Tyler O'Neal, Staff Editor ACADEMIA

UAB's proteomic analysis of 2,002 tumors identifies 11 pan-cancer molecular subtypes across 14 types of cancer

A new study that analyzed protein levels in 2,002 primary tumors from 14 tissue-based cancer types identified 11 distinct molecular subtypes, providing systematic knowledge that greatly expands a searchable online database that has become a go-to platform for cancer data analysis by users worldwide. To facilitate gene-level queries of data from more than 10,000 cancer patient transcriptome sequences and proteomics data from 2,000 patients, researchers have developed a user-friendly cancer data analysis web platform called UALCAN.

The University of Alabama at Birmingham Cancer Data analysis portal, or UALCAN, was developed and released to public use in 2017 as a user-friendly portal for pan-cancer omics data analysis, including transcriptomics, epigenetics, and proteomics. UALCAN has had nearly 920,000 site visits from researchers in more than 100 countries, and it has been cited more than 2,750 times.

“UALCAN is an effort to distribute comprehensive cancer data to researchers and clinicians in a user-friendly format to make discoveries and find needles in the haystack,” said Sooryanarayana Varambally, Ph.D., professor in the UAB Department of Pathology Division of Molecular and Cellular Pathology and director of UAB’s Translational Oncologic Pathology Research program. “Cancer detection, diagnosis, treatment, cure, and research need a global team effort, and making sense of the huge amount of data involved needs a way to analyze and interpret these data.”

Cancer is a complex disease, and its initiation, progression, and metastasis, the spread to distant organs, involves dynamic molecular changes in each type of cancer. Individual cancer patients show variations apart from some of the common genomic events.

In the new study, Varambally worked with longtime collaborator Chad Creighton, Ph.D., Baylor College of Medicine, Houston, Texas. Creighton led the proteomic study, “Proteogenomic characterization of 2002 human cancers reveals pan-cancer molecular subtypes and associated pathways.” This extends two early proteomics studies published in 2019 and 2021.

Previously the team performed RNA transcripts analysis, providing the data to researchers through UALCAN, to determine which pathways the myriad forms of cancer used to aid growth, spread and aggressiveness. In this recent study, the team performed and incorporated a large-scale proteomics analysis. The data and results provide new ideas for further research and possible therapeutic interventions.

A proteome is the complement of proteins expressed in a cell or tissue, and these can be measured quantitatively through recent technological advances in mass spectrometry. In cells, DNA makes mRNA, and mRNA makes protein, processes known as the central dogma of molecular biology. Proteins are major functional moieties of cells, crucial in cell metabolism, structure, growth, signaling, and movement.

The cancer types represented in the UALCAN proteomic dataset include breast, colorectal, gastric, glioblastoma, head, and neck, liver, lung adenocarcinoma, lung squamous, ovarian, pancreatic, pediatric brain, prostate, renal, and uterine cancers. The number of tumors in each cancer type in the study ranged from 76 to 230, with an average of 143. Intriguingly, the pan-cancer, proteome-based subtypes the current study found to cut across tumor lineages.

The compendium proteomic dataset came from 17 individual studies. Corresponding multi-omics data were available for most of these tumors, including mRNA levels, DNA somatic small mutations and insertions/deletions, and DNA somatic copy number alterations.

In general, the researchers found the protein expression of genes across tumors broadly correlated with corresponding mRNA levels or copy number alterations. However, there were some notable exceptions.

They identified 11 distinct proteome-based pan-cancer subtypes — named s1 through s11 — that can provide insights into the deregulated pathways and processes in tumors that make them cancerous. Each subtype spanned multiple tissue-based cancer types, though subtype s11 was specific to brain tumors, spanning glioblastomas and pediatric brain tumors. 

Each subtype expressed specific gene categories, some seen before in a previous, less comprehensive proteomic study. Three subtypes showed new gene categories: subtype s7 with “axon guidance” and “frizzled binding” genes, subtype s10 with “DNA repair” and “chromatin organization” genes, and subtype s11 with “synapse,” “dendrite” and “axon” genes.

At the DNA level, the study detailed differences among the proteome-based subtypes in overall copy number alterations of genes, and somatic mutations in subtypes associated with higher pathway activity, as inferred by proteome or transcriptome data.

“Our study results provide a framework for understanding the molecular landscape of cancers at the proteome level to integrate and compare the data with other molecular correlates of cancers,” Varambally said. “The associated datasets and gene-level associations represent a resource for the research community, including helping to identify gene candidates for functional studies and further develop candidates as diagnostic markers or therapeutic targets for a specific subset of cancers.

“Furthermore, this study reinforces the notion that cancers should be comprehensively surveyed at the protein level, though expression profiling on tumors has historically been mostly limited to the RNA transcript level. Many of the analyses in this ever-evolving cancer data analysis platform are based on user or expert requests, and the team is indebted to the support and encouragement from the researchers who use this platform to make discoveries that make a difference in cancer research.” 

Some of the large datasets for the UAB site are generated by consortiums like The Cancer Genome Atlas, or TCGA, and the Clinical Proteomic Tumor Analysis Consortium, or CPTAC, of the National Cancer Institute. Since the researchers also strive to address cancer health disparities, UALCAN provides an option to analyze the data based on patient race or ethnicity, where it is available.

Precision targeting of cancer requires the identification of individual or subclass-specific genomic and molecular alterations. To help cancer researchers perform various data analyses for a better understanding of these large datasets, Darshan Shimoga Chandrashekar, Ph.D., led the development of the UALCAN portal under the mentorship of Varambally. Updates to this continuously evolving portal were recently published in Neoplasia.

