Category Archives: Stem Cells

Scientists in the Perelman School of Medicine have uncovered the molecular causes of a congenital form of dilated cardiomyopathy (DCM), an often-fatal heart disorder.

This inherited form of DCMwhich affects at least several thousand people in the United States at any one time and often causes sudden death or progressive heart failureis one of multiple congenital disorders known to be caused by inherited mutations in a gene called LMNA. The LMNA gene is active in most cell types, and researchers have not understood why LMNA mutations affect particular organs such as the heart while sparing most other organs and tissues.

In a study published in Cell Stem Cell, the Penn Medicine scientists used stem cell techniques to grow human heart muscle cells containing DCM-causing mutations in LMNA. They found that these mutations severely disrupt the structural organization of DNA in the nucleus of heart muscle cellsbut not two other cell types studiedleading to the abnormal activation of non-heart muscle genes.

Were now beginning to understand why patients with LMNA mutations have tissue-restricted disorders such as DCM even though the gene is expressed in most cell types, says study co-senior author Rajan Jain, an assistant professor of cardiovascular medicine and cell and developmental biology at the Perelman School of Medicine.

This story is by Sophie Kluthe. Read more at Penn Medicine News.

Link:
Stem cell study illuminates the cause of an inherited heart disorder | Penn Today - Penn Today

JSP191 combined with low dose total body irradiation (TBI) and fludarabine is a safe, well-tolerated treatment option capable of clearing minimal residual disease (MRD)positive acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) in older adult patients undergoing nonmyeloablative allogeneic hematopoietic cell transplantation (HCT), according to a poster presented at the 2021 Transplantation and Cellular Therapy Meetings.

While the results of this phase 1 trial (NCT04429191) are early, the investigators emphasized that these data are the first to demonstrate that the antiCD117 monoclonal antibody is safe and effective in this disease cohort.

We are developing a first-in-class monoclonal antibody (mAb), JSP191, which targets and depletes normal and MDS/AML disease-initiating hematopoietic stem cells, wrote the investigators. JSP191 acts by inhibiting stem cell factor binding to CD117 present on HSC. We and others showed in pre-clinical models that HSC depletion can be enhanced by combining anti-CD117 mAb with low dose total body radiation.

The anti-CD117 monoclonal antibody was administered to a total of 6 patients intravenously at a dose of 0.6 mg/kg. Of note, the study population consisted of patients aged 60 years or older with MRD detected via cytogenetics, difference from normal flow cytometry, or next-generation sequencing (NGS).

The dual primary end points of the study are the safety and tolerability of JSP191 combined with low dose total body radiation and fludarabine and of JSP191 pharmacokinetics. The secondary end points include engraftment and donor chimerism, MRD clearance, event-free survival, and overall survival, among others.

The team used serum concentration of JSP191 determined by pharmacokinetics to establish the predicted JSP191 clearance and safety for the administration of fludarabine at 30 mg/m2 per day for 3 days, at days 4, -3, and -2 leading up to transplant.

At 28 days following transplant, 5 out of 6 patients showed signs of complete (>95%) donor CD15 myeloid chimerism in the peripheral blood.

To this point, there has been no evidence of significant infusion toxicities or JSP191-related serious adverse events. Also, a reduction or elimination of MRD in all subjects was seen at 28 days following transplant.

The research team explained that blood stem cell transplantation may offer the only curative therapy for many forms of both AML and MDS. Even though the current standard-of-care conditioning regimens administered before blood stem cell transplantation are well tolerated, they remain associated with increased relapse rates due to the prevalence of disease-causing hematopoietic stem cells and inadequate graft versus leukemia effect.

Further accrual for this study continues, while correlative analyses focusing on JPS191s impact with disease-initiating hematopoietic stem cells are ongoing.

References:

Muffly L, Kwon HS, Chin M, et al. Phase 1 study of JSP191, an anti-CD117 monoclonal antibody, with low dose irradiation and fludarabine in older adults with MRD-positive AML/MDS undergoing allogeneic HCT. Presented at the 2021 Transplantation and Cellular Therapy Meetings, held February 8-12, 2021. Abstract LBA5.

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JSP191 With Low Dose Irradiation and Fludarabine is Safe and Effective for Patients with MRD+ AML/MDS - Cancer Network

ELK CITY, Idaho, Feb.10, 2021 /PRNewswire/ --Therapeutic Solutions International, Inc., (OTC Markets: TSOI), announced today acquisition of the JadiCell, cell therapy, for use in the treatment of acute respiratory distress syndrome and other lung pathologies.

"Having worked with the Team at Therapeutic Solutions International for over 4 years, I am glad to place our highly promising and clinically advanced stem cell therapy into this innovative and cutting-edge company," said Dr. Amit Patel, inventor of the JadiCell. "Therapeutic Solutions International is unique in that it is currently running clinical trials in the area of nutraceuticals, as well as developing preclinical and clinical stage immunotherapies. There are numerous synergies to be had with the existing work and expertise in the Company."

"While there is a lot of excitement about various approaches to lung inflammation, there are very few therapies that not only potently block pathological immunity while concurrently induce regeneration of pulmonary tissues," said Dr. James Veltmeyer, Chief Medical Officer of the Company. "To date, by far the most promising regenerative therapy our scientists have worked with for acute respiratory distress syndrome (ARDS) has been the JadiCell. I am honored to work with our team of experts such as Dr. Francesco Marincola and Dr. Santosh Kesari in leading the JadiCell through Phase III and into the hands of patients."

"It is a significant accomplishment to acquire rights to this extremely promising and cost-effective technology that is scalable and functions as a 'cellular drug,'" said Famela Ramos, Vice President of Business Development. "To our knowledge this is the only stem cell therapy for lung pathologies that does not require animal components and can be generated in sufficient quantities to address the multi-billion-dollar market of ARDS."

"Dr. Patel and his team have been strong collaborators with us since our first licensing deal using the JadiCell for Chronic Traumatic Encephalopathy," stated Timothy Dixon, President and CEO of the Company. "Having worked with these cells, we appreciate that to date they are by far the most effective at production of cytokines, stimulation of regeneration, and inhibition of pathological inflammation. We are extremely confident in our ability to take these cells to the finish line in treatment of end stage lung disease."

About Therapeutic Solutions International, Inc.Therapeutic Solutions International is focused on immune modulation for the treatment of several specific diseases. The Company's corporate website is http://www.therapeuticsolutionsint.com, and our public forum is https://board.therapeuticsolutionsint.com/

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Therapeutic Solutions International Acquires Stem Cell Therapy That Successfully Completed FDA Double Blind Placebo Controlled Efficacy Study for Lung...

JSP191 in combination with low-dose total body irradiation (TBI) and fludarabine has demonstrated efficacy and tolerability in older patients with minimal residual disease (MRD)positive acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) who are undergoing nonmyeloablative allogeneic hematopoietic cell transplantation (HCT), according to a poster presented at the 2021 Transplantation and Cellular Therapy Meetings.1

While the results of this phase 1 trial (NCT04429191) are early, the investigators emphasized that these data are the first to demonstrate that the antiCD117 monoclonal antibody is safe and effective in this disease cohort.

We are developing a first-in-class monoclonal antibody (mAb), JSP191, which targets and depletes normal and MDS/AML disease-initiating hematopoietic stem cells, wrote the investigators. JSP191 acts by inhibiting stem cell factor binding to CD117 present on HSC. We and others showed in pre-clinical models that HSC depletion can be enhanced by combining anti-CD117 mAb with low dose total body radiation.

The anti-CD117 monoclonal antibody was administered to a total of 6 patients intravenously at a dose of 0.6 mg/kg. Of note, the study population consisted of patients aged 60 years or older with MRD detected via cytogenetics, difference from normal flow cytometry, or next-generation sequencing (NGS).

The dual primary end points of the study are the safety and tolerability of JSP191 combined with low dose total body radiation and fludarabine and of JSP191 pharmacokinetics. The secondary end points include engraftment and donor chimerism, MRD clearance, event-free survival, and overall survival, among others.

The team used serum concentration of JSP191 determined by pharmacokinetics to establish the predicted JSP191 clearance and safety for the administration of fludarabine at 30 mg/m2 per day for 3 days, at days 4, -3, and -2 leading up to transplant.

