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On its way for an IPO: mRNA platform Immune-Oncology, Moderna, is recruiting 100 new Life Scientists in Cambridge, MA

Posted in BioTechnology - Venture Creation, BioTechnology - Venture Creation, Venture Capital, Computational Biology/Systems and Bioinformatics, Diagnostic Immunology, Drug Delivery Platform Technology, Drug Development Process, Drug Discovery Chemistry, Genetics & Innovations in Treatment, Genome Biology, Genomic Testing: Methodology for Diagnosis, Human Immune System in Health and in Disease, Immuno-Oncology & Genomics, Immunodiagnostics, Immunotherapy, Infectious Disease Immunodiagnostics, mRNA platform in Drug DIscovery, RNA Biology, RNA Biology, Cancer and Therapeutics, tagged mRNA-based therapy, vascular endothelial growth factor-A (VEGF-A) on September 7, 2016| Leave a Comment »

On its way for an IPO: mRNA platform, Moderna, Immune Oncology is recruiting 100 new Life Scientists in Cambridge, MA

Curator: Aviva Lev-Ari, PhD, RN

 

Deals:

Moderna has now raised $1.9 billion from investors like AstraZeneca – 9% stack [AstraZeneca’s Pascal Soriot helped get that all started with a whopping $240 million upfront in its 2013 deal, which was tied to $180 million in milestones.], with another $230 million on the table from grants. In addition to the financing announcement this morning, Moderna is also unveiling a pact to develop a new Zika vaccine, with BARDA putting up $8 million to get the program started while offering an option on $117 million more to get through a successful development program.

Novel Strategy in Biotech:

in biotech. Instead of grabbing one or two new drugs and setting out to gather proof-of-concept data to help establish its scientific credibility, the company has harvested a huge windfall of cash and built a large organization before even entering the clinic. And it did that without turning to an IPO.

Pipeline include:

• The deal with AstraZeneca covers new drugs for cardiovascular, metabolic and renal diseases as well as cancer.
• partners filed a European application to start a Phase I study of AZD8601, an investigational mRNA-based therapy that encodes for vascular endothelial growth factor-A (VEGF-A)
• Moderna CEO spelled out plans to get the first 6 new drugs in the clinic by the end of 2016.
• The first human study was arranged for the infectious disease drug mRNA 1440, which began an early stage study in 2015.
• Moderna built up a range of big preclinical partnerships.
• CEO Bancel says the number of drugs in development has swelled to 11, with the first set of data slated to be released in 2017.
• Moderna also plans to add about 10 drugs to the clinic by next summer,

 

SOURCES

UPDATED: Booming Moderna is raising $600M while ramping up manufacturing and clinical studies

by john carroll – August 29, 2016

http://endpts.com/booming-moderna-is-adding-600m-while-ramping-up-manufacturing-and-clinical-studies/ 

$1.9B in: Moderna blueprints $100M facility, plans to double the pipeline after a $474M megaround

by john carroll – September 7, 2016

http://endpts.com/moderna-blueprints-100m-facility-plans-to-double-the-pipeline-after-a-474m-megaround/?utm_source=Sailthru&utm_medium=email&utm_campaign=Issue:%202016-09-07%20BioPharma%20Dive%20%5Bissue:7155%5D&utm_term=BioPharma%20Dive

 

Moderna Therapeutics Deal with Merck: Are Personalized Vaccines here?

Curator & Reporter: Stephen J. Williams, PhD – August 11, 2016

https://pharmaceuticalintelligence.com/2016/08/11/moderna-therapeutics-deal-with-merck-are-personalized-vaccines-here/

 

at #JPM16 – Moderna Therapeutics turns away an extra $200 million: with AstraZeneca (collaboration) & with Merck ($100 million investment)

Reporter: Aviva Lev-Ari, PhD, RN – January 13, 2016

https://pharmaceuticalintelligence.com/2016/01/13/at-jpm16-moderna-therapeutics-turns-away-an-extra-200-million-with-astrazeneca-collaboration-with-merck-100-million-investment/

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A New Computational Method illuminates the Heterogeneity and Evolutionary Histories of cells within a Tumor

Posted in Cancer Informatics, Cell Biology, Computational Biology/Systems and Bioinformatics, Gene Regulation and Evolution, Genome Biology, Next Generation Sequencing (NGS), Statistical Methods for Research Evaluation, tagged and phylogenies by simultaneously modeling single-nucleotide variants, clonal fractions, clonal genotypes, multi-state perfect phylogeny mixture deconvolution problem, phylogenetic trees, single-nucleotide variants, tumor genome sequencing data, variant allele frequency estimates on August 11, 2016| Leave a Comment »

A New Computational Method illuminates the Heterogeneity and Evolutionary Histories of cells within a Tumor

Reporter: Aviva Lev-Ari, PhD, RN

 

Start Quote

Numerous computational approaches aimed at inferring tumor phylogenies from single or multi-region bulk sequencing data have recently been proposed. Most of these methods utilize the variant allele fraction or cancer cell fraction for somatic single-nucleotide variants restricted to diploid regions to infer a two-state perfect phylogeny, assuming an infinite-site model such that each site can mutate only once and persists. In practice, convergent evolution could result in the acquisition of the same mutation more than once, thereby violating this assumption. Similarly, mutations could be lost due to loss of heterozygosity. Indeed, both single-nucleotide variants and copy number alterations arise during tumor evolution, and both the variant allele fraction and cancer cell fraction depend on the copy number state whose inference reciprocally relies on the relative ordering of these alterations such that joint analysis can help resolve their ancestral relationship (Figure 1). To tackle this outstanding problem, El-Kebir et al. (2016) formulated the multi-state perfect phylogeny mixture deconvolution problem to infer clonal genotypes, clonal fractions, and phylogenies by simultaneously modeling single-nucleotide variants and copy number alterations from multi-region sequencing of individual tumors. Based on this framework, they present SPRUCE (Somatic Phylogeny Reconstruction Using Combinatorial Enumeration), an algorithm designed for this task. This new approach uses the concept of a ‘‘character’’ to represent the status of a variant in the genome.

Commonly, binary characters have been used to represent single-nucleotide variants— that is, the variant is present or absent. In contrast, El-Kebir et al. use multi-state characters to represent copy number alterations, which may be present in zero, one, two, or more copies in the genome.

SPRUCE outperforms existing methods on simulated data, yielding higher recall rates under a variety of scenarios. Moreover, it is more robust to noise in variant allele frequency estimates, which is a significant feature of tumor genome sequencing data. Importantly, El-Kebir and colleagues demonstrate that there is often an ensemble of phylogenetic trees consistent with the underlying data. This uncertainty calls for caution in deriving definitive conclusions about the evolutionary process from a single solution.”

