Proteome patterns between treated and control cells

Proteome patterns between treated and control cells

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We did 4 experiments to compare the amount of certain proteins in treated and untreated cells. Each experiment was done separately. Because of the high cost of experiment, we were able to perform only one pair (one treated and one untreated) sample for each experiment. We want to see which proteins are differentially expressed (minimum 1.5 fold up/down).

First approach: We have compared protein levels of all 4 treated (as a group) with the 4 untreated (as a second group). There is of course variability between all experiments, because of the nature of the cells. We have a list of the proteins that are differentially expressed as a result of the treatment, however this list is not very long.

My question is (second approach): Can we compare the proteins levels pairwise for each experiment (treated vs its respective control) and make 4 corresponding lists, and then compare these four list using a statistical tool and find which proteins are consistently up- or down-regulated? DO you think that these two different approach will generate different lists of the affected proteins?

If I understand you did a treatment to some cells and compared them with non-treated ones. Instead of running the four experiments at the same time, you did one treated and one untreated at a time. Then you did proteomics for each sample. Is the treatment the same in all four experiments?

Edit after the further comments of the OP: So, since the four treated samples are the same and the four untreated also (same conditions except from the treatment and the treatment is the same), then the way to go is as what your collaborator did.

Identification and quantification of the detected proteins is one thing and every sample of the four are replicates. Comparison between the two conditions is another thing. The software he uses for ID can combine the same samples and already perform the statistical analysis, so the list you have now has higher credibility in terms of the proteins it includes and their levels for your cells when they are treated or not.

Using only one of your replicates, although it might give you different number of detected proteins or different amounts of each, has lower credibility, because it's only one sample out of four. In plain words, the presence or absence or the amount of a protein might be an artifact or insignificant.

What has to be clear is that the comparison between treated and untreated conditions is done after you have received the statistically correct list of detected proteins.

Thus, and in accordance to what I had said before the edit, if you take the list for each treated sample and compare them with any of the lists of the untreated ones, it will lead to conclusions that won't be as statistically significant as when you combine all treated together and all untreated together (as your colleague did). In plain words, your conclusions will have a higher chance to be wrong.

Every statistical analysis you do yourself for each sample should eventually lead to a similar consensus as your colleague got using the statistical analysis of his software.

As a sidenote, considering how many types of proteins are in a cell, now that you have a quite short list might not be a bad thing at all and you can proceed by:

  1. Concluding that the treatment had minor effect in the proteins that you expected it would affect (if they are not present in your lists as significant different)
  2. Trying to understand, identify and hypothesize on the role of the proteins that made it in your comparison threshold, as they have a much higher probability of being indeed different between the two conditions.

Splitting the samples in your analysis might have a point if the conditions of the experiment were not exactly the same or the treatment level is different etc. In that case you could split the samples accordingly, but that would definitely reduce your certainty level for your conclusions.

Proteomics of SARS-CoV-2-infected host cells reveals therapy targets

A new coronavirus was recently discovered and named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infection with SARS-CoV-2 in humans causes coronavirus disease 2019 (COVID-19) and has been rapidly spreading around the globe 1,2 . SARS-CoV-2 shows some similarities to other coronaviruses however, treatment options and an understanding of how SARS-CoV-2 infects cells are lacking. Here we identify the host cell pathways that are modulated by SARS-CoV-2 and show that inhibition of these pathways prevents viral replication in human cells. We established a human cell-culture model for infection with a clinical isolate of SARS-CoV-2. Using this cell-culture system, we determined the infection profile of SARS-CoV-2 by translatome 3 and proteome proteomics at different times after infection. These analyses revealed that SARS-CoV-2 reshapes central cellular pathways such as translation, splicing, carbon metabolism, protein homeostasis (proteostasis) and nucleic acid metabolism. Small-molecule inhibitors that target these pathways prevented viral replication in cells. Our results reveal the cellular infection profile of SARS-CoV-2 and have enabled the identification of drugs that inhibit viral replication. We anticipate that our results will guide efforts to understand the molecular mechanisms that underlie the modulation of host cells after infection with SARS-CoV-2. Furthermore, our findings provide insights for the development of therapies for the treatment of COVID-19.

