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Posts Tagged ‘driver gene’


Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

This image has an empty alt attribute; its file name is morethanthes.jpg
Seeing “through” the cancer with the power of data analysis — possible with the help of artificial intelligence. Credit: MPI f. Molecular Genetics/ Ella Maru Studio
Image Source: https://medicalxpress.com/news/2021-04-sum-mutations-cancer-genes-machine.html

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low-risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) and Artificial Intelligence (AI) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions by predicting new algorithms.

In the majority of human cancers, heritable loss of gene function through cell division may be mediated as often by epigenetic as by genetic abnormalities. Epigenetic modification occurs through a process of interrelated changes in CpG island methylation and histone modifications. Candidate gene approaches of cell cycle, growth regulatory and apoptotic genes have shown epigenetic modification associated with loss of cognate proteins in sporadic pituitary tumors.

On 11th November 2020, researchers from the University of California, Irvine, has established the understanding of epigenetic mechanisms in tumorigenesis and publicized a previously undetected repertoire of cancer driver genes. The study was published in “Science Advances

Researchers were able to identify novel tumor suppressor genes (TSGs) and oncogenes (OGs), particularly those with rare mutations by using a new prediction algorithm, called DORGE (Discovery of Oncogenes and tumor suppressor genes using Genetic and Epigenetic features) by integrating the most comprehensive collection of genetic and epigenetic data.

The senior author Wei Li, Ph.D., the Grace B. Bell chair and professor of bioinformatics in the Department of Biological Chemistry at the UCI School of Medicine said

Existing bioinformatics algorithms do not sufficiently leverage epigenetic features to predict cancer driver genes, even though epigenetic alterations are known to be associated with cancer driver genes.

The Study

This study demonstrated how cancer driver genes, predicted by DORGE, included both known cancer driver genes and novel driver genes not reported in current literature. In addition, researchers found that the novel dual-functional genes, which DORGE predicted as both TSGs and OGs, are highly enriched at hubs in protein-protein interaction (PPI) and drug/compound-gene networks.

Prof. Li explained that the DORGE algorithm, successfully leveraged public data to discover the genetic and epigenetic alterations that play significant roles in cancer driver gene dysregulation and could be instrumental in improving cancer prevention, diagnosis and treatment efforts in the future.

Another new algorithmic prediction for the identification of cancer genes by Machine Learning has been carried out by a team of researchers at the Max Planck Institute for Molecular Genetics (MPIMG) in Berlin and the Institute of Computational Biology of Helmholtz Zentrum München combining a wide variety of data analyzed it with “Artificial Intelligence” and identified numerous cancer genes. They termed the algorithm as EMOGI (Explainable Multi-Omics Graph Integration). EMOGI can predict which genes cause cancer, even if their DNA sequence is not changed. This opens up new perspectives for targeted cancer therapy in personalized medicine and the development of biomarkers. The research was published in Nature Machine Intelligence on 12th April 2021.

In cancer, cells get out of control. They proliferate and push their way into tissues, destroying organs and thereby impairing essential vital functions. This unrestricted growth is usually induced by an accumulation of DNA changes in cancer genes—i.e. mutations in these genes that govern the development of the cell. But some cancers have only very few mutated genes, which means that other causes lead to the disease in these cases.

The Study

Overlap of EMOGI’s positive predictions with known cancer genes (KCGs) and candidate cancer genes
Image Source: https://static-content.springer.com/esm/art%3A10.1038%2Fs42256-021-00325-y/MediaObjects/42256_2021_325_MOESM1_ESM.pdf

The aim of the study has been represented in 4 main headings

  • Additional targets for personalized medicine
  • Better results by combination
  • In search of hints for further studies
  • Suitable for other types of diseases as well

The team was headed by Annalisa Marsico. The team used the algorithm to identify 165 previously unknown cancer genes. The sequences of these genes are not necessarily altered-apparently, already a dysregulation of these genes can lead to cancer. All of the newly identified genes interact closely with well-known cancer genes and be essential for the survival of tumor cells in cell culture experiments. The EMOGI can also explain the relationships in the cell’s machinery that make a gene a cancer gene. The software integrates tens of thousands of data sets generated from patient samples. These contain information about DNA methylations, the activity of individual genes and the interactions of proteins within cellular pathways in addition to sequence data with mutations. In these data, a deep-learning algorithm detects the patterns and molecular principles that lead to the development of cancer.

