Collected data could give healthcare providers and clinical laboratories a practical view of individuals’ oral microbiota and lead to new diagnostic assays
When people hear about microbiome research, they usually think of the study of gut bacteria which Dark Daily has covered extensively. However, this type of research is now expanding to include more microbiomes within the human body, including the oral microbiome—the microbiota living in the human mouth.
One example is coming from Genefitletics, a biotech company based in New Delhi, India. It recently launched ORAHYG, the first and only (they claim) at-home oral microbiome functional activity test available in Asia. The company is targeting the direct-to-consumer (DTC) testing market.
According to the Genefitletics website, the ORAHYG test can decode the root causes of:
“Using oral microbial gene expression sequencing technology and its [machine learning] model, [Genefitletics] recently debuted its oral microbiome gene expression solution, which bridges the gap between dentistry and systemic inflammation,” ETHealthworld reported.
“The molecular insights from this test would give an unprecedented view of functions of the oral microbiome, their interaction with gut microbiome and impact on metabolic, cardiovascular, cognitive, skin, and autoimmune health,” BioSpectrum noted.
“Microbes, the planet Earth’s original inhabitants, have coevolved with humanity, carry out vital biological tasks inside the body, and fundamentally alter how we think about nutrition, medicine, cleanliness, and the environment,” Sushant Kumar (above), founder and CEO of Genefitletics, told the Economic Times. “This has sparked additional research over the past few years into the impact of the trillions of microorganisms that inhabit the human body on our health and diverted tons of funding into the microbiome field.” Clinical laboratories may eventually see an interest and demand for testing of the oral microbiome. (Photo copyright: ETHealthworld.)
Imbalanced Oral Microbiome Can Trigger Disease
The term microbiome refers to the tiny microorganisms that reside on and inside our bodies. A high colonization of these microorganisms—including bacteria, fungi, yeast, viruses, and protozoa—live in our mouths.
“Mouth is the second largest and second most diverse colonized site for microbiome with 770 species comprising 100 billion microbes residing there,” said Sushant Kumar, founder and CEO of Genefitletics, BioSpectrum reported. “Each place inside the mouth right from tongue, throat, saliva, and upper surface of mouth have a distinctive and unique microbiome ecosystem. An imbalanced oral microbiome is said to trigger onset and progression of type 2 diabetes, arthritis, heart diseases, and even dementia.”
The direct-to-consumer ORAHYG test uses a saliva sample taken either by a healthcare professional or an individual at home. That sample is then sequenced through Genefitletics’ gene sequencing platform and the resulting biological data set added to an informatics algorithm.
Genefitletics’ machine-learning platform next converts that information into a pre-symptomatic molecular signature that can predict whether an individual will develop a certain disease. Genefitletics then provides that person with therapeutic and nutritional solutions that can suppress the molecules that are causing the disease.
“The current industrial healthcare system is really a symptom care [system] and adopts a pharmaceutical approach to just make the symptoms more bearable,” Kumar told the Economic Times. “The system cannot decode the root cause to determine what makes people develop diseases.”
Helping People Better Understand their Health
Founded in 2019, Genefitletics was created to pioneer breakthrough discoveries in microbial science to promote better health and increase longevity in humans. The company hopes to unravel the potential of the oral microbiome to help people fend off illness and gain insight into their health.
“Microorganisms … perform critical biological functions inside the body and transform our approach towards nutrition, medicine, hygiene and environment,” Kumar told CNBC. “It is important to understand that an individual does not develop a chronic disease overnight.
“It starts with chronic inflammation which triggers pro-inflammatory molecular indications. Unfortunately, these molecular signatures are completely invisible and cannot be measured using traditional clinical grade tests or diagnostic investigations,” he added. “These molecular signatures occur due to alteration in gene expression of gut, oral, or vaginal microbiome and/or human genome. We have developed algorithms that help us in understanding these alterations way before the clinical symptoms kick in.”
Genefitletics plans to utilize individuals’ collected oral microbiome data to determine their specific nutritional shortcomings, and to develop personalized supplements to help people avoid disease.
