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Reporter: Gail S. Thornton, M.A.

LPBI Update

Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Newsletter #1 – February 2020

Welcome to the premier issue of LPBI Group News, where readers can find relevant news and updates about science, business and medical innovation. This newsletter is distributed as a service for our readers.

The Conference Forum Highlights Immuno-Oncology 360° in New York

The Conference Forum is hosting Immuno-Oncology 360°, which reports on current data and developments of immuno-oncology in the science and business communities. The summit takes place on February 26-28 at the Crowne Plaza Times Square in New York.

Please visit www.io360summit.com to register and use code LPBI20 for a 20% discount. 

Ahead of the conference, Immuno-Oncology 360° has created a series celebrating their women speakers in the work they are doing to fight cancer. To read the series, visit: https://theconferenceforum.org/conferences/immuno-oncology-360/io360%cb%9a-leadership-interviews/

This information is published in conjunction with the Immuno-Oncology 360° Summit.

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Venture Summit Attracts Top Innovators in Silicon Valley

Leaders in Pharmaceutical Business Intelligence (LPBI) Group is one of the sponsors of Venture Summit | West, “Where Innovation Meets Capital.”

The meeting will be held on March 23-24 at the Santa Clara Convention Center, Silicon Valley.

 

Special offer:  Register Now & Save $450 off (Use discount code “LPBI-VIP”)

For more information, please visit: https://pharmaceuticalintelligence.com/2019/12/17/venture-summit-west-where-innovation-meets-capital-march-23rd-24th-2020-santa-clara-convention-center-silicon-valley/

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e-Proceedings of 15th Annual Personalized Medicine Conference at Harvard Medical School

The 15th Annual Personalized Medicine Conference at Harvard Medical School, Boston last year [November 13-14, 2019], entitled  The Paradigm Evolves, explored the science, business and policy issues facing personalized medicine. In today’s world, scientists need to understand how molecular diagnostics augmented by artificial intelligence, data analytics and digital health empowers physicians and patients in their health care decisions.

Please visit for LPBI Group coverage of the meeting, including social media activities at the conference:

https://pharmaceuticalintelligence.com/2019/07/19/15th-annual-personalized-medicine-conference-at-harvard-medical-school-the-paradigm-evolves-november-13-14-2019-%e2%80%a2-harvard-medical-school-boston-ma/

https://pharmaceuticalintelligence.com/2019/11/15/tweets-and-retweets-by-aviva1950-and-by-pharma_bi-for-15th-annual-personalized-medicine-conference-at-harvard-medical-school-the-paradigm-evolves-november-13-14-2019-%e2%80%a2/

  •   3D Medical BioPrinting Technology Featured in Podcast

LPBI Group leaders, Aviva Lev-Ari, Ph.D., R.N., Stephen Williams, Ph.D., and Irina Robu, Ph.D., spoke with Partners in Health and Biz, a half-hour audio podcast that reaches 40,000 listeners, about the topic of 3D Medical BioPrinting Technology: A Revolution in Medicine.

Please click on this link to hear the podcast. https://www.youtube.com/watch?v=laozyrfi29c.

The topic is also the title of a recently offered e-book by the LPBI Group on 3D BioPrinting, available on Amazon/Kindle Direct [https://www.amazon.com/Medical-BioPrinting-Technologies-Patient-centered-Patient-Centered-ebook/dp/B078QVDV2W]. 

The 3D BioPrinting technology is being used to develop advanced medical practices that will help with previously difficult processes, such as delivering drugs via micro-robots, targeting specific cancer cells and even assisting in difficult eye operations.

The table of contents in this book includes: Chapter 1: 3D Bioprinting: Latest Innovations in a Forty year-old Technology. Chapter 2: LPBI Initiative on 3D BioPrinting, Chapter 3: Cardiovascular BioPrinting, Chapter 4: Medical and Surgical Repairs – Advances in R&D Research, Chapter 5: Organ on a Chip, Chapter 6: FDA Regulatory Technology Issues, Chapter 7: DNA Origami, Chapter 8: Aptamers and 3D Scaffold Binding, Chapter 9: Advances and Future Prospects, Chapter 10: BioInks and MEMS, Chapter 11: BioMedical MEMS, Chapter 12: 3D Solid Organ Printing and Chapter 13: Medical 3D Printing: Sources and Trade Groups – List of Secondary Material. 

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New e-Book: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology

LPBI Group’s latest e-book entitled, Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology, offers the reader content curation with embedded videos and audio podcasts, real-time conference e-Proceedings by LPBI’s scientists and professors and archived tweets of quotes from speakers at leading biotechnology conferences.

Please click on this link on Amazon/Kindle Direct: https://www.amazon.com/dp/B08385KF87

 

The book integrates in a single volume four distinct perspectives: basic science, technologies and methodologies, clinical aspects and business and legal aspects of genomics research. “The materials in this book represents the scientific frontier in Biological Sciences and Medicine related to the genomics aspects of disease onset,” said Aviva Lev-Ari, Ph.D., R.N., and founder of LPBI Group.

