Feeds:
Posts
Comments

Archive for the ‘Cancer Screening’ Category

Al is on the way to lead critical ED decisions on CT

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

Artificial intelligence (AI) has infiltrated many organizational processes, raising concerns that robotic systems will eventually replace many humans in decision-making. The advent of AI as a tool for improving health care provides new prospects to improve patient and clinical team’s performance, reduce costs, and impact public health. Examples include, but are not limited to, automation; information synthesis for patients, “fRamily” (friends and family unpaid caregivers), and health care professionals; and suggestions and visualization of information for collaborative decision making.

In the emergency department (ED), patients with Crohn’s disease (CD) are routinely subjected to Abdomino-Pelvic Computed Tomography (APCT). It is necessary to diagnose clinically actionable findings (CAF) since they may require immediate intervention, which is typically surgical. Repeated APCTs, on the other hand, results in higher ionizing radiation exposure. The majority of APCT performance guidance is clinical and empiric. Emergency surgeons struggle to identify Crohn’s disease patients who actually require a CT scan to determine the source of acute abdominal distress.

Image Courtesy: Jim Coote via Pixabay https://www.aiin.healthcare/media/49446

Aid seems to be on the way. Researchers employed machine learning to accurately distinguish these sufferers from Crohn’s patients who appear with the same complaint but may safely avoid the recurrent exposure to contrast materials and ionizing radiation that CT would otherwise wreak on them.

The study entitled “Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department” was published on July 9 in Digestive and Liver Disease by gastroenterologists and radiologists at Tel Aviv University in Israel.

Retrospectively, Jacob Ollech and his fellow researcher have analyzed 101 emergency treatments of patients with Crohn’s who underwent abdominopelvic CT.

They were looking for examples where a scan revealed clinically actionable results. These were classified as intestinal blockage, perforation, intra-abdominal abscess, or complex fistula by the researchers.

On CT, 44 (43.5 %) of the 101 cases reviewed had such findings.

Ollech and colleagues utilized a machine-learning technique to design a decision-support tool that required only four basic clinical factors to test an AI approach for making the call.

The approach was successful in categorizing patients into low- and high-risk groupings. The researchers were able to risk-stratify patients based on the likelihood of clinically actionable findings on abdominopelvic CT as a result of their success.

Ollech and co-authors admit that their limited sample size, retrospective strategy, and lack of external validation are shortcomings.

Moreover, several patients fell into an intermediate risk category, implying that a standard workup would have been required to guide CT decision-making in a real-world situation anyhow.

Consequently, they generate the following conclusion:

We believe this study shows that a machine learning-based tool is a sound approach for better-selecting patients with Crohn’s disease admitted to the ED with acute gastrointestinal complaints about abdominopelvic CT: reducing the number of CTs performed while ensuring that patients with high risk for clinically actionable findings undergo abdominopelvic CT appropriately.

Main Source:

Konikoff, Tom, Idan Goren, Marianna Yalon, Shlomit Tamir, Irit Avni-Biron, Henit Yanai, Iris Dotan, and Jacob E. Ollech. “Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department.” Digestive and Liver Disease (2021). https://www.sciencedirect.com/science/article/abs/pii/S1590865821003340

Other Related Articles published in this Open Access Online Scientific Journal include the following:

Al App for People with Digestive Disorders

Reporter: Irina Robu, Ph.D.

https://pharmaceuticalintelligence.com/2019/06/24/ai-app-for-people-with-digestive-disorders/

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

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

https://pharmaceuticalintelligence.com/2021/05/04/machine-learning-ml-in-cancer-prognosis-prediction-helps-the-researcher-to-identify-multiple-known-as-well-as-candidate-cancer-diver-genes/

Al System Used to Detect Lung Cancer

Reporter: Irina Robu, Ph.D.

https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/

Artificial Intelligence: Genomics & Cancer

https://pharmaceuticalintelligence.com/ai-in-genomics-cancer/

Yet another Success Story: Machine Learning to predict immunotherapy response

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

https://pharmaceuticalintelligence.com/2021/07/06/yet-another-success-story-machine-learning-to-predict-immunotherapy-response/

Systemic Inflammatory Diseases as Crohn’s disease, Rheumatoid Arthritis and Longer Psoriasis Duration May Mean Higher CVD Risk

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/10/09/systemic-inflammatory-diseases-as-crohns-disease-rheumatoid-arthritis-and-longer-psoriasis-duration-may-mean-higher-cvd-risk/

Autoimmune Inflammatory Bowel Diseases: Crohn’s Disease & Ulcerative Colitis: Potential Roles for Modulation of Interleukins 17 and 23 Signaling for Therapeutics

Curators: Larry H Bernstein, MD FCAP and Aviva Lev-Ari, PhD, RN https://pharmaceuticalintelligence.com/2016/01/23/autoimmune-inflammtory-bowl-diseases-crohns-disease-ulcerative-colitis-potential-roles-for-modulation-of-interleukins-17-and-23-signaling-for-therapeutics/

Inflammatory Disorders: Inflammatory Bowel Diseases (IBD) – Crohn’s and Ulcerative Colitis (UC) and Others

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/gama-delta-epsilon-gde-is-a-global-holding-company-absorbing-lpbi/subsidiary-5-joint-ventures-for-ip-development-jvip/drug-discovery-with-3d-bioprinting/ibd-inflammatory-bowl-diseases-crohns-and-ulcerative-colitis/

Read Full Post »

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

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

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

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

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

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

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

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

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

The Study

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

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

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

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

The Study

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

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

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

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

Marsico says

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

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

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

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

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

Clues for future directions

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

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

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

Marsico explains

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

Main Source

New prediction algorithm identifies previously undetected cancer driver genes

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

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

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

Other Related Articles published in this Open Access Online Scientific Journal include the following:

AI System Used to Detect Lung Cancer

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/

Deep Learning extracts Histopathological Patterns and accurately discriminates 28 Cancer and 14 Normal Tissue Types: Pan-cancer Computational Histopathology Analysis

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/10/28/deep-learning-extracts-histopathological-patterns-and-accurately-discriminates-28-cancer-and-14-normal-tissue-types-pan-cancer-computational-histopathology-analysis/

Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis

Curator & Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/12/08/evolution-of-the-human-cell-genome-biology-field-of-gene-expression-gene-regulation-gene-regulatory-networks-and-application-of-machine-learning-algorithms-in-large-scale-biological-data-analysis/

Cancer detection and therapeutics

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/05/02/cancer-detection-and-therapeutics/

Free Bio-IT World Webinar: Machine Learning to Detect Cancer Variants

Reporter: Stephen J. Williams, PhD

https://pharmaceuticalintelligence.com/2016/05/04/free-bio-it-world-webinar-machine-learning-to-detect-cancer-variants/

Artificial Intelligence: Genomics & Cancer

https://pharmaceuticalintelligence.com/ai-in-genomics-cancer/

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

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

Read Full Post »

Happy 80th Birthday: Radioiodine (RAI) Theranostics: Collaboration between Physics and Medicine, the Utilization of Radionuclides to Diagnose and Treat: Radiation Dosimetry by Discoverer Dr. Saul Hertz, the early history of RAI in diagnosing and treating Thyroid diseases and Theranostics

 

Guest Author: Barbara Hertz

 203-661-0777

htziev@aol.com

Celebrating eighty years of radionuclide therapy and the work of Saul Hertz

First published: 03 February 2021

Both authors contributed to the development, drafting and final editing of this manuscript and are responsible for its content.

Abstract

March 2021 will mark the eightieth anniversary of targeted radionuclide therapy, recognizing the first use of radioactive iodine to treat thyroid disease by Dr. Saul Hertz on March 31, 1941. The breakthrough of Dr. Hertz and collaborator physicist Arthur Roberts was made possible by rapid developments in the fields of physics and medicine in the early twentieth century. Although diseases of the thyroid gland had been described for centuries, the role of iodine in thyroid physiology had been elucidated only in the prior few decades. After the discovery of radioactivity by Henri Becquerel in 1897, rapid advancements in the field, including artificial production of radioactive isotopes, were made in the subsequent decades. Finally, the diagnostic and therapeutic use of radioactive iodine was based on the tracer principal that was developed by George de Hevesy. In the context of these advancements, Hertz was able to conceive the potential of using of radioactive iodine to treat thyroid diseases. Working with Dr. Roberts, he obtained the experimental data and implemented it in the clinical setting. Radioiodine therapy continues to be a mainstay of therapy for hyperthyroidism and thyroid cancer. However, Hertz struggled to gain recognition for his accomplishments and to continue his work and, with his early death in 1950, his contributions have often been overlooked until recently. The work of Hertz and others provided a foundation for the introduction of other radionuclide therapies and for the development of the concept of theranostics.

SOURCE

https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/acm2.13175

 

 

SOURCE

https://www.youtube.com/watch?v=34Qhm8CeMuc

 

http://www.wjnm.org/article.asp?issn=1450-1147;year=…

http://www.wjnm.org/text.asp?2019/18/1/8/250309

Abstract

Dr. Saul Hertz was Director of The Massachusetts General Hospital’s Thyroid Unit, when he heard about the development of artificial radioactivity. He conceived and brought from bench to bedside the successful use of radioiodine (RAI) to diagnose and treat thyroid diseases. Thus was born the science of theragnostics used today for neuroendocrine tumors and prostate cancer. Dr. Hertz’s work set the foundation of targeted precision medicine.

Keywords: Dr. Saul Hertz, nuclear medicine, radioiodine

 

How to cite this article:
Hertz B. A tribute to Dr. Saul Hertz: The discovery of the medical uses of radioiodine. World J Nucl Med 2019;18:8-12

 

How to cite this URL:
Hertz B. A tribute to Dr. Saul Hertz: The discovery of the medical uses of radioiodine. World J Nucl Med [serial online] 2019 [cited 2021 Mar 2];18:8-12. Available from: http://www.wjnm.org/text.asp?2019/18/1/8/250309

 

 

  • Dr Saul Hertz (1905-1950) discovers the medical uses of radioiodine

Barbara Hertz, Pushan Bharadwaj, Bennett Greenspan»

Abstract » PDF» doi: 10.24911/PJNMed.175-1582813482

 

SOURCE

http://saulhertzmd.com/home

 

  • Happy 80th Birthday: Radioiodine (RAI) Theranostics

Thyroid practitioners and patients are acutely aware of the enormous benefit nuclear medicine has made to mankind. This month we celebrate the 80th anniversary of the early use of radioiodine(RAI).

Dr. Saul Hertz predicted that radionuclides “…would hold the key to the larger problem of cancer in general,” and may just be the best hope for diagnosing and treating cancer successfully.  Yes, RAI has been used for decades to diagnose and treat disease.  Today’s “theranostics,” a term that is a combination of “therapy” and “diagnosis” is utilized in the treatment of thyroid disease and cancer. 

            This short note is to celebrate Dr. Saul Hertz who conceived and brought from bench to bedside the medical uses of RAI; then in the form of 25 minute iodine-128.  

On March 31st 1941, Massachusetts General Hospital’s Dr. Saul Hertz (1905-1950) administered the first therapeutic use of Massachusetts Institute of Technology (MIT) cyclotron produced RAI.  This landmark case was the first in Hertz’s clinical studies conducted with MIT, physicist Arthur Roberts, Ph.D.

[Photo – Courtesy of Dr Saul Hertz Archives ]

Dr Saul Hertz demonstrating RAI Uptake Testing

            Dr. Hertz’s research and successful utilization of radionuclides to diagnose and treat diseases and conditions, established the use of radiation dosimetry and the collaboration between physics and medicine and other significant practices.   Sadly, Saul Hertz (a WWII veteran) died at a very young age.  

 

About Dr. Saul Hertz

Dr. Saul Hertz (1905 – 1950) discovered the medical uses of radionuclides.  His breakthrough work with radioactive iodine (RAI) created a dynamic paradigym change integrating the sciences.  Radioactive iodine (RAI) is the first and Gold Standard of targeted cancer therapies.  Saul Hertz’s research documents Hertz as the first and foremost person to conceive and develop the experimental data on RAI and apply it in the clinical setting.

Dr. Hertz was born to Orthodox Jewish immigrant parents in Cleveland, Ohio on April 20, 1905. He received his A.B. from the University of Michigan in 1925 with Phi Beta Kappa honors. He graduated from Harvard Medical School in 1929 at a time of quotas for outsiders. He fulfilled his internship and residency at Mt. Sinai Hospital in Cleveland. He came back to Boston in 1931 as a volunteer to join The Massachusetts General Hospital serving as the Chief of the Thyroid Unit from 1931 – 1943.

Two years after the discovery of artifically radioactivity, on November 12, 1936 Dr. Karl Compton, president of the Massachusetts Institute of Technology (MIT), spoke at Harvard Medical School.  President Compton’s topic was What Physics can do for Biology and Medicine. After the presentation Dr. Hertz spontaneously asked Dr. Compton this seminal question, “Could iodine be made radioactive artificially?” Dr. Compton responded in writing on December 15, 1936 that in fact “iodine can be made artificially radioactive.”

