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Double Mutant PI3KA Found to Lead to Higher Oncogenic Signaling in Cancer Cells

Curator: Stephen J. Williams, PhD

PIK3CA (Phosphatidylinsitol 4,5-bisphosphate (PIP2) 3-kinase catalytic subunit α) is one of the most frequently mutated oncogenes in various tumor types ([1] and http://www.sanger.ac.uk/genetics/CGP/cosmic). Oncogenic mutations leading to the overactivation of PIK3CA, especially in context in of inactivating PTEN mutations, result in overtly high signaling activity and associated with the malignant phenotype.

In a Perspective article (Double trouble for cancer gene: Double mutations in an oncogene enhance tumor growth) in the journal Science[2], Dr. Alex Toker discusses the recent results of Vasan et al. in the same issue of Science[3] on the finding that double mutations in the same allele of PIK3CA are more frequent in cancer genomes than previously identified and these double mutations lead to increased PI3K pathway activation, increased tumor growth, and increased sensitivity to PI3K inhibitors in human breast cancer.

 

 

From Dr. Melvin Crasto blog NewDrugApprovals.org

Alpelisib: PIK3CA inhibitor:

Alpelisib: New PIK3CA inhibitor approved for HER2 negative metastatic breast cancer

 

FDA approves first PI3K inhibitor for breast cancer

syn https://newdrugapprovals.org/2018/06/25/alpelisib-byl-719/

Today, the U.S. Food and Drug Administration approved Piqray (alpelisib) tablets, to be used in combination with the FDA-approved endocrine therapy fulvestrant, to treat postmenopausal women, and men, with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, PIK3CA-mutated, advanced or metastatic breast cancer (as detected by an FDA-approved test) following progression on or after an endocrine-based regimen.

The FDA also approved the companion diagnostic test, therascreen PIK3CA RGQ PCR Kit, to detect the PIK3CA mutation in a tissue and/or a liquid biopsy. Patients who are negative by

May 24, 2019

Today, the U.S. Food and Drug Administration approved Piqray (alpelisib) tablets, to be used in combination with the FDA-approved endocrine therapy fulvestrant, to treat postmenopausal women, and men, with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, PIK3CA-mutated, advanced or metastatic breast cancer (as detected by an FDA-approved test) following progression on or after an endocrine-based regimen.

The FDA also approved the companion diagnostic test, therascreen PIK3CA RGQ PCR Kit, to detect the PIK3CA mutation in a tissue and/or a liquid biopsy. Patients who are negative by the therascreen test using the liquid biopsy should undergo tumor biopsy for PIK3CA mutation testing.

“Piqray is the first PI3K inhibitor to demonstrate a clinically meaningful benefit in treating patients with this type of breast cancer. The ability to target treatment to a patient’s specific genetic mutation or biomarker is becoming increasingly common in cancer treatment, and companion diagnostic tests assist oncologists in selecting patients who may benefit from these targeted treatments,” said Richard Pazdur, M.D., director of the FDA’s Oncology Center of Excellence and acting director of the Office of Hematology and Oncology Products in the FDA’s Center for Drug Evaluation and Research. “For this approval, we employed some of our newer regulatory tools to streamline reviews without compromising the quality of our assessment. This drug is the first novel drug approved under the Real-Time Oncology Review pilot program. We also used the updated Assessment Aid, a multidisciplinary review template that helps focus our written review on critical thinking and consistency and reduces time spent on administrative tasks.”

Metastatic breast cancer is breast cancer that has spread beyond the breast to other organs in the body (most often the bones, lungs, liver or brain). When breast cancer is hormone-receptor positive, patients may be treated with anti-hormonal treatment (also called endocrine therapy), alone or in combination with other medicines, or chemotherapy.

The efficacy of Piqray was studied in the SOLAR-1 trial, a randomized trial of 572 postmenopausal women and men with HR-positive, HER2-negative, advanced or metastatic breast cancer whose cancer had progressed while on or after receiving an aromatase inhibitor. Results from the trial showed the addition of Piqray to fulvestrant significantly prolonged progression- free survival (median of 11 months vs. 5.7 months) in patients whose tumors had a PIK3CA mutation.

Common side effects of Piqray are high blood sugar levels, increase in creatinine, diarrhea, rash, decrease in lymphocyte count in the blood, elevated liver enzymes, nausea, fatigue, low red blood cell count, increase in lipase (enzymes released by the pancreas), decreased appetite, stomatitis, vomiting, weight loss, low calcium levels, aPTT prolonged (blood clotting taking longer to occur than it should), and hair loss.

Health care professionals are advised to monitor patients taking Piqray for severe hypersensitivity reactions (intolerance). Patients are warned of potentially severe skin reactions (rashes that may result in peeling and blistering of skin or mucous membranes like the lips and gums). Health care professionals are advised not to initiate treatment in patients with a history of severe skin reactions such as Stevens-Johnson Syndrome, erythema multiforme, or toxic epidermal necrolysis. Patients on Piqray have reported severe hyperglycemia (high blood sugar), and the safety of Piqray in patients with Type 1 or uncontrolled Type 2 diabetes has not been established. Before initiating treatment with Piqray, health care professionals are advised to check fasting glucose and HbA1c, and to optimize glycemic control. Patients should be monitored for pneumonitis/interstitial lung disease (inflammation of lung tissue) and diarrhea during treatment. Piqray must be dispensed with a patient Medication Guide that describes important information about the drug’s uses and risks.

Piqray is the first new drug application (NDA) for a new molecular entity approved under the Real-Time Oncology Review (RTOR) pilot program, which permits the FDA to begin analyzing key efficacy and safety datasets prior to the official submission of an application, allowing the review team to begin their review and communicate with the applicant earlier. Piqray also used the updated Assessment Aid (AAid), a multidisciplinary review template intended to focus the FDA’s written review on critical thinking and consistency and reduce time spent on administrative tasks. With these two pilot programs, today’s approval of Piqray comes approximately three months ahead of the Prescription Drug User Fee Act (PDUFA) VI deadline of August 18, 2019.

The FDA granted this application Priority Review designation. The FDA granted approval of Piqray to Novartis. The FDA granted approval of the therascreen PIK3CA RGQ PCR Kit to QIAGEN Manchester, Ltd.

https://www.fda.gov/news-events/press-announcements/fda-approves-first-pi3k-inhibitor-breast-cancer?utm_campaign=052419_PR_FDA%20approves%20first%20PI3K%20inhibitor%20for%20breast%20cancer&utm_medium=email&utm_source=Eloqua

 

Alpelisib

(2S)-1-N-[4-methyl-5-[2-(1,1,1-trifluoro-2-methylpropan-2-yl)pyridin-4-yl]-1,3-thiazol-2-yl]pyrrolidine-1,2-dicarboxamide

PDT PAT WO 2010/029082

CHEMICAL NAMES: Alpelisib; CAS 1217486-61-7; BYL-719; BYL719; UNII-08W5N2C97Q; BYL 719
MOLECULAR FORMULA: C19H22F3N5O2S
MOLECULAR WEIGHT: 441.473 g/mol
  1. alpelisib
  2. 1217486-61-7
  3. BYL-719
  4. BYL719
  5. UNII-08W5N2C97Q
  6. BYL 719
  7. Alpelisib (BYL719)
  8. (S)-N1-(4-Methyl-5-(2-(1,1,1-trifluoro-2-methylpropan-2-yl)pyridin-4-yl)thiazol-2-yl)pyrrolidine-1,2-dicarboxamide
  9. NVP-BYL719

Alpelisib is an orally bioavailable phosphatidylinositol 3-kinase (PI3K) inhibitor with potential antineoplastic activity. Alpelisib specifically inhibits PI3K in the PI3K/AKT kinase (or protein kinase B) signaling pathway, thereby inhibiting the activation of the PI3K signaling pathway. This may result in inhibition of tumor cell growth and survival in susceptible tumor cell populations. Activation of the PI3K signaling pathway is frequently associated with tumorigenesis. Dysregulated PI3K signaling may contribute to tumor resistance to a variety of antineoplastic agents.

Alpelisib has been used in trials studying the treatment and basic science of Neoplasms, Solid Tumors, BREAST CANCER, 3rd Line GIST, and Rectal Cancer, among others.

 

SYN 2

POLYMORPHS

https://patents.google.com/patent/WO2012175522A1/en

(S)-pyrrolidine-l,2-dicarboxylic acid 2-amide l-(4-methyl-5-[2-(2,2,2-trifluoro-l,l- dimethyl-ethyl)-pyridin-4-yl]-thiazol-2-yl)-amidei hereafter referred to as compound I,

is an alpha-selective phosphatidylinositol 3 -kinase (PI3K) inhibitor. Compound I was originally described in WO 2010/029082, wherein the synthesis of its free base form was described. There is a need for additional solid forms of compound I, for use in drug substance and drug product development. It has been found that new solid forms of compound I can be prepared as one or more polymorph forms, including solvate forms. These polymorph forms exhibit new physical properties that may be exploited in order to obtain new pharmacological properties, and that may be utilized in drug substance and drug product development. Summary of the Invention

In one aspect, provided herein is a crystalline form of the compound of formula I, or a solvate of the crystalline form of the compound of formula I, or a salt of the crystalline form of the compound of formula I, or a solvate of a salt of the crystalline form of the compound of formula I. In one embodiment, the crystalline form of the compound of formula I has the polymorph form SA, SB, Sc, or SD.

In another aspect, provided herein is a pharmaceutical composition comprising a crystalline compound of formula I. In one embodiment of the pharmaceutical composition, the crystalline compound of formula I has the polymorph form SA, SB,Sc, or So.

In another aspect, provided herein is a method for the treatment of disorders mediated by PI3K, comprising administering to a patient in need of such treatment an effective amount of a crystalline compound of formula I, particularly SA, SB, SC,or SD .

In yet another aspect, provided herein is the use of a crystalline compound of formula I, particularly SA, SB, SC, or SD, for the preparation of a medicament for the treatment of disorders mediated by PI3K.

 

Source: https://newdrugapprovals.org/?s=alpelisib&submit=

 

Pharmacology and Toxicology from drugbank.ca

Indication

Alpelisib is indicated in combination with fulvestrant to treat postmenopausal women, and men, with advanced or metastatic breast cancer.Label This cancer must be hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, and PIK3CA­ mutated.Label The cancer must be detected by an FDA-approved test following progression on or after an endocrine-based regimen.Label

Associated Conditions

Contraindications & Blackbox Warnings

Learn about our commercial Contraindications & Blackbox Warnings data.