The book addresses:

  • aspects of life: the Cell, the Organ, the Human Body and Human Populations;
  • methodologies of genomic data analysis: Next Generation Sequencing, Gene Editing, AI, Single Cell Genomics, Evolution Biology Genomics, Simulation Modeling in Genomics, Genotypes and Phenotypes Modeling, measurement of Epigenomics effects on disease, and developments in Pharmaco-Genomics.

Additionally, artificial Intelligence in medicine is covered in Part 3 of the e-Book, which represents the frontier in this emerging field, with topics, such as the science, technologies and methodologies, clinical aspects, business and legal implications as well as the latest machine learning algorithms harnessed for medical diagnosis.

This e-book is significant because it:

  • contains 326 articles on topics, such as gene editing, bioinformatics and genome ontology;
  • incorporates 74 e-Proceedings created in real time by the Book’s authors and editors
  • includes four collections of Tweets representing quotes from speakers at global leading conferences on Genomics
  • has 13 locations of Videos and Audio Podcasts that serve to enrich the e-Reader’s experience.

We welcome your comments and suggestions. Please send them to Aviva Lev-Ari at avivalev-ari@alum.berkeley.edu.

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Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

Curator: Stephen J. Williams, PhD

 

 

From the POLICY FORUM ETHICS AND DIVERSITY Section of Science

Ethics of inclusion: Cultivate trust in precision medicine

 See all authors and affiliations

Science  07 Jun 2019:
Vol. 364, Issue 6444, pp. 941-942
DOI: 10.1126/science.aaw8299

Precision medicine is at a crossroads. Progress toward its central goal, to address persistent health inequities, will depend on enrolling populations in research that have been historically underrepresented, thus eliminating longstanding exclusions from such research (1). Yet the history of ethical violations related to protocols for inclusion in biomedical research, as well as the continued misuse of research results (such as white nationalists looking to genetic ancestry to support claims of racial superiority), continue to engender mistrust among these populations (2). For precision medicine research (PMR) to achieve its goal, all people must believe that there is value in providing information about themselves and their families, and that their participation will translate into equitable distribution of benefits. This requires an ethics of inclusion that considers what constitutes inclusive practices in PMR, what goals and values are being furthered through efforts to enhance diversity, and who participates in adjudicating these questions. The early stages of PMR offer a critical window in which to intervene before research practices and their consequences become locked in (3).

Initiatives such as the All of Us program have set out to collect and analyze health information and biological samples from millions of people (1). At the same time, questions of trust in biomedical research persist. For example, although the recent assertions of white nationalists were eventually denounced by the American Society of Human Genetics (4), the misuse of ancestry testing may have already undermined public trust in genetic research.

There are also infamous failures in research that included historically underrepresented groups, including practices of deceit, as in the Tuskegee Syphilis Study, or the misuse of samples, as with the Havasupai tribe (5). Many people who are being asked to give their data and samples for PMR must not only reconcile such past research abuses, but also weigh future risks of potential misuse of their data.

To help assuage these concerns, ongoing PMR studies should open themselves up to research, conducted by social scientists and ethicists, that examines how their approaches enhance diversity and inclusion. Empirical studies are needed to account for how diversity is conceptualized and how goals of inclusion are operationalized throughout the life course of PMR studies. This is not limited to selection and recruitment of populations but extends to efforts to engage participants and communities, through data collection and measurement, and interpretations and applications of study findings. A commitment to transparency is an important step toward cultivating public trust in PMR’s mission and practices.

From Inclusion to Inclusive

The lack of diverse representation in precision medicine and other biomedical research is a well-known problem. For example, rare genetic variants may be overlooked—or their association with common, complex diseases can be misinterpreted—as a result of sampling bias in genetics research (6). Concentrating research efforts on samples with largely European ancestry has limited the ability of scientists to make generalizable inferences about the relationships among genes, lifestyle, environmental exposures, and disease risks, and thereby threatens the equitable translation of PMR for broad public health benefit (7).

However, recruiting for diverse research participation alone is not enough. As with any push for “diversity,” related questions arise about how to describe, define, measure, compare, and explain inferred similarities and differences among individuals and groups (8). In the face of ambivalence about how to represent population variation, there is ample evidence that researchers resort to using definitions of diversity that are heterogeneous, inconsistent, and sometimes competing (9). Varying approaches are not inherently problematic; depending on the scientific question, some measures may be more theoretically justified than others and, in many cases, a combination of measures can be leveraged to offer greater insight (10). For example, studies have shown that American adults who do not self-identify as white report better mental and physical health if they think others perceive them as white (1112).

The benefit of using multiple measures of race and ancestry also extends to genetic studies. In a study of hypertension in Puerto Rico, not only did classifications based on skin color and socioeconomic status better predict blood pressure than genetic ancestry, the inclusion of these sociocultural measures also revealed an association between a genetic polymorphism and hypertension that was otherwise hidden (13). Thus, practices that allow for a diversity of measurement approaches, when accompanied by a commitment to transparency about the rationales for chosen approaches, are likely to benefit PMR research more than striving for a single gold standard that would apply across all studies. These definitional and measurement issues are not merely semantic. They also are socially consequential to broader perceptions of PMR research and the potential to achieve its goals of inclusion.