Shortly thereafter, a collaboration between Dr. Hertz (MGH) and Dr. Arthur Roberts, a physicist of MIT, was established. In late 1937, Hertz and Roberts created and produced animal studies  involving 48 rabbits that demonstrated that the normal thyroid gland concentrated Iodine 128 (non cyclotron produced), and the hyperplastic thyroid gland took up even more Iodine.  This was a GIANT step for Nuclear Medicine.

In early 1941, Dr. Hertz administer the first therapeutic treatment of MIT Markle Cyclotron produced radioactive iodine (RAI) at the Massachusetts General Hospital.  This  led to the first series of twenty-nine patients with hyperthyroidism being treated successfully with RAI. ( see “Research” RADIOACTIVE IODINE IN THE STUDY OF THYROID PHYSIOLOGY VII The use of Radioactive Iodine Therapy in Hyperthyroidism, Saul Hertz and Arthur Roberts, JAMA Vol. 31 Number 2).

In 1937, at the time of the rabbit studies Dr Hertz conceived of RAI in therapeutic treatment of thyroid carsonoma.  In 1942 Dr Hertz gave clinical trials of RAI to patients with thyroid carcinoma.

After serving in the Navy during World War II, Dr. Hertz wrote to the director of the Mass General Hospital in Boston, Dr. Paxon on March 12, 1946, “it is a coincidence that my new research project is in Cancer of the Thyroid, which I believe holds the key to the larger problem of cancer in general.”

Dr. Hertz established the Radioactive Isotope Research Institute, in September, 1946 with a major focus on the use of fission products for the treatment of thyroid cancer, goiter, and other malignant tumors. Dr Samuel Seidlin was the Associate Director and managed the New York City facilities. Hertz also researched the influence of hormones on cancer.

Dr. Hertz’s use of radioactive iodine as a tracer in the diagnostic process, as a treatment for Graves’ disease and in the treatment of cancer of the thyroid remain preferred practices. Saul Hertz is the Father of Theranostics.

Saul Hertz passed at 45 years old from a sudden death heart attack as documented by an autopsy. He leaves an enduring legacy impacting countless generations of patients, numerous institutions worldwide and setting the cornerstone for the field of Nuclear Medicine. A cancer survivor emailed, The cure delivered on the wings of prayer was Dr Saul Hertz’s discovery, the miracle of radioactive iodine. Few can equal such a powerful and precious gift. 

To read and hear more about Dr. Hertz and the early history of RAI in diagnosing and treating thyroid diseases and theranostics see –

http://saulhertzmd.com/home

 

   References in https://www.wjnm.org/article.asp?issn=1450-1147;year=2019;volume=18;issue=1;spage=8;epage=12;aulast=Hertz

 

Top

 

1.
Hertz S, Roberts A. Radioactive iodine in the study of thyroid physiology. VII The use of radioactive iodine therapy in hyperthyroidism. J Am Med Assoc 1946;131:81-6.  Back to cited text no. 1
2.
Hertz S. A plan for analysis of the biologic factors involved in experimental carcinogenesis of the thyroid by means of radioactive isotopes. Bull New Engl Med Cent 1946;8:220-4.  Back to cited text no. 2
3.
Thrall J. The Story of Saul Hertz, Radioiodine and the Origins of Nuclear Medicine. Available from: http://www.youtube.com/watch?v=34Qhm8CeMuc. [Last accessed on 2018 Dec 01].  Back to cited text no. 3
4.
Braverman L. 131 Iodine Therapy: A Brief History. Available from: http://www.am2016.aace.com/presentations/friday/F12/hertz_braverman.pdf. [Last accessed on 2018 Dec 01].  Back to cited text no. 4
5.
Hofman MS, Violet J, Hicks RJ, Ferdinandus J, Thang SP, Akhurst T, et al. [177Lu]-PSMA-617 radionuclide treatment in patients with metastatic castration-resistant prostate cancer (LuPSMA trial): A single-centre, single-arm, phase 2 study. Lancet Oncol 2018;19:825-33.  Back to cited text no. 5
6.
Krolicki L, Morgenstern A, Kunikowska J, Koiziar H, Krolicki B, Jackaniski M, et al. Glioma Tumors Grade II/III-Local Alpha Emitters Targeted Therapy with 213 Bi-DOTA-Substance P, Endocrine Abstracts. Vol. 57. Society of Nuclear Medicine and Molecular Imaging; 2016. p. 632.  Back to cited text no. 6
7.
Baum RP, Kulkarni HP. Duo PRRT of neuroendocrine tumours using concurrent and sequential administration of Y-90- and Lu-177-labeled somatostatin analogues. In: Hubalewska-Dydejczyk A, Signore A, de Jong M, Dierckx RA, Buscombe J, Van de Wiel CJ, editors. Somatostatin Analogues from Research to Clinical Practice. New York: Wiley; 2015.  Back to cited text no. 7

 

SOURCE

From: htziev@aol.com” <htziev@aol.com>

Reply-To: htziev@aol.com” <htziev@aol.com>

Date: Tuesday, March 2, 2021 at 11:04 AM

To: “Aviva Lev-Ari, PhD, RN” <AvivaLev-Ari@alum.berkeley.edu>

Subject: Dr Saul Hertz : Discovery for the Medical Uses of RADIOIODINE (RAI) MARCH 31ST: 80 Years

 

Other related articles published in this Open Access Online Scientific Journal include the following:

 

Experience with Thyroid Cancer

Author: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/11/23/my-experience-with-thyroid-cancer/

 

New Guidelines and Meeting Information on Advanced Thyroid Cancer as Reported by Cancer Network (Meeting Highlights)

Reporter: Stephen J. Williams, Ph.D.

https://pharmaceuticalintelligence.com/2015/10/20/new-guidelines-and-meeting-information-on-advanced-thyroid-cancer-as-reported-by-cancer-network-meeting-highlights/

The Experience of a Patient with Thyroid Cancer

Interviewer and Curator: Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2014/07/14/the-experience-of-a-patient-with-thyroid-cancer/

 

Parathyroids and Bone Metabolism

Writer and Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/02/10/parathyroids-and-bone-metabolism/

 

Thyroid Function and Disorders

Writer and Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/02/05/thyroid-function-and-disorders/

Summary and Perspectives: Impairments in Pathological States: Endocrine Disorders, Stress Hypermetabolism and Cancer

Author and Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2014/11/09/summary-and-perspectives-impairments-in-pathological-states-endocrine-disorders-stress-hypermetabolism-cancer/

Introduction to Impairments in Pathological States: Endocrine Disorders, Stress Hypermetabolism and Cancer

Author and Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2014/11/08/introduction-to-impairments-in-pathological-states-endocrine-disorders-stress-hypermetabolism-cancer/

Metformin, Thyroid-Pituitary Axis, Diabetes Mellitus, and Metabolism

Larry H, Bernstein, MD, FCAP, Author and Curator
and Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/9/27/2014/Metformin,_thyroid-pituitary_ axis,_diabetes_mellitus,_and_metabolism

Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy: Commentary of Bioinformatics Approaches

Author and Curator: Larry H Bernstein, MD, FCAP and Article Architect: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2014/09/18/autophagy-modulating-proteins-and-small-molecules-candidate-targets-for-cancer-therapy-commentary-of-bioinformatics-approaches/

 

Neural Activity Regulating Endocrine Response

Writer and Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/02/13/neural-activity-regulating-endocrine-response/

 

Pituitary Neuroendocrine Axis

Writer and Curator: Larry H. Bernstein, MD, FCA

https://pharmaceuticalintelligence.com/2015/02/04/pituitary-neuroendocrine-axis/

On the Influence of Hormones on Cancer

VOLUME 4: Human Reproductive System, Genomic Endocrinology and Cancer Types

(Series D: BioMedicine & Immunology) Kindle Edition. On Amazon.com  since February 2, 2021

http://www.amazon.com/dp/B08VTFWVKM

Read Full Post »

Inhibitory CD161 receptor recognized as a potential immunotherapy target in glioma-infiltrating T cells by single-cell analysis

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

 

Brain tumors, especially the diffused Gliomas are of the most devastating forms of cancer and have so-far been resistant to immunotherapy. It is comprehended that T cells can penetrate the glioma cells, but it still remains unknown why infiltrating cells miscarry to mount a resistant reaction or stop the tumor development.

Gliomas are brain tumors that begin from neuroglial begetter cells. The conventional therapeutic methods including, surgery, chemotherapy, and radiotherapy, have accomplished restricted changes inside glioma patients. Immunotherapy, a compliance in cancer treatment, has introduced a promising strategy with the capacity to penetrate the blood-brain barrier. This has been recognized since the spearheading revelation of lymphatics within the central nervous system. Glioma is not generally carcinogenic. As observed in a number of cases, the tumor cells viably reproduce and assault the adjoining tissues, by and large, gliomas are malignant in nature and tend to metastasize. There are four grades in glioma, and each grade has distinctive cell features and different treatment strategies. Glioblastoma is a grade IV glioma, which is the crucial aggravated form. This infers that all glioblastomas are gliomas, however, not all gliomas are glioblastomas.

Decades of investigations on infiltrating gliomas still take off vital questions with respect to the etiology, cellular lineage, and function of various cell types inside glial malignancies. In spite of the available treatment options such as surgical resection, radiotherapy, and chemotherapy, the average survival rate for high-grade glioma patients remains 1–3 years (1).

A recent in vitro study performed by the researchers of Dana-Farber Cancer Institute, Massachusetts General Hospital, and the Broad Institute of MIT and Harvard, USA, has recognized that CD161 is identified as a potential new target for immunotherapy of malignant brain tumors. The scientific team depicted their work in the Cell Journal, in a paper entitled, “Inhibitory CD161 receptor recognized in glioma-infiltrating T cells by single-cell analysis.” on 15th February 2021.

To further expand their research and findings, Dr. Kai Wucherpfennig, MD, PhD, Chief of the Center for Cancer Immunotherapy, at Dana-Farber stated that their research is additionally important in a number of other major human cancer types such as 

  • melanoma,
  • lung,
  • colon, and
  • liver cancer.

Dr. Wucherpfennig has praised the other authors of the report Mario Suva, MD, PhD, of Massachusetts Common Clinic; Aviv Regev, PhD, of the Klarman Cell Observatory at Broad Institute of MIT and Harvard, and David Reardon, MD, clinical executive of the Center for Neuro-Oncology at Dana-Farber.

Hence, this new study elaborates the effectiveness of the potential effectors of anti-tumor immunity in subsets of T cells that co-express cytotoxic programs and several natural killer (NK) cell genes.

The Study-

IMAGE SOURCE: Experimental Strategy (Mathewson et al., 2021)

 

The group utilized single-cell RNA sequencing (RNA-seq) to mull over gene expression and the clonal picture of tumor-infiltrating T cells. It involved the participation of 31 patients suffering from diffused gliomas and glioblastoma. Their work illustrated that the ligand molecule CLEC2D activates CD161, which is an immune cell surface receptor that restrains the development of cancer combating activity of immune T cells and tumor cells in the brain. The study reveals that the activation of CD161 weakens the T cell response against tumor cells.

Based on the study, the facts suggest that the analysis of clonally expanded tumor-infiltrating T cells further identifies the NK gene KLRB1 that codes for CD161 as a candidate inhibitory receptor. This was followed by the use of 

  • CRISPR/Cas9 gene-editing technology to inactivate the KLRB1 gene in T cells and showed that CD161 inhibits the tumor cell-killing function of T cells. Accordingly,
  • genetic inactivation of KLRB1 or
  • antibody-mediated CD161 blockade

enhances T cell-mediated killing of glioma cells in vitro and their anti-tumor function in vivo. KLRB1 and its associated transcriptional program are also expressed by substantial T cell populations in other forms of human cancers. The work provides an atlas of T cells in gliomas and highlights CD161 and other NK cell receptors as immune checkpoint targets.

Further, it has been identified that many cancer patients are being treated with immunotherapy drugs that disable their “immune checkpoints” and their molecular brakes are exploited by the cancer cells to suppress the body’s defensive response induced by T cells against tumors. Disabling these checkpoints lead the immune system to attack the cancer cells. One of the most frequently targeted checkpoints is PD-1. However, recent trials of drugs that target PD-1 in glioblastomas have failed to benefit the patients.

In the current study, the researchers found that fewer T cells from gliomas contained PD-1 than CD161. As a result, they said, “CD161 may represent an attractive target, as it is a cell surface molecule expressed by both CD8 and CD4 T cell subsets [the two types of T cells engaged in response against tumor cells] and a larger fraction of T cells express CD161 than the PD-1 protein.”