LEARN MORE

 

Pharmacodynamics

Alpelisib does not prolong the QTcF interval.Label Patients taking alpelisib experience a dose dependent benefit from treatment with a 51% advantage of a 200mg daily dose over a 100mg dose and a 22% advantage of 300mg once daily over 150mg twice daily.6 This suggests patients requiring a lower dose may benefit from twice daily dosing.6

Mechanism of action

Phosphatidylinositol-3-kinase-α (PI3Kα) is responsible for cell proliferation in response to growth factor-tyrosine kinase pathway activation.3 In some cancers PI3Kα’s p110α catalytic subunit is mutated making it hyperactive.3 Alpelisib inhibits (PI3K), with the highest specificity for PI3Kα.Label

TARGET ACTIONS ORGANISM
APhosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform inhibitor Humans

Absorption

Alpelisib reached a peak concentration in plasma of 1320±912ng/mL after 2 hours.4 Alpelisib has an AUClast of 11,100±3760h ng/mL and an AUCINF of 11,100±3770h ng/mL.4 A large, high fat meal increases the AUC by 73% and Cmax by 84% while a small, low fat meal increases the AUC by 77% and Cmax by 145%.Label

Volume of distribution

The apparent volume of distribution at steady state is 114L.Label

Protein binding

Alpelisib is 89% protein bound.Label

Metabolism

Alpelisib is metabolized by hydrolysis reactions to form the primary metabolite.Label It is also metabolized by CYP3A4.Label The full metabolism of Alpelisib has yet to be determined but a series of reactions have been proposed.4,5 The main metabolic reaction is the substitution of an amine group on alpelisib for a hydroxyl group to form a metabolite known as M44,5 or BZG791.Label Alpelisib can also be glucuronidated to form the M1 and M12 metabolites.4,5

Hover over products below to view reaction partners

Route of elimination

36% of an oral dose is eliminated as unchanged drug in the feces and 32% as the primary metabolite BZG791 in the feces.Label 2% of an oral dose is eliminated in the urine as unchanged drug and 7.1% as the primary metabolite BZG791.Label In total 81% of an oral dose is eliminated in the feces and 14% is eliminated in the urine.Label

Half-life

The mean half life of alprelisib is 8 to 9 hours.Label

Clearance

The mean apparent oral clearance was 39.0L/h.4 The predicted clearance is 9.2L/hr under fed conditions.Label

Adverse Effects

Learn about our commercial Adverse Effects data.

LEARN MORE

 

Toxicity

LD50 and Overdose

Patients experiencing an overdose may present with hyperglycemia, nausea, asthenia, and rash.Label There is no antidote for an overdose of alpelisib so patients should be treated symptomatically.Label Data regarding an LD50 is not readily available.MSDS In clinical trials, patients were given doses of up to 450mg once daily.Label

Pregnancy, Lactation, and Fertility

Following administration in rats and rabbits during organogenesis, adverse effects on the reproductive system, such as embryo-fetal mortality, reduced fetal weights, and increased incidences of fetal malformations, were observed.Label Based on these findings of animals studies and its mechanism of action, it is proposed that alpelisib may cause embryo-fetal toxicity when administered to pregnant patients.Label There is no data available regarding the presence of alpelisib in breast milk so breast feeding mothers are advised not to breastfeed while taking this medication and for 1 week after their last dose.Label Based on animal studies, alpelisib may impair fertility of humans.Label

Carcinogenicity and Mutagenicity

Studies of carcinogenicity have yet to be performed.Label Alpelisib has not been found to be mutagenic in the Ames test.Label It is not aneugenic, clastogenic, or genotoxic in further assays.Label

Affected organisms

Not Available

Pathways

Not Available

Pharmacogenomic Effects/ADRs 

 

Not Available

 

Source: https://www.drugbank.ca/drugs/DB12015

References

  1. Yuan TL, Cantley LC: PI3K pathway alterations in cancer: variations on a theme. Oncogene 2008, 27(41):5497-5510.
  2. Toker A: Double trouble for cancer gene. Science 2019, 366(6466):685-686.
  3. Vasan N, Razavi P, Johnson JL, Shao H, Shah H, Antoine A, Ladewig E, Gorelick A, Lin TY, Toska E et al: Double PIK3CA mutations in cis increase oncogenicity and sensitivity to PI3Kalpha inhibitors. Science 2019, 366(6466):714-723.

 

 

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National Cancer Institute Director Neil Sharpless says mortality from delays in cancer screenings due to COVID19 pandemic could result in tens of thousands of extra deaths in next decade

Reporter: Stephen J Williams, PhD

Source: https://cancerletter.com/articles/20200619_1/

NCI Director’s Report

Sharpless: COVID-19 expected to increase mortality by at least 10,000 deaths from breast and colorectal cancers over 10 years

By Matthew Bin Han Ong

This story is part of The Cancer Letter’s ongoing coverage of COVID-19’s impact on oncology. A full list of our coverage, as well as the latest meeting cancellations, is available here.

The COVID-19 pandemic will likely cause at least 10,000 excess deaths from breast cancer and colorectal cancer over the next 10 years in the United States.

Scenarios run by NCI and affiliated modeling groups predict that delays in screening for and diagnosis of breast and colorectal cancers will lead to a 1% increase in deaths through 2030. This translates into 10,000 additional deaths, on top of the expected one million deaths resulting from these two cancers.

“For both these cancer types, we believe the pandemic will influence cancer deaths for at least a decade,” NCI Director Ned Sharpless said in a virtual joint meeting of the Board of Scientific Advisors and the National Cancer Advisory Board June 15. “I find this worrisome as cancer mortality is common. Even a 1% increase every decade is a lot of cancer suffering.

“And this analysis, frankly, is pretty conservative. We do not consider cancers other than those of breast and colon, but there is every reason to believe the pandemic will affect other types of cancer, too. We did not account for the additional non-lethal morbidity from upstaging, but this could also be significant and burdensome.”

An editorial by Sharpless on this subject appears in the journal Science.

The early analyses, conducted by the institute’s Cancer Intervention and Surveillance Modeling Network, focused on breast and colorectal cancers, because these are common, with relatively high screening rates.

CISNET modelers created four scenarios to assess long-term increases in cancer mortality rates for these two diseases:

  1. The pandemic has no effect on cancer mortality

 

  1. Delayed screening—with 75% reduction in mammography and, colorectal screening and adenoma surveillance for six months

 

  1. Delayed diagnosis—with one-third of people delaying follow-up after a positive screening or diagnostic mammogram, positive FIT or clinical symptoms for six months during a six-month period

 

  1. Combination of scenarios two and three

 

Treatment scenarios after diagnosis were not included in the model. These would be: delays in treatment, cancellation of treatment, or modified treatment.

“What we did is show the impact of the number of excess deaths per year for 10 years for each year starting in 2020 for scenario four versus scenario one,” Eric “Rocky” Feuer, chief of the NCI’s Statistical Research and Applications Branch in the Surveillance Research Program, said to The Cancer Letter.

Feuer is the overall project scientist for CISNET, a collaborative group of investigators who use simulation modeling to guide public health research and priorities.

“The results for breast cancer were somewhat larger than for colorectal,” Feuer said. “And that’s because breast cancer has a longer preclinical natural history relative to colorectal cancer.”

Modelers in oncology are creating a global modeling consortium, COVID-19 and Cancer Taskforce, to “support decision-making in cancer control both during and after the crisis.” The consortium is supported by the Union for International Cancer Control, The International Agency for Research on Cancer, The International Cancer Screening Network, the Canadian Partnership Against Cancer, and Cancer Council NSW, Australia.

A spike in cancer mortality rates threatens to reverse or slow down—at least in the medium term—the steady trend of reduction of cancer deaths. On Jan. 8, the American Cancer Society published its annual estimates of new cancer cases and deaths, declaring that the latest data—from 2016 to 2017—show the “largest ever single-year drop in overall cancer mortality of 2.2%.” Experts say that innovation in lung cancer treatment and the success of smoking cessation programs are driving the sharp decrease (The Cancer LetterFeb. 7, 2020).

The pandemic is expected to have broader impact, including increases in mortality rates for other cancer types. Also, variations in severity of COVID-19 in different regions in the U.S. will influence mortality metrics.

“There’s some other cancers that might have delays in screening—for example cervical, prostate, and lung cancer, although lung cancer screening rates are still quite low and prostate cancer screening should only be conducted on those who determine that the benefits outweigh the harms,” Feuer said. “So, those are the major screening cancers, but impacts of delays in treatment, canceling treatment or alternative treatments—could impact a larger range of cancer sites.

“This model assumes a moderate disruption which resolves after six months, and doesn’t consider non-lethal morbidities associated with the delay. One thing I think probably is occurring is regional variation in these impacts,” Feuer said. “If you’re living in New York City where things were ground zero for some of the worst impact early on, probably delays were larger than other areas of the country. But now, as we’re seeing upticks in other areas of the country, there may be in impact in these areas as well”

How can health care providers mitigate some of these harms? For example, for people who delayed screening and diagnosis, are providers able to perform triage, so that those at highest risk are prioritized?

“From a strictly cancer control point of view, let’s get those people who delayed screening, or followup to a positive test, or treatment back on schedule as soon as possible,” Feuer said. “But it’s not a simple calculus, because in every situation, we have to weigh the harms and benefits. As we come out of the pandemic, it tips more and more to, ‘Let’s get back to business with respect to cancer control.’

“Telemedicine doesn’t completely substitute for seeing patients in person, but at least people could get the advice they need, and then are triaged through their health care providers to indicate if they really should prioritize coming in. That helps the individual and the health care provider  weigh the harms and benefits, and try to strategize about what’s best for any individual.”

If the pandemic continues to disrupt routine care, cancer-related mortality rates would rise beyond the predictions in this model.

“I think this analysis begins to help us understand the costs with regard to cancer outcomes of the pandemic,” Sharpless said. “Let’s all agree we will do everything in our power to minimize these adverse effects, to protect our patients from cancer suffering.”