End Quote

 

From Original Paper

Inferring Tumor Phylogenies from Multi-region Sequencing

Zheng Hu1,2 and Christina Curtis1,2,*

1Departments of Medicine and Genetics

2Stanford Cancer Institute

Stanford University School of Medicine, Stanford, CA 94305, USA

*Correspondence: cncurtis@stanford.edu

http://dx.doi.org/10.1016/j.cels.2016.07.007

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DISCUSSION – Genomics-driven personalized medicine for Pancreatic Cancer

Posted in Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Computational Biology/Systems and Bioinformatics, Exosomes, Genome Biology, Mutagenesis, Personalized and Precision Medicine & Genomic Research, tagged Akt, CHK or BCL2 inhibitors as agents, coupled with MTOR, docetaxel, EGFR, gemcitabine and MEK inhibition, germline BRCA deficiency is the basis for ongoing poly(ADP-ribose) polymerase (PARP) inhibitor, KRAS has been proposed to modify therapeutic dependency to EZH2 inhibitors, MEK and docetaxel inhibition, MEK and MTOR inhibitors, MEK inhibition, or MTOR (mammalian target of rapamycin) signaling., or tyrosine kinase inhibitors, Rare BRAF V600E mutations are identified in PDAC, Resistance to MEK inhibitors, Tyrosine Kinase on August 10, 2016| Leave a Comment »

DISCUSSION – Genomics-driven personalized medicine for Pancreatic Cancer

Reporter: Aviva Lev-Ari, PhD, RN

[bold face added, ALA]

Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer –>>> Personalized Tumor Models Could Help Identify Combination Therapies for Hard-to-Treat Cancers

 

Original article

Pancreatic Cancer – Genomics-driven personalized medicine

 

PDAC has a particularly poor prognosis, and even with new targeted therapies and chemotherapy, the survival is poor. Here, we show that patient-derived models can be developed and used to investigate therapeutic sensitivities determined by genetic features of the disease and to identify empirical therapeutic vulnerabilities. These data reveal several key points that are of prime relevance to pancreatic cancer and tumor biology in general.

The Challenges of Using Genetic Analysis to Inform Treatment in PDAC

Precision oncology is dependent on the existence of known vulnerabilities encoded by high-potency genetic events and drugs capable of exploiting these vulnerabilities. At present, the repertoire of actionable genetic events in PDAC is limited.

• Rare BRAF V600E mutations are identified in PDAC and could represent the basis for targeted inhibition, as our group and others have previously published (Collisson et al., 2012; Witkiewicz et al., 2015).

Similarly,

• germline BRCA deficiency is the basis for ongoing poly(ADP-ribose) polymerase (PARP) inhibitor clinical trials (Lowery et al., 2011).

As shown here, out of 28 cases, only one genetic event was identified that yielded sensitivity to a therapeutic strategy. In this case, existence of the matched model allowed us to confirm the biological relevance of the

• STAG2 mutation by showing sensitivity of the model to a DNA cross-linking agent.

Therefore, annotated patient-derived models provide a substrate upon which to functionally dissect the significance of novel and potentially actionable genetic events that occur within a tumor.

Another challenge of genomics-driven personalized medicine is

• assessing the effect of specific molecular aberrations on therapeutic response in the context of complex genetic changes present in individual tumors.

• KRAS has been proposed to modify therapeutic dependency to EZH2 inhibitors (Kim et al., 2015), and in the models tested, responses to this class of drugs were not uniformly present in cases harboring mutations in chromatin-remodeling genes.

This finding suggests that, although tumors acquire genetic alterations in specific genes, the implicated pathway may not be functionally inactive or therapeutically actionable. Therefore, annotated patient derived models provide a unique test bed for interrogating specific therapeutic dependencies in a genetically tractable system.

Empirical Definition of Therapeutic Sensitivities and Clinical Relevance

Cell lines offer the advantage of the ability to conduct high throughput approaches to interrogate many therapeutic agents. A large number of failed clinical trials have demonstrated the difficulty in treating PDAC. Based on the data herein, the paucity of clinical success is, most probably, due to the diverse therapeutic sensitivity of individual PDAC cases, suggesting that, with an unselected patient population, it will be veritably impossible to demonstrate clinical benefit. Additionally,

•  very few models exhibited an exceptional response to single agents across the breadth of a library encompassing 305 agents.
• We could identify only one tumor that was particularly sensitive to MEK inhibition and another model that was sensitive to
• EGFR and
• tyrosine kinase inhibitors.

In contrast to the limited activity of single agents, combination screens yielded responses at low-dose concentrations in the majority of models. Specific combinations were effective across several models, indicating that, by potentially screening more models, therapeutic sensitivity clades of PDAC will emerge. In the pharmacological screens performed in this study,

• MEK inhibition, coupled with MTOR, docetaxel, or tyrosine kinase inhibitors, was effective in _30% of models tested.
• Resistance to MEK inhibitors occurs through several mechanisms, including
• Upregulation of oncogenic bypass signaling pathways such as AKT, tyrosine kinase, or MTOR (mammalian target of rapamycin) signaling.

In the clinic, the MEK and MTOR inhibitors (e.g., NCT02583542) are being tested. An intriguing finding from the drug screen was

• sensitivity of a subset of models to combined MEK and docetaxel inhibition. This combination has been observed to synergistically enhance apoptosis and inhibit tumor growth in human xenograft tumor models (Balko et al., 2012; McDaid et al., 2005) and is currently being tested in a phase III study in patients with KRAS-mutated, advanced non-small-cell lung adenocarcinoma (Ja¨ nne et al., 2016).

Interestingly, in the models tested herein, there was limited sensitivity imparted through

•  the combination of gemcitabine and MEK inhibition.

This potentially explains why the combination of MEK inhibitor and gemcitabine tested in the clinic did not show improved efficacy over gemcitabine alone (Infante et al., 2014).

Another promising strategy that emerged from this study involves using

• CHK or BCL2 inhibitors as agents that drive enhanced sensitivity to chemotherapy.

Together, the data suggest that the majority of PDAC tumors have intrinsic therapeutic sensitivities, but the challenge is to prospectively identify effective treatment.

Patient-Derived Model-Based Approach to Precision Medicine

This study supports a path for guiding patient treatment based on the integration of genetic and empirically determined sensitivities of the patient’s tumor (Figure S7). In reference to defined genetic susceptibilities, the models provide a means to interrogate the voracity of specific drug targets. Parallel unbiased screening enables the discovery of sensitivities that could be exploited in the clinic. The model-guided treatment must be optimized, allowing for the generation of data in a time frame compatible with clinical decision making and appropriate validation.

In the present study, the majority of models were developed, cell lines were drug screened, and select hits were validated in PDX models within a 10- to 12-month window (Figure S7). This chronology would allow time to inform frontline therapy for recurrent disease for most patients who were surgically resected and treated with a standard of care where the median time to recurrence is approximately 14 months (Saif, 2013).

Although most models were generated from surgically resected specimens, two of the models (EMC3226 and EMC62) were established from primary tumor biopsies, indicating that this approach could be used with only a limited amount of tumor tissue available.