Study Practices, Improve Outcomes

Given the uncertainty and complexities of the current, early phase of PMR, the time is ripe for empirical studies that enable assessment and modulation of research practices and scientific priorities in light of their social and ethical implications. Studying ongoing scientific practices in real time can help to anticipate unintended consequences that would limit researchers’ ability to meet diversity recruitment goals, address both social and biological causes of health disparities, and distribute the benefits of PMR equitably. We suggest at least two areas for empirical attention and potential intervention.

First, we need to understand how “upstream” decisions about how to characterize study populations and exposures influence “downstream” research findings of what are deemed causal factors. For example, when precision medicine researchers rely on self-identification with U.S. Census categories to characterize race and ethnicity, this tends to circumscribe their investigation of potential gene-environment interactions that may affect health. The convenience and routine nature of Census categories seemed to lead scientists to infer that the reasons for differences among groups were self-evident and required no additional exploration (9). The ripple effects of initial study design decisions go beyond issues of recruitment to shape other facets of research across the life course of a project, from community engagement and the return of results to the interpretation of study findings for human health.

Second, PMR studies are situated within an ecosystem of funding agencies, regulatory bodies, disciplines, and other scholars. This partly explains the use of varied terminology, different conceptual understandings and interpretations of research questions, and heterogeneous goals for inclusion. It also makes it important to explore how expectations related to funding and regulation influence research definitions of diversity and benchmarks for inclusion.

For example, who defines a diverse study population, and how might those definitions vary across different institutional actors? Who determines the metrics that constitute successful inclusion, and why? Within a research consortium, how are expectations for data sharing and harmonization reconciled with individual studies’ goals for recruitment and analysis? In complex research fields that include multiple investigators, organizations, and agendas, how are heterogeneous, perhaps even competing, priorities negotiated? To date, no studies have addressed these questions or investigated how decisions facilitate, or compromise, goals of diversity and inclusion.

The life course of individual studies and the ecosystems in which they reside cannot be easily separated and therefore must be studied in parallel to understand how meanings of diversity are shaped and how goals of inclusion are pursued. Empirically “studying the studies” will also be instrumental in creating mechanisms for transparency about how PMR is conducted and how trade-offs among competing goals are resolved. Establishing open lines of inquiry that study upstream practices may allow researchers to anticipate and address downstream decisions about how results can be interpreted and should be communicated, with a particular eye toward the consequences for communities recruited to augment diversity. Understanding how scientists negotiate the challenges and barriers to achieving diversity that go beyond fulfilling recruitment numbers is a critical step toward promoting meaningful inclusion in PMR.

Transparent Reflection, Cultivation of Trust

Emerging research on public perceptions of PMR suggests that although there is general support, questions of trust loom large. What we learn from studies that examine on-the-ground approaches aimed at enhancing diversity and inclusion, and how the research community reflects and responds with improvements in practices as needed, will play a key role in building a culture of openness that is critical for cultivating public trust.

Cultivating long-term, trusting relationships with participants underrepresented in biomedical research has been linked to a broad range of research practices. Some of these include the willingness of researchers to (i) address the effect of history and experience on marginalized groups’ trust in researchers and clinicians; (ii) engage concerns about potential group harms and risks of stigmatization and discrimination; (iii) develop relationships with participants and communities that are characterized by transparency, clear communication, and mutual commitment; and (iv) integrate participants’ values and expectations of responsible oversight beyond initial informed consent (14). These findings underscore the importance of multidisciplinary teams that include social scientists, ethicists, and policy-makers, who can identify and help to implement practices that respect the histories and concerns of diverse publics.

A commitment to an ethics of inclusion begins with a recognition that risks from the misuse of genetic and biomedical research are unevenly distributed. History makes plain that a multitude of research practices ranging from unnecessarily limited study populations and taken-for-granted data collection procedures to analytic and interpretive missteps can unintentionally bolster claims of racial superiority or inferiority and provoke group harm (15). Sustained commitment to transparency about the goals, limits, and potential uses of research is key to further cultivating trust and building long-term research relationships with populations underrepresented in biomedical studies.

As calls for increasing diversity and inclusion in PMR grow, funding and organizational pathways must be developed that integrate empirical studies of scientific practices and their rationales to determine how goals of inclusion and equity are being addressed and to identify where reform is required. In-depth, multidisciplinary empirical investigations of how diversity is defined, operationalized, and implemented can provide important insights and lessons learned for guiding emerging science, and in so doing, meet our ethical obligations to ensure transparency and meaningful inclusion.

References and Notes

  1. C. P. Jones et al Ethn. Dis. 18496 (2008).
  2. C. C. GravleeA. L. NonC. J. Mulligan
  3. S. A. Kraft et al Am. J. Bioeth. 183 (2018).
  4. A. E. Shields et al Am. Psychol. 6077 (2005).