 

For more Articles on COVID-19 please see our Coronavirus Portal at

https://pharmaceuticalintelligence.com/coronavirus-portal/

 

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Live Notes, Real Time Conference Coverage AACR 2020 #AACR20: Tuesday June 23, 2020 Noon-2:45 Educational Sessions


Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 Noon-2:45 Educational Sessions

Reporter: Stephen J. Williams, PhD

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

Register for FREE at https://www.aacr.org/

 

Presidential Address

Elaine R Mardis, William N Hait

DETAILS

Welcome and introduction

William N Hait

 

Improving diagnostic yield in pediatric cancer precision medicine

Elaine R Mardis
  • Advent of genomics have revolutionized how we diagnose and treat lung cancer
  • We are currently needing to understand the driver mutations and variants where we can personalize therapy
  • PD-L1 and other checkpoint therapy have not really been used in pediatric cancers even though CAR-T have been successful
  • The incidence rates and mortality rates of pediatric cancers are rising
  • Large scale study of over 700 pediatric cancers show cancers driven by epigenetic drivers or fusion proteins. Need for transcriptomics.  Also study demonstrated that we have underestimated germ line mutations and hereditary factors.
  • They put together a database to nominate patients on their IGM Cancer protocol. Involves genetic counseling and obtaining germ line samples to determine hereditary factors.  RNA and protein are evaluated as well as exome sequencing. RNASeq and Archer Dx test to identify driver fusions
  • PECAN curated database from St. Jude used to determine driver mutations. They use multiple databases and overlap within these databases and knowledge base to determine or weed out false positives
  • They have used these studies to understand the immune infiltrate into recurrent cancers (CytoCure)
  • They found 40 germline cancer predisposition genes, 47 driver somatic fusion proteins, 81 potential actionable targets, 106 CNV, 196 meaningful somatic driver mutations

 

 

Tuesday, June 23

12:00 PM – 12:30 PM EDT

Awards and Lectures

NCI Director’s Address

Norman E Sharpless, Elaine R Mardis

DETAILS

Introduction: Elaine Mardis

 

NCI Director Address: Norman E Sharpless
  • They are functioning well at NCI with respect to grant reviews, research, and general functions in spite of the COVID pandemic and the massive demonstrations on also focusing on the disparities which occur in cancer research field and cancer care
  • There are ongoing efforts at NCI to make a positive difference in racial injustice, diversity in the cancer workforce, and for patients as well
  • Need a diverse workforce across the cancer research and care spectrum
  • Data show that areas where the clinicians are successful in putting African Americans on clinical trials are areas (geographic and site specific) where health disparities are narrowing
  • Grants through NCI new SeroNet for COVID-19 serologic testing funded by two RFAs through NIAD (RFA-CA-30-038 and RFA-CA-20-039) and will close on July 22, 2020

 

Tuesday, June 23

12:45 PM – 1:46 PM EDT

Virtual Educational Session

Immunology, Tumor Biology, Experimental and Molecular Therapeutics, Molecular and Cellular Biology/Genetics

Tumor Immunology and Immunotherapy for Nonimmunologists: Innovation and Discovery in Immune-Oncology

This educational session will update cancer researchers and clinicians about the latest developments in the detailed understanding of the types and roles of immune cells in tumors. It will summarize current knowledge about the types of T cells, natural killer cells, B cells, and myeloid cells in tumors and discuss current knowledge about the roles these cells play in the antitumor immune response. The session will feature some of the most promising up-and-coming cancer immunologists who will inform about their latest strategies to harness the immune system to promote more effective therapies.

Judith A Varner, Yuliya Pylayeva-Gupta

 

Introduction

Judith A Varner
New techniques reveal critical roles of myeloid cells in tumor development and progression
  • Different type of cells are becoming targets for immune checkpoint like myeloid cells
  • In T cell excluded or desert tumors T cells are held at periphery so myeloid cells can infiltrate though so macrophages might be effective in these immune t cell naïve tumors, macrophages are most abundant types of immune cells in tumors
  • CXCLs are potential targets
  • PI3K delta inhibitors,
  • Reduce the infiltrate of myeloid tumor suppressor cells like macrophages
  • When should we give myeloid or T cell therapy is the issue
Judith A Varner
Novel strategies to harness T-cell biology for cancer therapy
Positive and negative roles of B cells in cancer
Yuliya Pylayeva-Gupta
New approaches in cancer immunotherapy: Programming bacteria to induce systemic antitumor immunity

 

 

Tuesday, June 23

12:45 PM – 1:46 PM EDT

Virtual Educational Session

Cancer Chemistry

Chemistry to the Clinic: Part 2: Irreversible Inhibitors as Potential Anticancer Agents

There are numerous examples of highly successful covalent drugs such as aspirin and penicillin that have been in use for a long period of time. Despite historical success, there was a period of reluctance among many to purse covalent drugs based on concerns about toxicity. With advances in understanding features of a well-designed covalent drug, new techniques to discover and characterize covalent inhibitors, and clinical success of new covalent cancer drugs in recent years, there is renewed interest in covalent compounds. This session will provide a broad look at covalent probe compounds and drug development, including a historical perspective, examination of warheads and electrophilic amino acids, the role of chemoproteomics, and case studies.

Benjamin F Cravatt, Richard A. Ward, Sara J Buhrlage

 

Discovering and optimizing covalent small-molecule ligands by chemical proteomics

Benjamin F Cravatt
  • Multiple approaches are being investigated to find new covalent inhibitors such as: 1) cysteine reactivity mapping, 2) mapping cysteine ligandability, 3) and functional screening in phenotypic assays for electrophilic compounds
  • Using fluorescent activity probes in proteomic screens; have broad useability in the proteome but can be specific
  • They screened quiescent versus stimulated T cells to determine reactive cysteines in a phenotypic screen and analyzed by MS proteomics (cysteine reactivity profiling); can quantitate 15000 to 20,000 reactive cysteines
  • Isocitrate dehydrogenase 1 and adapter protein LCP-1 are two examples of changes in reactive cysteines they have seen using this method
  • They use scout molecules to target ligands or proteins with reactive cysteines
  • For phenotypic screens they first use a cytotoxic assay to screen out toxic compounds which just kill cells without causing T cell activation (like IL10 secretion)
  • INTERESTINGLY coupling these MS reactive cysteine screens with phenotypic screens you can find NONCANONICAL mechanisms of many of these target proteins (many of the compounds found targets which were not predicted or known)

Electrophilic warheads and nucleophilic amino acids: A chemical and computational perspective on covalent modifier

The covalent targeting of cysteine residues in drug discovery and its application to the discovery of Osimertinib

Richard A. Ward
  • Cysteine activation: thiolate form of cysteine is a strong nucleophile
  • Thiolate form preferred in polar environment
  • Activation can be assisted by neighboring residues; pKA will have an effect on deprotonation
  • pKas of cysteine vary in EGFR
  • cysteine that are too reactive give toxicity while not reactive enough are ineffective

 

Accelerating drug discovery with lysine-targeted covalent probes

 

Tuesday, June 23

12:45 PM – 2:15 PM EDT

Virtual Educational Session

Molecular and Cellular Biology/Genetics

Virtual Educational Session

Tumor Biology, Immunology

Metabolism and Tumor Microenvironment

This Educational Session aims to guide discussion on the heterogeneous cells and metabolism in the tumor microenvironment. It is now clear that the diversity of cells in tumors each require distinct metabolic programs to survive and proliferate. Tumors, however, are genetically programmed for high rates of metabolism and can present a metabolically hostile environment in which nutrient competition and hypoxia can limit antitumor immunity.

Jeffrey C Rathmell, Lydia Lynch, Mara H Sherman, Greg M Delgoffe

 

T-cell metabolism and metabolic reprogramming antitumor immunity

Jeffrey C Rathmell

Introduction

Jeffrey C Rathmell

Metabolic functions of cancer-associated fibroblasts

Mara H Sherman

Tumor microenvironment metabolism and its effects on antitumor immunity and immunotherapeutic response

Greg M Delgoffe
  • Multiple metabolites, reactive oxygen species within the tumor microenvironment; is there heterogeneity within the TME metabolome which can predict their ability to be immunosensitive
  • Took melanoma cells and looked at metabolism using Seahorse (glycolysis): and there was vast heterogeneity in melanoma tumor cells; some just do oxphos and no glycolytic metabolism (inverse Warburg)
  • As they profiled whole tumors they could separate out the metabolism of each cell type within the tumor and could look at T cells versus stromal CAFs or tumor cells and characterized cells as indolent or metabolic
  • T cells from hyerglycolytic tumors were fine but from high glycolysis the T cells were more indolent
  • When knock down glucose transporter the cells become more glycolytic
  • If patient had high oxidative metabolism had low PDL1 sensitivity
  • Showed this result in head and neck cancer as well
  • Metformin a complex 1 inhibitor which is not as toxic as most mito oxphos inhibitors the T cells have less hypoxia and can remodel the TME and stimulate the immune response
  • Metformin now in clinical trials
  • T cells though seem metabolically restricted; T cells that infiltrate tumors are low mitochondrial phosph cells
  • T cells from tumors have defective mitochondria or little respiratory capacity
  • They have some preliminary findings that metabolic inhibitors may help with CAR-T therapy

Obesity, lipids and suppression of anti-tumor immunity

Lydia Lynch
  • Hypothesis: obesity causes issues with anti tumor immunity
  • Less NK cells in obese people; also produce less IFN gamma
  • RNASeq on NOD mice; granzymes and perforins at top of list of obese downregulated
  • Upregulated genes that were upregulated involved in lipid metabolism
  • All were PPAR target genes
  • NK cells from obese patients takes up palmitate and this reduces their glycolysis but OXPHOS also reduced; they think increased FFA basically overloads mitochondria
  • PPAR alpha gamma activation mimics obesity

 

 

Tuesday, June 23

12:45 PM – 2:45 PM EDT

Virtual Educational Session

Clinical Research Excluding Trials

The Evolving Role of the Pathologist in Cancer Research

Long recognized for their role in cancer diagnosis and prognostication, pathologists are beginning to leverage a variety of digital imaging technologies and computational tools to improve both clinical practice and cancer research. Remarkably, the emergence of artificial intelligence (AI) and machine learning algorithms for analyzing pathology specimens is poised to not only augment the resolution and accuracy of clinical diagnosis, but also fundamentally transform the role of the pathologist in cancer science and precision oncology. This session will discuss what pathologists are currently able to achieve with these new technologies, present their challenges and barriers, and overview their future possibilities in cancer diagnosis and research. The session will also include discussions of what is practical and doable in the clinic for diagnostic and clinical oncology in comparison to technologies and approaches primarily utilized to accelerate cancer research.