However, potential side effects of antibody-mediated blockade of the CLEC2D-CD161 pathway remain unknown and will need to be examined in a non-human primate model. The group hopes to use this finding in their future work by

utilizing their outline by expression of KLRB1 gene in tumor-infiltrating T cells in diffuse gliomas to make a remarkable contribution in therapeutics related to immunosuppression in brain tumors along with four other common human cancers ( Viz. melanoma, non-small cell lung cancer (NSCLC), hepatocellular carcinoma, and colorectal cancer) and how this may be manipulated for prevalent survival of the patients.

References

(1) Anders I. Persson, QiWen Fan, Joanna J. Phillips, William A. Weiss, 39 – Glioma, Editor(s): Sid Gilman, Neurobiology of Disease, Academic Press, 2007, Pages 433-444, ISBN 9780120885923, https://doi.org/10.1016/B978-012088592-3/50041-4.

Main Source

Mathewson ND, Ashenberg O, Tirosh I, Gritsch S, Perez EM, Marx S, et al. 2021. Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis. Cell.https://www.cell.com/cell/fulltext/S0092-8674(21)00065-9?elqTrackId=c3dd8ff1d51f4aea87edd0153b4f2dc7

Related Articles

VIDEOS on Cancer Biology, Cancer Genetics, Cancer Immunotherapy

19th Annual Koch Institute Summer Symposium on Cancer Immunotherapy, June 12, 2020 at MIT’s Kresge Auditorium

 

Other related articles published in this Open Access Online Scientific Journal include the following:

 

Single Cell Sequencing:

Part 4.1 in Genomics Volume 2

Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology 

On Amazon.com since 12/28/2019

https://www.amazon.com/dp/B08385KF87

 

4.1.3   Single-cell Genomics: Directions in Computational and Systems Biology – Contributions of Prof. Aviv Regev @Broad Institute of MIT and Harvard, Cochair, the Human Cell Atlas Organizing Committee with Sarah Teichmann of the Wellcome Trust Sanger Institute

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/09/03/single-cell-genomics-directions-in-computational-and-systems-biology-contributions-of-ms-aviv-regev-phd-broad-institute-of-mit-and-harvard-cochair-the-human-cell-atlas-organizing-committee-wit/

 

4.1.4   Cellular Genetics

https://www.sanger.ac.uk/science/programmes/cellular-genetics

 

4.1.5   Cellular Genomics

https://www.garvan.org.au/research/cellular-genomics

 

4.1.6   SINGLE CELL GENOMICS 2019 – sometimes the sum of the parts is greater than the whole, September 24-26, 2019, Djurönäset, Stockholm, Sweden http://www.weizmann.ac.il/conferences/SCG2019/single-cell-genomics-2019

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/05/29/single-cell-genomics-2019-september-24-26-2019-djuronaset-stockholm-sweden/

 

4.1.7   Norwich Single-Cell Symposium 2019, Earlham Institute, single-cell genomics technologies and their application in microbial, plant, animal and human health and disease, October 16-17, 2019, 10AM-5PM

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/06/04/norwich-single-cell-symposium-2019-earlham-institute-single-cell-genomics-technologies-and-their-application-in-microbial-plant-animal-and-human-health-and-disease-october-16-17-2019-10am-5pm/

 

4.1.8   Newly Found Functions of B Cell

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

https://pharmaceuticalintelligence.com/2019/05/23/newly-found-functions-of-b-cell/

 

4.1.9 RESEARCH HIGHLIGHTS: HUMAN CELL ATLAS

https://www.broadinstitute.org/research-highlights-human-cell-atlas

 

CRISPR – 200 articles in the Journal

 

Chapter 21 in Genomics Volume 1

Genomics Orientations for Personalized Medicine. On Amazon.com since 11/23/2015

http://www.amazon.com/dp/B018DHBUO6

 

Glioblastoma – 150 articles in the Journal

Most recent

 

Immunotherapy may help in glioblastoma survival

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

https://pharmaceuticalintelligence.com/2019/03/16/immunotherapy-may-help-in-glioblastoma-survival/

 

New Treatment in Development for Glioblastoma: Hopes for Sen. John McCain

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/07/25/new-treatment-in-development-for-glioblastoma-hopes-for-sen-john-mccain/

 

Funding Oncorus’s Immunotherapy Platform: Next-generation Oncolytic Herpes Simplex Virus (oHSV) for Brain Cancer, Glioblastoma Multiforme (GBM)

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/12/28/funding-oncoruss-immunotherapy-platform-next-generation-oncolytic-herpes-simplex-virus-ohsv-for-brain-cancer-glioblastoma-multiforme-gbm/

 

Glioma, Glioblastoma and Neurooncology

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/10/19/glioma-glioblastoma-and-neurooncology/

 

Positron Emission Tomography (PET) and Near-Infrared Fluorescence Imaging:  Noninvasive Imaging of Cancer Stem Cells (CSCs)  monitoring of AC133+ glioblastoma in subcutaneous and intracerebral xenograft tumors

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2014/01/29/positron-emission-tomography-pet-and-near-infrared-fluorescence-imaging-noninvasive-imaging-of-cancer-stem-cells-cscs-monitoring-of-ac133-glioblastoma-in-subcutaneous-and-intracerebral-xenogra/

 

Gamma Linolenic Acid (GLA) as a Therapeutic tool in the Management of Glioblastoma

Eric Fine* (1), Mike Briggs* (1,2), Raphael Nir# (1,2,3)

https://pharmaceuticalintelligence.com/2013/07/15/gamma-linolenic-acid-gla-as-a-therapeutic-tool-in-the-management-of-glioblastoma/

 

 

Read Full Post »

Noninvasive blood test can detect cancer 4 years before conventional diagnosis

Reporter : Irina Robu, PhD

Several international researchers at  Fudan University and at Singlera Genomics have developed a noninvasive blood test, PanSeer that can detect whether a patient with five common type of cancers such as stomach, esophageal, colorectal, lung and liver cancer; four years before the condition can be diagnosed by the current methods. Early detection is significant for the reason that the survival of cancer patients increases when the disease is identified at early stages, as the tumor can be surgically removed or treated with suitable drugs. Yet, only a partial number of early screening tests exist for a few cancer types.

The blood test detected cancer in 91 percent of samples from individuals who have been asymptomatic when the samples were collected, but only diagnosed with cancer one to four years later. It was found that the test can accurately detect cancer in 88 percent from samples of 113 patience who were diagnosed. The blood test also detects cancer free samples 95 percent of the time.

What is clear is that the study is unique, in that the scientists had access to blood samples from patients who were asymptomatic but not diagnosed yet. This permitted the researchers to design a test that can find a cancer marker much earlier than conventional diagnosis. The sample were collected as part of 10-year longitudinal study started in 2007 by Fudan University in China.

The researchers highlight that the PanSeer assay is improbable to predict which patients will later go on to develop cancer. As a substitute, it is most possible identifying patients who already have cancerous growths, but continue  to be asymptomatic for current detection methods. The team decided that further large-scale longitudinal studies are needed to confirm the potential of the test for the early detection of cancer in pre-diagnosis individuals.

SOURCE

https://www.universityofcalifornia.edu/news/non-invasive-blood-test-can-detect-cancer-4-years-conventional-diagnosis-methods?utm_source=fiat-lux

Read Full Post »

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

UPDATED: 10/11/2021

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.”

UPDATED: 10/11/2021

Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multicenter Study during the COVID-19 Outbreak

Source:

Mengyuan DaiDianbo LiuMiao LiuFuxiang ZhouGuiling LiZhen ChenZhian ZhangHua YouMeng WuQichao ZhengYong XiongHuihua XiongChun WangChangchun ChenFei XiongYan ZhangYaqin PengSiping GeBo ZhenTingting YuLing WangHua WangYu LiuYeshan ChenJunhua MeiXiaojia GaoZhuyan LiLijuan GanCan HeZhen LiYuying ShiYuwen QiJing YangDaniel G. TenenLi ChaiLorelei A. MucciMauricio Santillana and Hongbing Cai. Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multicenter Study during the COVID-19 Outbreak

Abstract

The novel COVID-19 outbreak has affected more than 200 countries and territories as of March 2020. Given that patients with cancer are generally more vulnerable to infections, systematic analysis of diverse cohorts of patients with cancer affected by COVID-19 is needed. We performed a multicenter study including 105 patients with cancer and 536 age-matched noncancer patients confirmed with COVID-19. Our results showed COVID-19 patients with cancer had higher risks in all severe outcomes. Patients with hematologic cancer, lung cancer, or with metastatic cancer (stage IV) had the highest frequency of severe events. Patients with nonmetastatic cancer experienced similar frequencies of severe conditions to those observed in patients without cancer. Patients who received surgery had higher risks of having severe events, whereas patients who underwent only radiotherapy did not demonstrate significant differences in severe events when compared with patients without cancer. These findings indicate that patients with cancer appear more vulnerable to SARS-CoV-2 outbreak.

Significance: Because this is the first large cohort study on this topic, our report will provide much-needed information that will benefit patients with cancer globally. As such, we believe it is extremely important that our study be disseminated widely to alert clinicians and patients.

Introduction

A new acute respiratory syndrome coronavirus, named SARS-CoV-2 by the World Health Organization (WHO), has rapidly spread around the world since its first reported case in late December 2019 from Wuhan, China (1). As of March 2020, this virus has affected more than 200 countries and territories, infecting more than 800,000 individuals and causing more than 40,000 deaths (2).

With more than 18 million new cases per year globally, cancer affects a significant portion of the population. Individuals affected by cancer are more susceptible to infections due to coexisting chronic diseases, overall poor health status, and systemic immunosuppressive states caused by both cancer and anticancer treatments (3). As a consequence, patients with cancer who are infected by the SARS-CoV-2 coronavirus may experience more difficult outcomes than other populations. Until now, there is still no systematic evaluation of the effects that the SARS-CoV-2 coronavirus has of patients with cancer in a representative population. A recent study reported a higher risk of severe events in patients with cancer when compared with patients without cancer (4); however, the small sample size of SARS-CoV-2 patients with cancer used in the study limited how representative it was of the whole population and made it difficult to conduct more insightful analyses, such as comparing clinical characteristics of patients with different types of cancer, as well as anticancer treatments (5, 6).

Using patient information collected from 14 hospitals in Hubei Province, China, the epicenter of the 2019–2020 COVID-19 outbreak, we describe the clinical characteristics and outcomes [death, intensive care unit (ICU) admission, development of severe/critical symptoms, and utilization of invasive mechanical ventilation] of patients affected by the SARS-CoV-2 coronavirus for 105 hospitalized patients with cancer and 536 patients without cancer. We document our findings for different cancer types and stages, as well as different types of cancer treatments. We believe the information and insights provided in this study will help improve our understanding of the effects of SARS-CoV-2 in patients with cancer.

Results

Patients Characteristics

In total, 105 COVID-19 patients with cancer were enrolled in our study for the time period January 1, 2020, to February 24, 2020, from 14 hospitals in Wuhan, China. COVID-19 patients without cancer matched by the same hospital, hospitalization time, and age were randomly selected as our control group. Our patient population included 339 females and 302 males. Patients with cancer [median = 64.00, interquartile range (IQR) = 14.00], when compared with those without cancer (median = 63.50, IQR = 14.00) had similar age distributions (by design), experienced more in-hospital infections [20 (19.04%) of 105 patients vs. 8 (1.49%) of 536 patients;P < 0.01], and had more smoking history [36 (34.28%) of 105 patients vs. 46 (8.58%) of 536 patients; P < 0.01], but had no significant differences in sex, other baseline symptoms, and other comorbidities (Table 1). With respect to signs and symptoms upon admission, COVID-19 patients with cancer were similar to those without cancer except for a higher prevalence of chest distress [15 (14.29%) of 105 patients vs. 36 (6.16%) of 536 patients; P = 0.02].

Table 1.

Characteristics of COVID-19 patients with and without cancer

Clinical Outcomes

Compared with COVID-19 patients without cancer, patients with cancer had higher observed death rates [OR, 2.34; 95% confidence interval (CI), (1.15–4.77); P = 0.03], higher rates of ICU admission [OR, 2.84; 95% CI (1.59–5.08); P < 0.01], higher rates of having at least one severe or critical symptom [OR, 2.79; 95% CI, (1.74–4.41); P < 0.01], and higher chances of needing invasive mechanical ventilation (Fig. 1A). We also conducted survival analysis on occurrence of any severe condition which included death, ICU admission, having severe symptoms, and utilization of invasive mechanical ventilation (see cumulative incidence curves in Fig. 1B). In general, patients with cancer deteriorated more rapidly than those without cancer. These observations are consistent with logistic regression results (Supplementary Fig. S1), after adjusting for age, sex, smoking, and comorbidities including diabetes, hypertension, and chronic obstructive pulmonary disease (COPD). According to our multivariate logistic regression results, patients with cancer still had an excess OR of 2.17 (P = 0.06) for death (Supplementary Fig. S1A), 1.99 (P < 0.01) for experiencing any severe symptoms (Supplementary Fig. S1B), 3.13 (P < 0.01) for ICU admission (Supplementary Fig. S1C), and 2.71 (P = 0.04) for utilization of invasive mechanical ventilation (Supplementary Fig. S1D; Supplementary Table S1). The consistency of observed ORs between the multivariate regression model and unadjusted calculation reassures the association between cancer and severe events even in the presence of other factors such as age differences.

