XL platform represents a preclinical tool for functional gene validation and proof-of-concept studies to identify novel druggable vulnerabilities in colorectal cancer.

A personalized approach to acute myeloid leukemia therapy: current options

Therapeutic options for acute myeloid leukemia (AML) have remained unchanged for nearly the past 5 decades, with cytarabine and anthracyclines and use of hypomethylating agents for less intensive therapy. Implementation of large-scale genomic studies in the past decade has unraveled the genetic landscape and molecular etiology of AML. The approval of several novel drugs for targeted therapy, including midostaurin, enasidenib, ivosidenib, gemtuzumab–ozogamicin, and CPX351 by the US Food and Drug Administration has widened the treatment options for clinicians treating AML. This review focuses on some of these novel therapies and other promising agents under development, along with key clinical trial findings in AML.

Precision medicine for multiple sclerosis promotes preventative medicine

Multiple sclerosis (MS) is a chronic, lifelong disease, currently without a cure that is responsible for significant neurological injury in young adults. Precision medicine for MS aims to provide a more exacting and refined approach toward management by providing recommendations based on disease subtype, clinical status, existing radiological data, para-clinical data, and other biological markers. To achieve better outcomes, the three stages of care-diagnosis, treatment, and management-should be optimized. However, as the temporal profile of disease behavior is highly variable in MS, and unlike outcomes from other chronic conditions (i.e., hypertension, diabetes mellitus, etc.), should precision medicine for MS be one that focuses more on disease prevention and lifestyle modifications beyond recommendations for the use of disease-modifying therapies? As scientific advancements continue within the field of neuroimmunology, and until reliable biomarkers that predict disease outcomes are available, success may be better achieved by focusing on modifiable factors to reduce future disability.

Prognostic scoring system for osteosarcoma using network-regularized high-dimensional Cox-regression analysis and potential therapeutic targets

With the recent emphasis on the importance of personalized genomic medicine, studies have performed prognostic stratification using gene signatures in cancers. However, these studies have not considered gene networks with clinical data. Therefore, this study aimed to develop a novel prognostic score using grouped variable selection for patients with osteosarcoma. We assessed messenger RNA (mRNA) expression and clinical data from Gene Expression Omnibus to develop a novel prognostic scoring system for patients with osteosarcoma. Variable selection using Network-Regularized high-dimensional Cox-regression analysis with information regarding gene networks obtained from six large pathway databases was performed. We determined the risk score on the linear combination of regression coefficients and mRNA expression values. Log-rank test, UNO's c-index, and area under the curve (AUC) values were determined to evaluate the discriminatory power between the low- and high-risk groups. A recently reported next-generation Connectivity Map was used to identify future therapeutic targets for osteosarcoma. Our novel model had significantly high discriminatory power in predicting overall survival. An optimal c-index of 0.967 was obtained and time-dependent receiver operating characteristic analysis revealed an acceptable predictive value of AUC between 0.953 and 1.000. Knockdown of BACE2 or ING2 and linifanib treatment may improve the prognosis of patients with osteosarcoma. Herein, this novel prognostic scoring system would not only facilitate a more accurate prediction of patient prognosis, but also contribute to the selection of suitable therapeutic alternatives for osteosarcoma patients.

A systems biology approach towards understanding and treating non-neovascular age-related macular degeneration

Age-related macular degeneration (AMD) is the most common cause of blindness among the elderly in the developed world. While treatment is effective for the neovascular or “wet” form of AMD, no therapy is successful for the non-neovascular or “dry” form. Here we discuss the current knowledge on dry AMD pathobiology and propose future research directions that would expedite the development of new treatments. In our view, these should emphasize system biology approaches that integrate omic, pharmacological, and clinical data into mathematical models that can predict disease onset and progression, identify biomarkers, establish disease causing mechanisms, and monitor response to therapy.

No effective therapies exist for dry age-related macular degeneration. In this perspective, the authors propose that research should emphasize system biology approaches that integrate various ‘omics’ data into mathematical models to establish pathogenic mechanisms on which to design novel treatments, and identify biomarkers that predict disease progression and therapeutic response.

Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties

A key goal of precision medicine is predicting the best drug therapy for a specific patient from genomic information. In oncology, cancers that appear similar pathologically can vary greatly in how they respond to the same drug. Fortunately, data from high-throughput screening programs often reveal important relationships between genomic variability of cancer cells and their response to drugs. Nevertheless, many current computational methods to predict compound activity against cancer cells require large quantities of genomic, epigenomic, and additional cellular data to develop and to apply. Here we integrate recent screening data and machine learning to train classification models that predict the activity/inactivity of compounds against cancer cells based on the mutational status of only 145 oncogenes and a set of compound structural descriptors. Using IC50 values of 1 μM as activity cutoffs, our predictive models have sensitivities of 87%, specificities of 87%, and yield an area under the receiver operating characteristic curve equal to 0.94. We also develop regression models to predict log(IC50) values of compounds for cancer cells; the models achieve a Pearson correlation coefficient of 0.86 for cross-validation and up to 0.65-0.73 against blind test sets. Predictive performance remains strong when as few as 50 oncogenes are included. Finally, even when 40% of experimental IC50 values are missing from screening data, they can be imputed with sufficient reliability that classification accuracy is not diminished. The presented models are fast to generate and may serve as easily implemented screening tools for personalized oncology medicine, drug repurposing, and drug discovery.

Towards the Delivery of Precision Veterinary Cancer Medicine

We introduce a next phase in the evolution of medicine affecting human and veterinary patients. This evolution, genomic cancer medicine (Pmed), involves expansion of genomic and molecular biology into clinical medicine. The implementation of these new technologies has already begun and is a commercial reality. We introduce the underpinnings for this evolution, and focus on application in complex disease states. Pet owners have begun requesting Pmed technologies. To meet this demand, it is important to be aware of the opportunities and obstacles associated with available Pmed offerings as well as the current state of the field.

Future directions in soft tissue sarcoma treatment

Sarcoma is a broad term for mesenchymal malignancies that arise from soft tissue or bone. Despite classification by histologic subtype, clinical behavior and response to therapy have great variability. Modern genetic sequencing techniques have been able to identify additional genetic variability and subsequently new targeted therapies. In this review, we discuss the current state of STS diagnostics and treatment and explore some of the more promising areas in which progress is being made. We discuss therapies targeting PDGFRα/KIT, β-Catenin/APC/NOTCH, IDH-1/2 mutations, MDM2 amplifications, EZH2/INI1 expression loss, ALK fusion, and ASPSCR1-TFE3 fusion. We also discuss the progress that has been made within immunotherapies. While soft tissue sarcomas still portend a poor prognosis, these targeted therapies and immunotherapies provide treatment with less toxic side effects.

Aging Well: Using Precision to Drive Down Costs and Increase Health Quality

Efforts to provide patients with individualized treatments have led to tremendous breakthroughs in healthcare. However, a precision medicine approach alone will not offset the rapid increase in prevalence and burden of chronic non-communicable illnesses that is continuing to pervade the world's aging population. With rapid advances in technology, it is now possible to collect digital metrics to assess, monitor and detect chronic disease indicators, much earlier in the disease course, potentially redefining what was previously considered asymptomatic to pre-symptomatic. Data science and artificial intelligence can drive the discovery of digital biomarkers before the emergence of overt clinical symptoms, thereby transforming the current healthcare approach from one centered on precision medicine to a more comprehensive focus on precision health, and by doing so enable the possibility of preventing disease altogether. Presented herein are the challenges to the current healthcare model and the proposition of first steps for reversing the prevailing intractable trend of rising healthcare costs and poorer health quality.

Drug resistance: origins, evolution and characterization of genomic clones and the tumor ecosystem to optimize precise individualized therapy

Progress in understanding and overcoming fatal intrinsic and acquired resistance is slow, with only a few exceptions. Despite advances in modern genome and transcriptome analysis, the controversy of the three different theories on drug resistance and tumor progression, namely dynamic intratumor heterogeneity, pre-existing minor genomic clones and tumor ecosystem, is unresolved. Moreover, evidence on transcriptional heterogeneity suggests the necessity of a drug bank for individualized, precise drug-sensitivity prediction. We propose a cancer type- and stage-specific clinicogenomic and tumor ecosystemic concept toward cancer precision medicine, focusing on early therapeutic resistance and relapse.