 

Jorge S Reis-Filho, Thomas J Fuchs, David L Rimm, Jayanta Debnath

DETAILS

Tuesday, June 23

12:45 PM – 2:45 PM EDT

 

High-dimensional imaging technologies in cancer research

David L Rimm

  • Using old methods and new methods; so cell counting you use to find the cells then phenotype; with quantification like with Aqua use densitometry of positive signal to determine a threshold to determine presence of a cell for counting
  • Hiplex versus multiplex imaging where you have ten channels to measure by cycling of flour on antibody (can get up to 20plex)
  • Hiplex can be coupled with Mass spectrometry (Imaging Mass spectrometry, based on heavy metal tags on mAbs)
  • However it will still take a trained pathologist to define regions of interest or field of desired view

 

Introduction

Jayanta Debnath

Challenges and barriers of implementing AI tools for cancer diagnostics

Jorge S Reis-Filho

Implementing robust digital pathology workflows into clinical practice and cancer research

Jayanta Debnath

Invited Speaker

Thomas J Fuchs
  • Founder of spinout of Memorial Sloan Kettering
  • Separates AI from computational algothimic
  • Dealing with not just machines but integrating human intelligence
  • Making decision for the patients must involve human decision making as well
  • How do we get experts to do these decisions faster
  • AI in pathology: what is difficult? =è sandbox scenarios where machines are great,; curated datasets; human decision support systems or maps; or try to predict nature
  • 1) learn rules made by humans; human to human scenario 2)constrained nature 3)unconstrained nature like images and or behavior 4) predict nature response to nature response to itself
  • In sandbox scenario the rules are set in stone and machines are great like chess playing
  • In second scenario can train computer to predict what a human would predict
  • So third scenario is like driving cars
  • System on constrained nature or constrained dataset will take a long time for commuter to get to decision
  • Fourth category is long term data collection project
  • He is finding it is still finding it is still is difficult to predict nature so going from clinical finding to prognosis still does not have good predictability with AI alone; need for human involvement
  • End to end partnering (EPL) is a new way where humans can get more involved with the algorithm and assist with the problem of constrained data
  • An example of a workflow for pathology would be as follows from Campanella et al 2019 Nature Medicine: obtain digital images (they digitized a million slides), train a massive data set with highthroughput computing (needed a lot of time and big software developing effort), and then train it using input be the best expert pathologists (nature to human and unconstrained because no data curation done)
  • Led to first clinically grade machine learning system (Camelyon16 was the challenge for detecting metastatic cells in lymph tissue; tested on 12,000 patients from 45 countries)
  • The first big hurdle was moving from manually annotated slides (which was a big bottleneck) to automatically extracted data from path reports).
  • Now problem is in prediction: How can we bridge the gap from predicting humans to predicting nature?
  • With an AI system pathologist drastically improved the ability to detect very small lesions

 

Virtual Educational Session

Epidemiology

Cancer Increases in Younger Populations: Where Are They Coming from?

Incidence rates of several cancers (e.g., colorectal, pancreatic, and breast cancers) are rising in younger populations, which contrasts with either declining or more slowly rising incidence in older populations. Early-onset cancers are also more aggressive and have different tumor characteristics than those in older populations. Evidence on risk factors and contributors to early-onset cancers is emerging. In this Educational Session, the trends and burden, potential causes, risk factors, and tumor characteristics of early-onset cancers will be covered. Presenters will focus on colorectal and breast cancer, which are among the most common causes of cancer deaths in younger people. Potential mechanisms of early-onset cancers and racial/ethnic differences will also be discussed.

Stacey A. Fedewa, Xavier Llor, Pepper Jo Schedin, Yin Cao

Cancers that are and are not increasing in younger populations

Stacey A. Fedewa

 

  • Early onset cancers, pediatric cancers and colon cancers are increasing in younger adults
  • Younger people are more likely to be uninsured and these are there most productive years so it is a horrible life event for a young adult to be diagnosed with cancer. They will have more financial hardship and most (70%) of the young adults with cancer have had financial difficulties.  It is very hard for women as they are on their childbearing years so additional stress
  • Types of early onset cancer varies by age as well as geographic locations. For example in 20s thyroid cancer is more common but in 30s it is breast cancer.  Colorectal and testicular most common in US.
  • SCC is decreasing by adenocarcinoma of the cervix is increasing in women’s 40s, potentially due to changing sexual behaviors
  • Breast cancer is increasing in younger women: maybe etiologic distinct like triple negative and larger racial disparities in younger African American women
  • Increased obesity among younger people is becoming a factor in this increasing incidence of early onset cancers

 

 

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

 

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Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM

Reporter: Stephen J. Williams, PhD

 Minisymposium: Evaluating Cancer Genomics from Normal Tissues through Evolution to Metastatic Disease

Oncologic therapy shapes the fitness landscape of clonal hematopoiesis

April 28, 2020, 4:10 PM – 4:20 PM

Presenter/Authors
Kelly L. Bolton, Ryan N. Ptashkin, Teng Gao, Lior Braunstein, Sean M. Devlin, Minal Patel, Antonin Berthon, Aijazuddin Syed, Mariko Yabe, Catherine Coombs, Nicole M. Caltabellotta, Mike Walsh, Ken Offit, Zsofia Stadler, Choonsik Lee, Paul Pharoah, Konrad H. Stopsack, Barbara Spitzer, Simon Mantha, James Fagin, Laura Boucai, Christopher J. Gibson, Benjamin Ebert, Andrew L. Young, Todd Druley, Koichi Takahashi, Nancy Gillis, Markus Ball, Eric Padron, David Hyman, Jose Baselga, Larry Norton, Stuart Gardos, Virginia Klimek, Howard Scher, Dean Bajorin, Eder Paraiso, Ryma Benayed, Maria Arcilla, Marc Ladanyi, David Solit, Michael Berger, Martin Tallman, Montserrat Garcia-Closas, Nilanjan Chatterjee, Luis Diaz, Ross Levine, Lindsay Morton, Ahmet Zehir, Elli Papaemmanuil. Memorial Sloan Kettering Cancer Center, New York, NY, University of North Carolina at Chapel Hill, Chapel Hill, NC, University of Cambridge, Cambridge, United Kingdom, Dana-Farber Cancer Institute, Boston, MA, Washington University, St Louis, MO, The University of Texas MD Anderson Cancer Center, Houston, TX, Moffitt Cancer Center, Tampa, FL, National Cancer Institute, Bethesda, MD

Abstract
Recent studies among healthy individuals show evidence of somatic mutations in leukemia-associated genes, referred to as clonal hematopoiesis (CH). To determine the relationship between CH and oncologic therapy we collected sequential blood samples from 525 cancer patients (median sampling interval time = 23 months, range: 6-53 months) of whom 61% received cytotoxic therapy or external beam radiation therapy and 39% received either targeted/immunotherapy or were untreated. Samples were sequenced using deep targeted capture-based platforms. To determine whether CH mutational features were associated with tMN risk, we performed Cox proportional hazards regression on 9,549 cancer patients exposed to oncologic therapy of whom 75 cases developed tMN (median time to transformation=26 months). To further compare the genetic and clonal relationships between tMN and the proceeding CH, we analyzed 35 cases for which paired samples were available. We compared the growth rate of the variant allele fraction (VAF) of CH clones across treatment modalities and in untreated patients. A significant increase in the growth rate of CH mutations was seen in DDR genes among those receiving cytotoxic (p=0.03) or radiation therapy (p=0.02) during the follow-up period compared to patients who did not receive therapy. Similar growth rates among treated and untreated patients were seen for non-DDR CH genes such as DNMT3A. Increasing cumulative exposure to cytotoxic therapy (p=0.01) and external beam radiation therapy (2×10-8) resulted in higher growth rates for DDR CH mutations. Among 34 subjects with at least two CH mutations in which one mutation was in a DDR gene and one in a non-DDR gene, we studied competing clonal dynamics for multiple gene mutations within the same patient. The risk of tMN was positively associated with CH in a known myeloid neoplasm driver mutation (HR=6.9, p<10-6), and increased with the total number of mutations and clone size. The strongest associations were observed for mutations in TP53 and for CH with mutations in spliceosome genes (SRSF2, U2AF1 and SF3B1). Lower hemoglobin, lower platelet counts, lower neutrophil counts, higher red cell distribution width and higher mean corpuscular volume were all positively associated with increased tMN risk. Among 35 cases for which paired samples were available, in 19 patients (59%), we found evidence of at least one of these mutations at the time of pre-tMN sequencing and in 13 (41%), we identified two or more in the pre-tMN sample. In all cases the dominant clone at tMN transformation was defined by a mutation seen at CH Our serial sampling data provide clear evidence that oncologic therapy strongly selects for clones with mutations in the DDR genes and that these clones have limited competitive fitness, in the absence of cytotoxic or radiation therapy. We further validate the relevance of CH as a predictor and precursor of tMN in cancer patients. We show that CH mutations detected prior to tMN diagnosis were consistently part of the dominant clone at tMN diagnosis and demonstrate that oncologic therapy directly promotes clones with mutations in genes associated with chemo-resistant disease such as TP53.

  • therapy resulted also in clonal evolution and saw changes in splice variants and spliceosome
  • therapy promotes current DDR mutations
  • clonal hematopoeisis due to selective pressures
  • mutations, variants number all predictive of myeloid disease
  • deferring adjuvant therapy for breast cancer patients with patients in highest MDS risk group based on biomarkers, greatly reduced their risk for MDS

5704 – Pan-cancer genomic characterization of patient-matched primary, extracranial, and brain metastases

Presenter/AuthorsOlivia W. Lee, Akash Mitra, Won-Chul Lee, Kazutaka Fukumura, Hannah Beird, Miles Andrews, Grant Fischer, John N. Weinstein, Michael A. Davies, Jason Huse, P. Andrew Futreal. The University of Texas MD Anderson Cancer Center, TX, The University of Texas MD Anderson Cancer Center, TX, Olivia Newton-John Cancer Research Institute and School of Cancer Medicine, La Trobe University, AustraliaDisclosures O.W. Lee: None. A. Mitra: None. W. Lee: None. K. Fukumura: None. H. Beird: None. M. Andrews: ; Merck Sharp and Dohme. G. Fischer: None. J.N. Weinstein: None. M.A. Davies: ; Bristol-Myers Squibb. ; Novartis. ; Array BioPharma. ; Roche and Genentech. ; GlaxoSmithKline. ; Sanofi-Aventis. ; AstraZeneca. ; Myriad Genetics. ; Oncothyreon. J. Huse: None. P. Futreal: None.

Abstract: Brain metastases (BM) occur in 10-30% of patients with cancer. Approximately 200,000 new cases of brain metastases are diagnosed in the United States annually, with median survival after diagnosis ranging from 3 to 27 months. Recently, studies have identified significant genetic differences between BM and their corresponding primary tumors. It has been shown that BM harbor clinically actionable mutations that are distinct from those in the primary tumor samples. Additional genomic profiling of BM will provide deeper understanding of the pathogenesis of BM and suggest new therapeutic approaches.
We performed whole-exome sequencing of BM and matched tumors from 41 patients collected from renal cell carcinoma (RCC), breast cancer, lung cancer, and melanoma, which are known to be more likely to develop BM. We profiled total 126 fresh-frozen tumor samples and performed subsequent analyses of BM in comparison to paired primary tumor and extracranial metastases (ECM). We found that lung cancer shared the largest number of mutations between BM and matched tumors (83%), followed by melanoma (74%), RCC (51%), and Breast (26%), indicating that cancer type with high tumor mutational burden share more mutations with BM. Mutational signatures displayed limited differences, suggesting a lack of mutagenic processes specific to BM. However, point-mutation heterogeneity revealed that BM evolve separately into different subclones from their paired tumors regardless of cancer type, and some cancer driver genes were found in BM-specific subclones. These models and findings suggest that these driver genes may drive prometastatic subclones that lead to BM. 32 curated cancer gene mutations were detected and 71% of them were shared between BM and primary tumors or ECM. 29% of mutations were specific to BM, implying that BM often accumulate additional cancer gene mutations that are not present in primary tumors or ECM. Co-mutation analysis revealed a high frequency of TP53 nonsense mutation in BM, mostly in the DNA binding domain, suggesting TP53 nonsense mutation as a possible prerequisite for the development of BM. Copy number alteration analysis showed statistically significant differences between BM and their paired tumor samples in each cancer type (Wilcoxon test, p < 0.0385 for all). Both copy number gains and losses were consistently higher in BM for breast cancer (Wilcoxon test, p =1.307e-5) and lung cancer (Wilcoxon test, p =1.942e-5), implying greater genomic instability during the evolution of BM.
Our findings highlight that there are more unique mutations in BM, with significantly higher copy number alterations and tumor mutational burden. These genomic analyses could provide an opportunity for more reliable diagnostic decision-making, and these findings will be further tested with additional transcriptomic and epigenetic profiling for better characterization of BM-specific tumor microenvironments.