Figure 1.

Severe conditions in patients with and without cancer, and patients with different types, stages, and treatments of cancer. Severe conditions include death, ICU admission, having severe/critical symptoms, and usage of invasive mechanical ventilation. Incidence and survival analysis of severe conditions among COVID-19 patients with cancer and without cancer (A and B), among patients with different types of cancer (C and D), among patients with metastatic and nonmetastatic cancers (E and F), among patients with lung cancer, other cancers than lung with lung metastasis, and other cancers than lung without lung metastasis (G and H), and patients receiving different types of cancer treatments (I and J). P values indicate differences between cancer subgroups versus patients without cancer. For ACEGI, *, P < 0.05; **, P < 0.01. OR, 95% CI, and P values between different subgroups are listed in Supplementary Table S2. For BDFHJ, HR, 95% CI, and P values are listed in Supplementary Table S3.

Cancer Types

Information regarding potential risks of severe conditions in SARS-CoV-2 associated with each type of cancer was calculated. We compared different conditions among cancer types (Table 2). Lung cancer was the most frequent cancer type [22 (20.95%) of 105 patients], followed by gastrointestinal cancer [13 (12.38%) of 105 patients], breast cancer [11 (10.48%) of 105 patients], thyroid cancer [11 (10.48%) of 105 patients], and hematologic cancer [9 (8.57%) of 105 patients]. As shown in Fig. 1C and D and Supplementary Table S2, patients with hematologic cancer including leukemia, lymphoma, and myeloma have a relatively high death rate [3 (33.33%) of 9 patients], high ICU admission rate [4 (44.44%) of 9 patients], high risks of severe/critical symptoms [6 (66.67%) of 9 patients], and high chance of utilization of invasive mechanical ventilation [2 (22.22%) of 9 patients]. Patients with lung cancer had the second-highest risk levels, with death rate [4 (18.18%) of 22 patients], ICU admission rate [6 (27.27%) of 22 patients], risks of severe/critical symptoms [11 (50.00%) of 22 patients], and the chance of utilization of invasive mechanical ventilation [4 (18.18%) of 22 patients; Table 2].

Table 2.

Severe events in 105 patients with cancer for each type of cancer

Cancer Stage

We found that patients with metastatic cancer (stage IV) had even higher risks of death [OR, 5.58; 95% CI (1.71–18.23); P = 0.01], ICU admission [OR, 6.59; 95% CI (2.32–18.72); P < 0.01], having severe conditions [OR, 5.97; 95% CI (2.24–15.91); P < 0.01], and use of invasive mechanical ventilation [OR, 55.42; 95% CI (13.21–232.47); P < 0.01]. In contrast, patients with nonmetastatic cancer did not demonstrate statistically significant differences compared with patients without cancer, with all P > 0.05 (Fig. 1E and F; Supplementary Tables S2 and S3). In addition, when compared with patients without cancer, patients with lung cancer or other cancers with lung metastasis also showed higher risks of death, ICU admission rates, higher critical symptoms, and use of invasive mechanical ventilation, with all P values below 0.01, but other cancers without lung metastasis had no statistically significant differences (all P values > 0.05; Fig. 1G and H; Supplementary Table S3) when compared with patients without cancer.

Cancer Treatments

Among the 105 COVID-19 patients with cancer in our study, 13 (12.26%) had radiotherapy, 17 (14.15%) received chemotherapy, 8 (7.62%) received surgery, 4 (3.81%) had targeted therapy, and 6 (5.71%) had immunotherapy within 40 days before the onset of COVID-19 symptoms. All of the targeted therapeutic drugs were EGFR–tyrosine kinase inhibitors for treatment of lung cancer, and all of the immunotherapy drugs were PD-1 inhibitors for the treatment of lung cancer. A patient with cancer may have more than one type of therapy. Our observation suggested that patients who received immunotherapy tended to have high rates of death [2 (33.33%) of 6 patients] and high chances of developing critical symptoms [4 (66.67%) of 6 patients]. Patients who received surgery demonstrated higher rates of death [2 (25.00%) of 8 patients], higher chances of ICU admission [3 (37.50%) of 8 patients], higher chances of having severe or critical symptoms [5 (62.50%) of 8 patients], and higher use of invasive ventilation [2 (25.00%) of 8 patients] than other treatments excluding immunotherapy. However, patients with cancer who received radiotherapy did not show statistically significant differences in having any severe events when compared with patients without cancer, with all P values > 0.10 (Fig. 1I and J). Clinical details on the cancer diagnoses and cancer treatments are summarized in Supplementary Table S4.

Timeline of Severe Events

To evaluate the time-dependent evolution of the disease, we conducted the timeline of different events for COVID-19 patients with cancer (Fig. 2A) and COVID-19 patients without cancer (Fig. 2B) with death and other severe events marked in the figure. COVID-19 patients with cancer had a mean length of stay of 27.01 days (SD 9.52) and patients without cancer had a mean length of stay of 17.75 days (SD 8.64); the difference is significant (Wilcoxon test, P < 0.01). To better clarify the contributing factors that might influence outcomes, we also included logistic regression of COVID-19 patients with cancer adjusted by immunosuppression levels in Supplementary Table S5. However, no significant association between immunosuppression and severe outcomes was observed from the analysis (with all P > 0.05).

Figure 2.

Timeline of events for COVID-19 patients. A, Timeline of events in COVID-19 patients with cancer. B, Timeline of events in COVID-19 patients without cancer. For visualization purposes, patients without timeline information are excluded and only 105 COVID-19 patients without cancer are shown.

Discussion

The findings in this study suggest that patients with cancer infected with SARS-CoV-2 tend to have more severe outcomes when compared with patients without cancer. Patients with hematologic cancer, lung cancer, and cancers in metastatic stages demonstrated higher rates of severe events compared with patients without cancer. In addition, patients who underwent cancer surgery showed higher death rates and higher chances of having critical symptoms.

The SARS-CoV-2 virus has spread rapidly globally; thus, many countries have not been ready to handle the large volume of people affected by this outbreak due to a lack of knowledge about how this coronavirus affects the general population. To date, reports on the general population infected with SARS-CoV-2 suggest elderly males have a higher incidence and death rate (7, 8). Limited information is known about the outcome of patients with cancer who contract this highly communicable disease. Cancer is among the top causes of death. Asia, Europe, and North America have the highest incidence of cancer in the world (9), and at the moment of the writing of this study the SARS-CoV-2 virus is mainly spreading in these three areas (referred from https://www.cdc.gov/media/releases/2020/s0226-Covid-19-spread.htmlhttps://www.nytimes.com/2020/02/27/world/coronavirusnews.html). Although COVID-19 patients with cancer may share some epidemiologic features with the general population with this disease, they may also have additional clinical characteristics. Therefore, we conducted this study on patients with cancer with coexisting COVID-19 disease to evaluate the potential effect of COVID-19 on patients with cancer.

On the basis of our analysis, COVID-19 patients with cancer tend to have more severe outcomes when compared with the noncancer population. Although COVID-19 is reported to have a relatively low death rate of 2% to 3% in the general population (10), patients with cancer and COVID-19 not only have a nearly 3-fold increase in the death rate than that of COVID-19 patients without cancer, but also tend to have much higher severity of their illness. Altogether, these findings suggest that patients with cancer are a much more vulnerable population in the current COVID-19 outbreak. Our findings are consistent with those presented in a previous study based on 18 patients with cancer (4). Because of the limited number of patients with cancer in the previous study, the authors concluded that among patients with cancer, age is the only risk factor for the severity of the illness. On the basis of our data on 105 patients with cancer, we have discovered additional risk factors, including cancer types, cancer stage, and cancer treatments, which may contribute to the severity of the disease among patients with cancer.

Our data demonstrate that the severity of SARS-CoV-2 infection in patients is significantly affected by the types of tumors. From our analysis, patients with hematologic cancer have the highest severity and death rates among all patients with cancer, and lung cancer follows second. Patients with hematologic cancer in our study include patients with leukemia, myeloma, and lymphoma, who have a more compromised immune system than patients with solid tumors (11). These patients all had a rapidly deteriorating clinical course once infected with COVID-19. Because malignant or dysfunctional plasma cells, lymphocytes, or white blood cells in general in hematologic malignancies have decreased immunologic function (12–14), this could be the main reason why patients with hematologic cancer have very high severity and death rates. All patients with hematologic cancer are prone to the complications of serious infection (12–14), which can exacerbate the condition which could have worsened in patients with COVID-19. In our study, 55.56% of patients with hematologic cancer had severe immunosuppression, which may be the main reason for deteriorated outcomes. Although the small sample size limits representativity of the observation, we believe our finding can serve as an informative starting point for further investigation when a larger cohort from a wide range of healthcare providers becomes available. Among solid tumors, lung cancer is the highest risk category disease in patients with SARS-CoV-2 infection (Fig. 1C). Decreased lung function and severe infection in patients with lung cancer could contribute to the worse outcome in this subpopulation (15, 16).

In our analysis, we classified the SARS-CoV-2 infection–related high risk factors based on death, severe or critical illness, ICU admission, and the utilization of invasive mechanical ventilation. Using these parameters, we detected a multi-fold increase in risk in the cancer population, in contrast to the noncancer population. If there were primary or metastatic tumors in the lungs, patients were more prone to a deteriorated course in a short time. Intriguingly, when patients with cancer had only early-stage disease without metastasis, we did not observe any difference between the cancer and noncancer population in terms of COVID-19–related death rate or severity (Fig. 1E). The stage of cancer diagnosis seemed to play a significant role in the severity and death rate of COVID-19.

Patients with cancer received a wide range of treatments, and we also found that different types of treatments had different influences on severity and death when these patients contracted COVID-19. Recently, immunotherapy has assumed a very important role in treating tumors, which aids in treatment of cancer by blocking the immune escape of cancer cells. But in our study, in contrast to patients with cancer with other treatments, patients with immunotherapy had the highest death rate and the highest severity of illness, a very puzzling finding. According to pathologic studies on the patients with COVID-19, there were desquamation of pneumocytes and hyaline membrane formation, implying that these patients had acute respiratory distress syndrome (ARDS; ref. 17). ARDS induced by cytokine storm is reported to be the main reason for death of SARS-CoV-2–infected patients (18). It is possible that in this setting, immunotherapy induces the release of a large amount of cytokines, which can be toxic to normal cells, including lung epithelial cells (19–21), and therefore lead to a more severe illness. However, in this study the number of patients with immunotherapy was too small; further research with a large case population needs to be conducted in the future.

In addition, COVID-19 patients with cancer who are under active treatment or not under active treatment do not show differences in their outcomes, and there is a significant difference between COVID-19 patients with cancer but not with active treatment and patients without cancer (Supplementary Table S2). These results indicate that COVID-19 patients with both active treatment and just cancer history have a higher risk of developing severe events than noncancer COVID-19 patients. The possible reasons could be due to some known cancer-related complications, for example, anemia, hypoproteinaemia, or dyspnea in early phase of COVID-19 (22). We considered that cancer had a lifetime effect on patients and that cancer survivors always need routine follow-up after primary resection. Therefore, in clinical COVID-19 patient management, equivalent attention needs to be paid to those with cancer whether they are under active therapeutics or not during the outbreak of COVID-19.

This study has several limitations. Although the cohort of COVID-19 patients with cancer is one of the largest in Hubei province, China, the epicenter of the initial outbreak, a larger cohort from the whole country or even from multiple countries will be more representative. Large-scale national and international research collaboration will be necessary to achieve this. At the initial stage of the outbreak, data collection and research activities were not a priority of the hospitals. Therefore, it was not possible to record and collect some data that are potentially informative for our analysis in a timely manner. In addition, due to the urgency of clinical treatment, medical data used in this study were largely disconnected from the patients’ historical electronic medical records, which are mostly stored with a different healthcare provider than the medical center providing COVID-19 care. This left us with limited information about each patient.

Our study is the midsize cohort study on this topic and will provide much-needed information on risk factors of this population. We hope that our findings will help countries better protect patients with cancer affected by the ongoing COVID-19 pandemic.

Methods

Study Design and Patients

We conducted a multicenter study focusing on the clinical characteristics of confirmed cases of COVID-19 patients with cancer in 14 hospitals in Hubei province, China; all of the 14 hospitals served as government-designated hospitals for patients diagnosed with COVID-19. SARS-CoV-2–infected patients without cancer matched by the same hospital and hospitalization time were randomly selected as our control group. In addition, as age is one of the major predictors of severity of respiratory diseases like COVID-19 (4), we excluded from our analysis 117 younger COVID-19 patients without cancer so that median ages of patients with cancer (median = 64.0, IRQ = 14.00) and patients without cancers (median = 63.5, IQR = 14.00) would be comparable.