The company also produces DTC kits that analyze gut and vaginal microbiomes as well as a test that is used to evaluate an infant’s microbiome.
“The startup wants to develop comparable models to forecast conditions like autism, PCOS [polycystic ovarian syndrome], IBD [Inflammatory bowel disease], Parkinson’s, chronic renal [kidney] disease, anxiety, depression, and obesity,” the Economic Times reported.
Time will tell whether the oral microbiome tests offered by this company prove to be clinically useful. Certainly Genefitletics hopes its ORAHYG test can eventually provide healthcare providers—including clinical laboratory professionals—with a useful view of the oral microbiome. The collected data might also help individuals become aware of pre-symptomatic conditions that make it possible for them to seek confirmation of the disease and early treatment by medical professionals.
The speakers also noted that labs must learn to work collaboratively with payers—perhaps through health information technology (HIT)—to establish best practices that improve reimbursements on claims for novel genetic tests.
Harnessing the ever-increasing volume of diagnostic data that genetic testing produces should be a high priority for labs, said William Morice II, MD, PhD, CEO and President of Mayo Clinic Laboratories.
“There will be an increased focus on getting information within the laboratory … for areas such as genomics and proteomics,” Morice told the keynote audience at the Executive War College on Wednesday.
“Wearable technology data is analyzed using machine learning. Clinical laboratories must participate in analyzing that spectrum of diagnostics,” said William Morice II, MD, PhD (above), CEO and President of Mayo Clinic Laboratories. Morice spoke during this week’s Executive War College.
Precision Medicine Efforts Include Genetic Testing and Wearable Devices
For laboratories new to genetic testing that want to move it in-house, Morice outlined effective first steps to take, including the following:
Determine and then analyze the volume of genetic testing that a lab is sending out.
Research and evaluate genetic sequencing platforms that are on the market, with an eye towards affordable cloud-based options.
Build a business case to conduct genetic tests in-house that focuses on the long-term value to patients and how that could also improve revenue.
A related area for clinical laboratories and pathology practices to explore is their role in how clinicians treat patients using wearable technology.
For example, according to Morice, Mayo Clinic has monitored 20,000 cardiac patients with wearable devices. The data from the wearable devices—which includes diagnostic information—is analyzed using machine learning, a subset of artificial intelligence.
In one study published in Scientific Reports, scientists from Mayo’s Departments of Neurology and Biomedical Engineering found “clear evidence that direct seizure forecasts are possible using wearable devices in the ambulatory setting for many patients with epilepsy.”
Clinical laboratories fit into this picture, Morice explained. For example, depending on what data it provides, a wearable device on a patient with worsening neurological symptoms could trigger a lab test for Alzheimer’s disease or other neurological disorders.
“This will change how labs think about access to care,” he noted.
For Payers, Navigating Genetic Testing Claims is Difficult
While there is promise in genetic testing and precision medicine, from an administrative viewpoint, these activities can be challenging for payers when it comes to verifying reimbursement claims.
“One of the biggest challenges we face is determining what test is being ordered. From the perspective of the reimbursement process, it’s not always clear,” said Cristi Radford, MS, CGC, Product Director at healthcare services provider Optum, a subsidiary of UnitedHealth Group, located in Eden Prairie, Minnesota. Radford also presented a keynote at this year’s Executive War College.
Approximately 400 Current Procedural Terminology (CPT) codes are in place to represent the estimated 175,000 genetic tests on the market, Radford noted. That creates a dilemma for labs and payers in assigning codes to novel genetic tests.
During her keynote address, Radford showed the audience of laboratory executives a slide that charted how four labs submitted claims for the same high-risk breast cancer panel. CPT code choices varied greatly.
“Does the payer have any idea which test was ordered? No,” she said. “It was a genetic panel, but the information doesn’t give us the specificity payers need.”
In such situations, payers resort to prior authorization to halt these types of claims on the front end so that more diagnostic information can be provided.
“Plans don’t like prior authorization, but it’s a necessary evil,” said Jason Bush, PhD, Executive Vice President of Product at Avalon Healthcare Solutions in Tampa, Florida. Bush co-presented with Radford.