At the end of 2019, a cluster of cases of severe pneumonia of unknown cause was described in Wuhan (eastern China), and a SARS-like acute respiratory distress syndrome was noted in many patients. Early in January 2020, next-generation sequencing revealed that a novel coronavirus (named SARS-CoV-2) was the causal factor for the disease 1 , which was later designated COVID-19. SARS-CoV-2 shows high infectivity, which has resulted in rapid global spreading 2 .

Currently, there is no established therapy for the treatment of COVID-19. Treatment is based mainly on supportive and symptomatic care 4,5 . Therefore, the development of therapies that inhibit infection with or replication of SARS-CoV-2 are urgently needed. Molecular examination of infected cells by unbiased proteomics approaches offers a potent strategy for revealing pathways that are relevant for viral pathogenicity to identify potential drug targets. However, this strategy depends on the availability of cell-culture models that are amenable to virus infection and sensitive proteomics approaches that can be used for temporal infection profiling in cells. SARS-CoV-2 was recently successfully isolated using the human colon epithelial carcinoma cell line 6 Caco-2. SARS-CoV-2 replicates in gastrointestinal cells in vivo 7 and is frequently detected in stool—regardless of the occurrence of diarrhoea 8 . Caco-2 cells were extensively used to study infection with SARS-CoV and can be used for SARS-CoV-2 infection 6,9 . For proteome analysis, a method—multiplexed enhanced protein dynamics (mePROD) proteomics—was recently described that enables the determination of translatome and proteome changes at high temporal resolution 3 . Owing to the quantification of translational changes by naturally occurring heavy isotope labelling using stable isotope labelling by amino acids in cell culture (SILAC), this method does not affect cellular behaviour and therefore enables the perturbation-free and unbiased analysis of the response of cells to viral infection.

In this study, we used quantitative translatome and proteome proteomics to obtain an unbiased profile of the cellular response to SARS-CoV-2 infection in human cells. We monitored different time points after infection and identified key determinants of the host cell response to infection. These findings revealed pathways that are relevant for SARS-CoV-2 infection. We tested several drugs that target these pathways, including translation, proteostasis, glycolysis, splicing and nucleotide synthesis pathways. These drugs inhibited SARS-CoV-2 replication at concentrations that were not toxic to the human cells, potentially providing therapeutic strategies for the treatment of COVID-19.

Prokaryotic versus Eukaryotic Gene Expression

To understand how gene expression is regulated, we must first understand how a gene codes for a functional protein in a cell. The process occurs in both prokaryotic and eukaryotic cells, just in slightly different manners.

Prokaryotic organisms are single-celled organisms that lack a cell nucleus, and their DNA therefore floats freely in the cell cytoplasm. To synthesize a protein, the processes of transcription and translation occur almost simultaneously. When the resulting protein is no longer needed, transcription stops. As a result, the primary method to control what type of protein and how much of each protein is expressed in a prokaryotic cell is the regulation of DNA transcription. All of the subsequent steps occur automatically. When more protein is required, more transcription occurs. Therefore, in prokaryotic cells, the control of gene expression is mostly at the transcriptional level.

Eukaryotic cells, in contrast, have intracellular organelles that add to their complexity. In eukaryotic cells, the DNA is contained inside the cell&rsquos nucleus and there it is transcribed into RNA. The newly synthesized RNA is then transported out of the nucleus into the cytoplasm, where ribosomes translate the RNA into protein. The processes of transcription and translation are physically separated by the nuclear membrane transcription occurs only within the nucleus, and translation occurs only outside the nucleus in the cytoplasm. The regulation of gene expression can occur at all stages of the process (FIgure (PageIndex<1>)). Regulation may occur when the DNA is uncoiled and loosened from nucleosomes to bind transcription factors ( epigenetic level), when the RNA is transcribed (transcriptional level), when the RNA is processed and exported to the cytoplasm after it is transcribed ( post-transcriptional level), when the RNA is translated into protein (translational level), or after the protein has been made ( post-translational level).