At 28 days following transplant, 5 out of 6 patients showed signs of complete (>95%) donor CD15 myeloid chimerism in the peripheral blood.

To this point, there has been no evidence of significant infusion toxicities or JSP191-related serious adverse events. Also, a reduction or elimination of MRD in all subjects was seen at 28 days following transplant.

The research team explained that blood stem cell transplantation may offer the only curative therapy for many forms of both AML and MDS. Even though the current standard-of-care conditioning regimens administered before blood stem cell transplantation are well tolerated, they remain associated with increased relapse rates due to the prevalence of disease-causing hematopoietic stem cells and inadequate graft versus leukemia effect.

Further accrual for this study continues, while correlative analyses focusing on JPS191s impact with disease-initiating hematopoietic stem cells are ongoing.

References

Muffly L, Kwon HS, Chin M, et al. Phase 1 study of JSP191, an anti-CD117 monoclonal antibody, with low dose irradiation and fludarabine in older adults with MRD-positive AML/MDS undergoing allogeneic HCT. Presented at the 2021 Transplantation and Cellular Therapy Meetings, held February 8-12, 2021. Abstract LBA5.

Read more here:
JSP191 With Low-Dose Irradiation and Fludarabine Shows Promise in MRD+ AML/MDS - OncLive

BOSTON--(BUSINESS WIRE)--Gamida Cell Ltd. (Nasdaq: GMDA), an advanced cell therapy company committed to cures for blood cancers and serious hematologic diseases, today announced the results of a Phase 3 clinical study of omidubicel presented in an oral session at the Transplantation & Cellular Therapy Meetings of the American Society of Transplantation and Cellular Therapy (ASTCT) and Center for International Blood & Marrow Transplant Research (CIBMTR), or the TCT Meetings. Omidubicel is an advanced cell therapy under development as a potential life-saving allogeneic hematopoietic stem cell transplant solution for patients with hematologic malignancies.

This clinical data set was from the international, multi-center, randomized Phase 3 study of omidubicel that was designed to evaluate the safety and efficacy of omidubicel in patients with high-risk hematologic malignancies undergoing a bone marrow transplant compared to a comparator group of patients who received a standard umbilical cord blood transplant. This is the first presentation of these data in a peer-reviewed conference. The full presentation is available on the Gamida Cell website.

The results of this global Phase 3 study of omidubicel in patients with hematologic malignancies show that omidubicel resulted in faster hematopoietic recovery, fewer bacterial and viral infections and fewer days in hospital, all of which are meaningful results and represent potentially important advancements in care when considering the patient experience following transplant, said Mitchell Horwitz, M.D., principal investigator and professor of medicine at the Duke Cancer Institute. The comparator, a transplant with umbilical cord blood, has been historically shown to result in low incidence of graft versus host disease (GvHD) in relation to other graft sources, and in this study, omidubicel demonstrated a GvHD profile similar to the comparator. Moreover, previous studies have shown that engraftment with omidubicel is durable, with some patients in the Phase 1/2 study receiving their transplant more than 10 years ago. The data presented at this meeting indicate that omidubicel has the potential to be considered a new standard of care for patients who are in need of stem cell transplantation but do not have access to a matched donor.

Details of Phase 3 Efficacy and Safety Results Shared at the TCT Meetings

Patient demographics including racial and ethnic diversity and baseline characteristics were well-balanced across the two study groups. The studys intent-to-treat analysis included 125 patients aged 1365 years with a median age of 41. Diseases included acute lymphoblastic leukemia, acute myelogenous leukemia, chronic myelogenous leukemia, myelodysplastic syndrome or lymphoma. Patients were enrolled at more than 30 clinical centers in the United States, Europe, Asia, and Latin America.

Gamida Cell previously reported in May 2020 that the study achieved its primary endpoint, showing that omidubicel demonstrated a statistically significant reduction in time to neutrophil engraftment, a measure of how quickly the stem cells a patient receives in a transplant are established and begin to make healthy new cells, and a key milestone in a patients recovery from a bone marrow transplant. The median time to neutrophil engraftment was 12 days for patients randomized to omidubicel compared to 22 days for the comparator group (p<0.001).

All three secondary endpoints demonstrated a statistically significant improvement among patients who were randomized to omidubicel in relation to patients randomized to the comparator group (intent-to-treat). Platelet engraftment was significantly accelerated with omidubicel, with 55 percent of patients randomized to omidubicel achieving platelet engraftment at day 42, compared to 35 percent for the comparator (p = 0.028). The rate of infection was significantly reduced for patients randomized to omidubicel, with the cumulative incidence of first grade 2 or grade 3 bacterial or invasive fungal infection for patients randomized to omidubicel of 37 percent, compared to 57 percent for the comparator (p = 0.027). Hospitalization in the first 100 days after transplant was also reduced in patients randomized to omidubicel, with a median number of days alive and out of hospital for patients randomized to omidubicel of 60.5 days, compared to 48.0 days for the comparator (p = 0.005). The details of these data were first reported in December 2020.

Previously unpublished data from the study relating to exploratory endpoints also support the clinical benefit demonstrated by the studys primary and secondary endpoints. There was no statistically significant difference between the two patient groups related to grade 3/4 acute GvHD (14 percent for omidubicel, 21 percent for the comparator) or all grades chronic GvHD at one year (35 percent for omidubicel, 29 percent for the comparator). Non-relapse mortality was shown to be 11 percent for patients randomized to omidubicel and 24 percent for patients randomized to the comparator (p=0.09).

These clinical data results will form the basis of a Biologics License Application (BLA) that Gamida Cell expects to submit to the U.S. Food and Drug Administration (FDA) in the second half of 2021.

We believe that omidubicel has the potential to transform the field of hematopoietic bone marrow transplant by expanding access to this potentially curative cell therapy treatment for thousands of patients who are in need of a transplant but lack access to a matched related donor, said Julian Adams, Ph.D., chief executive officer of Gamida Cell. Sharing the results of the Phase 3 study of omidubicel with the transplant community is a major moment for Gamida Cell, and we are forever grateful to the patients who participated in this study, their caregivers, and the work of the investigators and their teams.

About Omidubicel

Omidubicel is an advanced cell therapy under development as a potential life-saving allogeneic hematopoietic stem cell (bone marrow) transplant solution for patients with hematologic malignancies (blood cancers). In both Phase 1/2 and Phase 3 clinical studies (NCT01816230, NCT02730299), omidubicel demonstrated rapid and durable time to engraftment and was generally well tolerated.1,2 Omidubicel is also being evaluated in a Phase 1/2 clinical study in patients with severe aplastic anemia (NCT03173937). The aplastic anemia investigational new drug application is currently filed with the FDA under the brand name CordIn, which is the same investigational development candidate as omidubicel. For more information on clinical trials of omidubicel, please visit http://www.clinicaltrials.gov.

Omidubicel is an investigational therapy, and its safety and efficacy have not been established by the FDA or any other health authority.

About Gamida Cell

Gamida Cell is an advanced cell therapy company committed to cures for patients with blood cancers and serious blood diseases. We harness our cell expansion platform to create therapies with the potential to redefine standards of care in areas of serious medical need. For additional information, please visit http://www.gamida-cell.com or follow Gamida Cell on LinkedIn or Twitter at @GamidaCellTx.

Cautionary Note Regarding Forward Looking Statements

This press release contains forward-looking statements as that term is defined in the Private Securities Litigation Reform Act of 1995, including with respect to timing of anticipated regulatory submissions, which statements are subject to a number of risks, uncertainties and assumptions, including, but not limited to the progress and expansion of Gamida Cells manufacturing capabilities and other commercialization efforts and clinical, scientific, regulatory and technical developments. In light of these risks and uncertainties, and other risks and uncertainties that are described in the Risk Factors section and other sections of Gamida Cells Annual Report on Form 20-F, filed with the Securities and Exchange Commission (SEC) on February 26, 2020, its Report on Form 6-K filed with the SEC on August 12, 2020, and other filings that Gamida Cell makes with the SEC from time to time (which are available at http://www.sec.gov), the events and circumstances discussed in such forward-looking statements may not occur, and Gamida Cells actual results could differ materially and adversely from those anticipated or implied thereby. Any forward-looking statements speak only as of the date of this press release and are based on information available to Gamida Cell as of the date of this release.