The UALCAN initiative and its continuous development involve contributions from a team of experts including bioinformaticians, computer scientists, statisticians, cancer biologists, pathologists, and oncologists. “It is a team science approach to enable the global cancer research team to tackle cancer,” Varambally said.

Co-first authors of this study are Yiqun Zhang and Fengju Chen, Baylor College of Medicine, and Chandrashekar, UAB Department of Pathology Division of Molecular and Cellular Pathology. 

Pathology is a department in the Marnix E. Heersink School of Medicine at UAB. Varambally is a senior scientist in the O’Neal Comprehensive Cancer Center and the Informatics Institute at UAB and is co-director of the Cancer Biology Theme of Graduate Biomedical Sciences at UAB. He holds an adjunct position at the Michigan Center for Translational Pathology, the University of Michigan, Ann Arbor.

Chinese scientists observe large-scale, ordered, tunable Majorana-zero-mode lattice

Tyler O'Neal, Staff Editor ACADEMIA

In a study, a joint research team led by Prof. GAO Hongjun from the Institute of Physics of the Chinese Academy of Sciences (CAS) has reported observation of a large-scale, ordered and tunable Majorana-zero-mode (MZM) lattice in the iron-based superconductor LiFeAs, providing a new pathway towards future topological quantum super computation. Fig. 1. Characterization of biaxial CDW region. (Image by Institute of Physics)

MZMs are zero-energy bound states confined in the topological defects of crystals, such as line defects and magnetic field-induced vortices. They are characterized by scanning tunneling microscopy/spectroscopy (STM/S) as zero-bias conductance peaks. They obey non-Abelian statistics and are considered building blocks for future topological quantum computation. 

MZMs have been observed in several topologically nontrivial iron-based superconductors, such as Fe (Te0.55Se0.45), (Li0.84Fe0.16)OHFeSe, and CaKFe4As4. However, these materials suffer from issues with alloying-induced disorder, uncontrollable and disordered vortex lattices, and the low yield of topological vortices, all of which hinder their further study and application. 

In this study, the researchers observed the formation of an ordered and tunable MZM lattice in the naturally strained superconductor LiFeAs. Using STM/S equipped with magnetic fields, the researchers found that local strain naturally exists in LiFeAs. Biaxial charge density wave (CDW) stripes along the Fe-Fe and As-As directions are produced by the strain, with wavevectors of λ1~2.7 nm and λ2~24.3 nm. The CDW with wavevector λ2 shows strong modulation of the superconductivity of LiFeAs.  Fig. 2. MZM in vortices. (Image by Institute of Physics)

Under a magnetic field perpendicular to the sample surface, the vortices emerge and are forced to align exclusively along with the As-As CDW stripes, forming an ordered lattice. The reduced crystal symmetry leads to a drastic change in the topological band structures at the Fermi level, thus transforming the vortices into topological ones hosting MZMs and forming an ordered MZM lattice. Moreover, the MZM lattice density and geometry are tunable by an external magnetic field. The MZMs start to couple with each other under high magnetic fields. 

This observation of a large-scale, ordered and tunable MZM lattice in LiFeAs expands the MZM family found in iron-based superconductors, thus providing a promising platform for manipulating and braiding MZMs in the future, according to the researchers. 

These findings may shed light on the study of topological quantum super computation using iron-based superconductors. 

Fig. 3. Majorana mechanism in LiFeAs. (Image by Institute of Physics)

Fig. 4. Tuning the MZM lattice with magnetic field. (Image by Institute of Physics)

QET Labs' breakthrough paves way for photonic sensing at the quantum limit

Tyler O'Neal, Staff Editor ACADEMIA

A Bristol-led team of physicists has found a way to operate mass manufacturable photonic sensors at the quantum limit. This breakthrough paves the way for practical applications such as monitoring greenhouse gases and cancer detection.  Photonic chip with a microring resonator nanofabricated in a commercial foundry. Photo credit: Joel Tasker, QET Labs

Sensors are a constant feature of our everyday lives. Although they often go unperceived, sensors provide critical information essential to modern healthcare, security, and environmental monitoring. Modern cars alone contain over 100 sensors and this number will only increase. 

Quantum sensing is poised to revolutionize today's sensors, significantly boosting the performance they can achieve. More precise, faster, and reliable measurements of physical quantities can have a transformative effect on every area of science and technology, including our daily lives. 

However, the majority of quantum sensing schemes rely on special entangled or squeezed states of light or matter that are hard to generate and detect. This is a major obstacle to harnessing the full power of quantum-limited sensors and deploying them in real-world scenarios. 

In a paper published today, a team of physicists at the Universities of Bristol, Bath, and Warwick have shown it is possible to perform high precision measurements of important physical properties without the need for sophisticated quantum states of light and detection schemes.  

The key to this breakthrough is the use of ring resonators – tiny racetrack structures that guide light in a loop and maximize its interaction with the sample under study. Importantly, ring resonators can be mass-manufactured using the same processes as the chips in our computers and smartphones. 

Alex Belsley, Quantum Engineering Technology Labs (QET Labs) Ph.D. student and lead author of the work, said: “We are one step closer to all integrated photonic sensors operating at the limits of detection imposed by quantum mechanics.” 

Employing this technology to sense absorption or refractive index changes can be used to identify and characterize a wide range of materials and biochemical samples, with topical applications from monitoring greenhouse gases to cancer detection.  

Associate Professor Jonathan Matthews, co-Director of QET Labs and co-author of the work, stated: “We are really excited by the opportunities this result enables: we now know how to use mass manufacturable processes to engineer chip-scale photonic sensors that operate at the quantum limit.”

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