Cancer Cell Drug Response Transcriptomes in 3D

The relevance of different in vitro culture models of cancer cells is a hot topic, but few systematic and definitive analyses in this area exist. In this issue of Cell Chemical Biology, Senkowski et al. (2016) address this issue by studying the transcriptomic profiles of drug-treated cancer cells cultured in two-dimensional and three-dimensional cultures. They describe biological findings with potential therapeutic implications and provide a unique data resource to mine.

pH-Sensitive Ratiometric Fluorescent Probe for Evaluation of Tumor Treatments

Determining therapeutic efficacy is critical for tumor precision theranostics. In order to monitor the efficacy of anti-cancer drugs (e.g., Paclitaxel), a pH-sensitive ratiometric fluorescent imaging probe was constructed. The pH-sensitive ratiometric fluorescent dye ANNA was covalently coupled to the N-terminal of the cell-penetrating TAT peptide through an amidation reaction (TAT-ANNA). The in vitro cellular experiments determined that the TAT-ANNA probe could penetrate the cell membrane and image the intracellular pH in real time. The in vivo experiments were then carried out, and the ratiometric pH response to the state of the tumor was recorded immediately after medication. The TAT-ANNA probe was successfully used to monitor the pharmacodynamics of anti-cancer drugs in vivo.

Technologies for circulating tumor cell separation from whole blood

The importance of early cancer diagnosis and improved cancer therapy has been clear for years and has initiated worldwide research towards new possibilities in the care strategy of patients with cancer using technological innovations. One of the key research fields involves the separation and detection of circulating tumor cells (CTC) because of their suggested important role in early cancer diagnosis and prognosis, namely, providing easy access by a liquid biopsy from blood to identify metastatic cells before clinically detectable metastasis occurs and to study the molecular and genetic profile of these metastatic cells. Provided the opportunity to further progress the development of technology for treating cancer, several CTC technologies have been proposed in recent years by various research groups and companies. Despite their potential role in cancer healthcare, CTC methods are currently mainly used for research purposes, and only a few methods have been accepted for clinical application because of the difficulties caused by CTC heterogeneity, CTC separation from the blood, and a lack of thorough clinical validation. Therefore, the standardization and clinical application of various developed CTC technologies remain important subsequent necessary steps. Because of their suggested future clinical benefits, we focus on describing technologies using whole blood samples without any pretreatment and discuss their advantages, use, and significance. Technologies using whole blood samples utilize size-based, immunoaffinity-based, and density-based methods or combinations of these methods as well as positive and negative enrichment during separation. Although current CTC technologies have not been truly implemented yet, they possess high potential as future clinical diagnostic techniques for the individualized therapy of patients with cancer. Thus, a detailed discussion of the clinical suitability of these new advanced technologies could help prepare clinicians for the future and can be a foundation for technologies that would be used to eliminate CTCs in vivo.

Cellular Metabolic Heterogeneity In Vivo Is Recapitulated in Tumor Organoids

Heterogeneous populations within a tumor have varying metabolic profiles, which can muddle the interpretation of bulk tumor imaging studies of treatment response. Although methods to study tumor metabolism at the cellular level are emerging, these methods provide a single time point “snapshot” of tumor metabolism and require a significant time and animal burden while failing to capture the longitudinal metabolic response of a single tumor to treatment. Here, we investigated a novel method for longitudinal, single-cell tracking of metabolism across heterogeneous tumor cell populations using optical metabolic imaging (OMI), which measures autofluorescence of metabolic coenzymes as a report of metabolic activity. We also investigated whether in vivo cellular metabolic heterogeneity can be accurately captured using tumor-derived three-dimensional organoids in a genetically engineered mouse model of breast cancer. OMI measurements of response to paclitaxel and the phosphatidylinositol-3-kinase inhibitor XL147 in tumors and organoids taken at single cell resolution revealed parallel shifts in metaboltruic heterogeneity. Interestingly, these previously unappreciated heterogeneous metabolic responses in tumors and organoids could not be attributed to tumor cell fate or varying leukocyte content within the microenvironment, suggesting that heightened metabolic heterogeneity upon treatment is largely due to heterogeneous metabolic shifts within tumor cells. Together, these studies show that OMI revealed remarkable heterogeneity in response to treatment, which could provide a novel approach to predict the presence of potentially unresponsive tumor cell subpopulations lurking within a largely responsive bulk tumor population, which might otherwise be overlooked by traditional measurements.