End Points and Assessments

There were four primary outcomes analyzed in this study: death, admission into the ICU, development of severe or critical symptoms, and utilization of invasive mechanical ventilation. The clinical definition of severe/critical symptoms follows the 5th edition of the 2019Novel Coronavirus Disease (COVID-19) Diagnostic Criteria published by the National Health Commission in China, including septic shock, ARDS, acute kidney injury, disseminated intravascular coagulation, and rhabdomyolysis.

Case Fatality Rate of Cancer Patients with COVID-19 in a New York Hospital System

Source:

Vikas MehtaSanjay GoelRafi KabarritiDaniel ColeMendel GoldfingerAna Acuna-VillaordunaKith PradhanRaja ThotaStan ReissmanJoseph A. SparanoBenjamin A. GartrellRichard V. SmithNitin OhriMadhur GargAndrew D. RacineShalom KalnickiRoman Perez-SolerBalazs Halmos and Amit Verma. Case Fatality Rate of Cancer Patients with COVID-19 in a New York Hospital System

Abstract

Patients with cancer are presumed to be at increased risk from COVID-19 infection–related fatality due to underlying malignancy, treatment-related immunosuppression, or increased comorbidities. A total of 218 COVID-19–positive patients from March 18, 2020, to April 8, 2020, with a malignant diagnosis were identified. A total of 61 (28%) patients with cancer died from COVID-19 with a case fatality rate (CFR) of 37% (20/54) for hematologic malignancies and 25% (41/164) for solid malignancies. Six of 11 (55%) patients with lung cancer died from COVID-19 disease. Increased mortality was significantly associated with older age, multiple comorbidities, need for ICU support, and elevated levels of D-dimer, lactate dehydrogenase, and lactate in multivariate analysis. Age-adjusted CFRs in patients with cancer compared with noncancer patients at our institution and New York City reported a significant increase in case fatality for patients with cancer. These data suggest the need for proactive strategies to reduce likelihood of infection and improve early identification in this vulnerable patient population.

Significance: COVID-19 in patients with cancer is associated with a significantly increased risk of case fatality, suggesting the need for proactive strategies to reduce likelihood of infection and improve early identification in this vulnerable patient population.

Introduction

The novel coronavirus COVID-19, or severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly throughout the world since its emergence in December 2019 (1). The virus has infected approximately 2.9 million people in more than 200 countries with more than 200,000 deaths at the time of writing (2). Most recently, the United States has become the epicenter of this pandemic, reporting an estimated 956,000 cases of COVID-19 infection, with the largest concentration in New York City (NYC) and its surrounding areas (approximately >203,000 cases or 35% of all U.S. infections; ref. 3).

Early data suggests that 14% to 19% of infected patients will develop significant sequelae with acute respiratory distress syndrome, septic shock, and/or multiorgan failure (1, 4, 5), and approximately 1% to 4% will die from the disease (2). Recent meta-analyses have demonstrated an almost 6-fold increase in the odds of mortality for patients with chronic obstructive pulmonary disease (COPD) and a 2.5-fold increase for those with diabetes, possibly due to the underlying pulmonary and immune dysfunction (6, 7). Given these findings, patients with cancer would ostensibly be at a higher risk of developing and succumbing to COVID-19 due to immunosuppression, increased coexisting medical conditions, and, in cases of lung malignancy, underlying pulmonary compromise. Patients with hematologic cancer, or those who are receiving active chemotherapy or immunotherapy, may be particularly susceptible because of increased immunosuppression and/or dysfunction.

According the NCI, there were approximately 15.5 million cancer survivors and an estimated 1,762,450 new cases of cancer diagnosed in the United States in 2019 (8). Early case series from China and Italy have suggested that patients with malignancy are more susceptible to severe infection and mortality from COVID-19 (9–12), a phenomenon that has been noted in other pandemics (13). Many of these descriptive studies have included small patient cohorts and have lacked cancer site–specific mortality data or information regarding active cancer treatment. As New York has emerged as the current epicenter of the pandemic, we sought to investigate the risk posed by COVID-19 to our cancer population with more granular data regarding cancer type and active treatment, and identify factors that placed patients with cancer at highest risk of fatality from COVID-19.

Results

Outcomes of 218 Cancer Patients with COVID-19 Show High Overall Mortality with Tumor-Specific Patterns

A total of 218 patients with cancer and COVID-19 were treated in Montefiore Health System (New York, NY) from March 18, 2020, to April 8, 2020. These included 164 (75%) patients with solid tumors and 54 (25%) with hematologic malignancies. This cohort included 127 (58%) males and 91 (42%) females. The cohort was predominantly composed of adult patients (215/218, 98.6%) with a median age of 69 years (range 10–92 years).

Sixty-one (28%) patients expired as a result of COVID-19disease at the time of analysis (Table 1). The mortality was 25% among all patients with solid tumors and was seen to occur at higher rates in patients with lung cancers (55%), gastrointestinal (GI) cancers [colorectal (38%), pancreatic (67%), upper GI (38%)], and gynecologic malignancies (38%). Genitourinary (15%) and breast (14%) cancers were associated with relatively lower mortality with COVID-19 infection.

Table 1.

Outcomes in patients with cancer and COVID-19

Hematologic malignancies were associated with higher rate of mortality with COVID-19 (37%). Myeloid malignancies [myelodysplastic syndromes (MDS)/acute myeloid leukemia (AML)/myeloproliferative neoplasm (MPN)] showed a trend for higher mortality compared with lymphoid neoplasms [non-Hodgkin lymphoma (NHL)/chronic lymphoid leukemia (CLL)/acute lymphoblastic leukemia (ALL)/multiple myeloma (MM)/Hodgkin lymphoma; Table 1]. Rates of ICU admission and ventilator use were slightly higher for hematologic malignancies than solid tumors (26% vs. 19% and 11% vs. 10%, respectively), but this did not achieve statistical significance.

Disease Characteristics of Cancer Patients with COVID-19 Demonstrate the Effect of Age, Comorbidities, and Laboratory Biomarkers on Mortality

Analysis of patient characteristics with mortality did not show any gender bias (Table 2). Older age was significantly associated with increased mortality, with median age of deceased cohort at 76 years when compared with 66 years for the nondeceased group (P = 0.0006; Cochran-Armitage test). No significant associations between race and mortality were seen.

Table 2.

Disease characteristics of patients with cancer with COVID-19 and association with mortality

COVID-19 disease severity, as evident from patients who needed ICU care and ventilator support, was significantly associated with increased mortality. Interestingly, active disease (<1 year) and advanced metastatic disease showed a trend for increased mortality, but the association did not achieve statistical significance (P = 0.09 and 0.06, respectively). Active chemotherapy and radiotherapy treatment were not associated with increased case fatality. Very few patients in this cohort were on immunotherapy, and this did not show any associations with mortality.

Analysis of comorbidities demonstrated increased risk of dying from COVID-19 in patients with cancer with concomitant heart disease [hypertension (HTN), coronary artery disease (CAD), and congestive heart failure (CHF)] and chronic lung disease (Table 2). Diabetes and chronic kidney disease were not associated with increased mortality in univariate analysis (Table 2).

We also analyzed laboratory values obtained prior to diagnosis of COVID-19 and during the time of nadir after COVID-19 positivity in our cancer cohort. Relative anemia pre–COVID-19 was associated with increased mortality, whereas pre-COVID platelet and lymphocyte counts were not (Table 3).Post–COVID-19 infection, lower hemoglobin levels, higher total white blood cell (WBC) counts, and higher absolute neutrophil counts were associated with increased mortality (Table 3). Analysis of other serologic biomarkers demonstrated that elevated D-dimer, lactate, and lactate dehydrogenase (LDH) in patients were significantly correlated with dying (Table 3).

Table 3.

Laboratory values of cancer patients with COVID-19 and association with mortality

Next, we conducted multivariate analyses and used variables that showed a significant association with mortality in univariate analysis (P < 0.05 in univariate was seen with age, ICU admission, hypertension, chronic lung disease, CAD, CHF, baseline hemoglobin, nadir hemoglobin, WBC counts, D-dimer, lactate, and LDH). Gender was forced in the model and we used a composite score of comorbidities from the sum of indicators for diabetes mellitus (DM), HTN, chronic lung disease, chronic kidney disease, CAD, and CHF capped at a maximum of 3. In the multivariate model (Supplementary Table S1), we observed that older age [age < 65; OR, 0.23; 95% confidence interval (CI), 0.07–0.6], higher composite comorbidity score (OR, 1.52; 95% CI, 1.02–2.33), ICU admission (OR, 4.83; 95% CI, 1.46–17.15), and elevated inflammatory markers (D-dimer, lactate, and LDH) were significantly associated with mortality after multivariate comparison in patients with cancer and COVID-19.

Interaction with the Healthcare Environment was a Prominent Source of Exposure for Patients with Cancer

A detailed analysis of deceased patients (N = 61; Supplementary Table S2) demonstrated that many were either nursing-home or shelter (n = 22) residents, and/or admitted as an inpatient or presented to the emergency room within the 30 days prior to their COVID-19 positive test (21/61). Altogether, 37/61 (61%) of the deceased cohort were exposed to the healthcare environment at the outset of the COVID-19 epidemic. Few of the patients in the cohort were on active oncologic therapy. The vast majority had a poor Eastern Cooperative Oncology Group performance status (ECOG PS; 51/61 with an ECOG PS of 2 or higher) and carried multiple comorbidities.

Patients with Cancer Demonstrate a Markedly Increased COVID-19 Mortality Rate Compared with Noncancer and All NYC COVID-19 Patients

An age- and sex-matched cohort of 1,090 patients at a 5:1 ratio of noncancer to cancer COVID-19 patients from the same time period and from the same hospital system was also obtained after propensity matching and used as control to estimate the increased risk posed to our cancer population (Table 4). We observed case fatality rates (CFR) were elevated in all age cohorts in patients with cancer and achieved statistical significance in the age groups 45–64 and in patients older than 75 years of age.

Table 4.

Comparison of cancer and COVID-19 mortality with all NYC cases (official NYC numbers up to 5 p.m., April 12, 2020) and a control group from the same healthcare facility

To also compare our CFRs with a larger dataset from the greater NYC region, we obtained official case numbers from New York State (current up to April 12, 2020; ref. 3). In all cohorts, the percentage of deceased patients was found to rise sharply with increasing age (Table 4). Strikingly, CFRs in cancer patients with COVID-19 were significantly, many-fold higher in all age groups when compared with all NYC cases (Table 4).

Discussion

To our knowledge, this is the first large report of COVID-19 CFRs among patients with cancer in the United States. The overall case fatality among COVID-19–infected patients with cancer in an academic center located within the current epicenter of the global pandemic exceeded 25%. In addition, striking tumor-specific discrepancies were seen, with marked increased susceptibility for those with hematologic malignancies and lung cancer. CFRs were 2 to 3 times the age-specific percentages seen in our noncancer population and the greater NYC area for all COVID-19 patients.

Our results seem to mirror the typical prognosis of the various cancer types. Among the most common malignancies within the U.S. population (lung, breast, prostate, and colorectal), there was 55% mortality among patients with lung cancer, 14% for breast cancer, 20% for prostate cancer, and 38% for colorectal cancer. This pattern reflects the overall known lethality of these cancers. The percent annual mortality (ratio of annual deaths/new diagnosis) is 59.3% for lung cancer, 15.2% for breast cancer, 17.4% for prostate cancer, and 36% for colorectal cancer (8). This suggests that COVID-19 infection amplifies the risk of death regardless of the cancer type.

Patients with hematologic malignancies demonstrate a higher mortality than those with solid tumors. These patients tend to be treated with more myelosuppressive therapy, and are often severely immunocompromised because of underlying disease. There is accumulating evidence that one major mechanism of injury may be a cytokine-storm syndrome secondary to hyperinflammation, which results in pulmonary damage. Patients with hematologic malignancy may potentially be more susceptible to cytokine-mediated inflammation due to perturbations in myeloid and lymphocyte cell compartments (14).

Many of the predictive risk factors for mortality in our cancer cohort were similar to published data among all COVID-19 patients. A recent meta-analysis highlighted the association of chronic diseases including hypertension (OR, 2.29), diabetes (OR, 2.47), COPD (OR, 5.97), cardiovascular disease (OR, 2.93), and cerebrovascular disease (OR, 3.89) with a risk for developing severe COVID-19 infection among all patients (15). In our cancer patient dataset, a large proportion of patients had at least one of these concurrent risk factors. In a univariate model, we observed significant associations of death from COVID-19 infection in patients with hypertension, chronic lung disease, coronary heart disease, and congestive heart failure. Serologic predictors in our dataset predictive for mortality included anemia at time of infection, and elevated LDH, D-dimer, and lactic acid, which correlate with available data from all COVID-19 patients.