[Editor’s note: Dark Daily offers a free webinar, “Learning from Payer Behavior to Increase Appeal Success,” that teaches labs how to better understand payer behavior. The webinar features recent trends in denials and appeals by payers that will help diagnostic organizations maximize their appeal success. Click here to stream this important webinar.]
Payers Struggle with ‘Explosion’ of Genetic Tests
In “UnitedHealth’s Optum to Offer Lab Test Management,” Dark Daily’s sister publication The Dark Report, covered Optum’s announcement that it had launched “a comprehensive laboratory benefit management solution designed to help health plans reduce unnecessary lab testing and ensure their members receive appropriate, high-quality tests.”
Optum sells this laboratory benefit management program to other health plans and self-insured employers. Genetic test management capabilities are part of that offering.
As part of its lab management benefit program, Optum is collaborating with Avalon on a new platform for genetic testing that will launch soon and focus on identifying test quality, streamlining prior authorization, and providing test payment accuracy in advance.
“Payers are struggling with the explosion in genetic testing,” Bush told Executive War College attendees. “They are truly not trying to hinder innovation.”
For clinical laboratory leaders reading this ebriefing, the takeaway is twofold: Genetic testing and resulting precision medicine efforts provide hope in more effectively treating patients. At the same time, the genetic test juggernaut has grown so large so quickly payers are finding it difficult to manage. Thus, it has become a source of continuous challenge for labs seeking reimbursements.
Heath information technology may help ease the situation. But, ultimately, stronger communication between labs and payers—including acknowledgement of what each side needs from a business perspective—is paramount.
Google designed the suite to ease radiologists’ workload and enable easy and secure sharing of critical medical imaging; technology may eventually be adapted to pathologists’ workflow
Clinical laboratory and pathology group leaders know that Google is doing extensive research and development in the field of cancer diagnostics. For several years, the Silicon Valley giant has been focused on digital imaging and the use of artificial intelligence (AI) algorithms and machine learning to detect cancer.
Now, Google Cloud has announced it is launching a new medical imaging suite for radiologists that is aimed at making healthcare data for the diagnosis and care of cancer patients more accessible. The new suite “promises to make medical imaging data more interoperable and useful by leveraging artificial intelligence,” according to MedCity News.
In a press release, medical technology company Hologic, and healthcare provider Hackensack Meridian Health in New Jersey, announced they were the first customers to use Google Cloud’s new suite of medical imaging products.
“Hackensack Meridian Health has begun using it to detect metastasis in prostate cancer patients earlier, and Hologic is using it to strengthen its diagnostic platform that screens women for cervical cancer,” MedCity News reported.
“Google pioneered the use of AI and computer vision in Google Photos, Google Image Search, and Google Lens, and now we’re making our imaging expertise, tools, and technologies available for healthcare and life sciences enterprises,” said Alissa Hsu Lynch (above), Global Lead of Google Cloud’s MedTech Strategy and Solutions, in a press release. “Our Medical Imaging Suite shows what’s possible when tech and healthcare companies come together.” Clinical laboratory companies may find Google’s Medical Imaging Suite worth investigating. (Photo copyright: Influencive.)
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Easing the Burden on Radiologists
Clinical laboratory leaders and pathologists know that laboratory data drives most healthcare decision-making. And medical images make up 90% of all healthcare data, noted an article in Proceedings of the IEEE (Institute of Electrical and Electronics Engineers).
More importantly, medical images are growing in size and complexity. So, radiologists and medical researchers need a way to quickly interpret them and keep up with the increased workload, Google Cloud noted.
“The size and complexity of these images is huge, and, often, images stay sitting in data siloes across an organization,” said Alissa Hsu Lynch, Global Lead, MedTech Strategy and Solutions at Google, told MedCity News. “In order to make imaging data useful for AI, we have to address interoperability and standardization. This suite is designed to help healthcare organizations accelerate the development of AI so that they can enable faster, more accurate diagnosis and ease the burden for radiologists,” she added.