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New Mutant KRAS Inhibitors Are Showing Promise in Cancer Clinical Trials: Hope For the Once ‘Undruggable’ Target

Curator: Stephen J. Williams, Ph.D.

The November 1st issue of Science highlights a series of findings which give cancer researchers some hope in finally winning a thirty year war with the discovery of drugs that target KRAS, one of the most commonly mutated oncogenes  (25% of cancers), and thought to be a major driver of tumorigenesis. Once considered an undruggable target, mainly because of the smooth surface with no obvious pockets to fit a drug in, as well as the plethora of failed attempts to develop such an inhibitor, new findings with recently developed candidates, highlighted in this article and other curated within, are finally giving hope to researchers and oncologists who have been hoping for a clinically successful inhibitor of this once considered elusive target.

 

For a great review on development of G12C KRas inhibitors please see Dr. Hobb’s and Channing Der’s review in Cell Selective Targeting of the KRAS G12C Mutant: Kicking KRAS When It’s Down

Figure 1Mechanism of Action of ARS853 showing that the inhibitors may not need bind to the active conformation of KRAS for efficacy

Abstract: Two recent studies evaluated a small molecule that specifically binds to and inactivates the KRAS G12C mutant. The new findings argue that the perception that mutant KRAS is persistently frozen in its active GTP-bound form may not be accurate.

 

Although the development of the KRASG12C-specific inhibitor, compound 12 (Ostrem et al., 2013), was groundbreaking, subsequent studies found that the potency of compound 12 in cellular assays was limited (Lito et al., 2016, Patricelli et al., 2016). A search for more-effective analogs led to the development of ARS853 (Patricelli et al., 2016), which exhibited a 600-fold increase of its reaction rate in vitro over compound 12 and cellular activities in the low micromolar range.

 

A Summary and more in-depth curation of the Science article is given below:

After decades, progress against an ‘undruggable’ cancer target

Summary

Cancer researchers are making progress toward a goal that has eluded them for more than 30 years: shrinking tumors by shutting off a protein called KRAS that drives growth in many cancer types. A new type of drug aimed at KRAS made tumors disappear in mice and shrank tumors in lung cancer patients, two companies report in papers published this week. It’s not yet clear whether the drugs will extend patients’ lives, but the results are generating a wave of excitement. And one company, Amgen, reports an unexpected bonus: Its drug also appears to stimulate the immune system to attack tumors, suggesting it could be even more powerful if paired with widely available immunotherapy treatments.

Jocelyn Kaiser. After decades, progress against an ‘undruggable’ cancer target. Science  01 Nov 2019: Vol. 366, Issue 6465, pp. 561 DOI: 10.1126/science.366.6465.561

The article highlights the development of three inhibitors: by Wellspring Biosciences, Amgen, and Mirati Therapeutics.

Wellspring BioSciences

 

In 2013, Dr. Kevan Shokat’s lab at UCSF discovered a small molecule that could fit in the groove of the KRAS mutant G12C.  The G12C as well as the G12D is a common mutation found in KRAS in cancers. KRAS p.G12C mutations predominate in NSCLC comprising 11%–16% of lung adenocarcinomas (45%–50% of mutant KRAS is p.G12C) (Campbell et al., 2016; Jordan et al., 2017), as well as 1%–4% of pancreatic and colorectal adenocarcinomas, respectively (Bailey et al., 2016; Giannakis et al., 2016).  This inhibitor was effective in shrinking, in mouse studies conducted by Wellspring Biosciences,  implanted tumors containing this mutant KRAS.

 

See Wellspring’s news releases below:

March, 2016 – Publication – Selective Inhibition of Oncogenic KRAS Output with Small Molecules Targeting the Inactive State

February, 2016 – Publication – Allele-specific inhibitors inactivate mutant KRAS G12C by a trapping mechanism

Amgen

 

Amgen press release on AMG510 Clinical Trial at ASCO 2019

 

THOUSAND OAKS, Calif., June 3, 2019 /PRNewswire/ — Amgen (NASDAQ: AMGN) today announced the first clinical results from a Phase 1 study evaluating investigational AMG 510, the first KRASG12C inhibitor to reach the clinical stage. In the trial, there were no dose-limiting toxicities at tested dose levels. AMG 510 showed anti-tumor activity when administered as a monotherapy in patients with locally-advanced or metastatic KRASG12C mutant solid tumors. These data are being presented during an oral session at the 55th Annual Meeting of the American Society of Clinical Oncology (ASCO) in Chicago.

“KRAS has been a target of active exploration in cancer research since it was identified as one of the first oncogenes more than 30 years ago, but it remained undruggable due to a lack of traditional small molecule binding pockets on the protein. AMG 510 seeks to crack the KRAS code by exploiting a previously hidden groove on the protein surface,” said David M. Reese, M.D., executive vice president of Research and Development at Amgen. “By irreversibly binding to cysteine 12 on the mutated KRAS protein, AMG 510 is designed to lock it into an inactive state. With high selectivity for KRASG12C, we believe investigational AMG 510 has high potential as both a monotherapy and in combination with other targeted and immune therapies.”