Figure (PageIndex<1>): Prokaryotic transcription and translation occur simultaneously in the cytoplasm, and regulation occurs at the transcriptional level. Eukaryotic gene expression is regulated during transcription and RNA processing, which take place in the nucleus, and during protein translation, which takes place in the cytoplasm. Further regulation may occur through post-translational modifications of proteins.

The differences in the regulation of gene expression between prokaryotes and eukaryotes are summarized below. The regulation of gene expression is discussed in detail in subsequent modules.

Table (PageIndex<1>): Differences in the Regulation of Gene Expression of Prokaryotic and Eukaryotic Organisms

Prokaryotic organisms Eukaryotic organisms
Lack nucleus Contain nucleus
DNA is found in the cytoplasm DNA is confined to the nuclear compartment
RNA transcription and protein formation occur almost simultaneously RNA transcription occurs prior to protein formation, and it takes place in the nucleus. Translation of RNA to protein occurs in the cytoplasm.
Gene expression is regulated primarily at the transcriptional level Gene expression is regulated at many levels (epigenetic, transcriptional, nuclear shuttling, post-transcriptional, translational, and post-translational)

Evolution of Gene Regulation

Prokaryotic cells can only regulate gene expression by controlling the amount of transcription. As eukaryotic cells evolved, the complexity of the control of gene expression increased. For example, with the evolution of eukaryotic cells came compartmentalization of important cellular components and cellular processes. A nuclear region that contains the DNA was formed. Transcription and translation were physically separated into two different cellular compartments. It therefore became possible to control gene expression by regulating transcription in the nucleus, and also by controlling the RNA levels and protein translation present outside the nucleus.

Some cellular processes arose from the need of the organism to defend itself. Cellular processes such as gene silencing developed to protect the cell from viral or parasitic infections. If the cell could quickly shut off gene expression for a short period of time, it would be able to survive an infection when other organisms could not. Therefore, the organism evolved a new process that helped it survive, and it was able to pass this new development to offspring.

Welcome to the Levin lab: investigating information storage and processing in biological systems

We work on novel ways to understand and control complex pattern formation. We use techniques of molecular genetics, biophysics, and computational modeling to address large-scale control of growth and form. We work in whole frogs and flatworms, and sometimes zebrafish and human tissues in culture. Our projects span regeneration, embryogenesis, cancer, and learning plasticity – all examples of how cellular networks process information. In all of these efforts, our goal is not only to understand the molecular mechanisms necessary for morphogenesis, but also to uncover and exploit the cooperative signaling dynamics that enable complex bodies to build and remodel themselves toward a correct structure. Our major goal is to understand how individual cell behaviors are orchestrated towards appropriate large-scale outcomes despite unpredictable environmental perturbations. Some general themes that run through our diverse research together include:

  1. We study bioelectrical signals that make up part of the language by which cells communicate to serve the patterning needs of the host organism. These natural voltage gradients exist in all cells (not just neurons), and regulate cell behavior and gene expression. We have developed new molecular tools to track and manipulate these biophysical conversations between cells and tissues in vivo. The results have yielded important findings about basic patterning, as well as new strategies to induce regenerative repair and reprogram tissues into new organs.
  2. We have projects in development, regeneration, and cancer, as well as in the plasticity of the brain and its connection to somatic tissues. These fields are treated as distinct by most labs, funding bodies, and educational programs, but we span them because we are seeking the most fundamental aspects of biological regulation, and we hypothesize that common rules of information processing might be discovered throughout these aspects of biology. While our work will eventually give rise to practical applications in bioengineering and biomedicine, we are fundamentally interested in synthetic biology and artificial life - the understanding of living systems as cohesive, computational entities that store and process information about their shape and their environment.
  3. We complement reductive analysis of molecular components with a synthesis designed to understand top-down controls and large-scale properties. For example, we analyze morphogenetic systems as primitive cognitive agents that manipulate information about their shape and make decisions about pattern regulation. We use techniques of artificial intelligence and neuroscience to find out what information biological tissues have, and how it is stored, processed, and communicated. Our focus on algorithmic (constructivist) computer models of patterning is an important component of linking genetic networks to complex 3-dimensional shape and its regulation in vivo.