  • are there genomic signatures different in brain mets versus non metastatic or normal?
  • 32 genes from curated databases were different between brain mets and primary tumor
  • frequent nonsense mutations in TP53
  • divergent clonal evolution of drivers in BMets from primary
  • they were able to match BM with other mutational signatures like smokers and lung cancer signatures

5707 – A standard operating procedure for the interpretation of oncogenicity/pathogenicity of somatic mutations

Presenter/AuthorsPeter Horak, Malachi Griffith, Arpad Danos, Beth A. Pitel, Subha Madhavan, Xuelu Liu, Jennifer Lee, Gordana Raca, Shirley Li, Alex H. Wagner, Shashikant Kulkarni, Obi L. Griffith, Debyani Chakravarty, Dmitriy Sonkin. National Center for Tumor Diseases, Heidelberg, Germany, Washington University School of Medicine, St. Louis, MO, Mayo Clinic, Rochester, MN, Georgetown University Medical Center, Washington, DC, Dana-Farber Cancer Institute, Boston, MA, Frederick National Laboratory for Cancer Research, Rockville, MD, University of Southern California, Los Angeles, CA, Sunquest, Boston, MA, Baylor College of Medicine, Houston, TX, Memorial Sloan Kettering Cancer Center, New York, NY, National Cancer Institute, Rockville, MDDisclosures P. Horak: None. M. Griffith: None. A. Danos: None. B.A. Pitel: None. S. Madhavan: ; Perthera Inc. X. Liu: None. J. Lee: None. G. Raca: None. S. Li: ; Sunquest Information Systems, Inc. A.H. Wagner: None. S. Kulkarni: ; Baylor Genetics. O.L. Griffith: None. D. Chakravarty: None. D. Sonkin: None.AbstractSomatic variants in cancer-relevant genes are interpreted from multiple partially overlapping perspectives. When considered in discovery and translational research endeavors, it is important to determine if a particular variant observed in a gene of interest is oncogenic/pathogenic or not, as such knowledge provides the foundation on which targeted cancer treatment research is based. In contrast, clinical applications are dominated by diagnostic, prognostic, or therapeutic interpretations which in part also depends on underlying variant oncogenicity/pathogenicity. The Association for Molecular Pathology, the American Society of Clinical Oncology, and the College of American Pathologists (AMP/ASCO/CAP) have published structured somatic variant clinical interpretation guidelines which specifically address diagnostic, prognostic, and therapeutic implications. These guidelines have been well-received by the oncology community. Many variant knowledgebases, clinical laboratories/centers have adopted or are in the process of adopting these guidelines. The AMP/ASCO/CAP guidelines also describe different data types which are used to determine oncogenicity/pathogenicity of a variant, such as: population frequency, functional data, computational predictions, segregation, and somatic frequency. A second collaborative effort created the European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of molecular Targets to provide a harmonized vocabulary that provides an evidence-based ranking system of molecular targets that supports their value as clinical targets. However, neither of these clinical guideline systems provide systematic and comprehensive procedures for aggregating population frequency, functional data, computational predictions, segregation, and somatic frequency to consistently interpret variant oncogenicity/pathogenicity, as has been published in the ACMG/AMP guidelines for interpretation of pathogenicity of germline variants. In order to address this unmet need for somatic variant oncogenicity/pathogenicity interpretation procedures, the Variant Interpretation for Cancer Consortium (VICC, a GA4GH driver project) Knowledge Curation and Interpretation Standards (KCIS) working group (WG) has developed a Standard Operating Procedure (SOP) with contributions from members of ClinGen Somatic Clinical Domain WG, and ClinGen Somatic/Germline variant curation WG using an approach similar to the ACMG/AMP germline pathogenicity guidelines to categorize evidence of oncogenicity/pathogenicity as very strong, strong, moderate or supporting. This SOP enables consistent and comprehensive assessment of oncogenicity/pathogenicity of somatic variants and latest version of an SOP can be found at https://cancervariants.org/wg/kcis/.

  • best to use this SOP for somatic mutations and not rearangements
  • variants based on oncogenicity as strong to weak
  • useful variant knowledge on pathogenicity curated from known databases
  • the recommendations would provide some guideline on curating unknown somatic variants versus known variants of hereditary diseases
  • they have not curated RB1 mutations or variants (or for other RBs like RB2? p130?)

 

Follow on Twitter at:

@pharma_BI

@AACR

@CureCancerNow

@pharmanews

@BiotechWorld

#AACR20

 

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Personalized Medicine, Omics, and Health Disparities in Cancer:  Can Personalized Medicine Help Reduce the Disparity Problem?

Curator: Stephen J. Williams, PhD

In a Science Perspectives article by Timothy Rebbeck, health disparities, specifically cancer disparities existing in the sub-Saharan African (SSA) nations, highlighting the cancer incidence disparities which exist compared with cancer incidence in high income areas of the world [1].  The sub-Saharan African nations display a much higher incidence of prostate, breast, and cervix cancer and these cancers are predicted to double within the next twenty years, according to IARC[2].  Most importantly,

 the histopathologic and demographic features of these tumors differ from those in high-income countries

meaning that the differences seen in incidence may reflect a true health disparity as increases rates in these cancers are not seen in high income countries (HIC).

Most frequent male cancers in SSA include prostate, lung, liver, leukemia, non-Hodgkin’s lymphoma, and Kaposi’s sarcoma (a cancer frequently seen in HIV infected patients [3]).  In SSA women, breast and cervical cancer are the most common and these display higher rates than seen in high income countries.  In fact, liver cancer is seen in SSA females at twice the rate, and in SSA males almost three times the rate as in high income countries.

 

 

 

 

 

 

Reasons for cancer disparity in SSA

Patients with cancer are often diagnosed at a late stage in SSA countries.  This contrasts with patients from high income countries, which have their cancers usually diagnosed at an earlier stage, and with many cancers, like breast[4], ovarian[5, 6], and colon, detecting the tumor in the early stages is critical for a favorable outcome and prognosis[7-10].  In addition, late diagnosis also limits many therapeutic options for the cancer patient and diseases at later stages are much harder to manage, especially with respect to unresponsiveness and/or resistance of many therapies.  In addition, treatments have to be performed in low-resource settings in SSA, and availability of clinical lab work and imaging technologies may be limited.

Molecular differences in SSA versus HIC cancers which may account for disparities

Emerging evidence suggests that there are distinct molecular signatures with SSA tumors with respect to histotype and pathology.  For example Dr. Rebbeck mentions that Nigerian breast cancers were defined by increased mutational signatures associated with deficiency of the homologous recombination DNA repair pathway, pervasive mutations in the tumor suppressor gene TP53, mutations in GATA binding protein 3 (GATA3), and greater mutational burden, compared with breast tumors from African Americans or Caucasians[11].  However more research will be required to understand the etiology and causal factors related to this molecular distinction in mutational spectra.

It is believed that there is a higher rate of hereditary cancers in SSA. And many SSA cancers exhibit the more aggressive phenotype than in other parts of the world.  For example breast tumors in SSA black cases are twice as likely than SSA Caucasian cases to be of the triple negative phenotype, which is generally more aggressive and tougher to detect and treat, as triple negative cancers are HER2 negative and therefore are not a candidate for Herceptin.  Also BRCA1/2 mutations are more frequent in black SSA cases than in Caucasian SSA cases [12, 13].

Initiatives to Combat Health Disparities in SSA

Multiple initiatives are being proposed or in action to bring personalized medicine to the sub-Saharan African nations.  These include:

H3Africa empowers African researchers to be competitive in genomic sciences, establishes and nurtures effective collaborations among African researchers on the African continent, and generates unique data that could be used to improve both African and global health.

There is currently a global effort to apply genomic science and associated technologies to further the understanding of health and disease in diverse populations. These efforts work to identify individuals and populations who are at risk for developing specific diseases, and to better understand underlying genetic and environmental contributions to that risk. Given the large amount of genetic diversity on the African continent, there exists an enormous opportunity to utilize such approaches to benefit African populations and to inform global health.

The Human Heredity and Health in Africa (H3Africa) consortium facilitates fundamental research into diseases on the African continent while also developing infrastructure, resources, training, and ethical guidelines to support a sustainable African research enterprise – led by African scientists, for the African people. The initiative consists of 51 African projects that include population-based genomic studies of common, non-communicable disorders such as heart and renal disease, as well as communicable diseases such as tuberculosis. These studies are led by African scientists and use genetic, clinical, and epidemiologic methods to identify hereditary and environmental contributions to health and disease. To establish a foundation for African scientists to continue this essential work into the future work, the consortium also supports many crucial capacity building elements, such as: ethical, legal, and social implications research; training and capacity building for bioinformatics; capacity for biobanking; and coordination and networking.

The World Economic Forum’s Leapfrogging with Precision Medicine project 

This project is part of the World Economic Forum’s Shaping the Future of Health and Healthcare Platform

The Challenge

Advancing precision medicine in a way that is equitable and beneficial to society means ensuring that healthcare systems can adopt the most scientifically and technologically appropriate approaches to a more targeted and personalized way of diagnosing and treating disease. In certain instances, countries or institutions may be able to bypass, or “leapfrog”, legacy systems or approaches that prevail in developed country contexts.

The World Economic Forum’s Leapfrogging with Precision Medicine project will develop a set of tools and case studies demonstrating how a precision medicine approach in countries with greenfield policy spaces can potentially transform their healthcare delivery and outcomes. Policies and governance mechanisms that enable leapfrogging will be iterated and scaled up to other projects.

Successes in personalized genomic research in SSA

As Dr. Rebbeck states:

 Because of the underlying genetic and genomic relationships between Africans and members of the African diaspora (primarily in North America and Europe), knowledge gained from research in SSA can be used to address health disparities that are prevalent in members of the African diaspora.

For example members of the West African heritage and genomic ancestry has been reported to confer the highest genomic risk for prostate cancer in any worldwide population [14].