In the context of inoperable pancreatic cancer, application of data from a cell-line screen without in vivo validation in PDX would permit the generation of sensitivity data in the time frame compatible with treatment.

[We] acknowledge that model-guided treatment is also not without significant logistical hurdles, including the availability of drugs for patient treatment, clinically relevant time frames, patient-performance status, toxicity of combination regiments, and quality metrics related to model development and therapeutic response evaluation.

Additionally, it will be very important to monitor ex vivo genetic and phenotypic divergence with passage and try to understand the features of tumor heterogeneity that could undermine the efficacy of using models to direct treatment. As shown here, drug sensitivities remained stable with passage in cell culture and, importantly, were confirmed in PDX models, suggesting that the dominant genetic drivers and related therapeutic sensitivities are conserved.

In spite of these challenges, progressively more effort is going into the development of patient-derived models for guidance of disease treatment (Aparicio et al., 2015; Boj et al., 2015; Crystal et al., 2014; van de Wetering et al., 2015).

Several ongoing trials use PDX models to direct a limited repertoire of agents (e.g., NCT02312245, NCT02720796, and ERCAVATAR2015). Given the experience here, PDAC cell lines would provide the opportunity to rapidly interrogate a larger portfolio of combinations that could be used to guide patient care and provide a novel approach to precision medicine.

Validation of this approach would require the establishment of challenging multi-arm or N-of-1 clinical trials. However, considering the dire outcome for PDAC patients and the long-lasting difficulty in developing effective treatments, this non-canonical approach might be particularly impactful in pancreatic cancer.

SOURCE

Witkiewicz et al., 2016, Cell Reports 16, 1–15

August 16, 2016 ª 2016 The Author(s).

http://dx.doi.org/10.1016/j.celrep.2016.07.023

Agnieszka K. WitkiewiczPress enter key for correspondence information

Press enter key to Email the author

Uthra Balaji

Cody Eslinger

Elizabeth McMillan

William Conway

Bruce Posner

Gordon B. Mills

Eileen M. O’Reilly

Erik S. KnudsenPress enter key for correspondence information

Press enter key to Email the author

Publication stage: In Press Corrected Proof

Open Access

DOI: http://dx.doi.org/10.1016/j.celrep.2016.07.023

Resource Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer

Correspondence

awitki@email.arizona.edu (A.K.W.),

eknudsen@email.arizona.edu (E.S.K.)

Accession Numbers: GSE84023

Other related articles on this topic published in this Open Access Online Scientific Journal include the following:

Pancreatic Cancer: Articles of Note @PharmaceuticalIntelligence.com

Curator: Aviva Lev-Ari, PhD, RN

Pancreatic Cancer: Articles of Note @PharmaceuticalIntelligence.com

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Crowdsourcing Genetic Data Yields Discovery of DNA loci associated with Major Depressive Disorder (MDD) in European Descendants

Posted in Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Computational Biology/Systems and Bioinformatics, Exosomes, Gene Regulation, Gene Regulation and Evolution, Genetics & Innovations in Treatment, Genome Biology, Genomic Endocrinology, Genomic Testing: Methodology for Diagnosis, Innovations in Neurophysiology & Neuropsychology, Neurological Diseases, Next Generation Sequencing (NGS), Personalized and Precision Medicine & Genomic Research, tagged behavioral phenotypes, biological mechanisms of depression, diagnoses of depression, European ancestry, genome-wide association studies (GWAS), histone methylation, loci associated with the risk of developing major depressive disorder (MDD), neurobiology of depression, neurodevelopment, neurogenesis and synaptic modification, Tissue and gene enrichment assays, weighted genetic risk score (GRS) on August 9, 2016| Leave a Comment »

Crowdsourcing Genetic Data Yields Discovery of DNA loci associated with Major Depressive Disorder (MDD) in European Descendants

 

Reporter: Kelly Perlman, Life Sciences Student and Research Assistant, McGill University

 

UPDATED on 11/24/2019

Can AI help diagnose depression? It’s a long shot

At the moment, machine intelligence is just as subjective as human intelligence

Alejandra Canales

https://www.salon.com/2019/11/23/can-ai-help-diagnose-depression-its-a-long-shot_partner/amp?__twitter_impression=true

Researchers from Pfizer Global Research and Development, 23andMe, and the Massachusetts General Hospital have published a study in Nature Genetics, pinpointing 15 genetic loci associated with the risk of developing major depressive disorder (MDD) in individuals of European ancestry. Evidence from previous research suggests that MDD is heritable, but the details of the specific gene correlates are unclear. The identification of loci where single nucleotide polymorphisms (SNPs) related to MDD exist could provide better insight into the neurobiology of depression, and therefore better treatment options.

23andMe, a private biotechnology company situated in California, offers a DNA sequencing service in which consumers send in a saliva swab for testing, and later receive a report listing the findings of the analysis related to ancestry, physical and behavioral traits, along with risk of inheriting certain diseases. The participants of this study had agreed to provide the results of their genetic testing for scientific research.

The results of 75,607 participants with self-reported diagnoses of depression were compared to the results of 231,747 participants reporting having never experienced depression. This data was combined with the results of previously published MDD genome-wide association studies (GWAS). To test the whether these results could be replicated, another set of results from 23andMe was analyzed, in which there were 45,773 MDD subjects, and 106,354 controls.

After the joint analysis, 17 SNPs were identified at 15 different loci. Tissue and gene enrichment assays showed that the genes that were over-expressed in the CNS were related to functions including neurodevelopment, histone methylation, neurogenesis and synaptic modification.

The team then created a weighted genetic risk score (GRS) in which they compared the 17 SNPs with factors including medication use, comorbid diseases and behavioral phenotypes, all of which were correlated with the GRS. Of note, the GRS was very highly correlated with age of onset of MDD.

The crowdsourcing of genetic data proves to be an efficient and powerful tool for large-scale MDD studies. Pooling large subject databases together is essential in order to account for the heterogeneous nature of the disease. Despite not being able to precisely assess each subject’s disease phenotype, scientists can make more rapid headway by collaborating with biotechnology companies in the quest to better understand the biological mechanisms of depression. Ron Perlis, M.D., M.Sc., of the Massachusetts General Hospital and co-author of this paper explained that “finding genes associated with depression should help make clear that this is a brain disease, which we hope will decrease the stigma still associated with these kinds of illnesses”.