Photo credit: Image is modified after "The Neurologist" by Jose Perez

Proteome patterns between treated and control cells - Biology

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Clinical Characteristics

The typical clinical symptoms of COVID-19 are fever, fatigue, and dry cough. Atypical clinical symptoms include expectoration, headache, hemoptysis, nausea, vomiting, and diarrhea. Chemosensory dysfunction, such as loss of smell and taste, is also closely associated with COVID-19 infection but is usually recovered within 2 to 4 weeks after infection (Yan et al., 2020). Some confirmed patients are asymptomatic (Chang et al., 2020 Ki and Task Force for 2019-nCoV, 2020 Rothe et al., 2020) or have low fever, mild fatigue, or other symptoms, without presenting with pneumonia, and most recovered after 1 week (Prevention, 2020). A meta-analysis of a number of research studies was conducted, and the following abnormalities in blood indicators were found: decreased albumin (75.8%), increased C-reactive protein (58.3%), increased lactate dehydrogenase (LDH) (57.0%), decreased lymphocytes (43.1%), and increased erythrocyte sedimentation rate (ESR) (41.8%). In addition, chest X-ray examination revealed that most novel coronavirus pneumonia patients presented with bilateral lung injury (72.9%) which was primarily characterized by ground-glass opacities (68.5%) (Rodriguez-Morales et al., 2020). CT imaging analysis of 130 COVID-19 patients showed that their distribution centered in the subpleural and lobular zones, with the two possibly merged into a sheet or progressing into bilobal diffuse opacities, in severe cases (Figure 2). During the recovery period, the margins of consolidation opacities contract, the bronchi expand, and subpleural linear or fibrous opacities are the primary features (Wu J. et al., 2020). In addition, lung lesions in recovered coronavirus pneumonia patients disappear completely on CT, and there are no symptoms of fibrosis, which differs completely from SARS. Therefore, one tentative suggestion is that alveolar epithelial cells may become functional lesions.

Figure 2. The clinical symptoms, treatment and prevention of COVID-19 pneumonia. ARDS, acute respiratory distress syndrome LDH, lactate dehydrogenase ESR, erythrocyte sedimentation rate mNGS, metagenomic next-generation sequencing RT-PCR, reverse transcription- polymerase chain reaction RT-LAMP, reverse transcription loop-mediated isothermal amplification ELISA, enzyme-linked immunosorbent assay GICA, gold immunochromatography assay siRNA, small interfering RNA ASO, antisense oligonucleotides IFN- α, Interferon-α QPD, qingfei paidu decoction.