 

PERSPECTIVEGLOBAL HEALTH

Cancer in sub-Saharan Africa

  1. Timothy R. Rebbeck

See all authors and affiliations

Science  03 Jan 2020:
Vol. 367, Issue 6473, pp. 27-28
DOI: 10.1126/science.aay474

Summary/Abstract

Cancer is an increasing global public health burden. This is especially the case in sub-Saharan Africa (SSA); high rates of cancer—particularly of the prostate, breast, and cervix—characterize cancer in most countries in SSA. The number of these cancers in SSA is predicted to more than double in the next 20 years (1). Both the explanations for these increasing rates and the solutions to address this cancer epidemic require SSA-specific data and approaches. The histopathologic and demographic features of these tumors differ from those in high-income countries (HICs). Basic knowledge of the epidemiology, clinical features, and molecular characteristics of cancers in SSA is needed to build prevention and treatment tools that will address the future cancer burden. The distinct distribution and determinants of cancer in SSA provide an opportunity to generate knowledge about cancer risk factors, genomics, and opportunities for prevention and treatment globally, not only in Africa.

 

References

  1. Rebbeck TR: Cancer in sub-Saharan Africa. Science 2020, 367(6473):27-28.
  2. Parkin DM, Ferlay J, Jemal A, Borok M, Manraj S, N’Da G, Ogunbiyi F, Liu B, Bray F: Cancer in Sub-Saharan Africa: International Agency for Research on Cancer; 2018.
  3. Chinula L, Moses A, Gopal S: HIV-associated malignancies in sub-Saharan Africa: progress, challenges, and opportunities. Current opinion in HIV and AIDS 2017, 12(1):89-95.
  4. Colditz GA: Epidemiology of breast cancer. Findings from the nurses’ health study. Cancer 1993, 71(4 Suppl):1480-1489.
  5. Hamilton TC, Penault-Llorca F, Dauplat J: [Natural history of ovarian adenocarcinomas: from epidemiology to experimentation]. Contracept Fertil Sex 1998, 26(11):800-804.
  6. Garner EI: Advances in the early detection of ovarian carcinoma. J Reprod Med 2005, 50(6):447-453.
  7. Brockbank EC, Harry V, Kolomainen D, Mukhopadhyay D, Sohaib A, Bridges JE, Nobbenhuis MA, Shepherd JH, Ind TE, Barton DP: Laparoscopic staging for apparent early stage ovarian or fallopian tube cancer. First case series from a UK cancer centre and systematic literature review. European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology 2013, 39(8):912-917.
  8. Kolligs FT: Diagnostics and Epidemiology of Colorectal Cancer. Visceral medicine 2016, 32(3):158-164.
  9. Rocken C, Neumann U, Ebert MP: [New approaches to early detection, estimation of prognosis and therapy for malignant tumours of the gastrointestinal tract]. Zeitschrift fur Gastroenterologie 2008, 46(2):216-222.
  10. Srivastava S, Verma M, Henson DE: Biomarkers for early detection of colon cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 2001, 7(5):1118-1126.
  11. Pitt JJ, Riester M, Zheng Y, Yoshimatsu TF, Sanni A, Oluwasola O, Veloso A, Labrot E, Wang S, Odetunde A et al: Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features. Nature communications 2018, 9(1):4181.
  12. Zheng Y, Walsh T, Gulsuner S, Casadei S, Lee MK, Ogundiran TO, Ademola A, Falusi AG, Adebamowo CA, Oluwasola AO et al: Inherited Breast Cancer in Nigerian Women. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2018, 36(28):2820-2825.
  13. Rebbeck TR, Friebel TM, Friedman E, Hamann U, Huo D, Kwong A, Olah E, Olopade OI, Solano AR, Teo SH et al: Mutational spectrum in a worldwide study of 29,700 families with BRCA1 or BRCA2 mutations. Human mutation 2018, 39(5):593-620.
  14. Lachance J, Berens AJ, Hansen MEB, Teng AK, Tishkoff SA, Rebbeck TR: Genetic Hitchhiking and Population Bottlenecks Contribute to Prostate Cancer Disparities in Men of African Descent. Cancer research 2018, 78(9):2432-2443.

Other articles on Cancer Health Disparities and Genomics on this Online Open Access Journal Include:

Gender affects the prevalence of the cancer type
The Rutgers Global Health Institute, part of Rutgers Biomedical and Health Sciences, Rutgers University, New Brunswick, New Jersey – A New Venture Designed to Improve Health and Wellness Globally
Breast Cancer Disparities to be Sponsored by NIH: NIH Launches Largest-ever Study of Breast Cancer Genetics in Black Women
War on Cancer Needs to Refocus to Stay Ahead of Disease Says Cancer Expert
Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk
Ethics Behind Genetic Testing in Breast Cancer: A Webinar by Laura Carfang of survivingbreastcancer.org
Live Notes from @HarvardMed Bioethics: Authors Jerome Groopman, MD & Pamela Hartzband, MD, discuss Your Medical Mind
Testing for Multiple Genetic Mutations via NGS for Patients: Very Strong Family History of Breast & Ovarian Cancer, Diagnosed at Young Ages, & Negative on BRCA Test
Study Finds that Both Women and their Primary Care Physicians Confusion over Ovarian Cancer Symptoms May Lead to Misdiagnosis

 

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Ethics Behind Genetic Testing in Breast Cancer: A Webinar by Laura Carfang of survivingbreastcancer.org

Reporter: Stephen J. Williams, PhD

The following are Notes from a Webinar sponsored by survivingbreastcancer.org  on March 12,2020.

The webinar started with a brief introduction of attendees , most who are breast cancer survivors.  Survivingbreastcancer.org is an organization committed to supplying women affected with breast cancer up to date information, including podcasts, webinars, and information for treatment, care, and finding support and support groups.

Some of the comments of survivors:

  • being strong
  • making sure to not feel overwhelmed on initial diagnosis
  • get good information
  • sometimes patients have to know to ask for genetic testing as physicians may not offer it

Laura Carfang discussed her study results presented at  a bioethics conference in Clearwater, FL   on issues driving breast cancer patient’s  as well as at-risk women’s decision making process for genetic testing.  The study was a phenomenological study in order to determine, through personal lived experiences, what are pivotal choices to make genetic testing decisions in order to improve clinical practice.

The research involved in depth interviews with 6 breast cancer patients (all women) who had undergone breast cancer genetic testing.

Main themes coming from the interviews

  • information informing decisions before diagnosis:  they did not have an in depth knowledge of cancer or genetics or their inherent risk before the diagnosis.
  • these are my genes and I should own it: another common theme among women who were just diagnosed and contemplating whether or not to have genetic testing
  • information contributing to decision making after diagnosis: women wanted the option, and they wanted to know if they carry certain genetic mutations and how it would guide their own personal decision to choose the therapy they are most comfortable with and gives them the best chance to treat their cancer (the decision and choice is very personal)
  • communicating to family members and children was difficult for the individual affected;  women found that there were so many ramifications about talking with family members (how do I tell children, do family members really empathize with what I am going through).  Once women were tested they felt a great strain because they now were more concerned with who in their family (daughters) were at risk versus when they first get the diagnosis the bigger concern was obtaining information.
  • Decision making to undergo genetic testing not always linear but a nonlinear process where women went from wanting to get tested for the information to not wanting to get tested for reasons surrounding negative concerns surrounding knowing results (discrimination based on results, fear of telling family members)
  • Complex decision making involves a shift or alteration in emotion
  • The Mayo Clinic has come out with full support of genetic testing and offer to any patient.

Additional resources discussed was a book by Leslie Ferris Yerger “Probably Benign” which discusses misdiagnoses especially when a test comes back as “probably benign” and how she found it was not.

 

for more information on further Podcasts and to sign up for newsletters please go to https://www.survivingbreastcancer.org/

and @SBC_org

More articles on this Online Open Access Journal on Cancer and Bioethics Include:

Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk

Tweets and Re-Tweets by @Pharma_BI ‏and @AVIVA1950 at 2019 Petrie-Flom Center Annual Conference: Consuming Genetics: Ethical and Legal Considerations of New Technologies, Friday, May 17, 2019 from 8:00 AM to 5:00 PM EDT @Harvard_Law

Genomics & Ethics: DNA Fragments are Products of Nature or Patentable Genes?

Study Finds that Both Women and their Primary Care Physicians Confusion over Ovarian Cancer Symptoms May Lead to Misdiagnosis

 

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Showcase: How Deep Learning could help radiologists spend their time more efficiently

Reporter and Curator: Dror Nir, PhD

 

The debate on the function AI could or should realize in modern radiology is buoyant presenting wide spectrum of positive expectations and also fears.

The article: A Deep Learning Model to Triage Screening Mammograms: A Simulation Study that was published this month shows the best, and very much feasible, utility for AI in radiology at the present time. It would be of great benefit for radiologists and patients if such applications will be incorporated (with all safety precautions taken) into routine practice as soon as possible.

In a simulation study, a deep learning model to triage mammograms as cancer free improves workflow efficiency and significantly improves specificity while maintaining a noninferior sensitivity.

Background

Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist specificity or efficiency.

Purpose

To develop a DL model to triage a portion of mammograms as cancer free, improving performance and workflow efficiency.

Materials and Methods

In this retrospective study, 223 109 consecutive screening mammograms performed in 66 661 women from January 2009 to December 2016 were collected with cancer outcomes obtained through linkage to a regional tumor registry. This cohort was split by patient into 212 272, 25 999, and 26 540 mammograms from 56 831, 7021, and 7176 patients for training, validation, and testing, respectively. A DL model was developed to triage mammograms as cancer free and evaluated on the test set. A DL-triage workflow was simulated in which radiologists skipped mammograms triaged as cancer free (interpreting them as negative for cancer) and read mammograms not triaged as cancer free by using the original interpreting radiologists’ assessments. Sensitivities, specificities, and percentage of mammograms read were calculated, with and without the DL-triage–simulated workflow. Statistics were computed across 5000 bootstrap samples to assess confidence intervals (CIs). Specificities were compared by using a two-tailed t test (P < .05) and sensitivities were compared by using a one-sided t test with a noninferiority margin of 5% (P < .05).

Results

The test set included 7176 women (mean age, 57.8 years ± 10.9 [standard deviation]). When reading all mammograms, radiologists obtained a sensitivity and specificity of 90.6% (173 of 191; 95% CI: 86.6%, 94.7%) and 93.5% (24 625 of 26 349; 95% CI: 93.3%, 93.9%). In the DL-simulated workflow, the radiologists obtained a sensitivity and specificity of 90.1% (172 of 191; 95% CI: 86.0%, 94.3%) and 94.2% (24 814 of 26 349; 95% CI: 94.0%, 94.6%) while reading 80.7% (21 420 of 26 540) of the mammograms. The simulated workflow improved specificity (P = .002) and obtained a noninferior sensitivity with a margin of 5% (P < .001).

Conclusion

This deep learning model has the potential to reduce radiologist workload and significantly improve specificity without harming sensitivity.