1 Horwitz M.E., Wease S., Blackwell B., Valcarcel D. et al. Phase I/II study of stem-cell transplantation using a single cord blood unit expanded ex vivo with nicotinamide. J Clin Oncol. 2019 Feb 10;37(5):367-374.

2 Gamida Cell press release, Gamida Cell Announces Positive Topline Data from Phase 3 Clinical Study of Omidubicel in Patients with High-Risk Hematologic Malignancies, issued May 12, 2020. Last accessed August 31, 2020.

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Gamida Cell Presents Efficacy and Safety Results of Phase 3 Study of Omidubicel in Patients with Hematologic Malignancies at the 2021 TCT Meetings of...

If you've ever wondered why we get goosebumps, you're in good company -- so did Charles Darwin, who mused about them in his writings on evolution. Goosebumps might protect animals with thick fur from the cold, but we humans don't seem to benefit from the reaction much -- so why has it been preserved during evolution all this time?

In a new study, Harvard University scientists have discovered the reason: the cell types that cause goosebumps are also important for regulating the stem cells that regenerate the hair follicle and hair. Underneath the skin, the muscle that contracts to create goosebumps is necessary to bridge the sympathetic nerve's connection to hair follicle stem cells. The sympathetic nerve reacts to cold by contracting the muscle and causing goosebumps in the short term, and by driving hair follicle stem cell activation and new hair growth over the long term.

Published in the journalCell, these findings in mice give researchers a better understanding of how different cell types interact to link stem cell activity with changes in the outside environment.

"We have always been interested in understanding how stem cell behaviors are regulated by external stimuli. The skin is a fascinating system: it has multiple stem cells surrounded by diverse cell types, and is located at the interface between our body and the outside world. Therefore, its stem cells could potentially respond to a diverse array of stimuli -- from the niche, the whole body, or even the outside environment," said Ya-Chieh Hsu, the Alvin and Esta Star Associate Professor of Stem Cell and Regenerative Biology, who led the study in collaboration with Professor Sung-Jan Lin of National Taiwan University. "In this study, we identify an interesting dual-component niche that not only regulates the stem cells under steady state, but also modulates stem cell behaviors according to temperature changes outside."

A system for regulating hair growth

Many organs are made of three types of tissue: epithelium, mesenchyme, and nerve. In the skin, these three lineages are organized in a special arrangement. The sympathetic nerve, part of our nervous system that controls body homeostasis and our responses to external stimuli, connects with a tiny smooth muscle in the mesenchyme. This smooth muscle in turn connects to hair follicle stem cells, a type of epithelial stem cell critical for regenerating the hair follicle as well as repairing wounds.

The connection between the sympathetic nerve and the muscle has been well known, since they are the cellular basis behind goosebumps: the cold triggers sympathetic neurons to send a nerve signal, and the muscle reacts by contracting and causing the hair to stand on end. However, when examining the skin under extremely high resolution using electron microscopy, the researchers found that the sympathetic nerve not only associated with the muscle, but also formed a direct connection to the hair follicle stem cells. In fact, the nerve fibers wrapped around the hair follicle stem cells like a ribbon.

"We could really see at an ultrastructure level how the nerve and the stem cell interact. Neurons tend to regulate excitable cells, like other neurons or muscle with synapses. But we were surprised to find that they form similar synapse-like structures with an epithelial stem cell, which is not a very typical target for neurons," Hsu said.

Next, the researchers confirmed that the nerve indeed targeted the stem cells. The sympathetic nervous system is normally activated at a constant low level to maintain body homeostasis, and the researchers found that this low level of nerve activity maintained the stem cells in a poised state ready for regeneration. Under prolonged cold, the nerve was activated at a much higher level and more neurotransmitters were released, causing the stem cells to activate quickly, regenerate the hair follicle, and grow new hair.

The researchers also investigated what maintained the nerve connections to the hair follicle stem cells. When they removed the muscle connected to the hair follicle, the sympathetic nerve retracted and the nerve connection to the hair follicle stem cells was lost, showing that the muscle was a necessary structural support to bridge the sympathetic nerve to the hair follicle.

How the system develops

In addition to studying the hair follicle in its fully formed state, the researchers investigated how the system initially develops -- how the muscle and nerve reach the hair follicle in the first place.

"We discovered that the signal comes from the developing hair follicle itself. It secretes a protein that regulates the formation of the smooth muscle, which then attracts the sympathetic nerve. Then in the adult, the interaction turns around, with the nerve and muscle together regulating the hair follicle stem cells to regenerate the new hair follicle. It's closing the whole circle -- the developing hair follicle is establishing its own niche," said Yulia Shwartz, a postdoctoral fellow in the Hsu lab. She was a co-first author of the study, along with Meryem Gonzalez-Celeiro, a graduate student in the Hsu Lab, and Chih-Lung Chen, a postdoctoral fellow in the Lin lab.

Responding to the environment

With these experiments, the researchers identified a two-component system that regulates hair follicle stem cells. The nerve is the signaling component that activates the stem cells through neurotransmitters, while the muscle is the structural component that allows the nerve fibers to directly connect with hair follicle stem cells.

"You can regulate hair follicle stem cells in so many different ways, and they are wonderful models to study tissue regeneration," Shwartz said. "This particular reaction is helpful for coupling tissue regeneration with changes in the outside world, such as temperature. It's a two-layer response: goosebumps are a quick way to provide some sort of relief in the short term. But when the cold lasts, this becomes a nice mechanism for the stem cells to know it's maybe time to regenerate new hair coat."

In the future, the researchers will further explore how the external environment might influence the stem cells in the skin, both under homeostasis and in repair situations such as wound healing.

"We live in a constantly changing environment. Since the skin is always in contact with the outside world, it gives us a chance to study what mechanisms stem cells in our body use to integrate tissue production with changing demands, which is essential for organisms to thrive in this dynamic world," Hsu said.

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The real reason behind goosebumps - Jill Lopez

Abstract

Various characteristics of cancers exhibit tissue specificity, including lifetime cancer risk, onset age, and cancer driver genes. Previously, the large variation in cancer risk across human tissues was found to strongly correlate with the number of stem cell divisions and abnormal DNA methylation levels. Here, we study the role of synthetic lethality in cancer risk. Analyzing normal tissue transcriptomics data in the Genotype-Tissue Expression project, we quantify the extent of co-inactivation of cancer synthetic lethal (cSL) gene pairs and find that normal tissues with more down-regulated cSL gene pairs have lower and delayed cancer risk. Consistently, more cSL gene pairs become up-regulated in cells treated by carcinogens and throughout premalignant stages in vivo. We also show that the tissue specificity of numerous tumor suppressor genes is associated with the expression of their cSL partner genes across normal tissues. Overall, our findings support the possible role of synthetic lethality in tumorigenesis.

Cancers of different human tissues have markedly different molecular, phenotypic, and epidemiological characteristics, known as the tissue specificity in cancer. Various aspects of this intriguing phenomenon include a considerable variation in lifetime cancer risk, cancer onset age, and the genes driving the cancer across tissue types. The variation in lifetime cancer risk is known to span several orders of magnitude (1, 2). Such variation cannot be fully explained by the difference in exposure to carcinogens or hereditary factors and has been shown to strongly correlate with differences in the number of lifetime stem cell divisions (NSCD) estimated across tissues (2, 3). As claimed by Tomasetti and Vogelstein (2), these findings are consistent with the notion that tissue stem cell divisions can propagate mutations caused either by environmental carcinogens or random replication error (4). In addition, the importance of epigenetic factors in carcinogenesis has long been recognized (5), and Klutstein et al. (6) have recently reported that the levels of abnormal CpG island DNA methylation (LADM) across tissues are highly correlated with their cancer risk. Although both global (e.g., smoking and obesity) and various cancer typespecific (e.g., HCV infection for liver cancer) risk factors are well known (7), no factors other than NSCD and LADM have been reported to date to explain the across-tissue variance in lifetime cancer risk.