The Phase 1, first-in-human, open-label multicenter study enrolled 35 patients with various tumor types (14 non-small cell lung cancer [NSCLC], 19 colorectal cancer [CRC] and two other). Eligible patients were heavily pretreated with at least two or more prior lines of treatment, consistent with their tumor type and stage of disease. 

Canon, J., Rex, K., Saiki, A.Y. et al. The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature 575, 217–223 (2019) doi:10.1038/s41586-019-1694-1

Besides blocking tumor growth, AMG510 appears to stimulate T cells to attack the tumor, thus potentially supplying a two pronged attack to the tumor, inhibiting oncogenic RAS and stimulating anti-tumor immunity.

 

Mirati Therapeutics

 

Mirati’s G12C KRAS inhibitor (MRTX849) is being investigated in a variety of solid malignancies containing the KRAS mutation.

 

For recent publication on results in lung cancer see Patricelli M.P., et al. Cancer Discov. 2016; (Published online January 6, 2016)

For more information on Mirati’s KRAS G12C inhibitor see https://www.mirati.com/pipeline/kras-g12c/

 

KRAS G12C Inhibitor (MRTX849)

Study 849-001 – Phase 1b/2 of single agent MRTX849 for solid tumors with KRAS G12C mutation

Phase 1b/2 clinical trial of single agent MRTX849 in patients with advanced solid tumors that have a KRAS G12C mutation.

See details for this study at clinicaltrials.gov

 

Additional References:

Allele-specific inhibitors inactivate mutant KRAS G12C by a trapping mechanism.

Lito P et al. Science. (2016)

Targeting KRAS Mutant Cancers with a Covalent G12C-Specific Inhibitor.

Janes MR et al. Cell. (2018)

Potent and Selective Covalent Quinazoline Inhibitors of KRAS G12C.

Zeng M et al. Cell Chem Biol. (2017)

Campbell, J.D., Alexandrov, A., Kim, J., Wala, J., Berger, A.H., Pedamallu, C.S., Shukla, S.A., Guo, G., Brooks, A.N., Murray, B.A., et al.; Cancer Genome Atlas Research Network (2016). Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat. Genet.48, 607–616

Jordan, E.J., Kim, H.R., Arcila, M.E., Barron, D., Chakravarty, D., Gao, J., Chang, M.T., Ni, A., Kundra, R., Jonsson, P., et al. (2017). Prospective comprehensive molecular characterization of lung adenocarcinomas for efficient patient matching to approved and emerging therapies. Cancer Discov. 7, 596–609.

Bailey, P., Chang, D.K., Nones, K., Johns, A.L., Patch, A.M., Gingras, M.C., Miller, D.K., Christ, A.N., Bruxner, T.J., Quinn, M.C., et al.; Australian Pancreatic Cancer Genome Initiative (2016). Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531, 47–52.

Giannakis, M., Mu, X.J., Shukla, S.A., Qian, Z.R., Cohen, O., Nishihara, R., Bahl, S., Cao, Y., Amin-Mansour, A., Yamauchi, M., et al. (2016). Genomic correlates of immune-cell infiltrates in colorectal carcinoma. Cell Rep. 15, 857–865.

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Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line

Reporter: Stephen J. Williams, PhD

In a Genome Research report by Marie Nattestad et al. [1], the SK-BR-3 breast cancer cell line was sequenced using a long read single molecule sequencing protocol in order to develop one of the most detailed maps of structural variations in a cancer genome to date.  The authors detected over 20,000 variants with this new sequencing modality, whereas most of these variants would have been missed by short read sequencing.  In addition, a complex sequence of nested duplications and translocations occurred surrounding the ERBB2 (HER2) while full-length transcriptomic analysis revealed novel gene fusions within the nested genomic variants.  The authors suggest that combining this long-read genome and transcriptome sequencing results in a more comprehensive coverage of tumor gene variants and “sheds new light on the complex mechanisms involved in cancer genome evolution.”

Genomic instability is a hallmark of cancer [2], which lead to numerous genetic variations such as:

  • Copy number variations
  • Chromosomal alterations
  • Gene fusions
  • Deletions
  • Gene duplications
  • Insertions
  • Translocations

Efforts such as the Cancer Genome Atlas [3], and the International Genome Consortium (2010) use short-read sequencing technology to detect and analyze thousands of commonly occurring mutations however short-read technology has a high false positive and negative rate for detecting less common genetic structural variations {as high as 50% [4]}. In addition, short reads cannot detect variations in close proximity to each other or on the same molecule, therefore underestimating the variation number.

Methods:  The authors used a long-read sequencing technology from Pacific Biosciences (SMRT) to analyze the mutational and structural variation in the SK-BR-3 breast cancer cell line.  A split read and within-read mapping approach was used to detect variants of different types and sizes.  In general, long-reads have better alignment qualities than short reads, resulting in higher quality mapping. Transcriptomic analysis was performed using Iso-Seq.