Relevance of neuroendocrine tumours models assessed by kinomic profiling

Although there is evidence of a significant rise of neuroendocrine tumours (NETs) incidence, current treatments are largely insufficient due to somewhat poor knowledge of these tumours. Despite many efforts achieved to expose driver oncogene mutations in NETs, the genetic landscape of NETs is characterized by relatively few mutations and chromosomal aberrations per tumour compared with other tumour types. In addition, NETs display few actionable mutations providing compelling rationale for targeted therapies. Recent works aiming at characterizing currently used NETs in vitro models at the genomic level raised concerns on their reliability as bona fide tools to study NETs biology. However, the lack of actionable mutation in NETs implies that sole use of genomic is not sufficient to describe these models and establish appropriate therapeutic strategies. Several kinases and kinase-involving signalling pathways have been demonstrated as abnormally regulated in NETs. Yet, kinases have only been investigated regardless of their involvement in large intracellular signalling networks. In order to assess the validity of in vitro NETs models to study NETs biology, "next-generation" high throughput functional technologies based on "kinome-wide activity" will demonstrate the similarities between signalling pathways in NETs models and patients' samples. These approaches will significantly assist in identifying actionable alterations in NETs signalling pathways and guide patient stratification into early-phase clinical trials based on kinase inhibition targeted therapies.

The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders

Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.

Probing the biological obstacles of nanomedicine with gold nanoparticles

Despite massive growth in nanomedicine research to date, the field still lacks fundamental understanding of how certain physical and chemical features of a nanoparticle affect its ability to overcome biological obstacles in vivo and reach its intended target. To gain fundamental understanding of how physical and chemical parameters affect the biological outcomes of administered nanoparticles, model systems that can systematically manipulate a single parameter with minimal influence on others are needed. Gold nanoparticles are particularly good model systems in this case as one can synthetically control the physical dimensions and surface chemistry of the particles independently and with great precision. Additionally, the chemical and physical properties of gold allow particles to be detected and quantified in tissues and cells with high sensitivity. Through systematic biological studies using gold nanoparticles, insights toward rationally designed nanomedicine for in vivo imaging and therapy can be obtained. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Targeting tropomyosin receptor kinase for cancer therapy

NTRKs and their expression product tropomyosin receptor kinases (Trks) are widely distributed in mammals. While neural growth factor (NGF)-induced normal Trk activation plays a key role in nerve growth, NTRK alternations occurring in tumor cells were highly correlated to tumor progression and invasion. Recent clinical data from several pan-Trk inhibitors have demonstrated potential and broad applications in various cancers. This intrigues us to summarize the development of inhibitors targeting Trks with different mechanisms of action and their applications in cancer therapy. We believe that this perspective would be of great help in investigating novel anticancer drugs with better therapeutic index.

Biomaterial Scaffolds Recruit an Aggressive Population of Metastatic Tumor Cells In Vivo

For most cancers, metastasis is the point at which clinical treatment shifts from curative intent to extending survival. Biomaterial implants acting as a synthetic premetastatic niche recruit metastatic cancer cells and provide a survival advantage, and their use as a diagnostic platform requires assessing their relevance to disease progression. Here, we showed that scaffold-captured tumor cells (SCAF) were 30 times more metastatic to the lung than primary tumor (PT) cells, similar to cells derived from lung micrometastases (LUNG). SCAF cells were more aggressive in vitro, demonstrated higher levels of migration, invasion, and mammosphere formation, and had a greater proportion of cancer stem cells than PT. SCAF cells were highly enriched for gene expression signatures associated with metastasis and had associated genomic structural changes, including globally enhanced entropy. Collectively, our findings demonstrate that SCAF cells are distinct from PT and more closely resemble LUNG, indicating that tumor cells retrieved from scaffolds are reflective of cells at metastatic sites. SIGNIFICANCE: These findings suggest that metastatic tumor cells captured by a biomaterial scaffold may serve as a diagnostic for molecular staging of metastasis.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/8/2042/F1.large.jpg.

Prognostic significance of EIF4G1 in patients with pancreatic ductal adenocarcinoma

Background

Advances in genomics have greatly improved the survival rate in cancer patients. However, due to genetic heterogeneity, pancreatic ductal adenocarcinoma (PDAC) is still difficult to diagnose early, and its survival rate is extremely low. Therefore, we identified biomarkers that predict the prognosis of PDAC patients using independent cohort data.

