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Google AI improves accuracy of reading mammograms, study finds

Google AI improves accuracy of reading mammograms, study finds

Google CFO Ruth Porat has blogged about twice battling breast cancer.

Artificial intelligence was often more accurate than radiologists in detecting breast cancer from mammograms in a study conducted by researchers using Google AI technology.

The study, published in the journal Nature, used mammograms from approximately 90,000 women in which the outcomes were known to train technology from Alphabet Inc’s DeepMind AI unit, now part of Google Health, Yahoo news reported.

The AI system was then used to analyze images from 28,000 other women and often diagnosed early cancers more accurately than the radiologists who originally interpreted the mammograms.

In another test, AI outperformed six radiologists in reading 500 mammograms. However, while the AI system found cancers the humans missed, it also failed to find cancers flagged by all six radiologists, reports The New York Times.

The researchers said the study “paves the way” for further clinical trials.

Writing in NatureEtta D. Pisano, chief research officer at the American College of Radiology and professor in residence at Harvard Medical School, noted, “The real world is more complicated and potentially more diverse than the type of controlled research environment reported in this study.”

Ruth Porat, senior vice president and chief financial officer Alphabet, Inc., wrote in a company blog titled “Breast cancer and tech…a reason for optimism” in October about twice battling the disease herself, and the importance of her company’s application of AI to healthcare innovations.

She said that focus had already led to the development of a deep learning algorithm to help pathologists assess tissue associated with metastatic breast cancer.

“By pinpointing the location of the cancer more accurately, quickly and at a lower cost, care providers might be able to deliver better treatment for more patients,” she wrote.

Google also has created algorithms that help medical professionals diagnose lung cancer, and eye disease in people with diabetes, per the Times.

Porat acknowledged that Google’s research showed the best results occur when medical professionals and technology work together.

Any insights provided by AI must be “paired with human intelligence and placed in the hands of skilled researchers, surgeons, oncologists, radiologists and others,” she said.

Anne Stych is a staff writer for Bizwomen.
Industries:

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

 

Effective humoral immune responses to infection and immunization are defined by high-affinity antibodies generated as a result of B cell differentiation and selection that occurs within germinal centers (GC). Within the GC, B cells undergo affinity maturation, an iterative and competitive process wherein B cells mutate their immunoglobulin genes (somatic hypermutation) and undergo clonal selection by competing for T cell help. Balancing the decision to remain within the GC and continue participating in affinity maturation or to exit the GC as a plasma cell (PC) or memory B cell (MBC) is critical for achieving optimal antibody avidity, antibody quantity, and establishing immunological memory in response to immunization or infection. Humoral immune responses during chronic infections are often dysregulated and characterized by hypergammaglobulinemia, decreased affinity maturation, and delayed development of neutralizing antibodies. Previous studies have suggested that poor antibody quality is in part due to deletion of B cells prior to establishment of the GC response.

 

In fact the impact of chronic infections on B cell fate decisions in the GC remains poorly understood. To address this question, researchers used single-cell transcriptional profiling of virus-specific GC B cells to test the hypothesis that chronic viral infection disrupted GC B cell fate decisions leading to suboptimal humoral immunity. These studies revealed a critical GC differentiation checkpoint that is disrupted by chronic infection, specifically at the point of dark zone re-entry. During chronic viral infection, virus-specific GC B cells were shunted towards terminal plasma cell (PC) or memory B cell (MBC) fates at the expense of continued participation in the GC. Early GC exit was associated with decreased B cell mutational burden and antibody quality. Persisting antigen and inflammation independently drove facets of dysregulation, with a key role for inflammation in directing premature terminal GC B cell differentiation and GC exit. Thus, the present research defines GC defects during chronic viral infection and identify a critical GC checkpoint that is short-circuited, preventing optimal maturation of humoral immunity.

 

Together, these studies identify a key GC B cell differentiation checkpoint that is dysregulated during chronic infection. Further, it was found that the chronic inflammatory environment, rather than persistent antigen, is sufficient to drive altered GC B cell differentiation during chronic infection even against unrelated antigens. However, the data also indicate that inflammatory circuits are likely linked to perception of antigen stimulation. Nevertheless, this study reveals a B cell-intrinsic program of transcriptional skewing in chronic viral infection that results in shunting out of the cyclic GC B cell process and early GC exit with consequences for antibody quality and hypergammaglobulinemia. These findings have implications for vaccination in individuals with pre-existing chronic infections where antibody responses are often ineffective and suggest that modulation of inflammatory pathways may be therapeutically useful to overcome impaired humoral immunity and foster affinity maturation during chronic viral infections.