Besides lifetime cancer risk, cancer onset age, as measured by the median age at diagnosis, also varies among adult cancers (1). Although most cancers typically manifest later in life [more than 40 years old (1, 8)], some such as testicular cancer often have earlier onset (1). Many tumor suppressor genes (TSGs) and oncogenes are also tissue specific (911). For example, mutations in the TSG BRCA1 are predominantly known to drive the development of breast and ovarian cancer but rarely other cancer types (12). In general, factors explaining the overall tissue specificity in cancer could be tissue intrinsic (10, 13), and their elucidation can further advance our understanding of the forces driving carcinogenesis.

Synthetic lethality/sickness (SL) is a well-known type of genetic interaction, conceptualized as cell death or reduced cell viability that occurs under the combined inactivation of two genes but not under the inactivation of either gene alone. The phenomenon of SL interactions was first recorded in Drosophila (14) and then in Saccharomyces cerevisiae (15). In recent years, much effort has been made to identify SL interactions specifically in cancer, since targeting these cancer SLs (cSLs) has been recognized as a highly valuable approach for cancer treatment (1619). The effect of cSL on cancer cell viability has led us to investigate whether it plays an additional role even before tumors manifest, i.e., during carcinogenesis. In this study, we quantify the level of cSL gene pair co-inactivation in normal (noncancerous) human tissue as a measure of resistance to cancer development (termed cSL load, explained in detail below). We show that cSL load can explain a considerable level of the variation in cancer risk and cancer onset age across human tissues, as well as the tissue specificity of some TSGs. Together, these correlative findings support the effect of SL in impeding tumorigenesis across human tissues.

To study the potential effects of cSL in normal, noncancerous tissues, we define a measure called cSL load, which quantifies the level of cSL gene pair co-inactivation based on gene expression of normal human tissues from the Genotype-Tissue Expression (GTEx) dataset (20). Specifically, we used a recently published reference set of genome-wide cSLs that are common to many cancer types, identified from both in vitro and The Cancer Genome Atlas (TCGA) cancer patient data (21) via the identification of clinically relevant synthetic lethality (ISLE) (table S1A) (22, 23). For each GTEx normal tissue sample, we computed the cSL load as the fraction of cSL gene pairs (among all the genome-wide cSLs) that have both genes lowly expressed in that sample (Methods; illustrated in Fig. 1). We further defined tissue cSL load (TCL) as the median cSL load value across all samples of each tissue type in GTEx (Methods and table S2A). We then proceed to test our hypothesis that TCL can be a measure of the level of resistance to cancer development intrinsic to each human tissue (outlined in Fig. 1).

This diagram illustrates the computation of cSL load for each sample and each tissue type (i.e., TCL) and depicts the outline of this study, where we attempted to explain the tissue-specific lifetime cancer risk, cancer onset age, and TSGs using TCL. See main text and Methods for details.

SL is widely known to be context specific across species, tissue types, and cellular conditions (24). In theory, a cancer-specific cSL gene pair can be co-inactivated in the normal tissue without reducing normal cell fitness, while conferring resistance to the emergence of malignantly transformed cells due to the lethal effect specifically on the cancer cells. Different normal tissues can have varied TCLs (representing the levels of cSL gene pair co-inactivation) as a result of their specific gene expression profiles, and we hypothesized that normal tissues with higher TCLs should have lower cancer risk, as transforming cancerous cells in these tissues will face higher cSL-mediated vulnerability and lethality. To test this hypothesis, we obtained data on the tissue-specific lifetime cancer risk in humans (Methods) and correlated that with the TCL values computed for the different tissue types. We find a strong negative correlation between the TCL (computed from older-aged GTEx samples, age 50 years) and lifetime cancer risk across normal tissues (Spearmans = 0.664, P = 1.59 104; Fig. 2A and table S2A). This correlation is robust, as comparable results are obtained when this analysis is carried out in various ways (e.g., different cutoffs for low expression of genes, different cSL network sizes, and different cancer typenormal tissue mappings; fig. S1 and note S3). We also showed that this correlation is not confounded by the number of poised genes associated with bivalent chromatin, variation in cancer driver gene expression, and immune cell or fibroblast abundance (notes S11 to S13 and figs. S12 to S14). Notably, the cSL load varies with age due to age-related gene expression changes, and the correlation with lifetime cancer risk is not found when the TCL is computed on samples from the young population (20 age < 50 years, Spearmans = 0.0251, P = 0.901; fig. S2A); this is consistent with the observation that lifetime cancer risk is mostly contributed by cancers occurring in older populations (1). We still see a marked negative correlation between TCL and lifetime cancer risk when analyzing samples from all age groups together (Spearmans = 0.49, P = 0.01; fig. S2B). Repeating these analyses using different control gene pairs including (i) random gene pairs, (ii) shuffled cSL gene pairs, and (iii) degree-preserving randomized cSL network (same size as the actual cSL network; note S4) results in significantly weaker correlations (empirical P < 0.001; fig. S3, A to C, and note S4), confirming that the associations found with cancer risk results from a cSL-specific effect.

(A) Scatterplot showing Spearmans correlations between lifetime cancer risk and TCL computed for the older population (age 50 years) (ranked values are used as lifetime cancer risk spans several orders of magnitude.) (B) Lifetime cancer risks across tissues were predicted using linear models (under cross-validation) containing different sets of explanatory variables: (i) TCL only, (ii) the number of stem cell divisions (NCSD) only, and (iii) TCL and NSCD (27 data points). The prediction accuracy is measured by Spearmans , shown by the bar plots. The result of a likelihood ratio test between models (ii) and (iii) is also displayed. (C) A similar bar plot as in (B) comparing the predictive models for cancer risk involving the following variables: (i) TCL only, (ii) the LADM only, and (iii) TCL and LADM combined (21 data points only due to the smaller set of LADM data). A model containing all the three variables does not increase the prediction power (Spearmans = 0.77 under cross-validation) and is not shown. (D) Bar plot showing the correlations between lifetime cancer risk with TCLs computed (age 50 years) using subsets of cSLs: hcSLs, lcSLs, and all cSLs. Spearmans and P values are shown. The hcSLs and lcSLs are identified using data of matched TCGA cancer types and GTEx normal tissues (Methods), which correspond to only a subset of tissue types. To facilitate comparison, here, the correlation for all cSLs was also computed for the same subset of tissues, and therefore, the resulting correlation coefficient is different from that in (A).

While the randomized cSL networks used in the control tests described above provide significantly weaker correlations with cancer risk than those observed with cSLs, many of these correlations are still significant by themselves (fig. S3, B and C). This suggests that there may be a possible association between the expression of single genes in the cSL network (cSL genes) and cancer risk. To investigate this, we computed the tissue cSL single-gene load (SGL; the fraction of lowly expressed cSL genes) for each tissue (Methods). We do find a significant negative correlation between tissue SGL levels and cancer risk (Spearmans = 0.49, P = 0.01; fig. S3D and note S5). This correlation vanishes when we use random sets of single genes (fig. S3F). However, after controlling for the single-gene effect, the partial correlation between TCL and cancer risk is still highly significant (Spearmans = 0.69, P = 6.10 105; fig. S3G), pointing to the dominant role of the SL genetic interaction effect (note S5).

We next compared the predictive power of TCL to those obtained with the previously reported measures of NSCD (2, 3) and LADM (6), using the set of GTEx tissue types investigated here (Methods). We first confirmed the strong correlations of NSCD and LADM with tissue lifetime cancer risk in our specific dataset (Spearmans = 0.72 and 0.74, P = 2.6 105 and 1.3 104, respectively; fig. S4). These correlations are stronger than the one we reported above between TCL and cancer risk. However, adding TCL to either NSCD or LADM in linear regression models leads to enhanced predictive models of cancer risk compared to those obtained with NSCD or LADM alone [log-likelihood ratio (LLR) = 2.18 and 2.39, P = 0.037 and 0.029, respectively]. Furthermore, adding TCL to each of these factors increases their prediction accuracy under cross-validation (Spearmans s from 0.67 and 0.69 with NSCD and LADM alone to 0.71 and 0.77, respectively; Fig. 2, B and C). LADM and NSCD are significantly correlated (Spearmans = 0.66, P = 0.02), while the TCL correlates only in a borderline significant manner with either NSCD (Spearmans = 0.57, P = 0.06) or LADM (Spearmans = 0.52, P = 0.08). Together, these observations support the hypothesis that TCL is associated with tissue cancer risk, with a partially independent role from either NSCD or LADM.