Rapidly accumulating reports suggest that age and race may play a role in the severity of COVID-19 infection. In our cancer cohort, the median age of the patients succumbing to COVID-19 was 76 years, which was 10 years older than patients who have remained alive. The CDC has reported a disproportionate number of African Americans are affected by COVID-19 in the United States, accounting for 33% of all hospitalized patients while constituting only 13% of the U.S. population (15). However, the racial breakdown of our patients was proportional to the Bronx population as a whole, and race was not a significant predictor of mortality in our univariate or multivariate models. Our data might argue that the increased mortality noted in the larger NYC populations might also likely be driven by socioeconomic and health disparities in addition to underlying biological factors. Overall mortality with COVID-19 has been higher in the Bronx, which is a socioeconomically disadvantaged community with a mean per capita income of $19,721 (16, 17). Our patients with cancer were predominantly from the Bronx and potentially had increased mortality in part due to socioeconomic factors and comorbidities. Even after accounting for the increased mortality seen in COVID-19 in the Bronx, the many-fold magnitude increase in death rates within our cancer cohort can potentially be attributed to the vulnerability of oncology patients. This was evident in the comparison with a control group from the same hospital system that demonstrated a significant association of cancer with mortality in patients between 45 and 64 years of age and older than 75 years of age.

Interaction with the healthcare environment prior to widespread knowledge of the epidemic within NYC was a prominent source of exposure for our patients with cancer. Many of those who succumbed to COVID-19 infection were older and frail with significant impairment of pulmonary and/or immunologic function. These findings could be utilized to risk-stratify patients with cancer during this pandemic, or in future viral airborne outbreaks, and inform mitigation practices for high-risk individuals. These strategies could include early and aggressive social distancing, resource allocation toward more outpatient-based care and telemedicine, testing of asymptomatic high-risk patients, and institution of strict infection-control measures. Indeed, such strategies were implemented early in the pandemic at our center, possibly explaining the relatively low number of infected patients on active therapy.

There were several limitations to our study. Data regarding do not resuscitate or intubate orders were not included in the analysis and could have significantly affected the decision-making and mortality surrounding these patients. Although an attempt was made to control for those receiving active cancer treatment or with additional comorbidities, we could not fully account for the patients’ preexisting health conditions prior to COVID-19 infection. Differential treatment paradigms for COVID-19 infection and sequelae were not controlled for in our analysis. Because of the limited follow-up, the full clinical course of these patients may not be included. Future comparative studies to noncancer patients will be needed to fully ascertain the risk posed to oncology patients. Finally, though our data does include those who were tested and discharged within our health system, we cannot fully account for those who were tested in nonaffiliated outpatient settings, which may potentially bias our study to more severe cases. We also acknowledge that the mortality rate is highly dependent on the breadth of testing, and therefore understand that more widespread detection of viral infection would likely alter the results.

Our data suggest significant risk posed to patients with cancer infected with COVID-19, with an observed significant increase in mortality. The highest susceptibility appears to be in hematologic or lung malignancies, suggesting that proactive strategies to reduce likelihood of infection and improve early identification of COVID-19 positivity in the cancer patient population are clearly warranted. Overall, we hope and expect that our data from the current epicenter of the COVID-19 epidemic will help inform other healthcare systems, patients with cancer, and the public about the particular vulnerability of patients with cancer to this disease.

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

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

Read Full Post »

Live Conference Coverage AACR 2020 in Real Time: Monday June 22, 2020 8AM-Noon Sessions

Reporter: Stephen J. Williams, PhD

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

 

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

AACR VIRTUAL ANNUAL MEETING II

 

June 22-24: Free Registration for AACR Members, the Cancer Community, and the Public
This virtual meeting will feature more than 120 sessions and 4,000 e-posters, including sessions on cancer health disparities and the impact of COVID-19 on clinical trials

 

This Virtual Meeting is Part II of the AACR Annual Meeting.  Part I was held online in April and was centered only on clinical findings.  This Part II of the virtual meeting will contain all the Sessions and Abstracts pertaining to basic and translational cancer research as well as clinical trial findings.

 

REGISTER NOW

 

Monday, June 22

8:30 AM – 10:10 AM EDT

Virtual Special Session

Opening Ceremony

The Opening Ceremony will include the following presentations:
Welcome from AACR CEO Margaret Foti, PhD, MD (hc)

CHIEF EXECUTIVE OFFICER

MARGARET FOTI, PHD, MD (HC)

​American Association for Cancer Research
Philadelphia, Pennsylvania

  • Dr. Foti mentions that AACR is making progress in including more ethnic and gender equality in cancer research and she feels that the disparities seen in health care, and in cancer care, is related to the disparities seen in the cancer research profession
  • AACR is very focused now on blood cancers and creating innovation summits on this matter
  • In 2019 awarded over 60 grants but feel they will be able to fund more research in 2020
  • Government funding is insufficient at current levels

Remarks from AACR Immediate Past President Elaine R. Mardis, PhD, FAACR

  • involved in planning and success of the first virtual meeting (it was really well done)
  • # of registrants was at unprecedented numbers
  • the scope for this meeting will be wider than the first meeting
  • they have included special sessions including COVID19 and health disparities
  • 70 educational and methodology workshops on over 70 channels

AACR Award for Lifetime Achievement in Cancer Research

  • Dr. Philip Sharp is awardee of Lifetime Achievement Award
  • Dr. Sharp is known for his work in RNA splicing and development of multiple cancer models including a mouse CRSPR model
  • worked under Jim Watson at Cold Spring Harbor
    Presentation of New Fellows of the AACR Academy
  • Dr. Radcliffe for hypoxic factors
  • CART therapies
  • Dr. Semenza for HIF1 discovery
  • Dr Swanton for stratification of patients and tumor heterogeneity
  • these are just some of the new fellows

AACR-Biedler Prizes for Cancer Journalism

  • Writer of Article War of Nerves awarded; reported on nerve intervation of tumors
  • writer Budman on reporting and curation of hedgehog inhibitors in cancers
  • patient advocacy book was awarded for journalism
  • cancer survivor Kasie Newsome produced multiple segments on personalized cancer therapy from a cancer survivor perspective

Remarks from Speaker of the United States House of Representatives Nancy Pelosi

  • helped secure a doubling of funding for NCI and NIH in the 90s
  • securing COVID funding to offset some of the productivity issues related to the shutdown due to COVID
  • advocating for more work to alleviate health disparities

 

Remarks from United States Senator Roy Blunt

  • tireless champion in the Senate for cancer research funding; he was a cancer survivor himself
  • we need to keep focus on advances in science

Margaret Foti

DETAILS

Monday, June 22

10:10 AM – 12:30 PM EDT

Virtual Plenary Session

Bioinformatics and Systems Biology, Epidemiology, Immunology, Molecular and Cellular Biology/Genetics

Opening Plenary Session: Turning Science into Lifesaving Care

Alexander Marson, Antoni Ribas, Ashani T Weeraratna, Olivier Elemento, Howard Y Chang, Daniel D. De Carvalho

DETAILS

Monday, June 22

12:45 PM – 1:30 PM EDT

Awards and Lectures

How should we think about exceptional and super responders to cancer therapy? What biologic insights might ensue from considering these cases? What are ways in which considering super responders may lead to misleading conclusions? What are the pros and cons of the quest to locate exceptional and super responders?

Alice P Chen, Vinay K Prasad, Celeste Leigh Pearce

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Tumor Biology, Immunology

Experimental and Molecular Therapeutics, Immunology

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

 

Read Full Post »

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

 

Read Full Post »

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 27, 2020 Minisymposium on Signaling in Cancer 11:45am-1:30 pm

Reporter: Stephen J. Williams, PhD.

SESSION VMS.MCB01.01 – Emerging Signaling Vulnerabilities in Cancer
April 27, 2020, 11:45 AM – 1:30 PM
Virtual Meeting: All Session Times Are U.S. EDT
DESCRIPTION

All session times are U.S. Eastern Daylight Time (EDT). Access to AACR Virtual Annual Meeting I sessions are free with registration. Register now at http://www.aacr.org/virtualam2020

Session Type

Virtual Minisymposium

Track(s)

Molecular and Cellular Biology/Genetics

16 Presentations
11:45 AM – 1:30 PM
– Chairperson

J. Silvio Gutkind. UCSD Moores Cancer Center, La Jolla, CA

11:45 AM – 1:30 PM
– Chairperson

  • in 80’s and 90’s signaling focused on defects and also oncogene addiction.  Now the field is switching to finding vulnerabilities in signaling cascades in cancer

Adrienne D. Cox. University of North Carolina at Chapel Hill, Chapel Hill, NC

11:45 AM – 11:55 AM
– Introduction

J. Silvio Gutkind. UCSD Moores Cancer Center, La Jolla, CA

11:55 AM – 12:05 PM
1085 – Interrogating the RAS interactome identifies EFR3A as a novel enhancer of RAS oncogenesis

Hema Adhikari, Walaa Kattan, John F. Hancock, Christopher M. Counter. Duke University, Durham, NC, University of Texas MD Anderson Cancer Center, Houston, TX

Abstract: Activating mutations in one of the three RAS genes (HRAS, NRAS, and KRAS) are detected in as much as a third of all human cancers. As oncogenic RAS mediates it tumorigenic signaling through protein-protein interactions primarily at the plasma membrane, we sought to document the protein networks engaged by each RAS isoform to identify new vulnerabilities for future therapeutic development. To this end, we determined interactomes of oncogenic HRAS, NRAS, and KRAS by BirA-mediated proximity labeling. This analysis identified roughly ** proteins shared among multiple interactomes, as well as a smaller subset unique to a single RAS oncoprotein. To identify those interactome components promoting RAS oncogenesis, we created and screened sgRNA library targeting the interactomes for genes modifying oncogenic HRAS-, NRAS-, or KRAS-mediated transformation. This analysis identified the protein EFR3A as not only a common component of all three RAS interactomes, but when inactivated, uniformly reduced the growth of cells transformed by any of the three RAS isoforms. EFR3A recruits a complex containing the druggable phosphatidylinositol (Ptdlns) 4 kinase alpha (PI4KA) to the plasma membrane to generate the Ptdlns species PI4P. We show that EFR3A sgRNA reduced multiple RAS effector signaling pathways, suggesting that EFR3A acts at the level of the oncoprotein itself. As lipids play a critical role in the membrane localization of RAS, we tested and found that EFR3A sgRNA reduced not only the occupancy of RAS at the plasma membrane, but also the nanoclustering necessary for signaling. Furthermore, the loss of oncogenic RAS signaling induced by EFR3A sgRNA was rescued by targeting PI4K to the plasma membrane. Taken together, these data support a model whereby EFR3A recruits PI4K to oncogenic RAS to promote plasma membrane localization and nonclustering, and in turn, signaling and transformation. To investigate the therapeutic potential of this new RAS enhancer, we show that EFR3A sgRNA reduced oncogenic KRAS signaling and transformed growth in a panel of pancreatic ductal adenocarcinoma (PDAC) cell lines. Encouraged by these results we are exploring whether genetically inactivating the kinase activity of PI4KA inhibits oncogenic signaling and transformation in PDAC cell lines. If true, pharmacologically targeting PI4K may hold promise as a way to enhance the anti-neoplastic activity of drugs targeting oncogenic RAS or its effectors.

@DukeU

@DukeMedSchool

@MDAndersonNews

  • different isoforms of ras mutations exist differentially in various tumor types e.g. nras vs kras
  • the C terminal end serve as hotspots of mutations and probably isoform specific functions
  • they determined the interactomes of nras and kras and determined how many candidates are ras specific
  • they overlayed results from proteomic and CRSPR screen; EFR3a was a potential target that stuck out
  • using TCGA patients with higher EFR3a had poorer prognosis
  • EFR3a promotes Ras signaling; and required for RAS driven tumor growth (in RAS addicted tumors?)
  • EGFR3a promotes clustering of oncogenic RAS at plasma membrane

 

12:05 PM – 12:10 PM
– Discussion

12:10 PM – 12:20 PM
1086 – Downstream kinase signaling is dictated by specific KRAS mutations; Konstantin Budagyan, Jonathan Chernoff. Drexel University College of Medicine, Philadelphia, PA, Fox Chase Cancer Center, Philadelphia, PA @FoxChaseCancer

Abstract: Oncogenic KRAS mutations are common in colorectal cancer (CRC), found in ~50% of tumors, and are associated with poor prognosis and resistance to therapy. There is substantial diversity of KRAS alleles observed in CRC. Importantly, emerging clinical and experimental analysis of relatively common KRAS mutations at amino acids G12, G13, A146, and Q61 suggest that each mutation differently influences the clinical properties of a disease and response to therapy. For example, KRAS G12 mutations confer resistance to EGFR-targeted therapy, while G13D mutations do not. Although there is clinical evidence to suggest biological differences between mutant KRAS alleles, it is not yet known what drives these differences and whether they can be exploited for allele-specific therapy. We hypothesized that different KRAS mutants elicit variable alterations in downstream signaling pathways. To investigate this hypothesis, we created a novel system by which we can model KRAS mutants in isogenic mouse colon epithelial cell lines. To generate the cell lines, we developed an assay using fluorescent co-selection for CRISPR-driven genome editing. This assay involves simultaneous introduction of single-guide RNAs (sgRNAs) to two different endogenous loci resulting in double-editing events. We first introduced Cas9 and blue fluorescent protein (BFP) into mouse colon epithelial cell line containing heterozygous KRAS G12D mutation. We then used sgRNAs targeting BFP and the mutant G12D KRAS allele along with homology-directed repair (HDR) templates for a GFP gene and a KRAS mutant allele of our choice. Cells that successfully undergo HDR are GFP-positive and contain the desired KRAS mutation. Therefore, selection for GFP-positive cells allows us to identify those with phenotypically silent KRAS edits. Ultimately, this method allows us to toggle between different mutant alleles while preserving the wild-type allele, all in an isogenic background. Using this method, we have generated cell lines with endogenous heterozygous KRAS mutations commonly seen in CRC (G12D, G12V, G12C, G12R, G13D). In order to elucidate cellular signaling pathway differences between the KRAS mutants, we screened the mutated cell lines using a small-molecule library of ~160 protein kinase inhibitors. We found that there are mutation-specific differences in drug sensitivity profiles. These observations suggest that KRAS mutants drive specific cellular signaling pathways, and that further exploration of these pathways may prove to be valuable for identification of novel therapeutic opportunities in CRC.