 

References:

 

https://www.biorxiv.org/content/10.1101/849844v1

 

https://www.ncbi.nlm.nih.gov/pubmed/25656706

 

https://www.ncbi.nlm.nih.gov/pubmed/27653600

 

https://www.ncbi.nlm.nih.gov/pubmed/26912368

 

https://www.ncbi.nlm.nih.gov/pubmed/26799208

 

https://www.ncbi.nlm.nih.gov/pubmed/23001146

 

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

 

One of the most contagious diseases known to humankind, measles killed an average of 2.6 million people each year before a vaccine was developed, according to the World Health Organization. Widespread vaccination has slashed the death toll. However, lack of access to vaccination and refusal to get vaccinated means measles still infects more than 7 million people and kills more than 100,000 each year worldwide as reported by WHO. The cases are on the rise, tripling in early 2019 and some experience well-known long-term consequences, including brain damage and vision and hearing loss. Previous epidemiological research into immune amnesia suggests that death rates attributed to measles could be even higher, accounting for as much as 50 percent of all childhood mortality.

 

Over the last decade, evidence has mounted that the measles vaccine protects in two ways. It prevents the well-known acute illness with spots and fever and also appears to protect from other infections over the long term by giving general boost to the immune system. The measles virus can impair the body’s immune memory, causing so-called immune amnesia. By protecting against measles infection, the vaccine prevents the body from losing or “forgetting” its immune memory and preserves its resistance to other infections. Researchers showed that the measles virus wipes out 11% to 73% of the different antibodies that protect against viral and bacterial strains a person was previously immune to like from influenza to herpes virus to bacteria that cause pneumonia and skin infections.

 

This study at Harvard Medical School and their collaborators is the first to measure the immune damage caused by the virus and underscores the value of preventing measles infection through vaccination. The discovery that measles depletes people’s antibody repertoires, partially obliterating immune memory to most previously encountered pathogens, supports the immune amnesia hypothesis. It was found that those who survive measles gradually regain their previous immunity to other viruses and bacteria as they get re-exposed to them. But because this process may take months to years, people remain vulnerable in the meantime to serious complications of those infections and thus booster shots of routine vaccines may be required.

 

VirScan detects antiviral and antibacterial antibodies in the blood that result from current or past encounters with viruses and bacteria, giving an overall snapshot of the immune system. Researchers gathered blood samples from unvaccinated children during a 2013 measles outbreak in the Netherlands and used VirScan to measure antibodies before and two months after infection in 77 children who’d contracted the disease. The researchers also compared the measurements to those of 115 uninfected children and adults. Researchers found a striking drop in antibodies from other pathogens in the measles-infected children that clearly suggested a direct effect on the immune system resembling measles-induced immune amnesia.

 

Further tests revealed that severe measles infection reduced people’s overall immunity more than mild infection. This could be particularly problematic for certain categories of children and adults, the researchers said. The present study observed the effects in previously healthy children only. But, measles is known to hit malnourished children much harder, the degree of immune amnesia and its effects could be even more severe in less healthy populations. Inoculation with the MMR (measles, mumps, rubella) vaccine did not impair children’s overall immunity. The results align with decades of research. Ensuring widespread vaccination against measles would not only help prevent the expected 120,000 deaths that will be directly attributed to measles this year alone, but could also avert potentially hundreds of thousands of additional deaths attributable to the lasting damage to the immune system.