We have shown results that support the role of TCL in impeding cancer development, and we reason that such an effect is dependent on the notion that many of the cSLs are specific to cancer while having weaker or no lethal effects in normal tissues. We tested and found that the co-inactivation of cSL gene pairs is under much weaker negative selection in GTEx normal tissues versus matched TCGA cancers [Wilcoxon rank sum test P = 2.93 106 (fig. S5A), also shown using cross-validation (note S7)]. Moreover, we hypothesize that those cSLs with the highest specificity to cancer (i.e., with the strongest SL effect in cancer and no or the weakest effect on normal cells) should have the strongest effect on cancer development. To test this, we identified the subset of such cSLs (termed highly specific cSLs or hcSLs) and those with the lowest specificity to cancer (termed lowly specific cSLs or lcSLs; Methods) and recomputed the TCLs of all normal GTEx tissues using these two cSL subsets, respectively. The TCLs computed from the hcSLs correlate much stronger with cancer lifetime risk than those computed from the lcSLs (Spearmans = 0.593 versus 0.319; Fig. 2D), testifying that these cSLs with high functional specificity to cancer are more relevant to carcinogenesis. These hcSLs are enriched for cell cycle, DNA damage response, and immune-related genes [false discovery rate (FDR) < 0.05; table S5 and Methods], which are known to play key roles in tumorigenesis.

We have thus established that TCL in the older population is inversely correlated with lifetime cancer risk across tissues. We next hypothesized that higher cSL load in a given normal tissue in the young population may delay cancer onset, which typically occurs later (age >40 years) (1). To test this, we use the median age at cancer diagnosis (1) of a certain tissue as its cancer onset age (table S3 and Methods). We find that the TCL values (for age 40 years) are markedly correlated with cancer onset age (Spearmans = 0.502, P = 0.011; Fig. 3A). This result is again robust to variations in our methods to compute TCL and cancer onset age (fig. S6, table S3, and note S3). We note that the cancer onset age is not significantly correlated with lifetime cancer risk (Spearmans = 0.279, P = 0.28).

(A) Scatterplot showing Spearmans correlations between cancer onset age and TCL (age 40 years). (B) Bar plot showing the correlations between cancer onset age with TCLs computed (age 40 years) using subsets of cSLs: hcSLs, lcSL, and all cSLs. Spearmans and P values are shown. As in Fig. 2D, this analysis was done for a subset of GTEx normal tissues for which we had matched TCGA cancer types to identify the hcSLs and lcSLs (Methods); therefore, the correlation result for all cSLs is also different from that in (A).

Similar to our earlier analysis, we see that the TCLs computed from the hcSLs correlate much stronger with onset age than those from the lcSLs or all cSLs (Spearmans = 0.603 versus 0.157; Fig. 3B and fig. S7A) and also stronger than those obtained from control tests performed as before (empirical P < 0.001; fig. S7, B to D). As with the case of cancer risk, the observed correlation is dominated by the SL genetic interaction effects rather than the single-gene effects (fig. S7, E to G, and note S5).

To further corroborate the relevance of cSL load to carcinogenesis, we next investigated whether carcinogen treatment in normal (noncancer) cell lines and primary cells in vitro can lead to cSL load decrease. First, we analyzed gene expression data from a recent study where human primary hepatocytes, renal tube epithelial cells, and cardiomyocytes were treated with the carcinogen and hepatotoxin thioacetamide-S-oxide (25). We computed the cSL load in each cell type after treatment versus control and found a significant decrease of cSL load only in the hepatocytes (Wilcoxon rank sum test P = 0.014; Fig. 4A), which is consistent with thioacetamide-S-oxides role as a hepatotoxin and a carcinogen primarily in the liver. Second, we collected the gene expression signatures of chemotherapy drug treatments in a total of four primary cells and normal cell lines from the Connectivity Map (CMAP) (26). We quantified the drug-induced cSL load changes indirectly from the gene signatures (Methods), comparing the strongly mutagenic DNA-targeting drugs (n = 6) including alkylating agents and DNA topoisomerase inhibitors to the weak/nonmutagenic taxanes and vinca alkaloids (n = 5), which act on the cytoskeleton and not directly on DNA (27). We find that the strong mutagenic chemotherapy drugs lead to a significantly larger decrease in cSL load (Fig. 4B, P = 0.03 from a linear model controlling for cell type; Methods). The strong mutagenicity of alkylating agents and DNA topoisomerase inhibitors is consistent with their mechanisms of actions; they are also World Health Organization class I carcinogens (28), supported by incidence of secondary cancers in patients treated by these drugs for their primary cancers (29). In contrast, taxanes and vinca alkaloids have shown negative or weak/inconclusive results in mutagenic tests (27, 30). These results are not likely affected by cell death, as the cSL decreased specifically only for the two classes among all tested chemotherapy drugs. Although the CMAP dataset used for this analysis does not include cell viability information, the gene expression of the cells does not show an apoptotic signature after the drug treatment.

(A) Box plots showing the cSL loads in control versus thioacetamide-S-oxidetreated samples in human primary hepatocytes (liver), renal tube epithelial cells (kidney), and cardiomyocytes (heart), using the data from (25). One-sided Wilcoxon rank-sum test P values are shown. (B) Box plots showing the cSL load changes after treatment by different classes of chemotherapy drugs in four cell types, using the CMAP data (26). Asterisk indicates that the cSL load change is estimated indirectly from the CMAP drug treatment gene expression signatures (Methods). Strongly mutagenic drugs (n = 6), including alkylating agents (green points) and DNA topoisomerase inhibitors (purple points), lead to a significantly larger cSL load decrease compared to weak or nonmutagenic drugs (n = 5), including taxanes (red points) and vinca alkaloids (blue points); P = 0.03 from a linear model controlling for cell type. HA1E is an immortalized kidney cell line; PHH, primary human hepatocyte; ASC, adipose-derived stem cell; SKB, human skeletal myoblast. (C) Box plots showing the cSL load in samples of different stages of premalignant lesions in the lung (including normal tissue and lung squamous cell carcinoma) (28). The cSL load shows an overall decreasing trend from normal to different pre-cancer stages to cancer (one-sided Wilcoxon rank sum test of normal versus cancer P = 4.47 105; ordinal logistic regression has negative coefficient 28.7, P = 5.89 107).

Further beyond these in vitro findings, analyzing a recently published lung cancer dataset (31), we find that cSL load decreases progressively as cancers develop from normal tissues throughout the multiple stages of premalignant lesions in vivo (normal versus cancer Wilcoxon rank sum test P = 4.47 105, ordinal logistic regression P = 5.89 107 with negative coefficient 28.7; Fig. 4C). These results provide further evidence supporting cSL as a factor that may be involved in cancer development.

Given the role of cSLs in cancer development, we turned to ask whether cSL may also contribute to the tissue/cancer-type specificity of TSGs (10, 32). Specifically, we reasoned that the loss of function of a gene is unlikely to have cancer-driving effects in tissues where its cSL partner genes are lowly expressed, due to the synthetic lethal effect of such co-inactivation on the emerging cancer cells. In other words, this gene is unlikely to be a TSG in such tissues. To study this hypothesis, we obtained a list of TSGs together with the tissues in which their loss is annotated to have a tumor-driving function from the COSMIC database (table S6A) (11). We further identified the cSL partner genes of each such TSG using ISLE (Methods and table S6B) (22). In total, there are 23 TSGs for which we were able to identify more than one cSL partner gene. Consistent with our hypothesis, we find that in most of the cases, the cSL partner genes of TSGs have higher expression levels in the tissues where the TSGs are known drivers compared to the tissues where they are not established drivers (binomial test for the direction of the effect P = 0.023; Fig. 5A). We identified 10 TSGs whose individual effects are significant (FDR < 0.05) and cSL specific (as shown by the random control test), and all these 10 cases exhibit the expected direction of effect (labeled in Fig. 5A and table S6C; two example TSGs, FAS and BRCA1, are shown in Fig. 5B, details are in fig. S8 and Methods). Reassuringly, these findings disappear under randomized control tests involving random partner genes of the TSGs and shuffled TSGtissue type mappings (note S9), further consolidating the role of cancer-specific cSLs of normal tissues in cancer risk and development.