According to the press release, Google Cloud’s Medical Imaging Suite features include:
Imaging Storage: Easy and secure data exchange using the international DICOM (digital imaging and communications in medicine) standard for imaging. A fully managed, highly scalable, enterprise-grade development environment that includes automated DICOM de-identification. Seamless cloud data management via a cloud-native enterprise imaging PACS (picture archiving and communication system) in clinical use by radiologists.
Imaging Lab: AI-assisted annotation tools that help automate the highly manual and repetitive task of labeling medical images, and Google Cloud native integration with any DICOMweb viewer.
Imaging Datasets and Dashboards: Ability to view and search petabytes of imaging data to perform advanced analytics and create training datasets with zero operational overhead.
Imaging AI Pipelines: Accelerated development of AI pipelines to build scalable machine learning models, with 80% fewer lines of code required for custom modeling.
Imaging Deployment: Flexible options for cloud, on-prem (on-premises software), or edge deployment to allow organizations to meet diverse sovereignty, data security, and privacy requirements—while providing centralized management and policy enforcement with Google Distributed Cloud.
First Customers Deploy Suite
Hackensack Meridian Health hopes Google’s imaging suite will, eventually, enable the healthcare provider to predict factors affecting variance in prostate cancer outcomes.
“We are working toward building AI capabilities that will support image-based clinical diagnosis across a range of imaging and be an integral part of our clinical workflow,” said Sameer Sethi, Senior Vice President and Chief Data and Analytics Officer at Hackensack, in a news release.
The New Jersey healthcare network said in a statement that its work with Google Cloud includes use of AI and machine learning to enable notification of newborn congenital disorders and to predict sepsis risk in real-time.
Hologic, a medical technology company focused on women’s health, said its collaboration integrates Google Cloud AI with the company’s Genius Digital Diagnostics System.
“By complementing our expertise in diagnostics and AI with Google Cloud’s expertise in AI, we’re evolving our market-leading technologies to improve laboratory performance, healthcare provider decision making, and patient care,” said Michael Quick, Vice President of Research and Development and Innovation at Hologic, in the press release.
Hologic says its Genius Digital Diagnostics System combines AI with volumetric medical imaging to find pre-cancerous lesions and cancer cells. From a Pap test digital image, the system narrows “tens of thousands of cells down to an AI-generated gallery of the most diagnostically relevant,” according to the company website.
Hologic plans to work with Google Cloud on storage and “to improve diagnostic accuracy for those cancer images,” Hsu Lynch told MedCity News.
Medical image storage and sharing technologies like Google Cloud’s Medical Imaging Suite provide an opportunity for radiologists, researchers, and others to share critical image studies with anatomic pathologists and physicians providing care to cancer patients.
One key observation is that the primary function of this service that Google has begun to deploy is to aid in radiology workflow and productivity, and to improve the accuracy of cancer diagnoses by radiologists. Meanwhile, Google continues to employ pathologists within its medical imaging research and development teams.
Assuming that the first radiologists find the Google suite of tools effective in support of patient care, it may not be too long before Google moves to introduce an imaging suite of tools designed to aid the workflow of surgical pathologists as well.
Proof-of-concept study ‘highlights that using AI to integrate different types of clinically informed data to predict disease outcomes is feasible’ researchers say
Artificial intelligence (AI) and machine learning are—in stepwise fashion—making progress in demonstrating value in the world of pathology diagnostics. But human anatomic pathologists are generally required for a prognosis. Now, in a proof-of-concept study, researchers at Brigham and Women’s Hospital in Boston have developed a method that uses AI models to integrate multiple types of data from disparate sources to accurately predict patient outcomes for 14 different types of cancer.
The process also uncovered “the predictive bases of features used to predict patient risk—a property that could be used to uncover new biomarkers,” according to Genetic Engineering and Biotechnology News (GEN).
Should these research findings become clinically viable, anatomic pathologists may gain powerful new AI tools specifically designed to help them predict what type of outcome a cancer patient can expect.