 

References:

 

https://hms.harvard.edu/news/inside-immune-amnesia?utm_source=Silverpop

 

https://science.sciencemag.org/content/366/6465/599

 

www.who.int/immunization/newsroom/measles-data-2019/en/

 

https://www.ncbi.nlm.nih.gov/pubmed/20636817

 

https://www.ncbi.nlm.nih.gov/pubmed/27157064

 

https://www.ncbi.nlm.nih.gov/pubmed/30797735

 

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DISCOVER BRIGHAM | NOVEMBER 7, 2019, 10AM – 6PM

#DISCOVERBRIGHAM

@pharma_BI

@AVIVA1950

 Aviva Lev-Ari, PhD, RN will be attending and will cover presentations in real time

ABOUT BRIGHAM RESEARCH

Discover Brigham is hosted by the Brigham Research Institute (BRI), under the umbrella of Brigham Health. Launched in 2005, the BRI’s mission is to accelerate discoveries that improve human health by bridging the gaps between science, communication and funding. The BRI’s resources help to foster groundbreaking interdepartmental and interdisciplinary research. They provide a voice for the research community and raise the profile of Brigham Research.

Speakers

http://www.discoverbrigham.org/speakers/

 

AGENDA

http://www.discoverbrigham.org/agenda/

ASK A QUESTION WITH SLI.DO!

DO YOU WANT TO SUBMIT A QUESTION TO A SPEAKER OF A SESSION? YOU CAN DO IT THROUGH SLI.DO!

2. ENTER THE EVENT CODE: DB19. THEN HIT JOIN!
3. PICK THE SESSION YOU WANT TO ASK A QUESTION. THEN ASK YOUR QUESTION!
4. YOUR QUESTION WILL BE REVIEWED AND MAY BE FORWARDED TO THE CHAIR TO ASK THE SPEAKER(S).

IT WORKS ON ANY DEVICE, YOU DO NOT NEED TO INSTALL ANYTHING!

 

Registration will open at 9:00 AM and will be located throughout the hospital including

  • Schlager Atrium (formerly known as Cabot Atrium, 45 Francis Street Lobby),
  • Schuster Lobby (75 Francis Street Entrance),
  • Shapiro Cardiovascular Center (70 Francis Street Entrance), and the
  • Hale Building for Transformative Medicine (HBTM) 1st Floor (60 Fenwood Road).

 

Click here for directions to these locations.  

NAVIGATING THE BRIGHAM IS EASIER THAN EVER

Need directions to a clinic, conference room, public space, or help assisting someone who looks lost?

Try our browser-based wayfinding tool and mobile app, BWH Maps,
which provides real-time location tracking and directions in the hospital.

Look for BWH Maps on the Apple App Store and Google Play Store,
or visit maps.brighamandwomens.org.

REGISTRATION LOCATIONS

Please visit one of the registration desks listed below to check-in, receive your badge, and collect any necessary materials. Registration will begin starting at 9:00 AM at each of the locations below.

 

Click on each location below for directions. 

  • SCHLAGER ATRIUM, FORMERLY KNOWN AS CABOT ATRIUM (45 FRANCIS ST. LOBBY)
  • SCHUSTER LOBBY (75 FRANCIS ST. LOBBY)
  • CARL J. AND RUTH SHAPIRO
    CARDIOVASCULAR CENTER
  • HALE BUILDING FOR
    TRANSFORMATIVE MEDICINE

SESSION LOCATIONS

Below you will find directions to each of the session locations.

MARSHALL A. WOLF CONFERENCE ROOM

HALE BUILDING FOR TRANSFORMATIVE MEDICINE

SESSION ROOM

FROM 60 FENWOOD ROAD:
Enter at 60 Fenwood Rd lobby entrance.

STAIRS:
Take the lobby staircase to the 2nd floor. Walk past the balcony overlooking the atrium and take the stairs on the left (Stair 2) to the 3rd floor. Once on the 3rd floor, exit the stairwell and take a right. The room is to your right through the double glass door, straight ahead.

ELEVATOR:
Take S Elevator to 3rd floor. Take a right out of the elevator. The room is past the stairwell, on your right through the double glass doors.

HALE VTC 02006B CONFERENCE ROOM

HALE BUILDING FOR TRANSFORMATIVE MEDICINE

OVERFLOW ROOM FOR MARSHALL A. WOLF CONFERENCE ROOM

FROM 60 FENWOOD ROAD:
Enter at 60 Fenwood Rd lobby entrance.

STAIRS:
Take the lobby staircase to the 2nd floor. The conference room will be on your right near the display monitor.

ELEVATOR:
Enter at 60 Fenwood Rd main entrance and take the S Elevator to the 2nd floor. Once you exit the elevator, take a right and walk past the balcony overlooking the atrium and the conference room will be straight ahead near the display monitor.