Marsico says

Ideally, we obtain a complete picture of all cancer genes at some point, which can have a different impact on cancer progression for different patients

Unlike traditional cancer treatments such as chemotherapy, personalized treatments are tailored to the exact type of tumor. “The goal is to choose the best treatment for each patient, the most effective treatment with the fewest side effects. In addition, molecular properties can be used to identify cancers that are already in the early stages.

Roman Schulte-Sasse, a doctoral student on Marsico’s team and the first author of the publication says

To date, most studies have focused on pathogenic changes in sequence, or cell blueprints, at the same time, it has recently become clear that epigenetic perturbation or dysregulation gene activity can also lead to cancer.

This is the reason, researchers merged sequence data that reflects blueprint failures with information that represents events in cells. Initially, scientists confirmed that mutations, or proliferation of genomic segments, were the leading cause of cancer. Then, in the second step, they identified gene candidates that are not very directly related to the genes that cause cancer.

Clues for future directions

The researcher’s new program adds a considerable number of new entries to the list of suspected cancer genes, which has grown to between 700 and 1,000 in recent years. It was only through a combination of bioinformatics analysis and the newest Artificial Intelligence (AI) methods that the researchers were able to track down the hidden genes.

Schulte-Sasse says “The interactions of proteins and genes can be mapped as a mathematical network, known as a graph.” He explained by giving an example of a railroad network; each station corresponds to a protein or gene, and each interaction among them is the train connection. With the help of deep learning—the very algorithms that have helped artificial intelligence make a breakthrough in recent years – the researchers were able to discover even those train connections that had previously gone unnoticed. Schulte-Sasse had the computer analyze tens of thousands of different network maps from 16 different cancer types, each containing between 12,000 and 19,000 data points.

Many more interesting details are hidden in the data. Patterns that are dependent on particular cancer and tissue were seen. The researchers were also observed this as evidence that tumors are triggered by different molecular mechanisms in different organs.

Marsico explains

The EMOGI program is not limited to cancer, the researchers emphasize. In theory, it can be used to integrate diverse sets of biological data and find patterns there. It could be useful to apply our algorithm for similarly complex diseases for which multifaceted data are collected and where genes play an important role. An example might be complex metabolic diseases such as diabetes.

Main Source

New prediction algorithm identifies previously undetected cancer driver genes

https://advances.sciencemag.org/content/6/46/eaba6784  

Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms

https://www.nature.com/articles/s42256-021-00325-y#citeas

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Premalata Pati, PhD, PostDoc in Biological Sciences, Medical Text Analysis with Machine Learning

https://pharmaceuticalintelligence.com/2021-medical-text-analysis-nlp/premalata-pati-phd-postdoc-in-pharmaceutical-sciences-medical-text-analysis-with-machine-learning/

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Targeted Therapy for Triple Negative Breast Cancer

Curator: Larry H. Bernstein, MD, FCAP

LPBI

 

Triple-Negative Breast Cancer Target Is Found

May 17, 2016   Researchers at UC Berkeley discover a target that drives cancer metabolism in triple-negative breast cancer.
http://www.technologynetworks.com/Genotyping/news.aspx?ID=191502

UC Berkeley researchers have found a long-elusive Achilles’ heel within “triple-negative” breast tumors, a common type of breast cancer that is difficult to treat. The scientists then used a drug-like molecule to successfully target this vulnerability, killing cancer cells in the lab and shrinking tumors in mice.

“We were looking for targets that drive cancer metabolism in triple-negative breast cancer, and we found one that was very specific to this type of cancer,” said Daniel K. Nomura, an associate professor of chemistry and of nutritional sciences and toxicology at UC Berkeley and senior author for the study, which is published online ahead of print in Cell Chemical Biology.