“Experts analyze many pieces of evidence to predict how well a patient may do. These early examinations become the basis of making decisions about enrolling in a clinical trial or specific treatment regimens,” said Faisal Mahmood, PhD (above) in a Brigham press release. “But that means that this multimodal prediction happens at the level of the expert. We’re trying to address the problem computationally,” he added. Should they be proven clinically-viable through additional studies, these findings could lead to useful tools that help anatomic pathologists and clinical laboratory scientists more accurately predict what type of outcomes cancer patient may experience. (Photo copyright: Harvard.)
AI-based Prognostics in Pathology and Clinical Laboratory Medicine
The team at Brigham constructed their AI model using The Cancer Genome Atlas (TCGA), a publicly available resource which contains data on many types of cancer. They then created a deep learning-based algorithm that examines information from different data sources.
Pathologists traditionally depend on several distinct sources of data, such as pathology images, genomic sequencing, and patient history to diagnose various cancers and help develop prognoses.
For their research, Mahmood and his colleagues trained and validated their AI algorithm on 6,592 H/E (hematoxylin and eosin) whole slide images (WSIs) from 5,720 cancer patients. Molecular profile features, which included mutation status, copy-number variation, and RNA sequencing expression, were also inputted into the model to measure and explain relative risk of cancer death.
The scientists “evaluated the model’s efficacy by feeding it data sets from 14 cancer types as well as patient histology and genomic data. Results demonstrated that the models yielded more accurate patient outcome predictions than those incorporating only single sources of information,” states a Brigham press release.
“This work sets the stage for larger healthcare AI studies that combine data from multiple sources,” said Faisal Mahmood, PhD, Associate Professor, Division of Computational Pathology, Brigham and Women’s Hospital; and Associate Member, Cancer Program, Broad Institute of MIT and Harvard, in the press release. “In a broader sense, our findings emphasize a need for building computational pathology prognostic models with much larger datasets and downstream clinical trials to establish utility.”
Future Prognostics Based on Multiple Data Sources
The Brigham researchers also generated a research tool they dubbed the Pathology-omics Research Platform for Integrative Survival Estimation (PORPOISE). This tool serves as an interactive platform that can yield prognostic markers detected by the algorithm for thousands of patients across various cancer types.
The researchers believe their algorithm reveals another role for AI technology in medical care, but that more research is needed before their model can be implemented clinically. Larger data sets will have to be examined and the researchers plan to use more types of patient information, such as radiology scans, family histories, and electronic medical records in future tests of their AI technology.
“Future work will focus on developing more focused prognostic models by curating larger multimodal datasets for individual disease models, adapting models to large independent multimodal test cohorts, and using multimodal deep learning for predicting response and resistance to treatment,” the Cancer Cell paper states.
“As research advances in sequencing technologies, such as single-cell RNA-seq, mass cytometry, and spatial transcriptomics, these technologies continue to mature and gain clinical penetrance, in combination with whole-slide imaging, and our approach to understanding molecular biology will become increasingly spatially resolved and multimodal,” the researchers concluded.
Anatomic pathologists may find the Brigham and Women’s Hospital research team’s findings intriguing. An AI tool that integrates data from disparate sources, analyzes that information, and provides useful insights, could one day help them provide more accurate cancer prognoses and improve the care of their patients.
Two studies show the accuracy of perception-based systems in detecting disease biomarkers without needing molecular recognition elements, such as antibodies
Researchers from multiple academic and research institutions have collaborated to develop a non-conventional machine learning-based technology for identifying and measuring biomarkers to detect ovarian cancer without the need for molecular identification elements, such as antibodies.
Traditional clinical laboratory methods for detecting biomarkers of specific diseases require a “molecular recognition molecule,” such as an antibody, to match with each disease’s biomarker. However, according to a Lehigh University news release, for ovarian cancer “there’s not a single biomarker—or analyte—that indicates the presence of cancer.
“When multiple analytes need to be measured in a given sample, which can increase the accuracy of a test, more antibodies are required, which increases the cost of the test and the turnaround time,” the news release noted.