The clinical classification of COVID-19 is primarily divided into mild, normal, severe and critical, based on clinical symptoms, clinical indicators, and imaging (Kenneson and Cannon, 2007 General Office of National Health Commission, 2020b). An analysis of the clinical typing of 1,099 confirmed patients found that the proportion of severe patients was 15.7% (Guan et al., 2020a). Classification of the 8,866 patients in China found that the proportions of severe, normal, and mild cases were 25.5, 69.9, and 4.5%, respectively (Yang et al., 2020). In addition, a study reported 18.5% critically ill patients among 72,314 patients (Novel Coronavirus Pneumonia Emergency Response Epidemiology Team, 2020). In summary, most COVID-19 patients are of the normal and mild types. Analysis of clinical characteristics showed that critically ill patients presented with moderate to low fever and even no obvious fever, in some cases, with dyspnea presenting after 1 week. In severe cases, they progressed rapidly to acute respiratory distress syndrome (ARDS), septic shock, metabolic acidosis which was difficult to correct, and coagulopathy (Prevention, 2020), as well as injury to the kidney, heart, and other organs, and even multiple organ failure (Huang et al., 2020 Wang D. et al., 2020). These clinical symptoms suggest that SARS-CoV-2 infection, in addition to affecting the lungs, also has clinical presentations that involve invasion of other organs such as liver, kidney, heart, esophagus, bladder, ileum, and pancreas (Chen N. et al., 2020 Liu F. et al., 2020 Novel Coronavirus Pneumonia Emergency Response Epidemiology Team, 2020 Xu et al., 2020b Zou X. et al., 2020). Recent reports suggested that human liver ductal organoids were permissive to SARS-CoV-2 infection and support robust replication, which impaired the barrier and bile acid transporting functions of cholangiocytes, indicated a potential cause of liver damage by viral infection (Zhao et al., 2020a). However, liver damage in patients with SARS-CoV-2 infection may not be directly caused by viral infection, but by the systemic inflammatory response caused by therapeutic drugs or pneumonia (Chai et al., 2020). In addition, studies have confirmed that renal insufficiency is common in patients with COVID-19, which may be one of the main causes of COVID-19 eventually leading to multiple organ failure and even death (Li Z. et al., 2020). However, Xu et al. reported that, among 62 patients with SARS-CoV-2 infection in Zhejiang, kidney damage was rare (Xu X. W. et al., 2020). This may be due to factors such as the timely admission of diagnosed patients, small sample size, or the virulence of the virus may decrease with increasing passage number. In addition, the results of different analyses of the clinical characteristics of COVID-19 by different researchers are inconsistent, and may be affected by factors such as the region from which samples originated, sample size, methods of analysis, and the level of expertise in the local medical center. By comparing the sex-related hormones between 81 men of childbearing age and 100 men infected with novel coronavirus, it was found that serum luteinizing hormone (LH) increased significantly, but the ratio of testosterone (T) to LH and the ratio of male follicle stimulating hormone (FSH) to LH decreased significantly (Ma L. et al., 2020). Moreover, one study reported that ACE2 is highly expressed in renal tubular cells, leydig cells, and cells in seminiferous ducts in testis. Therefore, virus might directly bind to such ACE2 positive cells and damage the kidney and testicular tissue of patients (Fan C. et al., 2020). Shastri et al. reported that male subjects have delayed viral clearance of SARS-CoV2 than female subjects (Shastri et al., 2020). Taken together, these suggest that there is potential hypogonadism and attention should be paid to the effect of SARS-CoV-2 on the reproductive system. However, there are reports that the semen samples or testicular biopsy samples of 13 COVID-19 patients (12 recovered patients and 1 deceased) were all negative for SARS-CoV-2, suggesting that SARS-CoV-2 may not infect human reproductive system (Song et al., 2020). Therefore, further research is warranted to explore whether SARS-CoV-2 will influence the reproductive system. In addition, the pathological anatomy of patients with severe cases included bilateral diffuse alveolar injury and pulmonary interstitial mononuclear cell infiltration, with lymphocytes predominating (Xu et al., 2020b). In which case, type IV hypersensitivity may be involved in lung damage in patients with severe novel coronavirus pneumonia. In addition, at early stages of the disease, it is possible that dysfunctional antiviral IFN in type II alveolar epithelial cells causes type I hypersensitivity-like changes (complement mediated cell lysis fragments), leading to increased pulmonary exudation. In summary, early identification and timely treatment of critical cases, timely attention to the functions of various organs, and effective intervention are essential to prevent multiple organ failure and thus reduce mortality.

Aims and scope

Reproductive Biology and Endocrinology publishes and disseminates high-quality results from excellent research in the reproductive sciences.

The journal publishes on topics covering gametogenesis, fertilization, early embryonic development, embryo-uterus interaction, reproductive development, pregnancy, uterine biology, endocrinology of reproduction, control of reproduction, reproductive immunology, neuroendocrinology, and human reproductive medicine.

We will consider manuscripts examining non-human primates, rats, and mice if they inform the human condition. However, we will no longer review studies involving horses, dogs, sheep, cats, cows, pigs, birds, or fish.