  • Flourescent coselection of KRAS edits by CRSPR screen in a colorectal cancer line; a cell that is competent to undergo HR can undergo combination multiple KRAS
  • target only mutant allele while leaving wild type intact;
  • it was KRAS editing event in APC  +/- mouse cell line
  • this enabled a screen for kinase inhibitors that decreased tumor growth in isogenic cell lines; PKC alpha and beta 1 inhibitors, also CDK4 inhibitors inhibited cell growth
  • questions about heterogeneity in KRAS clones; they looked at off target guides and looked at effects in screens; then they used top two clones that did not have off target;  questions about 3D culture- they have not done that; Question ? dependency on AKT activity? perhaps the G12E has different downstream effectors

 

12:20 PM – 12:25 PM
– Discussion

12:25 PM – 12:35 PM
1087 – NF1 regulates the RAS-related GTPases, RRAS and RRAS2, independent of RAS activity; Jillian M. Silva, Lizzeth Canche, Frank McCormick. University of California, San Francisco, San Francisco, CA @UCSFMedicine

Abstract: Neurofibromin, which is encoded by the neurofibromatosis type 1 (NF1) gene, is a tumor suppressor that acts as a RAS-GTPase activating protein (RAS-GAP) to stimulate the intrinsic GTPase activity of RAS as well as the closely related RAS subfamily members, RRAS, RRAS2, and MRAS. This results in the conversion of the active GTP-bound form of RAS into the inactive GDP-bound state leading to the downregulation of several RAS downstream effector pathways, most notably MAPK signaling. While the region of NF1 that regulates RAS activity represents only a small fraction of the entire protein, a large extent of the NF1 structural domains and their corresponding mechanistic functions remain uncharacterized despite the fact there is a high frequency of NF1 mutations in several different types of cancer. Thus, we wanted to elucidate the underlying biochemical and signaling functions of NF1 that are unrelated to the regulation of RAS and how loss of these functions contributes to the pathogenesis of cancer. To accomplish this objective, we used CRISPR-Cas9 methods to knockout NF1 in an isogenic “RASless” MEF model system, which is devoid of the major oncogenic RAS isoforms (HRAS, KRAS, and NRAS) and reconstituted with the KRAS4b wild-type or mutant KRASG12C or KRASG12D isoform. Loss of NF1 led to elevated RAS-GTP levels, however, this increase was not as profound as the levels in KRAS-mutated cells or provided a proliferative advantage. Although ablation of NF1 resulted in sustained activation of MAPK signaling, it also unexpectedly, resulted in a robust increase in AKT phosphorylation compared to KRAS-mutated cells. Surprisingly, loss of NF1 in KRAS4b wild-type and KRAS-mutated cells potently suppressed the RAS-related GTPases, RRAS and RRAS2, with modest effects on MRAS, at both the transcript and protein levels. A Clariom™D transcriptome microarray analysis revealed a significant downregulation in the NF-κB target genes, insulin-like growth factor binding protein 2 (IGFBP2), argininosuccinate synthetase 1 (ASS1), and DUSP1, in both the NF1 knockout KRAS4b wild-type and KRAS-mutated cells. Moreover, NF1Null melanoma cells also displayed a potent suppression of RRAS and RRAS2 as well as these NF-κB transcription factors. Since RRAS and RRAS2 both contain the same NF-κB transcription factor binding sites, we hypothesize that IGFBP2, ASS1, and/or DUSP1 may contribute to the NF1-mediated regulation of these RAS-related GTPases. More importantly, this study provides the first evidence of at least one novel RAS-independent function of NF1 to regulate the RAS-related subfamily members, RRAS and RRAS2, in a manner exclusive of its RAS-GTPase activity and this may provide insight into new potential biomarkers and molecular targets for treating patients with mutations in NF1.
  • NF1 and SPRED work together to signal from RTK cKIT through RAS
  • NF1 knockout cells had higher KRAS and had increased cell proliferation
  • NF1 -/-  or SPRED loss had increased ERK phosphorylation and some increase in AKT activity compared to parental cells
  • they used isogenic cell lines devoid of all RAS isoforms and then reconstituted with specific RAS WT or mutants
  • NF1 and SPRED KO both reduce RRAS expression; in an AKT independent mannner
  • NF1 SPRED KO cells have almost no IGFBP2 protein expression and SNAIL so maybe affecting EMT?
  • this effect is independent of its RAS GTPAse activity (noncanonical)

12:35 PM – 12:40 PM
– Discussion

12:40 PM – 12:50 PM
1088 – Elucidating the regulation of delayed-early gene targets of sustained MAPK signaling; Kali J. Dale, Martin McMahon. University of Utah, Salt Lake City, UT, Huntsman Cancer Institute, Salt Lake City, UT

Abstract: RAS and its downstream effector, BRAF, are commonly mutated proto-oncogenes in many types of human cancer. Mutationally activated RAS or BRAF signal through the MEK→ERK MAP kinase (MAPK) pathway to regulate key cancer cell hallmarks such as cell division cycle progression, reduced programmed cell death, and enhanced cell motility. Amongst the list of RAS/RAF-regulated genes are those encoding integrins, alpha-beta heterodimeric transmembrane proteins that regulate cell adhesion to the extracellular matrix. Altered integrin expression has been linked to the acquisition of more aggressive behavior by melanoma, lung, and breast cancer cells leading to diminished survival of cancer patients. We have previously documented the ability of the RAS-activated MAPK pathway to induce the expression of ITGB3 encoding integrin β3 in several different cell types. RAS/RAF-mediated induction of ITGB3 mRNA requires sustained, high-level activation of RAF→MEK→ERK signaling mediated by oncogene activation and is classified as “delayed-early”, in that it is sensitive to the protein synthesis inhibitor cycloheximide. However, to date, the regulatory mechanisms that allow for induced ITGB3 downstream of sustained, high-level activation of MAPK signaling remains obscure. We have identified over 300 DEGs, including those expressing additional cell surface proteins, that display similar regulatory characteristics as ITGB3. We use integrin β3 as a model to test our hypothesis that there is a different mechanism of regulation for delayed-early genes (DEG) compared to the canonical regulation of Immediate-Early genes. There are three regions in the chromatin upstream of the ITGB3 that become more accessible during RAF activation. We are relating the chromatin changes seen during RAF activation to active enhancer histone marks. To elucidate the essential genes of this regulation process, we are employing the use of a genome-wide CRISPR knockout screen. The work presented from this abstract will help elucidate the regulatory properties of oncogenic progression in BRAF mutated cancers that could lead to the identification of biomarkers.

12:50 PM – 12:55 PM
– Discussion

12:55 PM – 1:05 PM
1090 – Regulation of PTEN translation by PI3K signaling maintains pathway homeostasis

Radha Mukherjee, Kiran Gireesan Vanaja, Jacob A. Boyer, Juan Qiu, Xiaoping Chen, Elisa De Stanchina, Sarat Chandarlapaty, Andre Levchenko, Neal Rosen. Memorial Sloan Kettering Cancer Center, New York, NY, Yale University, West Haven, CT, Memorial Sloan Kettering Cancer Center, New York, NY, Memorial Sloan Kettering Cancer Center, New York, NY @sloan_kettering

Abstract: The PI3K pathway is a key regulator of metabolism, cell proliferation and migration and some of its components (e.g. PIK3CA and PTEN) are frequently altered in cancer by genetic events that deregulate its output. However, PI3K signaling is not usually the primary driver of these tumors and inhibitors of components of the pathway have only modest antitumor effects. We now show that both physiologic and oncogenic activation of the PI3K signaling by growth factors and an activating hotspot PIK3CA mutation respectively, cause an increase in the expression of the lipid phosphatase PTEN, thus limiting the duration of the signal and the output of the pathway in tumors. Pharmacologic and physiologic inhibition of the pathway by HER2/PI3K/AKT/mTOR inhibitors and nutrient starvation respectively reduce PTEN, thus buffering the effects of inhibition and contributing to the rebound in pathway activity that occurs in tumors. This regulation is found to be a feature of multiple types of cancer, non-cancer cell line and PDX models thereby highlighting its role as a key conserved feedback loop within the PI3K signaling network, both in vitro and in vivo. Regulation of expression is due to mTOR/4EBP1 dependent control of PTEN translation and is lost when 4EBP1 is knocked out. Translational regulation of PTEN is therefore a major homeostatic regulator of physiologic PI3K signaling and plays a role in reducing the output of oncogenic mutants that deregulate the pathway and the antitumor activity of PI3K pathway inhibitors.

  • mTOR can be a potent regulator of PTEN and therefore a major issue when developing PI3K inhibitors

1:05 PM – 1:10 PM
– Discussion

1:10 PM – 1:20 PM
1091 – BI-3406 and BI 1701963: Potent and selective SOS1::KRAS inhibitors induce regressions in combination with MEK inhibitors or irinotecan

Daniel Gerlach, Michael Gmachl, Juergen Ramharter, Jessica Teh, Szu-Chin Fu, Francesca Trapani, Dirk Kessler, Klaus Rumpel, Dana-Adriana Botesteanu, Peter Ettmayer, Heribert Arnhof, Thomas Gerstberger, Christiane Kofink, Tobias Wunberg, Christopher P. Vellano, Timothy P. Heffernan, Joseph R. Marszalek, Mark Pearson, Darryl B. McConnell, Norbert Kraut, Marco H. Hofmann. Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria, The University of Texas MD Anderson Cancer Center, Houston, TX, The University of Texas MD Anderson Cancer Center, Houston, TX, Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria

  • there is rational for developing an SOS1 inhibitor (GEF); BI3406 shows better PK and PD as a candidate
  • most sensitive cell lines to inhibitor carry KRAS mutation; NRAS or BRAF mutations are not sensititve
  • KRAS mutation defines sensitivity so they created KRAS mut isogenic cell lines
  • found best to co inhibit SOS and MEK as observed plasticity with only SOS
  • dual combination in lung NSCLC pancreatic showed enhanced efficacy compared to monotherapy
  • SOS1 inhibition plus irinotecan enhances DNA double strand breaks; no increased DNA damage in normal stroma but preferentially in tumor cells
  • these SOS1 had broad activity against KRAS mutant models;
  • phase 1 started in 2019;

@Boehringer

1:20 PM – 1:25 PM
– Discussion

1:25 PM – 1:30 PM
– Closing Remarks

Adrienne D. Cox. University of North Carolina at Chapel Hill, Chapel Hill, NC

Follow on Twitter at:

@pharma_BI

@AACR

@GenomeInstitute

@CureCancerNow

@UCLAJCCC

#AACR20

#AACR2020

#curecancernow

#pharmanews

Read Full Post »

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 27, 2020 Opening Remarks and Clinical Session 9 am

Reporter: Stephen J. Williams, PhD.

9:00 AM Opening Session

9:00 AM – 9:05 AM
– Opening Video

9:05 AM – 9:15 AM
– AACR President: Opening Remarks Elaine R. Mardis. Nationwide Children’s Hospital, Columbus, OH

 

Dr. Mardis is the Robert E. and Louise F. Dunn Distinguished Professor of Medicine @GenomeInstitute at Washington University of St. Louis School of Medicine.