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Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line

Reporter: Stephen J. Williams, PhD

In a Genome Research report by Marie Nattestad et al. [1], the SK-BR-3 breast cancer cell line was sequenced using a long read single molecule sequencing protocol in order to develop one of the most detailed maps of structural variations in a cancer genome to date.  The authors detected over 20,000 variants with this new sequencing modality, whereas most of these variants would have been missed by short read sequencing.  In addition, a complex sequence of nested duplications and translocations occurred surrounding the ERBB2 (HER2) while full-length transcriptomic analysis revealed novel gene fusions within the nested genomic variants.  The authors suggest that combining this long-read genome and transcriptome sequencing results in a more comprehensive coverage of tumor gene variants and “sheds new light on the complex mechanisms involved in cancer genome evolution.”

Genomic instability is a hallmark of cancer [2], which lead to numerous genetic variations such as:

  • Copy number variations
  • Chromosomal alterations
  • Gene fusions
  • Deletions
  • Gene duplications
  • Insertions
  • Translocations

Efforts such as the Cancer Genome Atlas [3], and the International Genome Consortium (2010) use short-read sequencing technology to detect and analyze thousands of commonly occurring mutations however short-read technology has a high false positive and negative rate for detecting less common genetic structural variations {as high as 50% [4]}. In addition, short reads cannot detect variations in close proximity to each other or on the same molecule, therefore underestimating the variation number.

Methods:  The authors used a long-read sequencing technology from Pacific Biosciences (SMRT) to analyze the mutational and structural variation in the SK-BR-3 breast cancer cell line.  A split read and within-read mapping approach was used to detect variants of different types and sizes.  In general, long-reads have better alignment qualities than short reads, resulting in higher quality mapping. Transcriptomic analysis was performed using Iso-Seq.

Results: Using the SMRT long-read sequencing technology from Pacific Biosciences, the authors were able to obtain 71.9% sequencing coverage with average read length of 9.8 kb for the SK-BR-3 genome.

A few notes:

  1. Most amplified regions (33.6 copies) around the locus spanning the ERBB2 oncogene and around MYC locus (38 copies), EGFR locus (7 copies) and BCAS1 (16.8 copies)
  2. The locus 8q24.12 had the most amplifications (this locus contains the SNTB1 gene) at 69.2 copies
  3. Long-read sequencing showed more insertions than deletions and suggests an underestimate of the lengths of low complexity regions in the human reference genome
  4. Found 1,493 long read variants, 603 of which were between different chromosomes
  5. Using Iso-Seq in conjunction with the long-read platform, they detected 1,692,379 isoforms (93%) mapping to the reference genome and 53 putative gene fusions (39 of which they found genomic evidence)

A table modified from the paper on the gene fusions is given below:

Table 1. Gene fusions with RNA evidence from Iso-Seq and DNA evidence from SMRT DNA sequencing where the genomic path is found using SplitThreader from Sniffles variant calls. Note link in table is  GeneCard for each gene.

SplitThreader path

 

# Genes Distance
(bp)
Number
of variants
Chromosomes
in path
Previously observed in references
1 KLHDC2 SNTB1 9837 3 14|17|8 Asmann et al. (2011) as only a 2-hop fusion
2 CYTH1 EIF3H 8654 2 17|8 Edgren et al. (2011); Kim and Salzberg
(2011); RNA only, not observed as 2-hop
3 CPNE1 PREX1 1777 2 20 Found and validated as 2-hop by Chen et al. 2013
4 GSDMB TATDN1 0 1 17|8 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011)
5 LINC00536 PVT1 0 1 8 No
6 MTBP SAMD12 0 1 8 Validated by Edgren et al. (2011)
7 LRRFIP2 SUMF1 0 1 3 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011)
8 FBXL7 TRIO 0 1 5 No
9 ATAD5 TLK2 0 1 17 No
10 DHX35 ITCH 0 1 20 Validated by Edgren et al. (2011)
11 LMCD1-AS1 MECOM 0 1 3 No
12 PHF20 RP4-723E3.1 0 1 20 No
13 RAD51B SEMA6D 0 1 14|15 No
14 STAU1 TOX2 0 1 20 No
15 TBC1D31 ZNF704 0 1 8 Edgren et al. (2011); Kim and Salzberg
(2011); Chen et al. (2013); validated by
Edgren et al. (2011); Chen et al. (2013)

 

SplitThreader found two different paths for the RAD51B-SEMA6D gene fusion and for the LINC00536-PVT1 gene fusion. Number of Iso-Seq reads refers to full-length HQ-filtered reads. Alignments of SMRT DNA sequence reads supporting each of these gene fusions are shown in Supplemental Note S2.

 

 References

 

  1. Nattestad M, Goodwin S, Ng K, Baslan T, Sedlazeck FJ, Rescheneder P, Garvin T, Fang H, Gurtowski J, Hutton E et al: Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line. Genome research 2018, 28(8):1126-1135.
  2. Hanahan D, Weinberg RA: The hallmarks of cancer. Cell 2000, 100(1):57-70.
  3. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA et al: Mutational landscape and significance across 12 major cancer types. Nature 2013, 502(7471):333-339.
  4. Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Fritz MH et al: An integrated map of structural variation in 2,504 human genomes. Nature 2015, 526(7571):75-81.

 

Other articles on Cancer Genome Sequencing in this Open Access Journal Include:

 

International Cancer Genome Consortium Website has 71 Committed Cancer Genome Projects Ongoing

Loss of Gene Islands May Promote a Cancer Genome’s Evolution: A new Hypothesis on Oncogenesis

Identifying Aggressive Breast Cancers by Interpreting the Mathematical Patterns in the Cancer Genome

CancerBase.org – The Global HUB for Diagnoses, Genomes, Pathology Images: A Real-time Diagnosis and Therapy Mapping Service for Cancer Patients – Anonymized Medical Records accessible to

 

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Tumor Ammonia Recycling: How Cancer Cells Use Glutamate Dehydrogenase to Recycle Tumor Microenvironment Waste Products for Biosynthesis

Reporter: Stephen J. Williams, PhD

A feature of the tumorigenic process is the rewiring of the metabolic processes that provides a tumor cell the ability to grow and thrive in conditions of limiting nutrients as well as the ability to utilize waste products in salvage pathways for production of new biomass (amino acids, nucleic acids etc.) required for cellular growth and division 1-8.  A Science article from Spinelli et al. 9 (and corresponding Perspective article in the same issue by Dr. Chi V. Dang entitled Feeding Frenzy for Cancer Cells 10) describes the mechanism by which estrogen-receptor positive (ER+) breast cancer cells convert glutamine to glutamate, release ammonia  into the tumor microenvironment, diffuses into tumor cells and eventually recycle this ammonia by reductive amination of a-ketoglutarate by glutamate dehydrogenase (GDH) to produce glutamic acid and subsequent other amino acids needed for biomass production.   Ammonia can accumulate in the tumor microenvironment in poorly vascularized tumor. Thus ammonia becomes an important nitrogen source for tumor cells.

Mammalian cells have a variety of mechanisms to metabolize ammonia including

  • Glutamate synthetase (GS) in the liver can incorporate ammonia into glutamate to form glutamine
  • glutamate dehydrogenase (GDH) converts glutamate to a-ketoglutarate and ammonia under allosteric regulation (discussed in a post on this site by Dr. Larry H. Berstein; subsection Drugging Glutaminolysis)
  • the reverse reaction of GDH, which was found to occur in ER+ breast cancer cells, a reductive amination of a-ketoglutarate to glutamate11, is similar to the reductive carboxylation of a-ketoglutarate to citrate by isocitrate dehydrogenase (IDH) for fatty acid synthesis (IDH is overexpressed in many tumor types including cancer stem cells 12-15), and involved in immune response and has been developed as a therapeutic target for various cancers. IDH mutations were shown to possess the neomorphic activity to generate the oncometabolite, 2-hydroxyglutarate (2HG) 16-18. With a single codon substitution, the kinetic properties of the mutant IDH isozyme are significantly altered, resulting in an obligatory sequential ordered reaction in the reverse direction 19.

 

In the Science paper, Spinelli et al. report that ER+ breast cancer cells have the ability to utilize ammonia sources from their surroundings in order to produce amino acids and biomass as these ER+ breast cancer cells have elevated levels of GS and GDH with respect to other breast cancer histotypes.

GDH was elevated in ER+ luminal cancer cells and the quiescent epithelial cells in organoid culture

However proliferative cells were dependent on transaminases, which transfers nitrogen from glutamate to pyruvate or oxaloacetate to form a-ketoglutarate and alanine or aspartate. a-ketoglutarate is further metabolized in the citric acid cycle.

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1.    Reductive amination and transamination reactions of glutamic acid.  Source http://www.biologydiscussion.com/organism/metabolism-organism/incorporation-of-ammonia-into-organic-compounds/50870

Spinelli et al. showed GDH is necessary for ammonia reductive incorporation into a-ketoglutarate and also required for ER+ breast cancer cell growth in immunocompromised mice.

In addition, as commented by Dr. Dang in his associated Perspectives article, (quotes indent)

The metabolic tumor microenvironment produced by resident cells, such as fibroblasts and macrophages, can create an immunosuppressive environment 20.  Hence, it will be of great interest to further understand whether products such as ammonia could affect tumor immunity or induce autophagy  (end quote indent)

 

 

 

Figure 2.  Tumor ammonia recycling.  Source:  From Chi V. Dang Feeding Frenzy for cancer cells.  Rights from RightsLink (copyright.com)

Metabolic recycling of ammonia via glutamate dehydrogenase supports breast cancer biomass

Jessica B. Spinelli1,2, Haejin Yoon1, Alison E. Ringel1, Sarah Jeanfavre2, Clary B. Clish2, Marcia C. Haigis1 *

1.      1Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA. 2.      2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

* *Corresponding author. Email: marcia_haigis@hms.harvard.edu

Science  17 Nov 2017:Vol. 358, Issue 6365, pp. 941-946 DOI: 10.1126/science.aam9305

Abstract

Ammonia is a ubiquitous by-product of cellular metabolism; however, the biological consequences of ammonia production are not fully understood, especially in cancer. We found that ammonia is not merely a toxic waste product but is recycled into central amino acid metabolism to maximize nitrogen utilization. In our experiments, human breast cancer cells primarily assimilated ammonia through reductive amination catalyzed by glutamate dehydrogenase (GDH); secondary reactions enabled other amino acids, such as proline and aspartate, to directly acquire this nitrogen. Metabolic recycling of ammonia accelerated proliferation of breast cancer. In mice, ammonia accumulated in the tumor microenvironment and was used directly to generate amino acids through GDH activity. These data show that ammonia is not only a secreted waste product but also a fundamental nitrogen source that can support tumor biomass.

 

 

References

1          Strickaert, A. et al. Cancer heterogeneity is not compatible with one unique cancer cell metabolic map. Oncogene 36, 2637-2642, doi:10.1038/onc.2016.411 (2017).