Co-Editor-in-Chief, David B Seifer

David B. Seifer is a Professor of Obstetrics & Gynecology at Yale School of Medicine in the Division of Reproductive Endocrinology and Infertility at Yale New Haven Hospital Medical Center in New Haven, CT (USA). Dr. Seifer is a consultant for the Food and Drug Administration (USA) and has published over 150 medical studies in peer-reviewed journals.

His translational research has focused upon the biology of ovarian function, particularly the aging ovary. This research has resulted in a better understanding of normal and abnormal follicular development during ART and in identifying novel human ovarian growth factors (ie. neurotrophins, inhibin B and AMH) which inform the clinical practice of reproductive medicine. His clinical research has focused upon health care disparities among women with infertility and the role of vitamin D in female reproductive health and polycystic ovarian syndrome.


Repeating an experiment to be confident that an observed effect represents a real phenomenon is key in biology and the reproducibility (or lack thereof) of research has garnered much attention recently. One important factor to consider (and report) is whether the replicates for each experiment are biological replicates or technical replicates. Broadly speaking, biological replicates are biologically distinct samples (e.g. the same type of organism treated or grown in the same conditions), which show biological variation technical replicates are repeated measurements of a sample, which show variation of the measuring equipment and protocols.

Figure 1a shows the results of three experiments, each measuring the increase in expression of a protein in a knockout mouse model relative to the wild type, and the authors say in each case that “n = 7”. However, only Experiment 3 provides statistically significant support for their conclusions because of the inappropriate way the data were handled in Experiments 1 and 2.

a Increased expression of Proteins A, B and C in geneX knockout mice relative to wild type (WT). n = 7 for each experiment. b The same data as in a, but with individual data points plotted. Different colours refer to different mice. *P < 0.05, two-tailed t-test. Error bars show SD

In Fig. 1b, the data values for each of the seven “n” values are plotted in each experiment. Different colours represent different biological replicates (i.e. different mice). Experiment 3 used seven different mice, measured once each. Experiment 1 used three mice but measured one of them five times, resulting in five technical replicates (red crosses). This skews the calculated mean heavily in favour of results from that mouse. If those technical replicates were combined into a single value, and the results from each mouse were given equal weighting, the overall result would no longer be statistically significant, since mice #2 and #3 (blue and green crosses) show no real change compared with the wild type.

In Experiment 2, although seven values are given, these come from only two mice (two biological replicates), with each measured more than once. The n value should therefore be reported as 2, not 7, and p values shouldn’t be given. The authors should also be concerned that the technical replicates for mouse #2 (blue crosses) are very different, suggesting that their equipment was faulty.

Proteome patterns between treated and control cells - Biology

Foundational research — as opposed to applied research — is research conducted not to solve a specific problem, but to satisfy a driving curiosity about the unknown. Seemingly simple queries, such as ‘How do cells divide?’ or ‘How can plants control how their genes are expressed?’ can pave the way for more applied research in therapeutics, cancer biology and genetics, and open up vast new fields of study that reshape the way we understand biology.

Through basic research, Robert Weinberg discovered the first gene known to cause cancer in humans.

Ruth Lehmann has made key discoveries on the biogenesis of piRNAs and their potential role in maintaining germ cell genomic integrity while allowing for genetic variation.

Jing-Ke Weng probes plants used in traditional Chinese medicine to discover new drugs.

Since its beginnings in 1982, Whitehead Institute has explored the core questions that underlie our basic understanding of biology. And while our mission remains the same, much has changed in the intervening decades new research tools allow our scientists to look deeper and design better experiments than ever before. The more we understand about how biological systems work, the better we can design informed and effective therapies and treatments to meet the challenges of modern society.

Silvi's work on RNA structures can reveal insights about viruses such as HIV and SARS-CoV-2.

Whitehead Fellow Silvi Rouskin

Brit d’Arbeloff and David Page (both seated) with Page lab postdoctoral fellow Adrianna San Roman (left) and Sahin Naqvi (rear), then a Page lab graduate student and now a postdoctoral fellow at Stanford University.