Results: Using the SMRT long-read sequencing technology from Pacific Biosciences, the authors were able to obtain 71.9% sequencing coverage with average read length of 9.8 kb for the SK-BR-3 genome.

A few notes:

  1. Most amplified regions (33.6 copies) around the locus spanning the ERBB2 oncogene and around MYC locus (38 copies), EGFR locus (7 copies) and BCAS1 (16.8 copies)
  2. The locus 8q24.12 had the most amplifications (this locus contains the SNTB1 gene) at 69.2 copies
  3. Long-read sequencing showed more insertions than deletions and suggests an underestimate of the lengths of low complexity regions in the human reference genome
  4. Found 1,493 long read variants, 603 of which were between different chromosomes
  5. Using Iso-Seq in conjunction with the long-read platform, they detected 1,692,379 isoforms (93%) mapping to the reference genome and 53 putative gene fusions (39 of which they found genomic evidence)

A table modified from the paper on the gene fusions is given below:

Table 1. Gene fusions with RNA evidence from Iso-Seq and DNA evidence from SMRT DNA sequencing where the genomic path is found using SplitThreader from Sniffles variant calls. Note link in table is  GeneCard for each gene.

SplitThreader path

 

# Genes Distance
(bp)
Number
of variants
Chromosomes
in path
Previously observed in references
1 KLHDC2 SNTB1 9837 3 14|17|8 Asmann et al. (2011) as only a 2-hop fusion
2 CYTH1 EIF3H 8654 2 17|8 Edgren et al. (2011); Kim and Salzberg
(2011); RNA only, not observed as 2-hop
3 CPNE1 PREX1 1777 2 20 Found and validated as 2-hop by Chen et al. 2013
4 GSDMB TATDN1 0 1 17|8 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011)
5 LINC00536 PVT1 0 1 8 No
6 MTBP SAMD12 0 1 8 Validated by Edgren et al. (2011)
7 LRRFIP2 SUMF1 0 1 3 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011)
8 FBXL7 TRIO 0 1 5 No
9 ATAD5 TLK2 0 1 17 No
10 DHX35 ITCH 0 1 20 Validated by Edgren et al. (2011)
11 LMCD1-AS1 MECOM 0 1 3 No
12 PHF20 RP4-723E3.1 0 1 20 No
13 RAD51B SEMA6D 0 1 14|15 No
14 STAU1 TOX2 0 1 20 No
15 TBC1D31 ZNF704 0 1 8 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011); Chen et al. (2013)

 

SplitThreader found two different paths for the RAD51B-SEMA6D gene fusion and for the LINC00536-PVT1 gene fusion. Number of Iso-Seq reads refers to full-length HQ-filtered reads. Alignments of SMRT DNA sequence reads supporting each of these gene fusions are shown in Supplemental Note S2.

 

 References

 

  1. Nattestad M, Goodwin S, Ng K, Baslan T, Sedlazeck FJ, Rescheneder P, Garvin T, Fang H, Gurtowski J, Hutton E et al: Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line. Genome research 2018, 28(8):1126-1135.
  2. Hanahan D, Weinberg RA: The hallmarks of cancer. Cell 2000, 100(1):57-70.
  3. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA et al: Mutational landscape and significance across 12 major cancer types. Nature 2013, 502(7471):333-339.
  4. Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Fritz MH et al: An integrated map of structural variation in 2,504 human genomes. Nature 2015, 526(7571):75-81.

 

Other articles on Cancer Genome Sequencing in this Open Access Journal Include:

 

International Cancer Genome Consortium Website has 71 Committed Cancer Genome Projects Ongoing

Loss of Gene Islands May Promote a Cancer Genome’s Evolution: A new Hypothesis on Oncogenesis

Identifying Aggressive Breast Cancers by Interpreting the Mathematical Patterns in the Cancer Genome

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

 

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An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

HER2 is an important prognostic biomarker for 20–30% of breast cancers, which is the most common cancer in women. Overexpression of the HER2 receptor stimulates breast cells to proliferate and differentiate uncontrollably, thereby enhancing the malignancy of breast cancer and resulting in a poor prognosis for affected individuals. Current therapies to suppress the overexpression of HER2 in breast cancer mainly involve treatment with HER2-specific monoclonal antibodies. However, these monoclonal anti-HER2 antibodies have severe side effects in clinical trials, such as diarrhea, abnormal liver function, and drug resistance. Removing HER2 from the plasma membrane or inhibiting the gene expression of HER2 is a promising alternative that could limit the malignancy of HER2-positive cancer cells.

 

DNA origami is an emerging field of DNA-based nanotechnology and intelligent DNA nanorobots show great promise in working as a drug delivery system in healthcare. Different DNA-based nanorobots have been developed as affordable and facile therapeutic drugs. In particular, many studies reported that a tetrahedral framework nucleic acid (tFNA) could serve as a promising DNA nanocarrier for many antitumor drugs, owing to its high biocompatibility and biosecurity. For example, tFNA was reported to effectively deliver paclitaxel or doxorubicin to cancer cells for reversing drug resistance, small interfering RNAs (siRNAs) have been modified into tFNA for targeted drug delivery. Moreover, the production and storage of tFNA are not complicated, and they can be quickly degraded in lysosomes by cells. Since both free HApt and tFNA can be diverted into lysosomes, so,  combining the HApt and tFNA as a novel DNA nanorobot (namely, HApt-tFNA) can be an effective strategy to improve its delivery and therapeutic efficacy in treating HER2-positive breast cancer.