Unveiled in two sequential studies, the new method for detecting ovarian cancer uses machine learning to examine spectral signatures of carbon nanotubes to detect and recognize the disease biomarkers in a very non-conventional fashion.
“Carbon nanotubes have interesting electronic properties,” said Daniel Heller, PhD (above), in the Lehigh University news release. “If you shoot light at them, they emit a different color of light, and that light’s color and intensity can change based on what’s sticking to the nanotube. We were able to harness the complexity of so many potential binding interactions by using a range of nanotubes with various wrappings. And that gave us a range of different sensors that could all detect slightly different things, and it turned out they responded differently to different proteins.” This method differs greatly from traditional clinical laboratory methods for identifying disease biomarkers. (Photo copyright: Memorial Sloan-Kettering Cancer Center.)
Perception-based Nanosensor Array for Detecting Disease
In the Science Advances paper, the researchers described their development of “a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids.
“Perception-based machine learning (ML) platforms, modeled after the complex olfactory system, can isolate individual signals through an array of relatively nonspecific receptors. Each receptor captures certain features, and the overall ensemble response is analyzed by the neural network in our brain, resulting in perception,” the researchers wrote.
“This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements,” the researchers concluded.
In the Nature Biomedical Engineering paper, the researchers described a fined-tuned toolset that could accurately differentiate ovarian cancer biomarkers from biomarkers in individuals who are cancer-free.
“Here we show that a ‘disease fingerprint’—acquired via machine learning from the spectra of near-infrared fluorescence emissions of an array of carbon nanotubes functionalized with quantum defects—detects high-grade serous ovarian carcinoma in serum samples from symptomatic individuals with 87% sensitivity at 98% specificity (compared with 84% sensitivity at 98% specificity for the current best [clinical laboratory] screening test, which uses measurements of cancer antigen 125 and transvaginal ultrasonography,” the researchers wrote.
“We demonstrated that a perception-based nanosensor platform could detect ovarian cancer biomarkers using machine learning,” said Yoona Yang, PhD, a postdoctoral research associate in Lehigh’s Department of Chemical and Biomolecular Engineering and co-first author of the Science Advances article, in the news release.
How Perception-based Machine Learning Platforms Work
According to Yang, perception-based sensing functions like the human brain.
“The system consists of a sensing array that captures a certain feature of the analytes in a specific way, and then the ensemble response from the array is analyzed by the computational perceptive model. It can detect various analytes at once, which makes it much more efficient,” Yang said.
“SWCNTs have unique optical properties and sensitivity that make them valuable as sensor materials. SWCNTS emit near-infrared photoluminescence with distinct narrow emission bands that are exquisitely sensitive to the local environment,” the researchers wrote in Science Advances.
“Carbon nanotubes have interesting electronic properties,” said Daniel Heller, PhD, Head of the Cancer Nanotechnology Laboratory at Memorial Sloan Kettering Cancer Center and Associate Professor in the Department of Pharmacology at Weill Cornell Medicine of Cornell University, in the Lehigh University news release.
“If you shoot light at them, they emit a different color of light, and that light’s color and intensity can change based on what’s sticking to the nanotube. We were able to harness the complexity of so many potential binding interactions by using a range of nanotubes with various wrappings. And that gave us a range of different sensors that could all detect slightly different things, and it turned out they responded differently to different proteins,” he added.
The researchers put their technology to practical test in the second study. The wanted to learn if it could differentiate symptomatic patients with high-grade ovarian cancer from cancer-free individuals.
The research team used 269 serum samples. This time, nanotubes were bound with a specific molecule providing “an extra signal in terms of data and richer data from every nanotube-DNA combination,” said Anand Jagota PhD, Professor, Bioengineering and Chemical and Biomolecular Engineering, Lehigh University, in the news release.
This year, 19,880 women will be diagnosed with ovarian cancer and 12,810 will die from the disease, according to American Cancer Society data. While more research and clinical trials are needed, the above studies are compelling and suggest the possibility that one day clinical laboratories may detect ovarian cancer faster and more accurately than with current methods.