ZINNER BREAKOUT ROOM

CARL J. AND RUTH SHAPIRO CARDIOVASCULAR CENTER

SESSION ROOM

FROM 70 FRANCIS STREET:
The Zinner Breakout Room is located in the Carl J. and Ruth Shapiro Cardiovascular Center at 70 Francis Street, Boston, MA. Upon entering the building at the street level, walk straight towards the escalators in the rear of the building. The Zinner Conference Center is located on your right; the Breakout room is through the large doors on the left.

ZINNER BOARDROOM

CARL J. AND RUTH SHAPIRO CARDIOVASCULAR CENTER

OVERFLOW ROOM FOR ZINNER BREAKOUT ROOM

FROM 70 FRANCIS STREET:
The Zinner Boardroom is located in the Carl J. and Ruth Shapiro Cardiovascular Center at 70 Francis Street, Boston, MA. Upon entering the building at the street level, walk straight towards the escalator, keeping to the left side of the building. The Conference Center is located on your right; the Boardroom is through the large doors on the back wall.

BORNSTEIN FAMILY AMPHITHEATER

MAIN PIKE, 45 FRANCIS STREET LOBBY

SESSION ROOM

FROM 45 FRANCIS STREET:
Coming from 45 Francis Street lobby, walk towards the Main Pike (2nd floor hallway). Then take left on the Main Pike, 2nd door on right.

AGENDA

10:00 AM – 11:00 AM

Opening remarks

Elizabeth G. Nabel, MD, President Brigham Health, Prof. Medicine @HarvardMed

  • 8th event since 2012
  • show casing amazing research
  • Open to the Public: Patients, Families to educate
  • 90 Posters
  • Health equity perspective as DNA of the Brigham
  • Learn a new idea, meet someone new, create a new idea

Keynote Introduction

David Bates, MD @DBatesSafety

KEYNOTE

KYU RHEE, MD, MPP, VICE PRESIDENT & CHIEF HEALTH OFFICER, IBM CORPORATION & IBM WATSON HEALTH

MAIN PIKE, 45 FRANCIS STREET LOBBY
  • Partnership BWH & IBM WATSON
  • Big data of claims from providers to payers
  • Waiting rookms in Healthcare delivery
  • Government: ACA
  • AI Spring is here, no more Winter for AI
  • Health disparities, salaries, sexual orientation – improving health of populations
  • Science & Security
  • Red Hat – data security – big data statoscope
  • Healthcare Culture & Technology Culture: IBM & Amazon hire healthcare professionals
  • Cost: Burnout, managing population health,
  • Reduce physicians burnout
  • Culture Tech – Competition by IBM’s Project Debater

11:15 AM – 12:50 PM

1:00 – 1:50 PM

FROM 70 FRANCIS STREET:
The Zinner Breakout Room is located in the Carl J. and Ruth Shapiro Cardiovascular Center at 70 Francis Street, Boston, MA. Upon entering the building at the street level, walk straight towards the escalators in the rear of the building. The Zinner Conference Center is located on your right; the Breakout room is through the large doors on the left.

Aaron Goldman
HaeLin Jang
Greog K. Gerber
  • Microbiome – Bacteria and Fungus therapies – computational tools for applications on microbiome
  • Diagnostics
  • Microbiome in early childhood
  • temporal variability during adulthood
  • host disease bacteriptherapeutics: C-Diff
  • Bugs as drugs
  • Gnotobiotic mice model for c-Diff in mice
  • MDSINE – Microbial dynamin model interaction model
  • cancer microbiome: Bacteria causing cancer, cancer changing the bacteria environment

 

Jeff Karp BENG PhD @MrJeffKarp

  • tissue based patch to seal open foramane ovale. Project remained in Academic settings however
  • GLUE component was commercialized
  • bioinspiration from living organs in Nature, slugs
  1. Viscose secretions
  2. Hydrophobic secretions and snails and sand castle worms

1:00 – 1:50 PM

Lina Matta, PharmD
Joji Suzuki, MD
Lisa WIchmann
Kevin Elias, MD
Daiva Braunfelds,MBA HPH
Elizabeth Cullen, MS