Opening remarks:  Dr. Mardis gave her welcome from her office.  She expressed many thanks to healthcare workers and the hard work of scientists and researchers.  She also expressed some regret for the many scientists who had wonderful research to present and how hard it was to make the decision to go virtual however she feels there now more than ever still needs a venue to discuss scientific and clinical findings.  Some of the initiatives that she has had the opportunity to engage in the areas of groundbreaking discoveries and clinical trials.  606,000 lives will be lost in US this year from cancer.  AACR is being vigilant as also an advocacy platform and public policy platform in Congress and Washington.  The AACR has been at the front of public policy on electronic cigarettes.  Blood Cancer Discovery is their newest journal.  They are going to host joint conferences with engineers, mathematicians and physicists to discuss how they can help to transform oncology.  Cancer Health Disparity Annual Conference is one of the fastest growing conferences.  They will release a report later this year about the scope of the problem and policy steps needed to alleviate these disparities.  Lack of racial and ethnic minorities in cancer research had been identified an issue and the AACR is actively working to reduce the disparities within the ranks of cancer researchers.   Special thanks to Dr. Margaret Foti for making the AACR the amazing organization it is.

 

9:15 AM – 9:30 AM- AACR Annual Meeting Program Chair: Review of Program for AACR Virtual Annual Meeting Antoni Ribas. UCLA Medical Center, Los Angeles, CA

Antoni Ribas, MD PhD is Professor, Medicine, Surgery, Molecular and Medical Pharmacology; Director, Parker Institute for Cancer Immunotherapy Center at UCLA; Director, UCLA Jonsson Comprehensive Cancer Center Tumor Immunology Program aribas@mednet.ucla.edu

The AACR felt it was important to keep the discourse in the cancer research field as the Annual AACR meeting is the major way scientists and clinicians discuss the latest and most pertinent results.  A three day virtual meeting June 22-24 will focus more on the translational and basic research while this meeting is more focused on clinical trials.  There will be educational programs during the June virtual meeting.  The COVID in Cancer part of this virtual meeting was put in specially for this meeting and there will be a special meeting on this in July.  They have created an AACR COVID task force.  The AACR has just asked Congress and NIH to extend the grants due to the COVID induced shutdown of many labs.

9:30  Open Clinical Plenary Session (there are 17 sessions today but will only cover a few of these)

9:30 AM – 9:31 AM
– Chairperson Nilofer S. Azad. Johns Hopkins Sidney Kimmel Comp. Cancer Center, Baltimore, MD @noza512

9:30 AM – 9:31 AM
– Chairperson Manuel Hidalgo. Weill Cornell Medicine, New York, NY

9:30 AM – 9:35 AM
– Introduction Nilofer S. Azad. Johns Hopkins Sidney Kimmel Comp. Cancer Center, Baltimore, MD

9:35 AM – 9:45 AM
CT011 – Evaluation of durvalumab in combination with olaparib and paclitaxel in high-risk HER2 negative stage II/III breast cancer: Results from the I-SPY 2 TRIAL Lajos Pusztai, et al

see https://www.abstractsonline.com/pp8/#!/9045/presentation/10593

AbstractBackground: I-SPY2 is a multicenter, phase 2 trial using response-adaptive randomization within molecular subtypes defined by receptor status and MammaPrint risk to evaluate novel agents as neoadjuvant therapy for breast cancer. The primary endpoint is pathologic complete response (pCR, ypT0/is ypN0)). DNA repair deficiency in cancer cells can lead to immunogenic neoantigens, activation of the STING pathway, and PARP inhibition can also upregulate PD-L1 expression. Based on these rationales we tested the combination of durvalumab (anti-PDL1), olaparib (PARP inhibitor) and paclitaxel in I-SPY2.
Methods: Women with tumors ≥ 2.5 cm were eligible for screening. Only HER2 negative (HER2-) patients were eligible for this treatment, hormone receptor positive (HR+) patients had to have MammaPrint high molecular profile. Treatment included durvalumab 1500 mg every 4 weeks x 3, olaparib 100 mg twice daily through weeks 1-11 concurrent with paclitaxel 80 mg/m2 weekly x 12 (DOP) followed by doxorubicin/cyclophosphamide (AC) x 4. The control arm was weekly paclitaxel x 12 followed by AC x 4. All patients undergo serial MRI imaging and imaging response at 3 & 12 weeks combined with accumulating pCR data are used to estimate, and continuously update, predicted pCR rate for the trial arm. Regimens “graduation with success” when the Bayesian predictive probability of success in a 300-patient phase 3 neoadjuvant trial in the appropriate biomarker groups reaches > 85%.
Results: A total of 73 patients received DOP treatment including 21 HR- tumors (i.e. triple-negative breast cancer, TNBC) and 52 HR+ tumors between May 2018 – June 2019. The control group included 299 patients with HER2- tumors. The DOP arm graduated in June 2019, 13 months after enrollment had started, for all HER2- negative and the HR+/HER2- cohorts with > 0.85% predictive probabilities of success. 72 patient completed surgery and evaluable for pCR, the final predicted probabilities of success in a future phase III trial to demonstrate higher pCR rate with DOP compared to control are 81% for all HER2- cancers (estimated pCR rate 37%), 80% for TNBC (estimated pCR rate 47%) and 74.5% for HR+/HER2- patients (estimated pCR rate 28%). Association between pCR and germline BRCA status and immune gene expression including PDL1 will be presented at the meeting. No unexpected toxicities were seen, but 10 patients (14%) had possibly immune or olaparib related grade 2/3 AEs (3 pneumonitis, 2 adrenal insufficiency, 1 colitis, 1 pancreatitis, 2 elevated LFT, 1 skin toxicity, 2 hypothyroidism, 1 hyperthyroidism, 1 esophagitis).
Conclusion: I-SPY2 demonstrated a significant improvement in pCR with durvalumab and olaparib included with paclitaxel compared to chemotherapy alone in women with stage II/III high-risk, HER2-negative breast cancer, improvement was seen in both the HR+ and TNBC subsets.

  • This combination of durvalumab and olaparib is safe in triple negative breast cancer
  • expected synergy between PARP inhibitors and PDL1 inhibitors as olaparib inhibits DNA repair and would increase the mutational burden, which is in lung cancer shown to be a biomarker for efficacy of immune checkpoint inhibitors such as Opdivio
  • three subsets of breast cancers were studied: her2 negative, triple negative and ER+ tumors
  • MRI imaging tumor size was used as response
  • olaparib arm had elevation of liver enzymes and there was a pancreatitis
  • however paclitaxel was used within the combination as well as a chemo arm but the immuno arm alone may not be better than chemo alone but experimental arm with all combo definitely better than chemo alone
  • they did not look at BRCA1/2 status, followup talk showed that this is a select group that may see enhanced benefit; PARP inhibitors were seen to be effective only in BRCA1/2 mutant ovarian cancer previously

 

10:10 AM – 10:20 AM
CT012 – Evaluation of atezolizumab (A), cobimetinib (C), and vemurafenib (V) in previously untreated patients with BRAFV600 mutation-positive advanced melanoma: Primary results from the phase 3 IMspire150 trial Grant A. McArthur,

for abstract please see https://www.abstractsonline.com/pp8/#!/9045/presentation/10594

AbstractBackground: Approved systemic treatments for advanced melanoma include immune checkpoint inhibitor therapy (CIT) and targeted therapy with BRAF plus MEK inhibitors for BRAFV600E/K mutant melanoma. Response rates with CITs are typically lower than those observed with targeted therapy, but CIT responses are more durable. Preclinical and clinical data suggest a potential for synergy between CIT and BRAF plus MEK inhibitors. We therefore evaluated whether combining CIT with targeted therapy could improve efficacy vs targeted therapy alone. Methods: Treatment-naive patients with unresectable stage IIIc/IV melanoma (AJCC 7th ed), measurable disease by RECIST 1.1, and BRAFV600 mutations in their tumors were randomized to the anti­-programmed death-ligand 1 antibody A + C + V or placebo (Pbo) + C + V. A or Pbo were given on days 1 and 15 of each 28-day cycle. Treatment was continued until disease progression or unacceptable toxicity. The primary outcome was investigator-assessed progression-free survival (PFS). Results: 514 patients were enrolled (A + C + V = 256; Pbo + C + V = 258) and followed for a median of 18.9 months. Investigator-assessed PFS was significantly prolonged with A + C + V vs Pbo + C + V (15.1 vs 10.6 months, respectively; hazard ratio: 0.78; 95% confidence interval: 0.63-0.97; P=0.025), an effect seen in all prognostic subgroups. While objective response rates were similar in the A + C + V and Pbo + C + V groups, median duration of response was prolonged with A + C + V (21.0 months) vs Pbo + C + V (12.6 months). Overall survival data were not mature at the time of analysis. Common treatment-related adverse events (AEs; >30%) in the A + C + V and Pbo + C + V groups were blood creatinine phosphokinase (CPK) increase (51.3% vs 44.8%), diarrhea (42.2% vs 46.6%), rash (40.9% in both arms), arthralgia (39.1% vs 28.1%), pyrexia (38.7% vs 26.0%), alanine aminotransferase (ALT) increase (33.9% vs 22.8%), and lipase increase (32.2% vs 27.4%). Common treatment-related grade 3/4 AEs (>10%) that occurred in the A + C + V and Pbo + C + V groups were lipase increase (20.4% vs 20.6%), blood CPK increase (20.0% vs 14.9%), ALT increase (13.0% vs 8.9%), and maculopapular rash (12.6% vs 9.6%). The incidence of treatment-related serious AEs was similar between the A + C + V (33.5%) and Pbo + C + V (28.8%) groups. 12.6% of patients in the A + C + V group and 15.7% in the Pbo + C + V group stopped all treatment because of AEs. The safety profile of the A + C + V regimen was generally consistent with the known profiles of the individual components. Conclusion: Combination therapy with A + C + V was tolerable and manageable, produced durable responses, and significantly increased PFS vs Pbo + C + V. Thus, A + C + V represents a viable treatment option for BRAFV600 mutation-positive advanced melanoma. ClinicalTrials.gov ID: NCT02908672

 

 

10:25 AM – 10:35 AM
CT013 – SWOG S1320: Improved progression-free survival with continuous compared to intermittent dosing with dabrafenib and trametinib in patients with BRAF mutated melanoma Alain Algazi,

for abstract and more author information please see https://www.abstractsonline.com/pp8/#!/9045/presentation/10595

AbstractBackground: BRAF and MEK inhibitors yield objective responses in the majority of BRAFV600E/K mutant melanoma patients, but acquired resistance limits response durations. Preclinical data suggests that intermittent dosing of these agents may delay acquired resistance by deselecting tumor cells that grow optimally in the presence of these agents. S1320 is a randomized phase 2 clinical trial designed to determine whether intermittent versus continuous dosing of dabrafenib and trametinib improves progression-free survival (PFS) in patients with advanced BRAFV600E/K melanoma.
Methods: All patients received continuous dabrafenib and trametinib for 8-weeks after which non-progressing patients were randomized to receive either continuous treatment or intermittent dosing of both drugs on a 3-week-off, 5-week-on schedule. Unscheduled treatment interruptions of both drugs for > 14 days were not permitted. Responses were assessed using RECIST v1.1 at 8-week intervals scheduled to coincide with on-treatment periods for patients on the intermittent dosing arm. Adverse events were assessed using CTCAE v4 monthly. The design assumed exponential PFS with a median of 9.4 months using continuous dosing, 206 eligible patients and 156 PFS events. It had 90% power with a two-sided α = 0.2 to detect a change to a median with an a priori hypothesis that intermittent dosing would improve the median PFS to 14.1 months using a Cox model stratified by the randomization stratification factors.
Results: 242 patients were treated and 206 patients without disease progression after 8 weeks were randomized, 105 to continuous and 101 to intermittent treatment. 70% of patients had not previously received immune checkpoint inhibitors. There were no significant differences between groups in terms of baseline patient characteristics. The median PFS was statistically significantly longer, 9.0 months from randomization, with continuous dosing vs. 5.5 months from randomization with intermittent dosing (p = 0.064). There was no difference in overall survival between groups (median OS = 29.2 months in both arms p = 0.93) at a median follow up of 2 years. 77% of patient treated continuously discontinued treatment due to disease progression vs. 84% treated intermittently (p = 0.34).
Conclusions: Continuous dosing with the BRAF and MEK inhibitors dabrafenib and trametinib yields superior PFS compared with intermittent dosing.

  • combo of MEK and BRAF inhibitors can attract immune cells like TREGs so PDL1 inhibitor might help improve outcome
  • PFS was outcome endpoint
  • LDH was elevated in three patients (why are they seeing liver tox?  curious like previous study); are seeing these tox with the PDL1 inhibitors
  • there was marked survival over placebo group and PFS was statistically  with continuous dosing however intermittent dosing shows no improvement

Dr. Wafik el Diery gave a nice insight as follows

Follow on Twitter at:

@pharma_BI

@AACR

@GenomeInstitute

@CureCancerNow

@UCLAJCCC

#AACR20

#AACR2020

#curecancernow

#pharmanews

 

 

 

Read Full Post »

Older Posts »