Research in David Page’s lab explores how sex chromosomes affect women’s health and diseases.

Researchers at Whitehead Institute are world-renowned for their contributions to cancer biology, genomics and more, and the impact of our publications positions us as the top research institution in the world for molecular biology and genetics. The Institute's location in Kendall Square creates an exciting and collaborative scientific environment where our scientists often team up with researchers at Harvard, the Koch Institute, and MIT on cross-disciplinary studies. Whitehead Institute also prioritizes giving back to the community, hosting summer programs, lecture series, and other events for scientists and aspiring scientists.

Whitehead Institute’s faculty has received awards from the Genetic Society of America, and National Academy of Medicine, and more, and several PIs are members of the National Academy of Sciences. Seven faculty members are investigators for the Howard Hughes Medical Institute, and four over the past 38 years have won the National Medal of Science.

Whitehead’s public programs, for example the Expedition Bio program for middle school students, merge curiosity with real world science through hands-on activities, laboratory modules and discussions with researchers.

Proteome patterns between treated and control cells - Biology

Targeted therapy is a cancer treatment that uses drugs to target specific genes and proteins that are involved in the growth and survival of cancer cells. Targeted therapy can affect the tissue environment that helps a cancer grow and survive or it can target cells related to cancer growth, like blood vessel cells.

Doctors often use targeted therapy along with chemotherapy and other treatments. The U.S. Food and Drug Administration (FDA) has approved targeted therapies for many types of cancers. Research is also underway to find new targeted therapy treatments.

How does targeted therapy work?

There are many types of cells that make up every tissue in your body. For example, there are blood cells, brain cells, and skin cells. Each type has its own job. Cancer starts when certain genes in healthy cells change and become abnormal over time. This change is called a genetic mutation.

Genes tell cells how to make proteins to keep the cell working. If the genes mutate, these proteins change, too. This can make cells divide too much or too quickly and allow the cells to live much longer than they normally would. When this happens, the cells grow out of control and form a tumor. Learn more about the genetics of cancer.

To develop targeted therapies, researchers first identify the genetic changes that help a tumor grow and change. A potential target for this therapy would be a protein that is present in cancer cells but not healthy cells. This can be caused by a mutation. Once researchers have identified a mutation, they develop a treatment that targets that specific mutation.

Targeted therapies can do different things to the cancer cells they target:

Block or turn off signals that tell cancer cells to grow and divide

Prevent the cells from living longer than normal

To best match the best targeted therapy for your tumor, your doctor may order tests to learn about the genes, proteins, and other factors that are unique to your tumor. This helps find the most effective treatment. Like other treatments, targeted therapies can cause side effects, so it is important that your doctor matches your tumor to the best possible treatment and dose. The dose of targeted therapy that you will receive is based on many factors, like your body weight and your risk of developing severe side effects. Talk to your health care team about why they recommended a certain dose.

Are there different types of targeted therapy?

There are several different types of targeted therapy. The most common types are monoclonal antibodies or small-molecule drugs.

Monoclonal antibodies. Drugs called monoclonal antibodies block a specific target on the outside of cancer cells. The target might also be in the area around this cancer. Monoclonal antibodies can also send toxic substances right to cancer cells. For example, they can help chemotherapy and radiation therapy reach cancer cells better. Monoclonal antibodies are also a type of immunotherapy.

Small-molecule drugs. Drugs called small-molecule drugs can block the process that helps cancer cells multiply and spread. Angiogenesis inhibitors are an example of this type of targeted therapy. Angiogenesis is the process for making new blood vessels. A tumor needs blood vessels to bring it nutrients. The nutrients help it grow and spread. Angiogenesis inhibitors starve the tumor by keeping new blood vessels from forming in the tissue around it.

Other types of targeted therapy include other immunotherapies, angiogenesis inhibitors, and apoptosis inducers (therapies that start cell death, or apoptosis).