 

Details on specific significant genes:

http://www.genecards.org/cgi-bin/carddisp.pl?gene=OLFM4

http://www.genecards.org/cgi-bin/carddisp.pl?gene=TMEM161B

http://www.genecards.org/cgi-bin/carddisp.pl?gene=MEF2C

http://www.genecards.org/cgi-bin/carddisp.pl?gene=MEIS2

http://www.genecards.org/cgi-bin/carddisp.pl?gene=TMCO5A

http://www.genecards.org/cgi-bin/carddisp.pl?gene=NEGR1

 

SOURCES

Hyde, C. L., Nagle, M. W., Tian, C., Chen, X., Paciga, S. A., Wendland, J. R., . . . Winslow, A. R. (2016). Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nature Genetics Nat Genet. doi:10.1038/ng.3623

Major Depressive Disorder Loci Discovered in Large GWAS Enabled by 23andMe Participants’ Data. (2016, August 01). Retrieved August 09, 2016, from https://www.genomeweb.com/microarrays-multiplexing/major-depressive-disorder-loci-discovered-large-gwas-enabled-23andme

 

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Through Data Science and Cloud Technology: Stanford Medicine and Google will transform Patient Care and Medical Research

Posted in Accountable Care Organizations, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, HealthCare IT, Healthcare Reform, Personalized and Precision Medicine & Genomic Research on August 9, 2016| Leave a Comment »

 Through Data Science: Stanford Medicine and Google will transform Patient Care and Medical Research

Reporter: Aviva Lev-Ari, PhD, RN

 

Stanford Medicine integrates research, medical education and health care at its three institutions –Stanford University School of Medicine, Stanford Health Care (formerly Stanford Hospital & Clinics), and Lucile Packard Children’s Hospital Stanford. For more information, please visit the Office of Communication & Public Affairs site at http://mednews.stanford.edu.

 

Stanford’s forthcoming Clinical Genomics Service, which puts genomic sequencing into the hands of clinicians to help diagnose disease, will be built using Google Genomics, a service that applies the same technologies that power Google Search and Maps to securely store, process, explore and share genomic data sets.

The Clinical Genomics Service will enable physicians at Stanford Health Care and Stanford Children’s Health to order genome sequencing for patients who have distinctive or unusual symptoms that might be caused by a wayward gene. The genomic data would then go to the Google Cloud Platform to join masses of aggregated and anonymous data from other Stanford patients. “As the new service launches,” said Euan Ashley, MRCP, DPhil, a Stanford associate professor of medicine and of genetics, “we’ll be doing hundreds and then thousands of genome sequences.”

The Clinical Genomics Service aims to make genetic testing a normal part of health care for patients. “Genetic testing is built into the whole system,” said Ashley. A physician who thinks a genome-sequencing test could help a patient can simply request sequencing along with other blood tests, he said. “The DNA gets sequenced and a large amount of data comes back,” he said. At that point, Stanford can use Google Cloud to analyze the data to decide which gene variants might be responsible for the patient’s health condition. Then a data curation team will work with the physician to narrow the possibilities, he said.

“This collaboration will enable Stanford to discover new ways to advance medicine to the benefit of Stanford patients and families,” said Ed Kopetsky, chief information officer at Lucile Packard Children’s Hospital Stanford and Stanford Children’s Health. “Together, Stanford Medicine and Google are making a major contribution and commitment in curing diseases that afflict children not just in our community, but throughout the world. It’s an extraordinary investment, and we’re proud to play such a large role in transforming patient care and research.”

Read more at the SOURCE

 

Stanford Medicine, Google team up to harness power of data science for health care

By JENNIE DUSHECK

Jennie Dusheck is a science writer for the medical school’s Office of Communication & Public Affairs. Email her at dusheck@stanford.edu.

SOURCE

http://med.stanford.edu/news/all-news/2016/08/stanford-medicine-google-team-up-to-harness-power-of-data-science.html

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CancerBase.org – The Global HUB for Diagnoses, Genomes, Pathology Images: A Real-time Diagnosis and Therapy Mapping Service for Cancer Patients – Anonymized Medical Records accessible to

Posted in CANCER BIOLOGY & Innovations in Cancer Therapy, Computational Biology/Systems and Bioinformatics, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Genome Biology, Imaging-based Cancer Patient Management on July 28, 2016| Leave a Comment »

CancerBase.org – The Global HUB for Diagnoses, Genomes, Pathology Images: A Real-time Diagnosis and Therapy Mapping Service for Cancer Patients – Anonymized Medical Records accessible to anyone on Earth

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 10/29/2019

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Yu Fu1, Alexander W Jung1, Ramon Viñas Torne1, Santiago Gonzalez1,2, Harald Vöhringer1, Mercedes Jimenez-Linan3, Luiza Moore3,4, and Moritz Gerstung#1,5 # to whom correspondence should be addressed 1) European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK. 2) Current affiliation: Institute for Research in Biomedicine (IRB Barcelona), Parc Científic de Barcelona, Barcelona, Spain. 3) Department of Pathology, Addenbrooke’s Hospital, Cambridge, UK. 4) Wellcome Sanger Institute, Hinxton, UK 5) European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.

Correspondence:

Dr Moritz Gerstung European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Hinxton, CB10 1SA UK. Tel: +44 (0) 1223 494636 E-mail: moritz.gerstung@ebi.ac.uk

Abstract

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Here we use deep transfer learning to quantify histopathological patterns across 17,396 H&E stained histopathology image slides from 28 cancer types and correlate these with underlying genomic and transcriptomic data. Pan-cancer computational histopathology (PC-CHiP) classifies the tissue origin across organ sites and provides highly accurate, spatially resolved tumor and normal distinction within a given slide. The learned computational histopathological features correlate with a large range of recurrent genetic aberrations, including whole genome duplications (WGDs), arm-level copy number gains and losses, focal amplifications and deletions as well as driver gene mutations within a range of cancer types. WGDs can be predicted in 25/27 cancer types (mean AUC=0.79) including those that were not part of model training. Similarly, we observe associations with 25% of mRNA transcript levels, which enables to learn and localise histopathological patterns of molecularly defined cell types on each slide. Lastly, we find that computational histopathology provides prognostic information augmenting histopathological subtyping and grading in the majority of cancers assessed, which pinpoints prognostically relevant areas such as necrosis or infiltrating lymphocytes on each tumour section. Taken together, these findings highlight the large potential of PC-CHiP to discover new molecular and prognostic associations, which can augment diagnostic workflows and lay out a rationale for integrating molecular and histopathological data.

https://www.biorxiv.org/content/10.1101/813543v1

 

Key points

● Pan-cancer computational histopathology analysis with deep learning extracts histopathological patterns and accurately discriminates 28 cancer and 14 normal tissue types

● Computational histopathology predicts whole genome duplications, focal amplifications and deletions, as well as driver gene mutations

● Wide-spread correlations with gene expression indicative of immune infiltration and proliferation

● Prognostic information augments conventional grading and histopathology subtyping in the majority of cancers

 