(A) For each tissue-specific TSG gene Gi, the expression levels of its cSL partner genes in the tissue type(s) where gene Gi is a TSG were compared to those where gene Gi is not an established TSG, using GTEx normal tissue expression data. The volcano plot summarizes the result of comparison with linear models. Positive linear model coefficients (x axis) mean that the expression levels of the cSL partner genes are, on average, higher in the tissue(s) where gene Gi is a TSG. Many cases have near-zero P values and are represented by points (half-dots) on the top border line of the plot. Overall, there is a dominant effect of the cSL partner genes of TSGs having higher expression levels in the tissues where the TSGs are known drivers (binomial test P = 0.023). All TSGs with FDR < 0.05 that also passed the random control tests are labeled. (B) Examples of two well-known TSGs, FAS and BRCA1, are given. The heatmaps display the normalized expression levels of their cSL partner genes (rows) in tissues of where these two genes are known to be TSGs [according to the annotation from the COSMIC database (11)] and in tissues where they are not established TSGs (columns), respectively. High and low expressions are represented by red and blue, respectively. For clarity, one typical tissue type where the TSG is a known driver (e.g., testis for FAS) and three other tissue types where the TSG is not an established driver (and the least frequently mutated) are shown.

In this work, we show that the cSL load in normal tissues is a strong predictor of tissue-specific lifetime cancer risk and is much stronger than the pertaining predictive power observed on the individual gene level. Consistently, we find that higher cSL load in the normal tissues from young people is associated with later onset of the cancers of that tissue. As far as we know, no other factor has been previously reported to be predictive of cancer onset age across tissues. Furthermore, cSL load decreases upon carcinogen treatment in vitro and during cancer development through stages of precancerous lesions in vivo. Last, we show that the activity status of cSL partners of TSGs can explain their tissue-specific inactivation.

We have shown that the correlation between cSL and cancer risk in normal tissues may be explained by the fact that many of the cSLs are specific to cancer and have weak or no functional lethal effect in the normal tissues (Figs. 2D and 3B and fig. S5); therefore, normal tissues can bear relatively high cSL loads without being detrimentally affectedquite to the contrary, they become more resistant to cancer due to the latent effect of these cSLs on potentially emerging cancer cells. We emphasize that while we quantified the cSL loads using the normal tissue data from GTEx, the set of cSLs we used was derived exclusively in cancer from completely independent cancer datasets (and without using any information regarding lifetime cancer risk, onset, or tumor suppressor tissue specificity), so there is no circularity involved. The cSL load in normal tissues was computed to reflect the summed effects of individual cSL gene pairs. The underlying assumption is that the low expression of each cSL gene pair is synthetic sick (i.e., reducing cell fitness to some extent) and that the effects from different cSL gene pairs are additive, consistent with the ISLE method of cSL identification (22). Many experimental screenings of SL interactions also rely on techniques such as RNA interference that inhibits gene expression rather than completely knocks out a gene (33), and it is evident that most of the resulting SL gene pairs have milder than lethal effects. While these cSLs likely act via a diverse range of biological pathways and thus do not provide pathway-specific mechanisms, the additive cancer-specific lethal effect of such cSL gene pairs, however, could form a negative force impeding cancer development from normal tissues.

Obviously, as we are studying the across-tissue association between cSL load and cancer risk, it is essential to focus on cSLs that are common to many cancer types (i.e., pan-cancer). Therefore, we focused on cSLs identified computationally by ISLE via the analysis of the pan-cancer TCGA patient data (22). In contrast, most experimentally identified cSLs are obtained in specific cancer cell lines and are thus less likely to be pan-cancer [and possibly, less clinically relevant (22)]. However, for completeness, we also compiled a set of experimentally identified cSLs from published studies (22, 34) (note S1 and table S1B). The corresponding TCL values computed using this set of cSLs correlate significantly with lifetime cancer risk but not with cancer onset age; the correlation with cancer risk is also markedly weaker than that obtained from ISLE-derived cSLs [Spearmans = 0.433, P = 0.024 (fig. S9A), control tests and detailed analysis are explained in note S4]. These experimentally identified cSLs can explain some cases of tissue-specific TSGs including BRCA1 and BRCA2 (fig. S9E) but do not result in overall significant accountability for a large proportion of TSGs present in the analysis (like in Fig. 5A). This corroborates the importance of pan-cancer cSLs and their relevance to cancer risk.

TCL is not likely to be a corollary of NSCD and LADM [while LADM was thought to be closely related to NSCD (6)], as the cSL load is computed by analyzing expression data of bulk tissues, where stem cells occupy only a minor proportion. We have shown that TCL significantly adds to either NSCD or LADM in predicting lifetime cancer risk (Fig. 2, B and C), which also suggests that cSL load is an independent factor correlated with cancer risk with unique underlying mechanisms. Furthermore, NSCD is measured as the product of the rate of tissue stem cell division and the number of stem cells residing in a tissue (2), and we confirmed that TCL is correlated with lifetime cancer risk independent of both of these components (partial Spearmans = 0.510 and 0.567, P = 0.007 and 0.002, respectively; fig. S10, A and B). We additionally tested and verified that proliferation indices computed for the bulk normal tissues do not correlate with lifetime cancer risk across tissues (Spearmans = 0.062, P = 0.77; fig. S10C and note S10). Furthermore, we verified that our observed correlations are not confounded by the number of samples from each cancer or tissue type (fig. S11).

Since cSL load can vary with age, one may wonder whether cSL load could be extended to correlate with age-specific cancer risk within a tissue (as opposed to across tissues). However, variations in cancer risk across tissues and across ages can be driven by different factors. We did not find a consistent correlation between cSL load computed by age range and age-specific cancer risk in all tissue types (note S14 and fig. S15). Another extension to our current research question is studying the effect of higher-order genetic interactions on cancer risk, which is plausible but challenging to study due to the limited knowledge available on such complex interactions.

While revealing cSL as a previously unknown factor associated with cancer development, our study has several limitations. First, because of the importance of using pan-cancer cSLs as discussed above, we mainly relied on the cSLs computationally inferred by ISLE (22) as one of the most comprehensive pan-cancer cSL datasets. However, current cSL prediction algorithms are far from perfect and should not be regarded as the gold standard for general cSL identification. Only a minor fraction of the large number of predicted cSLs have been experimentally validated only in specific cell types. The cSLs inferred by ISLE should be best viewed as a set of candidate cSL pairs that emerge from genetic screen data in vitro but with further support from patient and phylogenetic data. Future studies that provide experimentally validated pan-cancer cSLs are needed to consolidate our current findings. Second, we have relied on analyzing the gene expression data of bulk tissues from GTEx and not the expression data of the specific cells of origin of the corresponding cancers. More refined future analysis is desirable using single-cell data across normal human tissues as such data becomes more widely available. Last, our study does not establish a causal relationship between the cSL load and the risk of cancer, as it is challenging to experimentally perturb a large number of cSLs simultaneously. The results shown are descriptive and association based, and the causal role of SLs in carcinogenesis remains to be studied mechanistically.

Together, our findings demonstrate strong associations between SL and cancer risk, onset time, and context specificity of tumor suppressors across human tissues. This suggests that beyond the effect on cancer after it has developed, cSL could also play an important role during the entire course of carcinogenesis, although further studies are needed to establish causality. While SL has been attracting tremendous attention as a way to identify cancer vulnerabilities and target them, this is the first time that its potential role in mediating cancer development is uncovered.