Triple-negative breast cancers account for about one in five breast cancers, and they are deadlier than other forms of breast cancer, in part because no drugs have been developed to specifically target these tumors.

Triple-negative breast cancers do not rely on the hormones estrogen and progesterone for growth, nor on human epidermal growth factor receptor 2 (HER2). Because they do not depend on these three targets, they are not vulnerable to modern hormonal therapies or to the HER2-targeted drug Herceptin (trastuzumab).

Instead, oncologists treat triple-negative breast cancer with older chemotherapies that target all dividing cells. If triple-negative breast cancer spreads beyond the breast to distant sites within the body, an event called metastasis, there are few treatment options.

Tumor cells develop abnormal metabolism, which they rely on to get the energy boost they need to fuel their rapid growth. In their new study, the research team used an innovative approach to search for active enzymes that triple-negative breast cancers use differently for metabolism in comparison to other cells and even other tumors.

Inhibiting cancer metabolism

They discovered that cells from triple-negative breast cancer cells rely on vigorous activity by an enzyme called glutathione-S-transferase Pi1 (GSTP1). They showed that in cancer cells, GSTP1 regulates a type of metabolism called glycolysis, and that inhibition of GSTP1 impairs glycolytic metabolism in triple-negative cancer cells, starving them of energy, nutrients and signaling capability. Normal cells do not rely as much on this particular metabolic pathway to obtain usable chemical energy, but cells within many tumors heavily favor glycolysis.

Co-author Eranthie Weerapana, an associate professor of chemistry at Boston College, developed a molecule named LAS17 that tightly and irreversibly attaches to the target site on the GSTP1 molecule. By binding tightly to GSTP1, LAS17 inhibits activity of the enzyme. The researchers found that LAS17 was highly specific for GSTP1, and did not attach to other proteins in cells.

According to Nomura, LAS17 did not appear to have toxic side effects in mice, where it shrank tumors grown to an invasive stage from surgically transplanted, human, triple-negative breast cancer cells that had long been maintained in lab cultures.

The research team intends to continue studying LAS17, Nomura said, with the next step being to study tumor tissue resected from human triple-negative breast cancers and transplanted directly into mice.

“Inhibiting GSTP1 impairs glycolytic metabolism,” Nomura said. “More broadly, this inhibition starves triple-negative breast cancer cells, preventing them from making the macromolecules they need, including the lipids they need to make membranes and the nucleic acids they need to make DNA. It also prevents these cells from making enough ATP, the molecule that is the basic energy fuel for cells.”

Beyond the metabolic role they first sought to track down, GSTP1 also appears to aid signaling within triple-negative breast cancer cells, helping to spur tumor growth, the researchers found.

Technique identifies Achilles’ heels

Nomura said it was surprising that a single, unique target emerged from the research team’s search.

The method used by the researchers, called “reactivity-based chemoproteomics,” can quickly lead to specific targetable sites — the Achilles’ heels — on proteins of interest, and eventually to drug development strategies, Nomura said.

The approach is to search for protein targets that are actively functioning within cells, instead of first using the well-trod path of surveying all genes to identify the specific genes that have taken the first step toward protein production. With that more conventional strategy, the switching on, or “expression,” of genes is evidenced by the easily quantified molecule called messenger RNA, made by the cell from a gene’s DNA template.

Nomura’s team instead first used chemical probes that can react with certain configurations of two of the amino acid building blocks of protein — cysteine and lysine — known to be involved in several kinds of important structural and functional transitions that active proteins can undergo.

“A lot can happen after the first step in protein production, and we believe our method for identifying fully formed, active proteins is more useful for tracking down relevant differences in cellular physiology,” Nomura said.

The researchers analyzed and compared cells from five distinct triple-negative breast cancers that had been grown in cell cultures for generations, along with cells from four distinct breast cancers that were not triple negative.