Discussion

Here we presented PC-CHiP, a pan-cancer transfer learning approach to extract computational histopathological features across 42 cancer and normal tissue types and their genomic, molecular and prognostic associations. Histopathological features, originally derived to classify different tissues, contained rich histologic and morphological signals predictive of a range of genomic and transcriptomic changes as well as survival. This shows that computer vision not only has the capacity to highly accurately reproduce predefined tissue labels, but also that this quantifies diverse histological patterns, which are predictive of a broad range of genomic and molecular traits, which were not part of the original training task. As the predictions are exclusively based on standard H&E-stained tissue sections, our analysis highlights the high potential of computational histopathology to digitally augment existing histopathological workflows. The strongest genomic associations were found for whole genome duplications, which can in part be explained by nuclear enlargement and increased nuclear intensities, but seemingly also stems from tumour grade and other histomorphological patterns contained in the high-dimensional computational histopathological features. Further, we observed associations with a range of chromosomal gains and losses, focal deletions and amplifications as well as driver gene mutations across a number of cancer types. These data demonstrate that genomic alterations change the morphology of cancer cells, as in the case of WGD, but possibly also that certain aberrations preferentially occur in distinct cell types, reflected by the tumor histology. Whatever is the cause or consequence in this equation, these associations lay out a route towards genomically defined histopathology subtypes, which will enhance and refine conventional assessment. Further, a broad range of transcriptomic correlations was observed reflecting both immune cell infiltration and cell proliferation that leads to higher tumor densities. These examples illustrated the remarkable property that machine learning does not only establish novel molecular associations from pre-computed histopathological feature sets but also allows the localisation of these traits within a larger image. While this exemplifies the power of a large scale data analysis to detect and localise recurrent patterns, it is probably not superior to spatially annotated training data. Yet such data can, by definition, only be generated for associations which are known beforehand. This appears straightforward, albeit laborious, for existing histopathology classifications, but more challenging for molecular readouts. Yet novel spatial transcriptomic44,45 and sequencing technologies46 bring within reach spatially matched molecular and histopathological data, which would serve as a gold standard in combining imaging and molecular patterns. Across cancer types, computational histopathological features showed a good level of prognostic relevance, substantially improving prognostic accuracy over conventional grading and histopathological subtyping in the majority of cancers. It is this very remarkable that such predictive It is made available under a CC-BY-NC 4.0 International license. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. bioRxiv preprint first posted online Oct. 25, 2019; doi: http://dx.doi.org/10.1101/813543. The copyright holder for this preprint signals can be learned in a fully automated fashion. Still, at least at the current resolution, the improvement over a full molecular and clinical workup was relatively small. This might be a consequence of the far-ranging relations between histopathology and molecular phenotypes described here, implying that histopathology is a reflection of the underlying molecular alterations rather than an independent trait. Yet it probably also highlights the challenges of unambiguously quantifying histopathological signals in – and combining signals from – individual areas, which requires very large training datasets for each tumour entity. From a methodological point of view, the prediction of molecular traits can clearly be improved. In this analysis, we adopted – for the reason of simplicity and to avoid overfitting – a transfer learning approach in which an existing deep convolutional neural network, developed for classification of everyday objects, was fine tuned to predict cancer and normal tissue types. The implicit imaging feature representation was then used to predict molecular traits and outcomes. Instead of employing this two-step procedure, which risks missing patterns irrelevant for the initial classification task, one might directly employ either training on the molecular trait of interest, or ideally multi-objective learning. Further improvement may also be related to the choice of the CNN architecture. Everyday images have no defined scale due to a variable z-dimension; therefore, the algorithms need to be able to detect the same object at different sizes. This clearly is not the case for histopathology slides, in which one pixel corresponds to a defined physical size at a given magnification. Therefore, possibly less complex CNN architectures may be sufficient for quantitative histopathology analyses, and also show better generalisation. Here, in our proof-of-concept analysis, we observed a considerable dependence of the feature representation on known and possibly unknown properties of our training data, including the image compression algorithm and its parameters. Some of these issues could be overcome by amending and retraining the network to isolate the effect of confounding factors and additional data augmentation. Still, given the flexibility of deep learning algorithms and the associated risk of overfitting, one should generally be cautious about the generalisation properties and critically assess whether a new image is appropriately represented. Looking forward, our analyses revealed the enormous potential of using computer vision alongside molecular profiling. While the eye of a trained human may still constitute the gold standard for recognising clinically relevant histopathological patterns, computers have the capacity to augment this process by sifting through millions of images to retrieve similar patterns and establish associations with known and novel traits. As our analysis showed this helps to detect histopathology patterns associated with a range of genomic alterations, transcriptional signatures and prognosis – and highlight areas indicative of these traits on each given slide. It is therefore not too difficult to foresee how this may be utilised in a computationally augmented histopathology workflow enabling more precise and faster diagnosis and prognosis. Further, the ability to quantify a rich set of histopathology patterns lays out a path to define integrated histopathology and molecular cancer subtypes, as recently demonstrated for colorectal cancers47 .

Lastly, our analyses provide It is made available under a CC-BY-NC 4.0 International license. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

bioRxiv preprint first posted online Oct. 25, 2019; doi: http://dx.doi.org/10.1101/813543.

The copyright holder for this preprint proof-of-concept for these principles and we expect them to be greatly refined in the future based on larger training corpora and further algorithmic refinements.

https://www.biorxiv.org/content/biorxiv/early/2019/10/25/813543.full.pdf

 

July 27, 2016

By Andrew Myers

Illustration by Tricia Seibold and iStock/liuzishan

During his 2016 State of the Union address, President Barack Obama called on Vice President Joe Biden – who had months earlier lost his son Beau to brain cancer – to head a “moonshot” to significantly accelerate research into the disease. The president said he wanted to harness the spirit of American innovation that took us from zero to landing a man on the moon in a decade to similarly find new ways to prevent, diagnose and treat cancer.

One of those intrigued by that call to action was Stanford’s Jan Liphardt, an associate professor of bioengineering who specializes in biophysics, the tumor microenvironment and data analysis. Stanford Engineering talked to Liphardt about how he came to be involved with the moonshot and his approach to using data and the voice of patients to better understand cancer and how it can be treated, and how sharing information can better inform the course of cancer research.

How did you get involved in the National Cancer Moonshot?

In March, after the president’s charge, the vice president challenged scientists, doctors, industry and patients to give their best ideas to the moonshot. The White House also reached out to a few outsiders, myself included. The White House instructions were unusual: “Do something big and different. There is no money and you have 87 days. Go.”

I like a challenge, and this was a chance to serve, even in the face of administrative hurdles. So I looked for advice, teammates and support. Russ Altman, a colleague at Stanford, suggested it was time to give patients a way to volunteer their own health data in order to help find cures. I collaborated with Peter Kuhn, a professor of medicine and engineering at the University of Southern California, who’s known for carefully listening to cancer patients, advocates and their supporters. In short order we had links with advocates like AnneMarie Ciccarella, Sonja Durham, Lori Marx-Rubiner, Jack Whelan and Jack Park. That’s how we got to CancerBase.org.

What’s the idea the team came up with?