The cSL gene pairs computationally identified by the ISLE (identification of clinically relevant SL) pipeline were obtained from (22). We used the cSL network identified with FDR < 0.2 for the main text results, containing 21,534 cSL gene pairs, which is a reasonable size representing only about one cSL partner per gene on average. This also allows us to capture the effects of many weak genetic interactions. Nevertheless, we also used the cSL network with FDR < 0.1 (only 2326 cSLs) to demonstrate the robustness of the results to this parameter (notes S1 and S3). Each gene pair is assigned a significance score [the SL-pair score defined in (22)], that a higher score indicates that there is stronger evidence that the gene pair is SL in cancer. Out of these, we used 20,171 cSL gene pairs whose genes are present in the GTEx data (table S1A). The experimentally identified cSL gene pairs were collected from 18 studies [obtained from the supplementary data 1 of Lee et al. (22) except for those from Horlbeck et al. (34)]. Horlbeck et al. (34) provided a gene interaction (GI) score for each gene pair in two leukemia cell lines. Gene pairs with GI scores of <1 in either cell line were selected as cSLs. A total of 27,975 experimentally identified cSLs were obtained, out of which 27,538 have both their genes present in the GTEx data (table S1B).

The V6 release of GTEx (20) RNA sequencing (RNA-seq) data [gene-level reads per kilobase of transcript, per million mapped reads (RPKM) values] was obtained from the GTEx Portal (https://gtexportal.org/home/). The associated sample phenotypic data were downloaded from dbGaP (35) (accession number phs000424.vN.pN). For comparing the level of negative selection to co-inactivation of cSL gene pairs between normal and cancer tissues, the RNA-seq data of TCGA and GTEx as RNA-seq by expectation-maximization (RSEM) values that have been processed together with a consistent pipeline that helps to remove batch effects were downloaded from UCSC Xena (36). The expression data for each tissue type (normal or cancer) was normalized separately (inverse normal transformation across samples and genes) before being used for the downstream analyses. We mapped the GTEx tissue types to the corresponding TCGA cancer types (table S2B), resulting in one-on-many mappings, e.g., the normal lung tissue was mapped to both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).

Lifetime cancer risk denotes the chance a person has of being diagnosed with cancer during his or her lifetime. Lifetime cancer risk data (table S2A) are from Tomasetti and Vogelstein (2), which are based on the U.S. statistics from the SEER (Surveillance, Epidemiology, and End Results) database (1). We derived the cancer onset age based on the age-specific cancer incidence data from the SEER database with the standard formula (37). Specifically, for each cancer type, SEER provides the incidence rates for 5-year age intervals from birth to 85+ years old. The cumulative incidence (CI) for a specific age range S is computed from the corresponding age-specific incidence rates (IRi, i S) as CI = 5i S IRi, and the corresponding risk is computed as risk = 1 exp(CI). The onset age for each cancer type (table S3) was computed as the age when the CI from birth is 50% of the lifetime CI (i.e., birth to 85+ years old). Usually, the onset age defined as such is between two ages where the actual CI data are available, so the exact onset age was obtained by linear interpolation. Alternative parameters were used to define onset age (note S3) to show the robustness of the correlation between TCL and cancer onset age based on different definitions.

For each sample, we computed the number of cancer-derived SL gene pairs that have both genes lowly expressed and divided it by the total number of cSLs available to get the cSL load per sample. In the ISLE method described in (22), low expression was defined as having expression levels below the 33 percentile in each tissue or cell type. Thus, the ISLE-derived cSL gene pairs were shown to exhibit synthetic sickness effects when both genes in the gene pair are expressed at levels below the 33 percentile in each tissue, even though this appears to be a very tolerant cutoff (22). We therefore adopted the same criterion for low expression for the main results, although we also explored other low expression cutoffs to demonstrate the robustness of the results (note S3).

TCL of each tissue type is the median value of the cSL loads of all the samples (or a subpopulation of samples) in that tissue, with the cSL load of a sample computed as above. For example, TCL for the older population (age 50 years) is the median cSL load for the samples of age 50 years in each tissue type. For analyzing the correlation between the TCLs computed from GTEx normal tissues and cancer risk, we mapped the GTEx tissue types to the corresponding cancer types for which lifetime risk data are available from Tomasetti and Vogelstein (2), resulting in 16 GTEx types mapped to 27 cancer types (table S2A). Gallbladder nonpapillary adenocarcinoma and osteosarcoma of arms, head, legs, and pelvis are not mapped to GTEx tissues and excluded from our analysis. Similarly for the correlation between TCLs and cancer onset age, we mapped GTEx tissue types to the tissue sites from the SEER database (as given in the data slot site recode ICD-O-3/WHO 2008) by their names (table S3).

To investigate the effect on the single-gene level, we computed the cSL SGL in a paralleling way to the computation of the cSL load. Among all the unique genes constituting the cSL network (i.e., cSL genes), we computed the fraction of lowly expressed cSL genes for each sample as the cSL SGL, where low expression was defined in the same way as the computation of cSL load as elaborated above. Similarly, tissue cSL SGL is the median value of the cSL SGLs of all the samples in a tissue.

The lifetime cancer risks across tissue types were predicted with linear models containing three different sets of explanatory variables: (i) the number of total stem cell divisions (NSCD) alone, (ii) TCL alone, and (iii) NSCD together with TCL. LLR test was used to determine whether model (iii) (the full model) is significantly better than model (i) (the null model) in predicting lifetime cancer risks. The three models were also used to predict the lifetime cancer risks with a leave-one-out cross-validation procedure, and the prediction performances were measured by Spearman correlation coefficient. A similar analysis was performed to predict lifetime cancer risks across tissue types with three linear models involving the level of abnormal DNA methylation levels of the tissues (6): (i) the number of LADM alone, (ii) TCL alone, and (iii) LADM together with TCL.

For each pair of GTEx normalTCGA cancer of the same tissue type (table S2B), we computed the fraction of samples where a cSL gene pair i has both genes lowly expressed (defined above) among the normal samples (fni) and cancer samples (fci) and computed a specific score as rsi = fni fci. We selected the hcSLs as those whose specific scores are greater than the 75% percentile of all scores and lcSLs as those with a score below the 25% percentile (table S4, A and B). We compared SL significance scores between the hcSLs and lcSLs in each tissue using a Wilcoxon rank sum test. For each type of the GTEx normal tissues used in this analysis (i.e., those that can be mapped to TCGA cancer types), we also computed the TCL as above but using the hcSLs, lcSLs, or all cSLs, respectively, and analyzed their correlation with lifetime cancer risk or cancer onset age across the tissues.

We designed an empirical enrichment test as below to account for the fact that each cSL consists of two genes. For the hcSLs in each tissue type and each given pathway from the Reactome database (38), we computed the odds ratio (OR) for the overlap between the genes in hcSLs and the genes within the pathway based on the Fishers exact test procedure, with the background being all the genes in the ISLE-inferred cSLs. A greater than 1 OR indicates that the hcSLs are positively enriched for the genes of the pathway. To determine the significance of the enrichment, we repeatedly and randomly sampled the same number of cSLs as that of the hcSLs, computed the ORs similarly, and computed the empirical P value as the fraction of cases where the OR from the random cSLs is greater than that from the hcSLs. We corrected for multiple testing across pathways with the Benjamini-Hochberg method.

The phase I CMAP (26) data were downloaded from the Gene Expression Omnibus database (GSE92742). Level 5 data that represent the consensus perturbation-induced differential expression signature were used. We focused on CMAP data that involve treatment by specific classes of chemotherapy drugs (mutagenic: alkylating agents and DNA topoisomerase inhibitors; nonmutagenic: taxanes and vinca alkaloids) in normal cell lines or primary cells. We identified a total of 11 drugs tested in four cell types. Given the signature (z score) of a drug treatment in a cell, we estimated the drug-induced cSL load change as follows1|S|((i,j)SI(zi<0.5zj<0.5)(i,j)SI(zi>0.5zj<0.5))where S is the set of cSLs, and |S| is the total number of cSL gene pairs. A gene pair is denoted by (i, j), and zi and zj are the z scores of gene i and gene j, respectively. I() is the indicator function. Intuitively, the above formula quantifies the number of cSL gene pairs where both genes are down-regulated with a z score cutoff of 0.5 (i.e., contributing to cSL load increase), minus the number of cSL gene pairs where either gene is up-regulated with a z score cutoff of 0.5 (i.e., contributing to cSL load decrease), normalized by the total number of cSL gene pairs. We then tested whether the mutagenic drugs lead to a larger decrease in cSL load compared to nonmutagenic drugs with a linear model that controls for both cell type and drug.