The scientists used a chemical identification technique known as mass spectrometry to narrow down the set of proteins that had active lysines and cysteines to just those that were metabolic enzymes. Only then did they use the more conventional approach of measuring gene expression in the different cancer cell types.

GSTP1 was the only metabolically active enzyme that was specifically expressed only in triple-negative breast cancer cells compared to other breast cancer cell types, the researchers found. Separate analysis of databases of human breast cancer by UC San Francisco co-authors confirmed that GSTP1 is overexpressed in patients with triple-negative breast cancers in comparison to patients with other breast cancers.

In addition to Nomura and Weerapana, study authors included Sharon Louie, Elizabeth Grossman, Lucky Ding, Tucker Huffman and David Miyamoto, from UC Berkeley; Roman Camarda and Andrei Goga, from UC San Francisco, and Lisa Crawford, from Boston College. Study funders included the National Institutes of Health, the American Cancer Society, the U.S. Department of Defense, and the Searle Scholar Foundation.

 

Triple-negative breast cancer target is found

UC Berkeley researchers have found a long-elusive Achilles’ heel within “triple-negative” breast tumors, a common type of breast cancer that is difficult to treat. The scientists then used a drug-like molecule to successfully target this vulnerability, killing cancer cells in the lab and shrinking tumors in mice.

“We were looking for targets that drive cancer metabolism in triple-negative breast cancer, and we found one that was very specific to this type of cancer,” said Daniel K. Nomura, an associate professor of chemistry and of nutritional sciences and toxicology at UC Berkeley and senior author for the study, which is published online ahead of print on May 12 in Cell Chemical Biology.

Triple-negative breast cancers account for about one in five breast cancers, and they are deadlier than other forms of breast cancer, in part because no drugs have been developed to specifically target these tumors.

Triple-negative breast cancers do not rely on the hormones estrogen and progesterone for growth, nor on human epidermal growth factor receptor 2 (HER2). Because they do not depend on these three targets, they are not vulnerable to modern hormonal therapies or to the HER2-targeted drug Herceptin (trastuzumab).

Instead, oncologists treat triple-negative breast cancer with older chemotherapies that target all dividing cells. If triple-negative breast cancer spreads beyond the breast to distant sites within the body, an event called metastasis, there are few treatment options.

Tumor cells develop abnormal metabolism, which they rely on to get the energy boost they need to fuel their rapid growth. In their new study, the research team used an innovative approach to search for active enzymes that triple-negative breast cancers use differently for metabolism in comparison to other cells and even other tumors.

Inhibiting cancer metabolism

They discovered that cells from triple-negative breast cancer cells rely on vigorous activity by an enzyme called glutathione-S-transferase Pi1 (GSTP1). They showed that in cancer cells, GSTP1 regulates a type of metabolism called glycolysis, and that inhibition of GSTP1 impairs glycolytic metabolism in triple-negative cancer cells, starving them of energy, nutrients and signaling capability. Normal cells do not rely as much on this particular metabolic pathway to obtain usable chemical energy, but cells within many tumors heavily favor glycolysis.

for mor see.. http://news.berkeley.edu/2016/05/12/triple-negative-breast-cancer-target-is-found/

 

GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity

Sharon M. Louie, Elizabeth A. Grossman, Lisa A. Crawford….., Eranthie Weerapana, Daniel K. Nomura
Figure thumbnail fx1
  • We used chemoproteomics to profile metabolic drivers of breast cancer
  • GSTP1 is a novel triple-negative breast cancer-specific target
  • GSTP1 inhibition impairs triple-negative breast cancer pathogenicity
  • GSTP1 inhibition impairs GAPDH activity to affect metabolism and signaling