We thought for about a week: What would matter to the patients that Stanford and other research institutions serve? What would scale? Well, we’re not going to run a clinical trial, go near protected health information, invent a new drug or write a research proposal. There’s no time for that. Whatever it was, it had to be useful, scalable, legal and different. That pointed to data, the web, patients and decisions.

One thing jumped out: Right now, there’s significant friction in medical data sharing. People all over the world can already effortlessly share other kinds of information – pictures, movies, ideas, stories, tweets. Increasingly, they are using the same tools to share personal medical information. It’s remarkable what cancer patients already share: diagnoses, genomes, pathology images. But that information is not yet widely used to understand where they are with their diseases.

Ideally, everyone, including scientists and doctors, would have as much information as possible at their fingertips. Many patients think when they give data for research, magically scientists all over the world can dig into this information, find patterns and help. The practical reality is that it’s nearly impossible for any one scientist to access the amounts of data they would like.

So that’s the simple idea: A global map and give patients the tools they need to share their data – if they want to. They can donate information for the greater good. In return, we make a simple promise: When you post data, we’ll anonymize them and make them available to anyone on Earth in one second. We plan to display this information like real-time traffic data. HIPAA doesn’t apply to this direct data-sharing. The patients can give us whatever information they want, and they can tell us what they want us to do with it. We’re a conduit. Their data belong to them, not to us.

How does it work?

Today we ask just five basic questions. Over time we will add more. You join, give some information, and we’ll put you on a global map. Right now, some of the things we don’t know about cancer are incredibly simple: Where is everyone on Earth with cancer? How old are they? What is their diagnosis? Did their cancers metastasize? Global, instantaneous data sharing is the story.

In a second phase, we are going to see if we can plot all the information just like Waze does for traffic. Our role is to synthesize the information and plot it in ways that ordinary people can understand. Think of it this way – patients want to be able to chart their treatment path. Who went straight, who went left? People just getting on the highway are curious about what people did who came before them, and what happened to those people. Did they arrive at the destination easily and promptly? We’re a real-time diagnosis and therapy mapping service for cancer.

You say that giving patients a way to share their health data is important to help finding cures. Why?

Let me give you a specific example. At Stanford, I’m part of a team of cancer biologists and clinicians funded by the Stanford Cancer Institute to think about the next generation of screening for breast cancer in the U.S. Every year, the U.S. uses mammography to screen more than 40 million women for breast cancer. In this project, it quickly became clear that there is currently no central, easy-to-access repository of mammograms for research use.

That’s a major lost opportunity – our nation spends billions on screening, but we don’t store, share and analyze this information in a scalable and simple manner. In the traditional approach, our team would spend several hundred thousand dollars, and about three years, to assemble perhaps 1,000 mammograms. We would then use this tiny dataset to try to find something interesting, but since the dataset is so small, we would be blind to rare features of breast cancer and its predictors. It clearly makes a lot more sense to compare and explore 100 million images.

This sounds completely impossible until you realize that Instagram users upload 58 million images every day. Once you start to think about supposedly intractable research problems from a web or social networking perspective, new possibilities open. Imagine, for example, if there were a simple way for every single woman on Earth to upload and share her de-identified mammogram? Or more generally, imagine a world in which patients have the tools to globally share de-identified health data, if they want to. That’s exactly the idea behind CancerBase – let’s just give people those tools and see what happens.

How much data and how many people are needed to make this viable?

We think we are going to need several tens-of-thousands of members. There are approximately 50 million people on Earth with a cancer diagnosed in the last five years, and 200 million more people have an immediate family member with cancer. Almost 2 billion people are active on Twitter and Facebook – a quarter of the world’s population. If just a few percent of those people sign up, we could do something no one on Earth has done before.

Are there hopes to create a “developer community,” people who find ways to use your data that you didn’t even think about or have the time to work on?

Definitely. As much as we think we can predict what these data are useful for, we don’t really know. By making the anonymized data available to everyone within one second, they might start to do things that we never dreamed of. The more eyes look at these data, the better off everyone will be. The dream is to have cancer-relevant medical data flow unimpeded around the world in seconds, so that everyone, wherever they are, can see and use this information.

SOURCE

https://engineering.stanford.edu/news/how-data-can-help-us-understand-cancer-and-its-treatment

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A New Potential Target for Pancreatic Cancer Treatment: Rapid Screening Technique finds Gene Defending Tumors from DNA Damage @MD Anderson Cancer Center

Posted in CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Screening, Chemical Genetics, Computational Biology/Systems and Bioinformatics, tagged COMPASS complex regulating chromatin function, WD repeat-containing protein 5 (WDR5), WDR5 critical for cancer cell proliferation, WDR5 to be upregulated in prostate and bladder cancers on July 28, 2016| Leave a Comment »

A New Potential Target for Pancreatic Cancer Treatment: Rapid Screening Technique finds Gene Defending Tumors from DNA Damage @M. D. Anderson Cancer Center

Reporter: Aviva Lev-Ari, PhD, RN

Novel gene-hunting method implicates new culprit in pancreatic cancer

Researchers develop rapid screening technique; find gene defends tumors from DNA damage

Date:

June 23, 2016

Source:

University of Texas M. D. Anderson Cancer Center

Summary:

Using an innovative approach to identify a cancer’s genetic vulnerabilities by more swiftly analyzing human tumors transplanted into mice, researchers have identified a new potential target for pancreatic cancer treatment.

WDR5 emerges as robust “hit”

WD repeat-containing protein 5 (WDR5), a core part of the COMPASS complex regulating chromatin function, was implicated in multiple screens. Recent research by others had shown WDR5 to be upregulated in prostate and bladder cancers and critical for cancer cell proliferation.

The team confirmed WDR5 was highly expressed in pancreatic cancer compared to normal pancreas tissue and then conducted a series of experiments which showed knocking down the gene impaired cell proliferation and tumor growth and greatly increased survival in mice.

Subsequent experiments showed WDR5 works in concert with Myc to protect pancreatic cancer from DNA damage. There is no known method for targeting either WDR5 or Myc separately, Carugo said, but the team thinks there might be ways to block their interaction.

While the team targeted epigenetic regulators, Carugo noted the technique can be used with other shRNA libraries aimed at different classes of genes.

This technology is being widely adopted by MD Anderson’s moon shot teams to identify genetic vulnerabilities and cancer targets specific to various disease subtypes.

---

Story Source:

The above post is reprinted from materials provided by University of Texas M. D. Anderson Cancer Center. Note: Materials may be edited for content and length.

SOURCE

University of Texas M. D. Anderson Cancer Center. “Novel gene-hunting method implicates new culprit in pancreatic cancer: Researchers develop rapid screening technique; find gene defends tumors from DNA damage.” ScienceDaily. ScienceDaily, 23 June 2016. www.sciencedaily.com/releases/2016/06/160623115741.htm.