 

Researchers reported that a DNA framework-based intelligent DNA nanorobot for selective lysosomal degradation of tumor-specific proteins on cancer cells. An anti-HER2 aptamer (HApt) was site-specifically anchored on a tetrahedral framework nucleic acid (tFNA). This DNA nanorobot (HApt-tFNA) could target HER2-positive breast cancer cells and specifically induce the lysosomal degradation of the membrane protein HER2. An injection of the DNA nanorobot into a mouse model revealed that the presence of tFNA enhanced the stability and prolonged the blood circulation time of HApt, and HApt-tFNA could therefore drive HER2 into lysosomal degradation with a higher efficiency. The formation of the HER2-HApt-tFNA complexes resulted in the HER2-mediated endocytosis and digestion in lysosomes, which effectively reduced the amount of HER2 on the cell surfaces. An increased HER2 digestion through HApt-tFNA further induced cell apoptosis and arrested cell growth. Hence, this novel DNA nanorobot sheds new light on targeted protein degradation for precision breast cancer therapy.

 

It was previously reported that tFNA was degraded by lysosomes and could enhance cell autophagy. Results indicated that free Cy5-HApt and Cy5-HApt-tFNA could enter the lysosomes; thus, tFNA can be regarded as an efficient nanocarrier to transmit HApt into the target organelle. The DNA nanorobot composed of HApt and tFNA showed a higher stability and a more effective performance than free HApt against HER2-positive breast cancer cells. The PI3K/AKT pathway was inhibited when membrane-bound HER2 decreased in SK-BR-3 cells under the action of HApt-tFNA. The research findings suggest that tFNA can enhance the anticancer effects of HApt on SK-BR-3 cells; while HApt-tFNA can bind to HER2 specifically, the compounded HER2-HApt-tFNA complexes can then be transferred and degraded in lysosomes. After these processes, the accumulation of HER2 in the plasma membrane would decrease, which could also influence the downstream PI3K/AKT signaling pathway that is associated with cell growth and death.

 

However, some limitations need to be noted when interpreting the findings: (i) the cytotoxicity of the nanorobot on HER2-positive cancer cells was weak, and the anticancer effects between conventional monoclonal antibodies and HApt-tFNA was not compared; (ii) the differences in delivery efficiency between tFNA and other nanocarriers need to be confirmed; and (iii) the confirmation of anticancer effects of HApt-tFNA on tumors within animals remains challenging. Despite these limitations, the present study provided novel evidence of the biological effects of tFNA when combined with HApt. Although the stability and the anticancer effects of HApt-tFNA may require further improvement before clinical application, this study initiates a promising step toward the development of nanomedicines with novel and intelligent DNA nanorobots for tumor treatment.

 

References:

 

https://pubs.acs.org/doi/10.1021/acs.nanolett.9b01320

 

https://www.ncbi.nlm.nih.gov/pubmed/27939064

 

https://www.ncbi.nlm.nih.gov/pubmed/11694782

 

https://www.ncbi.nlm.nih.gov/pubmed/27082923

 

https://www.ncbi.nlm.nih.gov/pubmed/25365825

 

https://www.ncbi.nlm.nih.gov/pubmed/26840503

 

https://www.ncbi.nlm.nih.gov/pubmed/29802035

 

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Single-cell RNA-seq helps in finding intra-tumoral heterogeneity in pancreatic cancer

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Pancreatic cancer is a significant cause of cancer mortality; therefore, the development of early diagnostic strategies and effective treatment is essential. Improvements in imaging technology, as well as use of biomarkers are changing the way that pancreas cancer is diagnosed and staged. Although progress in treatment for pancreas cancer has been incremental, development of combination therapies involving both chemotherapeutic and biologic agents is ongoing.

 

Cancer is an evolutionary disease, containing the hallmarks of an asexually reproducing unicellular organism subject to evolutionary paradigms. Pancreatic ductal adenocarcinoma (PDAC) is a particularly robust example of this phenomenon. Genomic features indicate that pancreatic cancer cells are selected for fitness advantages when encountering the geographic and resource-depleted constraints of the microenvironment. Phenotypic adaptations to these pressures help disseminated cells to survive in secondary sites, a major clinical problem for patients with this disease.

 

The immune system varies in cell types, states, and locations. The complex networks, interactions, and responses of immune cells produce diverse cellular ecosystems composed of multiple cell types, accompanied by genetic diversity in antigen receptors. Within this ecosystem, innate and adaptive immune cells maintain and protect tissue function, integrity, and homeostasis upon changes in functional demands and diverse insults. Characterizing this inherent complexity requires studies at single-cell resolution. Recent advances such as massively parallel single-cell RNA sequencing and sophisticated computational methods are catalyzing a revolution in our understanding of immunology.