We obtained the list of TSGs and their associated tissue types from the COSMIC database (11) (https://cancer.sanger.ac.uk/cosmic/download, the Cancer Gene Census data; table S6A). For each TSG, their cSL partner genes were identified using the ISLE pipeline (22) with an FDR cutoff of 0.1 (table S6B). Here, the FDR cutoff is more stringent than that used for the pan-cancer genome-wide cSL network (FDR < 0.2 for the main results) since, here, FDR correction was performed for each TSG, corresponding to a much lower number of multiple hypotheses. As a result, the FDR correction has more power, and a relatively more stringent cutoff can give rise to a more reasonable number of cSL partner genes per TSG. We focused our analysis on 23 TSGs for which more than one cSL partner genes were identified (no cSL partner was identified for most of the other TSGs). The expression levels of the cSL partner genes were then compared between tissue type(s) where the TSG is a known driver and the rest of the tissues where the TSG is not an established driver with linear models. Specifically, the expression levels of the cSL partners were modeled with two explanatory variables: (i) driver status of the TSG in the tissue (binary) and (ii) cSL partner gene (categorical, indicating each of the cSL partner genes of a TSG). The coefficient and P value associated with variable (i) were used to analyze the general trend of differential expression among the cSL partner genes. Positive coefficients of variable (i) means that the expression levels of the cSL partner genes are, on average, higher in the tissue(s) where the TSG is a known driver compared to those in the tissues where the TSG is not an established cancer driver.

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Synthetic lethality across normal tissues is strongly associated with cancer risk, onset, and tumor suppressor specificity - Science Advances

Medical doctors wrestling with recurrent, non-reactive T-cell lymphoma, an intractable disease with no standard treatments, have recently got a green light.

A research team, led by Professor Yang Deok-hwan of the Department of Hematology at Chonnam National University Hwasun Hospital (CNUHH), said it has developed a new treatment method. For the first time in the world, they proved that the combined therapy of Copanlisib and Gemcitabine cell chemotherapeutic treatment showed high efficacy in treating the disease.

They conducted phase 1 and 2 clinical trials on 28 patients with P13K signal transduction inhibitor, Copanrai combining with Gemcitabine chemotherapy. The former inhibitor controls the P13K signal, and the latter suppresses the proliferation of malignant B cells, selectively blocking P13K subtypes.

Six other hospitals Seoul National University Hospital, Samsung Medical Center, Yonsei Severance Hospital, Chonbuk National University Hospital, Busan National University Hospital, and Kyungbuk National University Hospital also participated in the study.

Researchers found that 72 percent of patients showed favorable reactions to the treatment with minor adverse effects, and developed a new therapy that supplements old therapy using single P12K with the combined inhibitor treatment.

Recurring and non-reactive peripheral T-cell lymphoma is regarded as incurable cancer, which does not have a standardized treatment yet. In the past, salvage chemotherapy or hematopoietic stem cell transplants after high-dose chemotherapy were conducted to treat such disease after the first treatment failed; however, patients were non-reactive or lived for less than five months after the treatment.

The new method is receiving attention for using the next generation sequencing (NGS) approach to classify gene abnormalities or mutations in peripheral T-cell lymphoma in therapeutic and non-response groups.

We are conducting additional predictive systems for blood cancer patients using AI to research on developing prognosis prediction programs, Professor Yang said.

The study results will be published in the Annals of Oncology.

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New combo therapy offered against refractory T-cell lymphoma - Korea Biomedical Review

4-year-old kid whose artwork helps those in need gives encouraging message to others for 2021

Updated: 11:08 PM EST Jan 1, 2021

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From the moment you meet Juliet Leong, surrounded by her nature inspired artwork, it's apparent she is not your average 4.5 year old. I'm never gonna stop painting. The mini member of MENSA has lots of kid like hobbies, like swimming, martial arts and telling jokes. Why did the dinosaur across the world? I don't know why. Together the chicken wasn't born yet, but it's her paintings, which impress us the most. Her parents say they started a scribbles at the tender age of eight months, and by age three I have no idea how she did. It just blew me away. Here's the new Bob Ross. Juliet has created more than 100 works of art, with nearly a dozen fetching over $4000 for the Asian donor program. Sell the painting to help people are a counter and find a match with her mom's help. This kid prodigy makes painting tutorials on YouTube and even offers to give me a lesson were printed Banda line. As we wait for each layer to dry, Juliette asks to serenade us with her violin. Theun later challenges me now on business for a room toe, a math competition. I'm sweating and beats me. That was very easy when we're all done. Juliet, how do you think I did? I think good. You think good. She has these words of advice to me and anybody else watching. Who wants to make a difference in the new Year? You can do it if you focus in Piedmont. Dion Lim, ABC seven News

4-year-old kid whose artwork helps those in need gives encouraging message to others for 2021

Updated: 11:08 PM EST Jan 1, 2021

Juliette Leong, 4, is a very talented kid.She's made over 100 pieces of art, some of which have raised thousands of dollars for a nonprofit helping people with life-threatening diseases, KGO-TV reports."I'm never going to stop painting," the Bay Area child says.Her family says she started scribbling at less than a year old but her skills really took off by age 3.The artwork has helped the Asian American Donor Program, which is focused on connecting those in need with potential stem cells donors.Her talents don't stop there, though. Tap the video above to hear her advice for people as we take on 2021.

Juliette Leong, 4, is a very talented kid.

She's made over 100 pieces of art, some of which have raised thousands of dollars for a nonprofit helping people with life-threatening diseases, KGO-TV reports.

"I'm never going to stop painting," the Bay Area child says.

Her family says she started scribbling at less than a year old but her skills really took off by age 3.

The artwork has helped the Asian American Donor Program, which is focused on connecting those in need with potential stem cells donors.

Her talents don't stop there, though. Tap the video above to hear her advice for people as we take on 2021.

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4-year-old kid whose artwork helps those in need gives encouraging message to others for 2021 - WXII The Triad

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BMJ Open. 2020 Dec 18;10(12):e042085. doi: 10.1136/bmjopen-2020-042085.

ABSTRACT

INTRODUCTION: To date, no specific antivirus drugs or vaccines have been available to prevent or treat the COVID-19 pandemic. Mesenchymal stem cell (MSC) therapy may be a promising therapeutic approach that reduces the high mortality in critical cases. This protocol is proposed for a systematic review and meta-analysis that aims to evaluate the efficacy and safety of MSC therapy on patients with COVID-19.

METHODS AND ANALYSIS: Ten databases including PubMed, EMBASE, Cochrane Library, CINAHL, Web of Science, Chinese National Knowledge Infrastructure (CNKI), Chinese Scientific Journals Database (VIP), Wanfang database, China Biomedical Literature Database (CBM) and Chinese Biomedical Literature Service System (SinoMed) will be searched from inception to 1 December 2020. All published randomised controlled trials, clinical controlled trials and case series that meet the prespecified eligibility criteria will be included. The primary outcomes include mortality, incidence and severity of adverse events, respiratory improvement, days from ventilator, duration of fever, progression rate from mild or moderate to severe, improvement of such serious symptoms as difficulty breathing or shortness of breath, chest pain or pressure, and loss of speech or movement, biomarkers of laboratory examination and changes in CT. The secondary outcomes include dexamethasone doses and quality of life. Two reviewers will independently perform study selection, data extraction and assessment of bias risk. Data synthesis will be conducted using RevMan software (V.5.3.5). If necessary, subgroup and sensitivity analysis will be performed. Grading of Recommendations Assessment, Development and Evaluation system will be used to assess the strength of evidence.

ETHICS AND DISSEMINATION: Ethical approval is not necessary since no individual patient or privacy data have been collected. The results of this review will be disseminated in a peer-reviewed journal or an academic conference presentation.

PROSPERO REGISTRATION NUMBER: CRD42020190079.

PMID:33371042 | DOI:10.1136/bmjopen-2020-042085

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Efficacy and safety of mesenchymal stem cells for the treatment of patients infected with COVID-19: a systematic review and meta-analysis protocol -...