---

Alessandro Carugo et al. In Vivo Functional Platform Targeting Patient-Derived Xenografts Identifies WDR5-Myc Association as a Critical Determinant of Pancreatic Cancer. Cell Reports, June 2016 DOI:10.1016/j.celrep.2016.05.063

Cell Rep. 2016 Jun 28;16(1):133-47. doi: 10.1016/j.celrep.2016.05.063. Epub 2016 Jun 16.

In Vivo Functional Platform Targeting Patient-Derived Xenografts Identifies WDR5-Myc Association as a Critical Determinant of Pancreatic Cancer.

Carugo A¹, Genovese G², Seth S³, Nezi L², Rose JL², Bossi D⁴, Cicalese A⁴, Shah PK³, Viale A², Pettazzoni PF², Akdemir KC⁵, Bristow CA³,Robinson FS², Tepper J², Sanchez N², Gupta S⁶, Estecio MR⁷, Giuliani V³, Dellino GI⁸, Riva L⁹, Yao W², Di Francesco ME¹⁰, Green T², D’Alesio C⁴, Corti D², Kang Y¹¹, Jones P¹⁰, Wang H¹², Fleming JB¹¹, Maitra A⁶, Pelicci PG⁸, Chin L¹⁰, DePinho RA¹³, Lanfrancone L¹⁴, Heffernan TP¹⁵,Draetta GF¹⁶.

Author information

• ¹Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Molecular and Cellular Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Experimental Oncology, European Institute of Oncology, Milan 20139, Italy. Electronic address: acarugo@mdanderson.org.
• ²Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Molecular and Cellular Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ³Institute for Applied Cancer Science, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ⁴Department of Experimental Oncology, European Institute of Oncology, Milan 20139, Italy.
• ⁵Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ⁶Sheikh Ahmed Bin Zayed Al Nahyan Center for Pancreatic Cancer Research, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ⁷Department of Epigenetics and Molecular Carcinogenesis, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ⁸Department of Experimental Oncology, European Institute of Oncology, Milan 20139, Italy; Department of Oncology and Hemato-oncology, University of Milan, Milan 20139, Italy.
• ⁹Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milan 20139, Italy.
• ¹⁰Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Institute for Applied Cancer Science, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ¹¹Department of Surgical Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ¹²Department of Pathology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ¹³Department of Cancer Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
• ¹⁴Department of Experimental Oncology, European Institute of Oncology, Milan 20139, Italy. Electronic address: luisa.lanfrancone@ieo.eu.
• ¹⁵C-4 Therapeutics, Cambridge, MA 02142, USA. Electronic address: theffernan@c4therapeutics.com.
• ¹⁶Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Institute for Applied Cancer Science, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Molecular and Cellular Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address: gdraetta@mdanderson.org.

Abstract

Current treatment regimens for pancreatic ductal adenocarcinoma (PDAC) yield poor 5-year survival, emphasizing the critical need to identify druggable targets essential for PDAC maintenance. We developed an unbiased and in vivo target discovery approach to identify molecular vulnerabilities in low-passage and patient-derived PDAC xenografts or genetically engineered mouse model-derived allografts. Focusing on epigenetic regulators, we identified WDR5, a core member of the COMPASS histone H3 Lys4 (H3K4) MLL (1-4) methyltransferase complex, as a top tumor maintenance hit required across multiple human and mouse tumors. Mechanistically, WDR5 functions to sustain proper execution of DNA replication in PDAC cells, as previously suggested by replication stress studies involving MLL1, and c-Myc, also found to interact with WDR5. We indeed demonstrate that interaction with c-Myc is critical for this function. By showing that ATR inhibition mimicked the effects of WDR5 suppression, these data provide rationale to test ATR and WDR5 inhibitors for activity in this disease.

Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

PMID:

27320920

DOI:

10.1016/j.celrep.2016.05.063

SOURCE

http://www.ncbi.nlm.nih.gov/pubmed/27320920

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Topical Solution for Combination Oncology Drug Therapy: Patch that delivers Drug, Gene, and Light-based Therapy to Tumor

Posted in 3D Plotting Scaffolds, Advanced Drug Manufacturing Technology, Biopolymer Blend Open Porous, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Informatics, Cell Level, Computational Biology/Systems and Bioinformatics, Drug Delivery Platform Technology, Drug Development using MultiOrgan Chip, Drug Development/Formulation using 3D Printing, Genomic Expression, Loss of function gene, MicroEngineering Cell-Tissue & Systems, Nanotechnology for Drug Delivery, tagged a miR mimic (tumour suppressor miRNA), antagomiR (oncomiR inhibitor), anticancer platform to locally modulate the expression of endogenous miRs in cancer, Conjugation of RNA triple helices to dendrimers, natural tissue amines in the tumour, post-gel implantation, RNA-triple-helix adhesive scaffold upon interaction with dextran aldehyde, RNA-triple-helix hydrogels, self-assembled dual-colour RNA-triple-helix structure, self-assembled RNA-triple-helix conjugates remain functional in vitro and in vivo, therapeutic potential of miRNA (miR) in cancer, triplex nanoparticles on July 27, 2016| Leave a Comment »

Topical Solution for Combination Oncology Drug Therapy: Patch that delivers Drug, Gene, and Light-based Therapy to Tumor

Reporter: Aviva Lev-Ari, PhD, RN

 

NATURE MATERIALS | ARTICLE

Self-assembled RNA-triple-helix hydrogel scaffold for microRNA modulation in the tumour microenvironment

• João Conde,
• Nuria Oliva,
• Mariana Atilano,
• Hyun Seok Song
• & Natalie Artzi

• Affiliations
• Contributions
• Corresponding authors

Affiliations

1. Massachusetts Institute of Technology, Institute for Medical Engineering and Science, Harvard-MIT Division for Health Sciences and Technology, Cambridge, Massachusetts 02139, USA
   • João Conde,
   • Nuria Oliva,
   • Mariana Atilano,
   • Hyun Seok Song &
   • Natalie Artzi
2. School of Engineering and Materials Science, Queen Mary University of London, London E1 4NS, UK
   • João Conde
3. Grup d’Enginyeria de Materials, Institut Químic de Sarrià-Universitat Ramon Llull, Barcelona 08017, Spain
   • Mariana Atilano
4. Division of Bioconvergence Analysis, Korea Basic Science Institute, Yuseong, Daejeon 169-148, Republic of Korea
   • Hyun Seok Song
5. Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
   • Natalie Artzi
6. Department of Medicine, Biomedical Engineering Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
   • Natalie Artzi

Contributions

J.C. and N.A. conceived the project and designed the experiments. J.C., N.O., H.S.S. and M.A. performed the experiments, collected and analysed the data. J.C. and N.A. co-wrote the manuscript. All authors discussed the results and reviewed the manuscript.