Breast cancers possess fundamentally altered metabolism that fuels their pathogenicity. While many metabolic drivers of breast cancers have been identified, the metabolic pathways that mediate breast cancer malignancy and poor prognosis are less well understood. Here, we used a reactivity-based chemoproteomic platform to profile metabolic enzymes that are enriched in breast cancer cell types linked to poor prognosis, including triple-negative breast cancer (TNBC) cells and breast cancer cells that have undergone an epithelial-mesenchymal transition-like state of heightened malignancy. We identified glutathione S-transferase Pi 1 (GSTP1) as a novel TNBC target that controls cancer pathogenicity by regulating glycolytic and lipid metabolism, energetics, and oncogenic signaling pathways through a protein interaction that activates glyceraldehyde-3-phosphate dehydrogenase activity. We show that genetic or pharmacological inactivation of GSTP1 impairs cell survival and tumorigenesis in TNBC cells. We put forth GSTP1 inhibitors as a novel therapeutic strategy for combatting TNBCs through impairing key cancer metabolism and signaling pathways.

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Genetic association for breast cancer metastasis

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Gene Found in Brain Turns Out to be Key Driver of Breast Cancer   

GEN News Highlights  http://www.genengnews.com/gen-news-highlights/gene-found-in-brain-turns-out-to-be-key-driver-of-breast-cancer/81252361/

Researchers from the Wistar Institute report that a gene that was once thought to be found only in the brain is also expressed in breast cancer, and that it helps promote the growth and spread of the disease. Additionally, they showed how a version of the gene with edited RNA prevents metastasis. Their study (“The mRNA Edited Form of GABRA3 Suppresses GABRA3 Mediated Akt Activation and Breast Cancer Metastasis”) was published online in Nature Communications.

The causes of metastasis in breast cancer at a molecular level are not very well understood, so identifying regulatory genes that prompt this behavior could have a tremendous effect on survival, from early detection to the design of better treatment strategies.

“Metastatic breast cancer is ultimately what kills patients,” said Qihong Huang, M.D., Ph.D., associate professor in the Tumor Microenvironment and Metastasis Program at the Wistar Institute and lead author of the study. “While early detection is critical, it does not help patients whose disease has spread, and so we wanted to determine what was causing this to happen.”

The researchers analized The Cancer Genome Atlas (TCGA) and identified 41 genes inversely correlated with survival in breast cancer. Dr. Huang and colleagues focused on one gene in particular: GABAA receptor alpha3 (Gabra3). The gene was particularly intriguing, as prior to this study, researchers believed that Gabra3 was expressed only in brain tissue.

There were three main reasons the researchers determined it was worth studying. First, it is highly expressed in cancer tissues, but not in healthy breast tissues. Second, it’s a gene for a cell surface molecule, something that is potentially targeted by a drug. Finally, drugs that target Gabra3 are already available for treating other diseases such as insomnia.

The researchers showed that cells expressing Gabra3 were better at migrating and invading than their control counterparts, and Gabra3 showed metastasis-promoting activity in vivo. Animal models injected with the activated gene all developed metastatic lesions in their lungs. The gene functions by activating the AKT pathway, a cellular pathway essential to cell growth and survival in many types of cancer, including breast cancer.

In some instances, though, certain types of Gabra3 are actually able to suppress breast cancer metastasis. This activity is closely linked to the gene’s RNA. Dr. Huang and colleagues found that Gabra3 that had undergone RNA editing was found only in noninvasive breast cancers. When the RNA was edited, it suppressed the activation of the AKT pathway required for metastasis, meaning that breast cancer with this specific type of Gabra3 was unable to spread to other organs. This is particularly encouraging because interferons can increase RNA editing activity and could therefore prevent Gabra3 from activating the AKT pathway.

“We believe this is the first time that anyone has demonstrated the importance of RNA editing in breast cancer,” Dr. Huang said. “A combination strategy that that involves targeting Gabra3 while also upregulating the expression of RNA editing molecules could be an effective strategy for managing metastatic breast cancer.”

In addition to further studying the role of Gabra3 in breast cancer metastasis, Wistar is actively seeking collaborative development partners to advance the targeted use of existing GABA-A receptor antagonists in Gabra3 overexpressing tumors. Furthermore, Wistar is interested in collaborations to develop blood-brain barrier impermeable GABA-A receptor antagonists as next-generation oncology therapeutics.

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