2          Hui, S. et al. Glucose feeds the TCA cycle via circulating lactate. Nature 551, 115-118, doi:10.1038/nature24057 (2017).

3          Mashimo, T. et al. Acetate is a bioenergetic substrate for human glioblastoma and brain metastases. Cell 159, 1603-1614, doi:10.1016/j.cell.2014.11.025 (2014).

4          Sousa, C. M. et al. Erratum: Pancreatic stellate cells support tumour metabolism through autophagic alanine secretion. Nature 540, 150, doi:10.1038/nature19851 (2016).

5          Sousa, C. M. et al. Pancreatic stellate cells support tumour metabolism through autophagic alanine secretion. Nature 536, 479-483, doi:10.1038/nature19084 (2016).

6          Commisso, C. et al. Macropinocytosis of protein is an amino acid supply route in Ras-transformed cells. Nature 497, 633-637, doi:10.1038/nature12138 (2013).

7          Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57-70 (2000).

8          Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646-674, doi:10.1016/j.cell.2011.02.013 (2011).

9          Spinelli, J. B. et al. Metabolic recycling of ammonia via glutamate dehydrogenase supports breast cancer biomass. Science 358, 941-946, doi:10.1126/science.aam9305 (2017).

10        Dang, C. V. Feeding frenzy for cancer cells. Science 358, 862-863, doi:10.1126/science.aaq1070 (2017).

11        Smith, T. J. & Stanley, C. A. Untangling the glutamate dehydrogenase allosteric nightmare. Trends in biochemical sciences 33, 557-564, doi:10.1016/j.tibs.2008.07.007 (2008).

12        Metallo, C. M. et al. Reductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia. Nature 481, 380-384, doi:10.1038/nature10602 (2011).

13        Garrett, M. et al. Metabolic characterization of isocitrate dehydrogenase (IDH) mutant and IDH wildtype gliomaspheres uncovers cell type-specific vulnerabilities. Cancer & metabolism 6, 4, doi:10.1186/s40170-018-0177-4 (2018).

14        Calvert, A. E. et al. Cancer-Associated IDH1 Promotes Growth and Resistance to Targeted Therapies in the Absence of Mutation. Cell reports 19, 1858-1873, doi:10.1016/j.celrep.2017.05.014 (2017).

15        Sciacovelli, M. & Frezza, C. Metabolic reprogramming and epithelial-to-mesenchymal transition in cancer. The FEBS journal 284, 3132-3144, doi:10.1111/febs.14090 (2017).

16        Dang, L. et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462, 739-744, doi:10.1038/nature08617 (2009).

17        Gross, S. et al. Cancer-associated metabolite 2-hydroxyglutarate accumulates in acute myelogenous leukemia with isocitrate dehydrogenase 1 and 2 mutations. The Journal of experimental medicine 207, 339-344, doi:10.1084/jem.20092506 (2010).

18        Ward, P. S. et al. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting alpha-ketoglutarate to 2-hydroxyglutarate. Cancer cell 17, 225-234, doi:10.1016/j.ccr.2010.01.020 (2010).

19        Rendina, A. R. et al. Mutant IDH1 enhances the production of 2-hydroxyglutarate due to its kinetic mechanism. Biochemistry 52, 4563-4577, doi:10.1021/bi400514k (2013).

20        Zhang, X. et al. IDH mutant gliomas escape natural killer cell immune surveillance by downregulation of NKG2D ligand expression. Neuro-oncology 18, 1402-1412, doi:10.1093/neuonc/now061 (2016).

 

Other articles on this Open Access Journal on Cancer Metabolism Include:

 

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

 

Accumulation of 2-hydroxyglutarate is not a biomarker for malignant progression of IDH-mutated low grade gliomas

 

 

Protein-binding, Protein-Protein interactions & Therapeutic Implications [7.3]

Is the Warburg effect an effect of deregulated space occupancy of methylome?

Therapeutic Implications for Targeted Therapy from the Resurgence of Warburg ‘Hypothesis’

New Insights on the Warburg Effect [2.2]

The Inaugural Judith Ann Lippard Memorial Lecture in Cancer Research: PI 3 Kinase & Cancer Metabolism

Renal (Kidney) Cancer: Connections in Metabolism at Krebs cycle and Histone Modulation

Warburg Effect and Mitochondrial Regulation- 2.1.3

Refined Warburg Hypothesis -2.1.2

 

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Reporter: Gail S. Thornton

This article appeared on the web site of Harley Street Concierge, one of the U.K.’s leading independent providers of clinical, practical and emotion support for cancer patients. 

Cancer at Work: An Interview With Barbara Wilson

Whether you’re supporting an employee through cancer at work. Or you’re a cancer patient struggling to get the support you need. Either way, this Q and A with Barbara Wilson will help you out. Read on for a glimpse into Barbara’s personal experience with breast cancer. Find out where companies are falling short of supporting employees. Discover what you need to do if you’re feeling unsupported at work. And learn what’s unacceptable for Barbara in a modern and civilised society.

In a 2013 interview about cancer at work, you expressed amazement at “the lack of understanding there is about cancer. And what the impact is on individuals”. How would you say this has improved in the last 4 years? And what do you feel still needs to change?

There’s greater awareness and understanding about cancer at work. More organisations are aware of the difficulties people face. But many organisations don’t appreciate that recovery isn’t straightforward or quick. They also tend to rely on generic return to work policies. And these are inappropriate when it comes to supporting people recovering from cancer. A lot still depends on how far the local line manager is prepared to support an employee. And whether they’ll bend rules if need be about leave or sick pay.

You were diagnosed with breast cancer in 2005 and given the all clear in 2010. What did you learn about yourself through treatment and recovery?

 

I learned that I wasn’t immortal or superhuman! And also that life is precious and so it’s important to make the best of it. That doesn’t actually mean counting off things on your bucket list. Or living each day as if it’s your last. It’s about appreciating what you have, family, friends and the sheer joy of being alive.

“Life is precious. It’s about appreciating what you have, family, friends and the sheer joy of being alive.”

It’s a common misperception that people in remission want more family time or to travel the world. What reasons do your clients share with you for wanting to get back to work?

Yes. Before I had cancer, I remember asking a terminally ill employee why she still wanted to work. And she worked until a fortnight before her death. The simple answer is that it’s about feeling normal. Using your brain. Being with friends and colleagues rather than on your own. And losing yourself in your work. There are also financial reasons. But typically – and I can say this based on my own experience – it’s about being ‘you’ again rather than a cancer patient.

“I remember asking a terminally ill employee why she still wanted to work. And she worked until a fortnight before her death. Typically – and I can say this based on my own experience – it’s about being ‘you’ again rather than a cancer patient.”

You share tips for employers and HR professionals in this article for Macmillan. And you set out how to support a colleague during and after cancer treatment. What would you say to an employee who isn’t feeling supported by their employer or colleagues in this way?

In my experience there are two main reasons why people often aren’t supported.

1. Bosses and colleagues don’t understand the full impact of cancer treatment. They won’t understand what fatigue is or chemo brain or peripheral neuropathy. So they often expect people to get ‘back to normal’ work after 6 to 8 weeks. But recovery can take many months. This isn’t helped by the person often looking fit and well.

2. People don’t like talking about cancer at work. They feel awkward. And as a result often decide to say nothing. We advise people to be open from the outset. To understand their right to reasonable adjustments. And their responsibility to update their employer about their recovery and support needs. Employees recovering from cancer often have to take the lead. They have to guide their colleagues about the specific help they need. You can’t expect others to do it for you. It sounds wrong but that’s how it is.

“Bosses and colleagues often expect people to get ‘back to normal’ work after 6 to 8 weeks. But recovery can take many months. “

More than 100,000 people had to wait more than 2 weeks to see a cancer specialist in the UK last year. 25,153 had to wait more than 62 days to start treatment. What’s your reaction to these statistics?

It’s shocking. The worry for patients and their families during this period is totally debilitating. And on top of this it means that the cancer is growing unchecked. Where the cancer is aggressive, the delay may threaten lives. And it will certainly add to the overall costs of care. We really have to address this. It’s just not acceptable in a modern and civilised society.

“The worry for patients and their families during this period is totally debilitating. We really have to address this.”

Finally, can you tell us more about Working With Cancer?

Working With Cancer is a social enterprise and was established in June 2014. We support people affected by cancer to lead fulfilling and rewarding working lives. That means helping people to successfully return to work or remain in work. Or sometimes it’s about helping people to find work – depending on their personal needs. We work with corporate, charities and other third sector organisations to support people throughout the UK.

We coach people diagnosed with cancer to re-establish their working lives. And we train employers to understand how to manage work and cancer. We’ll advise teams about how to support a colleague affected by cancer. And we help carers juggle work whilst supporting their loved ones. Working With Cancer also helps organisations to update or improve their policies.

Barbara Wilson - Cancer at Work

About Barbara Wilson

Barbara Wilson is a senior HR professional with almost 40 years’ experience.  Roles include Group Head of Strategic HR at Catlin Group Ltd. Deputy Head of HR at Schroders Investment Management. And Chief of Staff to the Group HR Director at Barclays. After a breast cancer diagnosis, Barbara launched Working With Cancer. It’s a Social Enterprise providing coaching, training and consultancy to employers, employees, carers and health professionals.

 

For more information about Working With Cancer, click here to visit the websiteFollow this link to connect with Barbara on Twitter. Email admin@workingwithcancer.co.uk. Or call 07508 232257 or 07919 147784.

 

SOURCE

https://harleystreetconcierge.com/cancer-at-work/

Other posts on the JP Morgan 2019 Healthcare Conference on this Open Access Journal include:

2018

Top 10 Cancer Research Priorities

https://pharmaceuticalintelligence.com/2018/12/24/top-10-cancer-research-priorities/

Innovation + Technology = Good Patient Experience

https://pharmaceuticalintelligence.com/2018/12/24/innovation-technology-good-patient-experience/

2017

Inspiring Book for ALL Cancer Survivors, ALL Cancer Patients and ALL Cardiac Patients – The VOICES of Patients, Hospitals CEOs, Health Care Providers, Caregivers and Families: Personal Experience with Critical Care and Invasive Medical Procedures

https://pharmaceuticalintelligence.com/2017/10/24/inspiring-book-for-all-cancer-survivors-all-cancer-patients-and-all-cardiac-patients-the-voices-of-patients-hospitals-ceos-health-care-providers-caregivers-and-families-personal-experience-with/

2016

Funding Opportunities for Cancer Research

https://pharmaceuticalintelligence.com/2016/12/08/funding-opportunities-for-cancer-research/

2012

The Incentive for “Imaging based cancer patient’ management”

https://pharmaceuticalintelligence.com/2012/08/27/the-incentive-for-imaging-based-cancer-patient-management/

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