Some types of targeted therapies are specific to a type of cancer. Others are known as tumor-agnostic or site-agnostic treatments. They treat tumors anywhere in the body by focusing on the specific genetic change instead of the type of cell. Learn more about tumor-agnostic treatments.

Examples of targeted therapies

Targeted therapies are a rapidly growing field of cancer research and researchers are studying many new targets and drugs through clinical trials. Below are a few examples of targeted therapies that are available now. Ask your health care team for more information.

Breast cancer. About 20% to 25% of breast cancers have too much of a protein called human epidermal growth factor receptor 2 (HER2). This protein makes tumor cells grow. If the cancer is "HER2 positive", there are many targeted therapy options. Learn more about targeted therapy for breast cancer.

Chronic myeloid leukemia (CML). Almost all cases of chronic myeloid leukemia are driven by the formation of a gene called BCR-ABL. This gene leads to the production of an enzyme called the BCR-ABL protein. This protein causes normal myeloid cells to start behaving like cancer cells. This was the very first mutation and cancer treated with targeted therapy. Learn more about targeted therapy for chronic myeloid leukemia.

Colorectal cancer. Colorectal cancer often makes too much of a protein called epidermal growth factor receptor (EGFR). Drugs that block EGFR may help stop or slow cancer growth. These cancers have no mutation in the KRAS gene. Another option is a drug that blocks vascular endothelial growth factor (VEGF). This protein helps make new blood vessels. Learn more about targeted therapy for colorectal cancer.

Lung cancer. Drugs that block EGFR may also stop or slow lung cancer growth. This may be more likely if the EGFR has certain mutations. There are also drugs for lung cancer with mutations in the ALK and ROS genes. Doctors can also use angiogenesis inhibitors for some lung cancers. Learn more about targeted therapy for non-small cell lung cancer.

Lymphoma. In lymphoma, there is an overproduction of B cells, a type of white blood cell that fights infections. Targeted drugs that block the enzyme that leads to this overproduction of B cells have been very successful for the treatment of lymphomas and some B-cell leukemias. Learn more about targeted therapies for lymphoma.

Melanoma. About half of melanomas have a mutation in the BRAF gene. Researchers know certain BRAF mutations make good drug targets. So there are many FDA-approved BRAF inhibitors. But these drugs can be harmful if your tumor do not have the BRAF mutation. Learn more about targeted therapy for melanoma.

The list of examples above does not include every targeted therapy. You can learn more about targeted therapy in each cancer-specific section on Cancer.Net in the "Types of Treatment" and "Latest Research" pages. You can also learn more about the latest targeted therapy research on the Cancer.Net blog.

Are there limitations to targeted therapy?

As with any cancer treatment, targeted therapy may not be the best treatment for every person with cancer. It may seem simple to use a drug for your specific cancer, but targeted therapy is complex, and it does not always work. It is important to know that:

A targeted treatment will not work if the tumor does not have the target

Having the target does not mean the tumor will respond to the drug

The response to the treatment may not last over time

For example, the target may not be as important for the cancer’s growth as previously thought, so the drug does not provide much benefit. Or cells may become resistant to the targeted therapy, so the drug might work at first but then stop working.

Also, targeted therapy drugs may cause serious side effects. These are usually not the same as chemotherapy effects. For example, people who get targeted therapy often have skin, hair, nail, or eye problems. It is always important to talk with your doctor about the specific side effects possible for each drug in your treatment plan.

Targeted therapy is an important type of cancer treatment. But, so far, doctors can only treat a few cancers with targeted therapy. Most people with cancer also need surgery, chemotherapy, radiation therapy, or hormone therapy.

Questions to ask your health care team

Talk with your health care team about whether targeted therapy may be part of your treatment plan. If so, consider asking these questions:

What type of targeted therapy do you recommend? Why?

What are the goals of this treatment?

Will targeted therapy be my only treatment? If not, what other treatments will be a part of my treatment plan?

How will I receive targeted therapy treatment and how often?

What are the possible short-term and long-term side effects of targeted therapy?

How will this treatment affect my daily life? Will I be able to work, exercise, and perform my usual activities?


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