Materials and methods

To develop a novel prognostic biomarker, we used the gene expression and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Kaplan-Meier survival curve using median values of genes as cutoff showed that EIF4G1 was the only statistically significant gene in the 3 cohorts. We analyzed the prognostic significance of EIF4G1 using the time-dependent area under the curve (AUC) of Uno's C-index, the AUC value of the receiver operating characteristics (ROC) at 3 years, and multivariate Cox analysis. We also compared EIF4G1 levels between tumors and matched non-tumor tissues.

Results

EIF4G1 is the only prognostic gene in patients with PDAC, which was selected by Kaplan-Meier survival analysis. The survival curve showed that high expression of EIF4G1 was associated with poor prognosis of PDAC with a good discriminative ability in 3 independent cohorts. The risk stratifying ability of EIF4G1 was demonstrated by analyzing C-indices and AUC values. Multivariate Cox regression confirmed its prognostic significance. EIF4G1 expression was significantly higher in PDAC tissues than in the matched normal tissues.

Conclusion

EIF4G1 could be used as a novel prognostic marker for PDAC and to determine suitable treatment options.

A Microfabricated Sandwiching Assay for Nanoliter and High-Throughput Biomarker Screening

Cells secrete substances that are essential to the understanding of numerous immunological phenomena and are extensively used in clinical diagnoses. Countless techniques for screening of biomarker secretion in living cells have generated valuable information on cell function and physiology, but low volume and real-time analysis is a bottleneck for a range of approaches. Here, a simple, highly sensitive assay using a high-throughput micropillar and microwell array chip (MIMIC) platform is presented for monitoring of biomarkers secreted by cancer cells. The sensing element is a micropillar array that uses the enzyme-linked immunosorbent assay (ELISA) mechanism to detect captured biomolecules. When integrated with a microwell array where few cells are localized, interleukin 8 (IL-8) secretion can be monitored with nanoliter volume using multiple micropillar arrays. The trend of cell secretions measured using MIMICs matches the results from conventional ELISA well while it requires orders of magnitude less cells and volumes. Moreover, the proposed MIMIC is examined to be used as a drug screening platform by delivering drugs using micropillar arrays in combination with a microfluidic system and then detecting biomolecules from cells as exposed to drugs.

Metastatic carcinoma of unknown primary with complete metabolic response following sorafenib-based chemotherapy

Background:

The treatment of carcinoma of unknown primary based on histopathology and immunohistochemistry is generally chemotherapy. The use of molecular markers, genetic profiling platforms, and personalized medicine is under active investigation.

Case Report:

We report the case of a 56-year-old patient who presented to medical attention with palpable axillary adenopathy. Biopsy confirmed poorly differentiated adenocarcinoma. Formal staging revealed extensive metastatic disease to bone and liver. Initial chemotherapy proved ineffective. We describe the diagnostic evaluation, treatment, and achievement of durable remission using a novel sorafenib-based drug combination that was chosen through the application of a functional analytic laboratory platform.

Conclusion:

The clinical management of patients with carcinoma of unknown primary continues to present a considerable challenge for practicing oncologists. Laboratory platforms capable of examining cellular response to injury, growth factor withdrawal, and cytotoxic insult at the level of cellular function may provide insights for drug selection in this patient population.

Chasing the FOXO3: Insights into Its New Mitochondrial Lair in Colorectal Cancer Landscape

Colorectal cancer (CRC) poses a formidable challenge in terms of molecular heterogeneity, as it involves a variety of cancer-related pathways and molecular changes unique to an individual's tumor. On the other hand, recent advances in DNA sequencing technologies provide an unprecedented capacity to comprehensively identify the genetic alterations resulting in tumorigenesis, raising the hope that new therapeutic approaches based on molecularly targeted drugs may prevent the occurrence of chemoresistance. Regulation of the transcription factor FOXO3a in response to extracellular cues plays a fundamental role in cellular homeostasis, being part of the molecular machinery that drives cells towards survival or death. Indeed, FOXO3a is controlled by a range of external stimuli, which not only influence its transcriptional activity, but also affect its subcellular localization. These regulation mechanisms are mediated by cancer-related signaling pathways that eventually drive changes in FOXO3a post-translational modifications (e.g., phosphorylation). Recent results showed that FOXO3a is imported into the mitochondria in tumor cells and tissues subjected to metabolic stress and cancer therapeutics, where it induces expression of the mitochondrial genome to support mitochondrial metabolism and cell survival. The current review discusses the potential clinical relevance of multidrug therapies that drive cancer cell fate by regulating critical pathways converging on FOXO3a.