 

PDAC is the most common type of pancreatic cancer featured with high intra-tumoral heterogeneity and poor prognosis. In the present study to comprehensively delineate the PDAC intra-tumoral heterogeneity and the underlying mechanism for PDAC progression, single-cell RNA-seq (scRNA-seq) was employed to acquire the transcriptomic atlas of 57,530 individual pancreatic cells from primary PDAC tumors and control pancreases. The diverse malignant and stromal cell types, including two ductal subtypes with abnormal and malignant gene expression profiles respectively, were identified in PDAC.

 

The researchers found that the heterogenous malignant subtype was composed of several subpopulations with differential proliferative and migratory potentials. Cell trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. Furthermore, it was found a subset of ductal cells with unique proliferative features were associated with an inactivation state in tumor-infiltrating T cells, providing novel markers for the prediction of antitumor immune response. Together, the findings provided a valuable resource for deciphering the intra-tumoral heterogeneity in PDAC and uncover a connection between tumor intrinsic transcriptional state and T cell activation, suggesting potential biomarkers for anticancer treatment such as targeted therapy and immunotherapy.

 

References:

 

https://www.ncbi.nlm.nih.gov/pubmed/31273297

 

https://www.ncbi.nlm.nih.gov/pubmed/21491194

 

https://www.ncbi.nlm.nih.gov/pubmed/27444064

 

https://www.ncbi.nlm.nih.gov/pubmed/28983043

 

https://www.ncbi.nlm.nih.gov/pubmed/24976721

 

https://www.ncbi.nlm.nih.gov/pubmed/27693023

 

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scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Present day technological advances have facilitated unprecedented opportunities for studying biological systems at single-cell level resolution. For example, single-cell RNA sequencing (scRNA-seq) enables the measurement of transcriptomic information of thousands of individual cells in one experiment. Analyses of such data provide information that was not accessible using bulk sequencing, which can only assess average properties of cell populations. Single-cell measurements, however, can capture the heterogeneity of a population of cells. In particular, single-cell studies allow for the identification of novel cell types, states, and dynamics.

 

One of the most prominent uses of the scRNA-seq technology is the identification of subpopulations of cells present in a sample and comparing such subpopulations across samples. Such information is crucial for understanding the heterogeneity of cells in a sample and for comparative analysis of samples from different conditions, tissues, and species. A frequently used approach is to cluster every dataset separately, inspect marker genes for each cluster, and compare these clusters in an attempt to determine which cell types were shared between samples. This approach, however, relies on the existence of predefined or clearly identifiable marker genes and their consistent measurement across subpopulations.

 

Although the aligned data can then be clustered to reveal subpopulations and their correspondence, solving the subpopulation-mapping problem by performing global alignment first and clustering second overlooks the original information about subpopulations existing in each experiment. In contrast, an approach addressing this problem directly might represent a more suitable solution. So, keeping this in mind the researchers developed a computational method, single-cell subpopulations comparison (scPopCorn), that allows for comparative analysis of two or more single-cell populations.

 

The performance of scPopCorn was tested in three distinct settings. First, its potential was demonstrated in identifying and aligning subpopulations from single-cell data from human and mouse pancreatic single-cell data. Next, scPopCorn was applied to the task of aligning biological replicates of mouse kidney single-cell data. scPopCorn achieved the best performance over the previously published tools. Finally, it was applied to compare populations of cells from cancer and healthy brain tissues, revealing the relation of neoplastic cells to neural cells and astrocytes. Consequently, as a result of this integrative approach, scPopCorn provides a powerful tool for comparative analysis of single-cell populations.

 

This scPopCorn is basically a computational method for the identification of subpopulations of cells present within individual single-cell experiments and mapping of these subpopulations across these experiments. Different from other approaches, scPopCorn performs the tasks of population identification and mapping simultaneously by optimizing a function that combines both objectives. When applied to complex biological data, scPopCorn outperforms previous methods. However, it should be kept in mind that scPopCorn assumes the input single-cell data to consist of separable subpopulations and it is not designed to perform a comparative analysis of single cell trajectories datasets that do not fulfill this constraint.

 

Several innovations developed in this work contributed to the performance of scPopCorn. First, unifying the above-mentioned tasks into a single problem statement allowed for integrating the signal from different experiments while identifying subpopulations within each experiment. Such an incorporation aids the reduction of biological and experimental noise. The researchers believe that the ideas introduced in scPopCorn not only enabled the design of a highly accurate identification of subpopulations and mapping approach, but can also provide a stepping stone for other tools to interrogate the relationships between single cell experiments.

 

References:

 

https://www.sciencedirect.com/science/article/pii/S2405471219301887

 

https://www.tandfonline.com/doi/abs/10.1080/23307706.2017.1397554

 

https://ieeexplore.ieee.org/abstract/document/4031383

 

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0927-y

 

https://www.sciencedirect.com/science/article/pii/S2405471216302666

 

 

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