2:00 – 2:50 PM

3:00 – 3:50 PM

David Levin
Christopher baugh
Kathryn Britton
Joanne Feinberg Goldstein
Amrita Shahani
If patient meets criteria for Home Hospital : all services are sent home.
2016 – Pilot randomized controlled trial
2017-2018 – Repeat of Pilot on larger population
2018 – High-volume single arm innovation services
2019 – studies within home hospital wtth sensors at home
2020 – continue
Operation and Research lead to innovations

Anna Krichevsky, PhD HMS Initiative for RNA Medicine

  • paradox of organismal complexity and # protein encoding genes
  • Human genome, 70% Transcriptome Non-coding RNA only 2% encode proteins
  • Non-coding RNA small, long, multifunctional
  • biogenesis of offending RNAs can be drugged
  • RNA novel therapies: RNA as a Drug,
  • Indications: Brain Tumors and AD: MicroRNA (miRNA)the smallest Glioblastoma – only 4 drugs FDA approved in 25 years miRNA – 10b inhibition kills gliomacells miR-132 most neuroprotective RNA
  • Cardiovascular

Paul Anderson, MD, PhD

  • ALS and FTD – Fronto Temporal Dimensia
  • Riluzone 1970 – anti Anti-glutamateric
  • Edarabone 2017 drugs approved – anti-oxidative
  • Andogenesis role in Motor protection from Stress Cytoplasmatic tRNA – ANdiogenin (ANG) production
  • 20 amino acids
  • 5″-tiRNAs assemble G-quadruples – G4
  • point mutationin ANG (mANG) reduce its RNanase
  • G4-containing DNA analogs of 5″-tiRNA (Ala)

Marc Feinberg, MD

  • Cardiovascular: CAD, Insulin resistence – Vascular inflammation
  • Impaired angiogenesis: post MI repair CHF
  • MiRNA therapeutics for Atherosclerosis – miR-181b: Aortic ECs Athero (mice) CAD (Human)
  • miRNA _ Liposomes injected in the vessel wall – reduction of inflammation in vessel – microRNA Group
  • monocyte – How can we increase or amintain mir-181b expression in endothelial cells?
  • LncRNA Therapeutics for vascular Senescence and Atherosclerosis – no effect on leucocyte accumulation no difference in inflammation
  • DNA-dependent protein kinase (DNA-PK)
  • Does Loss SNHG12 triggers vascular senescence in the vessel wall

 

Clemens Scherzer, MD

  • The Protein RNA Brain
  • Dopamin p
  • BRAINCODE: 64% RNA: mRNA, ncRNA,
  • cell-type-spacific putative enhancer RNAs (eRNAs)
  • eRNAs indicate active genetic switches
  • central dogma in Biology: DNA, non-coding RNA, Protein
  • Top 10 Markers
  • Neuropsychiatric Disease: Parkinson: How do genetic variants function in specific brain cells: neurons, microglia, astrocytes
  • genetic variants of neuropsychiatric diseases over-localize to active eRNA sites in dopamine neurons
  • enhancers RNA – ADHD,
  • enhacers RNA – schizoprania, bipolar, addiction – antopsychotic Vlporic acid
  • BRAINCODE Project: BWH MGH HMS

5:00 – 6:00 PM

AWARDS & RECEPTION

SPECIAL PHOTO-OP TO CELEBRATE YOU!
WE WILL TAKE A GROUP PHOTO DURING THE RECEPTION AND AWARDS CEREMONY TO CELEBRATE YOU, OUR INNOVATORS!
THE PHOTO WILL BE DISPLAYED AT THE BRIGHAM IN THE HALE BUILDING. WE HOPE YOU CAN JOIN US IN CELEBRATING YOUR ACHIEVEMENTS.

SOURCE

http://www.discoverbrigham.org/agenda/

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Deep Learning extracts Histopathological Patterns and accurately discriminates 28 Cancer and 14 Normal Tissue Types: Pan-cancer Computational Histopathology Analysis

Reporter: Aviva Lev-Ari, PhD, RN

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.

SOURCE

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.

SOURCE

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

 

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

 

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

https://pharmaceuticalintelligence.com/2016/07/28/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/

 

631 articles had in their Title the keyword “Pathology”

https://pharmaceuticalintelligence.com/?s=Pathology

 

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