Nature Materials

15,

353–363

(2016)

doi:10.1038/nmat4497

Received

22 April 2015

Accepted

26 October 2015

Published online

07 December 2015

• Abstract•
• References•
• Author information•
• Supplementary information

The therapeutic potential of miRNA (miR) in cancer is limited by the lack of efficient delivery vehicles. Here, we show that a self-assembled dual-colour RNA-triple-helix structure comprising two miRNAs—a miR mimic (tumour suppressor miRNA) and an antagomiR (oncomiR inhibitor)—provides outstanding capability to synergistically abrogate tumours. Conjugation of RNA triple helices to dendrimers allows the formation of stable triplex nanoparticles, which form an RNA-triple-helix adhesive scaffold upon interaction with dextran aldehyde, the latter able to chemically interact and adhere to natural tissue amines in the tumour. We also show that the self-assembled RNA-triple-helix conjugates remain functional in vitro and in vivo, and that they lead to nearly 90% levels of tumour shrinkage two weeks post-gel implantation in a triple-negative breast cancer mouse model. Our findings suggest that the RNA-triple-helix hydrogels can be used as an efficient anticancer platform to locally modulate the expression of endogenous miRs in cancer.

SOURCE

http://www.nature.com/nmat/journal/v15/n3/abs/nmat4497.html#author-information

 

 

Patch that delivers drug, gene, and light-based therapy to tumor sites shows promising results

In mice, device destroyed colorectal tumors and prevented remission after surgery.

Helen Knight | MIT News Office
July 25, 2016

Approximately one in 20 people will develop colorectal cancer in their lifetime, making it the third-most prevalent form of the disease in the U.S. In Europe, it is the second-most common form of cancer.

The most widely used first line of treatment is surgery, but this can result in incomplete removal of the tumor. Cancer cells can be left behind, potentially leading to recurrence and increased risk of metastasis. Indeed, while many patients remain cancer-free for months or even years after surgery, tumors are known to recur in up to 50 percent of cases.

Conventional therapies used to prevent tumors recurring after surgery do not sufficiently differentiate between healthy and cancerous cells, leading to serious side effects.

In a paper published today in the journal Nature Materials, researchers at MIT describe an adhesive patch that can stick to the tumor site, either before or after surgery, to deliver a triple-combination of drug, gene, and photo (light-based) therapy.

Releasing this triple combination therapy locally, at the tumor site, may increase the efficacy of the treatment, according to Natalie Artzi, a principal research scientist at MIT’s Institute for Medical Engineering and Science (IMES) and an assistant professor of medicine at Brigham and Women’s Hospital, who led the research.

The general approach to cancer treatment today is the use of systemic, or whole-body, therapies such as chemotherapy drugs. But the lack of specificity of anticancer drugs means they produce undesired side effects when systemically administered.

What’s more, only a small portion of the drug reaches the tumor site itself, meaning the primary tumor is not treated as effectively as it should be.

Indeed, recent research in mice has found that only 0.7 percent of nanoparticles administered systemically actually found their way to the target tumor.

“This means that we are treating both the source of the cancer — the tumor — and the metastases resulting from that source, in a suboptimal manner,” Artzi says. “That is what prompted us to think a little bit differently, to look at how we can leverage advancements in materials science, and in particular nanotechnology, to treat the primary tumor in a local and sustained manner.”

The researchers have developed a triple-therapy hydrogel patch, which can be used to treat tumors locally. This is particularly effective as it can treat not only the tumor itself but any cells left at the site after surgery, preventing the cancer from recurring or metastasizing in the future.

Firstly, the patch contains gold nanorods, which heat up when near-infrared radiation is applied to the local area. This is used to thermally ablate, or destroy, the tumor.

These nanorods are also equipped with a chemotherapy drug, which is released when they are heated, to target the tumor and its surrounding cells.

Finally, gold nanospheres that do not heat up in response to the near-infrared radiation are used to deliver RNA, or gene therapy to the site, in order to silence an important oncogene in colorectal cancer. Oncogenes are genes that can cause healthy cells to transform into tumor cells.

The researchers envision that a clinician could remove the tumor, and then apply the patch to the inner surface of the colon, to ensure that no cells that are likely to cause cancer recurrence remain at the site. As the patch degrades, it will gradually release the various therapies.

The patch can also serve as a neoadjuvant, a therapy designed to shrink tumors prior to their resection, Artzi says.

When the researchers tested the treatment in mice, they found that in 40 percent of cases where the patch was not applied after tumor removal, the cancer returned.

But when the patch was applied after surgery, the treatment resulted in complete remission.

Indeed, even when the tumor was not removed, the triple-combination therapy alone was enough to destroy it.

The technology is an extraordinary and unprecedented synergy of three concurrent modalities of treatment, according to Mauro Ferrari, president and CEO of the Houston Methodist Research Institute, who was not involved in the research.

“What is particularly intriguing is that by delivering the treatment locally, multimodal therapy may be better than systemic therapy, at least in certain clinical situations,” Ferrari says.

Unlike existing colorectal cancer surgery, this treatment can also be applied in a minimally invasive manner. In the next phase of their work, the researchers hope to move to experiments in larger models, in order to use colonoscopy equipment not only for cancer diagnosis but also to inject the patch to the site of a tumor, when detected.

“This administration modality would enable, at least in early-stage cancer patients, the avoidance of open field surgery and colon resection,” Artzi says. “Local application of the triple therapy could thus improve patients’ quality of life and therapeutic outcome.”

Artzi is joined on the paper by João Conde, Nuria Oliva, and Yi Zhang, of IMES. Conde is also at Queen Mary University in London.

SOURCE

http://news.mit.edu/2016/patch-delivers-drug-gene-light-based-therapy-tumor-0725

Other related articles published in thie Open Access Online Scientific Journal include the following:

The Development of siRNA-Based Therapies for Cancer

Author: Ziv Raviv, PhD

https://pharmaceuticalintelligence.com/2013/05/09/the-development-of-sirna-based-therapies-for-cancer/

 

Targeted Liposome Based Delivery System to Present HLA Class I Antigens to Tumor Cells: Two papers

Reporter: Stephen J. Williams, Ph.D.

https://pharmaceuticalintelligence.com/2016/07/20/targeted-liposome-based-delivery-system-to-present-hla-class-i-antigens-to-tumor-cells-two-papers/

 

Blast Crisis in Myeloid Leukemia and the Activation of a microRNA-editing Enzyme called ADAR1

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/06/10/blast-crisis-in-myeloid-leukemia-and-the-activation-of-a-microrna-editing-enzyme-called-adar1/

 

First challenge to make use of the new NCI Cloud Pilots – Somatic Mutation Challenge – RNA: Best algorithms for detecting all of the abnormal RNA molecules in a cancer cell

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/07/17/first-challenge-to-make-use-of-the-new-nci-cloud-pilots-somatic-mutation-challenge-rna-best-algorithms-for-detecting-all-of-the-abnormal-rna-molecules-in-a-cancer-cell/

 

miRNA Therapeutic Promise

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/05/01/mirna-therapeutic-promise/

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